Krisna Gupta | Krisna 'imed' Gupta

Indonesia's router exports after the US FCC ban

Wed, 01 Apr 2026 10:00:00 +0700

On 23 March 2026, the US Federal Communications Commission (FCC) effectively banned foreign-made consumer routers from the American market. The ban targets Chinese manufacturers, but it catches every foreign producer — including Indonesia, which exported USD 43.6 million worth of routers to the US in 2025. This post looks at what happened, why Indonesia’s router exports to the US have been on a rollercoaster since 2021, and what the FCC ban means going forward.

The FCC ban

The FCC to include consumer-grade routers produced in foreign countries. In practice, this means new foreign-made router models can no longer receive equipment authorization — the certification needed to legally sell electronic devices in the US ( ).

A few important details:

New models of foreign-produced routers can no longer be marketed or sold in the US
Existing models that were previously authorized are not affected — consumers do not need to replace their current devices
An exemption exists for devices granted “Conditional Approval” by the Department of Defense or DHS
The FCC defines “production” broadly: it covers manufacturing, assembly, design, and development

Indonesia’s router exports to the US

Indonesia is a significant router exporter. Using BPS data for HS 851762 — the tariff line covering control and adaptor units including gateways, bridges, and routers — we can track how Indonesia’s router exports to the US have shifted over the past five years. (The HS code changed from 85176221 under HS2017 to 85176243 under HS2022, but covers the same product.)

Year	US Export	Total Export	US Share
2021	182.54	377.29	48.4%
2022	77.74	277.66	28.0%
2023	19.85	382.67	5.2%
2024	31.71	226.97	14.0%
2025	43.57	153.94	28.3%

All values in million USD.

The numbers tell a dramatic story. In 2021, nearly half of Indonesia’s router exports went to the US. By 2023, that share had collapsed to just 5%. Then it bounced back. Understanding why requires looking at both where Indonesian routers go and the US trade policies that shaped demand.

Where do Indonesian routers go?

Over the full 2021–2025 period, the US is the largest cumulative destination, but the ranking has shifted dramatically year to year.

Cumulative 2021–2025 (million USD):

United States — 355.4 (25.1%)
South Korea — 257.2 (18.1%)
Singapore — 244.6 (17.2%)
Japan — 122.5 (8.6%)
Mexico — 98.2 (6.9%)
Poland — 70.7 (5.0%)
China — 44.8 (3.2%)

In 2023, when total exports hit their peak of USD 382.7 million, the destination mix looked very different. Singapore dominated while the US barely registered:

2023 — peak year (million USD):

Singapore — 173.3 (45.3%)
South Korea — 43.5 (11.4%)
Japan — 38.6 (10.1%)
Poland — 36.0 (9.4%)
Mexico — 21.2 (5.5%)
United States — 19.8 (5.2%)

By 2025, the picture had flipped again. Singapore collapsed from the top spot (45.3%) to number 11 (2.2%), while the US climbed back to first:

2025 (million USD):

United States — 43.6 (28.3%)
South Korea — 36.6 (23.7%)
Japan — 16.7 (10.9%)
Poland — 6.5 (4.2%)
Panama — 5.5 (3.6%)

These swings are not random — they track closely with US trade policy toward China.

The US router import dynamics

The sharp drop from USD 183 million (2021) to USD 20 million (2023) is directly linked to the on-again, off-again tariffs on Chinese goods.

Under — the US trade law that authorizes tariffs in response to unfair trade practices — Chinese routers faced a 25% tariff (List 3). This made Chinese routers expensive and pushed US buyers toward alternatives, including Indonesian-made ones. That is why 2021 was a peak year for Indonesia’s US-bound exports.

But in March 2022, the US Trade Representative retroactively from October 2021, making Chinese routers competitive again. These exclusions were extended through September 2023. With cheaper Chinese supply restored, US importers shifted back. Indonesia’s US-bound exports cratered, while Singapore — likely serving as a re-export hub — surged from minor player to 45% of Indonesia’s total router exports in 2023.

Several factors brought the US back to the top of Indonesia’s destination list.

First, the Section 301 tariff exclusions expired in September 2023, reinstating the full 25% tariff on Chinese routers. Second, in August 2024, a — one of the world’s largest router makers — escalated into investigations by the DOJ, Commerce Department, and Department of Defense over cybersecurity vulnerabilities and alleged predatory pricing. This made US buyers wary of Chinese-made routers more broadly.

What the FCC ban means for Indonesia

The FCC ban lands at an awkward moment. Indonesia’s router exports to the US were just recovering — at USD 43.6 million, the US is once again Indonesia’s largest destination. But the ban covers all foreign-made consumer routers, not just Chinese ones. Indonesian manufacturers are .

Existing authorized models remain unaffected, but any new models produced in Indonesia would need Conditional Approval from the Department of Defense or DHS to access the US market — a much higher bar than standard FCC equipment authorization. Combined with already declining total exports (USD 154 million in 2025 versus the USD 383 million peak in 2023), this ban represents a significant new trade barrier for Indonesia’s electronics sector.

There is an irony here: Indonesia benefited when US policy pushed buyers away from Chinese routers, only to be hit by the next escalation of that same policy. And diversifying away from the US is easier said than done. The US is the world’s largest importer of routers, accounting for in this product category. Because the ban applies to all foreign-made routers — not just from specific countries — there is no comparable alternative market to pivot toward. The remaining demand is smaller and more fragmented.

One important caveat: this analysis has not examined investment in Indonesia’s electronics manufacturing sector, particularly facilities dedicated to router production. How much capacity Indonesia actually has — and whether it could scale up to pursue conditional approvals or pivot to other markets — remains an open question.

Indonesia's Fiscal Situation: Bleaker Than It Looks?

Tue, 24 Mar 2026 21:06:59 +0700

Together with Rizki Siregar and Riandy Laksono, I recently published a Survey of Recent Developments in the Bulletin of Indonesian Economic Studies (BIES). In that paper, we paint a rather pessimistic picture of the Indonesian economy, particularly on the fiscal side.

Unfortunately, due to word count constraints, there were several graphs we couldn’t include in the paper. This post supplements the narrative in our paper with additional visualizations. The data comes from the IMF’s Public Finances in Modern History database.

Government spending: back to “normal”?

At first glance, Indonesia’s government expenditure appears to have returned to pre-pandemic levels. During COVID, spending spiked — understandably, as stimulus was needed. But it has since gradually returned to normal.

But “real” spending is actually declining

If we look deeper, the picture may be a bit worse. Government expenditure consists of two components: primary expenditure (spending on maintenance and government programs) and interest payments on debt.

Naturally, what the economy actually feels is mainly the primary component. Interest payments also circulate — after all, many bondholders are Indonesian residents too. But a portion goes to Bank Indonesia and foreign creditors. As the share of interest payments grows, primary expenditure — the spending that more directly drives economic activity — has been trending downward. This means that even though total spending looks stable, fiscal space for development programs, infrastructure, and social protection is shrinking.

The revenue problem

Indonesia’s fiscal challenge isn’t just about spending being eroded by interest payments — it’s also about revenue being persistently low. In our paper, this is one of the key points we highlight.

Indonesia’s government revenue has consistently been at a low level — even before the pandemic. The chart below shows total government revenue as a percentage of GDP.

This revenue problem is no secret. The World Bank itself, in its Indonesia Economic Prospects report of December 2024 titled “Funding Indonesia’s Vision 2045,” highlighted Indonesia’s low tax ratio. According to the World Bank, Indonesia’s tax ratio is projected to remain at around 10% through 2027 — well below the average for middle-income countries. Moreover, a recent World Bank study found that Indonesia’s tax gap averaged 6.4% of GDP between 2016 and 2021, mostly from VAT and corporate income tax.

How does Indonesia compare?

Indonesia’s revenue problem becomes even clearer when compared with other G20 and ASEAN countries. The chart below shows total government revenue as a percentage of GDP, and Indonesia sits at the very bottom among G20 and Southeast Asian countries.

Closing: fiscal consolidation and tax capacity

In our paper, we make two recommendations. First, fiscal consolidation remains important. The government must be highly selective about its programs, especially those that are too large in scale and regressive.

Second, and more fundamentally: Indonesia must seriously improve its tax collection capacity. With a tax ratio still hovering around 10%, Indonesia simply doesn’t have enough fiscal space to fund ambitious development programs — let alone Vision 2045. Without substantive tax reform — from broadening the tax base, improving compliance, to reducing ineffective incentives — fiscal space will continue to narrow, and the quality of government spending will keep deteriorating.

Our full paper is available here.

Prabowonomics: Can Indonesia Really Grow at 8%?

Thu, 26 Feb 2026 00:00:00 +0000

Indonesia in BRICS: Between Economic Diversification and Personalised Politics

Wed, 04 Feb 2026 00:00:00 +0000

In: Felix Jantz (Ed.), ASEAN Goes BRICS: Understanding the Attraction of BRICS for ASEAN Countries. Friedrich Naumann Stiftung für die Freiheit, Regional Office Asia, Bangkok, 2026.

Intro to trade theory

Mon, 24 Nov 2025 11:15:00 +0700

The Economic Danger of Inequality

Sun, 16 Nov 2025 15:22:00 +0700

I recently finished Michael Pettis’s book “The Great Rebalancing: Trade, Conflict, and the Perilous Road Ahead for the World Economy.” The book discusses global economic imbalances caused by differences in savings and consumption rates between advanced and developing countries. Pettis argues that these imbalances could trigger a global economic crisis if left unaddressed.

I frequently use this argument when discussing international trade, for instance at a Lembaga Manajemen UI event some time ago.

In this post, I want to try unpacking the mechanism of inequality, saving rates, and their impact on the national (and global) economy, since an imbalance in one country will be mirrored by an imbalance in another. Then I’ll briefly discuss conditions in Indonesia.

Inequality, saving rate, and aftermath

In the book, Pettis argues that changes in saving rates are heavily influenced by income inequality in a country. Countries with high income inequality tend to have higher saving rates, because wealthy individuals tend to save more than poor individuals. Conversely, countries with lower income inequality tend to have lower saving rates, because poor individuals tend to spend most of their income on consumption.

He cites Federal Reserve Board Chairman Marriner Eccles:

“A mass production has to be accompanied by mass consumption.”

If the additional wealth created is not distributed well, then additional consumption will be smaller than that additional wealth, inequality rises along with a rising saving rate.

According to Pettis, this inequality process doesn’t only occur between individuals (for example between fellow workers or fellow capitalists) or between classes, but can also occur between economic agents. That is, inequality (and consequently saving rates) can emerge if companies and the state control more income than the public.

Corporate wealth is different from the wealth of the capitalist class¹. The capitalist class only receives money from companies if the company distributes dividends, or the company pays interest on debt (to bond holders, or banks, whose profits are transferred to bank owners and the public as deposit interest). If the company retains earnings, whether to let it sit or reinvest, it becomes corporate wealth. So national savings also has corporate savings behind it. And Pettis argues, the larger the retained earnings (not distributed as employee bonuses or shareholder dividends), the higher the national saving rate².

To illustrate, it’s roughly like this. First, let’s consider Earth as one closed economic system (we can’t yet trade with the Moon or Mars).

$$ GDP = C + I $$

All output can only become consumption, while the rest becomes investment for tomorrow. C and I are calculated from:

graph TD; A[Producer]-->B[tax income]; A-->M[costs beyond
taxes]; M-->C[worker
wages]; M-->D[Dividends/interest
capitalists]; M-->E[Retained earnings
companies]; C-->F[C workers]; C-->G[Savings
workers]; D-->H[C capitalists]; D-->I[Savings
capitalists]; E-->J[Savings
companies]; B-->K[I government]; B-->L[C state];

Total GDP from the production side is calculated from producer value added, while GDP from the expenditure side is C + I. Total C is workers’ C + capitalists’ C + state C, and total I is workers’ savings + capitalists’ savings + corporate I + government I. GDP from both sides must be equal. BPS calculates this every quarter.

According to Pettis, inequality leads to a higher saving rate vis-a-vis consumption rate. When inequality occurs through rising retained earnings rather than worker wages or capitalist dividends, total C automatically falls because reinvested earnings all become corporate I³.

Of course, the government’s share can also be increased by, for example, raising the tax ratio or expanding SOE roles. After that, it’s up to the government whether to increase C (e.g., subsidies or simply running administration by paying employee salaries) or increase I (e.g., building a high-speed rail or a new city. For example).

Let’s assume a sudden transfer from ordinary parties to wealthy parties, without affecting GDP (a strong assumption, but let’s set it aside for now). This wealthy party could be a transfer from poor to rich people, but could also be from the public to companies or to the state.

Pettis’s framework is quite simple. It can be illustrated with the following diagram:

graph TD; A[Inequality]-->B[Saving rate rises]; A-->C[Saving rate
unchanged]; B-->D[productive investment
rises]; B-->E[unproductive investment
rises]; C-->F[C of wealthy rises
proportionally]; C-->G[C of ordinary rises,
savings fall]; C-->H[Employment
falls]; D-->I[Sustainable]; E-->J[Unsustainable]; F-->K[Sustainable but
impossible]; G-->L[Unsustainable]; H-->M[Sustainable
but bad];

Although the diagram above looks dichotomous, in reality everything could occur in proportions simultaneously.

According to Pettis, the dominant scenario that occurred in Spain, Portugal, Italy, Greece, and the United States is the scenario where ordinary people’s consumption rises while their saving rate falls. This can manifest as rising household debt to finance daily consumption, or at minimum, people who struggle to save because their salary is barely enough to survive. In other words, savings that increase among the wealthy (for example tech companies in the US), are “compensated” by falling savings among ordinary people. High asset prices from wealthy people’s investments make ordinary people feel rich, so the inequality doesn’t feel so acute. But can asset prices keep rising forever?

But when talking about the US, which is a deficit country, their aggregate saving rate is actually very low despite tech companies having lots of money. This happens because the US’s low saving rate is offset by other countries’ high saving rates, especially China, Taiwan, Japan, and Germany. Particularly in China and Japan, state assets are quite high due to high foreign reserve accumulation. So the one saving abroad is the Central Bank. This is explained more fully in Pettis’s book and articles.

Which scenario is playing out in Indonesia?

What about Indonesia?

Recently, Pak Haryo also posted Indonesia’s GINI figures. The higher the number, the higher the income inequality in a country.

Dari data SWIID (Standardized World Income Inequality Database) saya bandingkan Gini IDN vs 3 negara tetangga

Negara2 lain gini_disp (Gini setelah pajak, biru) lebih rendah dari gini-mkt (Gini sebelum pajak, merah). Tapi data IDN kebalik. Apa perpajakan di IDN ~> REGRESIF? https://t.co/27CeKEot3l pic.twitter.com/kNi71CFShk
— Haryo Aswicahyono (@Aswicahyono) September 14, 2025

Indonesia’s GINI coefficient increased quite significantly (especially the post-transfer/tax measure) around the period after 2001. Around that time, China joined the WTO and prices for mineral and vegetable oil commodities surged sharply. This commodity price increase roughly plateaued after around 2011. The commodity industry is one with quite high inequality because it’s capital-intensive. When this industry grows, the income gains are enjoyed by relatively fewer workers. Pasaribu and Hill discuss this more fully.

I’m not too surprised because I once checked saving rates in this post. I tried to examine the dynamics of inequality and saving rates in Indonesia. My premise at the time was that I was quite taken aback by Indonesia’s net exports surging dramatically in 2022. This means our external savings rose significantly. I then started exploring BPS data, and yes, the saving rate of the bottom 20% of households was negative. I also found World Bank research showing a declining labor share of income. I ended up looking at GINI ratios not just recently but also during the commodity boom years, finding charts that are broadly similar to what Pak Haryo presented.

Clearly this doesn’t look great, especially since recently there has been a lot of discontent among the public, who increasingly feel that Indonesia’s economic growth isn’t being shared broadly. I also wanted to write about the government’s efforts to increase the saving rate, including converting SOEs from mandatory PNBP payments to the state (which is spent on consumption, presumably) into fresh funds for Danantara to be invested. Also the rising SAL balance, essentially reducing government consumption in favor of savings for financial sector injection.

Well, at least let’s hope this rising saving rate becomes productive investment and not unproductive speculation! That’s it for this post, hope it’s useful.

My use of the term “class” is completely value-neutral and purely definitional. The “classes” I describe aren’t really dichotomous. Workers who receive wages can also own capital that generates returns. ↩︎
Income inequality through capital income (interest and dividends) vs. wage income is also called “compositional inequality,” discussed quite thoroughly by Branko Milanovic, author of “Capitalism, Alone.” ↩︎
Why does it all become I? Because all income not consumed becomes I. Of course, if retained earnings just sit in a bank account, they’re still counted as I. This is simply accounting. ↩︎

Global Macroeconomic Imbalance

Thu, 13 Nov 2025 09:00:00 +0700

Global macroeconomic imbalance and the future of trade

Wed, 12 Nov 2025 13:30:00 +0700

Finding the Formula for Indonesia's Resilience

Mon, 15 Sep 2025 16:00:00 +0700

The Role of the Ministry of Foreign Affairs in Trade and Investment

Tue, 02 Sep 2025 08:00:00 +0700

Boosting Exports for Growth

Wed, 27 Aug 2025 09:00:00 +0700

Menggunakan Darts untuk melihat bedanya prediksi harga beras domestik dan internasional sejak Neraca Komoditas

Sat, 02 Aug 2025 11:00:00 +0700

I’m one of the regular readers of tweets by Haryo Aswicahyono, who often talks about economics. Just the other day, he tweeted mainly about how rice prices remain high, even though there’s an abundant supply. Here’s the tweet:

Biasanya kalau diviralin harga turun. Ini harga beras masih naik terus, padahal katanya stock melimpah.

Harga Pasar Modern: 15,900
Harga Pasar Tradisional: 15,700
Harga Pedagang Besar: 14,800
Harga Produsen (Petani): 13,100
ini harga rata2 se Indonesia yahttps://t.co/dLx2YHPm1w pic.twitter.com/KY80KG3sRK
— Haryo Aswicahyono (@Aswicahyono) July 4, 2025

At the time, I happened to be working on a paper about the Commodity Balance Sheet for the Center for Indonesian Policy Studies (CIPS), alongside Hasran and Rasya. Rice is one of the products covered by the Commodity Balance. So I was writing a bit about rice myself—and sure enough, I found the same thing: domestic rice prices remain high. The steep price stands in stark contrast to international rice prices, which have declined since early 2024.

Rice prices rose in mid-2023 due to climate conditions and India, a major exporter that banned its rice exports. But if we look at the trend, domestic and international rice prices had already started rising in mid-2022, around August. Both rose in parallel until early 2024, when they began to diverge. Domestic prices continued climbing, while international prices started falling. Interestingly, these persistently high domestic prices emerged at a time when domestic stock was abundant.

Given the surplus, the issue with high domestic rice prices might stem from distribution—from warehouses to markets—not moving quickly enough. I’m not too familiar with how the distribution chain works from Bulog to markets, such as how much rice is purchased from warehouses or the selling price determined by distributors. But if distribution were quicker, prices might better reflect the stock levels.

Commodity Balance Sheet

Part of the rice supply chain involves the Commodity Balance Sheet, a system designed to regulate imports. Rice may only be imported if forecast data suggests that domestic production will fall short of consumption. If a surplus is predicted, imports are halted. Imports can help lower domestic prices, since international rice prices are typically cheaper.

International prices often serve as reference points for analysts to track domestic trends. Broad factors affecting rice production, such as regional weather events, will influence both domestic and international prices. But if an event is isolated to Indonesia, only domestic prices will rise. That’s where imports become useful—if international prices remain stable while domestic supply is disrupted, imports help stabilize the market.

The speed of price stabilization depends on how fast imports can arrive. The Commodity Balance Sheet was reportedly designed to accelerate import decisions. Rice was added to this system in 2022. Before that, imports were still restricted but not using this mechanism. So—does the Commodity Balance Sheet actually work better?

In a paper co-written with Hasran and Rasya, I explored price movements in Indonesia without the Commodity Balance Sheet by estimating rice prices using other price indicators (a method called synthetic control). The results? You’ll have to wait until the paper’s out—lol.

In this blog post, let’s try a ‘lite’ version using forecasting. The premise: what would domestic rice prices look like if the Commodity Balance Sheet didn’t exist? We use Darts—specifically, Exponential Smoothing—to forecast post-2022 prices based on historical data, then compare those predictions to actual outcomes. The gap is attributed to the Commodity Balance Sheet. As a benchmark, we do the same for international rice prices, which weren’t affected by this system.

The time series is monthly. Domestic rice prices are sourced from PIHPS (Strategic Food Price Information Center), while international prices come from the IMF commodity market. I cut the data at August 2023, since prices start trending upward from then. A time series alone isn’t enough to capture all the dynamics at play.

#package
import pandas as pd
import plotly.express as px
from darts import TimeSeries
from darts.models import ExponentialSmoothing

# plot domestik vs internasional
df = pd.read_excel('beras.xlsx')
df['month'] = pd.Series(pd.date_range(start='2018-07-01', end='2025-05-10', freq='ME'))
dfl=df.melt(id_vars=['month'], var_name='jenis', value_name='harga')
fig = px.line(dfl, x="month", y="harga", title='harga beras bulanan (PIHPS)',color='jenis',
 color_discrete_sequence=px.colors.qualitative.G10,template='plotly_dark')
fig.update_layout(
 legend=dict(
 orientation="h", # Horizontal orientation
 yanchor="bottom", # Anchor the legend's y-position to the bottom
 y=1.02, # Adjust the y-position (can be slightly above the plot)
 xanchor="right", # Anchor the legend's x-position to the right
 x=1 # Adjust the x-position (can be slightly to the right of the plot)
 )
 )
fig.write_html('wew.html', full_html=False, include_plotlyjs='cdn')

df2=df[:-32]

series = TimeSeries.from_dataframe(df2, "month", "domestik")
train,val=series[:-8],series[-8:]
model=ExponentialSmoothing()
model.fit(train)
prediction=model.predict(len(val),num_samples=1000)
series.plot()
prediction.plot(label='prediksi',low_quantile=.05,high_quantile=.95)
plt.title('Prediksi harga beras domestik')
plt.legend()

<matplotlib.legend.Legend at 0x1ea12e5bd90>

series = TimeSeries.from_dataframe(df2, "month", "internasional")
train,val=series[:-8],series[-8:]
model=ExponentialSmoothing()
model.fit(train)
prediction=model.predict(len(val),num_samples=1000)
series.plot()
prediction.plot(label='prediksi',low_quantile=.05,high_quantile=.95)
plt.title('Prediksi harga beras internasional')
plt.legend()

<matplotlib.legend.Legend at 0x1ea12ef4a50>

Lol look at that confidence interval! It’s so wide that it’s almost useless. Of course there’re structural stuff in the rice market and our series isnt that long anyway. For fun, still okay i supposoe. Just dont be too serious about it.

The forecasting results show that domestic rice prices should have either declined or at least flattened since the Commodity Balance Sheet was implemented. But in reality, domestic prices actually increased. Meanwhile, international rice prices don’t seem too far off from the forecast. Technically, the forecast still falls within the 95% confidence interval—though just barely, haha. Is this because of the Commodity Balance Sheet? Possibly, but we’d need further analysis to confirm. Again, this is just a playful experiment, not a definitive conclusion. Treat it as a light-hearted exploration of forecasting with Darts, not a rigorous analysis.

Alright, that’s it for today’s post. Honestly, this entry is half a soft promo for the paper and half a playful experiment with Darts. Next time, we’ll try another, more exciting forecasting model!

Melihat Indonesia lewat kacamata Ray Dalio: Bagaimana Negara Bankrut

Sat, 28 Jun 2025 12:06:59 +0700

About Ray Dalio’s Book

I just finished reading Ray Dalio’s book titled How Countries Go Broke, which illustrates how nations fall into debt crises. It starts with the premise that the United States is currently facing a massive debt problem, which Dalio sees as entering the final stage of what he calls “The Big Debt Cycle.” This debt problem is driven not only by budget deficits but also by interest rates and inflation—which have remained super low since the 2000s—making debt easy to roll over. Because interest rates were kept so low, the U.S. (and other advanced economies) got too relaxed and addicted to borrowing. But after COVID-19, interest rates in developed countries started rising again, forcing the U.S. to reconsider how sustainable its debt truly is.

Economists like Kenneth Rogoff have already raised concerns about U.S. debt—and so has Ray Dalio, I think. But at a time when low inflation was seen as a bigger problem than high inflation, the debt kept piling up.

I saw a similar way of thinking in Australia, where they’ve been running a current account deficit for over 30 years without any crisis. My macroeconomics lecturer, , once said this might be risky, but at the time people in Australia weren’t too worried. Timo taught me that we need to be cautious about debt that looks easy on the surface. Politicians tend to make it worse, because increasing spending is how you win elections. That’s why debt discipline only kicks in when it’s already too late.

So has the U.S. left it too late? According to Ray Dalio, not yet. The U.S. can still do what he calls “beautiful deleveraging”—reducing debt in a painless way. That can be done by raising taxes, cutting government spending, and lowering interest rates at the same time. In other words, he believes it’s still doable. But if these steps aren’t taken, the U.S. could face an even worse debt crisis.

The problem is that today’s situation is tangled up with high political uncertainty, both domestically and geopolitically. On top of that, natural issues like the climate crisis and the uncertain effects of technology on the economy make things even more complex. Yup, in his book, Ray also talks about non-debt issues like geopolitical tension and climate-driven risks.

For me, the principle of debt sustainability that Ray Dalio presents isn’t exactly new. Timo had already covered it in his lectures. Even the concept of “beautiful deleveraging” is actually quite standard: it’s when governments and central banks work together—governments reduce debt (by trimming budgets a little and raising taxes) while central banks help out by lowering interest rates. But the way Dalio explains it is so simple and fluid—it’s like the “baby talk” version of macroeconomics class.

I think Ray Dalio is a great example of someone who learned macroeconomics the self-taught way. He’s obviously talked to many macro experts and has hands-on experience as a global investor, which means he had to understand the macroeconomic conditions of all the countries in his portfolio. I really appreciated the examples he gave—of which countries succeeded at beautiful deleveraging and which ones didn’t—it showed how much he’s learned from experience.

Applying Ray Dalio’s Indicators

In the book, he shares some indicators and example calculations to assess a country’s debt stability. One of them is the debt-to-income ratio (he says debt-to-GDP can be misleading). You can also use growth-adjusted versions to estimate the present value of debt. Overall, the indicators he uses are actually quite mainstream for investors analyzing economic fundamentals¹.

I tried looking at these indicators too, but went with the easy route—just used historical data without projecting into the future like Dalio did in the book. Mostly because, well… laziness 😆. I used historical data from the IMF dataset Public Finances in Modern History² (which you can ). Since the data layout is kind of messy, I manually merged it in Excel. This dataset contains indicators like:

Code	Description
prim	Primary government spending (% of GDP)
rev	Government revenue (% of GDP)
exp	Government expenditure (% of GDP)
ie	Interest paid on public debt (% of GDP)
pb	Government primary balance (% of GDP)
d	Gross public debt (% of GDP)
rltir	Real long-term gov bond yield (%)
rgc	Real GDP growth rate (%)

Let’s just stick to the basics. First is the debt-to-income ratio. In the table above, debt-to-GDP is labeled “d” and government revenue-to-GDP is labeled “rev”. So to get the debt-to-income ratio, just divide “d” by “rev”—GDP cancels out because it’s in both the numerator and the denominator.

Next, I’ll look at the difference between the interest rate on public debt and the GDP growth rate. If the interest you pay is higher than your revenue growth, then over time, debt becomes more expensive and potentially unsustainable. Now, we’re assuming that as GDP grows, a country’s revenue-raising capacity grows as well—so I’m using GDP growth as a proxy for revenue growth. A bit rough, but still helpful.

Let’s start by looking at the ratio of debt to government income. We’ve got the U.S. and Indonesia in the dataset, as well as Japan, Argentina, and Turkey—three countries that are kind of legendary when it comes to debt crises 😂.

We’re pretty familiar with the 1998 Asian financial crisis that hit Indonesia hard. You can see that Indonesia’s debt-to-income ratio peaked in 1999, when the country’s total debt was 6.7 times its revenue. That’s still better than Argentina, which went through two major in 1989 and 2002. Spikes like these are common during crises, when governments ramp up borrowing to stabilize the economy while revenues drop. And yep, there’s a visible spike for all countries in 2020 due to the pandemic.

But for the U.S. and Japan, the debt-to-income ratio has kept rising steadily since their respective crisis moments—Japan since around 1990, and both countries since the 2008 global financial crisis. At this point, it’s no longer a spike—it’s a long-term climb. That’s one of the strengths of issuing debt in your own currency, especially when most of the buyers are your own citizens. In cases like that, the debt ends up being supported by the central bank.

According to Ray Dalio, both the U.S. and Japan have reached what he calls Monetary Policy 3 (MP3): this is when the central bank starts buying government bonds to lower interest rates. That’s what keeps their borrowing costs down and allows debt levels to keep rising. To keep private sector balance sheets healthy, governments (and their central banks) have to sacrifice their own balance sheets. Oh, and by the way—looks like Argentina might be heading for another spike and could . Wild.

It looks like Indonesia’s ratio started climbing again around 2015 (gee, wonder who was in charge then 👀). But after 2020, the trend seems to be improving, mainly because the primary balance has started shrinking. Primary balance, by the way, is the difference between government revenue and spending excluding interest payments. A negative figure means the government is spending more (on things that drive consumption and investment) than it earns—before even factoring in interest payments.

In other words, a negative primary balance means the government is in expansion mode. A positive value, on the other hand, means revenue has actually surpassed spending. Still, overall there’s a deficit because debt payments need to be covered. Interestingly, in 2023 Indonesia’s primary balance started to turn positive—this means more of the deficit is now being used to repay or roll over debt.

By the way, we can see this low spending trend in the chart below, which shows primary expenditure—that is, total government spending minus interest payments. In other words, this chart reflects the government spending that actually fuels $G$ (government demand). From this, we can see that Indonesia’s debt has remained relatively manageable because government spending itself is relatively low.

What’s more, since the COVID period, the portion of government spending used for actual government consumption (excluding interest payments) has been gradually declining as a share of GDP. This signals that the government has been scaling back its spending—at least in relative terms.

Lastly, looking at the growth-minus-yield figure below, things still seem relatively okay. Indonesia is a country with high bond yields, but its economic growth is also pretty strong. That said, the issues of taxation and ICOR (Incremental Capital Output Ratio) still need serious attention if we want to avoid falling into the trap of unsustainable debt.

TL;DR

In summary, Indonesia still seems to be doing okay in terms of macroprudence—especially when compared to other countries. We issue most of our debt in our own currency (which can be backed by the central bank), which gives us a bit more flexibility. That said, we have to be cautious when comparing ourselves to countries like Japan or the U.S., because they’re in a category of their own. For Japan, we obviously don’t want to end up stuck in a negative interest rate spiral like they did. Investors pulled out, the currency kept depreciating, and young people there ended up in pretty bleak situations. The U.S.? Well, they literally print dollars. Although the dollar’s influence is slowly fading… so, not exactly a perfect scenario for them either.

Indonesia is generally praised for its macroprudence, even though the real sector of the economy is still kind of problematic. We’ve been keeping debt at around 3% of GDP each year—something Ray Dalio also recommends in his book. Our central bank is still holding strong, with foreign exchange reserves currently at an all-time high. As long as we maintain stronger macroprudence than our neighbors, Indonesia will likely remain an attractive place to park money.

Even though our indicators still look solid, we should be mindful of the signs of economic collapse that Ray Dalio talks about. For instance, he mentions that rising authoritarianism is often a red flag for countries heading toward bankruptcy. Then there’s the continuation of monetary policy into “MP3 territory” (meaning both public and private debt ends up being bought by the central bank). And investments that don’t lead to higher productivity? Definitely a red flag too.

About that central bank debt-buying cycle—Ray Dalio outlines a clear pattern in his book. First, the private sector collapses due to reckless debt management (say, construction or aviation firms playing fast and loose with money). Then the government steps in to rescue them—offering contracts or injecting capital. Where does the money come from? More debt. And when no one wants to lend anymore, the central bank swoops in to buy the government bonds in order to keep interest rates from spiking. So the sequence goes: private sector → government → central bank. Eventually, this can go hand-in-hand with a country turning to capital controls.

The real question is: have we reached that stage yet?

Who's buying Indonesia's government bonds? pic.twitter.com/DYR8RKy9ZC
— 🐦 (@dionbisara) March 19, 2025

Alright, that’s all for now. Hopefully, we’ll always stay in good shape, economically and otherwise. Don’t forget to mention me on X if you want to chat further.

Economists—especially macroeconomists—often argue that indicators used for individual firms don’t really apply to countries. That’s not entirely wrong, but I’ll set that aside for now so this blog post doesn’t go off the rails. ↩︎
Mauro, P., Romeu, R., Binder, A., & Zaman, A. (2015). A modern history of fiscal prudence and profligacy. Journal of Monetary Economics, 76, 55–70. ↩︎

Issues in Indonesia's State Budget

Fri, 09 May 2025 09:00:00 +0700

Trump Tariffs and Indonesia's Automotive Industry

Wed, 07 May 2025 13:30:00 +0700

The Middle Class Crisis and How to Fix It

Tue, 29 Apr 2025 13:30:00 +0700

Democracy, Economic Freedom, and Growth

Fri, 25 Apr 2025 14:00:00 +0700

Reviewing the National Economic Council Paper on the Economic Impact of Free Nutritious Meals

Mon, 24 Mar 2025 21:06:59 +0700

It was quite surprising yet interesting when I watched the by the National Economic Council (DEN) some time ago. The reason was that Luhut Binsar Panjaitan (LBP), the DEN chair, gave the spotlight to Professor Arif Anshory Yusuf (AAY) to present a teaser of his ex-ante model results on the impact of the Free Nutritious Meals program (MBG) on the economy. According to AAY, MBG has the potential to create 1.9 million jobs and reduce poverty by up to 5.8%. MBG, he said, is equivalent to giving IDR 600,000 per family if the average number of children in poor families is 3. This figure is 3x larger than PKH! He also added that to realize the maximum impact of MBG, “leakage” of food demand “that does not need to be imported” should be minimized by prioritizing domestic products. Those numbers sound off.

Then on the evening of March 23, 2025 Jakarta time, I watched Professor Arief Anshory Yusuf (AAY) present his latest paper written for the National Economic Council (DEN). The event was organized by Doctrine UK, an Indonesian student association in the United Kingdom. There, he presented a paper on the macroeconomic impact analysis of the Free Nutritious Meals program (MBG). The paper itself was shared in the event chat. The recording has been uploaded to YouTube and can be viewed .

After reviewing both the paper and the presentation, I felt compelled to write some notes about it. The main purpose is actually for myself, since this model is quite fun to explore and replicate. Additionally, at the event, AAY also represented DEN, which means the policies emerging from this paper can be considered as coming from the government and will likely become public policy. I’m also writing this for friends who aren’t very familiar with CGE¹ but want to learn. Lastly, it would be great if this post were read by AAY and DEN, but I’m sure someone of his caliber already receives plenty of input from people far more capable than me, so it’s fine if this goal isn’t achieved. Mostly, this post is for learning and sharing.

I greatly respect him as an academic because AAY is one of the few Indonesian economists who is very vocal about economic issues in Indonesia, especially on Higher Education and pro-poor and pro-job programs. He does not hesitate to criticize the government. Even his paper, which serves as the ex-ante justification for MBG, was shared with everyone, and he regularly presents and seeks opinions from many people across various circles. This level of transparency is something I haven’t found in other government policies.

Having said that, I have some notes that I believe are very important and worth writing about on this blog. I will focus on 2 caveats that he mentioned in his presentation. For other parts, that can be an opportunity for other friends who want to contribute comments. For example, commentary is also very interesting in my opinion, and easier to read than this post haha.

I will start by briefly summarizing the points I agree with, then move on to things I’m less sure about or disagree with.

Key points in my view

In his paper, AAY summarizes various studies about programs similar to MBG in other countries. In other countries, according to him, MBG has been quite successful. He also says that MBG is a very progressive program. I think it’s hard to disagree that MBG, as a concept, is a progressive program.

The paper also explains some details about MBG that are consistent with explanations from other sources (e.g., CISDI). He mentions a budget of 171 trillion rupiah, one of the largest welfare programs in the world, targeting approximately 82.9 million people.

Equally important: AAY mentions several official objectives of MBG: better nutrition, job creation, supporting local food systems, and improving student attendance and health. Nevertheless, he says that MBG should not be designed to pursue too many objectives at once, and I agree.

Finally, AAY uses input-output (IO) analysis and microsimulation to show the economic impacts in terms of increased output, value added, and employment, as well as poverty reduction from this program. Various simulations with different scenarios were conducted, but the results are consistently positive overall.

Important caveats

However, in his presentation, AAY stated two caveats that are very heroic and automatically dismiss much of the discussion about MBG:

This model is an ex-ante scenario assuming ideal implementation. All criticisms regarding operational aspects cannot be answered with this paper.
There is no general equilibrium aspect (supply, demand, market, and optimization, at least), so things like price changes and international trade are not reflected in this model.

In my personal opinion, these two things are arguably the most influential factors that could potentially derail this program. At the Doctrine UK event the other day, perhaps the majority of questions and discussions from participants were precisely about best practices and institutional readiness. I add some additional thoughts here beyond what was already raised by friends at the event, which can be read directly at the .

But this post will hyperfocus on point 2, something more relevant to AAY’s paper. I was quite surprised to hear AAY argue that IO analysis $\neq$ Computable General Equilibrium (CGE), and that IO does not account for price changes. In my opinion, that statement is not technically correct. I will also provide 3 arguments for why the results from the IO scenario may not be suitable for making claims as confident as what AAY showed at the DEN press conference above.

I will nerd out a bit about CGE to address caveat no. 2. This section will be too geeky. Read at your own risk. You’ve been warned.

The General Equilibrium Aspect

What is Input-Output Analysis (IO)?

In his paper, AAY explains IO in section 3.1.1., which is on page 4 of the paper.

There are 3 important terms AAY uses when explaining IO analysis: output, value added, and employment. Output is essentially the total production of an industry. Value added is total production minus inputs/raw materials. So value added consists of factors of production, mainly labor and capital. And labor equals employment.

The IO structure in AAY’s paper is exactly the same as a typical IO model, which is more or less like this:

$$ \begin{align*} x&=Ax+y \newline (I-A)x&=y \newline x&=(I-A)^{-1}y \end{align*} $$

where the resulting vector x represents input demand for sector $i$. That is, input demand from vector $i$ depends on the shift-share $A$ of each industry in $x$ for each industry in $x$, and final demand $y$.

In an Input-Output table, we can show output and value added if you open an actual IO table from BPS.

wew	s1	s2 … sj	y	sum
s1	$q_{1,1}$	$q_{1,2} \dots q_{1,j}$	$y_1$	$x_1$
s2	$q_{2,1}$	$q_{2,2} \dots q_{2,j}$	$y_2$	$x_2$
$\vdots$	$\ddots$	$\ddots$	$\vdots$	$\vdots$
si	$q_{i,1}$	$q_{i,2} \dots q_{i,j}$	$y_i$
va	$va_1$	$va_2 \dots va_j$	-	GDP
sum	$x_1$	$ x_2 \dots x_j$	GDP	-

Here, $q_{i,j}$ can be formed into a matrix equivalent to $Ax$ by setting $q_{i,j}=a_{i,j}x_i$. Hopefully that’s clear.

Usually, matrix $Ax$ is set through some optimization (you know, lagrange and stuff). Well, typically, matrix A in IO analysis is exogenous. In other words, the elements of matrix A are fixed and only expand along with output $x$. Therefore, all exogenous changes in the economy (in this case in $y$) are assumed to only expand according to matrix A.

Actually, IO is also CGE. It’s just that IO is a special case of CGE, where the output demand function has a perfect complement property. Perfect complement is a very useful assumption because the optimization becomes linear. Computationally, it becomes much easier since we only need linear algebra without any nonlinear optimization.

Additionally, Leontief is convenient because the optimization only expands according to the optimal share of each component.

For example, an input function $U=min(a_1x_1,a_2x_2)$ will have an optimal point at $a_1x_1=a_2x_2$ so the demand function for $x_1$ will be $x_1=\frac{a_2}{a_1}x_2$ and $\frac{a_2}{a_1}$ will be constant and fixed throughout changes in the consumption composite.

This method is relatively straightforward to implement in Python, as long as the IO matrix is available. I tried to replicate using a lite version of the IO table, . Of all the scenarios, I will try to replicate scenario A1 in the paper. From the 17-sector IO table, the lite version, I converted it into matrix A, x and y, and the data for the matrices and vectors can be downloaded . Note that the y matrix there still contains import values. That’s why in my simulation I construct y myself, not using y from there, so the imports are removed. Actually, you can also subtract the imports from final demand in the Excel file yourself.

import numpy as np
A=np.loadtxt('A.csv',delimiter=',') # matrix A
#y=np.loadtxt('y.csv',delimiter=',') ini final demand yang ada final importnya
u=np.loadtxt('x.csv',delimiter=',') # output
y=u-np.dot(A,u) # final demand tanpa final import
ia=np.identity(17)-A
invA=np.linalg.inv(ia)
np.savetxt('invA.csv', invA,delimiter=",") # save the change matrix result to a new file
x=np.dot(invA,y) ## Make sure replikasi final output di tabel.
x-u

array([ 0.00000000e+00, 4.76837158e-07, 9.53674316e-07, 1.19209290e-07,
-1.49011612e-08, -4.76837158e-07, 4.76837158e-07, 0.00000000e+00,
2.38418579e-07, 0.00000000e+00, 0.00000000e+00, -1.19209290e-07,
0.00000000e+00, -1.19209290e-07, 1.19209290e-07, -1.19209290e-07,
-1.19209290e-07])

## Simulasi MBG A1 di paper AAY
yy=y+np.array([0,0,0,0,0,0,0,0,171e6,0,0,0,0,-171e6,0,0,0]) ## + MBG shock - budget shock
xx=np.dot(invA,yy)
print(f'perubahan %output sektoral setelah +MBG-penghematan:\n {(xx-x)/x*100}') ## Percent change of sectoral output

perubahan %output sektoral setelah +MBG-penghematan:
[ 1.08864224 0.03824485 0.57615184 -0.79322713 -0.71996072
-0.27533884 0.34350708 -0.80514386 13.29429074 -1.13615204
0.0553673 -0.13483742 -1.60608706 -22.73385632 -0.14187647
-0.18583608 -0.23295487]

print(f'pertumbuhan output nasional adalah {(np.sum(xx)-np.sum(x))/np.sum(x)*100} persen') ## Percent change of total output

pertumbuhan output nasional adalah 0.046445748306372714 persen

## Put everything into a dataframe
import pandas as pd
sec=["Pertanian, Kehutanan, dan Perikanan", "Pertambangan dan Penggalian", "Industri Pengolahan", "Pengadaan Listrik dan Gas", "Pengadaan Air, Pengelolaan Sampah, Limbah dan Daur Ulang", "Konstruksi", "Perdagangan Besar dan Eceran; Reparasi Mobil dan Sepeda Motor", "Transportasi dan Pergudangan", "Penyediaan Akomodasi dan Makan Minum", "Informasi dan Komunikasi", "Jasa Keuangan dan Asuransi", "Real Estate", "Jasa Perusahaan", "Administrasi Pemerintahan, Pertahanan dan Jaminan Sosial Wajib", "Jasa Pendidikan", "Jasa Kesehatan dan Kegiatan Sosial", "Jasa lainnya"]
delta=(xx-x)/x*100
## Create pandas of the result
df=pd.DataFrame({'sektor':sec,'delta':delta})
#concat the national results
df2=pd.DataFrame({'sektor':'Total','delta':(np.sum(xx)-np.sum(x))/np.sum(x)*100},index=[17])
df=pd.concat([df,df2])
df

	sektor	delta
0	Pertanian, Kehutanan, dan Perikanan	1.088642
1	Pertambangan dan Penggalian	0.038245
2	Industri Pengolahan	0.576152
3	Pengadaan Listrik dan Gas	-0.793227
4	Pengadaan Air, Pengelolaan Sampah, Limbah dan ...	-0.719961
5	Konstruksi	-0.275339
6	Perdagangan Besar dan Eceran; Reparasi Mobil d...	0.343507
7	Transportasi dan Pergudangan	-0.805144
8	Penyediaan Akomodasi dan Makan Minum	13.294291
9	Informasi dan Komunikasi	-1.136152
10	Jasa Keuangan dan Asuransi	0.055367
11	Real Estate	-0.134837
12	Jasa Perusahaan	-1.606087
13	Administrasi Pemerintahan, Pertahanan dan Jami...	-22.733856
14	Jasa Pendidikan	-0.141876
15	Jasa Kesehatan dan Kegiatan Sosial	-0.185836
16	Jasa lainnya	-0.232955
17	Total	0.046446

indicator	original	replica
National output	0.06	0.046
Agriculture	1.37	1.08
manufacture	0.12	0.57

The A1 simulation results: the original is from AAY, the replica is mine. There are differences in the numbers, likely due to different tables and certainly different aggregation levels. But they’re relatively close, so I think it’s fairly accurate haha.

Three limitations of this model are immediately apparent.

First is the limitation of the constant shift-share assumption a la Leontief. This is immediately visible from the source of growth. The balanced budget reallocation produces positive additional output because the food service sector has a larger “total multiplier” (in the paper, terms $m_{ij}$ and $m_{ik}$, pp.6) than the government services sector. The problem is that this total multiplier is constant regardless of how much is added or subtracted. Without changes in marginal return, if the goal is output growth, we might as well move ALL output from government services to the food service sector (i.e., corner solution). But in the real world this doesn’t happen, because as a sector loses output, marginal productivity there rises, while a sector that is already bloated will experience diminishing returns, eliminating gains as more resources flow into it. Plus, on the demand side, there is also marginal utility that changes as consumption patterns concentrate on a single service. This is not a problem if the shock is relatively small. But given that the MBG shock raises the food service sector by 13.3% and cuts the government service sector by -22.73%, the constant marginal return assumption is likely to be problematic.

Second, the vector $y$ is treated as final demand without considering exports and imports. Indeed, the $y$ column in the BPS IO table originally contains capital formation and exports, which are aggregated into $y$ here. Meanwhile, in the first column there should be an imported raw materials row. In CGE, we often add a substitution function with Armington elasticity between imported and domestic inputs for each sector. Naturally, the absence of international trade (or more precisely, treating the shift-share of international trade as exogenous/constant) makes it difficult to forecast MBG’s impact on exports and imports. That is why AAY said at something like MBG will have a good impact as long as there is no “leakage,” that “materials that don’t need to be imported” should not be imported. I think this is not the best way to conclude from the model.

More importantly, in AAY’s paper, I could not find information about which table he used for the analysis. This is important because BPS has 2 types of IO tables: and . In the domestic table, only domestic input figures appear in the $Ax$ matrix, and all imports are pooled in row 2000. In the total table, the $Ax$ matrix already includes imports, so row 2000 is empty. There is no information in the paper about whether Armington calibration was performed for domestic vs. imported product choices.

If the domestic transaction table was used, then the $Ax$ matrix does not reflect total inputs (tends to be biased), implicitly treating imports as fixed, hence the “don’t leak” conclusion, which may be consistent with what the model says. But if the total transaction table was used, then the $Ax$ matrix already includes imported goods, and it may well be that “leakage” occurs in the model with no negative consequences whatsoever. Or maybe I’m the one misreading it and the procedure for removing imported input is actually explained somewhere in the paper. But it should be made more explicit.

However, the critique about the exogenous trade balance is actually very much related to the third critique: the absence of prices. The absence of prices becomes super relevant when we use a model with optimization and constraints. Without constraints, production in the MBG-recipient sector can seemingly expand as far as $Ax$. In reality, it is the supply-side constraint that would cause inflation. When there is a (budget) constraint, optimization must be performed, and consequently income effect and substitution effect will emerge (if using assumptions other than Leontief). In particular, this income effect will greatly affect the need for exports/imports, but more importantly, total consumption and poverty calculations.

Let me explain.

Constraint and prices

Under the Leontief assumption, when we shock final demand (in column $y_i$ where $i=$ the shocked sector), output from the shocked sector increases, and input demand from that sector increases, visible in column $s_j$ where $j=$ the sector experiencing the change in $y$. Like $\frac{dy}{dx}$ sort of thing.

For example, if demand in sector $k$ increases to $y_k+1$ so we have a vector $y_{new}$ shaped like this:

$$ y_{new}=y+ \begin{bmatrix} 0 \newline \vdots \newline 1 \newline \vdots \newline 0 \end{bmatrix} $$

let us call that new vector $d$ so that $y_{new}=y+d$. Then we compute $x_{new}=(I-A)^{-1}y_{new}$.

$$ \begin{align*} x_{new}&=(I-A)^{-1}(y+d) \newline x_{new}&=(I-A)^{-1}y+(I-A)^{-1}d \newline x_{new}&=x+(I-A)^{-1}d \end{align*} $$

The change in input demand from sector $k$ is exactly $(I-A)^{-1}$. AAY explains this on page 6 of his paper. Now, that term contains the identity matrix and matrix $A$, which we have made exogenous. This means the change in input demand from that sector depends only on $\Delta y$ and does not depend on price changes at all.

The BPS 2020 IO table is essentially a transaction matrix. Transactions imply current prices, where there is both quantity and price. Now, a simple IO exercise usually treats those matrix values as quantities. So if an element of the matrix (which is actually a value) moves, the movement is treated as a quantity movement.

The problem is that, with constraints, we can show that input demand usually depends on prices!

Consider typical Leontief production:

$$ \begin{align*} \max \min(a_1q_1,a_2q_2) \newline \text{subject to} \ p_1 q_1+p_2 q_2 \leq I \end{align*} $$

Optimization implies $a_1q_1=a_2q_2$, so $q_1=\frac{a_2}{a_1}q_2$. Substituting into the constraint:

$$ \begin{align*} p_1 \frac{a_2}{a_1}q_2+p_2 q_2 = I \newline q_2=\frac{I}{\frac{a_2}{a_2}p_2+\frac{a_2}{a_i}p_1} \end{align*} $$

If we replace income $I$ with the price of output 1 times the quantity of output 1, $p_1x_1$, then demand for output 2 becomes:

$$ q_2=\frac{p_1x_1}{\frac{a_2}{a_1}p_1+\frac{a_2}{a_2}p_2} $$

and we can generalize the equation above for $\max \min(a_1q_1,a_2q_2,\dots,a_Jq_J)$ to:

$$ q^j_i=\frac{p_ix_i}{\sum_{s=1}^S \frac{a^s_i}{a^j_i}p_s} $$

where $q^j_i$ is the material input from industry $j$ to industry $i$, $p_i$ is the output price of industry $i$, $x_i$ is the output of industry $i$. $q^j_i$ is a function of the isocost of industry $i$ (which equals total revenue) and input prices for each industry $s$ including $s=j$.

In the replication , $q^j_i$ is proxied by $a_{i,j}x_i$, where this term only changes because of $x_i$. Yet, as shown above, prices also matter. If $p=1$, then $q$ simply has relative shift share $a$ and final demand $x$, which is exactly what happens in the replication above.

Now, one can argue that price-dependent input demand is not a problem as long as we assume the sector is a price taker. That is, the demanding sector is so small that the market will always provide supply (i.e., there are infinitely many suppliers) without affecting price increases. But this is certainly not always realistic, especially since the sector in question has high demand for agricultural products, a sector that has historically always been plagued by bureaucratic trade governance, miscalculations, perpetually failed self-sufficiency programs, and so on. It seems difficult to expect super elastic supply from this sector.

Of course, we could also model the limitations from the supply composite labor side, where we could model a production nest. Indeed, in the IO table there is actually a separate labor share, which is also assumed to have a constant parameter $e$, just like value added which has a constant parameter $v$. Both assumptions can be relaxed.

But perhaps another time.

Implementation problems

I also intend to add some thoughts on the importance of considering implementation problems that will have a very significant impact on the conclusions about MBG’s impact.

I agree that if ALL schools receive this program, then it could be progressive, assuming the budget sacrificed for MBG comes from a line item that is usually enjoyed by the (hence lower marginal propensity to consume²). But there are several implementation possibilities suggesting this may not happen on the ground.

First, regarding the distribution of recipient schools. As I understand it, not all schools receive this program simultaneously. Some schools get MBG (treatment) and others do not (control). The problem arises if treatment does not occur randomly/exogenously. For example, if in general the schools that receive it first are those that tend to be more prepared in terms of infrastructure. Such schools are typically located in relatively wealthier areas. If this is the case, although AAY provides some anecdotes about poor community groups as beneficiaries, many participants may actually be in areas that are already fairly well-off.

Second is corruption. If this program is corrupted, the corrupt actors are most likely much wealthier than the beneficiaries. This means MBG would be dangerous if it becomes a vehicle for income transfer from the party whose budget was cut to the party that corrupts MBG. This is certainly not pro-poor. Indications of corruption (or at least failure to meet good nutritional standards) were already raised by participants at the Doctrine UK event the other day.

Lastly, the CGE scenario above may also be less effective if it turns out that not all of the 171T is used for food portions. Some of the money could also be used for operations and institution-building. Someone mentioned that the Ministry of Health already has institutions ready for use, so there’s no need to create new institutions like BGN and communal kitchens. A waste of resources.

Conclusion

In closing, I have tried to offer constructive criticism in this post. I focused on the IO model and tried to show that 3 assumptions in the IO model (constant shift-share, absence of international trade, and fixed nominal prices) could be violated. Of course, no model will be exactly the same as the real world. All models are wrong but that’s fine as long as the model is useful for explaining phenomena. It only becomes a problem when the assumptions that don’t hold have very important consequences for the conclusions drawn as a basis for policy.

I also want to once again express my appreciation for AAY for providing a level of transparency that is quite rare in government circles. It is very rare for anyone to document the studies used by the government as a policy guide, so that civil society like me can participate in the debate. As someone still junior, my input may not be that important, but it’s nice to be able to follow the thinking of Indonesia’s policy architects and learn from it. Hopefully this kind of openness will spread to all government sectors.

I’ll stop here for now. Maybe next time we can try modeling that production nest if I have some spare time. It’s gonna be fun! It’s been a long time since I’ve tinkered with CGE. Alright, see ya next post.

Honestly, I’m not that great at CGE either, so please take this post with a grain of salt. ↩︎
BTW, MPC <1 is what makes in-kind assistance like MBG potentially more effective at boosting consumption than cash transfers. Worth a separate discussion. ↩︎

Indonesian consumption growth, labor share, and saving rate

Sun, 23 Mar 2025 12:06:59 +0700

Recently, there have been many reports about the weakening of Indonesia’s middle class. The middle class in Indonesia has been shrinking since 2018 and now makes up only 17% of the total population. However, consumption growth often relies on the middle class. Unsurprisingly, Indonesia’s consumption growth has long lagged behind that of other countries in the region, except Thailand.

Indonesia’s consumption growth has been below the general pace of economic growth for quite some time. How is it possible for economic growth to outpace consumption growth?

One explanation is that this economic growth benefits those with a low consumption share. Consumption share refers to the portion of an individual’s income spent on consumption. Typically, this group consists of wealthy individuals or corporations. These entities already have high consumption levels. Any additional income is more likely to be allocated toward increasing assets (savings) or spending abroad. I previously discussed this issue in The Conversation Indonesia.

I will attempt to explore this theory by presenting two points: (1) Indonesia’s saving rate is increasing, and (2) the labor share is decreasing. I illustrate these two points using data from Total Economy - Integrated Institutional Sector Accounts (trillion rupiahs), 2016 - 2023 from BPS. This balance sheet is a very useful and interesting dataset as it includes calculations from all three methods of calculating Gross Domestic Product (GDP). I have made slight adjustments to the data, which can be viewed at the end of this post.

Increasing Saving Rate

The saving rate reflects the proportion of income saved by the population. A high saving rate indicates that people save more than they spend on consumption. As discussed above, the economic balance sheet does not differentiate the owners of the saving rate, but a significant increase in the saving rate typically signals a rising income share among groups more likely to save, such as wealthy individuals or corporations.

import seaborn as sns
import matplotlib as plt
import pandas as pd

data=pd.read_excel("neraca.xlsx",sheet_name="Sheet3")
dat=data[["tahun","LS","KS"]]
dat=dat.melt("tahun",var_name="jenis",value_name="nilai")
det=data[["tahun","srhh"]]

ax=sns.lineplot(data=det,x="tahun",y="srhh")
ax.set(xlabel="",ylabel="(%)",title="Saving rate (i.e., disposable income minus consumption),\nfrom Neraca Ekonomi BPS")

[Text(0.5, 0, ''),
Text(0, 0.5, '(%)'),
Text(0.5, 1.0, 'Saving rate (i.e., disposable income minus consumption),\nfrom Neraca Ekonomi BPS')]

Since 2016, Indonesia’s saving rate has continued to increase. COVID-19 forced everyone to reduce their savings, even increasing debt to maintain consumption. However, since then, the saving rate has risen again, even higher than usual. It seems that COVID-19 really dealt a blow to Indonesian consumers.

Some analysts say that one of the problems is weak wage growth. Reed, Pasha, and Gonzales, in their report for the World Bank Group, wrote about weak wage growth and profits in manufacturing companies with high foreign ownership. The image excerpt below is from the report.

In the economic balance sheet, we can also calculate how much primary income is allocated for various purposes. I divide it into three: taxes, employee expenses, and profits. Profits consist of income that has already been reduced by taxes and employee expenses. I’ve plotted this below, but I excluded taxes as the amount is very small, around 5% of the total balance sheet¹.

ax=sns.lineplot(data=dat,x="tahun",y="nilai",hue="jenis",palette="tab10")
ax.set(xlabel="",ylabel="(%)",title="Labor share (LS) and profit share (KS) of income, from Neraca Ekonomi BPS")

[Text(0.5, 0, ''),
Text(0, 0.5, '(%)'),
Text(0.5, 1.0, 'Labor share (LS) and profit share (KS) of income, from Neraca Ekonomi BPS')]

And it’s quite apparent that there has been progressive growth in the share of income absorbed by companies. As hypothesized above, an increase in the income share for companies is more likely to be saved, while an increase in the income share for employees is more likely to be consumed. So, at least based on this result, it makes sense.

Anyway, this post is actually just to share the chart. We can have a discussion outside of this post. Let me know what you think.

Below is the data I used. All of it was downloaded from Total Economy - Integrated Institutional Sector Accounts (trillion rupiahs), 2016 - 2023 with slight adjustments.

data

	tahun	L	K	T	va/output	LS	TS	KS	srhh
0	2016	40.576885	55.127270	4.295845	52.877975	41.728750	4.435895	53.835356	30.864658
1	2017	41.472019	53.934836	4.593145	53.108782	42.704020	4.746633	52.549347	31.795110
2	2018	39.977940	55.237447	4.784614	52.986106	41.343331	4.965863	53.690807	32.159447
3	2019	39.252272	55.950651	4.797077	52.971298	40.403369	4.954449	54.642181	31.793236
4	2020	37.304707	58.410155	4.285138	53.170697	38.341668	4.418889	57.239443	30.021169
5	2021	37.283856	58.021987	4.694157	53.484228	38.272325	4.833429	56.894245	33.807914
6	2022	35.453018	59.557783	4.989199	53.799988	36.094115	5.095040	58.810845	38.504459
7	2023	34.970118	60.019108	5.010774	53.400921	36.068580	5.184493	58.746927	36.506632

This 5% figure is quite interesting because Indonesia’s corporate income tax rate is much higher than that. This could make for an entire post on its own—maybe another time. ↩︎

Tips and Tricks for Writing

Wed, 19 Mar 2025 14:00:00 +0700

Indonesia and BRICS: A conversation with Lili Yan Ing and Krisna Gupta

Wed, 19 Feb 2025 14:00:00 +0700

In this Global Seminar, we talk with Dr Lili Yan Ing and Dr Krisna Gupta on the economics of Indonesia joining BRICS, a bloc formed by Brazil, Russia, India, and China in 2009 with the addition of South Africa in 2010. What are the benefits to Indonesia?

Sure, here’s the translation of your article:

the following english language article is translated from Bahasa Indonesia by Copilot

On February 19, 2025, I had the opportunity to share with the ANU Indonesia project about Indonesia’s membership in BRICS. I was very excited because I was on the same panel with Dr. Lili Yan Ing, the Secretary General of ERIA. She is an expert in international trade, and I read several of her works during my PhD studies. The panel was moderated by Dr. Arianto Patunru.

At the event, I started by outlining some popular reasons why Indonesia should join BRICS. Of course, as an economist, I used economic reasons and tried not to touch on international relations or defense politics. In my opinion, the economic reasons for joining BRICS do not seem very strong. See the details below, haha.

Unfortunately, BRICS has not yet become an “official” institution. Joining BRICS does not come with any commitments. Perhaps one of the advantages of Indonesia joining BRICS at this time is that Indonesia can have a voice during the formation of this institution.

However, we should not be too distracted by the BRICS issue at this time, because the more central issue is the cold war between the US and China. In my opinion, BRICS will be used by China as a vehicle to challenge US hegemony. Trump even said he would monitor and take action against BRICS members if they threatened the Dollar.

We don’t know how serious Trump is because his words are hard to trust. So far, Trump has been attacking Canada, the EU, Mexico, areas that are friends of the US and needed by the US in challenging China. But it is very central for Indonesia to continue engaging with the US.

The question is, should Indonesia move forward alone, or bring regional interests like ASEAN? Trump is very transactional. It is not impossible that the concessions he asks from Indonesia will sacrifice the interests of other countries in the region. Hopefully, it’s just me being pessimistic.

The talk was recorded and can be seen here

Checking BPS open unemployment rate data

Sat, 18 Jan 2025 14:06:59 +1100

Some time ago, my colleague Anton and I were invited to chat on the Indah G Show platform. It was a casual, mostly spontaneous conversation. Unfortunately, in these impromptu settings, I often don’t have data memorized, and there’s no opportunity to check data – or at least doing so would break the flow. Quite different from chatting on Twitter, which is asynchronous.

One such moment was when we were discussing the unemployment rate. At the time (the recording was around October or November 2024), news about deflation and unemployment was widespread, and many were claiming unemployment was high. I said that the unemployment rate at the time probably wasn’t that different from previous periods. I also mentioned that unemployment rates can vary by region. Unfortunately, checking provincial unemployment figures takes a bit of time.

Now it’s time to check the data. I claimed that unemployment was probably not higher than usual in 2024, and that it could be high in Aceh but low in Jakarta, so it’s not so bad (or at least not worse than typical).

I use the open unemployment rate by province table from BPS. BPS uses the Sakernas survey conducted in two different months: February and August. I plot the last 10 years, separately for February and August. I select a few provinces: Aceh and Jakarta (since I mentioned these on the podcast), plus Yogyakarta, Bali, and North Maluku (no particular reason). The data is shown below.

import seaborn as sns
import matplotlib as plt
import pandas as pd

data=pd.read_excel("ur.xlsx")
feb=data.query("bulan=='Februari'")
aug=data.query("bulan=='Agustus'")

ax=sns.lineplot(data=feb,x="tahun",y="ur",hue='daerah',palette='Paired')
sns.move_legend(ax,loc='lower right',bbox_to_anchor=(1.35,.5))
ax.set(xlabel="",ylabel="Unemployment rate (%)",title="Annual unemployment rate, February")

[Text(0.5, 0, ''),
Text(0, 0.5, 'Unemployment rate (%)'),
Text(0.5, 1.0, 'Annual unemployment rate, February')]

ax=sns.lineplot(data=aug,x="tahun",y="ur",hue='daerah',palette='Paired')
sns.move_legend(ax,loc='lower right',bbox_to_anchor=(1.35,.5))
ax.set(xlabel="",ylabel="Unemployment rate (%)",title="Annual unemployment rate, August")

[Text(0.5, 0, ''),
Text(0, 0.5, 'Unemployment rate (%)'),
Text(0.5, 1.0, 'Annual unemployment rate, August')]

Obviously, 2020 saw the COVID-19 pandemic, which caused unemployment rates to spike. Bali and Jakarta experienced far worse problems than other regions and the national average (red line), which makes sense given that both are highly service-oriented economies.

Overall, we can see that by August and February 2024, unemployment rates had largely returned to the pre-pandemic trend. Moreover, Jakarta has historically had a high unemployment rate – above the national average – just like Aceh. In fact, Jakarta’s unemployment rate is often higher than Aceh’s.

In short:

(1) the unemployment rate is probably not as bad as the news suggests, since it’s approaching the 10-year trend and has generally been improving since the COVID disruption. (2) Jakarta has an unemployment rate comparable to Aceh’s, and both are above the national average.

It’s also important to note that the unemployment rate alone is insufficient for labor market analysis, as many other indicators need to be considered. First, it’s important to know the labor force size, which can rise due to various factors (e.g., people who normally continue to university decide not to, thus entering the labor force), or even fall (people become so desperate about finding work that they give up and exit the labor force). Then there are hours worked (BPS defines someone as employed if they work at least 1 hour per week, so someone could be working but with reduced hours, indicating a weak labor market). And most importantly, the wages paid for those hours. I previously discussed the potential impact of minimum wages on the labor market here.

In other words, it’s quite possible that the unemployment rate hasn’t changed much, but what’s actually happening is a reduction in hours worked or wages. This is like when snack prices stay the same but the contents shrink.

Analyzing the labor market is not straightforward, which is why even among economists, labor economics is a whole specialization. Hopefully this helps you be more critical when you see these numbers going forward.

By the way, BPS has started disseminating more wage data – a very welcome development. Here’s one example of wage data on the BPS website. Also be careful looking at averages, since averages will be skewed and inflated by high earners, to some degree.

Hope this post helps clarify things.

## This is a copilot generated code.
import pandas as pd
from io import StringIO

# Tab-delimited data
data = """daerah\t2015\t2016\t2017\t2018\t2019\t2020\t2021\t2022\t2023
ACEH\t11226\t13627\t14809\t13814\t15065\t18099\t17037\t16772\t17585
DKI JAKARTA\t17012\t23181\t23826\t25238\t25236\t28420\t30662\t32685\t42354
DI YOGYAKARTA\t10440\t12070\t12281\t12554\t13275\t15771\t15098\t14916\t16478
BALI\t11038\t14852\t15624\t15889\t16408\t17775\t17662\t16857\t18521
MALUKU UTARA\t13607\t15226\t15760\t15864\t17425\t23338\t21131\t18278\t27078
INDONESIA\t11434\t14068\t14731\t15275\t15823\t17696\t18089\t17542\t19027"""

# Use StringIO to read the string data into a pandas dataframe
df = pd.read_csv(StringIO(data), delimiter='\t')
df_long = pd.melt(df, id_vars=['daerah'], var_name='year', value_name='value')

## Seaborn
ax=sns.lineplot(data=df_long,x="year",y="value",hue='daerah',palette='Paired')
sns.move_legend(ax,loc='lower right',bbox_to_anchor=(1.35,.5))
ax.set(xlabel="",ylabel="Rp",title="Hourly wage by province, Rupiah/hour")

[Text(0.5, 0, ''),
Text(0, 0.5, 'Rp'),
Text(0.5, 1.0, 'Hourly wage by province, Rupiah/hour')]

Probability Theory

Tue, 14 Jan 2025 08:00:00 +0700

Labor-Intensive Industry and Labor Conditions in Indonesia

Fri, 27 Dec 2024 14:00:00 +0700

Sustainable Trade Balance

Tue, 03 Dec 2024 09:30:00 +1100

Jakarta's Path to Global City Status vs Singapore

Fri, 29 Nov 2024 13:00:00 +1100

Checking Indonesia's five consecutive months of deflation

Sat, 05 Oct 2024 14:06:59 +1100

Indonesia has recently been buzzing about five consecutive months of deflation. Some analysts like have argued this signals weakening purchasing power among the middle class. But considers it not a serious concern because core inflation (inflation excluding food and energy prices, which tend to be volatile) on a month-to-month basis is still positive.

We can easily check this. I use data from , specifically Table VIII.1. I visualize it with seaborn, though I haven’t used Python in a while – I’ve been using R more recently, so apologies if the code isn’t the cleanest.

Let’s first look at overall inflation. Typically, cumulative-over-cumulative inflation can be set aside. Month-over-month usually hovers below 1%, and we can indeed see that m-o-m has been in negative territory for the last 5 months. Year-over-year inflation remains around 2%, consistent with Bank Indonesia’s target. But we can see that y-o-y inflation was relatively high throughout 2022, then started declining in 2023 through the present. What happened in 2022?

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import datetime
## Read data from the economist
url=f'https://docs.google.com/spreadsheets/d/1--ddaTIthvm3kQe97GTEqj1Rb1km0fwI/export?gid=1542512999#gid=1542512999&format=xlsx'
data=pd.read_excel(url,engine='openpyxl')
## Create the 'real' exchange rate measures
index=data.iloc[:,0:4]
index=pd.melt(index, id_vars='date').reset_index()
sns.lineplot(data=index,y='value',x='date',hue='variable')
plt.xticks(rotation=30)
plt.axhline(0)

<matplotlib.lines.Line2D at 0x20f57727650>

2022 was the beginning of the Russia-Ukraine war. Two prices surged dramatically: food and energy! Russia, a major oil and gas exporter, and Ukraine+Russia, major exporters of food commodities (specifically wheat) and fertilizers. This naturally had a massive impact on inflation in Indonesia. So could Sri Mulyani be right that the current deflation is driven by food and energy prices stabilizing?

By the way, the chart below is taken from the . I use the overall commodity price index, food only, and energy only, all normalized to January 2021. The spike is very clear, especially in energy. Food prices were also volatile but not as extreme as energy prices.

## Read data from the economist
url=f'https://docs.google.com/spreadsheets/d/1--ddaTIthvm3kQe97GTEqj1Rb1km0fwI/export?gid=1542512999#gid=1542512999&format=xlsx'
data=pd.read_excel(url,engine='openpyxl')
## Create the 'real' exchange rate measures
index=data[['date','pall','pfood','penergy']]
index=pd.melt(index, id_vars='date').reset_index()
sns.lineplot(data=index,y='value',x='date',hue='variable')
plt.axhline(100)
plt.xticks(rotation=30)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x20f58356c60>

How about in Indonesia? If Sri Mulyani is right, and food and energy were indeed the main drivers of inflation in previous years (so current deflation reflects price normalization), we should see a similar trend in Indonesian food and energy prices. Again, I use price indices normalized to January 2021. One complication is that SEKI appears to have shifted its base year in 2024, making it difficult to create continuous charts from 2021 onward. I had to cut at January 2024. Unfortunately SEKI doesn’t explicitly note this base year change.

Below I break down various price indices by category as defined in SEKI.

Variable	Meaning
food	FOOD, BEVERAGES AND TOBACCO
baju	CLOTHING AND FOOTWEAR
util	HOUSING, WATER, ELECTRICITY AND HOUSEHOLD FUEL
rt	FURNISHINGS, HOUSEHOLD EQUIPMENT AND ROUTINE MAINTENANCE
sehat	HEALTH
trans	TRANSPORTATION
itfin	INFORMATION, COMMUNICATION AND FINANCIAL SERVICES
rek	RECREATION, SPORTS AND CULTURE
pend	EDUCATION
resto	FOOD AND BEVERAGE PROVISION/RESTAURANTS
dll	PERSONAL CARE AND OTHER SERVICES
all	GENERAL

## Read data from the economist
url=f'https://docs.google.com/spreadsheets/d/1--ddaTIthvm3kQe97GTEqj1Rb1km0fwI/export?gid=1542512999#gid=1542512999&format=xlsx'
data=pd.read_excel(url,engine='openpyxl')
## Create the 'real' exchange rate measures
index=data[['date','food','baju','util','rt','sehat','trans','itfin','rek','pend','resto','dll','all']]
index=pd.melt(index, id_vars='date').reset_index()
sns.lineplot(data=index,y='value',x='date',hue='variable',style='variable')
plt.axhline(100)
plt.xticks(rotation=30)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x20f5a83c740>

We can see that before 2024, transportation prices were the biggest contributor to inflation. In July, there was a sudden significant increase in the transportation price index – probably due to fuel prices. But food prices truly surged from late 2021 through 2022. Remember the cooking oil shortage? It wasn’t just cooking oil – many food prices rose as countries imposed food export bans globally. Interestingly, IT and financial services prices have been in deflation for a long time. Why? The index has been declining continuously since January 2021. Unfortunately energy prices aren’t directly visible here, but we can treat the transportation price index as a proxy for energy prices, given that Indonesia’s largest energy import is fuel.

Now let’s move to 2024.

## Read data from the economist
url=f'https://docs.google.com/spreadsheets/d/1--ddaTIthvm3kQe97GTEqj1Rb1km0fwI/export?gid=1542512999#gid=1542512999&format=xlsx'
data=pd.read_excel(url,engine='openpyxl')
## Create the 'real' exchange rate measures
index=data[['date','fo','ba','ut','rumt','sht','trn','it','rekre','pnd','rest','lain','umum']]
index=pd.melt(index, id_vars='date').reset_index()
sns.lineplot(data=index,y='value',x='date',hue='variable',style='variable')
plt.axhline(100)
plt.xticks(rotation=30)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x20f5aa9f500>

Sure enough, the food price index drops significantly starting from March, in line with the normalization of international food commodity prices. By August, food prices returned to January 2021 levels! Food and beverages typically form the largest share of the average Indonesian consumer’s basket, so when food prices drop, overall inflation naturally falls. Transportation also appears to have stabilized – not declining, but not rising either. Could Sri Mulyani be right?

Education prices jumped suddenly in August – could this be related to the controversy over universities raising tuition fees? And IT & financial services continue their deflationary trend. Also, personal care and other services rose significantly – what’s going on there?

But no discussion of inflation and purchasing power would be complete without looking at the Wholesale Price Index (WPI, or IHPB in Indonesian). The WPI typically reflects the producer side and is often more volatile than the consumer price index. I use SEKI Table VIII.2. It also includes the international trade price index (IHPI) consisting of import and export wholesale prices.

## Read data from the economist
url=f'https://docs.google.com/spreadsheets/d/1--ddaTIthvm3kQe97GTEqj1Rb1km0fwI/export?gid=1542512999#gid=1542512999&format=xlsx'
data=pd.read_excel(url,engine='openpyxl')
## Create the 'real' exchange rate measures
index=data[['date','tani','tambang','industri','IHPB','impor','ekspor','IHPI']]
index=pd.melt(index, id_vars='date').reset_index()
sns.lineplot(data=index,y='value',x='date',hue='variable')
plt.axhline(100)
plt.xticks(rotation=30)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x20f5b212540>

Similar pattern. International trade prices started rising as early as 2021, peaking around late 2021/early 2022 before normalizing. Recently, the export price index has risen higher than the import price index, indicating cheap imports relative to exports. This is typically good news – it means our exports fetch high prices while imports are cheap. With modest exports, we can buy lots of imports! Of course, the recent drama around Chinese imports suggests that cheap imports aren’t necessarily good for everyone.

Agricultural product prices have been rising since H2 2022. But from the end of Q1, agricultural prices started stagnating and even declined from July. This seems consistent with falling food prices. Not only are food imports cheaper, but domestic prices are falling too. Meanwhile, the overall WPI appears fairly stable.

In summary, Indonesia’s five consecutive months of deflation appear to be driven primarily by the food sector. Given that food prices had previously surged, we probably shouldn’t be too worried about this deflation – it seems to reflect food prices returning to pre-Russia-Ukraine war levels.

Non-food prices are still growing, which suggests non-food demand remains healthy. Meanwhile, falling prices could be driven by weak demand or increased production. The production shock of 2022 appears to be subsiding, allowing supply to shift rightward again. In other words, this might not be something to worry about.

However, to complement this analysis, we need to wait for Indonesia’s Q3 GDP data, which should be released soon or at the latest next month. Only then can we conclude more confidently whether this deflation is normal or alarming. That said, I disagree with because the shift from formal to informal employment is not something we want. I’m also concerned about the , which I find very alarming. But on deflation specifically, we probably don’t need to panic just yet.

What do you think? Tag me on and let’s discuss.

By the way, the data I used for these visualizations can be downloaded .

Services, manufacturing and the Indonesian third unbundling

Tue, 24 Sep 2024 14:00:00 +1100

Indonesia's Declining Middle Class: A Warning Sign?

Wed, 18 Sep 2024 14:00:00 +1100

I was invited by The Conversation Indonesia to discuss the declining middle class trend. We discussed why this is happening and how significant the implications are for Indonesia’s economy. Personally, I believe the declining middle class trend should serve as an alarm for all of us.

Safe and Comfortable in the Workplace

Tue, 17 Sep 2024 14:00:00 +1100

Unbundlling, GVC and TiVA

Tue, 20 Aug 2024 09:00:00 +0700

A chapter for ERIA on Indonesian trade in services.

Mon, 15 Jul 2024 07:28:22 +0700

A working draft. Please don’t circulate.

Indonesian trade and industry data 2018-2023 from SEKI Bank Indonesia

Mon, 08 Jul 2024 15:10:34 +0700

The recent Indonesia’s aimed at Chinese products proposed by the Indonesian Minister of Trade, Zulkifli Hasan (Zulhas), startled businesses. Some love it, some others despise it. One thing for sure though, it created (still is, matter of fact) another confusion after series of blunder by the Ministry of Trade. This 200% tariff is no different, where Luhut just the 200% tariff hype. We also have no idea in what form this tariff will be (MFN? Anti dumping? Safeguard? or even some form of NTM?) What exactly the government want? No idea.

The main reason of such tariff is the recent mass layoff in the textile industry, and also ceramic tile. Zulhas mentioned that will be covered by his new tariff, textile and ceramic stuff included. That’s why me and Deasy Pane, my long time colleague, has a check and wrote for East Asia Forum about this. We host several data we use for the piece in this blog.

Various data in this post is taken from , with the exception being table 1 where we took them numbers from WITS.

We think this tariff is not only too sudden, too big, but also potentially misses Indonesia’s actual problem. First is the fact that Indonesia’s import from China is dominated by intermediate inputs and capital goods. Table 1 shows Indonesia’s import from China by BEC taken from WITS, while table 2 shows this in general (import from all countries), sourced from SEKI. Indonesia’s consumer products from China is actually dropping in share. In fact, Zulhas’ proposed tariff will cover important textile inputs like fabrics, threads, and cloth, which are needed to make shirts and pants and other wearing apparels. Tariff will hurt downstream producers, on top of the weak rupiah they have at the moment.

In fact, we argue it can possible harm Indonesia now that the US-China trade war hinders global value chain, where Chinese firms try to avoid US tariff by moving parts of their production chain to ASEAN. Vietnam and Bangladesh are actually benefited from this diversion BECAUSE they can import chinese stuff.

Table 1. Indonesian imports from China by broad economic category

Goods category	2018	2019	2020	2021	2022
Capital value	11,487,301.6	12,321,681.1	10,828,207.3	14,880,690.1	20,127,730.3
Consumer value	5,761,119.1	6,023,792.5	5,468,645.2	6,515,606.2	7,113,856.5
Intermediate value	28,039,338.4	26,302,772.6	23,054,342.8	34,354,081.1	39,842,624.6
Capital share	25.2	27.4	27.3	26.5	29.7
Consumer share	12.7	13.4	13.8	11.6	10.5
Intermediate share	61.6	58.5	58.2	61.1	58.8

Table 2. Indonesian imports by broad economic category

year	18	19	20	21	22	23
Share of consumption goods	14.37%	14.29%	13.31%	14.18%	14.19%	14.84%
Share of material goods	72.24%	71.79%	72.44%	74.01%	72.85%	70.11%
Share of capital goods	16.98%	17.61%	17.76%	15.25%	16.25%	18.63%
Growth of consumption goods	-	-8.75%	-18.35%	39.88%	21.46%	-7.04%
Growth of material goods	-	-9.32%	-23.75%	49.10%	21.70%	-3.10%
Growth of capital goods	-	-9.41%	-17.36%	42.96%	19.70%	-10.80%

Further, we look at Indonesia’s manufacturing (non oil & gas) growth, export and import. Table 3 shows that indeed textiles and ceramic stuff (under non-metal) seems to struggle. We can say the same in the category containing footwear and plastics. But indeed, in general, outside of electronics (perhaps including vastly invested batteries) and basic metal (basically nickel stuff), Indonesian manufacturing is struggling in 2023.

Table 3. Growth rate of Indonesian industries

Year	18	19	20	21	22	23
nonmigas	4.77	4.34	-2.52	3.67	5.01	4.69
Industri Makanan dan Minuman	7.91	7.78	1.58	2.54	4.90	4.47
Pengolahan Tembakau	3.52	3.36	-5.78	-1.32	-2.34	4.80
Industri Tekstil dan Pakaian Jadi	8.73	15.35	-8.88	-4.08	9.34	-1.98
Industri Kulit, Barang dari Kulit dan Alas Kaki	9.42	-0.99	-8.76	7.75	9.36	-0.34
Industri Kayu, Barang dari Kayu, Gabus dan Barang Anyaman dari Bambu, Rotan dan sejenisnya	0.75	-4.55	-2.16	-3.71	0.59	1.20
Industri Kertas dan Barang dari kertas, Percetakan dan Reproduksi Media Rekaman	1.43	8.86	0.22	-2.89	3.73	4.52
Industri Kimia, Farmasi dan Obat Tradisional	-1.42	8.48	9.39	9.61	0.69	0.11
Industri Karet, Barang dari Karet dan Plastik	6.92	-5.52	-5.61	1.08	-4.10	-3.63
Industri Barang Galian bukan logam	2.75	-1.03	-9.13	0.89	-2.00	4.11
Industri Logam Dasar	8.99	2.83	5.87	11.50	14.80	14.17
Industri Barang dari Logam, Komputer, Barang Elektronik, Optik dan Peralatan Listrik	-0.61	-0.51	-5.46	-1.62	6.71	13.67
Industri Mesin dan Perlengkapan	9.49	-4.13	-10.17	11.43	11.37	-0.03
Industri Alat Angkutan	4.24	-3.43	-19.86	17.82	10.67	7.63
Industri Furnitur	2.22	8.35	-3.36	8.16	-1.99	-2.04
Industri Pengolahan Lainnya, Jasa Reparasi dan Pemasangan Mesin dan Peralatan	-0.83	5.17	-0.88	-1.64	6.08	-2.10
PRODUK DOMESTIK BRUTO	5.17	5.02	-2.07	3.70	5.31	5.05

Table 4. growth of Indonesian Manufactures non oil and gas import by source

countries	19	20	21	22	23
USA	-11.30%	-8.60%	17.92%	6.72%	-1.11%
EU	-11.58%	-11.12%	8.31%	6.50%	17.18%
ASEAN	-7.56%	-20.58%	27.41%	11.54%	-8.21%
India	-14.91%	-7.83%	72.62%	9.77%	-16.75%
Japan	-12.17%	-31.71%	41.39%	11.66%	-4.95%
South Korea	-5.67%	-11.00%	38.55%	12.58%	-6.00%
Pakistan	-40.88%	-48.26%	-2.23%	-1.22%	62.29%
China	-1.32%	-11.38%	40.05%	21.65%	-7.62%
Australia	-7.77%	-13.23%	106.29%	7.33%	-1.57%

Table 5. growth of Indonesian Manufactures non oil and gas export by source

countries	19	20	21	22	23
Amerika Serikat	0.77%	4.61%	38.56%	9.88%	-18.18%
Uni Eropa	-15.06%	-1.97%	36.59%	16.04%	-20.85%
ASEAN	2.27%	-9.75%	28.59%	26.11%	-15.90%
Jepang	-15.33%	-6.21%	30.60%	37.74%	-18.88%
Korea Selatan	-18.64%	-8.25%	42.32%	33.48%	-19.00%
Pakistan	-19.50%	23.88%	61.01%	11.21%	-32.47%
Republik Rakyat Cina	5.99%	15.48%	70.96%	24.43%	-1.94%
Australia	-2.60%	14.52%	24.18%	7.53%	-7.50%

Analysis of the Effect of Indonesia-Japan Economic Partnership Agreement (IJEPA) on the Trade in Service Sector in Indonesia

Wed, 29 May 2024 09:12:42 +0700

Mobile Legends: Bang Bang

Sun, 26 May 2024 09:00:00 +0700

I’ve started playing video games less often. Currently, there are 3 games that I still play until now:

Guild Wars 2.
Monster Hunter Wilds.
Mobile Legends: Bang Bang.

Guild Wars 2

I’ve always enjoyed playing MMORPGs, and Guild Wars 2 is one game I still play to this day. I’ve been playing this game for a long time—probably since I was in graduate school for my master’s degree. I play it on and off, and since this game doesn’t have vertical progression, it’s great for casual on-and-off gameplay. I main mesmer and mostly do PvE content.

I play in the America region on the Dragonbrand server. Hit me up with my ID at Madboy.6804.

Monster Hunter Wilds

It’s a new game, and I’ve been a fan of Monster Hunter for a long time. I mainly use the insect glaive and bow, but I also enjoy using the long sword. I was introduced to the series by my Filipino PhD colleague back when Monster Hunter World came out. That’s why when Wilds launched, I bought it immediately. However, my Pinoy friend and I have both been busy lately, so we haven’t had time to play together—haha.

Mobile Legends: Bang Bang

As a former DotA player, I think it’s only natural that I eventually started playing this. Again, I play it on and off. I mostly play as support/roamer, occasionally filling in other lanes, but I definitely can’t handle assassin or jungle roles.

Add me my id 242310541.

The trade balance and why we can't buy nice things

Mon, 20 May 2024 15:01:51 +1000

Labor-machine substitution in agriculture

Sun, 25 Feb 2024 15:10:34 +0700

Today, February 25, 2024, there’s a lively online discussion about Indonesia’s increasingly high food prices. Some people are comparing rice prices abroad vs. in Indonesia, asking why other countries can keep prices low. The answer, of course, is that abroad (at least in Western countries) rice can be imported freely without government intervention. The follow-up question: if we’re an agrarian country, how can domestic production be more expensive than imports? There are many answers to this, and they’ve been addressed elsewhere – for instance in or . Excellent reads, both of them.

This post is related but focuses specifically on farmer numbers and productivity. Why? Because I’m currently teaching international economics and this is a perfect illustration of factor substitution as described in the Heckscher-Ohlin (HO) model. In Indonesia, there are many farmers but little farmland. Indonesia imports soybeans from the US, even though the US has even fewer farmers than Indonesia.

TLDR: This ultimately comes down to a discussion of labor intensity vs. capital intensity in agriculture, where in the US, machinery plays a far greater role than labor, while Indonesia is the opposite. For those who want to understand how economists analyze factor intensity, see the appendix at the bottom.

Let’s continue with the factor intensity discussion.

Heckscher-Ohlin and factor intensity

In Heckscher-Ohlin (HO) trade theory, there is substitution between labor and capital (machinery). Having fewer farmers is fine as long as they’re replaced with an appropriate amount of machinery.

Suppose there are 2 production options with the same output:

10 farmers with 10 hoes
1 farmer with 1 tractor

In case 1, most expenditure clearly goes to wages since you’re paying 10 people. In case 2, wages are a small share of total costs since you’re only paying 1 farmer. However, capital expenditure in case 1 is much smaller, especially since hoes are cheap (though as President Jokowi once said, ).

This is the key difference between the cost structure of Indonesian farmers and those in developed countries. In case 2, if the farmer asks for a raise, it won’t significantly increase total production costs. But in case 1, a wage increase burdens production costs heavily because 10 people need raises.

I compare the production cost structure of Indonesia vs. the United States. I use soybean production cost data for 2017 (because BPS only has Indonesian data through 2017). ; . Not all cost items are shown – only those present in both datasets.

Item	USA (%)	IDN (%)
Seed	9.25	6.53
Fertilizer	3.99	4.97
Chemicals	4.28	4.02
Labor	3.61	47.23
Rent/interest	0.13	0.12
Land	22.77	28.89
Taxes & insurance	1.70	0.86
Depreciation	14.50	1.98
Energy etc.	2.16	1.26

The contrast in labor and depreciation is stark. For Indonesian soybean farmers in 2017, 47% of production costs went to labor. For US farmers, labor was only 3.61% of total costs – even though we know US wage levels are much higher than Indonesia’s. This means that in the US, despite expensive wages, labor costs are a small share because they use far fewer farmers than Indonesia does.

Depreciation is also strikingly different. Capital depreciation in the US accounts for 14.5% of total production costs, while in Indonesia it’s only 2%. This means Indonesian farmers use far less machinery than US farmers. Moreover, the rental/interest share is identical in both cost structures, despite the US having much more machinery. This implies that interest rates in the US are cheap.

In other words, to grow soybeans, you can either use many farmers with minimal machinery (manual), or few farmers with expensive machines (semi-automation). Which strategy is adopted depends on the relative abundance of factors. In Indonesia, there are many workers suitable for farming while capital for buying machines is relatively scarce (production interest rates in Indonesia are very high). In the US it’s the opposite: suitable farm workers are fewer (hence expensive) but capital is abundant (pre-COVID interest rates in the US were very low). This is the substitution effect at the heart of the HO model.

Key takeaways & disclaimer

The main conclusions of this post:

For efficient agriculture, factor usage must match factor intensity. Some crops may need more machinery and fewer workers. A country that persists in using many workers in an industry that would be more efficient with machinery will have higher production costs than a country using machinery.
A country where labor is cheap relative to machinery will struggle to compete on price with a country where machinery is relatively cheaper, in capital intensive industries. Why? Because the labor-cheap country tends to use more labor even in industries that are inherently capital intensive, leading to inefficiency.
It’s actually more efficient to export labor intensive products and use the proceeds to import capital intensive products.

Disclaimer: the problems in Indonesian agriculture are complex. It’s not just about mechanization. The table above also shows that land constitutes a large share of production costs in both countries – land has no substitute in either the US or Indonesia. The difference is obviously that the US has far more land. There are also irrigation issues where Mekong River countries (Vietnam, Thailand) have advantages over Indonesia, even though their labor structures are similar (discussed in linked above). And more.

When we economists analyze the real world, there are of course many more considerations. For instance, factors of production aren’t limited to just labor and capital. In fact, Indonesia exports land-intensive products like mining and plantation commodities. Labor can also be divided into low-skill and high-skill categories. But if I kept going this post would never end.

Hope this HO case study is useful, especially for those studying international economics.

Appendix: A crash course on factor intensity

In standard micro and macroeconomics, we typically begin studying production with the general function:

$$Q=f(K,L)$$

where $K$ is the amount of capital used and $L$ is the number of workers employed to produce output $Q$. In introductory courses, production factors are described as two: capital and labor¹.

In a competitive economy, factors are compensated equal to their marginal productivity²:

$$\frac{df(K,L)}{dL}=w$$$$\frac{df(K,L)}{dK}=r$$

where $w$ is the wage and $r$ is rental/depreciation cost.

If production has a cost structure like:

$$TC=w \times L+r \times K$$

where total cost (TC) equals wages times workers plus rental times capital, then an economy is said to be labor intensive if labor’s share of total cost dominates capital’s share. That is, an economy is labor intensive if:

$$ \frac{w \times L}{TC} > \frac{r \times K}{TC} $$

Observing marginal productivity directly is impractical, while costs can still be calculated. Relying on the assumption that cost shares approximate productivity shares, we assess factor intensity through cost shares.

Ownership of factors of production was historically a major source of debate, pitting capitalists (owners of K) against workers (owners of L). The struggle was over who gets a larger share of Q. ↩︎
Theoretically, productivity is calculated using marginal product or the first derivative of Q with respect to each factor. In practice, this is extremely difficult. Very strict conditions are required. ↩︎

Hilirisasi dan Peningkatan Kompleksitas Ekonomi

Mon, 22 Jan 2024 14:44:52 +1000

Indonesia doubles down on nickel export bans and downstreaming

Mon, 27 Nov 2023 06:09:00 +1100

Greening the grid and what it takes

Thu, 23 Nov 2023 08:00:00 +0700

Visi Misi Pemilu 2024: Menakar janji industrialisasi dan hilirisasi Paslon Capres-Cawapres

Tue, 14 Nov 2023 06:09:00 +1100

The Heterogeneous Impact of Tariff and NTMs on Total Factor Productivity of Indonesian Firms

Mon, 30 Oct 2023 11:00:00 +0700

Indonesia’s stubborn logistics dilemma

Fri, 18 Aug 2023 00:00:00 +1100

Does ASEAN matter? How the summit tackles current challenges

Sat, 24 Jun 2023 09:00:00 +0700

The audio isn’t the best, but the two speakers are audible.

Public policy analysis: a discussion

Wed, 21 Jun 2023 14:00:00 +0700

The Complexity of Evaluating the Performance of the Directorate General of Agro-Industry

Fri, 09 Jun 2023 09:00:00 +0700

Demographic bonus: blessing or curse?

Thu, 25 May 2023 06:35:25 +1100

On Thursday, July 6, 2023, the Fiscal Policy Agency (BKF) at the Ministry of Finance held an event called fiscal day. One of the themes raised was a policy discussion on Indonesia’s demographic bonus. This discussion is highly relevant, especially now as Indonesia pursues investment and economic growth, trying to prevent “growing old before growing rich.”

BKF opened with interesting statistics. According to them, in 2020, Indonesia’s working-age population reached 191 million people, or about 70.7% of the total population. This is positive because it means the ratio of working to non-working population is quite high. By 2028-2031, this figure will begin to decline. Unfortunately, according to BKF, this demographic bonus could become a problem if the population isn’t productive.

According to BKF’s briefing document:

Unfortunately, human resource issues in Indonesia still face numerous challenges. Among them, Indonesia’s workforce is still dominated by primary school graduates and below at 39.76%. Workers with junior high school education make up 18.24%, senior high school 19.18%, and vocational high school 9.31%. Meanwhile, workers with higher education degrees remain very limited: Diploma I/II/III at 2.20%, and Diploma IV, Bachelor’s, Master’s, and Doctoral degrees at 9.31% (BPS, February 2023). This condition impacts relatively low labor productivity and competitiveness.

BKF also notes Indonesia’s low Human Development Index score.

Compared to neighboring countries, Indonesia’s HDI is similar to Vietnam’s, still higher than India’s, and still below Malaysia and Thailand. What’s quite surprising is how China and Vietnam are catching up with Indonesia while Indonesia appears to be standing still.

Several interesting points for discussion:

Is Indonesia’s current demographic bonus showing signs of becoming a blessing, or is it signaling trouble?
Are current government policies adequate? What sectors should be prioritized to maximize the demographic bonus?
What policies should the government prepare for the aging population era after the demographic bonus ends?
Discuss and formulate better policy recommendations and innovations related to the demographic bonus!

Blessing or disaster?

Looking at some youth statistics, there are indeed signs pointing toward disaster. The 2 charts below show Indonesian youth who are idle (NEET) or unemployed. The numbers are below India’s but still above neighboring countries.

Open unemployment by education level (%)

Education level	2020	2021	2022
Primary school and below	3.61	3.61	3.59
Junior high	6.46	6.45	5.95
Senior high	9.86	9.09	8.57
Vocational high	13.55	11.13	9.42
Diploma	8.08	5.87	4.59
Bachelor’s and above	7.35	5.98	4.80

The lowest unemployment rate is actually among primary school graduates and below, while vocational school graduates – who are supposed to be trained for work – have the highest rate. So blaming education per se may not be entirely accurate, since more education actually correlates with more unemployment, to some extent. Could there be a mismatch? This is of course on top of . According to SMERU:

Currently, only 37% of teachers have teaching qualifications as stipulated by the 2005 Teacher Law, and about 15% of teachers are absent on any given workday across Indonesia.

Not only that, according to BPS statistics, of those employed work in the informal sector. According to research, workers in the informal sector are at higher risk of income loss, especially during shocks.

Poorly educated youth lacking job opportunities may be prone to involvement in vigilantism. Incidentally, Indonesia also has a vigilantism problem. This has been studied by , who examines the prevalence of vigilantism in Indonesia and India, a country with similar youth unemployment. Additionally, according to , local vigilante activity increases near local election periods.

In short, it’s hard to be optimistic about Indonesia’s demographic bonus.

Are current policies sufficient?

According to BKF, several policies are related to leveraging the demographic bonus:

Allocating 20% of the state budget (APBN) for education spending. In 2023, this allocation reached IDR 612.2 trillion, distributed across several ministries/agencies (Kemendikbudristek, BRIN, Kemenag, etc.) and through Regional Transfers and Village Funds.
The Kartu Prakerja (pre-employment card) program. In 2020, the budget was IDR 10 trillion for 2 million participants. In 2023, the budget allocation dropped to IDR 2.67 trillion targeting 1 million participants.
Vocational education development through super tax deduction governed by Presidential Regulation No. 68/2022 on Vocational Education and Training Revitalization.

According to BKF, these policies seem inadequate. For example, the 20% APBN allocation hasn’t improved school quality or graduates (evident from PISA scores that have ).

That’s probably fair. If asked what the recipe for improving school quality is, I’d probably be confused too. But school improvement won’t change things quickly. Currently, even those responsible for improving education in Indonesia don’t seem to know what to do, while the demographic bonus will end soon. Education systems certainly need fixing, but we also seem to need quick-win policies.

The pre-employment card can also be critiqued. If education is problematic, then graduates’ ability to learn new concepts and skills may also be problematic.

We must also not forget that quality humans come from healthy mothers and non-stunted children. Indonesia’s policies are (both supply-side and demand-side policies) which may actually promote diabetes while lacking macro and micro nutrients from other foods. Maternal and child nutrition is critical, and access to nutritious food at affordable prices is essential. in this regard.

Lastly, employment also faces problems because investment may be insufficient. This is a classic issue. Indonesia is actually quite competitive, but many things discourage people from starting investments. There are many problems – legal uncertainty, bureaucratic red tape, and .

Interest rates are high, banks don’t accept production equipment as collateral, and the government keeps issuing bonds with attractive yields (crowding out). Domestic investors lack liquidity, while foreign investors are happy to enter Indonesia because interest rates abroad are low and Bank Indonesia is very committed to keeping the currency stable (by raising interest rates haha). Crowding out becomes more apparent when the export sector doesn’t help boost domestic liquidity.

For quick wins, we need jobs that can hire our current demographic, which is dominated by 12-year compulsory education graduates. Instead, we have Industry 4.0 policies that require educated workers and employ few 12-year-education graduates.

Post-demographic-bonus policies

This is actually a very difficult question. Indonesia currently doesn’t have a sufficient saving rate. It seems like it’ll be hard to prepare. The currency and economy are too insignificant for cheap refinancing like Japan or China. The only current path is to boost healthy economic growth and improve education. But this means massive investment and reaping returns as quickly as possible, preferably before 2035. Is it possible? The fear is that going all-out might lead to another 1998. It’s still difficult without strengthening education and institutions, which takes time.

Perhaps one way out is to prepare for overseas employment, if domestic conditions are this difficult to fix. There are many overseas jobs that provide decent wages and enough money for remittances. We’ve heard a lot about TKI (Indonesian migrant workers), but there are opportunities in sectors like senior care or nursing homes, given that other countries will age first. Recently, Indonesia and Australia established an engineering cooperation that may make it easier for Indonesian engineers to work in Australia in the future. But this is certainly not easy either.

Evaluation of existing programs must continue. BKF isn’t in a position to halt a policy, but at least they can conduct evaluation studies.

Goodbye dollar? Not quite yet

Thu, 25 May 2023 06:35:25 +1100

The topic of de-dollarization has been a hot-button issue lately. De-dollarization – the effort by many countries to reduce dependence on the US dollar (USD) – is reportedly being championed especially by BRICS nations (Brazil, Russia, India, China, and South Africa).

Indonesia is no exception. Bank Indonesia has established Local Currency Settlement (LCS) agreements with South Korea, Japan, China, Malaysia, and Thailand. In other words, Indonesia and its partner countries can now use their respective domestic currencies to settle cross-border payments without using the US dollar.

According to this article, LCS is important for minimizing exchange rate risk in bilateral transactions. That may be true if the currency usage focuses solely on trade in goods and services. But these days, currencies are also used for asset transactions. In fact, the use of currencies for asset trading may well dominate their use for goods and services.

Currency as a medium for asset exchange?

To answer this, some technical knowledge of the balance of payments (BoP) is needed. The BoP consists of two main accounts: the current account and the financial account.

In an open economy, we have:

$$ Y=C+I+G+(X-M) $$

Meanwhile, saving is income that is neither consumed nor taxed, so S=Y-C-G (assuming total taxes = G). Therefore:

$$ Y-C-G-I=X-M $$

Replacing Y-C-G with S:

$$ S-I=X-M $$

S-I is the financial account, while X-M is the current account. One balances the other.

Put simply, a current account surplus means you sell goods but get paid with IOUs. Or more precisely, you accumulate savings that you don’t spend right away.

Back to the currency question: for countries with trade surpluses, it means they store the proceeds from their exports in the form of foreign assets or investments. This is where a problem emerges: why would we hold another country’s currency?

Holding foreign currency may be useful if you plan to import from that country. But this logic breaks down when the countries in these partnerships are NEARLY ALL IN SURPLUS! See for yourself:

Above I show the trade balance deficit (as % of GDP) for BRICS, Indonesia, Japan, and South Korea. As you can see, nearly all of them run surpluses. In other words, they generally supply more goods to the world than they buy, and the proceeds are stored as assets rather than spent.

Undervaluation

This may be because these countries largely follow a mercantilist philosophy – an economic doctrine that prioritizes export promotion and dislikes imports to boost industrial competitiveness. Export-oriented countries typically prefer a weak currency, because a weak currency makes exports cheap and imports expensive, pushing X-M into surplus.

For these countries, currency intervention is fully justified to keep their currencies weak, even though they should arguably be valued higher. We can check for this by looking at undervaluation – comparing the nominal exchange rate with the “true” value. One approach is to use the Big Mac Index from The Economist.

#import wbdata as wb

import datetime

## Read data from the economist

url='https://raw.githubusercontent.com/TheEconomist/big-mac-data/master/output-data/big-mac-raw-index.csv'

bmi=pd.read_csv(url,parse_dates=['date'])

## Create the 'real' exchange rate measures

bmi['bmi']=bmi['dollar_ex']+bmi['dollar_ex']*bmi['USD']

bmi.iso_a3.unique() # Showing unique iso_a3 codes for the next cell

array(['ARG', 'AUS', 'BRA', 'GBR', 'CAN', 'CHL', 'CHN', 'CZE', 'DNK',
'EUZ', 'HKG', 'HUN', 'IDN', 'ISR', 'JPN', 'MYS', 'MEX', 'NZL',
'POL', 'RUS', 'SGP', 'ZAF', 'KOR', 'SWE', 'CHE', 'TWN', 'THA',
'USA', 'PHL', 'NOR', 'PER', 'TUR', 'VEN', 'EGY', 'COL', 'CRI',
'PAK', 'SAU', 'LKA', 'UKR', 'URY', 'ARE', 'IND', 'VNM', 'AZE',
'BHR', 'HRV', 'GTM', 'HND', 'JOR', 'KWT', 'LBN', 'MDA', 'NIC',
'OMN', 'QAT', 'ROU'], dtype=object)

b=[]

ctr=('CHN','IDN','IND','ZAF','RUS','BRA','JPN','KOR')

for i in ctr:

 bmii=bmi.loc[(bmi['iso_a3'] == i)]

 b.append(bmii)

b=pd.concat(b)

sns.lineplot(data=b,x='date',y='USD',hue='name',palette='tab10')

plt.axhline(0,color='grey')

<matplotlib.lines.Line2D at 0x2b17256fa48>

As we can see, except perhaps for Brazil, BRICS countries, Indonesia, Japan, and South Korea all have currencies that are undervalued relative to the USD. In other words, there are indications that these countries deliberately suppress their currencies to keep their products “competitive” in global markets.

Using the Big Mac Index isn’t exactly rigorous, but if you’re interested in diving deeper into currency misalignment, check out my colleague Wishnu Mahraddika’s publication here.

In other words, these countries appear to actively pursue trade surpluses! But not everyone can be in surplus at the same time, can they? This becomes even more stark when we compare trade balances in USD terms:

Who can absorb China’s surplus??

Uncle Sam to the rescue

So far, the surpluses of these net-exporter countries have been absorbed by none other than the United States.

As you can see, it is the US that has been absorbing these countries’ surpluses. How does the US government manage this? Through debt, of course! Everyone wants to sell to the US because it has deep pockets, and because everyone sells to the US, everyone holds US dollars, which makes the dollar the world’s primary reserve currency – a self-fulfilling network effect!

On top of that, the US has a relatively open financial account. It’s very easy for anyone to trade assets in the US. You can easily buy stocks and other financial products there. Capital controls are lax, so everyone feels safe parking their money in the US. The US is also where many creative and promising startups emerge.

Does de-dollarization reduce exchange rate risk?

The problem is that the countries in these partnerships (including Malaysia and Thailand) are almost all surplus countries. They maintain their surpluses by exchanging goods for foreign assets. The most popular foreign assets? Those of the country with the largest deficit in the world – the United States. Consequently, even if bilateral trade is settled in local currencies, those currencies end up being exchanged for dollars anyway to purchase US assets. Or perhaps bilateral trade is only a fraction of total trade with the US.

Again, as long as the US remains the world’s number one buyer of goods and services, it will be hard to avoid holding US dollars. And since we’ll ultimately transact with the US (whether buying assets or selling goods), the exchange rate relative to the USD will remain relevant.

That said, de-dollarization pressures may actually be coming more from the US side itself! US sanctions against Russia have made the entire world rethink the safety of USD-denominated assets. Additionally, the possibility of a US government bond default due to the debt ceiling may also erode global confidence in US assets.

And beyond exchange rates, the changing global order going forward could have even bigger implications for our economy. If BRICS and many countries do push through with de-dollarization, the giant surplus countries will need to rethink their domestic industries because they won’t be able to export like before (since the main buyer for many countries is the US). The impact could be far greater than “mere” exchange rate effects.

That said, I think LCS options like these are still attractive. The more payment systems available, the better, and efforts like LCS deserve our appreciation.

That’s it for this post – hope it’s useful.

Evaluasi subsidi kendaraan listrik: bagaimana sepeda motor bersubsidi belum mampu gaet masyarakat miskin

Wed, 08 Mar 2023 00:00:00 +1100

Isu lingkungan dan perkembangan teknologi ancam ambisi kendaraan listrik berbasis nikel Indonesia. Apa yang harus dilakukan pemerintah?

Wed, 08 Mar 2023 00:00:00 +1100

Jokowi resah tabungan mengendap Rp 690 triliun di bank, betulkah masyarakat yang harus belanja?

Thu, 02 Mar 2023 00:00:00 +1100

The Importance of Trade For MSEs in The Indonesian F&B Sector.

Mon, 30 Jan 2023 19:25:33 +0700

Recent FDI developments: Indonesia and the region

Sat, 07 Jan 2023 18:29:28 +0700

Indonesia was surprised on a holiday by the issuance of the as a replacement for the law currently being debated by the Constitutional Court (MK). This appears to have been driven by the government’s concerns about . The argument is that the Job Creation Law being contested at the Constitutional Court creates uncertainty for investors. This is compounded by the approaching election year, which makes the future of the law – currently in the government’s court – even more uncertain.

The government’s concerns seem well-founded. The global economy amid raging inflation in developed countries is not the best time for investment. Many investors are between staying in China or moving elsewhere, and those looking to diversify . All of this amid our pride in growing foreign investment in nickel smelters that were painstakingly attracted with an export ban that , backing out of its investment promise in the new capital, and of course , whose commitment has yet to materialize.

Looking at data, Indonesia appears to have attracted fairly high FDI. As shown in Figure 1, Indonesia’s FDI is second only to India.

However, we need to account for the size of the economy. While Indonesia’s FDI is fairly high, we should look at FDI per worker. After all, the capital built through investment will be used by workers. Think of it this way: two companies might each buy 10 computers, but that investment is more valuable in the company with 10 employees than the one with 20. In the first company, each employee gets a computer; in the second, employees have to share. That’s if they even get a computer – some might have to bring their own laptop (this is a personal gripe; please ignore).

On a per-capita basis, Indonesia clearly needs more FDI. By the way, Figure 1 doesn’t include China because adding China would make all other countries look tiny in comparison.

Looking at the trend in both figures, FDI growth in Indonesia hasn’t outpaced neighboring countries. Even the impact of the Job Creation Law isn’t clearly visible in the trend.

Lastly, as a country with a large domestic economy, it’s quite natural for Indonesia to depend more on domestic investment than foreign investment. shows that foreign investment is indeed relatively small compared to domestic investment.

Table 1. Domestic and foreign investment realization in million USD

Investment	2016	2017	2018	2019	2020	2021
Domestic	216,230.8	262,350.5	328,604.9	386,498.4	413,535.5	447,063.6
Foreign	28,964.1	32,239.8	29,307.9	28,208.8	28,666.3	31,093.1

On the other hand, this may also indicate that foreign investors haven’t been very interested in Indonesia from the start. While domestic investment keeps growing, FDI has stagnated. Can the Perppu change this? Let’s see!

The code to reproduce Figures 1 and 2 is below.

library(WDI)
library(tidyverse)

indi<-c(
 "fdi"="BX.KLT.DINV.CD.WD",
 "lab"="SL.TLF.TOTL.IN",
 "pop"="SP.POP.TOTL"
)

ctr<-c('BGD','CHN','IDN','MYS','IND','THA','VNM')

dat<-as_tibble(WDI(
 indicator = indi,
 country = ctr,
 start=2017
 ))
dat$weks<-dat$fdi/dat$pop
dat$wuks<-dat$fdi/dat$lab

dat |>
 subset(country!="China") |>
 ggplot(aes(x=year,y=fdi,color=country))+geom_line(linewidth=1.1)+theme_classic()+
 labs(title = "Total FDI, BoP, current USD (minus China)", y="Total FDI, current USD")+
 scale_y_continuous(labels = scales::label_number_si())
ggsave("pic1.svg")
ggplot(data=dat,aes(x=year,y=weks,color=country))+geom_line(linewidth=1.1)+theme_classic()+
 labs(title = "Total FDI per capita, current USD (with China)", y="FDI per capita, current USD")+
 scale_y_continuous(labels = scales::label_number_si())
ggsave("pic2.svg")
ggplot(data=dat,aes(x=year,y=wuks,color=country))+geom_line(linewidth=1.1)+theme_classic()+
 labs(title = "Total FDI per worker, current USD (with China)", y="FDI per worker, current USD")+
 scale_y_continuous(labels = scales::label_number_si())
ggsave("pic3.svg")

GVC, Structural Transformation, and Policy Implications for Indonesia's Food and Beverage Industry

Mon, 14 Nov 2022 13:00:00 +0700

3 Years of Jokowi-Ma'ruf: The Steep Road to Economic Recovery

Mon, 31 Oct 2022 15:01:51 +1000

Trade for Economic Recovery: Import Policies to Support Indonesia’s F&B Sector

Fri, 16 Sep 2022 19:25:33 +0700

Behind the Packaging: Understanding Global Supply Chains in Indonesia's Food Industry

Fri, 16 Sep 2022 08:47:06 +0700

A very engaging event. Most participants were students, though some were from government and the private sector. Don’t forget to visit the Di Balik Bungkus website here.

Guest Lecture Video

Projecting the long-run impact of an economic reform: the case of the Indonesian Omnibus Law

Fri, 16 Sep 2022 08:47:06 +0700

Video Recording

Slides

Global Value Chain Impact on Indonesia's Economy and the Way to Make It More Resilient

Thu, 15 Sep 2022 00:00:00 +0000

Book review: "How the World Became Rich: the Historical Origins of Economic Growth"

Fri, 19 Aug 2022 15:10:34 +0700

I just finished reading How the World Became Rich: the Historical Origins of Economic Growth.

This book is written by two highly accomplished economic historians. It discusses how the world became prosperous. In general, human progress has been extraordinary. The poorest person on earth today has a better standard of living than the wealthiest people in medieval times. For example, many diseases that killed aristocrats in the past now have treatments. There’s no denying that poverty still needs to be eradicated, and Koyama & Rubin argue that learning from history can offer solutions for making the world even more prosperous.

The book opens with Part One, which contains chapters explaining several factors that are prerequisites for a country to become prosperous. This section is like a synthesis of various similar books you may know, such as those by Acemoglu and Diamond. Several explanations are unpacked, including geography, demography, colonization, institutions, and culture.

The next part covers how these factors combine to explain why the first Industrial Revolution happened in England and nowhere else. According to Koyama & Rubin, it’s crucial to understand why England was first, because the Industrial Revolution is what truly lifted humanity toward sustained economic growth. Many regions and nations experienced golden ages – Rome, the Ottomans, Imperial China – but none achieved what England did. Why didn’t the Industrial Revolution start there? Why England, which was almost always poorer throughout pre-industrial history?

I really love Koyama & Rubin’s storytelling. They avoid convoluted language, making their explanations very accessible. The transitions between chapters flow beautifully, especially in Part Two. After discussing why England, they address why not elsewhere. Then they move to why the rest of Europe followed, then the United States, and finally the Asian Tigers (Japan, South Korea, Hong Kong, Taiwan, and Singapore).

Because the narrative flows so well, I think their book is also very suitable for a general audience. Some of their explanations may be old hat for economists, especially development economists. But even for economists, their synthesis is extremely helpful for framing the factors affecting development, particularly in answering why England came first.

As an Indonesian, my mind naturally connects the book’s content to the Indonesian context. They mention Indonesia once, when discussing the importance of infrastructure and colonialism for economic growth. They cite Dell & Olken (2020) ¹, whose finding is that the areas in Java that are most economically developed today are those that historically served as logistics routes for the sugar industry. This colonial legacy is a key factor in why some regions are wealthier than others. Koyama & Rubin also note that after independence, local elites in such countries tended to continue colonial institutions rather than dismantle them – essentially replacing foreign colonizers with domestic ones.

They also point out that trust among citizens tends to be very low in former colonies where local populations helped the colonizers (as proxy leaders, slave traders, etc.). Yet trust is critical for whether a country can build good democratic institutions.

Yes, in this book, Koyama & Rubin emphasize the importance of having institutions that constrain executive power. Leaders whose power is constrained are less likely to default on debts, so they can borrow at lower interest rates. Constrained leaders also can’t arbitrarily declare wars or suppress innovation (say, because it conflicts with the ruler’s ideology). In other words, developed countries are typically those where, as the saying goes, leaders can’t just do whatever they want.

Ora iso sak penake dewe!

Hal ini penting dan harus digaris bawahi bahwa setiap negara memiliki aturan tersendiri dan harus dihormati. Sama seperti kita jika hendak berbisnis di luar negeri. Harus patuh dan mengikuti aturan yang berlaku di sana.
— Sandiaga Salahuddin Uno (@sandiuno) July 30, 2022

One reason England got rich, according to the authors, was the parliamentary system of nobles and merchants that constrained royal power. Although parliaments existed in Spain and Portugal too, their ability to check the king and queen was not as effective as in England and the Low Countries (Netherlands, Belgium, Luxembourg).

Beyond constraining rulers, they also emphasize the importance of an educated citizenry. The reason Protestant countries tended to be more successful, they argue, is that Protestant nations helped improve human resources by teaching people to read. The original purpose was to get people to read the Bible, but it turned out to be useful for reading other things as well, enabling faster information dissemination. Protestant-based countries also tended to be more secular.

Oh, and when Koyama & Rubin described Mao Zedong’s thinking about the importance of a centralized economy and price controls, somehow I was reminded of certain leaders in Indonesia recently. Let’s hope the outcomes differ. The Mao era was terrifying.

In short, this book is extremely enjoyable. I highly recommend it because it’s essentially a synthesis of development economics packed into a slim volume (only about 170 pages) that is dense and up-to-date. It’s also very informative with many interesting citations worth following up on. For instance, if you want to dig deeper into the demographic impact on growth, just follow the citations in the demography chapter. The language isn’t complicated, and the narrative flows well. If you’re going to read just one book about how humanity became prosperous, pick this one!

N.B.: Dell & Olken (2020) complements two other articles about Indonesia that I also find fascinating: Kuipers (2022) ² and Bazzi, Koehler-Derrick and Marx (2020) ³. Must-reads if you’re into institutional economics or history.

Dell, Melissa, and Benjamin Olken. (2020). “The Development Effects of the Extractive Colonial Economy: The Dutch Cultivation System in Java.” Review of Economic Studies 87, no. 1. ↩︎
Kuipers, N. (2022). The Long-Run Consequences of The Opium Concessions for Out-Group Animosity on Java. World Politics, 1-38. https://doi.org/10.1017/S0043887122000041 ↩︎
Bazzi, S., Koehler-Derrick, G., & Marx, B. (2020). The Institutional Foundations of Religious Politics: Evidence from Indonesia. The Quarterly Journal of Economics, 135(2), 845-911. https://doi.org/10.1093/qje/qjz038 ↩︎

How Investment and Trade Shape the Economic Transformation of Indonesia

Wed, 17 Aug 2022 00:45:00 +1000

Digitalization, SMEs, and GVC in Indonesia: a short intro

Wed, 27 Jul 2022 13:00:00 +0700

The Heterogeneous Impact of Tariff and NTMs on Total Factor Productivity of Indonesian Firms

Mon, 13 Jun 2022 09:30:03 +0700

A Simple Model of Local Content Requirements

Tue, 29 Mar 2022 14:09:58 +0700

Discussions about increasing the use of domestic products and anti-import sentiments have long echoed among public officials. Lately, these conversations seem to be growing ever more frequent. Of course, using domestically produced goods is not inherently a bad thing. The problem arises when it is forced. Like any other kind of love, love for domestic products is no fun when it is coerced. One of the instruments Indonesia is currently pushing hard is TKDN.

What is TKDN?

, or Tingkat Kandungan Dalam Negeri (Local Content Requirement), has become increasingly popular in Indonesia¹. When mandated for a given industry, TKDN requires that a minimum share of a final product contains domestic content. This content can include labour, machinery, and raw materials. For example, , as of April 2022, must meet a 35% TKDN threshold, meaning all 5G devices on the market must have at least 35% local content (or, equivalently, at most 65% imported components).

TKDN effectively prevents firms from simply selling imported goods. Products must be manufactured domestically. Imported components are allowed but capped. The idea is that TKDN will encourage producers to set up factories in Indonesia. I wrote a bit about this effect in . The resulting investment would create jobs. In a country with high informality like Indonesia, formal employment can be a good avenue for improving welfare². TKDN is also expected to build production capacity and technological capability among domestic suppliers of intermediate goods.

TKDN is popular with governments partly because it is “free” (unlike subsidies, it does not require budget outlays). Moreover, anti-import policies tend to be popular with the public, potentially boosting politicians’ approval.

At first glance, raising TKDN does seem beneficial. If investment flows in, there should be additional value added. However, it is important to understand that the concept of value is neither static nor exogenous. The value of a product can rise or fall depending on its inputs. For instance, does a 5G device with high domestic content perform as well as a fully imported one? Can a high-TKDN product compete in global markets? Does building a smelter really capture more value ?

The answer is not necessarily yes. Several theoretical papers dissect how value and productivity shift between intermediate-input producers and final-good producers³ ⁴ ⁵. There is indeed a trade-off, and TKDN does not automatically produce outcomes as optimal as they may appear, especially if TKDN fails to raise productivity in the sector it aims to protect.

In this post, I try to illustrate this trade-off using a very simple model.

The Model

Suppose there is a final-good producer that often needs imported inputs. These inputs could in principle be substituted domestically, but for the firm, imports are cheaper and offer higher value added. Real-world examples might include or .

If we focus solely on the input-sourcing decision, the firm has the production function:

$$F(M,N) = \alpha M + N$$

where $M$ is the quantity of imported inputs and $N$ is the quantity of domestically sourced inputs. Meanwhile, $\alpha$ represents the quality of imported raw materials relative to domestic ones. If $\alpha > 1$, then imported goods are of higher quality. We also assume imported inputs are cheaper ($P_M

The firm is required to purchase a minimum share $k$ of domestic raw materials:

$$ P_NN \geq kP(\alpha M + N) $$

$$ \text{where } k \in (0,1) $$

The firm wants to meet demand $F(M,N)=Q$ at the lowest possible cost. That is, the firm solves:

$$ \min P_MM+P_NN $$

$$ s.t. \ \alpha M+ N \geq Q $$

$$ P_NN \geq kP(\alpha M + N) $$

$$ k \in (0,1) $$

Since the problem is linear, we can use linprog. I will use the following parameters:

$$ P_M=1 $$

$$ P_N=3 $$

$$ α=1.5 $$

$$ k=0 $$

$$ P=9 $$

$$ Q=30 $$

First, let us define the function:

from scipy.optimize import linprog
def TKDN(Pm=1,Pn=3,α=1.5,δ=1,k=0,P=9,Q=30):
 c_ex1 = np.array([Pm, Pn])
 A_ex1 = np.array([[-α,-1],[k*P*α, k*P-Pn]])
 b_ex1 = np.array([-δ*Q,0])
 res_ex1 = linprog(c_ex1, A_ub=A_ex1, b_ub=b_ex1, method='revised simplex')
 names=[f'imported inputs = {res_ex1["x"][0]}',
 f'domestic inputs = {res_ex1["x"][1]}',
 f'production cost = {Pm*res_ex1["x"][0]+Pn*res_ex1["x"][1]}',
 f'firm profit = {Q*P-Pm*res_ex1["x"][0]-Pn*res_ex1["x"][1]}']
 for i in names:
 print(i)

The default numbers above should not be taken literally – $P_M=1$ is simply a numeraire, or base price. Because the problem is linear (and therefore invariant to affine transformations), $P_N=3$ can be interpreted as meaning domestic raw materials are three times more expensive than imported ones.

Winners and Losers

Now let us use the function we just defined. Remember the linprog dependency from scipy. For the no-TKDN case, we set $k=0$:

TKDN()

imported inputs = 20.0
domestic inputs = 0.0
production cost = 20.0
firm profit = 250.0

What about a TKDN where domestic content must be at least 20% of total final-product value?

TKDN(k=0.2)

imported inputs = 8.0
domestic inputs = 18.0
production cost = 62.0
firm profit = 208.0

As we can see, TKDN causes the final-good producer’s profit to fall and total cost to rise. However, the domestic input supplier gains additional revenue of $18 \times P_N$. In other words, TKDN tends to benefit domestic raw material sellers but hurts final-good producers.

When TKDN is set to 40%, the model crashes – the final-good producer is better off not producing at all.

If we tabulate TKDN from 0 to 30% (in 10% increments):

k	M	N	C	$\pi$
0	20	0	20	250
10%	14	9	41	229
20%	8	18	62	208
30%	2	27	83	187

What about total domestic value added? Although TKDN raises value added for the input producer, it reduces value added for the final-good producer. With TKDN=0, the input producer’s value added is 0, while the final-good producer’s profit is 250. At TKDN=10%, the input supplier gains 9 but the final-good producer loses $250-229=21$. So total value added actually declines.

What about the government? Assuming duty-free imports, government revenue comes from corporate income tax. With TKDN=0, the input supplier pays no tax. The final-good producer pays corporate income tax of $t \pi$ where $t$ is the tax rate. If we assume the domestic input supplier’s cost is 0, then the supplier’s income is $P_N \times N$. Total government revenue becomes:

$$GR=t(P_N-c)N+t \pi$$

where c is the marginal cost, linear in output. I use c=2 so the domestic firm needs to charge $P_N=3$ to earn a margin of 1, the same as the importer. If domestic costs were low, they could sell at a lower price and win orders from the final-good producer.

Let us calculate government revenue at a 20% tax rate:

t=0.2 # corporate income tax
Pn=3 # domestic input price
c=2 # so firms can't sell below 3
brp=('0%','10%','20%','30%')
taxs=((Pn-c)*0*t+250*t,
(Pn-c)*9*t+229*t,
(Pn-c)*18*t+208*t,
(Pn-c)*27*t+187*t)
for x,y in zip(brp,taxs):
 print(f'At TKDN={x}, total GR = {y}')

At TKDN=0%, total GR = 50.0
At TKDN=10%, total GR = 47.6
At TKDN=20%, total GR = 45.2
At TKDN=30%, total GR = 42.8

The higher the TKDN, the lower the total income tax from the two firms. Although tax from the domestic firm rises, tax paid by the final-good producer falls because profits shrink. So even though TKDN appears not to require fiscal expenditure like subsidies, the inefficiency it generates carries a hidden cost to the state budget as well.

This is on top of the budget spent on enforcing TKDN rules. For instance, to verify that a firm complies with TKDN, it must be surveyed for a TKDN certificate. The first two product certificates are paid for by the government, funded from the state budget. This does not include the systems and human resources the government must allocate for TKDN enforcement. Moreover, for the third product onward, the firm must bear the certification cost itself, further reducing profits and consequently the corporate income tax paid.

So Is TKDN Good or Bad?

The issues above illustrate potential problems created by TKDN. This linear model, while somewhat arbitrary, does a reasonable job of explaining TKDN’s short-run effects. More representative (but more complex) models can be found in the footnotes.

One important caveat is that we cannot simply assume $Q$ and $P, P_M, P_N$ remain constant under TKDN. It is possible that instead of becoming more efficient, domestic input producers become complacent under protection and show no improvement at all. Rather than improving, they may even raise prices because the final-good producer has no alternative as long as TKDN binds.

Furthermore, TKDN makes domestic final-good producers less competitive against foreign ones. Why? Because foreign producers can access cheap, high-quality inputs without TKDN constraints. As a result, domestic final-good producers cannot compete in global markets and are forced to rely on the domestic market. Unable to compete with imported finished goods either, they will demand protection too.

This is why IMEI blocking exists. Domestic phone manufacturers do not want to compete with imported phones that are not subject to TKDN. Has the public ever been told how much of the state budget is spent on implementing IMEI blocking?

Finally, once TKDN is in place, it is very hard to roll back. Firms that have already made large investments to comply with TKDN would suffer significant losses if the policy were suddenly removed and firms that did not invest could enter the previously protected market. Therefore, firms that have already invested to meet TKDN have a strong incentive to lobby the government to maintain and enforce TKDN rules.

TKDN can be considered successful if domestic input producers actually learn and improve. For example, if their industry grows and becomes efficient enough to match import prices ($P_N \leq P_M$), or improves quality to surpass imports ($\alpha \in (0,1)$). If this happens, $k$ is no longer binding and TKDN can be declared a success, depending on how large and how fast the domestic industry’s improvement is.

Therefore, policymakers who have already adopted TKDN should continuously monitor $\alpha$ and $P_N$ (or more precisely $\frac{P_N}{P_M}$). If there is no progress, it is hard to expect domestic industry to become efficient and competitive, and the desired advantages will not materialise. Coercion alone is unlikely to succeed. The government may need to complement TKDN with other policies that promote efficiency. So yes, the regulatory burden just keeps growing.

Alternatives to TKDN

There are several alternatives to TKDN⁶:

Improve the business climate. This one seems obvious. But nothing beats this approach. It is hard for industry to advance when upstream sectors are protected, and both importing and exporting carry high administrative costs. Customs officials still engage in .
Leverage CSR. Ask multinational companies whether they can help develop local firms as suppliers.
Provide training for workers and component suppliers. Training is essential for improving the quality of local businesses and workers. A government that understands industry should be able to identify what training is needed and deliver it. This also costs money, but it is far better spent than on enforcing TKDN.
Improve logistics processes and physical infrastructure. This too seems obvious.
If all else fails, using tariffs and subsidies, though not ideal, is still better than TKDN. Of course, the main problem with tariffs is trade agreements and retaliation. Tariffs are easier to quantify than TKDN, making them more likely to face challenges at the WTO. Subsidies burden the budget directly, unlike TKDN which burdens the budget and the economy indirectly. Such is life when your policymakers and politicians cannot calculate hidden costs.

Update

A new book on TKDN has just been released for those who want to learn more. It is an edited volume by Lili Yan Ing and Grossman⁷. One particularly interesting chapter is by Yessi Vadila⁸.

I will discuss it further some other time. Mention me on if you have comments, criticisms, or suggestions. Thanks.

Fernando, O., & Ing, L. Y. (2022). Indonesia’s Local Content Requirements: An Assessment on Consistency with Free Trade Agreement Commitments. ERIA Discussion Paper Series, 420. ↩︎
Gupta, K., & Mardjono, E. (2021). Indonesia Emas Berkelanjutan 2045: Kumpulan Pemikiran Pelajar Indonesia Sedunia Seri 01 Ekonomi. LIPI Press. ↩︎
Grossman, G. M. (1981). The Theory of Domestic Content Protection and Content Preference. The Quarterly Journal of Economics, 96(4), 583-603. ↩︎
Veloso, F. M. (2006). Understanding Local Content Decisions: Economic Analysis and an Application to the Automotive Industry. Journal of Regional Science, 46(4), 747-772. ↩︎
Lin, S.-H., & Weng, Y. (2020). Can strengthening the local content requirements meet a government’s need to raise industrial productivity and production? Journal of Applied Economics, 23(1), 316-328. ↩︎
Hufbauer, G., Schott, J. J., Wada, E., Cimino, C., & Vieiroand, M. (2013). Local Content Requirements : A Global Problem. Peterson Institute for International Economics. ↩︎
Ing, L. Y., & Grossman, G. M. (2023). Local content requirements: promises and pitfalls. Routledge. ↩︎
Vadila, Y., & Christian, D. (2023). Effects of Local Content Requirements on Trade. In B. Hoekman & K. Saggi (Eds.), The Political Economy of Trade Policy: Theory, Evidence and Applications. Routledge. ↩︎

Klaim sukses hilirisasi nikel berbasis larangan ekspor masih memiliki segudang masalah

Fri, 25 Mar 2022 00:00:00 +1100

Why DMO, DPO, and price ceilings failed to control cooking oil prices and supply

Wed, 16 Mar 2022 10:26:01 +0700

(update 7 April 2022: added no. 8 in table 1)

Introduction

In recent days, the government has been grappling with rising cooking oil prices. The government is trying to keep cooking oil prices low. The President with ensuring price stability. The instruments used by the Ministry of Trade (MoT) include imposing a Domestic Market Obligation (DMO) on palm oil as the raw material for cooking oil, as well as setting a Domestic Price Obligation (DPO) for palm oil and a maximum retail price (HET) for cooking oil. Below is a summary of issued up to the time of writing.

Table 1: Timeline of MoT regulations on CPO and cooking oil

No	Date	Regulation	Issue
1	11 January 2022	Permendag 1/2022	BPD PKS subsidy for cooking oil in simple packaging, with HET of IDR 14,000 per liter.
2	18 January 2022	Permendag 2/2022	DMO obligation for 9 tariff lines begins.
3	19 January 2022	Permendag 3/2022	Revision of no. 1, subsidy expanded to all packaging types.
4	1 February 2022	Permendag 6/2022	HET split into 3: IDR 11,500 for bulk, IDR 13,500 for simple packaging, IDR 14,000 for premium packaging.
5	8 February 2022	Permendag 8/2022	Revision of no. 2, DMO expanded to 60 tariff lines.
6	10 February 2022	Kepmendag 129/2022	DMO set at 20%.
7	9 March 2022	Kepmendag 170/2022	DMO set at 30%.
8	20 March 2022	Permendag 12/2022	DMO revoked.

In the latest development, the government will for packaged cooking oil. Meanwhile, bulk cooking oil will follow an HET of IDR 14,000 per liter. This became effective on 16 March 2022. Why release the HET? Because under the HET described in no. 4 of table 1, supply shortages occurred, leaving many people unable to buy cooking oil – some even while queuing for it.

Underlying Assumptions

The DMO instrument is naturally premised on the assumption that cooking oil is expensive because the raw material is expensive. Indeed, around July, palm oil prices appeared to start rising. According to data from (figure 1), the price of Crude Palm Oil (CPO) began to climb around July 2021, and rose again in September 2021.

Figure 1. CPO prices

By the way, look at what happened to prices after January. They went even higher after Indonesia implemented the DMO and DPO.

Meanwhile, data presented by the Trade Minister at a (minute 5) on 9 March 2022 showed a slight increase in July 2021, with a sharper spike around November 2021. It seems this November spike triggered the government’s response. The Minister also showed a decline in cooking oil prices in February 2022, citing it as evidence that the HET successfully brought prices down.

Armed with this information, it seems reasonable for the MoT to deduce that rising CPO prices were the root cause of expensive cooking oil. If so, it is logical for the MoT to think the solution lies in restricting exports (in this case via the DMO), thereby suppressing domestic CPO prices.

In other words, because the Minister felt the problem was on the buyer-competition side, the MoT helped domestic CPO buyers by forcing exporters to divert 20% (later 30%) of their production to the domestic market.

Unfortunately, cooking oil remained expensive. The DMO and DPO should have ensured sufficient raw material supply for cooking oil. The next deduction: CPO was being channeled to the domestic market, but for products other than cooking oil. This is why the DMO was expanded from 9 to 60 products (table 1, no. 5), to cover downstream CPO products like soap.

Yet cooking oil remained scarce despite all these policies. The next deduction: there must be a mafia, speculators, or leakage abroad. This is how the MoT came to involve the police.

But could there be other problems?

Why the Price Ceiling Failed

The two problems above touch on fundamental supply and demand dynamics that push prices upward, even in the absence of mafias or speculators. Therefore, if these two issues are real, the HET will also fail. Beyond these two factors, several other things made the HET worsen the situation.

Distribution Costs

Distribution costs vary across regions and can undermine the HET. Cooking oil factories appear to be concentrated in western Indonesia, such as Sumatra and Java¹. Shipping to other regions is necessary. If this is the case, cooking oil prices should vary across different regions even under normal circumstances. Using from PIHPS, I created the chart below. It contains monthly cooking oil prices at traditional markets since January 2018 across all Indonesian provinces. I highlighted a few provinces.

Figure 2. Cooking oil prices

If price variation exists even in normal times, then the HET threatens shortages in regions with difficult distribution, especially at a time when energy and shipping costs are at historic highs.

Other Issues

Actually, the government has long wanted to regulate packaged cooking oil – since around 2016 if I recall correctly. There was a regulation requiring retailers to sell cooking oil only in packaging, not in bulk. But this regulation was repeatedly postponed because packaged cooking oil would become too expensive if imposed on all producers and consumers. In fact, based on these repeated postponements, one could have predicted the HET would cause problems. I don’t think any HET has ever succeeded in Indonesia.

Mafia?

Many also claim mafias are involved, or that cooking oil is a cartel industry. I think these accusations are reasonably grounded, given Indonesia’s poor reputation for in starting businesses, and its ranking as the world’s 8th most economy according to The Economist. Moreover, nearly 50% of cooking oil sales are controlled by just .

Dealing with mafias is difficult though. It is probably not just a cooking oil problem. Especially if vertical integration from plantations to downstream products is actually more efficient when done by large companies. If these “mafias” grew large because they are efficient, recklessly tinkering with the industry could actually reduce efficiency. The government may have chosen to intervene in the industry because it lacks the tools to prosecute the big players (for various reasons). But intervention in cooking oil risks hurting the small players, since the big ones can always maneuver. Don’t forget, according to Ministry of Agriculture data, about 40% of palm oil plantations are smallholder estates that may benefit from supplying to larger factories.

Besides, figure 2 shows a sharp spike in November 2021. If mafias played a significant role, prices should have been high all along. Why only November? Did the mafia just form that month? Did they just hold a price-fixing meeting? Probably not. The sharp rise actually correlates with the point when CPO consumption by biodiesel producers surpassed CPO for food.

I think the mafia argument is very plausible. In fact, new mafias may have emerged because of the HET, which increased incentives for hoarding and smuggling due to price disparities. However, it seems hard to use the mafia as the primary explanation for the sharp rise in cooking oil prices. Even if mafias play a large role, the HET and DMO are probably not the right solutions – they may actually create incentives for both existing and aspiring mafias.

Conclusion

This problem may have actually gotten worse because the DMO, DPO, and HET failed to stabilize prices and supply. People who lose confidence in the government’s ability to secure supply may start hoarding, which only worsens the situation.

Meanwhile, producers unable to meet the DMO or unable to supply cooking oil at the HET price will be forced to stop production. This will inevitably have knock-on effects on employment and consumption more broadly – particularly ironic as the fasting month approaches.

Can we blame the MoT alone? Of course not. As an agency tasked by the President, it is perfectly reasonable for the MoT to use DMO, DPO, and HET since those are the instruments at its disposal. Some alternative solutions – such as increasing export levies – require coordination with the Ministry of Finance (which was ). The biofuel program falls under the Ministry of Energy and Mineral Resources. Social assistance falls under the Ministry of Social Affairs. Then there is the supply side: labor, weather, fertilizer prices. State land use? That is under the Ministry of Environment and Forestry. Distribution has always been a chronic problem in Indonesia. Not to mention mafias. All of these factors, which fundamentally push cooking oil prices up, lie outside the MoT’s jurisdiction.

If all you have is a hammer, every problem looks like a nail.

Now the MoT has agreed to release the HET along with raising the export levy to 80%. I think this is a reasonable step. The government now has funds for cooking oil subsidies, at least for bulk oil (while biodiesel subsidies continue). Cooking oil is starting to reappear, and supply is recovering fairly quickly (from hoarders?). In my view, higher prices with available goods are better than low prices with nothing on the shelves. At least if the product is available, we can all buy it and consume it frugally.

Prices may surge initially after the release. But as supply increases and production restarts – since prices will be more palatable for producers – I am fairly confident we will start seeing cooking oil prices gradually decline, though they will likely remain above IDR 14,000 (remember, there are many fundamental issues at play). Prices may stay elevated due to increased demand as Ramadan approaches. We’ll see.

For now, I very much appreciate the MoT’s decision to finally release prices to the market. Time to support Pak Lutfi and the team at the MoT.

Kementerian Perdagangan. Profil Komoditas Minyak Goreng. ↩︎

Managing food inflation, Indonesian style

Fri, 04 Mar 2022 07:29:50 +0700

Many have said the possible increase the price of various important commodities. However, Indonesia may be able to limit the inflation of some of these goods. This is because Indonesia imposes quota restriction in many of those goods.

Amid quota restriction, domestic price of these goods are constantly higher than international prices. When domestic prices started to rise, the government needs only to relax quota, buy from the cheaper international market, increase supply and limit the price hike. As long as international price does not rise more than domestic prices, then the government can always use quota to manage domestic prices.

In this blog, I try to plot international prices vs domestic prices of 4 commodities: rice, chicken, beef and sugar. All of them is controlled by quota.

## Download international prices from Nasdaq Data Link (previously quandl)
rice = quandl.get("ODA/PRICENPQ_USD", paginate=True, start_date="2006-11-30")
chicken = quandl.get("ODA/PPOULT_USD", paginate=True, start_date="2006-11-30")
beef = quandl.get("ODA/PBEEF_USD", paginate=True, start_date="2006-11-30")
sugar = quandl.get("ODA/PSUGAUSA_USD", paginate=True, start_date="2006-11-30")
## Just remembering definitions from quandl
legends=[
 'Rice; 5 percent broken milled white rice; Thailand nominal price quote; US$ per metric ton',
 'Poultry (chicken); Whole bird spot price; Ready-to-cook; whole; iced; Georgia docks; US cents per pound',
 'Beef; Australian and New Zealand 85% lean fores; CIF U.S. import price; US cents per pound',
 'Sugar; U.S. import price; contract no.14 nearest futures position; US cents per pound (Footnote: No. 14 revised to No. 16)'
]

### Turns everything to USD/kg
rice['USD']=rice['Value']/1000
chicken['USD']=chicken['Value']/100*2.20462
beef['USD']=beef['Value']/100*2.20462
sugar['USD']=sugar['Value']/100*2.20462

## Download domestic prices from CEIC. I have the file in my local computer
sembako=pd.read_excel('harga_sembako.xlsx',index_col='Date')

## Plotting
fig, axs = plt.subplots(figsize=(14,12),ncols=2,nrows=2)
fig.subplots_adjust(hspace=0.3, wspace=.25)
sns.lineplot(data=rice,x='Date',y='USD',
 linewidth=2, ax=axs[0,0],legend=True)
sns.lineplot(data=beef,x='Date',y='USD',
 linewidth=2, ax=axs[0,1],legend=True)
sns.lineplot(data=chicken,x='Date',y='USD',
 linewidth=2, ax=axs[1,0],legend=True)
sns.lineplot(data=sugar,x='Date',y='USD',
 linewidth=2, ax=axs[1,1],legend=True)
sns.lineplot(data=sembako,x='Date',y='rice',
 linewidth=2, ax=axs[0,0],legend=True)
sns.lineplot(data=sembako,x='Date',y='beef',
 linewidth=2, ax=axs[0,1],legend=True)
sns.lineplot(data=sembako,x='Date',y='chicken',
 linewidth=2, ax=axs[1,0],legend=True)
sns.lineplot(data=sembako,x='Date',y='sugar',
 linewidth=2, ax=axs[1,1],legend=True)
axs[0,0].set_title('Rice (beras), USD/kg')
axs[0,0].set_ylabel('')
axs[0,0].set_xlabel('')
axs[0,1].set_title('Beef (Daging sapi), USD/kg')
axs[0,1].set_ylabel('')
axs[0,1].set_xlabel('')
axs[1,0].set_title('Chicken (Ayam), USD/kg')
axs[1,0].set_ylabel('')
axs[1,0].set_xlabel('')
axs[1,1].set_title('Sugar (Gula), USD/kg')
axs[1,1].set_ylabel('')
axs[1,1].set_xlabel('')
fig.text(x=0.25, y=0.95, s='International vs Indonesian domestic price of 4 commodities', fontsize=20)
fig.legend(bbox_to_anchor=(0.7,0.05),labels=['International Price, IMF via Nasdaq data link','Domestic price, BPS via CEIC'],fontsize=16)

<matplotlib.legend.Legend at 0x1a7ebe45a00>

As we can see, international price is consistently higher than domestic price of all 4 goods. Well, mostly. We can also see that while international prices are quite volatile especially during the pandemic, domestic prices are relatively more stable. This is because of the quota management.

Chicken is a bit different. Domestic vs international price converges around 2017. I am not sure why. It is possible this is just a US thingy cuz the chicken price is taken from the US market. I believe Indonesia’s chicken are mostly imported from South America. Unfortunately I can’t find the data from IMF Macroeconomic Statistics.

Domestic prices is downloaded from CEIC (source is National Statistics Body, the BPS), where BPS surveys domestic markets every month.International prices, taken from IMF Cross Country Macroeconomic Statistics via Quandle, are:

Rice; 5 percent broken milled white rice; Thailand nominal price quote; US$ per metric ton
Poultry (chicken); Whole bird spot price; Ready-to-cook; whole; iced; Georgia docks; US cents per pound
Beef; Australian and New Zealand 85% lean fores; CIF U.S. import price; US cents per pound
Sugar; U.S. import price; contract no.14 nearest futures position; US cents per pound (Footnote: No. 14 revised to No. 16)

Sekian postingan kali ini. Semoga bermanfaat.

Notes from watching "Ngobrol Tempo" on "Import Substitution of Electronics Products"

Fri, 04 Mar 2022 07:17:38 +0700

This morning I watched “Ngobrol Tempo” on YouTube. The show featured officials from the Ministry of Industry, the Ministry of Trade, and Gabel, the electronics manufacturers’ association. The topic was how to pursue import substitution for electronics products, amid the Ministry of Industry’s policy to cut imports by 35%. I found the program quite interesting and was very pleased with how Bu Ratna from Tempo moderated the discussion. Several points struck me as particularly noteworthy.

The Government’s Typical Mercantilist View

The first, naturally, is the government’s characteristically mercantilist outlook. By mercantilist I mean the state sees itself as an individual firm: a competitive nation is one whose exports exceed imports. Imports mean losing, exports mean winning. Exports mean profit, imports mean loss. This mercantilist view is quite popular not just in the Indonesian government but also among the general public, and even in other countries’ governments.

In this show, the mercantilist view was quite segmented, focusing on electronics products. They even brought up sectoral trade deficits, noting that in certain HS codes, Indonesia is a net importer. Interesting, because I usually hear the government talking about bilateral trade deficits. Here they went as far as sectoral trade deficits. Very specific. I wonder, if there were a show about CPO, would we discuss the CPO surplus? Probably not.

It is indeed difficult to use exports and imports as indicators of industrial competitiveness. It is hard to pin micro-level conclusions on trade deficits, which are fundamentally a macro phenomenon. In today’s world of rapid financial transactions, you cannot discuss trade deficits without discussing financial flows. As a country that targets capital inflow, it is natural for the current account to be negative. Moreover, Indonesia imports a lot of capital goods and components, which arguably represents good use of FDI.

Trade deficits must also be viewed alongside overall trade. Indonesia’s exports in other products are enormous. You cannot expect surpluses in everything. Do not forget dutch disease, where exports in one sector reduce competitiveness in another. So trying to discuss a deficit in a single sector seems problematic. Besides examining how to curb imports, the government should also scrutinize the financial management of companies in high-export sectors. Are they saving a lot? Holding cash? Paying dividends? Reinvesting? Are their savings overseas or domestic? How much does the state earn from export industries?

Comparing with Vietnam

The second hallmark is comparing with Vietnam. This has been trendy not just in government but also among consultants and academics. Comparing with Vietnam has intensified because of Global Value Chains (GVC). They say that in the early 2000s, Indonesia’s share of world trade was 0.9% while Vietnam’s was 0.25%, but by 2019, Indonesia’s share was 1% while Vietnam’s was 2%.

Some problems with comparing Indonesia to Vietnam:

Vietnam is a small country. Its GDP and $\frac{\text{GDP}}{\text{capita}}$ are still below Indonesia’s. They don’t have a large market, so they are compelled to exploit the global market. Indonesia? Its domestic market alone is enormous. That is why it is “easy” to pursue protectionist policies.
Vietnam’s exports are large but so are its imports. That is, the value added is small. What is called “Vietnam’s huge electronics exports” is actually the work of many countries. Vietnam just happens to be the final assembly location. If you look only at trade statistics, you overestimate Vietnam’s electronics manufacturing.
Vietnam is called a GVC success story not because it is protectionist, but precisely because it embraced globalization. That is why its imports are so large. Because it embraced globalization, it also managed to sign trade agreements with other countries faster than Indonesia. The fact that Indonesia’s textiles face 15% tariffs to the EU while Vietnam pays 0% is partly due to this.
As shown below, while Vietnam’s $\frac{\text{trade}}{\text{GDP}}$ ratio is enormous, its trade balance is not that different from Indonesia’s. Don’t forget that Vietnam ran trade deficits continuously from 1986 to 2011. If I recall correctly, the late 90s was when they started attracting FDI.

So given the differences in size and approach (import substitution vs. export expansion), there is little point in overly comparing ourselves with Vietnam. Vietnam also lacks natural resources to export and its domestic market is too small. It is natural for them to depend on the global market. That said, comparing institutions (ease of doing business, logistics management, trade agreements) remains perfectly reasonable.

Oh, and this should also be linked to the mercantilist school. Just because a country exports a lot does not mean it is doing well. Vietnam’s per capita income is still below Indonesia’s. They make fancy phones, but how many Vietnamese can buy them? This is because the value added that actually stays in Vietnam is tiny (labor-intensive). Indonesia wants to go 4.0, right?

Furthermore, we are in the same boat, aren’t we? Indonesia exports massive amounts of coal and CPO, yet many regions still struggle with electricity and cooking oil is now expensive. Is that good? What is the point of exports if ordinary people’s income and consumption barely change?

Public Policy

The show also touched on several interesting policy points. The panelists were asked what policies are being or will be implemented to boost the competitiveness of electronics products and curb imports. The speakers explicitly mentioned WTO constraints on their policymaking. In other words, their policies must be in line with international rules such as trade agreements and the WTO.

Trade Barriers

Several trade-related policies were mentioned:

Non-tariff barriers (NTBs) such as SNI and TKDN (local content requirements).
Trade remedy and anti-dumping measures.
Reviewing trade agreements, including the Information Technology Agreement.
Technical considerations for import approval (effectively another NTB).
Use of the Commodity Balance Sheet (Neraca Komoditas – also effectively an NTB).

These NTBs will be focused on 25 electronics products, mostly consumer electronics. They claimed that the AC import approval regulation (Permendag 64/2020) successfully attracted foreign investment. According to Gabel, thanks to this new NTB, domestic AC supply rose from 20% of total AC sales in Indonesia (i.e., 80% imported) to 30% (imports down to 70%). As usual, the ones complaining are AC retailers whose stock comes from imports.

I have heard the first two points many times. But the last two are fairly new. Apparently, the Ministry of Industry wants to introduce import quota licensing for downstream electronics products through technical considerations. If implemented, products like TVs, refrigerators, and ACs would need to apply for an import permit from the Ministry of Industry, specifying the quantity they wish to import. The Ministry would then issue a technical approval specifying the allowed import quota.

The problem is that this kind of technical-approval-based import licensing is precisely what the Commodity Balance Sheet is meant to eliminate. Eventually, products covered by the Commodity Balance Sheet should be importable according to the balance sheet without technical approval, just through the Ministry of Trade. The Commodity Balance Sheet is supposed to be fully implemented by 2023, so why push for technical approvals?

One more thing: using technical approvals or Commodity Balance Sheets will also face WTO problems because they are basically quota restrictions. This is what happened with Brazilian chicken. The question now is whether major exporting countries to Indonesia – such as China, Japan, and South Korea – will file a dispute like Brazil did.

Investment Policy: Expertise or Financing?

On investment, it seems new investments will receive tax holidays or corporate income tax reductions. This incentive is premised on the logic that foreign investment will help increase domestic production capacity. Once again, Vietnam is frequently cited as a model for attracting foreign investment and boosting exports. Why? Because foreign investment brings not just physical capital but also know-how (both managerial and technical) and international production networks (foreign suppliers and buyers). Foreign investment also absorbs a lot of labor and outsources various things from domestic producers (spillover). All these advantages make the potential tax revenue loss from tax holidays worthwhile.

Gabel raised an interesting point about Indonesia’s electronics industry. They said Indonesia is already quite competitive in ASEAN. However, Indonesian consumers are still low-end. Household electricity is still dominated by 900 watts. The best-selling AC in Indonesia is the 1/2 HP model, to avoid circuit breakers tripping. Refrigerators are also small because many Indonesian workers are paid daily, so they shop daily – no need for a large fridge. As a result, the electronics market in Indonesia can be considered a commodity: easy to copy with no differentiation advantages.

If that is the case, the role of foreign investment in terms of technical know-how is almost nil. Since the products are still low-end, complex high-end features and large-capacity manufacturing are not really needed. If know-how is not really needed, shouldn’t domestic investment suffice?

But investing in Indonesia is difficult. Interest rates are high, banks do not accept production equipment as collateral, and the government keeps issuing bonds with attractive yields (crowding out). Domestic investors lack liquidity, while foreign investors are happy to enter Indonesia because interest rates abroad are low and Bank Indonesia zealously maintains a stable exchange rate (by raising interest rates, LOL). Crowding out intensifies when the export sector fails to boost domestic liquidity.

Fragmented Policy

Back to the first discussion: trade is a macro issue. There is so much to consider. Competition, domino effects, etc. Expensive electricity, extortion, and protection of upstream industries (like steel) all play crucial roles. Exports from other sectors (CPO and coal, for instance), the money supply, exchange rates, and interest rates all have massive effects. It is certainly difficult to use a macro lens (total imports) to make policy for micro phenomena (the competitiveness of the electronics industry using trade barriers).

On upstream protection, it was also amusing to hear India mentioned as having cheaper automotive products than Indonesia, even though Indonesia has been pampering its automotive sector since the 80s (lots of incentives and protection). Don’t forget that steel in India is cheaper and more competitive than in Indonesia. If steel is cheap, literally almost every other industry will also be cheaper (including construction and capital goods).

From this show, I still feel that the policies being adopted are somewhat fragmented and poorly coordinated. Both ministries need to talk with other institutions handling capital flows, interest rates, energy, export commodities like mining, CPO and coal, and so much more. Fair enough – the speakers were from the Ministry of Industry and the Ministry of Trade, so the policies discussed naturally focused on trade barriers.

But Gabel’s secretary general made an interesting point: if import substitution is to be taken seriously, it must be discussed nationally. Too many factors are at play. I have heard the 35% import substitution program trumpeted by the Ministry of Industry too many times while others (Ministry of Finance, Ministry of Agriculture, Ministry of Energy, Bank Indonesia, etc.) seem to never mention it.

One more thing. No country can export everything. Not every sector needs to be a champion with a trade surplus. Not every country that exports a lot in one area (Vietnam in electronics) also exports a lot in another (Vietnam does not export coal or CPO). Inefficient policies will only reduce people’s purchasing power, and we will be back to selling low-end products.

BTW, the show I watched is below. Let me know what you think! Mention me at @imedkrisna

Russia's Invasion of Ukraine: What Should Indonesia Do?

Wed, 02 Mar 2022 09:00:43 +1000

On 24 February 2022, Russia attacked Ukraine. It was not entirely a surprise, as US intelligence had already indicated that Russia would invade Ukraine in the near future. At the time of writing, the war was still ongoing. Russian forces were attacking from three directions, and Ukraine’s Black Sea coastline had been almost entirely taken by Russia.

Meanwhile, Russia faced sanctions from Western countries that made it difficult for Russia to trade with the outside world. This caused the value of Russia’s currency, the Ruble, to plummet sharply, forcing the Central Bank of Russia to raise its policy rate from 9.5% to 20% in a single day.

How does this affect the Indonesian economy?

Trade Impact

Indonesia has relatively limited trade ties with Ukraine and Russia. Total imports from both countries account for only about 1% of Indonesia’s total imports, while investment from Russia and Ukraine in Indonesia is also far from significant.

Nevertheless, both are major sources of certain imported goods. Ukraine supplied approximately 24% of Indonesia’s total wheat imports in 2020. Meanwhile, Russian fertiliser accounted for about 15% of Indonesia’s total fertiliser imports. Russia is also the primary supplier of military equipment to Indonesia. Although both countries purchase significant quantities of Indonesian palm oil products, their combined transactions accounted for only about 0.5% of Indonesia’s total palm oil exports in 2020.

Figure 1. Indonesia’s Exports and Imports with Russia and Ukraine in 2020

This means that if Indonesia fails to find alternative wheat suppliers, there is a possibility that prices for wheat-based food products will rise. Wheat is primarily used for flour milling, which feeds not just consumers but also producers of instant noodles, pasta, bread, pastries, and traditional snacks. Even before the war, global wheat prices had been climbing due to supply bottlenecks caused by weather problems.

For wheat, it appears that Ukraine’s share had already fallen to about 10% in 2021.

I also tried looking at the 2021 data (from BPS), apparently the Ukrainian share in 2021 for wheat is lower, at around ~10%, mainly due to stronger production from AUS
— Faris Abdurrachman (@faris_sina) February 27, 2022

It is not only wheat. Agricultural commodity prices were likely to keep climbing. Disruptions to global fertiliser supply risked pushing fertiliser prices even higher, on top of already elevated levels due to high gas prices and China’s fertiliser export ban. We had already seen rapid increases in prices of commodities such as corn and soybeans. A fertiliser shortage would accelerate these increases further.

Regarding gas prices, Russia as the world’s third-largest oil and gas supplier plays a critically important role. The war and sanctions would make it difficult for Russia to sell oil, gas, and coal internationally, especially if the United States failed to persuade OPEC nations to raise production. On top of that, gas prices had been rising steeply.

WTI Crude Price

Figure 2. Commodity prices across various global markets, IMF via Nasdaq Data Link

Russia’s invasion of Ukraine was also expected to disrupt shipping routes in the Black Sea and force rerouting of flights. As a result, logistics for countries in the region – particularly wheat and oil suppliers – would be affected. Moreover, Indonesia, which was aggressively reopening Bali to tourists without quarantine, could be hit if disrupted flight routes reduced foreign tourists’ interest in visiting.

Flights are being rerouted around Russia.

I took the Tokyo to Anchorage and then onto London flight 35 years ago (cold war era?).

It was spectacular. Midnight sun the entire trip. The icebergs were amazing too. pic.twitter.com/yJijJiFvH1
— Harrison Bergeron (@GranPelotas) February 27, 2022

The bottom line: inflation, which was already rising, would be pushed even higher by Russia’s invasion of Ukraine.

On the other hand, Indonesia is also a country that benefits from rising commodity prices. Coal, palm oil, and nickel are Indonesia’s traditional export goods. Of course, the nickel export ban and potential future export bans on other commodities become more costly because of the high opportunity cost.

Furthermore, rising commodity prices would increase the incentive for commodity exporters to sell abroad. Expect the cooking oil crisis and PLN’s energy supply issues to potentially worsen going forward.

Climate Change & Tech

The war was forcing other countries, particularly continental Europe, to divert their resources away from other issues. The European Union, already in the midst of an energy crisis, was expected to be further hit by the disruption of supplies from Russia. They might have to rely on government subsidies to contain energy and food inflation. They might also have to fall back on more readily available energy sources like coal – of which Indonesia has plenty, potentially driving coal prices even higher.

In addition, European countries were beginning to recognise the need to strengthen their military capabilities and increase defence spending. This naturally meant less budget for other priorities such as climate change. Meanwhile, high commodity prices (Russia is a significant copper supplier) risked increasing the cost of materials essential for manufacturing solar panels and wind turbines.

Ukraine is a key supplier of materials used in semiconductor manufacturing, such as neon, argon, krypton, and xenon gases. These disruptions could affect the semiconductor supply chain in the medium term.

New car production was already disrupted by the semiconductor shortage (one reason used car prices rose last year). Now, the steel supply chain for automobiles would be further strained because Russia and Ukraine are important suppliers of nickel, palladium, and steel in general.

The C-Factor

Some people were comparing the Ukraine-Russia situation with the Taiwan-China dynamic. The Taiwan-China conflict and China’s Nine-Dash Line claim in the South China Sea are geopolitical conflicts much closer to Indonesia. China and Taiwan are also economically closer to Indonesia than Ukraine and Russia. So far, China had been the most reliable neighbour Russia could count on in facing economic sanctions from the West. If China were also drawn in – or if China were inspired to become more aggressive in the South China Sea – the impact could be extremely serious, not just for Indonesia but for the world.

Let us hope that does not happen…

What Should Indonesia Do?

So far, Indonesia’s inflation remained relatively under control. Import quotas on staple goods, which have traditionally been tightly controlled, could be relaxed if inflation started to bite. Indonesia’s staple goods have actually been more expensive than world market prices due to import restrictions and various other complications. So relaxing import controls could be used to stabilise price changes. Moreover, rice price increases seemed more contained than those of wheat, corn, or soybeans. As long as rice remains the staple food, things should be manageable.

Fertiliser seemed more worrying, as fertiliser is used for all crops. Indonesia’s gas prices had also been rising with the increasing demand from newly operational smelters. On top of that, if non-subsidised fertiliser becomes more expensive, the incentive to divert subsidised fertiliser to the black market grows stronger. The distribution system for subsidised fertiliser already has many problems as it is.

The Indonesian government itself needed to find ways to capture more benefits from rising commodity prices. Indonesia’s tax ratio had been problematic lately, and the state’s capacity to tax commodities was not as strong as during the oil boom era. Yet Indonesia urgently needed funds for various projects, from food estates to the new capital city (IKN). So how to increase government revenue? Rather than using Domestic Market Obligations or export bans whose effectiveness is questionable, perhaps the government would be better off raising export levies. The revenue could be used to mitigate the impact of inflation.

It may sound cliched, but global coordination is needed now more than ever. With Russia (and perhaps China? who knows) cut off from global markets, the number of countries that can coordinate will shrink further (and we may end up with competing blocs). Indonesia is currently in a unique position: a member of RCEP, the world’s largest trade agreement, and holder of the G-20 presidency, representing countries with more than 80% of global GDP. Indonesia must leverage this opportunity to cooperate on multiple issues. The top priority right now is limiting the impact of Russia’s invasion of Ukraine on Ukraine, the EU, and the world as a whole. But beyond that, global cooperation is also urgently needed to manage current account balances across countries, increase the production and distribution of vaccines and pandemic-fighting equipment, and accelerate efforts to address the global climate crisis.

Menakar Efektivitas Neraca Komoditas

Wed, 23 Feb 2022 09:28:58 +0700

The Advent of A New Trade Governance after the Omnibus Law: Neraca Komoditas

Mon, 07 Feb 2022 19:25:33 +0700

Oil Boom vs Commodity Boom: Can Raw Materials Export Ban Save the State Budget?

Mon, 31 Jan 2022 11:37:13 +0700

A couple of days ago, I was forwarded a news from Asia Nikkei by my colleague, Deasy Pane. The news is about Indonesia’s plan to ban exports of raw minerals (the likes of bauxite, tin, gold and copper) in the middle of 2023. This is not very surprising, knowing that nickel ore exports was banned in the start of 2020, with Indonesian President is willing to “fight in any way” of possible WTO complaint. Based on the strong export performance of nickel’s downstream industries, it is safe to say that the Indonesian President surely will expand the export ban.

What surprised me with the Asia Nikkei news, however, is the quote from Sri Mulyani Indrawati, the Indonesian Finance Minister.

It is not nationalism. We are the biggest economy in ASEAN, and you cannot allow this economy to just depend on commodities without value added

As a former World Bank managing director as well as highly regarded economist in Indonesia, it is quite shocking that she believed an export ban on mineral is the most efficient way to improve value added.

Tax Ratio?

This talk about increasing value added from commodities is interesting. Deasy suspect that the frustration comes from the fact that the Indonesian Government gets very little benefit from the exported minerals. This is unlike the oil boom in the 70s where Indonesia were able to finance its many programmes (like education, health, and Import Substitution Strategy (ISS)) mainly from exporting oil. Obviously that gets me thinking, as a person who also oversee Indonesia’s taxation office, could it be the case that the Finance Minister feel that the same? That today’s commodity boom doesn’t improve Government revenue? It is true that the Finance Minister has been stating the needs to increase revenue for some time. Prolly her overseer asked too many super important program while she needs to keep the fiscal rule in check. Make sense.

I use World Development Indicators (WDI) to check this out. Thankfully it has option to embed the graph so readers can consult to the graph below.

As you can see, unfortunately, the WDI’s data has holes and I am too lazy to look at MoF’s website to complete it. Sorry lol.

Anyway, to emphasize my point about the oil boom and the commodity boom, I downloaded the data and adds some shades which show the two booms.

import wbdata as wb
import datetime

## Downloading data from World Development Indicators
tanggal=(datetime.datetime(1960,1,1), datetime.datetime(2020,1,1))
a=wb.get_dataframe({"GC.TAX.TOTL.GD.ZS" : "TAX"}, country=["IDN"], data_date=tanggal, convert_date=True, keep_levels=True)
a=a.reset_index()

## Plotting
plt.figure(figsize=(10,6),dpi=80)
sns.lineplot(data=a,x='date',y='TAX',marker="o")
plt.axvspan(datetime.datetime(1972,1,1), datetime.datetime(1981,1,1),alpha=.5,color='green')
plt.axvspan(datetime.datetime(2003,1,1), datetime.datetime(2008,1,1),alpha=.5,color='green')
plt.annotate('Commodity\nBoom', xy=(datetime.datetime(2003,1,1),11), xytext=(datetime.datetime(2003,1,1),11),color='white')
plt.annotate('Oil Boom', xy=(datetime.datetime(1974,1,1),11), xytext=(datetime.datetime(1974,1,1),11),color='white')
plt.title("Indonesia's tax-to-GDP ratio, 1972-2019, World Development Indicators")
plt.xlabel('Year')
plt.ylabel('%')

Text(0, 0.5, '%')

The oil boom shade range from 1972 to 1981, while the commodity boom shade is 2003-2008. Obviously, Indonesia’s tax ratio since the Asian Financial Crisis is significantly lower. However, as you can see that the tax ratio keeps declining since the Global Financial Crisis in 2008 and the taper tantrum episode in 2013.

But the picture is clear. When the oil boom started, Indonesia’s tax-to-GDP ratio climbed significantly. The same cannot be said on the Commodity Boom. If you are the person overseeing the taxation office, I think it kinda makes sense to think that the raw material exports contribute very little to Indonesia’s welfare.

Caveats and conclusion

This graph, of course, simplify a lot of things. Indonesia’s oil company was (still is) a monoply, unlike mining and palm oil where private players are abundant (by private of course including individuals with strong political connection). SOEs revenue might also not included in the World Bank’s tax ratio calculation, but that emphasize an even stronger point on raw materials contribution to the general government revenue.

Surely there are more things to consider if one wants to take a deeper look in this issue (ie., raw material exports to the country’s welfare). But at least one thing is clear. Indonesia is ridden with many problems within its ease of doing business from red tapes, corruption, inconsistent rule of law, etc. In fact, nickel mining is ridden with this problem as well. Fixing these stuff will surely help Indonesia’s investment climate much more efficiently than any kind of restrictive trade policy. The fact that Sri Mulyani agrees with export ban as a tool to attract foreign investment and improve value added suggests that even her does not know how to fix those other problems.

If the best finance minister in the world can’t save us all, then who can? Certainly not me and you. Lol.

Unpacking the Neraca Komoditas

Thu, 27 Jan 2022 14:30:00 +0700

To access the paper, click the thumbnail above or alternatively click here

The recorded event:

Nickel ore export ban and the export performance of the downstream industries

Wed, 12 Jan 2022 23:50:30 +0700

This post is inspired by this tweet (in Indonesian).

Baru nyadar dampak pelarangan bijih nikel ternyata *sebesar* itu. Indonesia sekarang jadi eksportir stainless steel terbesar dunia, salah satu produk turunan feronikel.https://t.co/qonkL4o33d
— Faris Abdurrachman (@faris_sina) January 11, 2022

That tweet’s quoting artikel dari investor.id basically sez that Indonesia has now become the top global export of stainless steel. The export value is 1,6 billion USD. Amazing stuff!

Moreover, that article mentioned the cause. It is the nickel ore export ban. Indeed, nickel is one of the most important component in the production of stainless steel. In fact, most of exported nickel is used by the steel industry.

Let’s check out the data.

Ekspor Stainless Steel

As usual, my go to trade database is the UN COMTRADE. The HS-Code is guessed. Lol

wew=pd.read_csv('stainlesssteel.csv')
wew.query('Year == 2020 & `Commodity Code`>7200')

	Year	Commodity Code	Commodity	Netweight (kg)	Trade Value (US$)	USD/ton
7	2020	720260	Ferro-alloys; ferro-nickel	2874022704	4739112331	1648.947423
11	2020	7218	Stainless steel in ingots or other primary for...	1087854208	1630911693	1499.200611
15	2020	7219	Stainless steel; flat-rolled products of width...	1801937373	2993037641	1661.010913

1.6 billion USD indeed! Let’s plot its time series.

sns.lineplot(data=wew.query('`Commodity Code` == 7218'),x="Year",y="Trade Value (US$)",color="green")
sns.lineplot(data=wew.query('`Commodity Code` == 7219'),x="Year",y="Trade Value (US$)",color="orange")
sns.lineplot(data=wew.query('`Commodity Code` == 720260'),x="Year",y="Trade Value (US$)",color="magenta")
plt.legend(labels=['7218','7219','ferro-nickel'])

<matplotlib.legend.Legend at 0x220e4898ca0>

Whoa seems so clear as day that the two goods’ export skyrocketed in 2016.

wew.query('(Year == 2016 | Year == 2020) & `Commodity Code`>7200')

	Year	Commodity Code	Commodity	Netweight (kg)	Trade Value (US$)	USD/ton
7	2020	720260	Ferro-alloys; ferro-nickel	2874022704	4739112331	1648.947423
11	2020	7218	Stainless steel in ingots or other primary for...	1087854208	1630911693	1499.200611
15	2020	7219	Stainless steel; flat-rolled products of width...	1801937373	2993037641	1661.010913
23	2016	720260	Ferro-alloys; ferro-nickel	411438077	571298623	1388.540961
28	2016	7218	Stainless steel in ingots or other primary for...	24614238	30434580	1236.462408
33	2016	7219	Stainless steel; flat-rolled products of width...	49912515	74135846	1485.315777

Berkat Ekspor Nikel?

Salah satu hal yang menjadi penunjang kenaikan ekspor stainless steel tersebut adalah, katanya, adalah pelarangan ekspor nikel. Nikel adalah barang mentah. Adalah keinginan Pemerintah Indonesia untuk menghasilkan nilai tambah di dalam negeri. Daripada ekspor nikel mentah lalu negara lain yang menikmati jualan baja, kenapa tidak kita saja yang bikin baja? Berdasarkan logika tersebut, maka dilaranglah ekspor nikel agar penghasil baja Indonesia dapat meningkatkan produksinya dan menghasilkan nilai tambah ekstra di dalam negeri. Secara natural, pertanyaan berikutnya adalah berapakah nilai ekspor nikel yang turun? Apakah sebanding dengan naiknya ekspor baja?

Lagi-lagi untuk menjawabnya, saya menggantungkan diri pada data dari UNCOMTRADE dan juga tebak-tebakan kode HS. Menurut penerawangan saya, Nikel adalah barang yang dikasih kode HS 2604 yaitu nickel ores and concentrates. Langsung saja kita coba ilustrasikan dengan grafik.

sns.lineplot(data=wew.query('`Commodity Code` == 2604'),x="Year",y="Trade Value (US$)")
plt.axhline(color='grey')

<matplotlib.lines.Line2D at 0x220e495deb0>

Trade value nikel naik secara signifikan sejak commodity boom dimulai sekitar 2005an, dan sempat berkurang saat krisis global 2008. Kemudan, nilai ekspor dari nikel mendadak mendekati nol pada tahun 2014 dan baru naik lagi menjelas 2017-an. Sebenarnya larangan ekspor ini sudah pernah diberlakukan pada tahun 2014, namun pada 2017 dibolehkan lagi. Kita bisa lihat kenaikan kapasitas ekspor yang luar biasa mendekati larangan ekspor tersebut. Sekali lagi ketika dilarang lagi di 2020, para produsen meningkatkan ekspor besar-besaran pada 2019-nya.

Kalau dilihat dari grafik nikel dan stainless steel, sepertinya emang kelihatan ya dampak dari pelarangan ekspor bijih nikel. Ekspor produk turunannya langsung meningkat pesat. Apalagi memang saat ini sebagian besar nikel dunia digunakan untuk produksi stainless steel. Apalagi, kenaikan nilai ekspor 3 produk nikel di atas jauh melebihi kehilangan nilai ekspor nikel akibat larangan ekspor. Nilai ekspor bijih nikel saat lagi di puncak paling sekitar 1,7 milyar Dolar AS. angka ini jauh lebih kecil daripada naiknya nilai ekspor tiga produk stainless steel di atas sejak 2016. Dilihat dari data ini, sepertinya memang penambahan nilai tambah di dalam negeri telah berhasil dilakukan.

Produksi yang berpindah

Larangan ekspor Indonesia tentunya membuat produsen stainless steel di negara lain kesulitan. Indonesia adalah pemasok bijih nikel terbesar dunia. Tentunya larangan ekspor nikel oleh Indonesia akan memiliki dampak yang besar terhadap pasar global nikel. Mungkin ada 2 cara mainstream menghadapi larangan ekspor nikel Indonesia. Pertama adalah protes baik secara langsung maupun lewat World Trade Organization (WTO), seperti yang dilakukan oleh Uni Eropa. Tapi cara ini kurang efektif, pertama selain karena rentan untuk dicuekin, protes ke WTO bisa makan waktu bertahun-tahun.

Cara kedua adalah dengan memindahkan pabrik ke Indonesia sekalian, beli nikelnya secara domestik, dan mengekspor stainless steelnya balik ke negara masing-masing. Inilah yang dilakukan oleh Republik Rakyat Tiongkok (RRT). Bahkan Taiwan pun memutuskan untuk mengurangi produksi stainless steel mereka dan membelinya dari Indonesia.

Hal ini membuat saya kepingin ngelihat peta ekspor stainless steel dunia, yang lagi-lagi bisa kita dapat dari UNCOMTRADE. Saya akan coba lihat beberapa negara yang katanya big player di stainless steel. Saya coba masukin Amerika Serikat, Inggris, Uni Eropa (termasuk inggris wkkw), Indonesia, China, India dan Swedia. Saya cuma pake stainless steel yang 7218 yak karena ini yang jadi pemantik di twit di atas.

waks=pd.read_csv('stainlesssteel2.csv').sort_values(by=['Year','Trade Value (US$)'])
pd.set_option('display.max_rows', None)
b=[]
ctr=('IDN','GBR','EU2','CHN','IND','SWE')
for i in ctr:
 bmii=waks.loc[(waks['Reporter ISO'] == i)]
 b.append(bmii)
b=pd.concat(b)
b.query('Year == 2020')

	Year	Reporter	Reporter ISO	Commodity Code	Commodity	Netweight (kg)	Trade Value (US$)
642	2020	Indonesia	IDN	7218	Stainless steel in ingots or other primary for...	1.087854e+09	1630911693
897	2020	United Kingdom	GBR	7218	Stainless steel in ingots or other primary for...	1.408406e+08	326187431
3	2020	EU-28	EU2	7218	Stainless steel in ingots or other primary for...	4.993958e+07	179001341
550	2020	China	CHN	7218	Stainless steel in ingots or other primary for...	2.600670e+05	1247579
819	2020	India	IND	7218	Stainless steel in ingots or other primary for...	6.883925e+06	14563591
683	2020	Rep. of Korea	KOR	7218	Stainless steel in ingots or other primary for...	2.022914e+06	2851620

fig, ax =plt.subplots(1,2,figsize=(15,7))
sns.lineplot(data=b,x="Year",y="Trade Value (US$)",hue="Reporter", ax=ax[0])
sns.lineplot(data=b,x="Year",y="Netweight (kg)",hue="Reporter", ax=ax[1])

<AxesSubplot:xlabel='Year', ylabel='Netweight (kg)'>

Gambar di atas, sebelah kiri adalah dalam Dolar Amerika sementara sebelah kanan dalam kilogram. Nilai ekspor ngedrop pada 2016 kayaknya gara-gara Presiden AS memberlakukan tariff terhadap baja. Tapi sekotar 2017 Uni Eropa berhasil nego dikit. Tapi jelas kelihatan bahwa seiring kenaikan ekspor Indonesia, ekspor negara lain menurun, terutama negara-negara barat. India dan RRT merupakan produsen, namun mereka menggunakan sebagian besar produknya untuk kepentingan domestik sehingga ekspornya kurang banyak.

Kalo RRT wajar sih, karena memang perusahaan China lah yang pindah ke Indonesia. Jadi sebenernya produsennya dia lagi dia lagi tapi pindah lokasi aja ke Indonesia. India dan Uni Eropa sama-sama lagi mengatur strategi bagaimana mengurangi impor dari Indonesia, sementara Taiwan memilih untuk mengikuti arus dan beli aja dari Indonesia daripada proteksi. Sementara itu, Produsen di Korea Selatan memilih untuk mengubah jenis baja yang mereka produksi, mengurangi stainless steel grade rendah dan meningkatkan produksi stainless steel yang lebih tinggi.

Kalau lihat strateginya korsel, berarti sepertinya stainless steel sendiri memiliki banyak grade meski ada di HS-Code yang sama. Berhubung saya sendiri bukan pemerhati nikel dan stainless steel yang berdedikasi, saya ga punya cukup info untuk menggali hal ini. Tapi kita dapat kira-kira kualitas stainless steelnya dari harga. Kita asumsikan aja kalo harganya mahal berarti stainless steelnya lebih bagus. BTW Korsel ga saya masukin karena produksi mereka juga kebanyakan dipakai sendiri jadi ekspornya dikit.

Harga juga kayaknya macem-macem ya, tapi kita proksi aja pakai $\frac{\text{Nilai Ekspor}}{\text{Berat}}$

b['price']=b['Trade Value (US$)']/b['Netweight (kg)']
sns.lineplot(data=b,x="Year",y="price",hue="Reporter")
plt.legend(bbox_to_anchor=(1,0.5))

<matplotlib.legend.Legend at 0x220e47ede50>

Daaan memang Stainless Steel Indonesia bisa dibilang yang paling murah diantara negara-negara di atas. Tentunya kita harus berhati-hati menggunakan harga ini ya. Kalau produknya sama, berarti Indonesia justru hebat karena bisa produksi dengan harga miring. Tapi wajar sih ya yang lain kan harus akses Nikel yang harga internasionalnya mahal, sementara produsen di Indonesia pakai produk sendiri yang ekspornya dilarang, jadi harga bahan baku bisa ditekan. Di samping itu, bisa saja di negara yang lebih maju, concern tentang emisi mengakibatkan biaya produksi yang lebih tinggi daripada negara yang tidak terlalu mengutamakan isu lingkungan.

Yaudah sih sekian dulu postingan hari ini. Masih banyak info yang perlu digali, tapi bisa jadi ini adalah contoh sukses penggunaan larangan ekspor untuk menarik investasi asing dan tenaga kerja untuk meningkatkan nilai tambah di dalam negeri. Gimana menurut anda? Apakah ini adalah hal yang baik? Mensen saya di @twitter ya buat berdiskusi!

Trade in Value Added: Indonesia's Position in the Global Value Chain

Mon, 13 Dec 2021 09:54:16 +1100

Global Value Chain (GVC) is a buzzword that frequently comes up among academics and consultants when discussing international trade and manufacturing growth. The essence of GVC is splitting the production of a finished good into several links in a value chain, with each link produced in a different country. Take bicycles as an example: to assemble one bicycle, a manufacturer sources components from various countries.

Source: World Development Report 2020, World Bank.

The image above is from the World Development Report 2020, which I consider a fairly comprehensive read on GVC. I once illustrated (in a very simple way) in one of my posts how GVC helps produce gado-gado sauce that successfully enters export markets. I also wrote a short article on the role of GVC during COVID-19. The longer version is here.

GVC is often invoked by government officials, who argue that Indonesia’s participation in GVC can drive the growth of its manufacturing sector. GVC has been one of the key strategies for ASEAN and East Asian countries in producing competitive manufactured goods. Indonesia is frequently said to underutilise GVC compared to its regional peers. Is this true?

One indicator that can be used to assess Indonesia’s GVC participation is Trade in Value Added (TiVA). TiVA is an indicator based on trade data that estimates how much of a country’s final goods consists of domestic versus imported content. A country that relies heavily on GVC will have a high share of imported content in its export value. In other words, using the bicycle example, TiVA shows how large the total imports of gears, chains, tyres, and so on are relative to the value of bicycle exports. The TiVA data used in this post is sourced from here. As usual, I will use Python. Readers can replicate the data with this code.

tiva=pd.read_excel('tiva.xlsx',sheet_name='pake')
# source: https://stats.oecd.org/Index.aspx?DataSetCode=TIVA_2021_C1
tiva=pd.melt(tiva, id_vars=['year'])
tiva2=pd.read_excel('tiva.xlsx',sheet_name='pake2')
# source: https://stats.oecd.org/Index.aspx?DataSetCode=TIVA_2021_C1
tiva2=pd.melt(tiva2, id_vars=['year'])

tiva.variable.unique() # check countries

array(['OECD', 'AUS', 'AUT', 'BEL', 'CAN', 'CHL', 'COL', 'CRI', 'CZE',
'DNK', 'EST', 'FIN', 'FRA', 'DEU', 'GRC', 'HUN', 'ISL', 'IRL',
'ISR', 'ITA', 'JPN', 'KOR', 'LVA', 'LTU', 'LUX', 'MEX', 'NLD',
'NZL', 'NOR', 'POL', 'PRT', 'SVK', 'SVN', 'ESP', 'SWE', 'CHE',
'TUR', 'GBR', 'USA', 'NON', 'ARG', 'BRA', 'BRN', 'BGR', 'KHM',
'CHN', 'HRV', 'CYP', 'IND', 'IDN', 'HKG', 'KAZ', 'LAO', 'MYS',
'MLT', 'MAR', 'MMR', 'PER', 'PHL', 'ROU', 'RUS', 'SAU', 'SGP',
'ZAF', 'TWN', 'THA', 'TUN', 'VNM', 'ROW', 'APEC', 'ASEAN', 'EASIA',
'EU27', 'EU28', 'EU15', 'EU13', 'EA19', 'G20', 'ZEUR', 'ZASI',
'ZNAM', 'ZSCA', 'ZOTH'], dtype=object)

The chart below shows the percentage of foreign content in the exports of selected countries. In other words, the y-axis represents $\frac{\text{foreign content}}{\text{total exports}}$ in aggregate. So we are counting not just bicycles, but all of Indonesia’s exports. For foreign content, we are counting not just gears, tyres, etc., but all intermediate imports such as chips and so on. This measure does not reflect individual industries but the economy as a whole.

b=[]
ctr=('MYS','THA','AUS','CHN','IDN','VNM')
for i in ctr:
 ttt=tiva.loc[(tiva['variable'] == i)]
 b.append(ttt)
b=pd.concat(b)
sns.lineplot(data=b,x='year',y='value',hue='variable')
plt.legend(title="",bbox_to_anchor=(0.8,-.12),ncol=3)
plt.ylabel("%")

Text(0, 0.5, '%')

As we can see, Indonesia (IDN) has a very small share of imported content relative to its total exports, especially compared to other ASEAN countries. This is actually quite understandable, given that Indonesia’s exports are dominated by raw commodities. Raw commodities do not require intermediate inputs from other countries to produce. By this indicator, Indonesia’s participation is even higher than Australia’s, a country that essentially has no manufacturing industry.

But perhaps the most striking case is Vietnam. Its foreign content share has been rising steadily.

The chart below shows total imports of raw materials and intermediate goods for each country. The data also comes from the TiVA database. By this metric, China is far ahead of the others. However, Indonesia remains the lowest. This is notable because Indonesia’s economy is relatively larger than its neighbours’. Again, look at Vietnam.

b2=[]
for i in ctr:
 t2=tiva2.loc[(tiva2['variable'] == i)]
 b2.append(t2)
b2=pd.concat(b2)
sns.lineplot(data=b2,x='year',y='value',hue='variable')
plt.legend(title="",bbox_to_anchor=(0.8,-.12),ncol=3)

<matplotlib.legend.Legend at 0x133fa61e190>

Vietnam’s economy is tiny. In 2019, Vietnam’s GDP was only 261.9 billion USD, less than a quarter of Indonesia’s 1,119 trillion USD in the same year. How can such a small economy import so many raw materials and intermediates as shown in the chart above, and keep growing? Who consumes the finished goods? Indonesia’s large imports make sense given its large population and big market. But Vietnam? Why import so much?

The answer: to export. Let us look at high-technology exports below. This data comes from the World Bank.

import wbdata as wb
import datetime
tanggal=(datetime.datetime(1995,1,1), datetime.datetime(2019,1,1))
a=wb.get_dataframe({"TX.VAL.TECH.CD" : "hitek"}, country=["IDN","VNM","THA","MYS"],
 data_date=tanggal, convert_date=True, keep_levels=True)
a=a.reset_index()
sns.lineplot(data=a,x='date',y='hitek',hue='country')
plt.legend(title="",bbox_to_anchor=(0.8,-.12),ncol=4)
plt.ylabel("USD")
plt.xlabel("year")

<matplotlib.legend.Legend at 0x133fa6be820>

You can clearly see the growth in high-technology exports. Vietnam is not a country with a strong research tradition compared to its regional neighbours. They do not do their own R&D. They simply assemble. By importing raw materials and intermediates like chips, they become producers of high-technology goods. By the way, I did not include China in the chart above because if China were included, the others would look tiny.

The chart below shows scientific journal publications, also sourced from the World Bank. This serves as an illustration of each country’s research capacity. Compare Vietnam with the rest.

import wbdata as wb
import datetime
tanggal=(datetime.datetime(1995,1,1), datetime.datetime(2019,1,1))
a=wb.get_dataframe({"IP.JRN.ARTC.SC" : "Journal"}, country=["IDN","VNM","THA","MYS"],
 data_date=tanggal, convert_date=True, keep_levels=True)
a=a.reset_index()
sns.lineplot(data=a,x='date',y='Journal',hue='country')
plt.legend(title="",bbox_to_anchor=(0.8,-.12),ncol=3)

<matplotlib.legend.Legend at 0x133fa7e5820>

Speaking of research, Indonesia’s publication count suddenly surged from 2016 onward. Impressive! How did that happen? What is the secret?

Who published in "predatory" journals (2015-2017)?
Source: https://t.co/j5TQBWgrbr pic.twitter.com/PLYXDx9QYX
— Arief Anshory Yusuf (@anshory72) February 8, 2021

What is the takeaway? I will leave that to the reader. That is all for today’s post. Hopefully it is useful! Mention me on twitter for feedback, suggestions, or discussion. Thanks!

On District/City Minimum Wages: Too High or Too Low?

Wed, 08 Dec 2021 00:12:12 +1100

Some time ago, Twitter was abuzz about minimum wages. The trigger seemed to be news about Minister of Manpower Ida Fauziyah commenting on how high minimum wages are in Indonesia. One widely shared report was tweeted by CNN Indonesia below.

Menaker Sebut Upah Minimum di Indonesia Terlalu Tinggi https://t.co/QcN7DKj8bx
— CNN Indonesia (@CNNIndonesia) November 18, 2021

As you can see, the tweet received about 1,000 replies, far above CNN Indonesia’s usual engagement level.

But what is even more interesting is this tweet from a highly influential account:

Menteri Tenaga Kerja Ida Fauziyah mengatakan tak layak buruh menuntut kenaikan upah karena upah minimum di Indonesia sudah terlalu tinggi. Benarkah terlalu tinggi? Menurut Global Wage Report 2020-2021: upah minimum RI ada di papan bawah negara2 Asean, hanya menang dari Myanmar. pic.twitter.com/16PDZcAB7M
— Farid Gaban (@faridgaban) November 17, 2021

The account holder is someone quite respected in the Indonesian Twittersphere who frequently offers opinions that I find very critical, sharp, and thought-provoking. The chart he tweeted was taken from the published by the International Labour Organization (ILO).

The chart shows minimum wages in several countries in both US dollars and PPP dollars. It shows that Indonesia’s minimum wage is very low, only slightly above Myanmar. This seems to prove that Indonesia’s minimum wage does not match the Minister’s claim that it is “too high.”

So what was the Minister’s basis for saying that? Is there a missing link? Looking at replies and quote-tweets, we find several clues. Here is one:

UMR $111 itu UMR Yogya bukan Indonesia. Jabar sudah Rp4.7 jt ($337). Lebih tinggi dari Cina, Thailand, Malaysia.. https://t.co/MIhUJfgSOJ
— ARK (@ngabdul) November 18, 2021

It appears the Global Wage Report used the lowest minimum wage in each country. However, Indonesia’s minimum wage regulations exhibit substantial variation. Some districts/cities have very low minimum wages, while others are quite high. What the Minister likely meant by “expensive” were minimum wages in industrial hubs, not the very lowest. But as you might expect, the screenshot, with the context provided, .

So are minimum wages in Indonesia actually high or low? Well, it probably depends on the benchmark. High or low compared to what?

Sampling UMK on Java Island, 2019

I tried to do a quick fact-check, at least for Java. Following the chart tweeted by @fgaban, I will use 2019 figures and PPP dollars as the unit. According to the chart, Indonesia’s minimum wage is 331 PPP dollars. I looked up 2019 UMK regulations for various districts/cities on Java. The PPP conversion factor comes from the , while UMK data comes from:

Banten https://indolabourdatabase.files.wordpress.com/2018/12/SK-UMK-Banten-2019.pdf
East Java https://surabaya.tribunnews.com/2https://indolabourdatabase.files.wordpress.com/2018/12/SK-UMK-Banten-2019.pdf018/11/16/berikut-ini-nilai-umk-2019-di-jawa-timur-surabaya-tertinggi
DIY https://maucash.id/umr-yogyakarta
Central Java https://jatengprov.go.id/publik/umk-35-kabupaten-kota-ditetapkan-semarang-tertinggi-banjarnegara-terendah/
West Java https://databoks.katadata.co.id/datapublish/2019/11/29/daftar-umk-di-jawa-barat-karawang-cetak-tertinggi
DKI https://www.kompas.com/tren/read/2019/11/22/161505465/membandingkan-ump-dki-jakarta-dengan-umk-daerah-sekitarnya?page=all

umk19=pd.read_csv('gabung.csv').query('year == 2019')
umk19=pd.melt(umk19, id_vars=['provname'], value_vars=['meanP','minP','maxP'])
sns.barplot(data=umk19,x="provname",y='value',hue='variable')
plt.axhline(331,color='orange',linewidth=2)
plt.xticks(rotation=30)

(array([0, 1, 2, 3, 4, 5]),
[Text(0, 0, 'DKI Jakarta'),
Text(1, 0, 'Jawa Barat'),
Text(2, 0, 'Jawa Tengah'),
Text(3, 0, 'DI Yogyakarta'),
Text(4, 0, 'Jawa Timur'),
Text(5, 0, 'Banten')])

The orange line above represents 331, the figure for Indonesia’s minimum wage according to the Global Wage Report 2020-2021. Indeed, the lowest minimum wage in Indonesia is in DIY (Yogyakarta), as several people who replied to @fgaban’s tweet pointed out. However, the average UMK on Java appears quite high. The closest to DIY are Central Java and East Java. But West Java and Banten have much higher average UMKs. Is this the effect of being near Jakarta?

By the way, Jakarta’s average, maximum, and minimum are all the same. Obviously, because it only has a provincial minimum wage (UMP). For other provinces, the UMK varies by district/city. The variation is quite different across provinces.

Some additional facts about the 2019 UMK (in Rupiah):

Province	Highest UMK	Amount	Lowest UMK	Amount
Banten	Kota Cilegon	3,913,078	Lebak	2,498,068
West Java	Karawang	4,594,324	Kota Banjar	1,831,884
Central Java	Kota Semarang	2,715,000	Banjarnegara	1,748,000
D.I. Yogyakarta	Kota Yogyakarta	1,846,400	Gunung Kidul	1,571,000
East Java	Kota Surabaya	3,871,052	multiple	1,763,267

The highest UMK is the red bar, the lowest is the green, and blue is the average. In East Java, the districts/cities with the lowest UMK include PAMEKASAN, NGAWI, PONOROGO, SITUBONDO, MADIUN, MAGETAN, PACITAN, SAMPANG, and TRENGGALEK (apologies for the all-caps – the labels came from Stata).

As shown above, the variation between the highest and lowest is remarkable. The biggest gap is probably in West Java, which is reportedly home to . Karawang’s UMK is even higher than DKI Jakarta’s! It is perhaps unsurprising that .

UMK Trends on Java, 2002-2014 (+2019)

Minimum wages are not the focus of my research. Fortunately, I have a colleague whose research centres on minimum wages. She is , whom I usually call Dika. She recently presented on the impact of minimum wages on poverty. She used Java UMK data from 2002 to 2014, and was kind enough to share it with me.

Below I plot UMK trends on Java for 2002-2014 using Dika’s data. I added my own collected 2019 data. I do not have figures for 2015 to 2018, so in the charts below you will see a straight line between 2014 and 2019. I include the same chart in both PPP dollars and Rupiah.

umk=pd.read_csv('gabung.csv')
fig, axs = plt.subplots(figsize=(10,5),ncols=2)
sns.lineplot(data=umk,x='year',y='maxP',hue='provname',
 style='provname',linewidth=2, ax=axs[0],legend=False)
sns.lineplot(data=umk,x='year',y='minP',hue='provname',
 style='provname',linewidth=2, ax=axs[1],legend=False)
axs[0].set_title('Highest UMK in each province')
axs[0].set_ylabel('PPP$')
axs[0].set_xticks([2002,2005,2008,2011,2014,2019])
axs[1].set_title('Lowest UMK in each province')
axs[1].set_ylabel('')
axs[1].set_xticks([2002,2005,2008,2011,2014,2019])
axs[0].set_ylim(100,1000) #a
axs[1].set_ylim(100,1000) #b
labels=['DKI Jakarta','Jawa Barat','Jawa Tengah','DI Yogyakarta','Jawa Timur','Banten']
plt.legend(title='',labels=labels, bbox_to_anchor=(0.7,-0.15), ncol=3)

<matplotlib.legend.Legend at 0x238ae9840d0>

fig, axs = plt.subplots(figsize=(10,5),ncols=2)
sns.lineplot(data=umk,x='year',y='maximum',hue='provname',
 style='provname',linewidth=2, ax=axs[0],legend=False)
sns.lineplot(data=umk,x='year',y='minimum',hue='provname',
 style='provname',linewidth=2, ax=axs[1],legend=False)
axs[0].set_title('Highest UMK in each province')
axs[0].set_ylabel('current IDR')
axs[0].set_xticks([2002,2005,2008,2011,2014,2019])
axs[1].set_title('Lowest UMK in each province')
axs[1].set_ylabel('')
axs[1].set_xticks([2002,2005,2008,2011,2014,2019])
labels=['DKI Jakarta','Jawa Barat','Jawa Tengah','DI Yogyakarta','Jawa Timur','Banten']
plt.legend(title='',labels=labels, bbox_to_anchor=(0.7,-0.15), ncol=3)

<matplotlib.legend.Legend at 0x238ac3f8730>

From both charts, we can see that UMK rose gradually but then jumped sharply around 2012/2013, and increased even more steeply by 2019. Unfortunately, I am not interested enough to fill in the 2015-2018 data, but minimum wages almost certainly did not decline, so the insights from those years would not add much. The trend is definitely upward toward 2019.

In any case, the charts above illustrate how rapidly UMK on Java has been rising, likely well above inflation.

So Are Indonesia’s Minimum Wages High or Low?

A proper investigation would need to go beyond Java and beyond 2019. I strongly recommend that interested readers look at more comprehensive studies or contact Dika to chat. However, I think our exercise here is reasonably valid, especially since both the highest and lowest UMKs are on Java. Java also remains the top destination for manufacturing investors, so I believe Java provides sufficient insight to continue the discussion.

Returning to the question of high or low, I think we can at least look at it from two angles. First, comparing wages between Indonesia and other countries. Second, comparing Indonesia’s current wages with its own past (in other words, the rate of increase).

On the first point, comparing Indonesia’s lowest minimum wage with the lowest minimum wage in other countries using PPP dollars is appropriate. This is the advantage of PPP dollars – inflation and consumer prices in each country are already incorporated, making cross-country comparisons valid.

However, the lowest minimum wage alone does not provide full context. If we look deeper into the report, there is actually a lot of information about the distribution of minimum wages. Figure 7.16 in the report gives some sense of the UMK distribution in Indonesia. Reading this chart alongside the one tweeted by @fgaban immediately gives a better picture of UMK distribution and adds context to the Minister’s comment.

But the most important chart is Figure 9.7 in the same report. This chart, about three pages after Figure 9.3 (which @fgaban posted), shows the lowest and highest minimum wages in several countries.

From the chart above, we can see that the minimum wage in the lowest-paying province in Indonesia is indeed relatively lower than in the Philippines, Vietnam, and Brazil. However, if we look at the highest minimum wage, Karawang’s UMK is higher than the minimum wages in Vietnam and the Philippines, and even higher than Shanghai’s minimum wage!

The second angle is comparing current UMK with historical levels. We can see that on Java, UMK has been rising very rapidly, especially in DKI Jakarta and surrounding areas. This gives businesses a clear signal that in the long term, minimum wages in these areas will keep rising, and at some point production there will no longer be attractive.

There is actually a third angle (sorry): comparing minimum wage regulations with the median and/or average wage. You may remember from high school statistics the difference between the mean and the median. The minimum wage is supposed to be the lowest wage that should be paid in a given area. Since it is the floor, there should naturally be a gap between it and the mean or median. If the minimum wage is too close to the median or mean, it may indicate that the minimum wage is set too high for the business sector.

Fortunately, this can also be seen in the same report.

The chart above shows $\frac{\text{minimum wage}}{\text{average wage}}$ (red bar) and $\frac{\text{minimum wage}}{\text{median wage}}$ (blue bar). 100% means the minimum wage equals the average or median wage. As we can see, Indonesia’s minimum wage is above the global average when measured by the ratios $\frac{\text{minimum wage}}{\text{average wage}}$ and $\frac{\text{minimum wage}}{\text{median wage}}$. By this measure, Indonesia’s minimum wage regulation appears somewhat high relative to the regional wage level (mean or median), compared with Vietnam, China, the Philippines, and Thailand.

In other words, there are many indicators for determining whether Indonesia’s minimum wage is “too high” or “too low.” We do not really know which measure the Minister used when she said minimum wages are too high. There are many layers between the original statement and the Twitter frenzy. The Minister made her statement at a press conference, which most of us did not attend. The press wrote it up, chose a “representative” headline, posted it on Twitter, and it was disseminated by netizens with their own added context. Communication distortion can occur at any level in this process. We do not know whether people who read the tweet also read the article, or the full report, and so on.

According to , the Minister used the Kaitz Index. The Kaitz Index is more appropriately linked to $\frac{\text{minimum wage}}{\text{median wage}}$. Using this measure, the Minister’s statement appears reasonably valid.

Minimum wages that are too high can also be seen through changes in the level of economic informality. The informal economy consists of economic actors (workers and business owners) who are not registered in business databases. Informal economic actors generally do not have tax identification numbers (and do not pay taxes), and rarely have protections such as employment insurance or pensions. Because they are unregistered, informal businesses are also more likely to pay below the minimum wage, and informal workers are generally paid below it.

In other words, the higher the minimum wage, the less incentive businesses have to formalise, because they would have to pay the minimum wage. Likewise, as the number of jobs paying above the minimum wage shrinks, workers are forced to join the informal economy and accept wages below the minimum. In other words, an excessively high minimum wage can lead to a growing “black market” in the labour market. Indonesia is one of the countries with a .

That is all for this post. So are minimum wages too high or too low? Ask Dika, not me. The main point is to always be critical when receiving information, whether from Twitter posts or any other source (including this blog!). Always cross-check and make the effort to read more in-depth studies, or better yet, conduct your own!

n.b.: The 2019 Java UMK data is shown below. For the 2002-2014 data, feel free to contact Dika.

pd.set_option('display.max_rows', None)
umk.query('year==2019')

Economic Issues in the SDGs Framework in Indonesia

Wed, 06 Oct 2021 20:29:40 +1100

Click presentation above to view my slides. Access the book here.

Recording:

Purchasing Power Parity and the Big Mac Index

Wed, 06 Oct 2021 14:06:59 +1100

We often see the news comparing GDP between countries. These GDPs are usually measured in constant USD or current USD. While these GDP measures are useful for comparing economic power (not the best vocab to use? Not sure what’s better tho), they are not very useful to compare standard of living. That’s because price level in one country is different than other countries. While Indonesian GDP is smaller than Australia if we measure it with current USD, Indonesian GDP is actually bigger if measured with GDP PPP.

Purchasing Power Parity (PPP) supposed to include difference in price level. Additionally, PPP says that long run exchange rate will move towards a rate where a piece of identical goods are the same everywhere. That is:

$$Q=\frac{P^*e}{P}\equiv 1$$$$e=\frac{P}{P^*}$$

Where P is domestic price index, while $P^*$ is foreign price index.

That means, if GDP calculated with current dollar is different compared to GDP calculated with PPP conversion (so called international dollar), then there’s a misalignment in the (short-term) exchange rate. That is, an exchange rate may be overvalued or undervalued. The Economist magazine captures this neatly and intuitively with an index called Big Mac Index.

About Big Mac Index

Big Mac index is first introduced as a meme by The Economist (a magazine). Big Mac Index is based on the theory of Purchasing Power Parity (PPP). The problem with PPP is that it is almost impossible to compare 2 baskets of goods as there are difference in quality, taste, and cost (among other differences). Big Mac provide excellent comparison point because the recipe should be exactly the same everywhere. That, and obviously now $P$ and $P^*$ are no longer an index because now we only consider one good, which is the Big Mac.

Big Mac Index shows whether official exchange rate leads to an equal price of Big Mac between countries. Undervaluation of a currency (e.g., of IDN), means that a USD converted to IDN can buy less burger in Indonesia.

Go here and pick a country, say Indonesia. According to the economist, $P=34,000 IDR$ while $P^*=5.65 USD$. If $Q=1$ then $e$ must be

$$ e=\frac{34,000 IDR}{5.65 \\USD}=6,017.7 \frac{IDR}{USD}$$

however, official exchange rate is 14,517.50 $\frac{IDR}{USD}$ which suggests an undervaluation by 58.5%

Is this persistant across time between countries?

import wbdata as wb

import datetime

## Read data from the economist

url='https://raw.githubusercontent.com/TheEconomist/big-mac-data/master/output-data/big-mac-raw-index.csv'

bmi=pd.read_csv(url,parse_dates=['date'])

## Create the 'real' exchange rate measures

bmi['bmi']=bmi['dollar_ex']+bmi['dollar_ex']*bmi['USD']

bmi.iso_a3.unique() # Showing unique iso_a3 codes for the next cell

array(['ARG', 'AUS', 'BRA', 'GBR', 'CAN', 'CHL', 'CHN', 'CZE', 'DNK',
'EUZ', 'HKG', 'HUN', 'IDN', 'ISR', 'JPN', 'MYS', 'MEX', 'NZL',
'POL', 'RUS', 'SGP', 'ZAF', 'KOR', 'SWE', 'CHE', 'TWN', 'THA',
'USA', 'PHL', 'NOR', 'PER', 'TUR', 'VEN', 'EGY', 'COL', 'CRI',
'PAK', 'SAU', 'LKA', 'UKR', 'URY', 'ARE', 'IND', 'VNM', 'AZE',
'BHR', 'HRV', 'GTM', 'HND', 'JOR', 'KWT', 'LBN', 'MDA', 'NIC',
'OMN', 'QAT', 'ROU'], dtype=object)

b=[]

ctr=('CHN','AUS','GBR','IDN','IND','EUZ')

for i in ctr:

 bmii=bmi.loc[(bmi['iso_a3'] == i)]

 b.append(bmii)

b=pd.concat(b)

sns.lineplot(data=b,x='date',y='USD',hue='name')

plt.axhline(0,color='grey')

<matplotlib.lines.Line2D at 0x217440dc9a0>

As above graph suggests, it seems that Big Mac Index is not converge to 0. This means, the official nominal exchange rate undervaluation/overvaluation persists especially for lower income countries.

GDP PPP

The World Bank has something like this, but a bit more official. They have the “International Comparison Program”, which data can be accessed easily and intuitively from data.worldbank.org. They consider price indices between countries, compares them, and make a “true value” of currencies. Converting economies using PPP conversion index will make comparing countries’ GDP to be more accurate.

For example, Indonesian statistics body calculates Indonesian GDP using IDR. Converting it straight away with current USD will cause the GDP to be undervalued because IDR itself is undervalued. However, if we convert it with the PPP Index (aka the “international dollar”), Indonesian economy will look much bigger. Check out this graph below:

ctr='IDN'

bmii=bmi.loc[(bmi['iso_a3'] == ctr)]

tanggal=(datetime.datetime(2000,1,1), datetime.datetime(2021,1,1))

ICP=wb.get_dataframe({"PA.NUS.PRVT.PP" : "ICP"}, country=ctr, data_date=tanggal, convert_date=True, keep_levels=True)

ICP=ICP.reset_index()

sns.lineplot(data=bmii,x='date',y='bmi')

sns.lineplot(data=ICP,x='date',y='ICP')

sns.lineplot(data=bmii,x='date',y='dollar_ex')

plt.legend(title='legend', labels=['Big Mac Index','ICP (World Bank)','Official exchange rate'])

<matplotlib.legend.Legend at 0x21745da97c0>

Graph above shows three different stuff: red line is the official IDR/USD from the Big Mac Index; blue line is the Big Mac Index converter; green line is the World Bank’s version of PPP converter (the international dollar conversion) for Indonesia. As you can see, Big Mac Index is a bit more expensive than the overall PPP which may make sense (believe it or not, Big Mac in Indonesia is considered among the expensive side compared to usual foodstuffs). But both are definitely below the official IDR/USD line.

Below is the difference in GDP if we use normal USD vs International dollar:

ctr=['IDN']

PPP=wb.get_dataframe({"NY.GDP.MKTP.PP.CD" : "PPP"}, country=ctr, data_date=tanggal, convert_date=True, keep_levels=True)

NGDP=wb.get_dataframe({"NY.GDP.MKTP.CD" : "NGDP"}, country=ctr, data_date=tanggal, convert_date=True, keep_levels=True)

PPP=PPP.reset_index()

NGDP=NGDP.reset_index()

sns.lineplot(data=PPP,x='date',y='PPP')

sns.lineplot(data=NGDP,x='date',y='NGDP')

plt.ylabel('$')

plt.xlabel('Year')

plt.legend(title='legend', labels=['GDP (current intl $)','GDP (current USD)'])

<matplotlib.legend.Legend at 0x21745cdb2b0>

Above graph shows how differ Indonesian GDP is when calculated with GDP PPP, right? Let’s see comparison with Australia. First is GDP with current USD:

Next is GDP PPP:

While current USD GDP of Australia is bigger than Indonesia, Indonesian GDP PPP is bigger. That means, Indonesian standard of living is actually pretty ok compared to Australia. However, comparing current GDP sometimes better because it reflects the true buying power of a country. If you can get Australian income but consume with Indonesian prices, then you will live like a king. LoL.

Why PPP seldom holds? Well, many things can be said about it. some includes:

cost of non-tradable is different (labour wage, property rent)
competition (imagine if there exists a country which only Maccas is allowed to sell burger)
Trade barrier (bad supply chain, tariff, non-tariff barriers like quota restriction)

That’s it for today’s post. C Ya later!

Global Value Chain dalam bumbu gado-gado

Tue, 31 Aug 2021 23:27:21 +1000

Hari ini saya sarapan gado-gado. Menurut saya, gado-gado (dan bumbu kacang secara umum) adalah sebuah fenomena yang luar biasa. Meskipun kacang pertama kali diperkenalkan oleh Portugis yang dibawa dari Mexico, Masyarakat Indonesialah yang pertama kali membuat bumbu kacang. Bumbu kacang ini sungguh versatile karena bisa dicampur apa aja. Dikasih sayur, bisa. Kasih gorengan, enak. Nasi uduk, hayu. Buat bumbu ayam bakar, ya bisa (alias jadi sate).

Orang bule juga ternyata sangat menyukai bumbu kacang. Seperti kita ketahui, orang bule kalo sarapan suka pake roti yang diolesin selai kacang. Tentunya sungguh wajar jika mereka juga suka bumbu kacang. Daripada salad dressing a la eropa, jelas lah enakan gado-gado. wkwkwk. Kesukaan rang bule akan bumbu kacang (ditambah dengan banyaknya orang Asia dan orang Indonesia di Australia) memberikan skala ekonomi yang cukup untuk importir bumbu kacang.

Tentunya sebagai insan yang ga bisa masak, saya lebih suka pake bumbu kacang yang udah jadi. Praktis. ga perlu ngulek-ngulek. Ntar kalo bikin sendiri mah udahlah nguleknya capek, makan waktu, kagak enak pula! Udahla mending kita beli bumbu jadi aja. Praktis dan udah pasti rasanya oke.

Saya suka beli bumbu ini, yang namanya “Enak Eco”, beli di toko Vietnam deket rumah. Harganya sekitar A$4. Mantap ya. Export quality wkwkw. Diekspor dari mana sih benda ini?

Ternyata bumbu ini adalah karya anak bangsa. Made in Malang, tepatnya diproduksi oleh PT Enak Jaya Makmur. Diimpor oleh Sony Trading di New South Wales.

Cabe lalu gado-gado: peran Global Value Chain

Beberapa waktu lalu, saya sempet bikin postingan tentang impor cabe yang ternyata lumayan banyak peminatnya. Memang ketika itu situasinya adalah harga cabe sedang rendah, sehingga petani merugi. Di postingan tersebut juga saya memperlihatkan bahwa cabe mentah sudah dibatasi impornya, sementara cabe kering impornya bebas.

Katanya sih cabe mentah dikonsumsi oleh masyarakat, sementara cabe kering dikonsumsi oleh industri. Meskipun ternyata ga juga, akan tetapi tidak dapat kita pungkiri bahwa industri berperan besar dalam mengimpor cabe, terutama melihat betapa besarnya pertumbuhan industri makanan dan minuman di Indonesia.

Menggunakan produk impor untuk diolah di dalam negeri menjadi produk ekspor bukanlah hal baru. Sejak zaman di mana banyak negara melakukan perjanjian dagang, adalah hal lumrah bagi perusahaan di suatu negara untuk menggunakan bahan baku dari negara lain. Seperti halnya coklat belgia yang cokelatnya datang dari pantai gading, gado-gado Indonesia pun cabenya datang dari negara lain. Vietnam, negara yang sejatinya berteknologi rendah, dapat menjadi eksportir ponsel pintar besar dunia karena mengimpor komponen. Ekonom memakai istilah “Global Value Chain” (GVC) untuk mendeskripsikan fenomena ini.

Di postingan cabe itu, kita juga jadi tahu bahwa ada negara lain yang sanggup bikin cabe lebih murah daripada Indonesia. Akan tetapi, mereka ga bisa bikin gado-gado seenak kita lho (mungkin). Hahaha. Dengan cabe yang murah, maka bumbu gado-gado juga bisa jadi murah. Akibatnya, produk ini jadi laris tidak hanya di pasar dalam negeri, tetapi juga di pasar luar negeri.

Tanpa berpanjang lebar lagi, mari kita coba cek ‘kandungan internasional’ di dalam bumbu gado-gado impor ini. haha.

Ingredients dengan bantuan UN COMTRADE

Tentunya saya ga begitu paham struktur industri bumbu gado-gado. Karena itu, kita akan pake kira-kira aja. Dari bungkus belakang bumbu ini, dapat dilihat bahwa bumbunya antara lain adalah kacang tanah, cabe (capsicum), coconut sugar, bawang putih, bawang merah, garem, gula, dan pengawet (hiii baru ngeh ada pengawetnya tidaaaak).

Dari list tersebut, saya tinggal googling HS Code, untuk mendapatkan kode HS buat kita tarik datanya dari UN COMTRADE. Di sini kita sekali lagi harus bikin caveat wkwkw. Pertama, Ga ada informasi bahan-bahan ini dibeli oleh PT Enak Jaya Makmur dalam bentuk apa. Misalnya, apa kacang tanahnya murni yg masih ada kulitnya? Atau udah dikupas dan disangrai? Cabe pun demikian. Basah? Kering? Bubuk? Kode HS nya beda-beda. haha! Karena tidak ada informasi ini, maka saya melakukan sedikit spekulasi. Jadi jangan terlalu percaya sama kode HS yang akhirnya saya pake wkwkwkw. Jika anda lebih tahu, kode yg saya pake di postingan ini bisa direplikasi dengan mudah, jadi bisa coba versi anda sendiri. Atau lebih baik lagi, kasih tau saya resep yg benernya melalui twitter wkwkwk.

Seperti halnya postingan cabe, kita tarik data-data yang diperlukan pake API dari UN COMTRADE.

# cari api key-nya di https://comtrade.un.org/Data/

api_key='/api/get?max=502&type=C&freq=A&px=HS&ps=2019&r=360&p=0&rg=2%2C1&cc=200811%2C170290%2C070320%2C070310%2C071220%2C250100%2C291619%2C170111%2C090421%2C170114'

url='https://comtrade.un.org/'+api_key+'&fmt=csv'

gado2=pd.read_csv(url)

gado2=gado2[['Year','Trade Flow','Commodity Code', 'Commodity',

 'Netweight (kg)', 'Trade Value (US$)']]

Setelah kita download datanya, lalu saya coba perlihatkan deskripsi dari komoditas yang saya pake. Itu 4 komoditas pertama harusnya ada 0 di depannya (jadi 070310, 6 digit) tapi saya terlalu malas untuk konversi jadi biar aja jadi 5 digit wkwkw.

pd.set_option('display.max_colwidth', None) # show full content of a column

desc=gado2[['Commodity Code','Commodity']] # ambil cuma kode dan nama barang

desc.drop_duplicates() # print

	kabid	year	umk	prov	kabname	provname	rerata	minimum	maximum	PPPC	inflation	PPP	meanP	minP	maxP
2002	3101	2019	4267349.00	31	KEPULAUAN SERIBU	DKI Jakarta	4267349.0	4267349.0	4267349.0	4751.936	3.031	898.02325	898.02325	898.02325	898.02325
2003	3171	2019	4267349.00	31	KODYA JAKARTA SELATAN	DKI Jakarta	4267349.0	4267349.0	4267349.0	4751.936

	Commodity Code	Commodity
0	70310	Vegetables, alliaceous; onions and shallots, fresh or chilled
2	70320	Vegetables, alliaceous; garlic, fresh or chilled
4	71220	Vegetables; onions, whole, cut, sliced, broken or in powder but not further prepared, dried
6	90421	Spices; fruits of the genus Capsicum or Pimenta, dried, neither crushed nor ground
8	170114	Sugars; cane sugar, raw, in solid form, other than as specified in Subheading Note 2 to this chapter, not containing added flavouring or colouring matter
10	170290	Sugars; n.e.c. in heading no. 1702, including invert sugar and other sugar and sugar syrup blends containing, in the dry state, 50% by weight of fructose
12	200811	Nuts; ground-nuts, whether or not containing added sugar, other sweetening matter or spirit
14	250100	Salt (including table salt and denatured salt); pure sodium chloride whether or not in aqueous solution; sea water
16	291619	Acids; unsaturated acyclic monocarboxylic, cyclic monocarboxylic, their anhydrides, halides, peroxides and peroxyacids; their halogenated, sulphonated, nitrated or nitrosated derivatives, n.e.c. in item no. 2916.1

Seperti dapat dilihat, deskripsinya agak menyebalkan wkwk. Saya sertakan juga bubuk bawang di situ. Untuk cabe, saya pake cabe kering. Saya ga gitu tau Coconut sugar pake HS yg mana, tapi jadinya di sini saya pake 170290. Feel free to disagree with my choices hahaha.

Oh iya satu lagi, saya pake data tahun 2019. 2020 ada tapi takut rusak gara-gara COVID. 2019 harusnya ga jauh beda dengan business as usual.

Okeh, setelah dapat barang-barang yang mau dilirik, mari kita tampilkan ekspor dan impornya bahan-bahan ini.

## Tarik ekspor doang

x1=gado2.query('`Trade Flow`=="Export"').reset_index()

x1=x1[['Commodity Code','Netweight (kg)','Trade Value (US$)']]

x1=x1.rename(columns={"Netweight (kg)": "Ekspor (kg)",

 "Trade Value (US$)": "Ekspor (US$)"})

## Tarik impor doang

m1=gado2.query('`Trade Flow`=="Import"').reset_index()

m1=m1[['Commodity Code','Netweight (kg)','Trade Value (US$)']]

m1=m1.rename(columns={"Netweight (kg)": "Impor (kg)",

 "Trade Value (US$)": "Impor (US$)"})

## Gabung dan bikin tabelnya enak dilihat

gabung=pd.merge(x1,m1,on='Commodity Code',how='outer')

gabung['Product description']=['Onion/Shallot','Garlic','Onion Powder','Dried Chili','Cane Sugar','Coconut Sugar','Ground-nuts','salt',

 'Perservatives']

gabung=gabung[['Commodity Code','Product description','Ekspor (kg)','Impor (kg)','Ekspor (US$)','Impor (US$)']]

## Print dengan format thousand separated by comma

gabung.style.format({"Ekspor (kg)": "{:,.0f}", "Impor (kg)": "{:,.0f}"

 , "Ekspor (US$)": "{:,.0f}", "Impor (US$)": "{:,.0f}"})

	Commodity Code	Product description	Ekspor (kg)	Impor (kg)	Ekspor (US$)	Impor (US$)
0	70310	Onion	8,786,797	111,934,997	10,588,352	56,596,401
1	70320	Garlic	25,804	465,344,332	11,977	529,965,497
2	71220	Onion Powder	6,224	6,098,177	19,052	17,914,797
3	90421	Dried Chili	321,959	39,132,270	934,735	65,280,761
4	170114	Cane Sugar	124,025	3,965,555,215	113,024	1,317,595,639
5	170290	Coconut Sugar	39,810,415	91,207,379	53,547,449	71,438,428
6	200811	Ground-nuts	2,768,870	3,126,465	8,220,574	5,334,185
7	250100	salt	542,077	2,595,397,085	155,069	95,522,423
8	291619	Perservatives	2,229	2,277,642	13,200	11,570,694

Seperti dapat dilihat pada tabel di atas, hampir semua bahan-bahan yang saya gunakan di sini memiliki impor yang jauh lebih tinggi daripada ekspornya. Dengan kata lain, kemungkinan kandungan internasionalnya (setidaknya dari sisi bahan baku) cukup tinggi. Bumbu gado-gado ternyata adalah produk GVC!! Kita beli bahan baku dari negara lain, kita olah, lalu kita jual lagi ke berbagai pasar di belahan dunia. Di konsep GVC, kita bisa bilang PT Eka melakukan “Backward participation”. Ga salah emang kalo Indonesia dibilang surganya makanan!

But at what cost?

Kebijakan pembatasan impor

Diantara bahan-bahan yang saya pake di atas, sebagian mengalami pembatasan impor, dan sebagian lain dapat diimpor secara bebas. Bawang merah, bawang putih, gula, kacang dan garam adalah barang-barang yang impornya diregulasi. Dengan kata lain, pemerintah menerapkan kuota impor, di mana hanya perusahaan tertentu yang ditunjuk oleh negara (biasanya BUMN) yang boleh impor.

Pembatasan ini dirasa perlu karena Pemerintah berupaya melindungi petani, peternak dan nelayan di Indonesia dari kompetisi internasional. Indonesia memang memiliki beragam masalah dalam memajukan sektor pertanian. Solusi paling mudah dan populer adalah dengan melakukan pembatasan impor. Kenapa bisa populer ya? Ntahlah.

Sayangnya, meski memiliki tujuan mulia, tidak semua hal yang diserahkan negara memiliki pengelolaan yang baik. Diantara kasus korupsi kuota impor yang paling terkenal mungkin adalah kasus suap kuota daging sapi yang melibatkan petinggi penting sebuah parpol. Namun kasus korupsi juga tidak jauh dari bumbu gado-gado yang saya beli. Diantaranya kasus suap kuota bawang putih, yang akhirnya pelakunya dipenjara meskipun di bawah tuntutan. Di samping itu, ada juga kasus korupsi gula yang melibatkan eks bos BUMN gula, dan ada juga tersangka pengadaan garam impor. Suap-menyuap ini, menurut KPPU, diakibatkan oleh buruknya transparansi penunjukkan perusahaan yang berhak mendapatkan kuota.

Tidak semuanya kasus korupsi, kadang pengendalian perdagangan juga tidak sempurna dan mengakibatkan adanya barang ilegal atau palsu, seperti di kasus bawang merah.

Salah satu penelitian yang mencoba mendokumentasikan hal-hal ini ditulis oleh kolega saya Felippa Amanta yang bisa dibaca di sini.

Namun pada prinsipnya, kebijakan pembatasan impor sejatinya adalah sebuah keberpihakan. Produsen bahan baku (petani, nelayan dan peternak) adalah pihak yang diuntungkan dari dilakukannya kebijakan seperti ini. Koruptor juga untung sih hahaha. Yang rugi siapa? Tentunya industri manufaktur dan konsumen secara umum. Dengan akses yang sulit terhadap bahan baku, maka akan sulit manufaktur kita berkembang.

Kalau ditanya ke orang ekonomi, maka pastinya jawabannya adalah soal efisiensi. Mana yang lebih memberikan nilai tambah tinggi? proteksi petani untuk konsumsi domestik, atau mengimpor bahan baku dan menghasilkan produk yang bisa diekspor? Biasanya sih keterbukaan perdagangan memberikan hasil yang lebih efisien. Bahkan, kalo kata dosen saya di sini, akan lebih baik jika “loser” dari perdagangan kita kompensasikan dari “gain” yang didapatkan dari perdagangan bebas. Dengan kata lain, kita pajakin eksportir (looking at you CPO dan batubara), lalu uangnya kita gunakan untuk membiayai program-program pemerintah yang beriorientasi pada pelayanan publik, seperti kesehatan, pendidikan dan infrastruktur. Di samping itu, fasilitasi untuk mempermudah perpindahan tenaga kerja dari yang kerja di sektor “loser” untuk pindah ke sektor “gainer” bisa juga jadi opsi. Sebenarnya anak muda yang memilih jadi produsen manufaktur daripada petani adalah sebuah awal menuju perubahan struktur ekonomi yang tinggi agrikultur menuju tinggi manufaktur.

Bisa jadi juga program-program seperti itu lumayan kompleks dan merepotkan. Program-program tersebut juga bisa aja dikorup juga. Di samping itu, pembatasan impor lebih mudah, dan disenangi tidak hanya masyarakat tetapi juga elit politik dan birokrat.

Mungkin juga ada alasan yang lebih fundamental. Lebih ideologis. Apa artinya efisien jika identitas kita sebagai negara agraris terkikis? Jika identitas sebuah bangsa adalah berdasarkan produk yang dia ekspor, apa mau kita berubah dari negara agraris jadi negara CPO? Atau negara pembuat gado-gado? Semua demi efisiensi?

Yha pada akhirnya ini adalah pertanyaan politik, bukan ekonomi. Ekonomi mah cuma sebuah alat aja, untuk membantu para pembuat visi untuk mencapai visi tersebut. Jika masyarakat memilih untuk menjaga identitas sebagai negara agraris dengan mengabaikan efisiensi, maka tentu itu wajar-wajar saja.

Sekian postingan ini! Mari kita selalu optimis menjadi bangsa yang besar, maju, dan berdaya saing! Dan tetap makan gado-gado!

The chili price mystery: low because of imports?

Sat, 28 Aug 2021 23:22:01 +1000

Chili is one of the most important commodities in Indonesia. Indonesians love chili. Even while studying abroad, I still bought sambal because chili is just that good. It’s not only vital in various Indonesian dishes but also contains very high levels of vitamin C!

One morning, I saw a somewhat viral video of a farmer angrily kicking and stomping on his chili plants.

Ngene Iki Lurrrrrr akibaté, nek Rego / Harga si-Lombok terjun bebas, murahnya kebangetan,..petani tekor, modal tanam tekadang pinjaman / kredit Bank, tahu kenapa...! pic.twitter.com/IjTEcvZxO0
— Susilo Samin Blora (@Susilo_Blora) August 26, 2021

According to the account I embedded above, the cause appears to be the collapse of chili prices during harvest. This is obviously heartbreaking. Imagine – months of effort, cost, time, and energy, all lost when output prices plummet.

Another post I found quite interesting. I’ve embedded it below:

lanjutannya. diambil dari sini sepertinya ya https://t.co/Lz4vNibZb6 pic.twitter.com/lthDKPFjvc
— Krisna 'imed' Gupta (@iMedKrisna) August 28, 2021

That post was interesting for two reasons. First, the account linked the phenomenon to the Job Creation Law (UUCK), claiming that now chili imports no longer need to consider domestic farmer production. Second, the account took an image and headline from a Bisnis.com article titled “Chili Supply is in Surplus, So Why Are We Importing 27,851 Tons?”.

Of course, what’s interesting is that the article confirms the imports were dried/powdered chili for the manufacturing industry. Quoting the article:

Bisnis.com, JAKARTA – The Ministry of Agriculture states that the supply of various chilies for consumption in Indonesia is in surplus. Indonesia also imports chili to meet industrial needs.

Director of Horticultural Product Processing and Marketing at the Ministry of Agriculture, Bambang Sugiharto, explained that the 27,851 tons of chili imported in the first semester of 2021 was to meet industrial needs. Chili was imported in the form of dried chili, crushed or ground chili, not fresh chili for consumption.

Based on BPS data, national chili production in 2020 reached 2.77 million tons. This figure represents a 7.11% increase compared to 2019. Additionally, Indonesia exported various chilies valued at USD 25.18 million in 2020, up 69.86% from 2019.

Unfortunately, the account didn’t provide a link to the actual article for its followers to read. Just a screenshot of the headline. Be careful, folks! Always question headline screenshots and always read the full article before sharing with a particular prejudice!

But regardless, I found the narrative quite interesting. The account linked the phenomenon to imports. In other words, chili prices fell because imports happened during the chili harvest season. Second, the account linked these imports to the UUCK.

It’s obviously hard to prove this comprehensively and coherently. But maybe we can at least estimate a few things.

First, for imports to affect domestic prices, we need to at least examine the magnitude of imports relative to local production and exports. According to the article excerpt above, national production is 2.77 million tons. Moreover, Indonesia is actually a chili exporter!

Second, the claim that high chili imports are caused by the UUCK. This means that before the UUCK, imports should have been lower or even insignificant, then risen in 2020 and 2021.

Let’s check with export-import data. For this post, I use UN COMTRADE’s API because the data is quite comprehensive and accessible to anyone. Skip this section if you don’t want to see the code.

I’ll pull total Indonesian export and import data to and from other countries. There are 2 goods I’ll extract data for. First is Fresh Chili with HS 6-digit code 070960, which is alleged to be heavily imported since the UUCK took effect. Second is Dried Chili (non-crushed) with HS 6-digit code 090421, the product that was actually imported according to the article above. See – fresh chili and dried chili have different HS codes. Dangerous to just read headlines.

From these two datasets, let’s see whether we actually imported a lot of Fresh Chili starting from 2020, when the UUCK took effect. Unfortunately, UN COMTRADE doesn’t have 2021 data yet. BPS has monthly data, but since not everyone has access, we’ll hold off on 2021.

Pulling data from UN COMTRADE

Pull via API from UN COMTRADE.

# cari api key-nya di https://comtrade.un.org/Data/
api_key='/api/get?max=502&type=C&freq=A&px=HS&ps=2016%2C2017%2C2018%2C2019%2C2020&r=360&p=0&rg=2%2C1&cc=070960%2C090421'
url='https://comtrade.un.org/'+api_key+'&fmt=csv'
cabe=pd.read_csv(url)
# cek nama variabel
cabe.columns

Index(['Classification', 'Year', 'Period', 'Period Desc.', 'Aggregate Level',
'Is Leaf Code', 'Trade Flow Code', 'Trade Flow', 'Reporter Code',
'Reporter', 'Reporter ISO', 'Partner Code', 'Partner', 'Partner ISO',
'2nd Partner Code', '2nd Partner', '2nd Partner ISO',
'Customs Proc. Code', 'Customs', 'Mode of Transport Code',
'Mode of Transport', 'Commodity Code', 'Commodity', 'Qty Unit Code',
'Qty Unit', 'Qty', 'Alt Qty Unit Code', 'Alt Qty Unit', 'Alt Qty',
'Netweight (kg)', 'Gross weight (kg)', 'Trade Value (US$)',
'CIF Trade Value (US$)', 'FOB Trade Value (US$)', 'Flag'],
dtype='object')

Lots of variables. Let’s keep only the important ones. I’ll make two types of tables, one for Fresh Chili and one for Dried Chili.

# Ambil variabel yg penting aja
cabe=cabe[['Year','Trade Flow','Commodity Code', 'Commodity',
 'Netweight (kg)', 'Trade Value (US$)']]
# Menyiapkan cabe mentah
xcabe_mentah=cabe.query('`Commodity Code` == 70960 & `Trade Flow` == "Export"').sort_values(by=['Year']).reset_index()
xcabe_mentah=xcabe_mentah[['Year','Netweight (kg)','Trade Value (US$)']]
xcabe_mentah=xcabe_mentah.rename(columns={"Netweight (kg)": "Ekspor (kg)",
 "Trade Value (US$)": "Ekspor (US$)"})
mcabe_mentah=cabe.query('`Commodity Code` == 70960 & `Trade Flow` == "Import"').sort_values(by=['Year']).reset_index()
mcabe_mentah=mcabe_mentah[['Year','Netweight (kg)','Trade Value (US$)']]
mcabe_mentah=mcabe_mentah.rename(columns={"Netweight (kg)": "Impor (kg)",
 "Trade Value (US$)": "Impor (US$)"})
#cabe_mentah=cabe_mentah.set_index('Year')
#cabe_mentah['Tahun']=pd.to_datetime(cabe_mentah['Year'],format='%Y')
xcabe_kering=cabe.query('`Commodity Code` == 90421 & `Trade Flow` == "Export"').sort_values(by=['Year']).reset_index()
xcabe_kering=xcabe_kering[['Year','Netweight (kg)','Trade Value (US$)']]
xcabe_kering=xcabe_kering.rename(columns={"Netweight (kg)": "Ekspor (kg)",
 "Trade Value (US$)": "Ekspor (US$)"})
mcabe_kering=cabe.query('`Commodity Code` == 90421 & `Trade Flow` == "Import"').sort_values(by=['Year']).reset_index()
mcabe_kering=mcabe_kering[['Year','Netweight (kg)','Trade Value (US$)']]
mcabe_kering=mcabe_kering.rename(columns={"Netweight (kg)": "Impor (kg)",
 "Trade Value (US$)": "Impor (US$)"})

Fresh Chili

First let’s check the fresh chili export-import table, along with the charts.

cm=pd.merge(xcabe_mentah,mcabe_mentah,on='Year',how='outer')
print(cabe.Commodity.iloc[2])
print(cabe['Commodity Code'].iloc[2])
cm

Vegetables; fruits of the genus capsicum or of the genus pimenta, fresh or chilled
70960

	Year	Ekspor (kg)	Ekspor (US$)	Impor (kg)	Impor (US$)
0	2016	433825	587063	NaN	NaN
1	2017	309464	635331	NaN	NaN
2	2018	152292	387459	120.0	526.0
3	2019	156327	316311	2112.0	4216.0
4	2020	418972	669580	6.0	37.0

fig, axes = plt.subplots(1, 2, figsize=(15, 5))
fig.suptitle('Ekspor dan Impor Cabai Mentah (HS6:070960),Indonesia, 2016-2020 (UN COMTRADE)')
sns.lineplot(ax=axes[0], data=cm ,x='Year', y='Ekspor (kg)',marker='o')
axes[0].set_title('Ekspor (kg)')
axes[0].xaxis.set_ticks([2016,2017,2018,2019,2020])
sns.lineplot(ax=axes[1], data=mcabe_mentah ,x='Year', y='Impor (kg)',marker='o')
axes[1].set_title('Impor (kg)')
axes[1].xaxis.set_ticks([2016,2017,2018,2019,2020])

[<matplotlib.axis.XTick at 0x26ed40c7ca0>,
<matplotlib.axis.XTick at 0x26ed40c7c70>,
<matplotlib.axis.XTick at 0x26ed40f9520>,
<matplotlib.axis.XTick at 0x26ed41123a0>,
<matplotlib.axis.XTick at 0x26ed4126df0>]

As shown in the table and chart above, our fresh chili imports are tiny. In 2020, when the UUCK took effect, imports didn’t increase – they actually fell. UN COMTRADE data says in 2020, Indonesia imported 6 kilograms of chili. This figure is clearly far below the 2.77 million ton production level. UNCOMTRADE has no fresh chili import data for 2016 and 2017, probably because we simply didn’t import fresh chili in those years.

In fact, we’re actually a net exporter of fresh chili. Quite interesting actually. This means our country is fairly competitive in chili production (perhaps?). Far from importing, we’re exporting a lot! If so, why are farmers losing money? Are chili farmers just being dramatic?

Not necessarily! Many factors could be at play. It could be distribution to markets, someone might be hoarding supply, not all farmers may have access to export markets, consumer demand might be weak, or planting seasons might not be coordinated. For instance, during planting season everyone plants and nobody harvests, so chili is scarce at market. During harvest, everyone harvests simultaneously, creating a supply glut. When supply is high, prices crash.

There are many possible issues I don’t fully understand. But clearly, imports are not the problem. Don’t get me wrong – there are many problems caused by import rents (and export rents), but apparently not for chili.

Dried Chili (non-crushed)

One problem we could hypothesize is weak industrial absorption of farmers’ chili. This would be reflected in dried chili imports. The logic: if there’s an oversupply of fresh chili, those chilies should be usable for dried chili production, reducing dried chili imports. Let’s look at how dried chili exports and imports look in the table and chart below.

ck=pd.merge(xcabe_kering,mcabe_kering,on='Year',how='outer')
print(cabe.Commodity.iloc[10])
print(cabe['Commodity Code'].iloc[10])
ck

Spices; fruits of the genus Capsicum or Pimenta, dried, neither crushed nor ground
90421

	Year	Ekspor (kg)	Ekspor (US$)	Impor (kg)	Impor (US$)
0	2016	680078	4497982	24374457	31019978
1	2017	673598	2393926	37490445	45749699
2	2018	223126	725423	34409357	51208284
3	2019	321959	934735	39132270	65280761
4	2020	446247	1095573	25674802	52323399

fig, axes = plt.subplots(1, 2, figsize=(15, 5))
fig.suptitle('Ekspor dan Impor Cabai Kering/Bubuk (HS6:090421),Indonesia, 2016-2020 (UN COMTRADE)')

sns.lineplot(ax=axes[0], data=ck ,x='Year', y='Ekspor (kg)',marker='o')
axes[0].set_title('Ekspor')
axes[0].xaxis.set_ticks([2016,2017,2018,2019,2020])
sns.lineplot(ax=axes[1], data=ck ,x='Year', y='Impor (kg)',marker='o')
axes[1].set_title('Impor')
axes[1].xaxis.set_ticks([2016,2017,2018,2019,2020])

[<matplotlib.axis.XTick at 0x26ed47da910>,
<matplotlib.axis.XTick at 0x26ed47da8e0>,
<matplotlib.axis.XTick at 0x26ed480bd90>,
<matplotlib.axis.XTick at 0x26ed483f400>,
<matplotlib.axis.XTick at 0x26ed482db50>]

Now dried chili is a different story. While there are exports, imports still dominate. The import scale in the chart is so large it had to be expressed in scientific notation (1e7 or $10^7$). Dried chili imports are large. I’m also not sure whether dried chili imports are controlled like fresh chili, but looking at the data, it shouldn’t have too significant an impact on farmer prices. Indonesia has a very large food industry, so it’s quite reasonable that it needs more supply than farmers can provide. Moreover, dried chili has a storage advantage – you can buy in bulk and store it. This means dried chili prices probably don’t need to depend on harvest timing. Advantageous for industry.

The problem, of course, is when consumers also switch to buying dried chili. If the price gap is really that wide, even those who prefer fresh chili might opt for dried. As mentioned above, dried chili imports are large. How much is actually used by industry? Should we ban dried chili imports too? What about industries that use chili (like producers of instant gado-gado sauce) that would have to buy chili at higher prices?

The chili mystery

Regardless, low chili prices for farmers remain a problem. I dug around a bit on this chili issue and found only 1 paper that seems to agree that the problems are many, but not imports.

Muflikh et al. (2021)¹ state that since 2016, Indonesia has completely stopped importing chili because the reference price policy has completely failed to control chili prices that are always volatile each year. This could influence why industry imports so much dried chili: abroad, dried chili producers have free access to cheap fresh chili. Since domestic industry finds it hard to import fresh chili, they just import dried chili instead. As I mentioned earlier, Indonesia’s food manufacturing industry is one of the largest and fastest growing.

Well, if the problem is low international prices, the problems of chili farmers are clearly far more fundamental than imports or the UUCK. Domestic fresh chili, despite farmers complaining about low prices, is still relatively more expensive than abroad. This remains problematic even amid the chili import ban. This shows domestic problems that are more fundamental than blaming imports.

Additionally, Muflikh et al. (2021) state:

The volatility is caused mainly by seasonal production, unorganised market governance, and consumer preference for fresh chillies.

In other words, Indonesia’s chili price problems seem to be largely domestic issues.

The UUCK mystery

Regarding the UUCK: chili price volatility has been happening for a long time, as have fresh chili import regulations and high dried chili imports. Fresh chili has been protected all along, and since 2016 there have been no fresh chili imports at all. Meanwhile, dried chili has never had import restrictions. What’s supposed to become “more freely imported” if it’s already free?

This means it’s hard to blame the UUCK, at least for now. We may need to see 2021 data to clearly blame the UUCK, or examine implementing regulations that specifically target fresh and dried chili imports. The UUCK is indeed a controversial law. There’s much we can criticize, but we should do so with a cool head and on target. Especially sharing headline screenshots without citing sources – that should be avoided.

I’ll stop here for now. I know quite a bit about international trade, but I’m no chili expert. Hopefully this post motivates those who want to advance agriculture, especially chili farming, in Indonesia. As I said, there seems to be room for a rigorous study on chili in Indonesia, which could make for a very interesting thesis topic. Even better if you can do fieldwork. Hehe.

That’s it for this post. Thanks for reading!

Updates

Ban on curly chili imports?

I’ve embedded some Twitter responses about chili in this section. If there’s anything interesting I’ll keep updating.

Hrg cabe dlm negeri lg hancur, hrg hancur sdh berbulan², klo pun dipanen malah petani tambah merugi. Sebagian petani membiarkan cabenya membusuk dilahan.

Masyarakat konsumsi cabe segar jng cabe kering, krn kita kalah bersaing dng hrg cabe kering impor.

Mari kita diskusikan ini. https://t.co/dZD3SwSPr7 pic.twitter.com/nZcyT4QfBe
— Petani Rasional (@PetaniRasional) August 28, 2021

If the price difference is that large, even those who love fresh chili might choose dried. Not just industry – household consumers might also opt for dried chili if fresh chili prices are high.

The solution: ban dried chili imports too? Maybe we’ll discuss that in another post.

Tujuan impor cabe kering yg harusnya untuk kebutuhan industri ((( KONON KATANYA ))), tetapi faktanya bocor sampai beredar dipasaran tradisional, ditambah lg petani dlm negri lg panen raya. Dampaknya harga cabe hancur dalam tempo waktu yg lama, petani menjerit. pic.twitter.com/coalvk1m7L
— Petani Rasional (@PetaniRasional) August 26, 2021

Grow other commodities

The tweet below is also interesting: grow commodities that can be exported. Do your own market research and forget the government. Of course, not all farmers can do this, and it doesn’t mean we should stop criticizing government agricultural programs.

Mas,
Aku tanam Lemon California, walau gak begitu banyak. Di bulang juni-juli kemarin juga kena badai harga. Di petani anjlok sampai 1500-2000 perkilo. Sedang harga dipasaran tetap sama. Kenapa gak dipasarkan langsung? Mana ada waktu dan tenaganya. Belum lagi biayanya untuk itu.
— Calon Ketua RT (@bukanboslagi) August 29, 2021

Subsidy difficulties and forming farmer groups

Poorly targeted subsidies seem to be par for the course in countries with bad bureaucracies. This tweet provides a more detailed account of the mess around fertilizer subsidies, from difficulty accessing them (hard to join farmer groups) to resale at market prices (instead of using the subsidized fertilizer, they resell it like rent seekers).

Pupuk subsidi itu hanya diperoleh kelompok tani. Jgn lupa masih sgt banyak petani yg tdk masuk kelompok tani. Bentuk kelompok tani jg gak mudah di daerah.
Jd distribusi pupuk subsidi gak semulus yg dibayangkan. Kacaunya anggota kel tani jual balik pupuk subsidi dgn harga pasar.
— Andreas S Kurniawan (@ansyaku) August 30, 2021

Muflikh, Y. N., Smith, C., Brown, C., & Aziz, A. A. (2021). Analysing price volatility in agricultural value chains using systems thinking: A case study of the Indonesian chilli value chain. Agricultural Systems, 192, 103179. https://doi.org/https://doi.org/10.1016/j.agsy.2021.103179 ↩︎

Awesome Stata 17 Upgrade: Trying Pystata by using PPML on USITC and WITS database

Sun, 22 Aug 2021 20:39:04 +1000

Finally got my hand on Stata 17. When I got my email about Stata 17, I was very excited on the new PyStata feature. It allows for calling python from Stata and calling Stata from Python. There are many other cool upgrades on Stata 17 especially for Macroeconometrician, but the PyStata is especially interesting for me!

It doesn’t take long for me to try using PyStata on my Jupyter Lab. While using Python on Stata is certainly super interesting, I of course like better using Stata on Jupyter. I write things from Jupyter (like this post), so running things straight from Jupyter would be super awesome. While there are many things we can do with Python, it’s still hard for me to say goodbye to Stata. For one, I use packages like Levinsohn-Petrin algorithm for my research. Moreover, Stata still has plenty of familiarity for my own compared with Pandas. Lastly, most of my colleagues don’t even speak python or r, so Stata remains to be my main tool for collaboration.

So this post is dedicated to myself being super excited to Trying PyStata for the first time! As a trade economist, what’s best but to start with some trade data? In this post, I take data from the internet with Pandas, then transfer it to Stata database (you know, the .dta), and then run a PPML a la Silva and Tenreyro. Yes there is a PPML package for Python called GME (I even blogged about it) but come on I am having fun with PyStata here!

There is of course a better starting place to read if you really want to go on with playing with your PyStata, but I hope this post is fun and useful enuff four you.

Since this post is all about using Stata command in Python environment (and not the other way around), I mainly use Stata Magic. Maybe if I manage to use Mata Magic and PyStata Magic I will blog about it as well but the Stata Magic is what I am super excited about for now.

Okay lets go on to the show!

Installing PyStata

obviously the first thing you should do is to have Stata 17 installed on your computer. Remember where it is installed (it will be used later) but if you just click next like I do then it’s gonna be in C:/Program Files/Stata17/. When we’re done installing Stata 17, then all we need to do is to install PyStata in our env using

pip install --upgrade --user stata_setup

Then we’re good to go!

Collecting the data (in Pandas)

I am using publicly available data for this post so everyone can try this. No, not car, don’t worry.

Since this will be about gravity regression, I will need gravity data. Let’s get it from USITC Dynamic Gravity Dataset for our trial here. Be mindful of the size, this dataset is suuuuuuuuuper huge!

weqs=pd.read_csv('https://www.usitc.gov/data/gravity/dgd_docs/release_2.1_2000_2016.zip')
weqs.columns # checking its variables

Index(['year', 'country_o', 'iso3_o', 'dynamic_code_o', 'country_d', 'iso3_d',
'dynamic_code_d', 'colony_of_destination_ever', 'colony_of_origin_ever',
'colony_ever', 'common_colonizer', 'common_legal_origin', 'contiguity',
'distance', 'member_gatt_o', 'member_wto_o', 'member_eu_o',
'member_gatt_d', 'member_wto_d', 'member_eu_d', 'member_gatt_joint',
'member_wto_joint', 'member_eu_joint', 'lat_o', 'lng_o', 'lat_d',
'lng_d', 'landlocked_o', 'island_o', 'region_o', 'landlocked_d',
'island_d', 'region_d', 'agree_pta_goods', 'agree_pta_services',
'agree_fta', 'agree_eia', 'agree_cu', 'agree_psa', 'agree_fta_eia',
'agree_cu_eia', 'agree_pta', 'capital_const_d', 'capital_const_o',
'capital_cur_d', 'capital_cur_o', 'gdp_pwt_const_d', 'gdp_pwt_const_o',
'gdp_pwt_cur_d', 'gdp_pwt_cur_o', 'pop_d', 'pop_o', 'hostility_level_o',
'hostility_level_d', 'common_language', 'polity_o', 'polity_d',
'sanction_threat', 'sanction_threat_trade', 'sanction_imposition',
'sanction_imposition_trade', 'gdp_wdi_cur_o', 'gdp_wdi_cap_cur_o',
'gdp_wdi_const_o', 'gdp_wdi_cap_const_o', 'gdp_wdi_cur_d',
'gdp_wdi_cap_cur_d', 'gdp_wdi_const_d', 'gdp_wdi_cap_const_d'],
dtype='object')

Since this is a test, I will use just some of the variable. First, I will only use Indonesia as a country of origin. Secondly, I will only use cross-section in the year 2016. Next, only several variable will be used, namely, export partner’s population, GDP and distance, as well as FTA with Indonesia. Obviously there are more stuff needs to be added if you want to do a real gravity analysis but this is just a test come on.

grav=weqs.query('country_o == "Indonesia"') # take only Indonesia as a country of origin
grav=grav.query('year==2016') # take only year 2016
grav=grav[['country_d','pop_d', 'agree_fta', 'gdp_wdi_cur_d', 'distance']] # take some variables only
grav=grav.rename(columns={'country_d':'Partner'})
grav.head() # check content

	Partner	pop_d	gdp_wdi_cur_d	distance
455937	Aruba	0.104822	2.646927e+09	18924.247763
455954	Afghanistan	NaN	1.936264e+10	6158.679225
455971	Angola	28.813463	1.011239e+11	10483.002562
455988	Anguilla	0.014764	NaN	18167.048245
456005	Aland Islands	NaN	NaN	10410.128664

Now that i have the gravity variables, let’s get the trade data. I will need Indonesia’s export value to partners as well as their import tariff. Again, we normally need to do more rigorous check on the export and tariff, but since this is just a test, I will use total export as well as MFN weighted average tariff.

To get from WITS i use world_trade_data package in python.

import world_trade_data as wits

# Grabbing Indonesia's export value to partners in 2016
exports = wits.get_indicator('XPRT-TRD-VL', reporter='idn',partner='all', year='2016',product='Total')

exports=exports.reset_index()
exports=exports[['Partner','Value']] # Take only necessary columns
exports=exports.rename(columns={'Value':'Export_Value'}) # rename to merge later

# Grabbing tariff from Indonesia's partners
tariff=wits.get_indicator('MFN-WGHTD-AVRG',reporter='all',year='2016',datasource='tradestats-tariff',product='Total')
tariff=tariff.reset_index()
tariff=tariff[['Reporter','Value']] # take only necessary columns
tariff=tariff.rename(columns={"Reporter": "Partner","Value":"Tariff"}) # rename to merge later

trade=pd.merge(exports,tariff,how='left',on='Partner') # merge the two dataset

now that I have my trade,tariff and gravity data, lets merge them to get the final data to feed to our Stata PPML.

idntrade=pd.merge(grav,trade,how='left',on='Partner')
idntrade['Export']=idntrade['Export_Value']/1000000
idntrade.head()

	Partner	pop_d	gdp_wdi_cur_d	distance	Export_Value	Tariff	Export
0	Aruba	0.104822	2.646927e+09	18924.247763	878.469	10.158305	0.000878
1	Afghanistan	NaN	1.936264e+10	6158.679225	16220.103	NaN	0.016220
2	Angola	28.813463	1.011239e+11	10483.002562	57713.955	9.379807	0.057714
3	Anguilla	0.014764	NaN	18167.048245	NaN	NaN	NaN
4	Aland Islands	NaN	NaN	10410.128664	NaN	NaN	NaN

Well, obviously we can merge and cook our data ini Stata as well but as you can see it’s kinda faster to do it in Pandas unless I need to generate anything new.

Run a PPML in Stata

Now that the data’s ready, we can begin running PPML in Stata! Now, we need to call Stata to our Python environment with the import command. This is where we need our Stata location.

import stata_setup
stata_setup.config("C:/Program Files/Stata17/", "mp")

 ___ ____ ____ ____ ____ ©
/__ / ____/ / ____/ 17.0
___/ / /___/ / /___/ MP—Parallel Edition
Statistics and Data Science Copyright 1985-2021 StataCorp LLC
StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800-STATA-PC https://www.stata.com
979-696-4600 stata@stata.com

Look at how georgeous Stata logo is.

Next, we load the dataset to Stata! I use Stata magic to do this. there is also a possibility calling Stata with API like this one and this one, but in Jupyter, using magic is somewhat easier for me.

Now lets call the data and describe it.

%%stata -d idntrade -force
describe

Contains data
Observations: 255
Variables: 8
-------------------------------------------------------------------------------
Variable Storage Display Value
name type format label Variable label
-------------------------------------------------------------------------------
Partner str45 %45s
pop_d double %10.0g
agree_fta double %10.0g
gdp_wdi_cur_d double %10.0g
distance double %10.0g
Export_Value double %10.0g
Tariff double %10.0g
Export double %10.0g
-------------------------------------------------------------------------------
Sorted by:
Note: Dataset has changed since last saved.

Wooohoo cool! Let’s try summarize

%%stata
su

 Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
Partner | 0
pop_d | 183 42.87855 150.9889 .005152 1403.5
agree_fta | 255 .0588235 .2357568 0 1
gdp_wdi_cu~d | 206 5.25e+11 2.26e+12 3.65e+07 1.87e+13
distance | 253 10594.37 4663.103 951.1062 19190.28
-------------+---------------------------------------------------------
Export_Value | 192 676920.4 2420380 .101 1.68e+07
Tariff | 119 6.739741 4.004538 0 18.73486
Export | 192 .6769204 2.42038 1.01e-07 16.78559

Awesome!! Now let’s do our ppml command.

%%stata
ppml Export Tariff pop_d agree_fta gdp_wdi_cur_d distance

note: checking the existence of the estimates
WARNING: Tariff has very large values, consider rescaling or recentering
WARNING: pop_d has very large values, consider rescaling or recentering
WARNING: gdp_wdi_cur_d has very large values, consider rescaling or recenterin
> g
WARNING: distance has very large values, consider rescaling or recentering
note: starting ppml estimation
note: Export has noninteger values
Iteration 1: deviance = 118.6375
Iteration 2: deviance = 101.5413
Iteration 3: deviance = 101.0093
Iteration 4: deviance = 101.0082
Iteration 5: deviance = 101.0082
Number of parameters: 6
Number of observations: 114
Number of observations dropped: 0
Pseudo log-likelihood: -102.58858
R-squared: .75786551
------------------------------------------------------------------------------
| Robust
Export | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
Tariff | -.0906943 .0542772 -1.67 0.095 -.1970757 .015687
pop_d | -.0002608 .0004712 -0.55 0.580 -.0011843 .0006626
agree_fta | 2.28588 .5362387 4.26 0.000 1.234871 3.336888
gdp_wdi_cu~d | 2.04e-13 3.58e-14 5.69 0.000 1.33e-13 2.74e-13
distance | -.0000199 .000065 -0.31 0.760 -.0001473 .0001075
_cons | -.488918 .7264721 -0.67 0.501 -1.912777 .9349412
------------------------------------------------------------------------------
Number of regressors dropped to ensure that the estimates exist: 0
Option strict is off

woohoo! The result is also great considering how under-specified the model is! High $R^2$ , Tariff is negative and strong, FTA and GDP are both highly important in determining foreign demand!

Now one thing I can’t quite work out is to extract the information from the regression back to pandas. I usually just go with outreg and do it manually, but it’s no fun if I can’t integrate the regression result in my notebook.

What I can do is to export the result to csv and read it thru Pandas.

%%stata
eststo
esttab using hehe.csv, plain replace wide

. eststo
(est1 stored)
. esttab using hehe.csv, plain replace wide
(output written to hehe.csv)
.

dff=pd.read_csv('hehe.csv')
dff

	Unnamed: 0	est1	Unnamed: 2
0	NaN	b	t
1	Tariff	-.0906943	-1.670947
2	pop_d	-.0002608	-.5535483
3	agree_fta	2.28588	4.262802
4	gdp_wdi_cur_d	2.04e-13	5.688106
5	distance	-.0000199	-.3056529
6	_cons	-.488918	-.6730031
7	N	114	NaN

The result is not the best so this is kinda bummer. But at least I have something. haha. There may be a better way to do this but let me look for it and post it later on. If you have a suggestion please mention me on twitter!

Anyway, that’s it for now. I hope this post is enjoyable!

Book Launch Seminar: Sustainable Golden Indonesia 2045 - Collected Thoughts of Indonesian Students Worldwide, Series 01 Economics

Fri, 20 Aug 2021 17:00:00 +1000

The event opened with remarks from the editor and the director of research and studies at PPID, as well as a keynote speech from Professor (Emeritus) Dorodjatun Kuntjoro-Jakti.

The event continued with a panel discussion. The panelists were the Director of Population and Social Security, Dr. Muhammad Cholifihani, Unpad lecturer and TNP2K researcher Dr. Raden Purnagunawan, and two book authors, Malik Cahyadin and Hildayanti Loekman.

The event can be viewed on the PPI Dunia YouTube Channel.

Below is the guest lecture by Professor Djatun.

Sekilas tentang Buku Kami

Thu, 19 Aug 2021 16:44:04 +1000

About the book

Indonesia Emas Berkelanjutan 2045: Kumpulan Pemikiran Pelajar Indonesia Sedunia

Series 01 Ekonomi

s.id/bukuppid

About the book

This book is a part of 12 book series on “Indonesia Emas Berkelanjutan 2045” (Sustaniable Golden Indonesia 2045) written by Overseas Indonesian Students Association Alliance (OISAA)
SDGs are used as our guide making sure the book covers sustainability and inclusivity.
This book in particular uses goals 1, 8 dan 10.
This book is published by LIPI Press and can be accessed here

About this book

Goals 1: Poverty eradication

Poverty eradication in Indonesia including social protection programs.

Goals 8: Decent work and economic Growth

Economic growth, labor productivity, tourism, (youth) employment and skill and education progress, among others

Goals 10: Reduced inequality

Sustainable migration, progress in reducing inequality in general but also among rural-urban, islands, and gender.

Thanks!

Kuliah Umum

Profesor (Emeritus) Dorodjatun Kuntjoro-Jakti

atau pergi ke YouTube

Indonesia Emas Berkelanjutan 2045: Kumpulan Pemikiran Pelajar Indonesia Sedunia Seri 01 Ekonomi

Sun, 08 Aug 2021 15:58:19 +1000

LIPI Press and Overseas Indonesian Students Association Alliance (OISAA) published a series called “Indonesia Emas Berkelanjutan 2045: Kumpulan Pemikiran Pelajar Indonesia Sedunia”, written in Bahasa Indonesia. This series in particular discusses the Indonesian Vision 2045 through the lens of Sustainable Development Goals (SDGs), in particular Poverty eradication (target 1), Sustainable and inclusive economic growth (target 8), and reduced inequality (target 10).

bahan-bahan narasumber

Keynote speech from Professor (Emeritus) Dorodjatun Kuntjoro-Jakti (in Indonesian)

Question 4 Tutorial 1 ECON3102 2021

Sun, 01 Aug 2021 17:32:43 +1000

About this post

This post is created to quickly demonstrate one of the possible answer of question 4 of tutorial 1 of ECON3102. I chose to use r’s WDI to show it but any other software and any other source of data can be used obviously. The reason why I use WDI is because it is convinient and easy to show some graph. If we use World Development Indicators we don’t even need to download it. Just use its graph function which is super convinient.

some packages:

library('WDI')
library('ggplot2')
library('tidyverse')
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## v purrr 0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library('ggdark')
library('patchwork')

A bit of aesthetic:

graph<-ggplot()+
dark_theme_bw(base_size=16)+
theme(text = element_text(color='#e0e0e0'),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "#e0e0e0"))
## Inverted geom defaults of fill and color/colour.
## To change them back, use invert_geom_defaults().

The question itself consist of two parts. Students are to find, download and sketch numbers for two countries: one is developed one while the other is an emerging economy. In this example, I use Indonesia and Australia.

There are two sub-questions in this exercise. The first one is to plot real GDP and show a bit of a business cycle (which I will do by showing GDP growth)

Question One

Real GDP

Quickly browse website of World Development Indicators to get all keys needed for the WDI package.

let’s check out real GDP.

realgdp<-as_tibble(WDI(indicator = 'NY.GDP.MKTP.KD',country = c('IDN','AUS')))
realgdp
## # A tibble: 122 x 4
## iso2c country NY.GDP.MKTP.KD year
## <chr> <chr> <dbl> <int>
## 1 AU Australia 1.45e12 2020
## 2 AU Australia 1.45e12 2019
## 3 AU Australia 1.42e12 2018
## 4 AU Australia 1.38e12 2017
## 5 AU Australia 1.35e12 2016
## 6 AU Australia 1.31e12 2015
## 7 AU Australia 1.28e12 2014
## 8 AU Australia 1.25e12 2013
## 9 AU Australia 1.22e12 2012
## 10 AU Australia 1.17e12 2011
## # ... with 112 more rows

Which only shows top 10 which contain Aussie. If we plot ’em:

graph+geom_line(data=realgdp, aes(x=year, y=NY.GDP.MKTP.KD,color=country),size=1.1)+
labs(title = 'Fig 1. Real GDP, Indonesia and Australia, 1960 - 2020',caption='source: World Development Indicators')+
ylab('2010 USD')+geom_vline(xintercept=c(2009,1998,1983))

Real GDP are going up for both countries. Note that, while generally the graph goes up, but it has some ‘brakes’ for a short while. There are 1980s oil crisis which apparently struck Australia a bit more. There’s also something in 90s that hit Aussies. Asian Financial Crisis in 1998 definitely crushed Indonesian economy. The GFC do affect Australia a bit but not as bad as the northern hemisphere.

Also, Indonesia seems to be able to catch up with Aussie (note the relatively smaller gap in the late 2010s). Is it a converge? It remains to be seen.

Also, f#ck COVID-19.

GDP growth

While I spoke a bit about business cycle in some points, seeing GDP growth give us better picture.

First we get GDP growth and calculate arithmatic mean of them. (for the sake of the graph. You should really use geometric mean if you want to calculate mean growth rate.)

gdpg<-as_tibble(WDI(indicator = 'NY.GDP.MKTP.KD.ZG',country = c('IDN','AUS'),start=1961))
growth<-tibble(
country = c('Australia','Indonesia'),
mean_growth = c(mean(gdpg$NY.GDP.MKTP.KD.ZG[gdpg$country=='Australia']),
mean(gdpg$NY.GDP.MKTP.KD.ZG[gdpg$country=='Indonesia']))
)
growth
## # A tibble: 2 x 2
## country mean_growth
## <chr> <dbl>
## 1 Australia 3.38
## 2 Indonesia 5.13
graph+geom_line(data=gdpg, aes(x=year, y=NY.GDP.MKTP.KD.ZG,color=country),size=1.1)+
labs(title = 'Fig 2. GDP growth, Indonesia and Australia, 1960 - 2020',caption='source: World Development Indicators')+
ylab('%')+
geom_hline(yintercept = 0) +
geom_hline(yintercept = as.numeric(growth[1,2])) +
geom_hline(yintercept = as.numeric(growth[2,2])) +
geom_vline(xintercept = 2009) +
geom_vline(xintercept = 1998)

Fig 2 shows that the growth is not a smooth line: There are ups and downs. You can see how bad 1998 AFC does to Indonesia, while GFC decrease both countries’ growth but not much.

Question 2

Collect the data for nominal GDP (current year) and its four main types of expenditures: household consumption, government consumption, gross fixed capital formation, and net exports. This is a bit annoying because I can’t find nex export in World Development Indicators.

indo<-as_tibble(WDI(indicator = c('NE.CON.PRVT.ZS','NE.CON.GOVT.ZS','NE.GDI.FTOT.ZS',
'NE.EXP.GNFS.CN','NE.IMP.GNFS.CN','NY.GDP.MKTP.CN'),country = 'IDN'))
ausi<-as_tibble(WDI(indicator = c('NE.CON.PRVT.ZS','NE.CON.GOVT.ZS','NE.GDI.FTOT.ZS',
'NE.EXP.GNFS.CN','NE.IMP.GNFS.CN','NY.GDP.MKTP.CN'),country = 'AUS'))
indoo<-tibble(
year=indo$year,
C=indo$NE.CON.PRVT.ZS,
I=indo$NE.GDI.FTOT.ZS,
G=indo$NE.CON.GOVT.ZS,
NX=(indo$NE.EXP.GNFS.CN-indo$NE.IMP.GNFS.CN)/indo$NY.GDP.MKTP.CN*100
)
indooo<-indoo %>% pivot_longer(!year,names_to='variable',values_to='value')
ausii<-tibble(
year=ausi$year,
C=ausi$NE.CON.PRVT.ZS,
I=ausi$NE.GDI.FTOT.ZS,
G=ausi$NE.CON.GOVT.ZS,
NX=(ausi$NE.EXP.GNFS.CN-ausi$NE.IMP.GNFS.CN)/ausi$NY.GDP.MKTP.CN*100
)
ausiii<-ausii %>% pivot_longer(!year,names_to='variable',values_to='value')
gindo<-graph+geom_line(data=indooo,aes(x=year,y=value,color=variable),size=1)+geom_vline(xintercept=2009)+geom_vline(xintercept=1998)+dark_theme_bw(base_size = 12)+labs(title = 'Indonesia')+geom_hline(yintercept=0)
gausi<-graph+geom_line(data=ausiii,aes(x=year,y=value,color=variable),size=1)+geom_vline(xintercept=2009)+geom_vline(xintercept=1998)+dark_theme_bw(base_size=12)+labs(title = 'Australia')+geom_hline(yintercept=0)
gindo+gausi+plot_layout(ncol = 2, guides = "collect")+ plot_annotation(
title = 'Fig 3. Component of GDP by consumption, Indonesia and Australia, 1960-2020',
caption = 'Source: World Development Indicators'
)

There are a few point we can discuss here. First up, while both countries generally export very similar things (natural resources), Australia runs long term trade deficit, while Indonesia mostly has trade surplus. Consequently, trade has higher GDP component in Indonesia.

In the context of developed vs emerging, it is generally true that developed countries have higher G. Make sense given their better capacity to tax and generally richer people with more population sit on the upper-ish tax brackett. In terms of combined private-government consumption, Australia is higher mainly because its government spend more (as a % of GDP).

Lastly, Indonesia’s GDP structure change a bit in the late 60s to late 90s due to its govermental transition. That is, consumption’s fraction of GDP goes down as investment go up. This is also a pivotal moment where Indonesia starts to grow faster (see fig 2).

One thing to note is while some countries may follow similar pattern, country’s economic growth and business cycle can be quite nuance and ungeneralisable. It depends on plethora of things: exposure to international market, political regime, etc. Some general insight is very useful especially if we are talking about small countries with high international exposure, but always be careful, be mindful of local wisdom and take everything with a grain of salt.

Try China vs US, for example. Will be fun.

New update of the recent second wave (or third?) of COVID-19 in Indonesia

Wed, 28 Jul 2021 10:18:28 +1000

It has been a while since i last wrote about COVID-19. Today i’d like to check out Indonesia’s statistic on COVID-19, especially since it seem to get worse these days, unfortunately, and so many people talked about possibility of the government intentionally undertest to push down new cases at the cost of human lives.

Penambahan kasus Covid-19 harian cenderung menurun. Hal ini terjadi seiring dengan turunnya jumlah pemeriksaan secara signifikan. Masih terlalu dini untuk menyimpulkan bahwa gelombang Covid-19 telah terkendali. #Humaniora #AdadiKompas @aik_arif https://t.co/eYvloMmpIC
— Harian Kompas (@hariankompas) July 21, 2021

I rely heavily on Our World in Data ¹ which give free access of COVID-19 data.

Grab the data and show 6 tops

url='https://covid.ourworldindata.org/data/owid-covid-data.csv' # simpan url
df=pd.read_csv(url, parse_dates=['date']) # download dari url. parse_dates untuk menjadikan kolom date jadi tipe waktu
df.head(6) # menampilkan 10 baris paling atas

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	extreme_poverty	cardiovasc_death_rate	diabetes_prevalence	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality
0	AFG	Asia	Afghanistan	2020-02-24	1.0	1.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
1	AFG	Asia	Afghanistan	2020-02-25	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
2	AFG	Asia	Afghanistan	2020-02-26	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
3	AFG	Asia	Afghanistan	2020-02-27	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
4	AFG	Asia	Afghanistan	2020-02-28	1.0	0.0	NaN	NaN	NaN	NaN	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN
5	AFG	Asia	Afghanistan	2020-02-29	1.0	0.0	0.143	NaN	NaN	0.0	...	NaN	597.029	9.59	NaN	NaN	37.746	0.5	64.83	0.511	NaN

6 rows × 60 columns

I am not super familiar with its variable. So let’s check them out with df.columns.

df.columns # untuk panggil list dari nama-nama variabel

Index(['iso_code', 'continent', 'location', 'date', 'total_cases', 'new_cases',
'new_cases_smoothed', 'total_deaths', 'new_deaths',
'new_deaths_smoothed', 'total_cases_per_million',
'new_cases_per_million', 'new_cases_smoothed_per_million',
'total_deaths_per_million', 'new_deaths_per_million',
'new_deaths_smoothed_per_million', 'reproduction_rate', 'icu_patients',
'icu_patients_per_million', 'hosp_patients',
'hosp_patients_per_million', 'weekly_icu_admissions',
'weekly_icu_admissions_per_million', 'weekly_hosp_admissions',
'weekly_hosp_admissions_per_million', 'new_tests', 'total_tests',
'total_tests_per_thousand', 'new_tests_per_thousand',
'new_tests_smoothed', 'new_tests_smoothed_per_thousand',
'positive_rate', 'tests_per_case', 'tests_units', 'total_vaccinations',
'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations',
'new_vaccinations_smoothed', 'total_vaccinations_per_hundred',
'people_vaccinated_per_hundred', 'people_fully_vaccinated_per_hundred',
'new_vaccinations_smoothed_per_million', 'stringency_index',
'population', 'population_density', 'median_age', 'aged_65_older',
'aged_70_older', 'gdp_per_capita', 'extreme_poverty',
'cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers',
'male_smokers', 'handwashing_facilities', 'hospital_beds_per_thousand',
'life_expectancy', 'human_development_index', 'excess_mortality'],
dtype='object')

There’s a huge chunk of variable names! Musta been a super hard work collecting all the data. Shout out to Hannah Ritchie et al.

Aight now let’s check new cases! New cases tends to be volatile, especially if there’s seasonality in the data itself. It is quite common to see seasonality on daily data just because of weekends. Thankfully, there’s new_cases_smoothed which I imagine take into account seasonality by plotting 7-day rolling average. I only take Indonesian data for this post.

indo=df[["iso_code","date","new_cases","new_cases_smoothed"]].query('iso_code == "IDN"')

Plot time!

sns.lineplot(data=indo,x='date',y='new_cases')
sns.lineplot(data=indo,x='date',y='new_cases_smoothed')
plt.xticks(rotation=45)

(array([18322., 18383., 18444., 18506., 18567., 18628., 18687., 18748.,
18809.]),
[Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, ''),
Text(0, 0, '')])

I try to make my own 7-day rolling average by copying codes from here

indo['cases_7day_ave'] = indo.new_cases.rolling(7).mean().shift(-3)
indo.head(10)

	iso_code	date	new_cases	new_cases_smoothed	cases_7day_ave
44074	IDN	2020-03-02	2.0	NaN	NaN
44075	IDN	2020-03-03	0.0	NaN	NaN
44076	IDN	2020-03-04	0.0	NaN	NaN
44077	IDN	2020-03-05	0.0	NaN	0.857143
44078	IDN	2020-03-06	2.0	NaN	2.428571
44079	IDN	2020-03-07	0.0	0.571	3.571429
44080	IDN	2020-03-08	2.0	0.857	4.571429
44081	IDN	2020-03-09	13.0	2.429	4.571429
44082	IDN	2020-03-10	8.0	3.571	9.285714
44083	IDN	2020-03-11	7.0	4.571	13.142857

Which confirms that new_cases_smoothed is indeed 7-day rolling average.

sns.lineplot(data=indo,x='date',y='new_cases')
sns.lineplot(data=indo,x='date',y='new_cases_smoothed')
sns.lineplot(data=indo,x='date',y='cases_7day_ave')
plt.xticks(rotation=45)
plt.legend(['new cases','new cases smoothed','7-day average bikinan sendiri'])
plt.ylabel('kasus')
plt.xlabel('tanggal')

Text(0.5, 0, 'tanggal')

A year and a half is a bit too long (dear god it’s already a year and a half??), so let’s cut it to just 2021.

indo2=indo.query('date>20210101') # ambil hanya setelah 1 Januari 2021
# lalu kita plot persis seperti di atas
sns.lineplot(data=indo2,x='date',y='new_cases')
sns.lineplot(data=indo2,x='date',y='new_cases_smoothed')
plt.xticks(rotation=45)
plt.legend(['new cases','new cases smoothed','7-day average bikinan sendiri'])
plt.ylabel('kasus')
plt.xlabel('tanggal')

Text(0.5, 0, 'tanggal')

Cases is indeed seem to go down even with the smoothed one. But is this because of undertesting? We can also see it from our dataset. We add positive rate to really make sure.

indo=df[["iso_code","date","new_tests","new_tests_smoothed",
 "new_cases","new_cases_smoothed","positive_rate"]].query('iso_code == "IDN"')
indo2=indo.query('date>20210101')

fig, axes = plt.subplots(1, 2, figsize=(18, 10))
fig.suptitle('Data tes baru dan positive rate Indonesia')
sns.lineplot(ax=axes[0],data=indo2,x='date',y='new_tests')
sns.lineplot(ax=axes[0],data=indo2,x='date',y='new_tests_smoothed')
axes[0].tick_params(labelrotation=45)
axes[0].legend(['new tests','new tests smoothed'])
axes[0].set_ylabel('tes baru')
axes[0].set_xlabel('tanggal')
axes[0].set_title('new cases')
sns.lineplot(ax=axes[1],data=indo2,x='date',y='positive_rate')
plt.xticks(rotation=45)
plt.ylabel('0-1')
plt.xlabel('tanggal')
axes[1].set_title('positive rate')

Text(0.5, 1.0, 'positive rate')

And yes test is indeed goes down. At the same time, positive rate seem to be trending down as well. This will depend on how testing is conducted in terms of selecting who gets to be tested and who’s not. We can be sure if we check hospitalisation and death. Unfortunately Indonesian hospitalisation number is non-existent in this dataset.

df.query('iso_code=="IDN"')[['weekly_icu_admissions','weekly_hosp_admissions']]

	weekly_icu_admissions	weekly_hosp_admissions
44074	NaN	NaN
44075	NaN	NaN
44076	NaN	NaN
44077	NaN	NaN
44078	NaN	NaN
...	...	...
44582	NaN	NaN
44583	NaN	NaN
44584	NaN	NaN
44585	NaN	NaN
44586	NaN	NaN

513 rows × 2 columns

On death (Dear God, bless all the lost souls and those who they left), situation is rather gloom.

indo=df[["iso_code","date","new_deaths","new_deaths_smoothed"]].query('iso_code == "IDN"')
indo2=indo.query('date>20210101')
sns.lineplot(data=indo2,x='date',y='new_deaths')
sns.lineplot(data=indo2,x='date',y='new_deaths_smoothed')
plt.xticks(rotation=45)
plt.legend(['kematian baru','kematian baru rerata bergerak 7 hari'])
plt.ylabel('kasus')
plt.xlabel('tanggal')

Text(0.5, 0, 'tanggal')

Judging from the death data, pandemic still far from over. Note that death may follow new cases, hence have a lag in its trending down. However, if we cannot trust test data, death data is also hard to be trusted. I think with unreliable data, it is hard to react on any news really, whether cases go up or down. It is hard to make a good case for the government, because people’s like: low cases: bad data! bad testing!. High cases: Government is stupid!

So yeah. I guess it is helping if we don’t overreact over the new cases because it might not reveal the true state of Indonesian COVID-19 Pandemic situation.

What about vaccination? Judging from all of our graph up there, new cases and positive rate shot up during June-ish. What happen during that month? Delta entrance? What kind of crowdy events happen during that time? What high mobility event took place during that date? The government might let high mobility events to take place amid vaccination program has started. So let me end this blog by posting vaccination speed between countries, including Indonesia.

Hannah Ritchie, Esteban Ortiz-Ospina, Diana Beltekian, Edouard Mathieu, Joe Hasell, Bobbie Macdonald, Charlie Giattino, Cameron Appel, Lucas Rodés-Guirao and Max Roser (2020) - “Coronavirus Pandemic (COVID-19)”. Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/coronavirus' [Online Resource] ↩︎

Is the Blackwidow V3 Mini Hyperspeed any good? Trying the yellow switch!

Mon, 05 Jul 2021 09:47:19 +1000

In my previous post, I wrote about . My last non-mechanical keyboard was the , which I bought for work from home. In that post I also described my journey to the , a keyboard that’s mechanical but technically optical.

Why mechanical keyboards and Razer?

I switched to mechanical keyboards because they’re widely recommended in typist forums. On top of that, mechanical keyboards can be programmed with macros and remapped keys with custom functions. This is super useful for my daily work. I’ve loaded my keyboard’s extra functions with various text snippets for commands and methods in languages I frequently use, such as (R)markdown syntax, Python, and Stata.

For adding functions and alternate key assignments, I’ve found Razer to be among the best. It can remap all keys and add functions to every key (Razer calls this ). For example, I can store the text {{< icon name="exclamation-triangle" pack="fas" >}} on the K key. I can bring up that text by pressing fn+K. And every key can do this.

By the way, the syntax above renders on my blog as this ->

The main drawback of Razer is the price. Fortunately, in Australia and on Amazon, there are frequent big sales, especially around the end of the financial year and Boxing Day. Razer also has a student discount program, which is very helpful for a student like me.

Of course, mechanical keyboards aren’t for everyone. They’re quite pricey and you may not need their features. True, a mechanical keyboard makes me type faster, but the difference isn’t huge. If your work doesn’t urgently require fast typing, you may not need one. Macros and Hypershift aren’t for everyone either. Also, if you’re switching from a thin keyboard to a mechanical one for the first time, it might feel uncomfortable – sore fingers and such. If you have no issues with your current keyboard, switching to mechanical probably isn’t a priority.

Mechanical keyboards also come with all sorts of switch types. Since they’re expensive, most people will only buy one. But which one? In this post, I want to compare three Razer switches I’ve tried. They are Razer Purple, Razer Green, and Razer Yellow.

There are probably many keyboards that offer better value than Razer at their price point. Nowadays there are tons of great mechanical keyboards at affordable prices, if you’re only after the switches. For instance, if you don’t care much about software, macros, wireless, or RGB, you might be better off with a different brand. But be careful with availability in Indonesia since importing can be complicated.

This time I’ll discuss the with the Yellow switch.

Blackwidow V3 mini hyperspeed yellow switch!

First, let’s discuss why the Yellow switch.

Why Yellow switch?

Before getting to Yellow, let’s compare the Razer switches I’ve tried.

Switches
Keyboard	Razer Blackwidow	Razer Huntsman	Razer Blackwidow V3 Mini
Feel	Clicky	Clicky	Linear
Actuation force (g)	50	45	45
Actuation travel (mm)	1.74	1.56	1.2
Total travel (mm)	4.0	3.5	3.5
typing speed (WPM)	84	84	81

From the stats above, the Yellow switch is the lightest. Its travel is also not as deep as the clicky switches. Razer’s Yellow switch is comparable to the Cherry MX Red. The feel is linear with fast actuation.

Gamers typically prefer fast actuation because the shorter travel means you don’t have to lift the key as high to ‘reset’ and press again. Great for games requiring fast reactions like First Person Shooters – strafing (side-to-side movement) can be faster.

But for typing, I still find clicky more comfortable. It’s true that clicky switches have longer travel, but you don’t have to press all the way to the bottom. The feedback from the click is still more satisfying for typing. You can see in the table that my speed dropped slightly with Yellow, and accuracy dropped noticeably. More typos because the switch is too light.

In terms of noise, clicky is definitely louder. The Razer Yellow is reasonably silent.

Why mini?

The Blackwidow V3 Mini is a 65% keyboard, meaning it has no numpad and no F1-F12 keys.

V3 mini in action

How do you manage without numpad and F keys? F keys are accessed via Fn + number. For example, to refresh (F5), press fn + 5. To rename (F2), press fn + 2. Numpad? Just doesn’t exist.

Isn’t that inconvenient? Absolutely! Especially since the ESC key is now next to the number 1, making backtick and ~ awkward because you need shift and fn simultaneously. I remapped CAPS LOCK to Fn for convenience. So now I have two Fn keys – the original one on the bottom right and Caps Lock.

The advantage is space. The mouse can be closer to your left hand since there’s more room on the right side. My desk isn’t very big and I keep lots of stuff on it, so having a smaller keyboard is a huge benefit.

V3 mini in action

I still fumble sometimes. I’ve had to make several key adjustments. Besides Caps Lock, I also remapped Pg Up, Pg Down, and Ins. Space for macros is reduced since the Huntsman had way more keys.

That’s why key remapping is so important for a 65% keyboard. For instance, the V3 Mini doesn’t have Home or End – two keys I use often. But it has INS, which I never use. So they need to be remapped. If you buy one without remapping capability, make sure the keys it includes are ones you actually use.

So far, the space savings and closer mouse position seem like a pretty good trade-off. Just need a bit more adjustment.

Conclusion

The downside? The price, of course. At this price, you could get a keyboard of similar quality. With the decreasing cost of mechanical switches, you can get comparable keyboards for perhaps half the price. I only bought this because I got a discount. I’m also not confident enough to try other keyboards when I’m unsure about their programmable button setup. But if you want a regular 65% mechanical keyboard, you’re probably better off buying something else.

For typing, clicky is still more comfortable in my opinion. Unless you’re a competitive gamer, clicky is probably the way to go. This keyboard is more silent, but clicky is still better for typing. Trade-offs. But I don’t regret buying the Yellow either.

That’s it!

Indonesian Students Association abroad: what is it for?

Sun, 27 Jun 2021 21:57:43 +1000

Twitter is currently buzzing because of this post by BEM UI (the Student Executive Board of the University of Indonesia):

JOKOWI: THE KING OF LIP SERVICE pic.twitter.com/EVkE1Fp7vz
— BEM UI (@BEMUI_Official) June 26, 2021

Among the replies, several were interesting, essentially questioning BEM UI’s legitimacy in representing all UI students’ views. Some said BEM UI should not be used as a political tool. Others even attacked the chairperson and took issue with the fact that the BEM UI chair is a member of HMI (Islamic Students Association).

In this post, I do not intend to discuss BEM UI’s critique itself – whether its substance is on point, whether its method was appropriate. What I find more interesting is the broader question of the function of student organizations. Should they be “politicized”?

Perhimpunan Pelajar Indonesia Dunia (PPID)

Wait, why the sudden jump to PPID (World Association of Indonesian Students)? Heh, sorry. I have to admit that I was never an active student organization member. I was the kind of student who went to class, went home, and played video games. I am not great at organizational work and tend to be short-sighted. So I don’t think I have the legitimacy to talk about student bodies in general, like BEM UI. I will focus on PPID.

My involvement with PPID was one of the rare organizational activities I undertook as a student. But even “involved” is generous – I mainly helped a bit with research. PPID has a wing called the Directorate of Research and Studies, led by Denny Irawan. I helped him with economics studies. This function may differ a bit from BEM’s, but that is precisely what drew me in.

Anyway, this post is really about my understanding of what PPI is actually for. One thing that struck me was an article by dr. Rully Tri Cahyono, former chair of PPI Groningen (the Netherlands), titled “The Shifting Role of Indonesian Student Associations”. In it, he critiques the current PPI for being too “party-oriented.”

According to him, PPI in the pre-1920s era – when it existed only in the Netherlands – was indeed formed by Indonesian students to, well, “hang out and have fun.” Just getting together. After the 1920s, as more Indonesian youth studied in the Netherlands, PPI began shifting to a more “political” direction, coinciding with the arrival of Mohammad Hatta and his cohort. The original article is worth reading – it is quite illuminating.

His message was: PPI, which now exists in many countries and even has a “world” version, is reverting to mere socializing? He envisions a PPI that returns to the post-1920s spirit: active in social and political movements.

Perhaps something like what BEM UI is doing right now? Only Pak Rully would know for sure, but my guess is yes.

A More Activist and Scientific PPI?

I honestly don’t know how many people share Pak Rully’s concern about PPI being too “party-focused.” But anecdotally, I have frequently encountered similar remarks even before joining PPI. Many say Indonesian student associations abroad have too much fun and not enough substance. After all, we are a group of highly privileged individuals who should have a cause to champion.

I am among those who believe Indonesian students should contribute intellectual output or research in written form. Again, anecdotally, I have heard comments like “person X only posts vacation photos. They should be posting achievements. Publications. Conference presentations.” Regardless of the vacation-posting debate, I think these observations have a point.

Especially as a scholarship recipient. Who knows how many more deserving people could have gone in my place? I feel a moral obligation to take my studies seriously and perform well. I felt that, anyway. Whether I actually performed is another matter.

I joined PPID because I wanted to help elevate the image of Indonesian students – particularly those in PPI – beyond just socializing, and to make the scientific side more visible. An incredible presumption, I know. But hey, at least I wasn’t alone.

Oh, one major project we had at PPID’s Ditlitka was writing a book to be published through LIPI Press. Stay tuned!

PPI: For Whom?

But the most lively discussion about the role of Indonesian student associations actually took place at a halal bi halal gathering in Canberra.

In late May 2021, PPI ACT (Canberra), ANUISA (the Indonesian student organization at ANU), and Indonesia Synergy – an organization I am also a member of – organized a halal bi halal event at my campus, ANU.

This event, disguised as a halal-bi-halal gathering with food, actually started with a free-flowing discussion about the role of PPI. The discussion was very engaging and illuminating. If I had to summarize, there were at least 5 reasons why PPI should exist.

1. Student Welfare

The first reason that immediately came up was student welfare. This is the core of the question “PPI for whom?” As an organization formed by students, PPI should naturally strive for the wellbeing of Indonesian students.

Living in a foreign country, especially for first-timers who know nobody, is not easy. Several examples of PPI’s welfare role emerged: Mas Haly from PPI ACT ran a kind of “people’s garage” to help students whose cars broke down, especially when they weren’t near home. PPI Australia organized webinars with relevant officials on Back for Good procedures and shipping belongings back to Indonesia. My friend Riandy noted that PPI was the only organization he could think of to contact when first arriving in Australia – for information and maybe even pooling airport pickups.

Welfare-related activities probably garner the easiest support, mainly because student welfare benefits everyone.

2. Advocacy

The second role is still welfare-related but leans more toward advocacy. Gatra, also a friend of mine, raised this idea by pointing to PARSA, the postgraduate student organization at ANU. PARSA frequently advocates for postgrad students’ interests – such as pushing for affordable housing or helping students who feel mistreated by the university. PARSA even has its own lawyer. And despite often being at odds with the university, part of PARSA’s funding actually comes from the university itself.

3. Socializing

Although many like to mock vacation trips and other recreational activities, these are undeniably important for students, and demand is high. I myself once joined a ski trip organized by ANUISA (or was it PARSA? Or PPI? I forget) at a reasonable price.

Socializing is not just for the students there either. Many friends back in Indonesia seem quite interested in posts, photos, and videos of life abroad.

Socializing isn’t only about travel. It can include halal-bi-halal gatherings or other social events. It can also involve coordinating festivals with the campus or embassy. These activities are especially helpful for those who get homesick.

4. Position Statements and Activism

This is the role that, I gather, dr. Rully wants to see more of, and also what BEM UI is doing now: taking a stance on the Indonesian President. The feeling is not exclusive to dr. Rully. I believe many Indonesian students have a strong desire to engage in activism and take positions on issues.

Not only that, public demand for Indonesian students to speak up is also high. I frequently hear things like “how can Indonesian students abroad stay silent while the KPK is being gutted?”

But this is precisely where Indonesian students often diverge. Unlike welfare-related matters that benefit ALL Indonesian students abroad, position statements or activism do not uniformly resonate. For example, when PPI is asked to take a stance on FPI, Papua, Palestine, or Myanmar, achieving 100% consensus is difficult. If PPI issues a statement in such circumstances, the question arises: whose views does it represent?

This echoes some of the replies to the BEM UI tweet that opened this post.

Moreover, Indonesia today is very different from the 1920s. As a layperson in history, I can only speculate about how easy it was to build consensus around independence from colonial rule. But it is probably not hard to imagine that the majority of PI members back then shared the same spirit of independence. If you were a nationalist, your position against the colonial government was clear.

Today, Indonesia is independent with a legitimate government. Activism becomes somewhat harder because the meaning of nationalism is more contestable. Does nationalism mean always agreeing with the current government? Would the nation be the same under a different (still Indonesian) administration? Should we comment on colonialism by other nations? These questions make activism somewhat more challenging, in my view, especially given diverse educational backgrounds and experiences.

5. Advancing Research and Knowledge

The last role is more intellectual. Indonesian students are now everywhere. In Australia alone, they are at many different universities. In other countries there are certainly many students who, while within the same discipline, have different specializations, use different techniques, and are trained by different supervisors. PPI can serve as a platform to connect these young intellectuals and create space for research collaboration.

As a student whose hobby was going to class and going home, I actually find this function very appealing and potentially beneficial.

So, Which One Is PPI?

In the end, our free-flowing discussion did not definitively settle which of these 5 reasons best justifies PPI’s existence. We all got hungry and if food wasn’t served soon, things might have gotten chaotic.

We briefly discussed “levels” within PPI. Welfare-related activities make more sense at the city or at most national level. Perhaps PPID (the world level) is less suited for that. But PPID could advocate for broader issues, like pushing for an umbrella legal protection for Indonesian citizens abroad, or something similar.

Personally, I think which function PPI ultimately adopts depends on its leadership and cabinet composition. For instance, Mas Haly’s garage program might not be continued by the next leadership. Similarly, the book-writing project (arguably function #5) may not carry over to the next PPID administration.

Don’t forget that participation in student organizations is voluntary. Unlike PARSA, PPI generally doesn’t have a large and consistent funding source, let alone the budget to pay people for specific welfare and advocacy functions. We cannot judge people who join PPI. Are they genuinely interested in organizing? Are they altruistic and intent on helping fellow students? Or do they have a personal agenda? Do they want to use PPI for personal political gain and social climbing? Even if so, is that wrong?

And don’t forget, student activities can take non-PPI forms too. IS, for instance, was formed somewhat accidentally to serve an intellectual function because ANU frequently hosts visitors worth having great discussions with. Before the pandemic, our most recent guests included Bu Mari Pangestu, Pak Adrianus from Kompolnas, Prof Arif Ansori Yusuf, and Pak Burhanuddin Muhtadi.

Activism also does not have to go through PPI. IS itself has engaged in activism on several occasions when Mbak Chitra was still in Canberra.

So, what is PPI’s function? Why should PPI exist? Any ideas? Reply here!

Why Indonesian Sugar is Expensive? A closer look on Indonesian trade policy (and Neraca Komoditas)

Sun, 27 Jun 2021 21:57:43 +1000

Sugar Prices

Sugar is one of the most important commodity in Indonesia. Sugar consumption in Indonesia is high. In 2018, Indonesian consumption of sugar reached 26.34 kg per capita, much higher than India, Vietnam and The Philippines¹. That is true even when Indonesian domestic sugar price is consistently higher if we compared to International market¹.

I downloaded data from CEIC Indonesia Premium Database² and FAO website³ and use it to plot the different prices of Indonesian sugar price in both traditional and modern market, and compare it with the world price.

url='secret!' # url is deleted because I can't freely distribute data from CEIC sorry
a=pd.read_csv(url) # taking data from google drive
a=pd.melt(a,id_vars='date',value_vars=['tprice','mprice','ip']) # melt it to fit my plot
# making a neat x axis
lol=pd.date_range('2016-07-01','2021-07-01',
 freq='6MS').strftime("%m/%Y").tolist()

# plotting
sns.lineplot(data=a,x='date',y='value',hue='variable',style='variable')
plt.xticks(ticks=lol,rotation=45)
plt.legend(labels=['traditional market','modern market','international market'])
plt.title('Sugar prices in Indonesian market and international market')
plt.xlabel('month/year')
plt.ylabel('USD/Kg')

Text(0, 0.5, 'USD/Kg')

As the graph above show, Indonesian domestic price of sugar is consistently higher than international market by about 3 times. One might wonder if international market price of sugar is so low, why don’t we import more of it? The obvious answer is because trade is restricted.

Sugar Trade

Indonesia import almost 70% of its sugar consumption from international market⁴. Not only Indonesians love its teh manis, but sugar is also play a crucial role in sugar refining industry. This industry is then supplying other industries like sugary drinks.

Previously I wrote that sugar-using industry also need to apply for sugar import to Ministry of Industry and Ministry of Trade. However according to Risetio, a person who once involved in this industry, sugar-using industry don’t import. Instead, they buy domestically from sugar refinery who do the import. Not sure why sugar-using industry don’t import directly. See thread below.

Balesnya di sini aja Med. Jadi kalau baca artikel tersebut, ada 1 kesalahan paling krusial. Perusahaan industri pengguna gula (industri makanan dan minuman) TIDAK meminta rekomendasi impor maupun persetujuan impor gula.
— Risetioz (@risetioz) July 10, 2021

The regulation that restrict trade of sugar is MOT regulations. To be precise, it’s Peraturan Menteri Perdagangan Nomor 14 Tahun 2020 which superseeds Peraturan Menteri Perdagangan Nomor 117 Tahun 2015. This regulation put a quota restriction on sugar. According to the regulation, imports are allowed only to supply domestic industry and control inflation. How much quota to import is determined in a coordination meeting held by Coordinating Ministry of Economy. I feel like this is basically Ministry of Agriculture asking for low quota to protect farmers, while Ministry of Industry asking for high quota to help sugar-using industries. But I might be wrong.

To get how much quoota to import next year, the flow chart looks like this:

sequenceDiagram participant A as Firms participant B as Ministry of Industry participant C as Surveyor participant D as Coordination meeting participant E as Ministry of Trade A->>B: Provide an import plan B->>C: Audit request C->>B: Report back B->>D: Argue for quota D->>E: Quota is settled

In short, the data Ministries are using come from (partially) sugar-using firms. Surveyor is needed to make sure that the asked quota is in-line to firms’ actual production capacity. A small firms asking for a big quota might turn out to be a reseller, a rent-seeker if you will, hence needs to be audited. This process happens every year.

Now that the quota has been settled, sugar refineries need to apply for the actual quota.

sequenceDiagram participant A as Firms participant B as Ministry of Industry participant C as Ministry of Trade participant D as Customs A->>B: Apply for a recommendation B->>A: Provide recommendation letter A->>C: Apply for the import quota C->>A: Provide the right to import A->>D: Use the right to import as a part of import documents

The process is rather tedious, involving many agencies and web applications. Ministries may only grant a fraction of the asked quota. This process is not very transparent, in the sense that you may not always know why your quota is cut, or how much quota is left for the taking¹. Additionally, some firms who get refined sugar intended for industry were also sometimes sold as white sugar in the consumer market which priced higher, creating an illegal trading¹.

The quota to import sugar for household is granted by the ministry only to 3 or 4 State-Owned Enterprises¹. These firms are also prone to badly time their imports and rent-seeking behavior can’t be excluded since these firms will always get the quota even when they don’t realize the quota¹.

If that is the case, let’s improve domestic sugar production! We should not rely on imported sugar, right? Well, that depends. Improving domestic sugar market is also a bit of a problem. Some of the on-farm problems include sufficient manpower, lack of monitoring of subsidized seeds and fertilizers, and inefficient mills¹. On-farm and off-farm is already highly subsidized, but productivity remain low. Not only the government spent a lot of money on improving productivity, it still turns out to be more expensive compared to international production of sugar.

New Solution?

There is this thing called ‘Neraca Komoditas’, which supposed to have an information on how much production and consumption of a commodity, including sugar. The difference between production and consumption on that data will be used to settle on how much quota to import. Setting the quota to import seems to be highly political. It is basically a battle between sugar producer and sugar consumer. It is clear that sugar is used by many industries under the Ministry of Industry which just finished a regulation which says it will guarantee a steady flow of industrial inputs.

Neraca Komoditas wants to cut a process of negotiating how much sugar is imported in the coordination meeting. Remember my impression about sugar producer vs sugar-using industries? The data for sugar production mainly comes from Ministry of Agriculture, while the data for sugar consumption comes from Ministry of Industry (like in the first diagram). When the two clashes, Ministry of Industry might accuse Ministry of Agriculture of inflating their figures, as Industry still have hard times getting sugar. With Neraca Komoditas as the one sole data which rule them all, this dispute is (hopefully?) no more. This is hypothesized to cut a bit of a red-tape as the coordination meeting may not last as long.

It will be much more interesting if Neraca Komoditas actually cuts the need to apply for recommendation letters. It is possible that the web portal SIINAS to actually remember how much quota firms planned for the import, the quota is set per firms instead of in aggregate. Firms can feel safer as ministries can no longer cut their proposed quota. It may reduce rent-seeking activity. It will be even better if ministries actually publish the data, the quota, and who gets how much. It will be easier for firms to check their actual allowance and for public to audit the system.

If the whole recommendation letters are still in place, then the red tape faced by industries seems to be staying. Neraca Komoditas might help bureaucrats in their debates of settling in a quota (remember, Neraca Komoditas is not only about sugar, but also a huge lists of ‘commodities’). But it seems like it won’t affect industries that much.

At the end of the day, it is still going to be the good ol’ quota. Neraca Komoditas might solve the data issue, but the old quota problem seems to not be addressed. If firms granted by the quota is still restricted, it won’t solve the problem. If how much quota left or how fast it is disbursed to the market is not clear, it won’t solve the problem. If industries which import industrial inputs will still mask their refined sugar to white crystal and do shadow trading, it won’t solve the problem.

There will be a material center for small business who can’t import their own inputs. Will it be another SOE? we’ll have to see.

It is easy to be pessimistic, but let’s give the benefit of the doubt to the government. Again.

What do you think? Please comment on my twitter!

Saputri, N. K., & Respatiadi, H. (2018). Policy reform to lower sugar prices in Indonesia. CIPS Indonesia. https://repository.cips-indonesia.org/media/270481-policy-reform-to-lower-sugar-prices-in-i-839081a1.pdf ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎
CEIC. “Average Retail Price: Sugar: Local”. CEIC Indonesia Premium Database. CEIC. ↩︎
FAO. “ICE Future US: Sugar”. GIEWS FPMA. https://fpma.apps.fao.org/giews/food-prices/tool/public/#/dataset/international ↩︎
Amanta, F. (2021). The cost of Non-Tariff Measures on Food and Agriculture in Indonesia. CIPS Policy Paper, 36. https://www.cips-indonesia.org/cost-of-ntm ↩︎

The Heterogenous Impact of Tariff and NTM on Total Factor Productivity of Indonesian Firms

Fri, 25 Jun 2021 09:00:00 +1000

Examining the National Economic Recovery (PEN) Stimulus Policy Amid the COVID-19 Pandemic Crisis

Sat, 01 May 2021 14:30:00 +1000

Summary

The COVID-19 pandemic has significantly impacted the Indonesian economy across several dimensions. The first dimension is negative economic growth reaching -2.1% in 2020, while inflation remained relatively low at 1.9%. The second dimension is shown by the significant contraction of most MSMEs in terms of production, employment, and emergency funds for business operations. Meanwhile, the next dimension is the increase in unemployment and the decline in worker income as a result of layoffs and reduced income from self-employment activities.

The Indonesian government responded to the COVID-19 pandemic impact by establishing a strategic program in the form of the National Economic Recovery (PEN) as the implementation of Government Regulation in Lieu of Law Number 1 of 2020. In 2020, the total fiscal stimulus for PEN reached IDR 696 trillion, increasing to IDR 699.44 trillion in 2021. Many parties responded positively and appreciated the PEN plan and its realization. However, several critical challenges still need to be addressed, such as the availability of an accurate and accountable database, particularly for the realization of PEN for social protection and MSMEs. Synchronization and accuracy of poverty data in both urban and rural areas also need improvement. This has implications for the effectiveness and efficiency of PEN fund realization given the relatively limited fiscal stimulus capacity.

Policy recommendations can be implemented by the government with emphasis on PEN planning and realization supported by accurate and accountable databases. The year 2021 marks the initial momentum for national economic recovery through fiscal stimulus within the PEN framework. To that end, PEN fund allocation has been set higher than the previous year. Additionally, the monitoring and evaluation process for PEN fund realization can be conducted more rigorously as a form of public accountability. This means that the increased PEN fund allocation can be balanced by good governance practices. To support PEN, the government can sharpen tax revenue sources and increase borrowing for productive sectors.

Notes on Professor Keyu Jin's Public Lecture titled

Mon, 26 Apr 2021 10:39:06 +1000

I finally got the time to watch this excellent lecture by Professor Keyu Jin from LSE. She talked about techno-nationalism and discuss whether it’s good or bad. Her discssion is quite detailed in that she started with the basic: why the high-tech economy is different.

According to her, these high-tech services has a different cost curve compared to our traditional manufacturing. In a traditional manufacturing firm, the marginal cost of selling one more product is very close to the cost of making a product. For tech giants, once the production is complete, the marginal cost for additional sale is zero. All you need to do is a copy paste, give product key, or give an access to the customer’s account. In other words, you don’t need to make anything else, hence no (or very very little) additional cost. Once the product is ready, the cost of servicing 100 or 10,000 customers is very similar.

Moreover, most tech services inhibit an increasing returns to scale. This returns to scale comes from customers’ data (network effect), not physical capital. Therefore, serving more customers is not only cost less, but also leads to more returns. Therefore, burning money to acquire customers can be justified. In fact, that is the main strategy: subsidies users so more come to the network, then monetize it later on.

All of this network effect even extends tech giants’ ability to increase their scope. For example, if a tech giant has the ability to track down your purchases, it can learn your income stability and your risk of default. This information, which can be granular, is very important for credit service provider. From ecommerce app, this giant can, in turn, provide credit service. This is already happening in China in the form of super apps.

The implication of zero marginal cost and increasing returns to scale amid network effect is that being big is very important for tech giants. Big firms are more efficient and better for consumers. However, there’s a catch, a room for techno-nationalism: local externalities.

Some tech services requires localization or local twist to it. She gave an example were Didi, the Chinese Uber, is better at localization than Uber. Didi’s Mexico goes so far as to train each of their driver on how to use the app effectively, including how to use it for receive payment and using it to spend. Uber does not do that. They relies on credit card and care less about the local operation. In this sense, it is good to limit the growth of global player and help support local player.

One very interesting for me to learn that she suggests payment is the key to super app. This is one reason why super app is very powerful in China, but not in the United States (US). in the US, credit and debit card is already settled. Practically everyone has the ability to pay using credit card, hence no need for the likes of Amazon or Google to invest in payment platform. This is different with China, where financial institution is not having the same extended reach.

To some extent, I find this fascinating because I can help but feel like Indonesia is very similar. Remember when you has to pay Uber with credit card in Indonesia? Gojek can survive perhaps because of Gopay. Similarly, e-wallet also rises.

Also in the local externalities, I already feel like this applies to entertainment. Many of Indonesian locals are actually enjoy local music (local pop band, dangdut, etc) which may be better or local artists than relying on the likes of Taylor Swift for entertainment. The same goes for influencers and TV shows, I guess. Does that mean we need to limit global entertainer?

Another interesting points from her: who owns data? It matters because this whole “big firm is efficient” ignores non-cost aspect of this whole ruckuss. Who we trust more as a global citizen: US firms or Chinese? western governments care a lot about privacy while the chinese is less so. Big is good in terms of economic efficiency perspective, but possibly not in the moral perspective. In fact, to me, this is the main reason why this whole techno-nationalism is rising in the first place.

Big global firms rise a question: what if a private firm becomes more powerful than a (small?) country?

Here’s the full video if you are interested in this stuff.

Economic Conditions During the COVID-19 Pandemic

Thu, 08 Apr 2021 12:30:00 +1100

Restrictive regulations on foreign trade may hurt economy

Fri, 12 Mar 2021 22:43:13 +1100

Economic recovery through export

Sat, 27 Feb 2021 14:00:00 +1100

Pendahuluan

During a pandemic, the best economic policy is health policy. Vaccine is often cited to be the silver bullet of Indonesian economy. Say it is true, the speed seems to suggest that we need to readjust our hope from the vaccine. Data from our world in data Shows that our speed need some bump if we are to meet the target.

That said, Vaccination does not seem to correlate with economic growth. Although Indonesia is a bit behind compare to Turkey and Brazil, the economic growth are somewhat similar. I pick weekly tracker from OECD, and this data shows that albeit vary in vaccination speed, growth are somewhat similar.

A note on weekly tracker, it uses machine learning to nowcast growth from google trend, so read it with a grain of salt. The data however is timely (hence nowcasting instead of forecasting).

Export recovery

Likewise, export performance doesn’t seem to be impacted by the pandemic. In fact, Indonesia’s export performance in terms of value is higher in November and December 2020 compared to the same month a year earlier.

Export was also the way most ASEAN countries escape the Asian Financial Crisis 1998.

A little note on that though, cuz the crisis during 1998 was pretty much concentrated in ASEAN and East Asia region, hence selling stuff to EU or US is a viable option. There was a huge drop in exchange rate too, which allows for increased competitiveness of Indonesian goods. Both doesn’t happen with the same extent as 1998, perhaps for the better.

That said, stimuli from advanced economies may provide the global demand Indonesia needs right now, if one’s to recover through export.

Often times, people ask me “what products do we need to push to the export market”? Very complicated question, but in this seminar, I just use Revealed Comparative Advantage (RCA) to try to attend to that question.

RCA

RCA is one metric we can look at to assess how competitive a country is on a certain product. In the RCA term, the higher the number, the more competitive that country is in producing that particular good.

I use UNCTAD’s data hence their defintion which can be read here. Buat yang suka rumus, silakan lihat di akhir post ini.

I ain’t running thru all of the HS codes. I aggregate them based on HS2 which can be seen here.

The picture below shows Indonesian RCA dynamics from 2000 to 2018. We can see that Indonesia’s RCAs are high in mining stuff and vegetable oil (which is basically Crude Palm Oil or CPO). There’s oil, gas and coal in fuels terdapat minyak bumi, gas alam dan batubara. Indonesian rubber is also highly competitive. RCA in minerals drops significantly, I would speculate the mineral ban ruling which was started to get very interesting in 2009 onward.

Indonesia also great in producing wood and wood products, also labour intensive stuff like footwear and textiles.

Harga-harga komoditas

Well, naturally Indonesia is a commodity exporter. Now is indeed a good time to be a commodity exporter since the prices of all of Indonesia’s export commodities are skyrocketting at the moment. Maybe not fuels, but still.

Export value

Data shows that Indonesia’s overall export value isn’t affected very much by the pandemic. CPO and fuels domination is clearly visible here. In fact, CPO grows compared to 2019. Fuel went down amid the pandemic, however, also the energy prices shock led by oil.

Transportation goods are dropping, one reason why Indonesia is exempting the tax for buying a car to exploit demand market. Textiles are hurt as well, perhaps amid the PPE viasco which lead to input for textiles disrupted. Textiles is a bit GVC heavy, so a disruption in the supply chain matters a lot.

RCA isn’t everything

RCA changes. Indonesian RCA also changes. Maybe Indonesia will not always be a commodity exporter forever. In fact, Indonesian government are heavily intervening through import substitution policies and trying to increase domestic value added by banning mineral export. As you can see from the graph above, mineral exports are going down while the export for metals, product of smelters, are increasing. I also hear anecdotes that the government is trying to improve CPO-processing industries.

On fuels, Indonesia may need to be careful since many advanced countries these days are pushing their zero emission target. Even China, in fact. This may reduce the coal price in the future.

Backward participation in GVC may be the best option for the governmen. Consequently, import will be a bit high since backward participation may require firms to import inputs and capital goods. Import may be as important as export itself because of this cuz import may reflect industrial activities.

Kinerja impor

For better or worse, import is a bit stagnant. Fuels import drops, likely because of the mobility issues amid the virus. Not just fuels, many other imports are dropping.

Capital goods (machinery and elect) and inputs (like metal (steel is important)) also drops, which can be a bad sign.

Strategi Pemerintah

Many of Indonesia’s manufacturer requires imported inputs, such as:

Soybean for tofu, tempe, chicken and beef;
iron, steel and chemical;
synthetic fibre, etc.

Moreover, domestic value added through export ban may take some time to realize. From nickel to battery to electric car could take years to materialized. By that time, maybe COVID is over (or maybe endemic we’ll see). Hence, speed matters.

If the government opt to go ahead with the ban (sunk cost is high anyway), then fiscal support in the short run is necessary. The government just need to make sure that these industries are growing and showing promises. Additionally, it may increase import, something the government really hate.

Retaliation is another matter with the export ban. It is kinda pointless trying to build industries with foreign debt and investment if the country can’t export its product to get foreign reserves. It also possibly limits economies of scale. This possible retaliation is worth keeping in mind. If we can ban import and export, why other countries can’t? RCEP may play a great role in this fiasco, but we’ll see.

All in all, let’s hope for the best, as usual.

rumus

$$ RCA_{Ai}=\frac{\frac{X_{Ai}}{\sum_{j \in P} X_{Aj}}}{\frac{X_{wi}}{\sum_{j \in P} X_{wj} }} \geq 1 $$

RCA negara $A$ untuk barang $i$ tergantung pentingnya ekspor barang $i$ untuk negara $A$, dibandingkan dengan pentingnya barang $i$ di seluruh dunia $w$.

kembali ke RCA

Agregasi

kembali ke RCA

Vaksin COVID-19 di Indonesia: harapan dan hambatan

Fri, 26 Feb 2021 15:00:00 +1100

Rasanya sangat lumrah jika ada ketertarikan yang tinggi terhadap program vaksinasi di Indonesia. Indonesia Synergy, sekumpulan mahasiswa di Canberra, mencoba mangakomodir keinginan beberapa warga Canberra terutama para mahasiswa untuk mengadakan diskusi santai mengenai beberapa permasalahan dan optimisme mengenai program vaksinasi massal ini.

target dan jenis vaksin

beberapa permasalahan di Indonesia

Kami mengundang Mbak Liza dan Bli Tarsa, dua orang peneliti di Australian National University (ANU). Dr Syarifah Munira adalah peneliti di Research School of Population Health, sementara Dr I Nyoman Sutarsa adalah dosen dan peneliti di Rural Clinical School, Medical School, The Australian National University. Bli Tarsa juga terafiliasi dengan Department of Public Health and Preventative Medicine, Faculty of Medicine Udayana University, Indonesia.

Bli Tarsa juga sempat menjadi pembicara di ANU Indonesia Project Global Seminar Series dua hari sebelum diskusi ini terjadi. Saya rekomen pada nonton rekamannya di youtube ANU Indonesia Project di sini.

Diskusi berjalan sangat lancar dan seru. Berikut sedikit kesimpulan dari diskusi hari itu.

Resensi ini adalah hasil interpretasi pribadi saya, dan belum tentu merefleksikan pemikiran kedua narasumber dengan ketepatan 100%

Diskusi dimulai dengan mengangkat soal efikasi. Mbak Liza menjelaskan bahwa efikasi sebenarnya digunakan untuk mengestimasi seberapa banyak populasi yang harus divaksinasi untuk mencapai herd immunity. Beliau juga menjelaskan bahwa desain vaksin-vaksin yang ada saat ini memberikan penekanan kepada penurunan gejala sedang sampai berat, sehingga dapat mengurangi beban rumah sakit. Bli Tarsa menambahkan bahwa mengukur efektivitas vaksin memerlukan waktu yang cukup lama, dan untuk saat ini melihat hospitalization rate dapat lebih menjelaskan hal tersebut.

Dari sana, diskusi bergerak ke arah target Pemerintah, apakah target ini realistis atau tidak. Menurut Bli Tarsa, target ini cukup realistis, namun dengan memerhatikan rumitnya kondisi logistik di lapangan. Pemerintah juga harus meningkatkan jumlah vaksinator, karena target tersebut akan sulit terealisasi jika hanya mengandalkan fasilitas dan tenaga kesehatan yang ada saat ini. Program seperti melatih sukarelawan vaksinator di komunitas (seperti RT/RW) dapat menjadi jalan keluar karena vaksin COVID-19 tidak terlalu sulit diinjeksi (cukup lewat otot) dan komunitas berpotensi memiliki data yang lebih baik dan personal. Ia juga menambahkan best practice dari banjar di Bali.

Diskusi yang mungkin paling seru terjadi ketika membahas privatisasi. Mbak Liza memberikan penekanan ekstra pada bahaya kegagalan pasar (market failure). Privatisasi berpotensi menggerus jatah pemerintah dan mendistribusikan vaksin tidak sesuai dengan target, yaitu nakes dahulu, lansia dan pekerja esensial seperti guru, baru masyarakat luas. Bli Tarsa setuju dengan hal tersebut, mengatakan bahwa approach terbaik tetap terpimpin atau top down. Namun Mbak Liza menambahkan bahwa skema Public-Private Partnership dapat berjalan selama distribusi tetap sesuai dengan kebutuhan epidemiologis, bukan semata-mata ekonomi.

situasi saat diskusi

Basis data juga menjadi perhatian serius. Bli Tarsa mengatakan pentingnya memiliki basis data, terutama karena approach yang beliau sarankan adalah distribusi dari atas, namun dilakukan melalui grass root seperti RT/RW. Dengan kata lain, pendataan yang nyambung menjadi sangat penting. Pendataan berguna tidak hanya untuk targeting orang yang belum divaksin tapi harus divaksin, tetapi juga memastikan bahwa orang yang eligible memang sudah divaksin. Tentunya kita tidak ingin ada kasus seperti tes PCR palsu: orang yang belum divaksin ngaku-ngaku divaksin demi bisa jalan-jalan.

Kedua narasumber juga tidak menyetujui sanksi seperti denda dan kurungan. approach yang lebih baik adalah dengan mengeluarkan semacam insentif. Contohnya di Australia, vaksin tidak diwajibkan. Namun ada kemungkinan pemerintah akan melobi perusahaan penerbangan untuk mewajibkan semua penumpang untung divaksin. Hal ini akan memberikan kebutuhan vaksinasi bagi setiap orang. Tentunya ini akan kembali lagi ke pentingnya pendataan untuk menghindari sertifikat vaksin palsu atau semacamnya.

Vaksin dalam negeri saat ini sepertinya masih cukup jauh dari realisasi. Vaksin merah-putih yang dikembangkan lembaga Eijkman baru sampai tahap 1 dan kemungkinan memerlukan setahun sampai siap diproduksi. Sementara itu, Vaksin nusantara / gotong-royong menggunakan teknologi yang pengembangannya dapat mencapai 10 tahun. Sepertinya agak sulit mengandalkan kedua vaksin ini untuk saat ini.

Poin terakhir adalah soal mutasi. Mutasi dapat menjadi momok apabila mutasi tersebut mengurangi efektivitas vaksin yang ada saat ini. Kemungkinan yang terjadi adalah keharusan melakukan re-vaksinasi, sehingga hal ini dapat membebani tidak hanya kapasitas produksi, namun juga kapasitas distribusi. Mbak Liza memberikan gambaran bahwa to some extent, hal ini dapat memberikan kepastian untuk produsen vaksin dalam meningkatkan kapasitas produksi. Bli Tarsa mengatakan bahwa saat ini mutasi masih terus dipantau, dan berharap bahwa Indonesia juga memberikan perhatian khusus dalam menguraikan strain-strain yang ada di Indonesia saat ini.

Diskusi ditutup dengan pesan dari kedua narasumber untuk senantiasa mengabarkan hal yang benar. Harapan yang tinggi juga diberikan kepada pemerintah untuk menyukseskan vaksinasi massal ini, sesuatu yang belum pernah dilakukan sebelumnya.

Berikut foto-foto pasca diskusi. Canberra sendiri sudah membolehkan penduduknya beraktivitas seperti biasa. Mulai senin, saya sendiri akan memulai pengajaran offline untuk pertama kali. Pemerintah Australia agak santai dalam menjalankan program vaksinasinya, partly karena COVID-19 di Australia sudah cukup terkendali.

situasi setelah diskusi

Indonesia's digital sector: opportunities and challenges

Mon, 08 Feb 2021 18:19:57 +1100

This post is about what I know regarding the digital economy in Indonesia. Indonesia (and indeed the whole world) is currently caught up in the hype of Industry 4.0, the digital economy, the Internet of Things, and similar trends. Digitalization seems unavoidable. While globalization has been fading, data exchange has been growing exponentially. Some of this content also appears in a collaborative book by PPI Australia on Realizing Indonesia 4.0. That book covers much more, including the impact of digitalization on healthcare, education, and more.

Disclaimer: I’m not an expert on Indonesia’s digital economy. This post is just written in my spare time because the sector is fascinating, widely discussed, and seemingly inevitable. Happy to discuss!

The digital economy and Indonesia’s readiness

What exactly is the digital economy? I’m not entirely sure of the definition myself. Australia’s communications department defines it as social and economic activities enabled by information technology, including e-banking, social media, Netflix, and other businesses using computing, cloud, and other smart devices.

the global network of economic and social activities that are enabled by information and communications technologies, such as the internet, mobile and sensor networks

By this definition, what makes an economic activity “digital” is when it involves computing and the internet. The digital economy has naturally attracted attention from many stakeholders. In a report titled Digital Globalization, McKinsey noted that global data flows grew 45-fold between 2005 and 2014. This coincides with so-called ‘slowbalization’ – the declining growth in trade of goods, services, and global investment since the 2008 global financial crisis. In other words, even as worldwide trade in goods, services, and investment has slowed, data exchange has grown at breakneck speed.

Information technology is changing how people do business. Much faster and better communication enables multinational corporations (MNCs) to operate globally. Outsourcing production processes and lean manufacturing are made possible by increasingly rapid inter-personal coordination thanks to the internet. MNCs can source labor, raw materials, and capital from around the world and market their products globally as well. This applies not just to goods but also services like Netflix.

Improving computing power and internet speed have even made machine-to-machine communication possible. In the past, outsourcing meant designers sending fax drawings to factories. Email helped designers send drawings faster and in higher quality. CAD-CAM and 3D printing have made it even easier, as designers just need to send CAD files¹. Machine-to-machine communication is also enabled by technologies like RFID, and ever-faster computing and internet speeds have enabled cloud computing.

Sensor technology has further accelerated human-machine communication. Traditionally, we communicate with machines by manually inputting data. You’ve probably used Excel and manually entered numbers. This blog was written using manual keyboard input. Biometric sensors let you “command” your smartphone to grant access via fingerprint. Today, smartwatches benefit from various sensors that can transmit body information like heart rate to machines, potentially saving your life.

This combination of human-human, machine-machine, and human-machine data exchange is the enabler behind everyone’s favorite buzzword: Industry 4.0.

But talking about Industry 4.0 in Indonesia still seems premature. The World Economic Forum’s 2018 report placed Indonesia in the nascent category – still far from ready. Key problems cited include a skills gap in the workforce, inadequate and uneven infrastructure, poor cyber security, and regulations and institutions that need strengthening.

There aren’t many comprehensive studies on Industry 4.0 adoption in Indonesia, but this CSIS study on Industry 4.0 in the manufacturing sector is extremely interesting. CSIS conducted its own survey showing that most manufacturing firms don’t yet feel the need to increase their Industry 4.0 adoption. One reason is that labor remains relatively cheap and industries rely on low-skilled, labor-intensive production. Rather than upgrading technology, relocating to regions with lower minimum wages still makes more economic sense. They added that most firms acknowledge they don’t really understand the practical implementation of the government’s “Making Indonesia 4.0” at the ground level.

While Industry 4.0 adoption in manufacturing still seems distant, at least one sector has been growing rapidly. The World Economic Forum identified two assets that give Indonesia a foundation for the digital world: market size driven by a large population, and relatively strong foreign investment. We can see how both factors play a crucial role in developing Indonesia’s digital economy: e-commerce.

E-commerce growth in Indonesia

As the world’s 4th most populous country, Indonesia is a highly promising market. A large market is essential for building economies of scale, allowing businesses to have many customers. Increasingly affordable smartphone and data plan prices have made internet access easier for Indonesians. Indonesia has also been called a nation that is hungry for internet content and social media.

(Indonesians) spend a higher-than-average amount of time on the Internet, primarily engaging in heavy social media usage and e-commerce ….. Their social media usage is among the highest of any population in the world.

According to a study by SMERU, Indonesia has five startups that have achieved unicorn or decacorn status: Gojek, Tokopedia, OVO, Bukalapak, and Traveloka. Gojek claims to have contributed 55 trillion rupiah to the economy, while Grab claims 48.9 trillion. Tokopedia claims a contribution equivalent to 1.5% of GDP – roughly comparable to the contribution of the entire aviation industry in Indonesia! And that’s just Tokopedia²…

E-commerce transactions in Indonesia have grown rapidly. A CSIS Policy Brief shows e-commerce transaction values rising exponentially from 32.29 trillion rupiah in 2014 to around 265.07 trillion rupiah in 2019. These figures don’t even include off-platform transactions such as purchases through Facebook or Kaskus.

Click for visualization from CSIS Policy Brief on e-commerce transactions in Indonesia. If you're using dark mode, be careful -- the image has a white background

E-commerce transaction value in Indonesia in trillion rupiah (Bank Indonesia, processed by CSIS)

This strong demand is forecast to keep growing. As competition in the smartphone and telecom industries drives down the prices of phones and data plans, and as economic growth enables a growing middle class, the potential for online shopping is projected to accelerate further.

With such large demand potential, investment needs have also increased. The Indonesian government facilitated this opportunity by removing e-commerce from the negative investment list through Presidential Regulation 44/2016. Foreign investment flooded into the sector. Major players include Tencent, Alibaba, Temasek, and various venture capitalists from Western countries. Even Google and Microsoft got in on the action. Tirto quoted then-BKPM chief Thom Lembong in 2019, who said that around 15-20% of foreign investment entering Indonesia was going to e-commerce.

“We estimate that 15-20 percent [of total investment] goes to e-commerce. That’s about $2-2.5 billion per year,” said Thomas at a forum titled “Unicorn Investment: For Whom?”

This massive capital enabled platforms to subsidize their way to scale-up. It’s an open secret that Gojek and Grab in their early days competed fiercely by pouring out subsidies, sacrificing short-term profits to grow their user base. The same happened in e-commerce, where platforms offered enormous promotions, discounts, and flash sales. This certainly benefited consumers, at least in the short term.

Another advantage of e-commerce is the incredible range of services it offers, including online storefronts, payment systems, advertising, and even warehousing. This allows merchants to focus purely on their products without worrying about payment systems and marketing. This is great for SMEs with limited capital. If the product is outsourced or imported, a seller doesn’t even need to think about production – just marketing!

The rapid growth of the digital sector presents an opportunity for the government to increase tax revenue. Digital products can be taxed by the consumer’s country. So even if Netflix is already taxed in the US, Indonesia has the right to levy taxes on Netflix purchases by Indonesian consumers. The digital services tax regulation is embedded in Government Regulation in Lieu of Law No. 1/2020 on financial system policy during the COVID-19 pandemic. Despite the COVID-19 framing, this tax will likely persist even after the pandemic ends.

The technical details are in Minister of Finance Regulation 48/PMK.03/2020, and this tax is called VAT on Trade through Electronic Systems (PPN PMSE). Collection works similarly to restaurant tax – the Ministry of Finance partners with platforms like Netflix, Amazon, Google, Spotify, etc., so consumers pay the tax to the platform, which then remits it to the government.

Challenges ahead

As data needs are projected to keep rising, network investment must continue. Currently, investment in Indonesia is inefficient, partly because building network infrastructure (towers, cables) is expensive while returns are insufficient to justify the investment. In Australia, the government subsidizes internet infrastructure development and provides access to internet providers. The logic is similar to building toll roads: the government builds the road, and all vehicle owners can access it by paying a fee. At least the sunk cost and fixed cost can be reduced. Indonesia has the Palapa Ring, but I’m not yet clear on how it’s being utilized.

The challenge isn’t just about speeding up and expanding internet access, but also about even distribution. You’ve certainly heard how the quality of online schooling varies enormously due to vastly different network quality. Some students enjoy smooth video and ample bandwidth, while others can barely hear audio. We just got 4G, and 5G is already on the horizon. Combined with heavy reliance on private investment for network infrastructure, private companies respond to market incentives. They must choose between upgrading networks in major cities or extending coverage to smaller towns. Being private companies, they make decisions based on profit. This could widen the internet access gap even further.

Next is the challenge specific to e-commerce growth without corresponding growth in production capacity. We discussed how demand-side drivers (market size) can stimulate supply-side responses through foreign investment. But without complementary supply-side improvements such as business climate and investment conditions and workforce quality, we’ll fall behind on production. E-commerce is merely a market facilitator – production can come from anywhere. If domestic production can’t compete, the result is more imports.

The Jakarta Post in February 2019 quoted the Finance Minister saying that e-commerce product imports grew 9.11 percent in 2019, putting pressure on the trade balance – something the Indonesian government is deeply concerned about.

The Finance Ministry’s Customs and Excise Directorate General recorded that the contribution of import duties from e-commerce products to state revenue was recorded at Rp 1.19 trillion ($84.65 million) last year, about Rp 99.2 billion per month on average. However, the total import of e-commerce products reached US$17.17 billion in 2018 or 9.11 percent higher than the previous year’s figure. Meanwhile, the country’s trade deficit was recorded at US$8.57 in 2018.

E-commerce enables individual purchases, creating a phenomenon called ‘small package in large numbers’. You may recall that Indonesia previously had a duty-free threshold of $75 for imports. In the past, retailers bought in bulk. Thanks to e-commerce, purchases are now individual. As a result, many small packages valued under $75 entered Indonesia. Total imports didn’t decrease, but proportional customs revenue fell short. This prompted the government to revise the duty-free threshold down to $3.

Finally, there are challenges around data security and privacy. It’s no secret that data security and privacy are increasingly important worldwide. Indonesia experienced 88 million cyberattacks in Q1 2020. We’ve also heard multiple times about platforms being breached by hackers, including incidents involving the Indonesian government.

Better policy

Beyond challenges like infrastructure and human resources, the government needs to improve its ability to regulate this sector. Fintech regulation, the industrial relationship between online platforms and driver-partners, cloud computing, and Industry 4.0 all need clarity and consistent enforcement. Then there’s data privacy and security, which remains contentious not just in Indonesia but globally. We also have issues with rampant hoax dissemination and the controversial enforcement of the ITE Law.

The digital sector emerged suddenly and has grown extremely fast – faster than our ability to adapt. Fortunately, with the enormous hype around the digital economy, an increasing number of talented thinkers are helping the government formulate sound policy. Now it’s just about execution.

(((just)))

Teresa C. Fort, Technology and Production Fragmentation: Domestic versus Foreign Sourcing, The Review of Economic Studies, Volume 84, Issue 2, April 2017, Pages 650-687, https://doi.org/10.1093/restud/rdw057 ↩︎
assuming their claims are accurate. ↩︎

Comparative advantage untuk vaksin

Thu, 04 Feb 2021 22:04:07 +1100

Ekspor sebagai strategi economic recovery pasca COVID-19

Krisna Gupta

IDM Coop: Badai Pandemi, Vaksinasi dan Harapan Pemulihan Ekonomi

Sabtu, 27 Februari 2021

Siapa saya?

Mahasiswa doktoral di Crawford School of Public Policy, Australian National University
Lebih lanjut tentang saya klik s.id/krisnagupta

Hari ini

Revealed Comparative advantage (RCA)
Statistik ekspor dan impor
Approach pemerintah

Slide ini

dapat diakses di s.id/krisnaidm

Kecepatan vaksin perlu ditingkatkan

Sumber: Our World in Data

GDP Terkendali

Data Weekly tracker dari OECD menunjukkan Indonesia tidak cukup dalam terkoreksi, namun recovery sepertinya masih butuh waktu.

Recovery dari ekspor

Indonesia dan negara ASEAN lainnya mengandalkan ekspor untuk recovery AFC 1998. Bedanya: COVID-19 adalah krisis global, tidak terjadi depresiasi rupiah yang dalam. Seiring dengan memulihnya kasus COVID-19, stimulus di negara tujuan ekspor bisa jadi peluang.

Revealed Comparative Advantage (RCA)

[RCA] adalah salah satu metrik untuk Ekspor andalan suatu negara
RCA menghitung besarnya kontribusi suatu negara terhadap produksi suatu komoditas
- Suatu negara dikatakan unggul jika $RCA\geq1$
- definisi formal ada di slide 18
Tabel agregasi yang digunakan di presentasi ini dapat dilihat di slide terakhir

RCA

Indonesia memiliki keunggulan di komoditas. Vegetable didorong oleh Crude Palm Oil (CPO), sementara mineral terpengaruh kebijakan lartas yang ramai sejak 2009-an.

RCA

Selain komoditas kayu, Indonesia juga unggul di industri kertas, tekstil dan persepatuan. Angka setelah 2016 cenderung stagnan. Barang modal sepertinya masih kurang kompetitif.

Recovery dengan ekspor

Ekspor adalah jalan keluar yang cukup baik:
- karena dapat mengamankan cadangan devisa;
- permintaan LN yang tinggi akan komoditas;
- cepat.
Bagaimana dengan kinerja ekspor belakangan ini?

Ekspor

Dominasi CPO, migas dan batubara sangat terlihat. CPO bahkan tumbuh di 2020 dibandingkan dengan 2019. Tahun-tahun ini juga saat dimulainya larangan ekspor mineral.

Ekspor

Sebagian besar komoditas melemah di 2020. Ekspor mobil sangat terdampak, karena itu pemerintah mencoba memanfaatkan pasar domestik melalui pembebasan pajak. Sementara ekspor metal meningkat seiring peningkatan kapasitas smelter.

RCA bukan segalanya

RCA selalu berubah
Indonesia sedang berstrategi meningkatkan nilai tambah dalam negeri.
- Mineral $\Rightarrow$ smelter $\Rightarrow$ baterai listrik $\Rightarrow$ mobil listrik
- turunan CPO
minyak, gas dan batubara tidak selamanya bisa diandalkan seiring target zero carbon emission dari berbagai negara.
Memanfaatkan GVC adalah (mungkin) cara terbaik
- ekspektasi impor meningkat
- berapa lama?

Impor

Impor cenderung stagnan. Menurunnya permintaan energi merupakan dampak menurunnya mobilitas dan aktivitas manufaktur.

Impor

Penurunan impor barang modal (mchinery & electronics) dan bahan baku (terutama baja) juga indikator melemahnya produksi

Apakah GVC solusi?

GVC dapat meningkatkan impor karena untuk kompetitif di pasar global, perusahaan harus mendapatkan akses bahan baku yang juga kompetitif.
Banyak bahan baku murah harus dipasok dari luar negeri:
- kedelai untuk tahu, tempe dan pakan ternak;
- besi baja dan bahan kimia;
- serat sintetis, dll
Larangan ekspor mineral butuh waktu untuk dituai
- investasi industri sel baterai butuh waktu
- bagaimana dengan kapasitas fiskal?

Strategi pemerintah

Permintaan dunia yang tinggi akan komoditas meberikan peluang. Pemerintah perlu mencermati kebijakan lartasnya.
PP turunan UU Cipta Kerja berorientasi ke penambahan nilai tambah dalam negeri dan investasi asing:
- berpeluang menekan neraca pembayaran.
- berapa lama sampai menuai hasil?
Dukungan fiskal yang cukup dapat menjadi solusi jangka pendek, sambil memantau peningkatan daya saing industri dalam negeri.
Waspada bahaya retaliasi: mineral dapat dibeli dari negara lain. Retaliasi berpotensi membatasi skala ekonomis.

Definisi formal RCA dan sumber data

Data RCA diambil dari UNCTAD
Definisi RCA menurut UNCTAD:

$$ RCA_{Ai}=\frac{\frac{X_{Ai}}{\sum_{j \in P} X_{Aj}}}{\frac{X_{wi}}{\sum_{j \in P} X_{wj} }} \geq 1 $$

RCA negara $A$ untuk barang $i$ tergantung pentingnya ekspor barang $i$ untuk negara $A$, dibandingkan dengan pentingnya barang $i$ di seluruh dunia $w$.

Klasifikasi

Kembali ke slide 8

Economics

Thu, 04 Feb 2021 22:04:07 +1100

Economics

BE4158

First meeting

IBE at Petra

Krisna Gupta
Monday, February $8^{th}$ 2021

Today’s content ☀

housekeeping
about studying economics
some main principles

Who am I? 👀

A PhD student of Economics at Crawford School of Public Policy, Australian National University (ANU).
Research about trade and industry, in particular how trade and investment policy shape Indonesian manufacturing industry.
Ocassionally in online portals and personal
An ocassional gamer, campsite musician and sometimes .

About the content of BE4158 📚

This course is based on .
covered are principle of microeconomics:
- Supply and demand
- the market and its limitation
- strategic thinking
and macroeconomics:
- GDP, inflation, unemployment, the government
- What does it mean for business
The content is very similar with Mankiw (2009) available at your .
books can be useful but are not required.

Some expectations from student

No prior knowledge is required.
Please be active and ask questions.
Good internet connection is assumed.
Good english is assumed and expected to be used thoroughly.
No cheating! Especially during exams.

Some rules

The lectures will be about 1.5 - 2 hours length.
I expect all students to pay attention.
- I would greatly appreciate you turning on your cams.
There will be a small quizzes in the middle of each class.
- make sure you pay attention.
You are welcome to go off cam and AFK at any time you like.
Feel free to interrupt my class with questions.
- alternatively, you can use chat if you are shy, or ask me later thru e-mail.

Structure of this course

week 1: introduction
- housekeeping
- some principles
week 2: supply and demand
- supply and demand curves
- perfect market equilibrium
week 3: Meddling with the market
- tax and quantity control
- price elasticity
week 4: firm problem
- production function
- marginal cost and average cost
- perfect competition

Structure of this course

week 5: Monopoly & oligopoly
- price discrimination
- game theory intro
week 6: Externalities & public goods
- deviation from perfect market
- policies that work
week 7: Welfare
- Poverty & inequality
- firms under different welfare setting

*** MIDTERM EXAM ***

Structure of this course

week 8: Macro basics
- accounting and growth
- inflation and international imbalances
week 9: GDP, inflation and unemployment
- Measuring economy and price
- unemployment
week 12: Long run growth
- Dynamics of economic growth
- Aggregate demand and supply
week 11: Fiscal policy
- demand shock
- the state budget

Structure of this course

week 12: Financial system
- Banks and money
- The central bank
week 13: Monetary policy
- interest rate
- central bank instruments
week 14: international economies
- trade policy
- international capital and exchange rate

Exams etc ✍🏾

evaluation	covers	%
quiz	everyday, best of 10	20
midterm exam	first 7 meetings	40
final exam	last 7 meetings	40

Only your best 10 will be used for graded quiz out of 14 meetings.
- the quiz will be given by the end of every lecture.
A bit of an intern quiz in the middle of each meeting, ungraded.
Slides should be enough to get a good grade.
However, to get an A, you should master the principle.

Some tools

All course materials are accessible through lentera and in pdf.
All announcement will be posted on lentera and google classroom.
- I believe it will patch through your Petra e-mail as well.
- Feel free to let me know what’s best for your learning.
The course is also available at my website, .
- It is in html format so you don’t have to download.
- Also can be viewed on your phone.
I will update the course on a weekly basis.

Some tools

The mid-lecture pop quiz (ungraded) will be conducted via poll everywhere.
- Please prepare a tab in your browser to
- You can also use your smartphone’s browser.
The tool for the end-lecture quiz (graded) is still being explored.
- we will test by the end of today’s lecture.
- We can also use google form.
- You are free to let me know your preference.

Questions?

A bit about you

Now let’s appoint a class rep!

for now, class rep’s responsibility is to just administering attendance.
any student is allowed to contact me directly.

About economics

Why economics?

Contrary to what normal people think, economics is not about (just) money.
Economics is all about making optimal decision under scarcity.
We use math principle to optimise our decision making.
This course is not going to dig too deep into the world of economic principles.
This course does not provide answers to your question: It teach how to use economic framework to shape your question.

Some questions asked by economists

How to best tackle climate change?
Is minimum wage good?
Should we introduce tax holiday to firms?
Should the government increase expenditure by more tax or more debt?
Should we introduce Universal Basic Income?
How to evaluate government program?
Should we ban mineral export?
can we sustain economic growth?
what to do during economic crisis?

Principles of Economics 💰

People make choices

because resources are scarce

resource can be anything you use to produce.
Money is the most mainstream limited resource.
- Buy only one holiday package.
- Buy only one portion of nasi goreng.
But there are other resources worth noting.
- There’s only one holiday season in a year.
- Two nasi gorengs would be too much for my stomach.

The true cost of something is its opportunity cost

money is just one type of cost.
The true cost of taking a holiday to Hong Kong is going somewhere else.
Your holiday can be spent on learning new stuff.
What is the cost of doing your degree?
- Going straight to work and get experiences.
- Doing other degrees (biology, chemistry, etc).
- enjoy life.
essentially, opportunity cost of something is something else you gave up to get it.

Pop quiz

Thinking at the margin

The principle of opportunity cost leads to a marginal thinking.
Understand your status quo, and think what action you can do to add a little improvement from it.
If you have two hours to study or play, how will you divide it?
- what if there’s an exam tommorow?
- If you are tired of studying, would you study more?

Specialisation and gains from trade

During your daily time, you make decisions on allocating how much time to study and to cook, among other things.
Since you will die if you don’t eat, you can’t allocate 0 hour to cook.
On the other hand, there are those who are very good cook and will cook for you for a little money.
In this case, it may be better if you spend 0 hour to cook, and order food from these chefs.
Trade is the reason why I can study economics: I don’t have to study how to be a doctor. I will just go to the hospital if I need a medical service.

Specialisation and gains from trade

Extending to firm level, many outsource part of their business from a third party.
- buying office probably better than developing our own softwares.
- why make our own server if we can use ?
- Use tokopedia instead of making your own e-store.
We can easily extend this to a country level:
- Japan makes car because they cannot plant palm oil.

Incentives matter

People move to cities because there are more opportunities there.
People will buy cheaper products given the same price.
incentives are not always about money:
- You study more on the subjects you like.

“ada gula ada semut”

Market and intervention

often times, market incentives are enough.
However, intervention is often used.
- education is an example where government intervene heavily.
- Gojek used subsidy to incentivies drivers and users.
Intervention may be temporary.
- The COVID-19 relieve bill (PEN) will likely stop after the pandemic.
- Gojek’s subsidy is removed once they reach economies of scale.

Things tend to move to equilibrium

When there is an incentive to do something, there will be more and more people exploit it.
As more people do it, the incentive decreases to a point where it is not worth it anymore.
examples:
- City becomes more crowded (and costly to live in).
- Stock price stops growing few moments after its IPO.
- Countries’ economic growth reduced after they get rich.
Movement towards equilibrium, however, varies.
- short run vs long run.
- individual vs aggregate.

Conclusion

We will see how these principles come up again and again as we delve deeper into the course.
The principle will be carried over to your overall degree, and life in general.
I am not trying to make you an economist, but the principles are very useful.

Testing Graded quiz

Housekeeping!

Joy is the class rep.
We’ll use Zoom over Google Meet.
Quizizz is used for everyday lecture.
what else?

Next week: Supply and demand

Electric vehicle production hub: investment driven by export bans?

Sat, 16 Jan 2021 14:53:17 +1100

Recently, Indonesia has been buzzing about the MoU between Indonesia and LG Energy Solution, a South Korean battery cell manufacturer. The deal roughly entails LG Energy Solution making a massive upstream-to-downstream investment in battery cell production in Indonesia. My colleague Wishnu Mahraddika and I wrote about this at The Conversation Indonesia. We were fairly optimistic about this development and hoped it would become a momentum for the automotive industry in Indonesia, particularly in electric vehicles.

Investment Driven by Trade Restrictions

What I did not discuss there in depth is the critical role of the nickel export ban. In recent years, the Indonesian government has been tightening nickel ore trade in order to increase domestic value-added. This has made it difficult for manufacturers outside Indonesia to source nickel. Investors who need nickel are effectively forced to invest in Indonesia.

But an export ban on minerals is not without problems. In the short run, the restriction has led to reduced investment and employment in the downstream sector. Nickel selling prices are regulated by the Ministry of Energy and Mineral Resources to maintain smelter economies of scale, resulting in selling prices below international levels. This price regulation and oversight process is considered ineffective due to lax enforcement.

Below are LME nickel prices, which I pulled from IMF Cross Country Macroeconomic Statistics via Quandl.

import quandl
import datetime
quandl.ApiConfig.api_key = 'vDm7zkSvztuywGuZuuvY'
mydata = quandl.get('ODA/PNICK_USD', start_date="2010-01-31", paginate=True)
mydata = mydata.reset_index()
sns.lineplot(data=mydata,x='Date',y='Value')
plt.axvline(x=pd.to_datetime("2013-12-31"),color='grey')
plt.axvline(x=pd.to_datetime("2016-12-31"),color='grey')
plt.axvline(x=pd.to_datetime("2019-12-31"),color='grey')
plt.text(pd.to_datetime("2014-02-28"),25000,'ban',fontsize=14)
plt.text(pd.to_datetime("2017-02-28"),22500,'relaxation',fontsize=14)
plt.text(pd.to_datetime("2020-02-28"),25000,'ban',fontsize=14)
plt.title('Nickel; melting grade; LME spot price;\nCIF European ports; US$ per metric ton\nIMF Cross Country Macroeconomic Statistics via Nasdaq data link')
plt.ylabel('US$')

Text(0, 0.5, 'US$')

As China’s performance improves and several countries see declining COVID-19 cases, combined with Joe Biden’s plans to boost U.S. domestic demand, demand for Indonesia’s export commodities is likely to rise. Some observers even predict another commodity price boom. We have already felt the impact of rising soybean prices, which has angered tempeh producers.

This restriction somewhat reduces the potential for post-COVID-19 export-led recovery. As long as downstream nickel industries lack export capacity, the restriction will put pressure on the balance of payments. This short-term sacrifice may pay off if battery cell and electric vehicle exports can significantly boost the trade surplus in the future.

Retaliation

Of course, the main problem with relying on exports is retaliation. This morning, the Twitter account of the Ministry of Trade (@Kemendag) tweeted that the European Union has filed a protest through the World Trade Organization (WTO). This is because Indonesia’s nickel export ban disrupts supply chains in Europe.

RI's new Trade Minister M Lutfi said he welcomes EU's appeal for @wto to rule in its favour on the EU-Indonesia nickel dispute (DS592).
— Kemendag (@Kemendag) January 15, 2021

This is just the initial filing. Other countries may join in supporting the EU in this case. If Indonesia loses, it will have to revise the nickel ban. This would impact investors who have already built smelters and other nickel processing facilities, since reopening nickel exports would push domestic nickel prices up, disrupting their return-on-investment calculations.

We also must not forget that nickel, while very important, is only part of what electric vehicles need. EV production still requires several products that Indonesia does not currently have. One can imagine how damaging retaliation would be to the entire supply chain.

China tried a similar export ban. In 2010, China blocked exports of rare-earth metals. A 2014 WTO ruling found that China had violated WTO rules and the ban had to be revised.

The WTO is currently weakened and it is unclear how long the EU-Indonesia case will take to resolve. But this could lead to retaliation. The EU could potentially ban or tax battery cell or EV exports from Indonesia. This could somewhat limit Indonesia’s ability to explore global markets.

Relying on trade restrictions is generally not the best way to attract investment. Primarily because trade restrictions are highly distortive and can make the economy inefficient. We would naturally prefer investment driven by efficient bureaucracy, legal certainty, productive workers, and so on. But unfortunately, using trade restrictions seems to be a much easier and faster option.

Staying Optimistic

But let us stay optimistic. This investment may generate trickle-down effects for other industries and the SME sector, as the government hopes. We also need to see whether the Omnibus Law (UU CK) succeeds in reforming Indonesia’s business environment to be more efficient and legally certain. The impact on supply chains has certainly been carefully considered by the government before issuing this policy.

Of course, we must be optimistic that the Ministry of Trade will successfully resolve this issue. Indonesia now has RCEP too, which may have its own mechanisms eventually. Even though we are forgoing some export-led recovery potential, hopefully it will all pay off with a thriving EV industry in the future. A healthy level of skepticism should be maintained, but of course we must all remain optimistic!

Update

Tesla is reportedly more sensitive to ESG considerations, while Indonesia’s ESG practices appear to be poor.
Although nickel investment dipped temporarily, it appears to be climbing again. This coincides with a sharpening rise in foreign investment in the base metals industry. Batteries (classified under KBLI 27 “electrical equipment”) have not yet shown a significant increase.

# sns.set_theme(style="white", palette="bright")
dat=pd.read_csv('investasi_nikel.csv',parse_dates=['tahun'])
sns.lineplot(data=dat,x="tahun",y="jutaUSD",hue="industri",style="industri",linewidth=2)
plt.title('Figure 1. Investment realization in mining,\nbase metals, and electrical equipment (BKPM)\n',fontsize=16)
plt.ylabel('Million USD',fontsize=14)
plt.xlabel('Year',fontsize=14)
plt.xlim(pd.to_datetime("2009"),)
plt.legend(labels=['Mining', 'Base metals', 'Electrical equipment'],title='Industry type (2-digit KBLI)',
 bbox_to_anchor=(0.95,-0.1),ncol=3,fontsize=14)
plt.ticklabel_format(style='plain', axis='y')
plt.axvline(x=pd.to_datetime("2014"),color='grey')
plt.axvline(x=pd.to_datetime("2017"),color='grey')
plt.axvline(x=pd.to_datetime("2020"),color='grey')
plt.text(pd.to_datetime("2012"),4e6,'ban',fontsize=14)
plt.text(pd.to_datetime("2015"),5e6,'relaxation',fontsize=14)
plt.text(pd.to_datetime("2018"),6.5e6,'ban',fontsize=14)

Text(2018-01-01 00:00:00, 6500000.0, 'ban')

Mengapa tren kendaraan listrik adalah momentum transformasi industri otomotif Indonesia

Fri, 15 Jan 2021 13:59:45 +1100

Mengapa tren kendaraan listrik adalah momentum transformasi industri otomotif Indonesia

Krisna Gupta, Australian National University and Wishnu Mahraddika, Australian National University

Tren mobil listrik mulai datang dan Indonesia berada di waktu serta tempat yang tepat.

Indonesia memiliki cadangan nikel terbesar di dunia, yang merupakan salah satu bahan baku pembuatan baterai untuk mobil listrik. Jumlahnya kurang lebih 21 juta ton atau 25% cadangan nikel dunia.

Investasi pun mulai berdatangan. Desember lalu pemerintah Indonesia telah menandatangani kesepakatan kerja sama dengan perusahaan asal Korea Selatan, LG Energy Solution untuk investasi senilai Rp 130 triliun untuk penambangan bahan baku dan pembuatan sel baterai.

Untuk itu pemerintah harus bisa memanfaatkan kesempatan ini untuk melakukan transformasi industri manufaktur. Momen ini juga perlu dijaga dan ditindaklanjuti dengan kebijakan yang memudahkan bisnis dan membuat Indonesia menjadi bagian dari Global Value Chain atau rantai pasok global, saat komponen produksi Indonesia digunakan di negara lain.

Dampak positif industri mobil listrik

Investasi sel baterai merupakan bagian dari keinginan pemerintah membangun pusat industri mobil listrik di Indonesia.

Pemerintah berencana membuat Indonesia menjadi negara pertama dengan industri hulu sampai hilir untuk baterai mobil listrik. Jadi mulai dari penambangan bahan baku untuk baterai sampai ke pembuatan baterai mobil listrik itu terjadi di Indonesia.

Sebuah laporan menyatakan LG Energy Solution tengah menggarap kerja sama dengan Hyundai untuk menjadi pemasok utama baterai listrik untuk mobil buatan Hyundai.

Saat ini, Hyundai Motor sedang membangun pabrik mobil senilai Rp 21 triliun di kawasan Deltamas, Bekasi, Jawa Barat dengan kapasitas produksi hingga 250.000 mobil per tahun, termasuk kendaraan listrik.

Pabrik ini akan mulai beroperasi secara komersial pada akhir 2021 dan menjadi pabrik Hyundai terbesar di kawasan ASEAN.

Investasi dari hulu ke hilir ini akan memiliki dampak yang sangat positif bagi perekonomian Indonesia. Pabrik Hyundai Motor saja akan mempekerjakan 23.000 orang untuk pabrik barunya.

Indonesia memiliki peluang yang sangat tinggi untuk meningkatkan nilai tambah barang tambangnya. Seiring dengan kebutuhan industri sel baterai untuk mengurangi kandungan kobalt yang telah menjadi terlalu mahal, peran nikel akan semakin penting di dalam industri mobil listrik.

Dengan cadangan nikel yang besar, Indonesia memiliki daya tarik yang tinggi bagi produsen sel baterai dan mobil listrik global.

Kedatangan investor-investor ini seolah menjawab keinginan Indonesia untuk melakukan transformasi ekonomi melalui investasi asing dan peningkatan nilai ekspor.

Indonesia tidak hanya memerlukan investasi asing untuk mendapatkan kapital atau modal untuk pembangunan di dalam negeri, namun juga menggunakan teknologi mereka.

Dorongan untuk meningkatkan nilai ekspor dari nikel ke sel baterai dan bahkan mobil listrik, akan membantu mengamankan nilai tukar rupiah dan neraca pembayaran atau transaksi antar negara.

Memanfaatkan pasar dunia

Untuk menaikkan posisi Indonesia di dalam rantai pasok global, pemerintah harus memastikan bahan baku lain untuk industri mobil listrik dapat dipasok dengan cepat, mudah dan murah. Ini penting untuk menjaga produk Indonesia tetap kompetitif di pasar global.

Belajar dari permasalahan di otomotif konvensional, kebutuhan bahan baku adalah salah satu permasalahan yang mengakibatkan Indonesia kalah bersaing dibandingkan Thailand.

Industri sel baterai dan mobil listrik memiliki rantai pasok yang lebih kompleks dibandingkan otomotif konvensional. Ini karena beberapa kebutuhan penting seperti semikonduktor atau bahan penghantar listrik yang tidak dapat dipasok dari dalam negeri saat ini.

Pada saat yang sama, Indonesia harus bisa menangkap permintaan global untuk mobil listrik karena permintaannya akan lebih besar jika dibandingkan dengan pasar domestik.

Saat ini harga mobil listrik masih relatif mahal dan menjadi pangsa pasar segmen premium atau menengah atas di Indonesia.

Sebagai contoh PT Hyundai Motors Indonesia (HMI) resmi menjual dua mobil listriknya, yakni Hyundai Ioniq EV dan Hyundai Kona EV, yang dibanderol dalam rentang harga Rp 600 jutaan, sementara mayoritas daya beli masyarakat masih untuk mobil di bawah Rp 250 juta.

Selain itu meningkatkan ketersediaan listrik dan stasiun pengisi daya bukan perkara mudah. Program konversi Bahan Bakar Gas (BBG) dapat menjadi pengingat betapa sulitnya membangun infrastruktur kendaraan secara menyeluruh.

Memanfaatkan bahan baku dan permintaan global artinya terlibat lebih jauh ke dalam rantai pasok global.

Untuk itu kuncinya adalah pemanfaatan perjanjian dagang.

Indonesia saat ini terlibat dalam berbagai perjanjian dagang penting, salah satunya adalah Regional Comprehensive Economic Cooperation (RCEP) atau Kemitraan Ekonomi Komprehensif Regional.

RCEP memiliki konsep Rule of Origin aturan asal barang untuk impor yang sangat fleksibel dan memungkinkan Indonesia memanfaatkan pasar RCEP secara efektif.

Untuk menangkap peluang ini, pemerintah harus memastikan seluruh kementerian terkait untuk terlibat melihat kembali peraturan seperti harmonisasi atau penyamaan standar teknis dan tingkat kandungan lokal.

Krisna Gupta, PhD Student at The Australian National University, Australian National University and Wishnu Mahraddika, PhD Scholar, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

A small trip to Tidbinbilla Natural Reserve in Canberra, Australia

Fri, 01 Jan 2021 09:04:02 +1100

Traveling isn’t really my forte. However, The year-end free entry to Tidbinbilla Natural Reserve can’t really be missed can’t it?

Tidbinbilla is a natural reserve in Canberra, Australia. Reachable from the city center by car, and you would prolly need around 40 minutes driving to get there. Here, you can walk in many bushwalking trail from the very easy, 1-2 km to the hard one with rocky and hilly climb. You will be able to see many Australian vegetation like eucalyptus and also its animals like birds, Kangaroos, Wallabies, Koala and Platypus.

Tidbinbilla is a very huge place. To explore all, you will need a whole day and a good prep. Check out its full map over here.

Me and wife only aim for its eucalyptus forest and a place called Sanctuary.

We first come to the visitor center to grab some maps, information and coffee. Maps and infos are necessary since you will prolly get no connection once you step inside the park. From here, you can actually go to a few walking trails to see Emu and Kangaroos. We actually saw both without even going to the trail!

We drive to the sanctuary from the visitor center.

First up, eucalyptus forest

We took the Koala Path, the easier bit.

From here, we went to the sanctuary. Unfortunately, no sight of Platypus 😢

According to the ranger, most animals including Platypus are shy and learn to avoid human. They can only be seen around early in the morning or later afternoon (something like 6pm-ish). Additionally, Platypus are said to be active around spring time. So I might need to go back here later in the different time!

Trying three cool keyboards: Logitech MX Keys, Razer BlackWidow and Razer Huntsman

Tue, 29 Dec 2020 19:31:47 +1100

This semester has been incredibly busy. Lots of work, a for one of my dissertation milestones, and teaching. On the teaching front, I was fortunate that during the pandemic, when the university cut staff heavily, I was among those kept on. The problem is that with fewer hires, I got buried in marking – checking assignments and final exams. Very stressful.

As the semester ended, so came Black Friday + Cyber Monday + Boxing Day – a string of discount days in Australia. I figured, since I rarely splurge, I could treat myself this time.

Among the discounted items, electronics got massive markdowns. Since I had some cash, I decided to try buying a keyboard to replace my current one. I’d been using the , a very sleek and comfortable keyboard. Based on forums, mechanical keyboards are supposedly better for typing because they provide better feedback. You don’t have to press as deep, making them more comfortable for typing. Having never tried typing on a mechanical keyboard, I thought I’d give it a shot.

I tried buying keyboards at , a major electronics retailer in Australia. Prices are fairly competitive, there are many locations, and the Belconnen store lets you try different switches in-person. Best of all, if you don’t like the product after a week or two, you can exchange it for something else. That’s the power of .

Thanks to the exchange policy, I got to try 3 different keyboards. I browsed various switches recommended for typing and bought based on discounts. The first keyboard I tried was the , mainly because I’m a huge Logitech fan. Their products are expensive but genuinely good. Unfortunately, it didn’t suit me. So I switched to a Razer product, the Razer BlackWidow. Still not quite right, so I ended up with the Razer Huntsman. Here are the specs:

Keyboard	Logitech MX Keys	Logitech G512 GX Brown	Razer BlackWidow	Razer Huntsman
Switches	Scissor
Feel	Tactile	Bump	Clicky	Clicky
Actuation force (g)	37.1	50	50	35.5
Actuation travel (mm)	1.26	1.9	1.74	1.56
Total travel (mm)	1.84	4.0	4.0	3.5
typing speed (WPM)	73	68	73	80

Some notes:

Actuation force: how hard you need to press the key. Higher means more finger fatigue during long typing sessions, but also fewer accidental key presses.
Actuation travel: how far you need to press before the keystroke registers. Higher means deeper press required.
Total travel: how far the key can be pressed to the bottom. Higher means deeper.
I tested typing speed using at medium difficulty for 3 minutes. WPM = Words per Minute.
Other figures from , except Logitech specs which are from the .

I’ll give my review and reasons why I didn’t like the Logitech G512 and Razer BlackWidow. I’m currently still trying the Huntsman and hoping it works out. I’ll focus on typing and switches, since switches are the main reason I tried mechanical keyboards. Macros are the most useful feature beyond that. I’m not particularly interested in RGB.

Note that I’m not a keyboard enthusiast. I’m just a regular user who browses around. I read various reviews from Rtings, Tom’s Hardware, etc. and watched YouTube videos from channels like RandomFrankP and Hardware Canucks. For serious reviews, I’d recommend those sources.

Logitech MX Keys

What I love	What I hate
Thin and compact	Programmable only for F1-F12 keys
Great for typing	Expensive
Wireless is superb
Battery lasts forever
Logitech Flow is amazing

In early COVID-19 days in Australia (around early March), my university allowed reallocating part of research student funds to purchase Work From Home (WFH) equipment, since campus was closing and all staff and students were required to work from home. Research funds are typically used for attending academic conferences, which obviously weren’t happening anytime soon due to the pandemic.

I chose the Logitech MX Keys as my home keyboard. No particular reason – I’m just a Logitech fan.

It turned out to be wonderful. The switches feel better than my office keyboard, in my opinion. Several standout features: First, it’s wireless, supporting both a dongle (2.4 GHz) and Bluetooth. The wireless can use a unified dongle, so the keyboard and mouse (also Logitech) share one dongle. Bluetooth can connect to 3 devices – other computers, tablets, and smartphones. Very helpful when I need to type on a tablet or phone. The wireless setup also keeps the desk clean. Switching between the wireless dongle and 3 Bluetooth devices is effortless because there are physical buttons on the keyboard.

Second, Logitech has a technology called Flow. If I copy text on my laptop, I can paste it on my tablet. Super helpful.

Third, the keyboard is very thin, reasonably compact, and has excellent build quality. The desk feels spacious, and hand position is comfortable because the keyboard is thin enough that you don’t need to raise your fingers high. No finger fatigue typing here since the actuation point is short and the force required is minimal.

Logitech G512 GX-Brown

What I love	What I hate
Fairly affordable	Cable is terrible
	The switches
	No macros

This was the first keyboard I bought – my first-ever mechanical keyboard. It was reasonably priced for a mechanical keyboard and on sale at the time. Being my first, I started cheap. Didn’t dare go expensive right away.

The GX-Brown switch is Logitech’s tactile switch without the click sound typical of mechanicals. The keyboard sends a signal when the plates connect upon pressing. Unfortunately, it’s hard to see in this gif I made from images on the Logitech website.

After trying it out, typing with the G512 was tiring and didn’t suit me. My fingers got sore, and my palm area near the pinky ached. I think because I’m used to thin keyboards, I always press all the way to the bottom. But with mechanicals, you’re supposed to hover – you only need to press to the actuation point.

From the table above, this keyboard (and mechanical keyboards in general) requires slightly more force. Probably not noticeable for short typing sessions, but after typing all day, you definitely feel it.

This keyboard is also much taller than the MX Keys. My hand position felt awkward, so I had to buy a fairly tall wrist rest to keep my hands level. Somewhat more comfortable, but still sore during extended typing. The keyboard height is compounded by the tall keycaps and longer travel distance. The differences look small on paper, but over a full day of typing, they add up.

As for speed, my typing speed actually dropped with this one, even though I switched to mechanical for better typing. Already uncomfortable AND slower – a truly bad investment. Luckily I could still exchange it.

I decided to try clicky switches, since supposedly clicky feels better and you can feel the actuation point more clearly, so you don’t have to press as deep.

Razer BlackWidow

What I love	What I hate
Razer Green switch is amazing	Good value for money
Fully programmable
RGB is wild

I exchanged for the Razer BlackWidow. This was my first Razer product. It has clicky switches – they make a clicking sound when pressed. In the gif below from Razer’s official website, you can see that when the key is pressed, a plate hits another plate inside. That’s when the keystroke registers and the computer detects the key press.

You can also see that the plate makes contact without needing to press the keycap all the way to the bottom.

The clicky switch is indeed better than the half-hearted tactile one. Truly satisfying to type with that click-click sound. But this keyboard still wasn’t perfectly comfortable. Fingers still got sore. Based on specs, this keyboard has the same travel distance and actuation force as the GX-Brown. I was probably still bottoming out despite the click feedback.

That said, the click-click sound is genuinely enjoyable. Makes you feel like you’re typing something really important. The downside, of course, is that it’s fairly noisy. Could be annoying in an office. My wife said the sound was noticeable, though not too disruptive – but hard to use while she’s sleeping¹.

This keyboard is fully programmable, so every key can be remapped and the options are extensive: you can set keys to alt, shift, launch programs or websites, and most importantly, store text snippets.

This keyboard is better than the Logitech GX-Brown. In my opinion, the Razer Green Switch is one of the best switches around, especially once you get used to the floating typing style. But there’s a learning curve if you’re coming from thin membrane keyboards. Once you adapt though, it’s great.

Razer Huntsman

What I love	What I hate
Razer Purple switch is amazing	Expensive
Fully programmable
RGB is wild

The last keyboard I tried is the Razer Huntsman. It uses new technology called opto-mechanical. While previous switches use electrical signals from metal plates, this keyboard uses laser to send signals from keyboard to computer. When a key is pressed, the laser connects to its receiver, sending a signal that the key has been pressed.

According to Razer, sending signals via laser makes it faaaaaaaaaar faster than metal plates. But it probably doesn’t make a real difference, especially for an old person like me whose reflexes are already snail-paced.

What drew me to try this is that it has lighter actuation force and shorter travel distance than regular mechanicals, while retaining the same clicky feeling as the Razer Green switch. This should be more comfortable on the fingers for typing, especially for someone just starting with mechanical keyboards.

The clicky feel isn’t quite as satisfying as the BlackWidow, in my opinion. But it’s still nice. More importantly, my hands have been fine so far. Typing feels better here than with the Green switch. Hopefully I’ll stick with this one.

In terms of speed, this switch has given me the fastest typing speed so far (80 WPM), even faster than the MX Keys at 73 WPM. It really is more comfortable to type on. Let’s see how it holds up over the coming weeks. So far, the Huntsman is the most comfortable of the keyboards I’ve tried.

Conclusion

Typing on the MX Keys was already very comfortable. Even though you bottom out every keystroke (it’s so thin), your hands don’t get sore. The wireless features – switching between devices and copy-paste across them – are incredibly useful. The unified receiver keeps the desk clean with just one dongle for keyboard and mouse. Logitech really is the best at making office products.

But typing with clicky-clicky switches brings a joy I can’t quite explain. Mechanical keyboards also have extensive macros that are super useful for storing frequently-used commands and methods for coding. Maybe I just wasn’t used to thick keyboards after years on laptops and the MX Keys. So far I haven’t fully felt the mechanical advantage over MX Keys (aside from the click-clack). Oh, and the RGB grows on you over time.

Update: the Razer Huntsman has become my daily driver. After learning to type properly on a mechanical keyboard, Razer’s clicky switches are genuinely fantastic. My fastest speed is on the Huntsman. It’s lighter than typical clicky mechanical switches, so it’s decent for newcomers. The full programmability is also excellent if you need it. I think Razer has the best software for key programming and macros. I do miss the MX Keys’ versatile wireless capability, but programmability and clicky switches won me over.

Also read about my experience with the Yellow switch on the Razer Blackwidow V3 Mini .

My WFH workspace = the bedroom. Can’t help it – renting a small unit to save money. ↩︎

Mengeksplorasi & mengelaborasi data serta sumber bacaan

Mon, 14 Dec 2020 01:20:32 +1100

Litka

Sun, 13 Dec 2020 23:36:14 +1100

Mengeksplorasi & mengelaborasi

data

serta sumber bacaan

Krisna Gupta ft. Denny Irawan
Dirlitka PPI Dunia
krisna.netlify.app

Siapa tuh Krisna Gupta 👀

Ketua Komisi Ekonomi.

Sering dipanggil Imed.

Economics PhD Student di Australian National University.

Double degree Master in Econ dari UI / VU Amsterdam.

Meneliti dampak kebijakan perdagangan dan investasi.

Dosen Politeknik APP Jakarta.

Hobby ngeblog, nyanyi, ngefans L’arcenCiel.

beberapa riset dan publikasi 😎

Tentang hari ini 👍

literatur
Data
Diskusi

Literatur

kata kunci: relevan

Untuk menulis 10 ribu kata, anda harus membaca 100 ribu kata!

$$\max_{B,T,S} \{f(B,T,S): B+T+S \leq 24\}$$ $B>T>S$
Baca $\Rightarrow$ Tulis $\Rightarrow$ Speak
JANGAN KEBALIK!
hehe

Kenapa melakukan tinjauan literatur

Kita tidak bisa meneliti sesuatu yang kita tidak tau!
- mendapatkan informasi dan konteks
- mencari gap (target, debat, etc)
Meyakinkan pembaca bahwa tulisan kita relevan:
- terupdate
- penting

Mengenal konteks

Selalu ingat bahwa kita menulis tentang Indonesia.
Karena itu, anda memerlukan pendekatan konteks yang punya ekstra layer:
- Apa SDGs yang relevan.
- Bagaimana perkembangannya di Indonesia.
- Apakah akan tercapai secara sustainable di 2045.

Mulai dari mana?

United Nations (UN)
- ada website yang user friendly.
- ada report (download) hasil kerjasama dengan Bappenas.
Dokumen milik pemerintah.
- Selain Bappenas, lihat juga lembaga terkait.
- Peraturan perundang-undangan yang terkait.
Literatur ternama di bidang masing-masing.

Mulai dari mana?

Wikipedia adalah sahabat kita semua.
- gunakan sebagai pijakan, bukan literatur untuk disitasi.
- berlaku juga untuk informasi seperti blog orang lain.
Tanya rekan sejawat atau editor.

Data

Mengapa kita menggunakan data?

Data dapat menyampaikan konteks dengan efisien.
- pembaca orang sibuk. makanya harus efisien.
Data sangat powerful untuk menyampaikan pesan.
Data menunjukkan seberapa paham kita dengan isu.
Data biasanya cukup obyektif.

Bagaimana menggunakan data?

data bukanlah cerita! Ia memperkuat.
- Data adalah support, bukan inti tulisan anda.
- lebih baik jika sudah ada kerangka berpikir.
Data tidak harus yang berupa statistik.
- bisa juga referensi seperti pasal atau argumen orang lain.
Data harus relevan!
- jangan pasang data yang anda ga pake.
Hati-hati dengan informasi yang partisan.

Beberapa sumber yang dapat digunakan.

ada website yang user friendly.
Website-nya World Bank juga cukup user friendly:
- SDGs
- maupun World Development Indicators
Tanya kolega atau editor.

Tools untuk visualisasi.

Microsoft Excel / Google sheet adalah tool paling gampang dan mainstream.
- jika anda nulis bareng, usahakan pake tools yang semua bisa pake.
Hanya pakai pie chart untuk data fraction.
Gunakan grafik garis untuk menunjukkan trend (biasanya data time series).

Mengambil visualisasi pihak lain.

Jika anda mengambil visualisasi dari publikasi pihak lain, selalu sertakan sumber.
jangan lupa, yang penting adalah data support argumen. Datanya boleh pinjem, tapi argumennya harus punya anda.

Bagaimana jika tidak ada data yang mendukung cerita kita?

Cari terus sampai dapat!
Jika masih tidak ada, be humble! Alim == Pendiam
Bagaimana jika data yang kita dapatkan malah melawan argumen kita?

*then, change your mind*
Menulis adalah proses pendewasaan logika
Makes mistakes is an important part of life

The End

Price trends of Indonesia's export commodities

Fri, 04 Dec 2020 21:43:58 +1100

COVID-19 has certainly hit exports and imports. However, in recent months, Indonesia’s trade balance has been showing increasing signs of surplus. I often explain that Indonesian manufacturing is heavily linked to Global Value Chains (GVCs), so imports are needed for production and exports. Of course, amid COVID-19, production appears to have been hampered, and Indonesia’s imports of semi-finished goods and capital seem to have declined. The government also dislikes imports, especially expensive capital imports that erode the rupiah.

Now, if you want exports only and don’t like lots of imports, the best bet is commodity exports. Coal, CPO, and other mining products can simply be exported without complex production processes or value-added from imports. Indonesia is indeed very resource-rich and has often depended on commodity exports, from petroleum (a long time ago), to coal and other minerals, to the current star: CPO.

As China recovers and Western governments like the US and EU maintain high levels of support, demand for these goods keeps improving. This is reflected in the rising prices of commodities in the market. Below are some visualizations of key Indonesian commodity prices sourced from IMF.

The red vertical line marks late February 2020 / early March 2020, when COVID-19 was first detected in Indonesia.

One important note about coal: its trend had been declining recently. This aligns with the world moving away from coal toward renewable energy. With China also jumping on the decarbonization bandwagon, relying on coal may not be sustainable going forward, and Indonesia needs to find new ways to maintain a stable current account.

Next time I’ll update with export volumes too, not just prices. For now, this is what we have.

<matplotlib.lines.Line2D at 0x1fcd1b7c610>

<matplotlib.lines.Line2D at 0x1fcd1c6c3d0>

<matplotlib.lines.Line2D at 0x1fcd1bf0580>

<matplotlib.lines.Line2D at 0x1fcd1e74100>

<matplotlib.lines.Line2D at 0x1fcd1d56430>

<matplotlib.lines.Line2D at 0x1fcd1db49d0>

Kebijakan untuk meningkatkan partisipasi perusahaan-perusahaan Indonesia dalam rantai pasok global

Fri, 04 Dec 2020 21:34:47 +1100

Selengkapnya lihat di sini

The heterogenous impact of tariff and NTM on total factor productivity of Indonesian firms

Fri, 04 Dec 2020 21:12:42 +1100

Artikel ini telah diterima untuk diterbitkan di Bulletin of Indonesian Economic Studies (BIES) dan sedang mengalami proses edit. Artikel versi pre-edit dapat diakses di sini. Artikel ini telah dipresentasikan di Economics PhD Seminar di Crawford School of Public Policy. Slide presentasi dapat diakses di sini

Revisiting 3-period intertemporal consumption model: work, retire, afterlife

Wed, 02 Dec 2020 10:48:50 +1100

I discussed this theme in my medium blog(https://medium.com/@imedkrisna/extending-a-simple-2-period-consumption-problem-the-third-period-afterlife-9223cf5467d). I recycle this with a bit of an addition to a more generalised utility function and exploiting python this time so I don’t have to do it by hand, which is nice. One reason why I left Medium ha ha ha.

Work and retirement

It started with a standard, very simple undergrad style intertemporal consumption model which demonstrate consumption smoothing

$$ \begin{equation} \max_{C_1,C_2} u(C_1)+\beta u(C_2) \\ \begin{split} s.t.: \ & C_1 = Y - s \\ & C_2 = s(1+r) \end{split} \end{equation} $$

I extend the above model with a third period: the afterlife.

Bekerja, pensiun dan masuk surga.

Modelnya diekstensi jadi:

$$ \begin{equation} \max_{C_1,C_2} u(C_1)+\beta u(C_2)+\delta u(C_3) \\ \begin{split} s.t.: \ & C_1 = Y -s -p \\ & C_2 = s(1+r) \\ & C_3 = p(1+x) \\ & s \geq 0 \end{split} \end{equation} $$

Seperti dapat dilihat, kali ini kita punya 3 $C$, di mana $C_3$ adalah konsumsi di akherat. Ketika masih di periode satu, kita punya opsi tidak hanya menyisihkan pendapatan $Y$ untuk menabung sebanyak $s$, tapi juga beramal sebanyak $p$. Tidak seperti tabungan, amal tersebut tidak dapat kita gunakan untuk konsumsi di periode 2. Akan tetapi, kita akan menikmati hasil amal kita ketika sudah meninggal. Di akherat, kita akan konsumsi sebanyak $p(1+x)$ di mana $x$ adalah seberapa besar amalam kita berlipat ganda di surga nanti.

dengan substitusi, constraintnya bisa disederhanakan menjadi:

$$ C_1 + \frac{C_2}{(1+r)} + \frac{C_3}{1+x} = Y $$

Optimisasinya tidak berbeda jauh dengan dua periode:

$$ \mathcal{L}(C_t)=u(C_1)+\beta u(C_2)+\delta u(C_3) + \lambda (Y - C_1 - \frac{C_2}{(1+r)} - \frac{C_3}{1+x}) \\ \begin{split} F.O.C.: \ & \frac{d\mathcal{L}}{dC_1}=u'(C_1)-\lambda=0 \\ & \frac{d\mathcal{L}}{dC_2}=\beta u'(C_2)-\lambda \frac{1}{(1+r)}=0 \\ & \frac{d\mathcal{L}}{dC_3}=\delta u'(C_3)-\lambda \frac{1}{(1+x)}=0 \end{split} $$

Dari F.O.C. yang pertama didapat:

$$ u'(C_1) = \lambda $$

Ganti semua $\lambda$:

$$ \begin{align*} u'(C_2)&=\frac{u'(C_1)}{\beta (1+r)} \\ u'(C_3)&=\frac{u'(C_1)}{\delta (1+x)} \end{align*} $$

Supaya bisa diitung dengan mudah, kita kasihkan standard CRRA utility function $u(C_t)=\frac{C_t^{1-\gamma}}{1-\gamma}$. Dengan begini, problem 3 periode di atas dapat kita selesaikan. Kita bisa saja selesaikan dengan tidak terlalu sulit menggunakan pensil dan kertas, tapi kali ini mari kita manfaatkan Python untuk menghitung kelanjutan dari persamaan di atas. Pertama definisikan fungsi utilitas dan turunannya:

# kodenya boleh ngopas dari quantecon
def u(c, γ):
 '''utility function'''
 if γ ==1:
 return np.log(c)
 else:
 return c**(1-γ)/(1-γ)

def u_prime(c, γ):
 '''Derivative of utility'''
 if γ == 1:
 return 1 / c
 else:
 return c**(-γ)

Kemudian kita dapat manfaatkan SciPy untuk menghitung persamaan konsumsi 3 periode di atas. Ada beberapa parameter yang digunakan di problem ini, berikut artinya:

$\gamma$ = kadar takut risiko kita $\delta$ = faktor diskon surga. semakin besar, semakin agamis $\beta$ = faktor diskon periode dua. Semakin besar, semakin penyabar. Kalo kecil berarti tipe YOLO. $Y$ = Income / penghasilan semasa muda. $r$ = tingkat suku bunga $z$ = seberapa besar amalan kita dilipat-gandakan oleh Yang Maha Kuasa.

Saya pake parameter-parameter di bawah ini karena iseng aja. Tentu saja anda bisa install python, kopas kodenya dan utik-utik sendiri parameternya sesuai karakteristik anda.

from scipy.optimize import fsolve
γ=2.00001
δ=0.95
β=0.9
Y=150
r=0.1
z=0.1

def func(c):
 return[u_prime(c[1],γ)-u_prime(c[0],γ)/β/(1+r),
 u_prime(c[2],γ)-u_prime(c[0],γ)/δ/(1+z),
 c[0]+c[1]/(1+r)+c[2]/(1+z)-Y]

root=fsolve(func,[1,1,1])
data = [root, u(root,γ),(root/sum(root))]
tabel=pd.DataFrame(data, columns=["Masa muda", "Pensiun","Surga"])
tabel['Variabel']=['C','U','Share']
tabel = tabel[['Variabel', "Masa muda", "Pensiun","Surga"]]
tabel

	Variabel	Masa muda	Pensiun	Surga
0	C	52.931443	52.666122	54.109290
1	U	-0.018891	-0.018987	-0.018480
2	Share	0.331429	0.329767	0.338804

Dengan parameter di atas, dapat dilihat bahwa model ini mengarah ke orang yang sangat-sangat menjunjung tinggi comsumption smoothing. Penghasilan yang ia dapatkan $(Y)$ dibagi rata untuk konsumsi, menabung dan juga beramal. Kombinasi ini memiliki total utility yang so pasti maksimal. Ingat, yang dimaksimalkan bukanlah $u$ untuk masing-masing periode tetapi $u(C_1)+\beta u(C_2)+ \delta u(C_3)$.

Angka ini bisa berubah tidak hanya berdasarkan karakteristik si individu (alias parameter $\beta$ dan $\delta$) tetapi juga tergantung dengan kondisi di luar seperti besarnya tingkat suku bunga $r$. seandainya saya naikin suku bunga, ceteris paribus, kira-kira apa yang akan terjadi?

r=0.4
root=fsolve(func,[1,1,1])
data = [root, u(root,γ),(root/sum(root))]
tabel=pd.DataFrame(data, columns=["Masa muda", "Pensiun","Surga"])
tabel['Variabel']=['C','u','Share']
tabel = tabel[['Variabel', "Masa muda", "Pensiun","Surga"]]
tabel

	Variabel	Masa muda	Pensiun	Surga
0	C	54.922854	61.650715	56.145014
1	u	-0.018206	-0.016220	-0.017810
2	Share	0.317990	0.356943	0.325066

Secara umum semua naik karena kali ini tabungannya menghasilkan lebih banyak uang untuk konsumsi. Namun dapat dilihat bahwa share-nya jadi banyakan tabungan dibandingkan konsumsi (“Masa muda”) ataupun beramal (“Surga”). Hasilnya akan lebih kelihatan jika misalnya kita naikkan suku bunga tapi menurunkan $Y$. Ia akan punya total lifetime income yang sepadan, tapi akan lebih banyak mengalokasikan ke menabung karena dengan suku bunga yang tinggi, opportunity cost dari konsumsi jadi tinggi.

Nah sekarang bagaimana jika ada argumen bahwa amal kita akan dibayarkan dengan berkali lipat bahkan sampai tidak terhingga? Artinya, parameter yang harus kita utik-utik adalah $z$. Yuk dicoba.

z=99
root=fsolve(func,[1,1,1])
data = [root, u(root,γ),(root/sum(root))]
tabel=pd.DataFrame(data, columns=["Masa muda", "Pensiun","Surga"])
tabel['Variabel']=['C','u','Share']
tabel = tabel[['Variabel', "Masa muda", "Pensiun","Surga"]]
tabel

	Variabel	Masa muda	Pensiun	Surga
0	C	78.978540	88.653140	769.778825
1	u	-0.012661	-0.011279	-0.001299
2	Share	0.084252	0.094572	0.821176

Menurut model ini, maka yang terjadi adalah kita akan menghabiskan tabungan kita untuk amal. Seperti anda lihat di tabel di atas, share amal tiba-tiba naik jadi 83% dari total pendapatan. Yang lucu adalah total income yang dihabiskan di dunia jadi ikut naik. Tentu saja ini mustahil ha ha ha. Tapi jelas naiknya gara-gara “bunga” yang didapatkan dari amal. Bukannya mustahil sih. Bisa saja kan amalan kita di dunia dinikmati sebagian ketika kita masih hidup? Makanya guys jangan lupa berbuat baik.

Tentu saja model ini ngaco

Jangan lupa bahwa ini model yang sangat sederhana sekali dan banyak caveat-nya. Saya sudah tuliskan beberapa di blog medium di atas. Bagaimanapun juga, banyak juga peluang untuk memperlebarnya. Misalnya, dibikin $p$ boleh negatif dengan konsekuensi $Y$ naik, yang bisa kita terjemahkan sebagai perilaku korupsi: Berdosa demi meningkatkan pendapatan dan akan dibayar di neraka nanti. Ini cuma satu contoh. Ada banyak aplikasi lain dengan model yang lebih ribet yang bisa kita buat. hahha.

Yaudah segini dulu. semoga tulisan ini menghibur dan menginspirasi anda untuk beramal dan berbuat kebajikan bagi masyarakat!

Statistik ekspor impor Indonesia di 2018 dengan negara-negara RCEP

Sat, 28 Nov 2020 13:39:10 +1100

Just a bit of stats on RCEP. To connect it a bit with the Global Value Chain, I am using Stage of Production (SoP) UNCTAD:

	productcode	isgroup	nomenclaturecode	grouptype	productdescription
27	UNCTAD-SoP1	Yes	H0	Stages-Of-Processing	Raw materials
28	UNCTAD-SoP2	Yes	H0	Stages-Of-Processing	Intermediate goods
29	UNCTAD-SoP3	Yes	H0	Stages-Of-Processing	Consumer goods
30	UNCTAD-SoP4	Yes	H0	Stages-Of-Processing	Capital goods

Just a very long code to aggregate RCEP. Sorry too lazy to make it DRY. Skip these if you don’t need it to go straight to the stats.

The stats is:

	Trade Value	All goods	Raw materials	intermediate goods	Consumption goods	Capital Goods
0	export RCEP	$101,578,213.46	$29,583,083.81	$28,876,371.04	$32,692,137.50	$10,003,962.16
1	export World	$180,215,034.44	$45,863,376.29	$49,419,462.61	$68,578,923.17	$15,547,001.92
2	import RCEP	$125,215,957.00	$7,859,326.12	$40,495,720.49	$33,166,747.09	$42,937,992.48
3	import World	$188,711,171.62	$24,893,192.22	$61,575,534.29	$44,976,972.35	$56,497,488.96

This is, by the way, in 2018. There are RCEP countries in the World cuz i am too lazy to substract them. Also, I think it is useful to see how much Indonesians are trading with the rest of the world.

I wrote once on my blog about how important imports are for Indonesian production. It is indeed reflected in this 2018 stat where the fraction of consumption goods in Indonesian import is just mere:

23.83376244573637 %

which actually a decrease.

As for export, the other way around is true. That is, most exports are consumption goods. Even with this simple stat, it is so hard to convince Indonesian government that it is important to keep a free flow of goods not just export but also imports, to participate in the GVC. Indeed, increasing imports to help exports may seem very counter-intuitive for a country so used to exporting natural resources.

But of course, back to RCEP, what’s important is how big is the fraction of RCEP to Indonesia’s total trade.

	importance	All goods	Raw materials	intermediate goods	Consumption goods	Capital Goods
0	export	0.563650	0.645026	0.584312	0.476708	0.643466
1	import	0.663532	0.315722	0.657659	0.737416	0.759998

And as you can see, RCEP countries are indeed very important to Indonesia’s export and import. more than 70% Indonesia’s consumption goods and capital goods came from RCEP countries in 2018. Notably, Indonesian consumption goods export to RCEP countries is rather small, which makes sense because Indonesia’s export destination for consumption goods also consist of plenty of developed nations like US, which reflects the importance of the extended GSP tariff preference from the US. The small importance of raw materials import is probably due to agriculture stuffs such as beef, soybean and wheat which many come from the US and Latin America.

That’s all for this post and I hope it’s useful.

Kuliah tamu di Universitas Indonesia tentang tinjauan literatur

Mon, 16 Nov 2020 11:44:06 +1500

FDI, industry, and trade policy

Wed, 11 Nov 2020 21:17:11 +1100

This post briefly discusses FDI developments in Indonesia.

FDI Trends Since 1970

The chart below shows Indonesia’s net FDI inflows as a percentage of GDP. The data source is the World Bank’s World Development Indicators.

The chart is accompanied by a narrative from Linblad (2015). According to him, there are at least three major phases in Indonesia’s FDI story: before 1985 (the oil boom era), after 1985 through the crisis, and the reform period.

FDI and high-tech industry

Again using World Development Indicator data, I searched for indicators containing the keyword “high-tech,” and WDI returned these four indicators:

id name
----------------- ------------------------------------------------------------------------------------
NV.MNF.TECH.ZS.UN Medium and high-tech Industry (including construction) (% manufacturing value added)
TX.MNF.TECH.ZS.UN Medium and high-tech exports (% manufactured exports)
TX.VAL.TECH.CD High-technology exports (current US$)
TX.VAL.TECH.MF.ZS High-technology exports (% of manufactured exports)

I use the first one for the visualization below.

There doesn’t seem to be much of a link between FDI and high-tech manufacturing growth. In fact, it almost looks inverted: when FDI rises as a share of GDP, high-tech manufacturing actually declines. Of course, manufacturing also includes construction and CPO processing. Besides, FDI also flows into mining and services like e-commerce and banking. FDI may not be going into high-tech manufacturing at all – it may be going into industries with low high-tech value added. This makes sense: FDI flows into the most profitable industries, and when those industries grow, factors of production shift toward them. High-tech manufacturing growth dropped in 2005 and never recovered. The mid-2000s was the peak of the commodity boom and the era of so-called ‘sunset industries.’

Two Non-Tariff Measures (NTMs): TKDN and Export Ban

TKDN stands for Tingkat Komponen Dalam Negeri (Local Content Requirement). One of the most prominent TKDN cases involves smartphones.

The chart below shows realized FDI (source: BKPM) in the electronics, software, and IT industries. TKDN for smartphones became mandatory in 2016. We can see that FDI in these three industries suddenly surged after 2016.

<Figure size 504x360 with 0 Axes>

Below is the mineral export ban. In 2014, mineral exports were banned, though the ban was relaxed in 2017. Investment in base metals increased while mining of metal ores declined. Investors appear to have shifted into mineral processing due to the raw commodity export ban.

Text(2017-01-01 00:00:00, 300000, 'Relaxed')

How about FDI and industrial growth?

This time I use the World Bank Global Economic Monitor (GEM) to get Indonesia’s industrial production data. Since it’s in constant USD, I convert it to growth rates ($\Delta log$), though this is still not ideal.

id name
--------- -----------------------------------------------
IPTOTNSKD Industrial Production, constant US$
IPTOTSAKD Industrial Production, constant US$, seas. adj.
<function matplotlib.pyplot.show(*args, **kw)>

Industrial growth, as we can see above, was briefly negative in 2006. It has since stagnated at around 4-5%. There doesn’t seem to be any relationship with FDI. Again, it’s possible that FDI isn’t going into the manufacturing sector at all.

Litrev

Fri, 06 Nov 2020 21:03:23 +1100

Tinjauan literatur

Kuliah Tamu Seminar Ekonomi Internasional

Senin, 9 November 2020

Krisna ‘Imed’ Gupta
Australian National University
PhD Candidate in Economics

A bit about me 😃

PhD student at Crawford School of Public Policy, Australian National University. My working thesis is the impact of trade and investment policy to Indonesian firms.
Lecturer at
- Statistics and international finance
Teaching Assistant includes:
- PPIE, UI: Macroeconomics 2, Microeconomics 2
- CBE, ANU: Macro 3, Micro 2, Applied forecasting
Sparetime activity includes blogging, twitter-ranting, casual sports & singing

beberapa riset dan publikasi 😎

Tentang hari ini 👀

Tips and trick
Some tools
diskusi

Silakan gunakan raise hand atau chat jika ingin bertanya di tengah jalan

Slide dapat diakses di ,

pake ini juga bisa $\rightarrow$

First up: a quiz 🤣

please navigate to:

Masukan username anda

Mulai memilih!

Literature Review!

What, Why, How

Literature review?

Peneliti pemula seringkali bingung bagaimana memulai tinjauan literatur
Sebenarnya, tinjauan literatur (dan referencing) adalah skill yang kita perlukan untuk semua jenis pekerjaan!

Literature review is actually an investigation!

Untuk menulis 10 ribu kata, anda harus membaca 100 ribu kata!

$$\max_{B,T,S} \{f(B,T,S): B+T+S \leq 24\}$$ $B>T>S$
Baca $\Rightarrow$ Tulis $\Rightarrow$ Speak
JANGAN KEBALIK!
hehe

Kenapa melakukan tinjauan literatur

Kita tidak bisa meneliti sesuatu yang kita tidak tau!
- mendapatkan konteks
- metode termutakhir
- mencari gap di literatur: metode / konteks
Meyakinkan pembaca bahwa riset kita relevan:
- terupdate
- penting
Bonus: membantu mengarahkan yang baru mau terjun ke bidang kita!

Beberapa kesalahan pemula

Problem	Komentar
Kurang terorganisir	fokus ke isu, bukan paper. Gunakan mindmap dapat membantu
Kurang relevan	Seminal paper biasanya jadul. Penting, tapi fokus ke yang lebih baru
Kurang kritis	Ingat, anda bukan hanya merangkum, tapi juga mencari gap
Takut salah	Mahasiswa adalah tempatnya salah. Yang penting kita belajar dari situ

Mulai dari mana?

Kata kunci-nya adalah relevan!
- meneliti tentang Kazakhstan bagi audience Indonesia mungkin kurang rame
Paling mudah adalah mulai dari isu yang relevan dengan kita:
- Misal, sesuatu yang kita sering dengar (misalnya korupsi daging sapi, omnibus law)
- Bisa juga yang relevan dengan kerjaan kita
Bisa juga mulai dari metode.

Mulai dari mana?

Sebagian besar pertanyaan di bidang ilmu ekonomi adalah tentang hubungan dua variabel:

Does X causes Y?

$$ Y = X\beta + \varepsilon $$

Kita bisa mulai mencari menggunakan kata kunci Y atau X, atau keduanya.

Mulai dari mana?

beberapa pertanyaan yang bisa dijawab, misalnya:
- $Y \Rightarrow X$ atau $X \Rightarrow Y$?
- bagaimana dengan $Z$?
- di Amerika, $X \Rightarrow Y$. Bagaimana dengan di Indonesia?
- apa lagi kira-kira?

Tabel dapat membantu proses berpikir kita

Author	Y	X	sign	context	method
Amiti & Konings 2007	TFP	input tariff	+	Indonesia, firm level	levpet, gravity
Kee, Nicita, Olarreaga 2009	import val	NTM count	+	global	cross-section gravity
Fugazza, Olarreaga, Ugarte 2017	export val	NTM count	+,-	Peru	gravity, firm size
Cadot et al 2015	export val	NTM count, Reg. dist.	+	global	gravity cross-section

Contoh di atas adalah pencarian yang berfokus pada $X$: Bagaimana Non-Tariff Measures (NTMs) mempengaruhi perdagangan dan produktivitas (dan lainnya kalau ada)

Diisi juga kontribusi atau gap

Author	+	-
Amiti & Konings 2007	Jurnal AER, konteks Indonesia, firm level, TFP	Tidak ada NTM
Kee, Nicita, Olarreaga 2009	Seminal utk AVE	Terlalu global dan makro
Fugazza, Olarreaga, Ugarte 2017	heterogenous firm, firm level	konteks Peru, export may not be best measure, no PPML
Cadot et al 2015	Seminal NTM, konsep regulatory distance	Cross section, terlalu makro

Saya dapat berkontribusi di konteks Indonesia, firm level dengan TFP. Trade policy measures adalah tariff dan NTMs. Measures untuk firm level export dapat dipakai untuk robustness. Data ada, metode ada, export lebih baik dimodelkan dengan PPML. Sip!

Mulai dari mana?

Literaturnya literatur: akan ada author(s) yang disebut berkali-kali oleh literatur yang anda sudah temukan.
- Trade context: Krugman, Melitz, Grossman & Helpman, Bernard, Kee, Cadot
- General equilibrium: Hertel, Horridge, McKibbin, Dixon, Costinot
- Indonesian context: Sjoholm, Hill, Aswicahyono, Patunru
Bisa juga tanya dosen

Mulai dari mana?

report lembaga penting seperti IMF, World Bank dan Kementerian/Lembaga.
textbook dan referensinya.
Google (scholar) sangat berguna karena repositori-nya sangat luas. Namun harus agak berhati-hati. Pastikan yang anda sitasi adalah yang peer-reviewed.
Mulai dari wikipedia jika anda bener-bener newb juga boleh. Jangan lupa lihat sitasi yang ada di Wikipedia, jangan Wikipedianya langsung ya.
Jika ragu, tanya pembimbing anda.

Final tips

Tinjauan literatur sangat penting dilakukan meskipun skripsi anda tidak punya bab berjudul “tinjauan literatur”
Baca bab literature review orang lain, dan coba bedah strukturnya
Minta tolong orang lain membaca bab literature review anda (kalau ada)
tanya dosen / pembimbing / teman anda yang pintar

butuh berapa referensi?

Rule of thumb saya: minimal 10 referensi utama di 5 tahun terakhir, tentang metode.
Dari 10 itu, ambil referensi mereka yang paling relevan (cek abstrak).
Gunakan referensi lain untuk setting konteks.
- Misalnya, saya pake untuk pembukaan saja, bukan metodenya.
Tapi jumlahnya kira-kira aja. Sebaiknya tanya pembimbing.

Tools you can use

Mindmap

Beberapa mindmap bisa digunakan, tapi favorit saya adalah connected papers ( )
connected papers akan menunjukkan interkoneksi antar artikel. Cara cepat untuk mengetahui jaringan sitasi di sebuah isu.
ukuran node menunjukkan besarnya sitasi, sementara ketebalan warna menunjukan keterbaruan.
contohnya ketika kita mencari
Menariknya, koneksi berbasis isu juga ditampilkan. Lebih keliatan jika kita cari artikel yang belum ada sitasinya. Misalnya

Menggunakan reference manager

Reference manager adalah piranti lunak yang sangat membantu dalam me-manage daftar pustaka kita.
Dengan Reference manager, kita bisa dengan cepat melakukan sitasi dan membuat daftar pustaka dengan format yang kita inginkan.
Reference manager juga membantu kita mengelompokan dan memasang tag di referensi-referensi kita.
Zotero dan Mendeley termasuk software gratisan yang paling terkenal. Google Scholar Library juga layak coba

Menggunakan google scholar

gunakan AND atau & untuk irisan, dan OR untuk *union*

Indonesia AND Australia # will show only Indonesia & AUstralia included

Indonesia OR Australia # will show results also when only one of them is present

Menggunakan google scholar

gunakan tanda petik untuk mencari satu rangkaian kata

"the case of indonesia"

gunakan author:"nama-author" untuk mencari karya peneliti tersebut

author:"Costinot"

gunakan `..` untuk memberi interval di pencarian kita

Indonesia AND Trade 2010..2018

Menggunakan google scholar

pakai tanda bintang untuk mencari lanjutan. Misalnya, indust* akan mencarikan industri, industry, industrial dan industrialisasi.
gunakan tanda tanya untuk mencari produk yang ejaannya berbeda. Misalnya, labo?r akan menemukan sesuatu yang ada kata labor dan labour
Trik asterisk juga bisa digunakan untuk ejaan berbeda, misalnya globali* akan menemukan globalization dan globalisation
Selengkapnya bisa google wildcard search

Google scholar dan Reference manager

Saya akan menunjukkan bagaimana cara melakukan sitasi langsung dari google scholar!
Sekalian sedikit demonstrasi pake advanced search
Kita mulai dengan mencari

Dan tools terakhir adalah....

No, seriously, your lecturer is your best tool!! Exploit him!!

Kesimpulan

Tinjauan literatur sejatinya adalah investigasi
Kata kuncinya adalah relevan!
Gunakan semua resources yang ada!
Jika bingung, tanya pembimbing

References

Lange, Cheryl. (N.D.). . Studysmarter research series. The University of Western Australia.
Rowland, DR. (N.D.). . The University of Queensland.

Indonesian tariff dynamics, 2000-2018

Thu, 05 Nov 2020 22:26:15 +1100

A couple of weeks ago, a colleague of mine, Deasy Pane from Bappenas, invited me for a discussion about Indonesia’s trade and investment situation policy. Indeed, that’s the beauty of zoom right? Collabs know no boundaries! With us was Donny and Chandra, also trade anthusiast at ANU. Some of Bappenas’ employee were also there.

In fact, one of Deasy’s Bappenas colleague asked us about Indonesian tariff dynamics. Indeed, Indonesian manufacturing import was a bit volatile during 2000s and early 2010s, which could be driven by changes in tariff and Indonesian overall protectionism dynamics. Commodity boom might also a factor here. Generally, protectionism in terms of tariff was declining during this time. However, the dynamics on overall protectionism is not so straight-forward if we include other measures, and in fact even in terms of tariff, it rises a little bit during Jokowi’s presidency.

Additionally, since my last post I was showing how to use World Bank’s API with Python,, might as well shows how to use WITS’ API this time around. To install:

pip install world_trade_data --upgrade

To show tariff regulation (instead of how it might change welfare), I use effective applied simple average tariff and MFN simple average tariff. Here’s what they are:

import pandas as pd
import world_trade_data as wits
tariff=wits.get_indicators('tradestats-tariff')
tariff=tariff.filter(['name','definition'])
tariff=tariff.loc[['AHS-SMPL-AVRG','MFN-SMPL-AVRG']]
#pd.set_option('display.max_colwidth', None)
tariff

	name	definition
indicatorcode
AHS-SMPL-AVRG	AHS Simple Average (%)	Effectively Applied Simple Average tariff (%)
MFN-SMPL-AVRG	MFN Simple Average (%)	Most Favored Nation ( (MFN) Simple Average tar...

Using the API

import pandas as pd
import datetime
#pd.set_option('display.max_rows', 20)
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
pd.options.display.max_rows=999
b=wits.get_products('tradestats-tariff')
b=b.loc[(b.grouptype=='Sector') & (
 b.index!='Total')].productdescription.values

# MFN tariff, weighted average
a=wits.get_indicator(indicator='MFN-SMPL-AVRG',
reporter='IDN',datasource='tradestats-tariff',
 year=['2000','2019'])
a=a.reset_index()
a=a.set_index('ProductCode')
c,d,e=a.loc[b[0:4]],a.loc[b[5:10]],a.loc[b[11:]]
c,d,e=c.reset_index(),d.reset_index(),e.reset_index()
a=a.reset_index()

# Effective applied tariff, weighted average
o=wits.get_indicator(indicator='AHS-SMPL-AVRG',
reporter='IDN',datasource='tradestats-tariff',
 year=['2000','2019'])
o=o.reset_index()
o=o.set_index('ProductCode')
p,q,r=o.loc[b[0:4]],o.loc[b[5:10]],o.loc[b[11:]]
p,q,r=p.reset_index(),q.reset_index(),r.reset_index()
o=o.reset_index()

For some reason, Indonesian tariff is missing in 2014 and 2015. If you see from all of my graph below, you will see a jump from 2013 to 2016. Seems like the API extract the year not as datetime object. It’s a bit of a pain to change it myself cuz then I need to add two NA rows per sectors and I am too lazy to do that.

There are 16 different sectors WITS is giving us, HS-based. I split them into three broader groups so the graph is much more readable. In total, there are 6 graphs in this post. Here’s the sectors:

array(['Animal', 'Vegetable', 'Food Products', 'Minerals', 'Fuels',
'Chemicals', 'Plastic or Rubber', 'Hides and Skins', 'Wood',
'Textiles and Clothing', 'Footwear', 'Stone and Glass', 'Metals',
'Mach and Elec', 'Transportation', 'Miscellaneous'], dtype=object)

Effective Applied Tariff, simple average

Three graphs below are the effective applied tariff. After 2008, Indonesia was very diligent in doing FTAs. Among them are ASEAN FTA driven (AFTA). The AFTA turned to plenty of AFTA+1 in 2010. Among them are AKFTA (with South Korea), ACFTA (China), AANZFTA (Australia and New Zealand), and AIFTA with India. Additionally There’s also IJEPA with Japan, perhaps the only bilateral one Indonesia had before IACEPA comes along.

These FTAs eventually lower effective applied tariff.

# FIGURES FOR AHS
plt.figure(figsize=(9,6))
sns.lineplot(data=p,x='Year',y='Value',hue='ProductCode')
plt.axvline('2016', color='black')
plt.axvline('2008', color='black')

<matplotlib.lines.Line2D at 0x165b71fc790>

Firstly, primary products. You can straight away see a far higher tariff in food producing sector compared to animal, vegetables, and minerals. The GFC clearly increased Indonesian food protectionism (as with other countries during that time). Overall, other sectors aren’t that highly protected by tariff, which hover around 2-4%. Same thing can’t be said for NTMs, however. But that’s for another post.

plt.figure(figsize=(9,6))
sns.lineplot(data=q,x='Year',y='Value',hue='ProductCode')
plt.axvline('2016', color='black')
plt.axvline('2008', color='black')

<matplotlib.lines.Line2D at 0x165bdbb2760>

In some manufacturing, you see a higher tariff. These sectors were highly protected before Asian Financial Crisis (AFC) in 1998. It was lowered after AFC, but also after GFC, interestingly. Indeed, bad times lead to good policy. Another interesting thing is that Indonesia was famed to have a premature deindustrialisation during 2005s if my memory serves. These are times where textiles got more protection, as you can see from the graph.

Lastly, these tariff shot up in 2016 (remember, 2014 and 2015 are missing). What happened? What huge event happened in 2015/2015?

plt.figure(figsize=(9,6))
sns.lineplot(data=r,x='Year',y='Value',hue='ProductCode')
plt.axvline('2016', color='black')
plt.axvline('2008', color='black')

<matplotlib.lines.Line2D at 0x165bdc9ef10>

On this group, it seems like in general tariff are progressing to the lower end. Especially after AFC and GFC, protection given to transportation is stripped up, but also in metals, machinery, electronics, and other equipments. These are also sectors that have large intensity of Global Value Chain (GVC). Make sense to lower the trade cost of these stuff as low as possible. Again, you can see a bit of a jump in 2016.

MFN, simple average

Here’s MFN! In general, we should expect a higher level of tariff using MFN. But the trends are pretty much the same: aside from food products, crisis reduce the overall tariff level. 2016, everything jumped up.

This time, let me hear more from you! Please do comment on this blog or mention me on twitter for a discussion!

# FIGURES FOR AHS
plt.figure(figsize=(9,6))
sns.lineplot(data=c,x='Year',y='Value',hue='ProductCode')
plt.axvline('2016', color='black')
plt.axvline('2008', color='black')

<matplotlib.lines.Line2D at 0x165bdd830a0>

plt.figure(figsize=(9,6))
sns.lineplot(data=d,x='Year',y='Value',hue='ProductCode')
plt.axvline('2016', color='black')
plt.axvline('2008', color='black')

<matplotlib.lines.Line2D at 0x165bdedc280>

plt.figure(figsize=(9,6))
sns.lineplot(data=e,x='Year',y='Value',hue='ProductCode')
plt.axvline('2016', color='black')
plt.axvline('2008', color='black')

<matplotlib.lines.Line2D at 0x165bde1e8b0>

Indonesia's export and import for COVID-19-related goods: July 2020

Sun, 01 Nov 2020 23:10:58 +1100

This is an updated figures on COVID-19-related goods export and import in Indonesia. I was once presenting about it in ANU Indonesia Project’s global seminar series, and I cited Indonesian monthly trade data until March 2020. WCO/WHO classification second edition was used to identify what are considered COVID-19-related goods.

This time around, I’ve got my hands on the trade data until July 2020, and I am using WCO/WHO third revision classification. Illustration is conducted in R codes. So let’s see what happens.

Exports are generally similar to last year’s

First, let’s take a look at export.

# Loading and cleaning the data
library(tidyverse)
library(lubridate)

expor<-read.csv('Book1.csv') # read the data
a<-colnames(expor) # add column names to a for easy rename
expor<-expor %>% # rename variable names
 rename(hs=a[1],deskripsi=a[2],tujuan=a[3],periode=a[4],kg=a[5],usd=a[6])

# make periode into time series
expor$periode<-as.Date(paste0(as.character(expor$periode), '01'),format='%Y%m%d')

# Make aggregation to all countries
exporall<-expor %>% group_by(hs,periode) %>% summarise((expor=sum(usd)))
a<-colnames(exporall)
exporall<-exporall %>% rename(usd=a[3])

# Make aggregation on WCO goods classification
wco<-read.csv('wco.csv') # adding wco classification
wco<-wco %>% distinct(hshs, .keep_all = TRUE) # drop duplicates
wco$hshs<-as.character(wco$hshs) #make hshs a string so i can join them
exporall$hshs<-substr(as.character(exporall$hs),start=1,stop=6) # taking the first 6 digits
final<-inner_join(exporall,wco,by="hshs")
finall<-final %>% group_by(desc,periode) %>% summarise(expor=sum(usd))
a<-colnames(finall)
finall<-finall %>% rename(usd=a[3])
finall$usd6<-finall$usd/1000000

# plot
ggplot(finall,aes(x=periode,y=usd6, color=desc)) +
 geom_line(aes(linetype=desc),size=1.1) +
 scale_x_date(date_labels="%b-%y",date_breaks = "2 month")+
 # theme_ipsum() +
 # theme(axis.text.x=element_text(angle=60, hjust=1)) +
 geom_vline(xintercept=as.Date('20200301',format='%Y%m%d')) +
 labs(x='tahun',y="Million USD",title="Indonesia's export of COVID-19 related goods",subtitle="BPS,Compiled by Pusdatin Kemenperin")

Apart from other consumables and PPE, Indonesia isn’t really comparatively advantageous in producing health equipments in general. PPE jump in February was the infamous skyrocketed export in masks due to demand surge by China and Singapore, the two countries experienced COVID-19 at the early stage. In March, Indonesia consulted to export ban on PPEs and other stuffs, but export was still quite strong amind commitment to buyers.

During the talk, I mentioned how Indonesia can be a powerhouse in making PPEs in a competitive way, provided the inputs. However, export ban seems to prevent this. It is also still possible to say production did increase but it was mostly consumed domestically.

Indonesian export value tends to reduced during the ramadhan season which happened in June in 2019, but May in 2020. The export ban and many other trade measures was revoked in June 2020, so it will be interesting to take a look at the numbers after July 2020.

Testing kits imports were raising, but most imports went down in July

More things can be said on imports.

As you can see, import plays a significant role in Indonesian health service even before COVID-19. While Indonesia can produce surgical gown competitively, capital-intensive PPE such as masks are still mostly imported. Moreover, Indonesia also imports a lot of other COVID-19-related goods quite extensively.

Indonesia’s first case was in early March. And as you can see, the number of imports shot up right after March, especially testing kits and PPEs. The trend weakend on May (ramadhan?) except for the testing kits import. Interestingly, coming July, all imports went down except for other medical equipments. I mentioned about revoked rules (yes, including for easing up imports for private enterprises), so this may reflect that. It could also reflect improvement on domestic production but I am not sure. Sadly domestic production numbers are a bit harder to get.

Notably, Imports are generally decreased in February (take this with a grain of salt but I am thinking Chinese New Year. China is a major source of import).

Despite its ability in improving growth and standard of living, trade has never been championed as the tool to improve production. On the countrary, countries are trying to look inward, including (especially?) Indonesia. I am quite sceptical Indonesian economy can crank-up essential production in a short time alone, but I highly hope I am wrong.

Here’s what covered in WCO Classification.

section	desc	hs	deskripsi
1	testing kit etc	38220010	Plates, sheets, film, foil & strip of plastics impregnated or coated with diagnostic reagents or laboratory
1	testing kit etc	38220020	Paperboard, cellulose wadding & web of cellulose fibres impregnated or coated with diagnostic reagents or laboratory
1	testing kit etc	38220030	Sterilisation indicator strips & tapes
1	testing kit etc	38220090	Diagnostic/laboratory reagents on a backing prepared diagnostic/laboratory reagents Oth than HS 3822.00.10 - 3822.00.30
1	testing kit etc	30021500	Oth blood fractions, whet/not modified/obtain by biotechnological process,put up in measured doses
1	testing kit etc	90278010	Oth instruments&apparatus,exposure meters
1	testing kit etc	90278030	Oth instruments&apparatus oth exposure meters,electricallyoperated
1	testing kit etc	90278040	Oth instruments&apparatus oth exposure meters,not electrically operated
1	testing kit etc	38210010	Prepared culture media for the development of microorganisms
1	testing kit etc	38210090	Other than prepared culture media for the development of microorganisms
2	PPE	48189000	Cellulose wadding or webs of cellulose fibres, oth thn hs code 4818.10.00 - 4818.50.00
2	PPE	63079030	Umbrella covers in pre-cut triangular form
2	PPE	63079040	Surgical masks
2	PPE	63079061	Safety harnesses safety suitable for industrial use
2	PPE	63079069	Safety harnesses suitable not for industrial use
2	PPE	63079070	Fans and handscreens
2	PPE	63079080	Laces for shoes,boots,corsets & the like
2	PPE	63079090	Oth made up articls excl umbrella covers/surgical masks/safety harnesses/fans&handscreens/laces shoes,boots,corsets
2	PPE	90200000	Breathing appliances&gas masks,excl protective masks having neither mechanical part nor replaceable filters
2	PPE	90049010	Corrective spectacles
2	PPE	90049050	Protective goggles
2	PPE	90049090	Spectacles & the like excl corrective, protective googles
2	PPE	39269010	Floats for fishing nets
2	PPE	39269020	Fans and handscreens, frames and handles therefor, and parts thereof
2	PPE	39269032	Plastic moulds with denture imprints
2	PPE	39269039	Hygienic, medical and surgical articles oth plastic moulds with denture imprints
2	PPE	39269041	Police shields
2	PPE	39269042	Protective masks for use in welding and similar work
2	PPE	39269044	Life saving cushions for the protection of persons falling from heights
2	PPE	39269049	Other safety and protective devices
2	PPE	39269053	Transmission or conveyor belts or belting
2	PPE	39269055	Plastic J-hooks or bunch blocks for detonators
2	PPE	39269059	Other articles for industrial uses
2	PPE	39269060	Point of poultry feeders
2	PPE	39269070	Padding for articles of apparel or clothing accessories
2	PPE	39269081	Cards for shoe lasts
2	PPE	39269082	Cards for prayer beads
2	PPE	39269089	Cards for jewellery or other small objects of personal adornment
2	PPE	39269091	Point of a kind used for grain storage
2	PPE	39269092	Empty capsules of a kind suitable for pharmaceutical use
2	PPE	39269099	Other articles of plastics & other materials of headings 39.01 to 39.14. oth than 3926.10.00-3926.90.92
2	PPE	40151100	Gloves, mittens and mitts for surgical, of vulcanised rubber other than hard rubber
2	PPE	40151900	Gloves, mittens and mitts for not surgical, of vulcanised rubber other than hard rubber
2	PPE	61161010	Divers’ gloves,knitted or crocheted, Impregnated,coated or covered with plastics or rubber
2	PPE	61161090	Other gloves, mittens and mitts, knitted or crocheted,impregnated, coated or covered with plastics or rubber
2	PPE	62160010	Protective work gloves, mittens and mitts
2	PPE	62160091	Other gloves, mittens and mitts,of wool or fine animal hair
2	PPE	62160092	Other gloves, mittens and mitts,of cotton
2	PPE	62160099	Other gloves, mittens and mitts,of oth textile material excwool or fine animal hair or cotton
2	PPE	65050010	Hats & oth headgear of a kind used for religious purposes,knitted/crocheted
2	PPE	65050020	Hair-nets of any materials
2	PPE	65050090	Oth hats&oth headgear,not for hair-nets/religious purposes,knitted/crochet
2	PPE	62114210	Garments for fencing or wrestling,of cotton,women’s/girl’s
2	PPE	62114220	Prayer cloaks, of cotton, women’s or girl’s
2	PPE	62114290	Other garments exc for fencing/wrestling/prayer cloaks, ofcotton, womenâ€™s or girl’s
2	PPE	40159010	Lead aprons, of vulcanised rubber other than hard rubber
2	PPE	40159020	Divers’ suits (wet suits), of vulcanised rubber other than hard rubber
2	PPE	40159090	Articles apparel&clothing accessories,oth thn 40151100-40159020 for all purposes, vulcanised rubber oth thn hard rubber
2	PPE	48185000	Articles of apparel and clothing accessories
2	PPE	62101011	Garments used for protection from chemical substances, radiation or fire, of fabrics of heading 56.02 or 56.03
2	PPE	62101019	Other protective work garment (exc used for protection fromchemical substance,radiation/fire),of fabric head 5602/5603
2	PPE	62101090	Garments of fabrics of heading 56.02 or 56.03, other than protective work garments
2	PPE	62104010	Menâ€™s or boysâ€™ garments, used for protection from fire
2	PPE	62104020	Menâ€™s or boysâ€™ garments, used for protection from chemicalsubstances or radiation
2	PPE	62104090	Other menâ€™s or boysâ€™ protective work garments (exc used forprotection from fire/chemical subtances/radiation)
2	PPE	62105010	Women’s or girls’ garments, used for protection from fire
2	PPE	62105090	Other women’s or girls’ protective work garments (exc usedfor protection from fire/chemical subtances/radiation)
2	PPE	40169911	Parts&access kind used for vehicles of Chapter 87 for vehicles of head 8702,8703, 8704 or 8705,oth thn weatherstripping
2	PPE	40169912	Parts and accessories of vulcanised rubber other than hard rubber, of a kind used for vehicles of heading 87.11
2	PPE	40169913	Weatherstripping of vulcanised rubber oth thn hard rubber, of a kind used on motor vehicles of heading 8702,8703 or 8704
2	PPE	40169915	Parts&accessories of vulcanised rubber oth thn hard rubber,of a kind used for vehicles of head 8709, 8713, 8715 or 87.16
2	PPE	40169916	Bicycle mudguards of vulcanised rubber other than hard rubber
2	PPE	40169917	Bicycle parts of vulcanised rubber other than hard rubber
2	PPE	40169918	Other bicycle accessories of vulcanised rubber other than hard rubber
2	PPE	40169919	Parts&accessories of a kind used for vehicles of Chapter 87 of vulcanised rubber exc hard rubber oth thn 4016.99.11-20
2	PPE	40169920	Parts and accessories of rotochutes of heading 88.04 of vulcanised rubber other than hard rubber
2	PPE	40169930	Rubber bands, of vulcanised rubber other than hard rubber
2	PPE	40169940	Wall tiles of vulcanised rubber other than hard rubber
2	PPE	40169951	Rubber rollers of vulcanised rubber other than hard rubber
2	PPE	40169952	Tyre mould bladders of vulcanised rubber other than hard rubber
2	PPE	40169953	Electrical insulator hoods of vulcanised rubber other than hard rubber
2	PPE	40169954	Rubber grommets and rubber covers for automotive wiring harnesses of vulcanised rubber other than hard rubber
2	PPE	40169959	Oth articles a kind used in machinery/mechanical/electric appliances,or for oth technical uses,oth thn 40169951-40169954
2	PPE	40169960	Rail pads of vulcanised rubber other than hard rubber
2	PPE	40169970	Structural bearings including bridge bearings of vulcanised rubber other than hard rubber
2	PPE	40169991	Table coverings of vulcanised rubber other than hard rubber
2	PPE	40169999	Other articles of vulcanised rubber other than hard rubber, n.e.c in 4016
3	sterilization product	22071000	Undenatured ethyl alcohol of an alcoholic strength by volume of 80 % vol or higher
3	sterilization product	22089010	Medicated samsu of an alcoholic strength by volume not exceeding 40 % vol
3	sterilization product	22089099	oth than samsu, arrack and bitters of an alcoholic strength by volume exceeding 114 % vol but not exceed 40 % vol
3	sterilization product	38089410	Disinfectants containing mixtures of coal tar acid & alkalis
3	sterilization product	38089420	Disinfectants other than those containing mixtures of coal tar acid & alkalis
3	sterilization product	38089490	Disinfectants other than HS 3808.94.10 - 3808.94.20
3	sterilization product	84192000	Medical, surgical or laboratory sterilisers
3	sterilization product	28470010	Hydrogen peroxide, whether or not solidified with urea. In liquid form
3	sterilization product	28470090	Hydrogen peroxide, whether or not solidified with urea. Oth than liquid form
3	sterilization product	30049010	Transdermal therapeutic system patches for the treatment of cancer or heart diseases
3	sterilization product	30049020	Closed sterile water for inhalation, pharmaceutical grade
3	sterilization product	30049030	Antiseptics
3	sterilization product	30049041	Anaesthetics,Containing procaine hydrochloride
3	sterilization product	30049049	Anaesthetics,Other than Containing procaine hydrochloride
3	sterilization product	30049051	Analgesics,antipyretic&oth med cont para cetamol/dipyrone,acetylsalicylic,taken o
3	sterilization product	30049052	Analges,antipyretic&ot.medicament for the treat.of coughs/colds,contain/not antihistamine,contain chlrpeniramine maleate
3	sterilization product	30049053	Analges,antipyretic&oth.medicament for the treat.of cough/cold,contain/not antihistamine,contain diclofenac,taken orally
3	sterilization product	30049054	Analgesic,antipyretic&oth medicament for treatment of cough/cold,contain/not antihistamine,contain piroxicam/ibuprofen
3	sterilization product	30049055	Analgesic,antipyretic&oth medicaments for the treatment of cough/cold,contain/not antihistamines,other,in liniment form
3	sterilization product	30049059	Analgesic,antipyretic&oth med cont acety lsalicylic acd,paractaml/dpyron,not oral
3	sterilization product	30049064	Antimalarials, Containing artemisinin other than of subheading 3004.60.10
3	sterilization product	30049065	Antimalarials, Not Containing primaquine & artemisinin other than of subheading 3004.60.10, Herbal medicaments
3	sterilization product	30049069	Antimalarials, Not Containing primaquine & artemisinin other than of subheading 3004.60.10, Herbal medicaments
3	sterilization product	30049071	Anthelmintic, Containing piperazine or mebendazole (INN)
3	sterilization product	30049072	Anthelmintic Not containing piperazine or mebendazole (INN) for Herbal medicaments
3	sterilization product	30049079	Anthelminti containing piperazine or mebendazole (INN) for Herbal medicaments
3	sterilization product	30049082	Other medicaments for the treatment of HIV/AIDS
3	sterilization product	30049089	Oth medicaments for other intractable diseases oth than HIV/AIDS
3	sterilization product	30049091	Other medicaments containing sodium chloride or glucose, for infusion
3	sterilization product	30049092	Other medicaments containing sorbitol or salbutamol, for infusion
3	sterilization product	30049093	Other medicaments containing sorbitol or salbutamol, in other forms
3	sterilization product	30049094	Other medicaments containing cimetidine (INN) or ranitidine (INN) other than for injection
3	sterilization product	30049096	Nasaldrop medicaments containing naphazoline, xylometazoline or oxymetazoline
3	sterilization product	30049098	Herbal medicaments
3	sterilization product	30049099	Other medicaments except HS 3004.10.15 to 3004.90.98
3	sterilization product	29051200	Propan-1-ol (propyl alcohol) & propan-2-ol (isopropyl alcohol)
3	sterilization product	29151100	Formic acid
3	sterilization product	29151200	Salts of formic acid
3	sterilization product	29182100	Salicylic acid & its salts
3	sterilization product	85394900	Ultra-violet/infra-red lamps
3	sterilization product	85395000	Light-emitting diode (LED) lamps
3	sterilization product	85437010	Electric fence energisers
3	sterilization product	85437020	Remote control apparatus,oth radio remote control apparatus
3	sterilization product	85437030	Electrical machines with translation/dictionary functions
3	sterilization product	85437040	Equipment/machine for removal dust particle&curingmaterial by UV light for mfg of printed circuit boards
3	sterilization product	85437090	Oth equipment/machine for removal dust particle&curingmaterial by UV light for mfg of printed circuit boards
4	Oxygen teraphy	90192000	Ozone therapy,oxygen therapy, aerosoltherapy, artificialrespiration/other therapeutic respiration apparatus
4	Oxygen teraphy	90183910	Catheters
4	Oxygen teraphy	90183990	Cannulae and the like
4	Oxygen teraphy	90268010	Oth instruments/apparatus for measuring/checking the flow,level,pressure,electrically operated
4	Oxygen teraphy	90268020	Oth instruments/apparatus for measuring/checking the flow,level,pressure, not electrically operated
4	Oxygen teraphy	90181900	Other electro-diagnostic apparatus used in medical
5	other medical	90221200	Computed tomography apparatus
5	other devices	90181200	Ultrasonic scanning apparatus,used in medical
5	other devices	90181100	Electro-cardiographs,used in medical
5	other devices	90189020	Intravenous administration set
5	other devices	90189030	Electronic instruments and appliances,used in medical
5	other devices	90189090	Other instruments and appliances,used in medical
5	other devices	90251911	Thermometers&pyrometers,elect operated,temperature gauges for motor vehicles
5	other devices	90251919	Thermometers&pyrometers,elect operated,oth temperature gauges for motor vehicles
5	other devices	90251920	Thermometers&pyrometers not electrically operated
5	other devices	84131910	Pump fitted with measuring device,electrically operated,excl for dispensing fuel/lubricant in filling-station/garage
5	other devices	84131920	Pump fitted with measuring device,not-electrically operatedexcl for dispensing fuel/lubricant in filling-station/garage
5	other devices	90282020	Water meters
5	other devices	90282090	Liquid meters other than water meters.
5	other devices	73249010	Other, including parts, flushing water closets or urinals (fixed type)
5	other devices	73249091	Parts of kitchen sinks or bathtubs
5	other devices	73249093	Parts of flushing water closets or urinals (fixed type)
5	other devices	73249099	Other parts of kitchen sinks or bathtubs/flushing water closets or urinals (fixed type)
6	other consumables	28044000	Oxygen
6	other consumables	30059010	Bandages
6	other consumables	30059020	Gauze
6	other consumables	30059090	Wadding & similar articles, except bandage & gauze
6	other consumables	30051010	Adhesive dressings & other articles having an adhesive layer, impregnated or coated with pharmaceutical substances
6	other consumables	30051090	Adhesive dressings&other articles having an adhesive layer,except impregnated or coated with pharmaceutical substances
6	other consumables	34012020	Soap chips
6	other consumables	34012091	Soap in other forms of a kind used for flotation deinking of recycled paper, other than soap chips
6	other consumables	34012099	Soap in other forms, other than soap chips & soap of a kind used for flotation deinking of recycled paper
6	other consumables	34011140	Medicated soap including disinfectant soap
6	other consumables	34011150	Other soap including bath soap
6	other consumables	34011160	Soap&org surf-active prod&prep, of felt/nonwovens,impregnated,coated/covered w/ soap/detergent
6	other consumables	34011190	Soap&org surf-active prod&prep,form of bars,cakes,mould pcs/shapes,&pap.wad,felt etc,for toilet use exc HS3401.11.40-60
6	other consumables	34013000	Organic surfaceactive products&prep for washing the skin,in the form of liq/cream&put up for retSale,containing soap/not
6	other consumables	90183110	Disposable syringes, with or without needles
6	other consumables	90183190	Syringes, with/without needles oth than disposable syringes
6	other consumables	90183200	Tubular metal needles and needles for sutures
6	other consumables	39232910	Aseptic bags of oth plastics,reinforced or not with aluminium foil,widthâ‰¥315mm&lengthâ‰¥410mm,incorporating a sealed gland
6	other consumables	39232990	AsepticBags of oth plastics,reinforced/not w aluminiumfoil (exc retort pouches),widthâ‰¥315mm&lengthâ‰¥410mm,not SealedGland
6	other consumables	30067000	Gel prep designed to be used in hum or vet med as a lubricant for parts of the body for surg operations or physical exam
6	other consumables	37011000	For Xray
6	other consumables	37021000	For Xray
6	other consumables	87059050	Oth special purpose motor vhcl,not design for person & goodStreet cleaning vhcl, cesspool emptiers, mobile clinics
6	other consumables	87059090	Oth special purpose motor vhcl,not design for person & goodother special vhcl
6	other consumables	94029010	Furniture designed for medical,surgical/ veterinarypurposes &parts thereof, oth than chairs
6	other consumables	94029020	Commodes
6	other consumables	94029090	Oth furniture designed for medical, surgical/veterinarypurpose&part thereof, oth than chairs and commodes
6	other consumables	84213920	Air purifiers
6	other consumables	84213990	Filtering/purifying mach & aparatus for gases
6	other consumables	73110023	Containers for compressed/liquefied gas,of iron/steel seamless steel cylinders,capacity>30L for LPG
6	other consumables	73110024	Containers for compressed/liquefied gas,of iron/steel seamless steel cylinders,capacity>30L &<110L for LPG
6	other consumables	73110025	Oth containers for compressed/liquefied gas seamless steel cylinders, for Liquefied Petroleum Gas (LPG)
6	other consumables	73110026	Oth containers for compressed/liquefied gas seamless steel cylinders, capacity<30L
6	other consumables	73110027	Oth containers for compressed/liquefied gas,seamless steel cylinders,capacity>30L&<110L
6	other consumables	73110029	Oth containers for compressed/liquefied gas seamless steel cylinders, capacity>30L
6	other consumables	73110091	Other Containers for compressed or liquefied gas, of iron or steel with capacity < 7,3L
6	other consumables	73110092	Other Containers for compressed or liquefied gas, of iron or steel with capacity > 7,3L &< 30L
6	other consumables	73110094	Other Containers for compressed or liquefied gas, of iron or steel with capacity > 30L &< 110L
6	other consumables	73110099	Other Containers for compressed or liquefied gas, of iron or steel with capacity > 110L
6	other consumables	76130000	Aluminium containers for compressed or liquefied gas.
6	other consumables	63062200	Tents of synthetic fibres
6	other consumables	63062910	Tents of oth textiles of cotton
6	other consumables	63062990	Tents of oth textiles, oth than cotton
6	other consumables	84248910	Hand-operated household sprayers of capacity <= 3 l
6	other consumables	84248920	Spray heads with dip tubes
6	other consumables	84248940	Wet processing equipment;apparatus for the spot applicationof liquid;apparatus for the application of dry film/similar
6	other consumables	84248950	Mechanical appliances, electrically operated, oth than foragricultural/horticultural
6	other consumables	84248990	Mechanical appliances, not electrically operated, oth thanfor agricultural/horticultural
6	other consumables	70171010	Quartz reactor tubes&holders glass,for production of semiconductor wafers
6	other consumables	70171090	Laboratory/pharmaceutical glass of fused quartz/oth fusedsilica; other
6	other consumables	70172000	Laboratory,hygienic/pharmaceutical w/coef< 5x106/kelvin,0-3000c
6	other consumables	70179000	Laboratry,hygienic/pharmaceutical glasswre,nor fused quartz/fused silica

A quick look at Indonesia's current account and balance of trade using World Bank's API in Python

Wed, 21 Oct 2020 21:07:25 +1100

Indonesia has been always famed for its lack of depth and innovation in its financial market. Banks are powerful and aim mostly at consumer banking. Indeed, the role of foreign investment is quite central to Joko ‘Jokowi’ Widodo’s development policy, from building infrastructures and the same-price gasoline policy, both relies on SOE’s corporate bonds, to attracting FDI to provide jobs. The high influx of foreign investment of course leads to a current account deficit, which means increase the surge of imports. This is a basic GDP accounting where current account balance is $=S-I=X-M$.

Interestingly, minimizing Current Account Deficit (CAD) has been something Indonesian government set as their de facto policy targeting. Targeting CAD may be important(?) for stability, but might not be something you would want if you aim for growth. In this blog, I try to have a look at Indonesia’s CAD, what’s causing it, and a bit of insight from looking at it. My visualization relies on Python’s Seaborn and , wbdata. My learning resources are and World Bank’s and .

Using wbdata

install it first using:

pip install -U wbdata

We then import it and see what are the source.

import wbdata as wb

wb.get_source()

 id name
---- --------------------------------------------------------------------
1 Doing Business
2 World Development Indicators
3 Worldwide Governance Indicators
5 Subnational Malnutrition Database
6 International Debt Statistics
11 Africa Development Indicators
12 Education Statistics
13 Enterprise Surveys
14 Gender Statistics
15 Global Economic Monitor
16 Health Nutrition and Population Statistics
18 IDA Results Measurement System
19 Millennium Development Goals
20 Quarterly Public Sector Debt
22 Quarterly External Debt Statistics SDDS
23 Quarterly External Debt Statistics GDDS
24 Poverty and Equity
25 Jobs
27 Global Economic Prospects
28 Global Financial Inclusion
29 The Atlas of Social Protection: Indicators of Resilience and Equity
30 Exporter Dynamics Database – Indicators at Country-Year Level
31 Country Policy and Institutional Assessment
32 Global Financial Development
33 G20 Financial Inclusion Indicators
34 Global Partnership for Education
35 Sustainable Energy for All
36 Statistical Capacity Indicators
37 LAC Equity Lab
38 Subnational Poverty
39 Health Nutrition and Population Statistics by Wealth Quintile
40 Population estimates and projections
41 Country Partnership Strategy for India (FY2013 - 17)
43 Adjusted Net Savings
44 Readiness for Investment in Sustainable Energy
45 Indonesia Database for Policy and Economic Research
46 Sustainable Development Goals
50 Subnational Population
54 Joint External Debt Hub
57 WDI Database Archives
58 Universal Health Coverage
59 Wealth Accounts
60 Economic Fitness
61 PPPs Regulatory Quality
62 International Comparison Program (ICP) 2011
63 Human Capital Index
64 Worldwide Bureaucracy Indicators
65 Health Equity and Financial Protection Indicators
66 Logistics Performance Index
67 PEFA 2011
68 PEFA 2016
69 Global Financial Inclusion and Consumer Protection Survey
70 Economic Fitness 2
71 International Comparison Program (ICP) 2005
72 PEFA_Test
73 Global Financial Inclusion and Consumer Protection Survey (Internal)
75 Environment, Social and Governance (ESG) Data
76 Remittance Prices Worldwide (Sending Countries)
77 Remittance Prices Worldwide (Receiving Countries)
78 ICP 2017
79 PEFA_GRPFM
80 Gender Disaggregated Labor Database (GDLD)
81 International Debt Statistics: DSSI

So many databases, but we will get what we want from World Development Indicators, number 2 on the above list. Now, let’s search current account balance in the database

wb.search_indicators('current account balance',source=2)

id name
----------------- ------------------------------------------
BN.CAB.XOKA.CD Current account balance (BoP, current US$)
BN.CAB.XOKA.GD.ZS Current account balance (% of GDP)

I will use one with % of GDP. I will take the current account balance only for Indonesia and only from 1981 to 2019. Don’t forget to create a datetime tuple to limit the year you’re taking.

import pandas as pd
import datetime
tanggal=(datetime.datetime(1981,1,1), datetime.datetime(2019,1,1))
a=wb.get_dataframe({"BN.CAB.XOKA.GD.zS" : "Current account balance (% of GDP)"}, country=["IDN"], data_date=tanggal, convert_date=True, keep_levels=True)
a=a.reset_index()
a.head()

	country	date	Current account balance (% of GDP)
0	Indonesia	2019-01-01	-2.714101
1	Indonesia	2018-01-01	-2.939161
2	Indonesia	2017-01-01	-1.594657
3	Indonesia	2016-01-01	-1.819151
4	Indonesia	2015-01-01	-2.035042

Let’s visualize it with seaborn

import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
plt.figure(figsize=(13.5,8))
graph1=sns.lineplot(x="date", y="Current account balance (% of GDP)",data=a)
graph1.axhline(0, color='black')
graph1.axvline(datetime.datetime(1998,1,1), color='red')
graph1.text(datetime.datetime(1999,1,1),-6, "Asian Financial Crisis",color='red',size='x-large')
plt.show()

The above graph is Indonesia’s current account balance. I suppose it is clear to us that Indonesia were running a current account deficit since the collapse of oil price in the early 80s. Financial liberalization were then took place around the same period and was getting more progressive up until right before Asian Financial Crisis (AFC) in 1998. Indeed, running a mismanaged CAD is somewhat dangerous when the investment is not allocated efficiently. Those investment were not productive and Indonesia failed to earn enough foreign currency to pay back its debt. The economy was then crash, and perhaps haunt us until now. This is, perhaps, the reason why we hate CAD so much.

Indonesia was having a positive current account since then, but starting 2011, the CAD started to happen. Since then, CAD has been happening consistently up until now. The anti-import sentiment was return, and the government has been rising Non-Tariff Measures to combat it. In fact, according to , NTM shot up by almost 30% in numbers, from 2015 to 2018.

What causes our CAD?

Interestingly, Indonesia’s trade balance (export - import) has always been positive. We have been exporting enough to offset our import. Our deficit was mainly driven by a negative net primary income. The negative net primary income was driven by interest payment from portfolio and FDI. Indonesia’s foreign investment inflow has largely been used to actually pay these interests. That’s one reason why Indonesian policy makers talk a lot on speeding up growth and improving export relative to import, because reducing net primary income deficit is much harder in the short run.

b=wb.get_dataframe({"BN.GSR.FCTY.CD" : "Net Primary Account (Current USD)"}, country=["IDN"], data_date=tanggal, convert_date=True, keep_levels=True)
b=b.reset_index()
b.head()
c=wb.get_dataframe({"BN.GSR.MRCH.CD" : "Net Trade in goods (Current USD)"}, country=["IDN"], data_date=tanggal, convert_date=True, keep_levels=True)
c=c.reset_index()
b["Net Trade in goods (Current USD)"]=c["Net Trade in goods (Current USD)"]/1000000
b["Net Primary Account (Current USD)"]=b["Net Primary Account (Current USD)"]/1000000
b=b.set_index('date')
del b['country']
plt.figure(figsize=(13.5,8))
graph2=sns.lineplot(data=b)
graph2.axhline(color='black')
graph2.set(ylabel='Juta USD',xlabel='Tahun')
plt.show()

Increasing export or reducing import?

If we take a look at the graph below, Indonesia’s import follows its export closely.

b=wb.get_dataframe({"BX.GSR.MRCH.CD" : "Goods Export (Current USD)"}, country=["IDN"], data_date=tanggal, convert_date=True, keep_levels=True)
b=b.reset_index()
b.head()
c=wb.get_dataframe({"BM.GSR.MRCH.CD" : "Goods Import (Current USD)"}, country=["IDN"], data_date=tanggal, convert_date=True, keep_levels=True)
c=c.reset_index()
b["Goods Export (Current USD)"]=b["Goods Export (Current USD)"]/1000000
b["Goods Import (Current USD)"]=c["Goods Import (Current USD)"]/1000000
b=b.set_index('date')
del b['country']
plt.figure(figsize=(13.5,8))
graph2=sns.lineplot(data=b)
graph2.set(ylabel='Juta USD',xlabel='Tahun')
plt.show()

Improving export in the short run can be challenging, especially given the current global situation. Indonesia’s two main industries mining and plantation, are still performing quite well, but these two commodities are very sensitive to price volatility, and .

Unfortunately, trying to reduce import is also not ideal. Indonesian imports are currently consist of many industrial supplies and capital goods, needed for firms to operate. In fact, for firms operate in the Global Value Chain (GVC), imported inputs are crucial for them to be competitive in the global market. This is perhaps the reason why Indonesian import and export are moving together, because its exporting firms need imports to be able to export. Reducing imports can be bad both in the short run (industrial supplies) and in the long run (capital goods).

# This one's not using WBdata. I use UNCOMTRADE i used for my other project
import pandas as pd
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
c=pd.read_csv('data.csv')
c=c.rename(columns = {'Trade Value (US$)' : 'Trade Value (Million US$)'})
c['Commodity']=c['Commodity'].replace({'Capital goods (except transport equipment), and parts and accessories thereof':'Capital goods'})
c['Commodity']=c['Commodity'].replace({'Transport equipment, and parts and accessories thereof':'Transport eq & parts'})
c['Trade Value (Million US$)']=c['Trade Value (Million US$)']/1000000
plt.figure(figsize=(13.5,8))
g=sns.lineplot(x="Period", y="Trade Value (Million US$)", hue="Commodity",data=c)
g.legend(loc='upper left', ncol=1)

<matplotlib.legend.Legend at 0x1be0560a148>

Indeed, Indonesia face challenge again during this times. I personally think that reducing import is a bad move. What is important is to make sure its investment pays off and keep .

The heterogeneous impacts of tariffs and NTM on total factor productivity of Indonesian firms

Fri, 02 Oct 2020 11:00:00 +1000

COVID-19 death rate in Indonesia

Sun, 27 Sep 2020 09:37:03 +1000

This morning, as usual, I prepared breakfast, did the dishes, all while listening to a podcast. One of my favorite, Babbage aired the recent development of COVID-19, including the decreasing death rate across the world.

As cases grow, we should expect a still increasing death rate as well. What happens is the contrary. Slavea Chankova, the healthcare-correspondent at The Economist, said that healthcare workers now have the knowledge to better treat COVID-19 patients. They also able to reduce the use of ICU bed, which is a good thing.

Other publications also say similar things. A post from The Conversation says that the decreased death rate is the result of increased proportion of young age. As test capacity increases, the death rate converges to the ’true’ death rate.

Furthermore, The Conversation post shows a graph where albeit increased number of new cases, new deaths are going down. This is in UK btw.

What about Indonesia?

The case in Indonesia is a bit different. I try to re-create the graph for Indonesian context using data from Our World in Data

import pandas as pd
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
c=pd.read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv")
c['date']=pd.to_datetime(c['date'])

y1=c['new_cases'][c['iso_code']=='IDN']
y2=c['new_deaths'][c['iso_code']=='IDN']
x=c['date'][c['iso_code']=='IDN']
df=pd.DataFrame({'date':x,'new_cases':y1,'new_deaths':y2})
plt.figure(figsize=(15,5))
ax = df.plot(x='date', y='new_cases', legend=False)
ax2 = ax.twinx()
df.plot(x='date', y='new_deaths', ax=ax2, legend=False, color="r")
ax.figure.legend()
plt.show()

#nt=sns.load_dataset("c")
#sns.lineplot(x=x, y=y, data=c)
# sns.lineplot(x=x, y=y4, data=c)

As you can see from the graph above, Indonesia’s new deaths are still on the increase as new cases goes up. New cases in Indonesia is still increasing, with new record breaking number of cases every day. Indeed, Indonesia, albeit its ability to ramp-up testing capacity, is still being criticised for not testing enough.

Death Ratio is converging

But when in comes to death rate, Indonesia is indeed converging to the world. The Jakarta Post, citing the satgas’ spokeperson Wiku Adisasmito, report that Indonesia’s death rate is constantly decreasing since April, although still above the world’s average.This may be at least a silver lining, although in Indonesia there’s also a ruckuss about the government trying to propose change the definition of COVID-19 death.

using $death rate = \frac{total cases}{total deaths} * 100$ , I try to illustrate Indonesia’s death rate and compare them to some countries I feel comparable. Data from Our World in Data.

cn=c[(c['iso_code'] == 'IDN') | (c['iso_code'] == 'IND') | (c['iso_code'] == 'BRA') | (c['iso_code'] == 'USA') | (c['iso_code'] == 'VNM') | (c['iso_code'] == 'MYS')]
cn['rate']=cn['total_deaths']/cn['total_cases']*100
plt.figure(figsize=(15,5))
#nt=sns.load_dataset("c")
#sns.lineplot(x=x, y=y, data=c)
sns.lineplot(x="date", y="rate", hue="iso_code", style="iso_code",data=cn)

Apart from Vietnam, the deathrate among these countries seem to have similar patterns. The explanation might be similar: better knowledge on how to treat patient, more, converging tests, and increased fraction of young population.

So, all is good?

Not necessarily. Remember, Indonesia’s case is still increasing fast, and with it, its new deaths. Hospital capacity and the availability of healthcare workers are increasingly become issues in handling COVID-19. This is still a big concern.

Naively speaking, there are two possible ways to handle this. One is to decrease the viral infection’s sped (so called flattening the curve), and the other is to improve healthcare capacity. Which one’s easier?

Another big recent news in Indonesia is the resignation of Professor Akmal Taher, a respectable doctor, from Indonesia’s COVID-19 task force. His reason was that improving testing and tracing would be easier to be done from outside of the task force. To me, this is very interesting development. What other place with better authority and resource to improve testing and tracing? Resource-wise, there can’t be a better place?

Does this means the top structure of the task force does not put testing and tracing as the main strategy? It is possible that the task force is prioritizing healthcare infrastructure, which is not what Akmal Taher would want. Me, being nobody, have not enough information to assess the best strategy in handling COVID-19, but it does seem that Indonesian government is having a lot of problems in terms of conducting a proper test and trace. Does that mean it is easier to buy hospital beds and recruit doctors and nurses?

Alas, this post provides more question than answer. So what do you think? Testing and tracing or improving healthcare capacity? Or both?

Strategi sukses meraih beasiswa pendidikan lanjut

Fri, 18 Sep 2020 09:00:00 +0700

When Instrumental Variable (IV) is problematic

Wed, 16 Sep 2020 18:46:31 +1000

As a tutor for both Australia Awards Scholarship and ANU, I get the privilege to learn many courses. There’s a lot of great content in ANU in terms of courses which I can’t cope with. But as a tutor, I am ‘forced’ to learn many of these courses so that’s definitely something. Haha!

This time, i’d like to write about a demonstration of weak instrument that I learned from a course at CBE.

What’s an IV?

One problem with OLS is endogeneity/reverse causality. This happens when our independent variable is actually depending on something else. This something else is then absorbed by the error term and violate $E[MXM\epsilon] \neq 0$.

We often use IV as one of the treatment to this problem. IV is a third variable $Z$ that should be independent from $Y$ but have high correlation with $X$. IV will establish $X$ causes $Y$ but not $Y$ causes $X$.

Problem IV

However, when the instrument $Z$ is not explaining $X$ very well, IV may lead to even more bias. We try to simulate this using R below.

Setting

Here, we try to use finance setting. Let $X$ be a firm’s cash flow while $Y$ is firm’s borrowing. We try to establish causal relationship between $X$ and $Y$. When firm’s cash flow is negative, it will need to borrow from financial market to survive for the next revenue collection time. However, when firm’s cash flow is positive, it may also borrow if the positive cash flow is a sign of positive shock and the firm needs fund to expand. We can use technological shock $Z$ as an instrument for $X$.

Generate 2,000 observation, where half experienced a flat negative shock $( − 3 + rand())$ while half experience positive shock $X = Z + rand()$.

data <- data.frame(
id=c(1:2000)
)
# generate data observation
data$lnz <- rnorm(2000, mean = 0.5, sd=1)
data$Z <- exp(data$lnz)
data$U <- rnorm(2000,mean = 0, sd=0.1)
data$B1 <- rbinom (2000, 1, 0.5) # to explain about probability 1
data$B2 <- 1-data$B1 # to explain about probability 1
# generate firm cashflow
data$X <- data$B1*data$Z - 3*data$B2 + data$U
# generate firm borrowing
data$E <- rnorm(2000,mean = 0, sd=0.1)
data$R <- 0.5*data$U + 0.5*data$E
data$X1 <- ifelse (data$X>0, 1, 0)
data$X2 <- ifelse (data$X<0, 1, 0)
data$Y <- (data$X*data$X1) - (data$X*data$X2) + data$R
head(data)
## id lnz Z U B1 B2 X E R
## 1 1 0.53446900 1.7065418 -0.02069438 1 0 1.685847 -0.16523761 -0.09296600
## 2 2 -0.25843412 0.7722599 -0.01968836 0 1 -3.019688 -0.09326667 -0.05647752
## 3 3 0.58658233 1.7978335 0.05513357 0 1 -2.944866 -0.10034947 -0.02260795
## 4 4 1.48663543 4.4221917 -0.18736333 0 1 -3.187363 -0.10648732 -0.14692532
## 5 5 1.07463211 2.9289152 0.04403934 0 1 -2.955961 0.03063488 0.03733711
## 6 6 0.05710789 1.0587700 0.03335503 1 0 1.092125 0.07427162 0.05381333
## X1 X2 Y
## 1 1 0 1.592881
## 2 0 1 2.963211
## 3 0 1 2.922258
## 4 0 1 3.040438
## 5 0 1 2.993298
## 6 1 0 1.145938

Positive cash flow $X$ have a 1-on-1 relationship with positive $Z$, but a flat $-3$ with negative $Z$.

Let’s try plot $Z$ and $X$

library(ggplot2)
a<-ggplot(data, aes(Z,X, colour=B1)) +
geom_point()
a

Blue is those experience positive shock while black is those who get negative shock. Positive shock has 45^∘ from $X = 0$ while all the negative ones are flat around $X = -3$. Look at that. What a bad pattern. Horrible for researchers 🤣🤣.

Now let’s try $X$ and $Y$:

b<-ggplot(data,aes(X,Y,colour=X1)) +
geom_point()
b

Firm’s borrowing sudah pasti positif, dan kita buat sedemikian sehingga perusahaan meminjam sesuai cash flow (alias koefisien beta-nya = 1). Nah, ketika $X$ = − 3, mereka minjamnya $Y$ = 3, makanya yang hitam ngumpul di ($X$, $Y$) = ( − 3, 3) semua, sementara yang biru punya tren lurus dengan sudut 45^∘ alias $Y$ = $X$.

Jika kita, ceritanya nggak tau hubungan $X$, $Y$ dan $Z$ di dunia nyata, meregresikan $X$ dan $Y$ dengan OLS biasa, kira-kira seperti apa bentuknya?

b +
geom_smooth(method = "lm")

Keliatan bias banget yah?

Bagaimana dengan koefisiennya? Coba anda bisa nebak gak? ingat bahwa ketika $X$ positif, $X$ naik 1 beriringan dengan $Y$ naik sebanyak 1, sementara ketika $X$ negatif, $X$ turun 1 beriringan dengan $Y$ naik 1 juga. Sementara itu ada 50% $X$ yang negatif, dan 50% $X$ yang positif. Jika kita regresi biasa, kira-kira koefisiennya berapa? Coba tebak!

# Regression
mod <- lm(Y~X, data = data)
summary(mod)
##
## Call:
## lm(formula = Y ~ X, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0007 -1.7264 0.9776 1.1259 15.2347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.88313 0.04075 70.76 <2e-16 ***
## X 0.32862 0.01145 28.70 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.82 on 1998 degrees of freedom
## Multiple R-squared: 0.2919, Adjusted R-squared: 0.2916
## F-statistic: 823.7 on 1 and 1998 DF, p-value: < 2.2e-16

Jawabannya (hampir) benar! (hampir) 0.5! Jadi sebenarnya dengan OLS, kita sudah mendapatkan koefisien yang (hampir) sesuai dengan data yang kita regenerasi, yaitu (hampir) 0.5! Yah tentu saja hasilnya masih salah wkwkwk karena 0.32 masih agak jauh dari 0.5 yha, tapi kan ceritanya kita gak tau dunia nyata bentuknya kayak gimana he he he.

Regresi IV malah bikin tambah jelek

Tentu saja OLS belum ngasih kita koefisien yang kita inginkan ya (tentu saja masalah bias masih ada). Yang jadi masalah, regresi IV malah bikin koefisiennya makin menjauh dari 0.5!

Coba kita regresi $Y$ dan $X$ dengan menggunakan $Z$ sebagai instrumen:

library (AER)
modelIV <- ivreg(formula=data$Y~data$X | data$Z)
summary(modelIV)
##
## Call:
## ivreg(formula = data$Y ~ data$X | data$Z)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.366 -3.175 -2.877 3.252 3.884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.01206 0.07358 40.94 <2e-16 ***
## data$X 1.08897 0.05915 18.41 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.26 on 1998 degrees of freedom
## Multiple R-Squared: -1.271, Adjusted R-squared: -1.272
## Wald test: 339 on 1 and 1998 DF, p-value: < 2.2e-16

Setelah di-treat dengan IV, koefisien X dan Y malah bermasalah. Tapi sebenernya kan memang kita meregresikan X dan Y dengan koefisien 1 (meskipun aslinya 50% doang, 50%-nya lagi koefisien -1) Tentu saja, IV-nya hanya menangkap 50% dari fenomena, dan 50%-nya lagi akhirnya diserahkan ke intercept (dan error). OLS emang masih ada bias, tapi IV malah bikin tambah parah. Inilah salah satu contoh problem menggunakan IV, jika hubungan IV nya sendiri bias.

Cara terbaik? Tentu saja memisahkan regresinya jadi dua. Contoh jika kita regresi hanya shock positif saja:

dataX<-data
dataX<-dataX[!(dataX$B2=="1"),]
modelIV2 <- ivreg(formula=dataX$Y~dataX$X | dataX$Z)
summary(modelIV2)
##
## Call:
## ivreg(formula = dataX$Y ~ dataX$X | dataX$Z)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.228315 -0.047317 0.002166 0.045417 0.201270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0020950 0.0028174 0.744 0.457
## dataX$X 0.9997967 0.0006977 1432.905 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06716 on 999 degrees of freedom
## Multiple R-Squared: 0.9995, Adjusted R-squared: 0.9995
## Wald test: 2.053e+06 on 1 and 999 DF, p-value: < 2.2e-16

Ini benar. Intercept = 0, dan koefisiennya 1, sesuai dengan yang kita buat. Tentu saja yang negatif tidak perlu kita IV karena ketika negatif, $X$ akan otomatis -3. Artinya, untuk yang negatif, $X$ sudah eksogen! Tidak perlu ditreatment apa-apa (meskipun, coba lihat bagaimana saya generate error term?)

Intinya, hati-hati menggunakan IV

Tentu saja di dunia nyata kita tidak tau fungsi asli dari $Y=f(X)$, dan biasanya memang IV kita gunakan untuk memperbaiki kelemahan OLS. Sayangnya IV dapat berbahaya jika instrumennya tidak menjelaskan dengan baik penyebaran dari variabel endogen yang di-treat, atau jika anda tidak tau-tau banget hubungan si instrumen dengan variabel yang di-treat. Bukannya memperbaiki, malah bikin OLS tambah parah. Tentu saja masalah lainnya adalah jika ternyata instrumennya punya korelasi dengan Y. Di contoh kita ini tentu saya Y independen terhadap Z, tapi di dunia nyata, sulit banget cari instrumen yang benar-benar independen. Contoh, jika ternyata shock-nya berpengaruh ke sektor finansial, otomatis si-shock ini akan punya pengaruh ke borrowing. Wkkwkwk.

Biasanya yang kita lakukan di paper adalah menunjukkan hasil regresi OLS dan IV, sehingga bisa dilihat pola-nya apakah IV memperbaiki OLS atau tidak. Tentu saja justifikasi dari publikasi lain soal keabsahan IV juga harus ditulis di paper kita wkwk. Di Crawford sendiri, jika anda menggunakan IV, siap-siaplah ditanya-tanyain soal kekuatan instrumennya. Ha ha ha ha!

IV memang ribet. Moga-moga kita tidak harus berurusan dengan IV. wwkwkw. Ok sekian semoga postingannya berguna.

webinar dan bedah buku bidang ekonomi Mewujudkan Indonesia 4.0: Kumpulan pemikiran pelajar Indonesia di Australia

Thu, 10 Sep 2020 13:30:00 +0700

Pada hari Kamis 10 September 2020, saya berkesempatan menjadi host sekaligus moderator di acara webinar dan bedah buku Mewujudkan Indonesia 4.0: Kumpulan pemikiran pelajar Indonesia di Australia. Acara ini terselenggara berkat kerjasama Perkumpulan Pelajar Indonesia di Australia atau PPI Australia dan Sobat Cyber Indonesia.

Acara pada hari ini adalah salah satu dari serangkaian acara grand launching buku Mewujudkan Indonesia 4.0: Kumpulan pemikiran pelajar Indonesia di Australia yang diterbitkan oleh Yayasan Pustaka Obor Indonesia. Grand Launching buku ini telah dilaksanakan di KBRI Australia dan di dunia maya tepat seminggu sebelumnya pada tanggal 3 September 2020. Kemudian, kami menyelenggarakan serangkaian acara bedah buku untuk setiap aspek atau bidang yang dibahas di buku ini. Jangan lewatkan rangkaian acara lainnya yang detilnya dapat anda lihat di Instagram PPI Australia di @ppiaustralia

Acara kali ini fokus pada aspek ekonomi, industri dan investasi dari buku ini. Beberapa narasumber yang turut berpartisipasi adalah:

Professor doctor Suharnomo, Dekan FEB Universitas Diponegoro sekaligus Ketua Asosiasi Fakultas Ekonomi dan Bisnis Indonesia (AFEBI).
Aditia Prasta, ST., MM, Kepala Indonesia Investment Promotion Center (IIPC) London.
Monasisca Noviani, pelajar di Monash University, Melbourne dan salah satu penulis buku Mewujudkan Indonesia 4.0: Kumpulan pemikiran pelajar Indonesia di Australia

Acara ini sendiri dibuka dengan keynote speak dari Menteri Perindustrian, Bapak Dr. Agus Gumiwang Kartasasmita, M.Si. Beliau memberikan sedikit gambaran mengenai kondisi perekonomian dan industri manufaktur di Indonesia, serta bagaimana COVID-19 membuat Indonesia harus mengupdate sedikit aspek dari Making Indonesia 4.0, utamanya memberikan tekanan pentingnya berinvestasi di industri farmasi dan alat kesehatan.

Acara ini disiarkan di Sobat Cyber Indonesia. Paparan dari Menteri Perindustrian dan para narasumber dapat diakses di google drive, atau ketuk slides di atas.

Berikut rekamannya dari kanal Sobat Cyber Indonesia. Acara dimulai dari menit 22:55, jadi langsung diskip saja ke menit 22:55 yah!

A ketoprak lunch and reflections on Global Value Chains (GVC)

Mon, 24 Aug 2020 18:53:20 +1000

The picture above is my lunch. My wife made ketoprak today to cure our longing for Indonesian food. I suddenly craved bakwan and my wife brilliantly suggested turning it into ketoprak.

This may be the best ketoprak in the world (this sentence is written under my wife’s supervision). But no, seriously, the ketoprak was great. I am a loyal bakwan (or bala-bala, depending on where you are from) fan, but what probably made the ketoprak so good was the peanut sauce. The sambal kacang was amazing. Just a little lacking in spiciness.

But my wife is far from a chef. What made it delicious was that the peanut sauce was store-bought, not homemade. A few days ago we went to an Asian grocery store and bought this brand of bumbu pecel (peanut sauce mix).

Imported peanut sauce mix

GVC in the Household

Of course, my wife is no culinary maestro. Making peanut sauce from scratch requires a great many ingredients. Different recipes call for different seasonings. And supposedly, even with 100% identical ingredients, if different hands do the grinding, the taste will differ.

In other words, making peanut sauce from scratch is not only tedious – it may well be more expensive than buying it ready-made, with no guarantee of being tastier. Meanwhile, the product we bought has a ton of certifications: halal, ISO, and HACCP. If my wife were asked to get all those certifications, how much would it cost?

This is one of the strengths of GVC. With just one producer, how many households can produce their own ketoprak? And with store-bought peanut sauce, you are more flexible. You don’t have to make ketoprak – you could make pecel sayur, pecel ayam, pecel lele – all with the same sauce.

Peanut sauce is not only competitively priced (many brands, all cheap), but can also be supplied to households even in other countries. Countries that don’t have many pecel vendors can now enjoy pecel easily and cheaply.

Blocking Peanut Sauce Imports

Imagine a husband who doesn’t like his wife cooking with store-bought sauce. He insists everything must be made from scratch. He plans to ban purchases of peanut sauce and even budget subsidies so that his wife can do everything herself: grow the peanuts, grind the sauce, all the way to finished ketoprak.

You can immediately picture what would happen. Making peanut sauce is not something every wife can do efficiently. Just for peanut sauce, wives would lose time for other, more productive activities. If I gave my wife money to make peanut sauce from scratch, she would probably still buy the ready-made version and use the leftover money to upgrade the ketoprak – say, by adding chicken. Pecel ayam, anyone?

Especially if the wife is super productive. If she is a homemaker, time for housework and childcare would have to be cut to make pecel. If she has a career, her work hours would be reduced. Or maybe her leisure time. All for homemade pecel that might not even taste better.

But perhaps some households take genuine pride in homemade pecel. That’s fine too. Maybe in the long run, the wife’s skills improve enough to open her own pecel restaurant. Great. But not every woman wants to become a pecel vendor. If all women became pecel vendors, who would do everything else? Men? Meh.

Welfare Effect on Pecel Vendors?

Those most annoyed by factory-made peanut sauce are probably pecel vendors. If all households make their own pecel with store-bought sauce, vendors’ profits could drop as their customers cook at home. But if the husband was already a pecel customer, he’s unlikely to stop buying pecel from vendors entirely. That would be hypocritical.

Top pecel vendors will still want to compete with factory-made sauce. By making their pecel tastier and more special, adding better toppings, offering a pecel-and-coffee combo, or providing free Wi-Fi. All of this makes consumers happier.

Of course, my household is not part of any anti-import club. Blocking peanut sauce imports only hurts households that want to make peanut-sauce-based food. Making pecel from scratch is inefficient for some households and diverts time, effort, and money from more productive activities.

And trust me, good peanut sauce is a lifesaver. OK, that’s enough. I’m going to go finish my pecel. Until next time!

Diversification as a strategy to survive

Fri, 21 Aug 2020 15:41:24 +1000

Just recently I finished a book titled The Gene: an Intimate History by Siddharta Mukherjee. The book started by a touching stories about Dr. Mukherjee and his family history, that his gene is ’tainted’ by abnormality. He has several family members with unusual mental conditions, and that may be printed in his gene as well.

The book wave a history of our understanding of the genes, starting as far back as Mendel, and even socrates. It then moving on to Darwin’s highly influencial “the origin of species”, and continued to the finished “the genome project”. To me who takes humanity’s understanding of genes and DNA for granted, the history of finding ourselves is really a great story to be told. There are so much to take from this book if you are like me, a non-biologist. The story of how we misused the knowledge (the times of eugenics), how we understand genetic illness, how we understand reproduction.

A disclaimer before we go: I am no biologist so please read with a grain of salt.

Why do we have sex?

As the highest, most complex being on the planet, humans use sex as a means to reproduce. To make a baby, two types of individuals will be needed: one who produce egg, and another who produce sperm. The sperm then swim with style to the place where egg resides, and then inject its content (which is just a DNA, i.e. a blueprint) to the egg.

The sperm literally only adds copies of father’s DNA to the egg. The rest of the ingredients are coming from the ladies. So yeah boys, your sperm is not contributing all that much.

In fact, according to Dr. Mukherjee, it will be much more efficient for humanity if the girls can do Parthenogenesis, an automatic development of egg with no fertilization. There’s no need to find mates, risking a girl to enter an abusive relationship, no need for rules to govern marriage. Sounds much better. (I mean Dr. Mukherjee presented a much better cases than marriage problem but that’s all that stuck in my head sorry)

It turns out, efficiency isn’t the main reason why the most complex being, top of the food chain, the homo sapies, evolved sexual reproduction as its main way to reproduce. The main reason is to increase diversity.

Parthenogenesis (and maybe other asexual reproduction) creates an exact copy of the parent. This is amazing if the parent is the best-individual in the world. Why highly limited human like me should live on this planet if a kind, rich, and smart person (like, maybe, Taylor Swift) can filled this planet with billions of talented individual, right? Population who are an exact copy of Ms. Swift would rule the world, and nobodies like me will not survive for very long. Moreover, why would Ms. Swift marries me and taints her offspring with my genes (or anyone really since she is the best in the world) if she can just multiply and retain her superior gene?

It will be much more efficient if every single humans in the world is highly talented, very intelligent, and superbly attractive. No covidiots, no shit politician, no narcisstic leaders. Right? Humanity will be highly efficient.

The problem with efficiency

The problem with having an exact copy of a species is that once anything bad happened to that individual, it will also happen to everyone else. Take, for example, thalassemia.

Thalassemia is a disorder caused by a mutated of a certain genes that causes a human to produce small blood cells. In a worse case, a person could be dead before he/she was born or would require a lifetime blood tranfussion. Nobody wants to be born with a mutated gene. However, It turns out a type of Thalassemia called Alpha Thalassemia protects a person from a killer disease, Malaria. Alpha Thalassemia is more common in people living in tropical countries (yes, including Indonesia) and would explain why Malaria was such a huge problem for colonisers.

Now imagine if every human in the world have a “good” gene. Maybe the world today would have been ruled by evolved cockroaches.

In other words, humanity (and other species, obviously) is saved because it invested in a developing sex as a mean for reproduction. It allows for mixing genes with others, creating a diverse humanity. People with “good” gene are only best equipped living in a “good” condition. Blind people is only “a disabled” in a situation where the earth is filled with light. Savants are lucky to be living in a world filled with data and numbers. Highly “fit” people have less worth in the world where heavy-lifting is done by machines.

The book really change the way we think about “normal”.

Perhaps mixing genes is not efficient. Diversifying our gene may cost us having to lose a certain “superiority” and become less “efficient”. However, it allows for a greater chance of survival. In a long run, it is better to be diverse than efficient. In other words, diversification is a strategy for a dynamic efficiency.

Static efficiency vs dynamic efficiency

Humans have this tendency to maximising everything, trying to reach maximum potential by sorting “the bad” out, and only opting “the good”. Search engines and recommendation algorithm direct us to webpages more “suitable” to our characteristics, making us further away from engaging in a diverse forums and dialogue. We always want our children married with the “best” candidate. Once we think we know how the world work, model it in our minds (and our computers), we will do a policy best fit with that model.

Of course there always the problem “all models are wrong”. But even if that model is right, we will never know whether it will always work. Had humans succeeded in deleting the Thalassemia mutation, maybe we all are dead by now from Malaria hundred of years ago. We are “normal” because the current world is suitable for “normal” people. We may need to invest in keeping “the freaks” alive.

The same adage exists in investing. You got that right: “never put the all your money in one basket”. If you have an idea of the best yielding investment, why would you not putting everything there? Why would you want to diversify? Unfortunately, we will never know what is the best yielding investment. Just like we will never know what kind of future we will have, what kind of virus, bacteria, or whatever disease can hurt humanity, what kind of world will exist in the future, we will never know. The best chance of survival is to diversify.

In international trade, critics are there for the Just-in-Time (JIT) delivery. That is, making sure warehouses are lean and efficient, relying on third or even fourth parties. Everyone aims for this static efficiency to compete. Unfortunately, when problem happens to one of their supply chain, the whole production jeopardized. This was true when Fukushima exploded, was true when Thailand got heavily flooded, was true when coronavirus hit.

Countries respond by turning inward. With many leaders thinking that they can no longer rely on other countries, they campaign nationalistic production. They want to produce everything.

Sadly, it will work in the opposite direction. Concentrating production in their own countries are also problematic, assuming that nothing will ever happen to their own countries. The key, instead, is to diversify, sourcing their needs to more than one countries, while at the same time supplying to more than one countries. We need to better understand production network, not shying away from it.

Humanity has taught us that diversifying, albeit not efficient, is better in the long run. Who are we to defy nature?

Trade, industry and investment: dealing with Covid-19

Sun, 16 Aug 2020 19:33:41 +1000

the talk can be find in this link

paper here

IA-CEPA will not solve Indonesia’s FDI problem

Wed, 05 Aug 2020 14:45:04 +1000

COVID-19 punishes Indonesian commodity exporters

Mon, 06 Jul 2020 14:44:34 +1000

Indonesia's PPE export ban backfires

Tue, 16 Jun 2020 14:44:52 +1000

Daily COVID-19 Testing in Indonesia

Sun, 31 May 2020 00:00:00 +0000

The government seems increasingly eager to usher in the “New Normal” and start reopening the economy. A guidebook on how to handle the “New Normal” from BPOM has already been circulated. News about malls reopening was everywhere (though apparently Summarecon Mal Bekasi did not open after all). The economy was set to bounce back!

According to many sources (for example, this one), the key to a successful economic reopening is a high growth rate of daily testing – in other words, massive testing. But have we actually achieved sufficient massive testing?

import pandas as pd
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
c=pd.read_csv("https://covid.ourworldindata.org/data/owid-covid-data.csv")
c['date']=pd.to_datetime(c['date'])
y=c['new_tests'][c['iso_code']=='IDN']
y1=c['new_tests_smoothed'][c['iso_code']=='IDN']
x=c['date'][c['iso_code']=='IDN']
plt.figure(figsize=(15,5))
#nt=sns.load_dataset("c")
sns.lineplot(x=x, y=y, data=c)
sns.lineplot(x=x, y=y1, data=c)

The chart above shows the number of “new tests” per day. The data is taken from Our World in Data. It is updated daily based on government reports. The x-axis shows the date, while the y-axis represents the variable “new_tests” from the original dataset. In short, this is the daily test count.

The dots and the thin line show the raw daily test numbers, while the blue line shows the smoothed new tests.

As a brief aside, the code above was run on Python 3.7. The data is read directly from Our World in Data’s online portal, so every time you run it, you get the latest figures. If you have Python on your machine, feel free to copy the code above to see the most current data.

Indonesia’s daily testing clearly has a lot of gaps, and up until 26 May 2020, the daily test count was still very volatile. The ups and downs are somewhat random. Some opinions I have seen on Twitter suggest that daily test counts are hard to predict because they depend not only on the number of swabs conducted but also on the queue of samples at laboratories. So the numbers reported on a given day may actually be the results of swabs taken the day before.

I have not followed the COVID-19 testing debate too closely, to be honest. I had not updated on COVID-19 in a while, but the sudden buzz around reopening the economy piqued my curiosity. Also, Bio Farma reportedly succeeded in producing its own PCR test kits, claiming it can produce 100,000 per day. Assuming smooth logistics to labs and sufficient lab capacity, massive testing should be achievable. Hopefully this upward trend continues and remains sustainable during the New Normal.

By the way, the data comes from the central government, and the central government’s data reflects its own testing plus tests conducted by regional governments (and perhaps private entities). Although we do not have data beyond the central government’s reports, at least regional governments know their own numbers.

Also, other countries have somewhat erratic “new tests” data as well. New Zealand and Malaysia are also volatile, while Japan does not report daily new tests (though cumulative totals are available). Vietnam’s data suddenly drops and disappears midway. So perhaps this is why President Jokowi felt so confident.

import pandas as pd
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
c=pd.read_csv("https://covid.ourworldindata.org/data/owid-covid-data.csv")
c['date']=pd.to_datetime(c['date'])
plt.figure(figsize=(15,10))
#cn=c.drop(c[(c['iso_code'] != 'IDN')| c[(c['iso_code'] != 'VNM') | c[(c['iso_code'] != 'SGP') | c[(c['iso_code'] != 'AUS') | c[(c['iso_code'] != 'MYS')].index)
cn=c[(c['iso_code'] == 'IDN') | (c['iso_code'] == 'VNM') | (c['iso_code'] == 'MYS') | (c['iso_code'] == 'JPN') | (c['iso_code'] == 'NZL')]
sns.lineplot(x="date", y="new_tests", hue="iso_code", style="iso_code",data=cn)

Here I used raw daily tests, not daily tests per 1,000 population. If I used the per-capita measure, other countries would be so far above that Indonesia would look like a flat line. Anyway, I was trying to use the argument that “we are confident enough to reopen because testing capacity has been ramped up.” Using this argument, Indonesia’s upward trend does look reasonable. Whether it is sufficient or not, of course, is still a matter of debate – and I am sure many others have already weighed in.

In closing, there are of course many reasons behind the decision to reopen the economy, not just testing. I am not trying to discuss those other reasons. This post was simply meant to provide a brief snapshot of how Indonesia’s daily testing has been progressing. I will leave the rest to the experts.

COVID-19 dan Anjloknya Indeks PMI Indonesia

Tue, 05 May 2020 14:45:13 +1000

Book Review of "World Trade Evolution: Growth, Productivity and Employment ‐ edited by Lili Yan Ing and Miaojie Yu (eds)"

Wed, 15 Apr 2020 00:00:00 +0000

Industri 4.0 sebagai solusi daya saing industri indonesia?

Wed, 15 Apr 2020 00:00:00 +0000

A book chapter written in Indonesia. Available at online stores in Indonesia.

Book Title: Mewujudkan Indonesia 4.0: Kumpulan Pemikiran Pelajar Indonesia di Australia.

Editors: Denny Irawan, Wasisto Raharjo Jati, Oscar Karnalim, Asrul Sidiq (Eds)

ISBN: 978-602-433-918-0

The Importance of Financial Liberalisation to Economic Growth: The Case of Indonesia

Tue, 07 Apr 2020 00:00:00 +0000

Public Policy Practice the Indonesian Way

Tue, 25 Feb 2020 00:00:00 +0000

Are you a public policy observer? You do not have to be a professional. Even someone who comments on government policies in their spare time – and not as their main job – is justified in doing so, because those policies may affect their daily life. A simple example: a friend of mine who sells model kits on the side is deeply affected by the government’s policy on Standar Nasional Indonesia (SNI) for children’s toys, even though he never studied public policy.

As an economist trained at the Crawford School of Public Policy, I am always enthusiastic when discussing public policy. If you are a civil servant (PNS), you might also enjoy talking about public policy. My school was filled with scholarship recipients, most of them civil servants from various countries.

However, at school, one typically learns about public policy in general terms – the principles of public policy, and perhaps a bit of economics. These principles are very useful when you engage with public policy (whether officially or as an observer), but they cannot be applied directly in practice to the Indonesian context.

The Importance of Understanding Public Policy Practice in the Indonesian Context

There are at least two things that I think graduates of public policy schools need to recognise. The first is context. Although the principles are universal, the context of each country, in different situations, at different times, can differ. Not every recipe works in every place at every time. Esther Duflo, one of the 2019 Nobel laureates in economics, has said:

“Instead of relying on our intuition, or that of others, we set up large-scale, rigorous randomised controlled trials to understand what works, what does not work, and why.”

In other words, it is important for policymakers to be cautious about what they learned in school, because what works in one context may not work in their own country (or even their province or village). Starting small with pilot projects to test a policy’s impact can be a useful first step before generalising.

The second point concerns public policy provision – how a policy is implemented in a given country. In other words, for an observer or policymaker in Indonesia, understanding Indonesia’s constitution is essential. I think everyone knows that Indonesia has various levels of policy. The highest level is legislation (Undang-undang) decided by the DPR (parliament). Below that are Government Regulations, cascading down to Ministerial Decrees. Beyond that, there are even Director General Regulations. At the regional level, there are Regional Regulations (Peraturan Daerah) decided by the DPRD (regional parliament), and Governor’s Regulations decided by the governor.

This second point is important for understanding the extent to which stakeholders are involved in implementing a policy you are observing or working on. When you encounter a policy you disagree with, you must be able to trace which level of authority the problem lies at. For instance, regarding trade restrictions, you should point to Law No. 7 of 2014 on Trade, which falls under the DPR’s domain. If what you disagree with is import restrictions specifically, then the target should be Minister of Trade Regulation No. 54 of 2009 on General Import Provisions. This should be addressed to the Minister of Trade. If what you want to protest is the procedure for obtaining an import licence, say for textile products, then the criticism should be directed at the Minister of Industry. Each issue may involve a different institution. Criticism that is more precisely targeted will be far sharper than criticism addressed vaguely to “the government” or to the president. The same principle applies to the various levels of regional government (DPRD, governor, mayor, village head, etc.).

In a university public policy essay, you usually do not need to target your criticism at a specific institution. But in practice, knowing which institution and which regulation your criticism relates to is far more practical and more likely to lead to a solution.

Want to Be the Minister of Industry? Read This!

When you become a public policy observer, it is more efficient to specialise. You focus on a particular issue and consistently offer criticism on that topic. Criticising other issues is fine too, but in your area of specialisation, your criticism can be sharper and better targeted.

Having spent a long time working at the Ministry of Industry, industry-related issues are naturally important to me. If you have also decided to specialise in industrial policy, then we are in the same boat.

There are several regulations you must know if you want to be a critic of industrial policy (or even become the Minister of Industry).

The first thing anyone who wants to dedicate themselves to industry (whether as a critic or as minister) needs to understand is the legal basis. All Ministries/Agencies are established to carry out the mandate of legislation. Some agencies have more than one law, like the Ministry of Education, but in the case of the Ministry of Industry, the primary legislation is Law No. 3 of 2014 on Industry. Article 5(2) of this law grants the Minister of Industry the authority to “administer government affairs in the field of industry.”

There are a total of 125 articles in this law, most of which concern what the state regulates regarding industry. Several responsibilities fall under specific Directorates General. This is where industrial policy critics become important: if the answer to a problem is something like “the government should regulate this,” then one must go back to the Industry Law. Returning to the law is important to determine what the government already regulates, and which article of Law 3/2014 is closest to the problem at hand, so that the solution can be precisely targeted using the tools available to the government.

Besides Law 3/2014, there are at least two more regulations that industry critics need to understand. The first is Minister of Industry Regulation No. 30 of 2017 on Types of Industries Under the Guidance of Directorates General and Bodies Within the Ministry of Industry. Long name, right? But this regulation essentially identifies which industries fall under the Ministry of Industry’s jurisdiction. If your industry is not listed here, it may be regulated by a different ministry. Some industries may have more than one supervising ministry, but this regulation can still serve as a guide.

The next regulation is Minister of Industry Regulation No. 35 of 2018 on the Organisation and Administration of the Ministry of Industry. This regulation describes the Ministry’s organisational structure. Industry critics would benefit from understanding which part of the Ministry handles which industry-related issues. If additional functions need to be added or existing ones removed, the decision to seek reorganisation should be grounded in this regulation: which parts change, what differs from the status quo. Which functions are added and which are removed – all based on this regulation. One more thing to keep in mind: the Ministry’s general functions are already set out in Law 3/2014, so if major additions or reductions are needed, the law itself would need to be amended.

In my view, if a political party wants to place its cadre in the position of Minister of Industry or on Commission VI, training on these matters is essential. It might be more useful than training on patriotism, nationalism, and the like. Just my opinion, of course. I have never been a party chair.

Too Complicated. I Do Not Want to Be a Minister. I Will Just Be an Observer!

If you just want to be an observer (like me), you do not need to read all of that. Not knowing it is fine too. As an observer, it is enough to know which regulation is directly related to the issue you are discussing. For example, if you are looking at IMEI regulations, just review Minister of Communication and Information Technology Regulation No. 11 of 2019 on the Control of Telecommunication Devices Connected to Cellular Mobile Networks Through International Mobile Equipment Identity (IMEI) Identification, and critique its contents. If that regulation was based on a preceding regulation, simply trace back to it. Related regulations are usually listed in the preamble. Just look it up.

In my view, officials need to understand regulations from general to specific (top-down). For ordinary citizens like myself (and perhaps you too, because if you were an official you probably would not be reading this blog), it is easier to start from the specific regulation and work upward if needed (bottom-up). After years of doing this bottom-up approach, you will automatically understand the broader policy framework. You will start to see patterns – the policies you frequently criticise will keep pointing back to the same articles in the same law. Once you reach that point, you are ready to be an official.

What About Other Fields?

I am not deeply familiar with other fields. However, as a student working in industry, I inevitably need to know some related stakeholders. The two main ones are trade and investment. I have had to read Ministry of Trade regulations and Ministry of Finance regulations (related to import duties and corporate income tax) on several occasions. I have also visited the BKPM website multiple times to check investment-related regulations and data.

Critics in other fields should similarly familiarise themselves with the relevant regulations. I have repeatedly seen criticism directed at the government without specifying which level of government or which regulation. This is not inherently bad, but it would be far more useful if you can identify which regulation is being criticised, what changes are desired, and who should implement them. Without these, criticism becomes mere sniping and may not be very useful for problem-solving.

Generating public buzz around an issue is certainly important. But it is hard to solve problems if people just keep making noise without getting into the details.

In summary, when practising public policy, it is important to understand how the government implements public policy in Indonesia. In Indonesia, everything must be based on legislation. A good public policy practitioner must know whether the issue they want to critique already has a legal basis, whether there is already an institution handling it, so that the discussion can be more practical and not just abstract. With knowledge of the practical structure of public policy in Indonesia, your criticism can be sharper and more useful. Happy investigating!

Thorbecke 2019: Japan's Exchange Rate and Electronics Exports

Tue, 25 Feb 2020 00:00:00 +0000

For my first paper review post, I will cover a paper by Thorbecke that is certainly interesting. The paper, which examines how Japan’s electronics export competitiveness was affected by exchange rate movements, was recently published. It is fascinating to see how a manufacturing giant like Japan has been displaced by South Korea and Taiwan.

The paper starts with a compelling premise. Japan has long been a powerhouse in exporting electronic components and parts. Competition in components and parts is quite fierce, so Japan has to compete on price with other countries (mainly Taiwan and South Korea). Trouble came when the Global Financial Crisis (GFC) hit. Japan suddenly became a safe haven. Capital flowed in, the Yen appreciated, and export prices became expensive relative to costs denominated in Yen. This caused Japan’s electronic components and parts industry to lose competitiveness against other countries – an effect that persists to this day.

The paper uses a Vector Auto Regressive (VAR) method. The estimation takes roughly the following form:

$$ \Delta p_{jt}^x = \beta_0 + \sum_{i=0}^p \beta_{1i} \Delta e_{jt-i} + \sum_{i=0}^p \beta{2i} \Delta p_{t-i}^f + \sum_{i=0}^p \beta_{3i} \Delta c_{jt-i} + \sum_{i=0}^q \beta_{4i} \Delta y_{t-i}^f + u_t $$

where $p^x$ denotes Japan’s export price to a destination country, $e$ is the exchange rate, $p^f$ is the international market price, $c$ represents production cost, and $y$ is the producer price index in OECD countries. All data in the paper come from the Bank of Japan and the OECD. The model itself is fairly straightforward and could readily be replicated for other country contexts – provided, of course, that comparable data are available as those supplied by the Bank of Japan. The key identification assumption is that the sudden exchange rate change is treated as exogenous.

Upon reading this paper, the first thing I noticed was how convincingly the initial premise was built. The author opens with a simple yet impactful narrative supported by clear findings. The charts used (such as producer price indices and export price indices) strongly support the storytelling in the opening section. The model is not overly complicated either, relying on the assumption that the exchange rate is exogenous. Having a natural experiment like a financial crisis is certainly advantageous for a researcher.

Just kidding. Crises are bad. Nobody wants a crisis. Hopefully we will not see another one.

Anyway, this paper is very concise – only 9 pages. The results are as expected, and with the key variable assumed to be exogenous, there is no need for complex methods to deal with endogeneity bias. It is a fairly accessible paper for someone who is just starting to learn about VAR.

Media and Politics: How Television Changed Democracy

Sat, 22 Feb 2020 00:00:00 +0000

Introduction: A Visit to the Museum of Australian Democracy

About a year ago, my wife and I visited a museum in Canberra called the Museum of Australian Democracy. The museum occupies Australia’s old Parliament House (the Parliament now works in a new building). Inside, there are all sorts of texts and explanations about political and democratic events in Australia, as well as information about former Prime Ministers. Taking photos outside is free, but entry costs 2 AUD per person. We thought it was well worth it. Here are a few photos of us.

Special thanks to my wife’s friend Ennoy, who visited Canberra and invited us out. Yes, she was the guest yet she was the one who dragged us out of the house – that is how lazy we were.

Besides reading and watching video clips of Australian politicians’ speeches from bygone eras, the museum also offers a free tour called the highlights tour. This free tour is led by a volunteer at scheduled times. Visitors simply gather at a designated point – in our case, the hall – and when the appointed time arrives, the group sets off together led by the tour guide. Our guide was an elderly gentleman whose name we unfortunately forgot (sorry, grandpa). Let us call him “The Elder.”

The Elder walked us through the museum while explaining historically significant rooms from when the building served as Parliament House, recounting the dramatic and historic moments associated with each space. Although the tour lasted about 45 minutes, we were so captivated by The Elder’s storytelling that fatigue barely registered, and his passion for Australian politics was palpable.

The Elder’s tour inspired me in two ways. First, I was inspired to do the same in Indonesia when I am old. Volunteer for tours at the DPR building, pointing and saying “to my right is the seat where the honourable Mr Roy Suryo was caught on camera sleeping.” Second, I was inspired at the tour’s final stop, in the office of the last Prime Minister to work there before moving to the new building. The Elder blamed the invention of television (TV) as the turning point that marked the decline of Australian democracy.

What Does TV Have to Do with Politics?

According to The Elder, before TV, democracy was about public policy. Politicians debated the pros and cons of public policies. The mass media of the time – mostly newspapers and radio – covered these policy debates. Not everyone participated in the discussion either. According to The Elder, only educated people with access to newspapers (and sufficient reading comprehension) engaged in public policy debates. As a result, during elections, people voted for candidates with the best policies.

When TV arrived, democracy shifted to being about personality. Since the advent of TV, politicians became more interested in showing their faces than debating public policy. Debates may still have occurred, but according to The Elder, the focus of campaigning shifted from public policy to the candidate’s persona. Elections became popularity contests about figures, no longer about public policy.

The Elder’s complaint about TV sounds a lot like my complaint about social media.

Anyway, returning to the TV-and-politics story, I became curious whether The Elder’s concern could be generalised. It turns out he is not alone. This article by David Kaiser tells a similar story. According to Kaiser, TV helps people forget real problems and focus on fleeting pleasures. Worse, TV helps create demagogues – politicians whose image is polished to perfection but who are actually incompetent. The Apprentice has even been accused of being a major factor behind Donald Trump’s victory in the 2016 US presidential election.

This article by Matt Sailor echoes the same point. TV allows politicians to speak face-to-face with a far larger audience than any previous medium. TV also enables campaign content that is less factual and more negative than what other media of the era produced. And yes, Sailor also concludes that TV helped create popularity contests among candidates rather than policy contests.

There are several other articles you can find online, most with a similar tone. Unfortunately, I have no intention of committing to a deep dive into academic journals on this topic. But at my university, a PhD student named Chris Hoy researched the role of TV in shaping perceptions of inequality, and his preliminary findings indicate a positive correlation.

Why might this happen? Perhaps because visual media has more features? Unlike newspapers, on TV we can see the politician’s body language, which – consciously or not – matters a great deal. Perhaps even more than verbal communication. It is also possible that on TV, characteristics like attractiveness and height can be leveraged more than in print. That reminds me of the claim that President SBY won partly because he is tall and handsome.

Indeed, if we look here, books are almost always better than their film adaptations. According to this article, one reason is that when reading a book, you visualise the scenes yourself, and each reader may imagine something different. Film constrains that imaginative space. Someone with an average face like mine might be better off writing than vlogging. Who knows, a reader who has never seen my face might imagine the author as handsome – then see the real thing and lose all interest.

OK, enough speculation. If you are exploring the role of TV (or social media) in democracy and politics, I would love to hear from you or at least get a comment! Thanks in advance!

Public Policy Academics Who Have Not Evolved

As an academic working in public policy, this naturally led me to reflect. Suppose it is true that the evolution of media has changed how we do politics and democracy. What about academics? When I read about Adam Smith, Alfred Marshall, Hicks, Arrow, Slutsky, and others, not once do I think about who they are as people. What ethnicity? What gender? Old, young, overweight, good-looking? None of it matters. It does not come up on the exam.

The same goes for discussing public policy. If you think about it, academics seem to have stayed in place when it comes to discussing public policy. Perhaps some of us have been “infected” by society – focusing on figures rather than policies. But academics as a whole still seem to focus on issues rather than personalities. In public policy journals, the authors still discuss the policies taken by a figure, not the figure themselves. The language in academic journals is always neutral and very different from what you find on Twitter, for example.

Could this be because academics still primarily operate through written media – in Scopus-indexed journal articles? Perhaps if activities in other media, such as TED Talks, tweeting, and so on, counted toward academic credit, academics could also shift their focus to personalities. Perhaps we too would start being snarky, getting into fights, hurling insults, and having Twitter wars with strangers, instead of focusing on policy pros and cons. If Twitter wars counted toward academic credit, perhaps academics would evolve along with the rest of society, and better understand how people think outside the ivory tower.

OK, time for a Twitter war!

That said, there is a lot of encouragement for academics to use non-written platforms. At my university, for instance, promoting your research on social media is strongly encouraged. We now also have many TED-Talk-style videos, and we have come to know celebrity scientists like Dawkins and Neil deGrasse Tyson. In Indonesia, the most famous economist is perhaps Faisal Basri Batubara, and even he is not that mainstream. Maybe soon we can slowly evolve too. Not now. Maybe in five years?

will IA-CEPA be the boost to the Australia-Indonesia relationship leaders are banking on

Tue, 18 Feb 2020 14:44:20 +1000

Jakpos

Fri, 31 Jan 2020 15:01:51 +1000

Boosting Global Competitiveness in Indonesia: is Industri 4.0 the answer?

Thu, 18 Apr 2019 10:18:40 +1000

Can cryptocurrency help business to be more competitive?

Thu, 18 Apr 2019 10:18:40 +1000

How Trump’s steel and aluminium tariff shapes future world trade

Thu, 18 Apr 2019 10:18:40 +1000

Strategi sukses meraih beasiswa pendidikan lanjut

Tue, 05 Feb 2019 00:00:00 +0000

Strategi sukses meraih

beasiswa pendidikan lanjut

I Made Krisna Gupta
18 September 2020
Presentasi ini dapat diakses di krisna.netlify.app/talk/beasiswa

Tentang Saya

Penerima beasiswa Australia Awards Indonesia.
Kandidat doktoral ekonomi di Crawford School of Public Policy, Australian National University (ANU).
Fungsional dosen di Politeknik APP Jakarta.
Pendidikan terakhir:
- M.Sc. in Economics at VU University
- M.S.E. Ekonomi di Universitas Indonesia
Akses CV saya di krisna.netlify.app/#CV

Beasiswa yang pernah dicoba (S2)

DAAD (Jerman)
Chevening (UK)
USAID (USA)
NESO (Belanda)
KOICA (Korea Selatan)
LPDP
Australia Awards Scholarship (Australia)
Twente (Belanda)
NTUST (Taiwan)
VU Fellowship Program (Belanda)

Beasiswa yang pernah dicoba (S3)

ANU PhD Scholarship (Australia)
Chevening (UK)
Australia Awards Scholarship (Australia)

Daftar isi

tips umum
tips spesifik
tips extra untuk S3
penutup

Berdasarkan pengalaman pribadi

Informasi adalah kunci:
- Kenali diri sendiri dulu
- ikuti berita tentang beasiswa
- rajin membaca laman tentang beasiswa
Proses mencari beasiswa agak berbeda tergantung dari:
- dalam negeri vs luar negeri
- S2 vs S3

Kenali diri sendiri dulu

Kampus atau program apakah yang anda butuhkan?
- Apakah gelarnya berguna untuk karir?
- coursework or by research?
- Ranking fakultas
- Apakah ada beasiswa kampus?
- Ikuti lebih dari satu program
Pertimbangan finansial dan keluarga
- Tidak semua ditanggung beasiswa
- Potensi LDR

Kenali diri sendiri dulu

Beasiswa adalah kompetisi.
Beberapa privilege yang menguntungkan:
- keunggulan akademik (IPK, TPA, bahasa)
- punya uang lebih
- kenal orang penting / spv
No privilege? Work harder!
- perbanyak membaca dan menulis
- perkaya kegiatan sosial
- jadi dosen/peneliti 😜

Keep yourself updated

Selalu terdepan dalam informasi.
Ikuti sumber berita yang ter-update.
- akun sosial media beasiswa.
- ikuti forum khusus beasiswa jurusan tertentu.
- ASN biasanya punya portal internal
Kenali syarat yang sejenis.
- kopi transkrip, surat rekomendasi, tes bahasa inggris, TPA, LOA.
Catat tanggal-tanggal penting.

Keep yourself organised

Organisasi yang baik akan lebih efisien.
- Buat folder untuk setiap aplikasi.
- menyiapkan checklist sangat membantu.
- punya reminder / pengingat.
siapkan waktu dan ongkos untuk translasi dan legalisir.
take your time. Kalau submisi on-line, hati-hati traffic di detik-detik akhir.
Punya partner akan sangat menolong.

Keep yourself motivated

Tidak ada faktor yang lebih penting daripada niat. Selalu luangkan waktu untuk update informasi dan aplikasi

Beberapa tips spesifik

Setiap beasiswa punya persyaratan khusus yang mungkin berbeda satu sama lain

Kampus dulu atau beasiswa dulu?

Beberapa beasiswa mensyaratkan beasiswa dulu / tidak perlu Letter of Acceptance (LoA)
- Australia Awards, DAAD, dan Chevening
Namun informasi mengenai program yang dituju tetap harus dicari
- Modal untuk menjawab interview
- Kadang kampus punya beasiswa juga
Khusus PhD, sangat umum diwajibkan memiliki bukti korespondensi dengan calon pembimbing

Mendapatkan LoA

Untuk beasiswa seperti LPDP, LoA cukup penting
LoA dari LN malah relatif mudah
- kampus DN biasanya ada ujian saringan masuk / TPA
- LN mostly administratif

Kecuali jika anda kesulitan dengan bahasa inggris

Tes bahasa inggris

Nomor satu harus dipikirkan untuk LN hunter
Pastikan tes apa yang diakui
IELTS vs TOEFL
- IELTS (menurut saya) lebih mudah
- institutional TOEFL? yes or no?
Perlukah mengambil les persiapan?
- IELTS secara umum lebih murah
- beli buku, online, partner
- practice makes perfect, ala bisa karena biasa

Menulis essay

beasiswa pada umumnya mensyaratkan essay
mulai dengan kerangka yang baik
- gunakan tujuan beasiswa sebagai acuan
- di mana posisi kita?
baca kembali dan tanya: paragraf ini menjawab pertanyaan apa?
minta partner untuk membaca
perbanyak latihan (sekalian IELTS/TOEFL)

Menulis essay

beberapa beasiswa menyediakan form khusus
Informasi yang biasanya ditanyakan:
- memilih program
- kontribusi hasil studi
- cerita sukses
Konteks sangatlah penting
- kaitkan dengan pekerjaan
- kolaborasi dengan institusi / negara donor
Contoh essay saya dulu apply AAS

Wawancara

Wawancara pada umumnya menanyakan pertanyaan yang mirip dengan di aplikasi.
persiapan yang baik mengurangi derajat kepanikan dan meningkatkan rasa percaya diri
beberapa persiapan yang saya rekomendasikan:
- antisipasi pertanyaan
- persiapkan jawaban
- baca yang banyak tentang beasiswa
- latihan
Saya bikin list potensi pertanyaan AAS

Tips ekstra S3 untuk ASN

Struktural/manajerial lebih sulit (mungkin)
- susah dapat ijin (kelamaan)
- tidak banyak benefit bagi institusi
- juga bagi anda pribadi
sepertinya menjadi dosen membantu
- institusi mendapat benefit
- angkatan saya (AAS) banyak fungsional
Ilmu dari S3 sangat dalam dan spesifik

Tips ekstra S3 & S2 untuk swasta

Apakah beasiswa berguna untuk karir?
- S3 harus mikir lebih ekstra
- 5 tahun kerja vs 5 tahun sekolah
beberapa kantor mau re-employ
- tapi sangat jarang yang begini
- siap-siap mantab (makan tabungan)
akan lebih baik jika sudah punya tujuan kerja
- misalnya dosen / peneliti

Pengalaman pribadi (dosen)

Kontribusi dosen sangat mudah dan terukur:
- dosen PhD di Indonesia masih sedikit
- publikasi akademis sangat sedikit
Tidak perlu berkhayal terlalu jauh
- kolaborasi riset antar lembaga
Kerjaan mendukung untuk PhD
- otomatis rajin membaca dan menulis
- mudah dikontekstualisasi dengan aplikasi

Mempersiapkan research proposal

Pastikan proposal riset anda mengakomodir target beasiswa
- Australia Awards memiliki sektor yang ditarget secara spesifik
- beberapa beasiswa punya slot khusus tentang riset di form-nya
Wajib lihat persyaratan di situs fakultas:
- beberapa kampus minta hanya 3 halaman maksimal
- ada juga yang membebaskan, bahkan sampai 15 halaman

Mempersiapkan research proposal

Informasi yang wajib ada di proposal riset:
- pendahuluan, tekankan di latar belakang, motivasi, obyektif
- metode dan data (kalau sudah punya rencana mau pakai data apa)
- timeline. Apakah bisa selesai dalam jangka waktu S3?
Demonstrasi bahwa anda mengikuti literatur di bidang anda.
Tunjukan hal-hal yang sudah anda kerjakan
Contoh salah satu proposal pendek

Tips memilih pembimbing

Approach pembimbing biasanya wajib untuk beasiswa S3.
- Sebaiknya diutamakan
- profesor seringkali punya extra funding
- Beasiswa S3 juga bisa datang dari lembaga riset.
Baca situs kampus tentang mengapproach profesor: apakah sentralistik atau personal
Makin sakti, makin sibuk.

Tips korespondensi

Pastikan akun surel anda professional.
Pastikan anda tau dia profesor atau bukan.
- Supaya tau cara menyapa.
Pastikan anda punya informasi tentang orang ini.
- bisa gunakan situs seperti research gate
- lebih baik lagi jika paper beliau anda cite di proposal
Makin sakti, makin sibuk.
- jika beliau minta revisi, langsung kerjakan.
Jangan lupa diprint.

Tips setelah dapat beasiswa

Banyak hal yang harus dipersiapkan setelah diterima ~~misalnya bucket list tempat jalan-jalan~~.

Simpan rapi kontrak beasiswa
Catat langkah-langkah berikutnya
Persiapkan SK TB
Persiapkan paspor dinas

Tips ketika sudah sekolah

beasiswa (and life in general) is a long-term project.
mencari beasiswa S2 dimulai sejak S1
mencari beasiswa S3 dimulai sejak S2
- sekolah S2 yang bener
- jalin relasi dengan pembimbing
Selalu ingat ilmu padi
- jangan cepat berpuas diri
- Dirasani? balas dengan karya

Tips ketika sudah sekolah

Pastikan anda memang ingin sekolah
- rumput tetangga effect
- ingat yang tidak dapat
Terutama bagi PhD
- rate depresi cukup tinggi
- 5 tahun sekolah vs 5 tahun bekerja
- Make sure you love what you do.
- supervisor matters

Penutup

baca, baca, dan baca
No privilege? work harder.
- or be prepare to make ‘sacrifice’
Persisten dan jangan menyerah
Bersiap menyambut peluang

Penutup

Ada ratusan yang gagal dari setiap 1 kelulusan
- don’t waste the privilege
Evaluasi pasca-lulus biasanya tidak ada
- anda harus memikirkan sendiri pola karir anda
Dosen / peneliti jadi semakin menarik
- Indonesia kekurangan dosen dan peneliti
- Kesempatan meneliti di institusi terbaik
- usahakan publikasi
We need more Indonesian researcher

Krisna Gupta | Krisna 'imed' Gupta

Indonesia's router exports after the US FCC ban

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What the FCC ban means for Indonesia

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Government spending: back to “normal”?

But “real” spending is actually declining

The revenue problem

How does Indonesia compare?

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Melihat Indonesia lewat kacamata Ray Dalio: Bagaimana Negara Bankrut

About Ray Dalio’s Book

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TL;DR

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Probability Theory

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Indonesian trade and industry data 2018-2023 from SEKI Bank Indonesia

Analysis of the Effect of Indonesia-Japan Economic Partnership Agreement (IJEPA) on the Trade in Service Sector in Indonesia

Mobile Legends: Bang Bang

Guild Wars 2

Monster Hunter Wilds

Mobile Legends: Bang Bang

The trade balance and why we can't buy nice things

Labor-machine substitution in agriculture

Heckscher-Ohlin and factor intensity

Key takeaways & disclaimer

Appendix: A crash course on factor intensity

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Indonesia doubles down on nickel export bans and downstreaming

Greening the grid and what it takes

Visi Misi Pemilu 2024: Menakar janji industrialisasi dan hilirisasi Paslon Capres-Cawapres

The Heterogeneous Impact of Tariff and NTMs on Total Factor Productivity of Indonesian Firms

Indonesia’s stubborn logistics dilemma

Does ASEAN matter? How the summit tackles current challenges

Public policy analysis: a discussion

The Complexity of Evaluating the Performance of the Directorate General of Agro-Industry

Demographic bonus: blessing or curse?

Blessing or disaster?