"A man writes because he doubts, because he is tormented."

Sunday, July 1, 2018

How Much Is the Lossfrom Tax Avoidance?

This is a short post based on the research by Cobham and Janský (2018) which continues from Crevelli, De Mooij, and Keen (2015).

Here's the context: many countries in the world suffer from loss of tax revenues due to tax avoidance and/or tax evasion. One of the most common methods used by multinational corporations is to set up a subsidiary in the so-called tax havens non-cooperative or low-tax jurisdictions to divert their profits.

Having a subsidiary in non-cooperative/low-tax jurisdictions is not necessarily indicative of tax avoidance. Businesses may have legitimate reason such as access to market, diversification, or centralization of one of their business functions. Nevertheless, the concern of tax revenue loss cannot simply be ignored.

But how much is lost?

The study by Crevelli et al. (2015) tries to estimate how much a country loses their tax revenue from tax avoidance. Cobham and Janský (2018) tests and re-estimates this research. I'd like to try estimating the loss of Indonesia.

The Setup
A company avoid taxes in a country by diverting profit to its subsidiaries in other countries. The tax base of the aforementioned country is thus reduced and "spillover" to other countries.

But how does a company choose the jurisdiction of its subsidiaries?

In a micro-data, such as commercial database (that contains financial reports and jurisdiction of subsidiaries/headquarter), this is much easier and clearer to measure. In fact, many of the measures and monitoring tools for base erosion and profit shifting as outlined in OECD's BEPS Action Plan 11 utilize micro-data from commercial database.

Measuring in a macro (country-level) setting thus requires us to make assumptions. Since a company may create a subsidiary for access to market, we must assume that company does not pick a country purely for its low tax. A company may be hesitant to create a subsidiary in a small or faraway country. Or, given a choice of two countries with similar tax rate, a company will choose the country which is closer or has bigger market than the other one. Thus, we weight the effect of tax rate in every country of the world based on two things: distance and market size (that is represented by GDP).

The Model
We first estimate the φ and λ based on the equation:

, where bit denotes the corporate tax base in country i in time t; τit the domestic tax rate (in this case Indonesia); W-it τit a weighted average of the tax rates in countries other than Indonesia; Xit a vector of controls such as trade openness (import + export divided by GDP), share of agriculture to GDP, and log of GDP per capita in constant 2011 dollar; and μt is time specific effect. I employ LASSO (Least Absolute Shrinkage and Selection Operator) in the estimation to penalize model overfitting, as I don't have much data to begin with.

After we get φ and λ we plug them to equation:
for short term effect (L) and


 for long term effect (LL), where Whτ-it denotes the average tax rates of tax haven countries as listed by Gravelle (2013).

The Results
The loss of Indonesia's tax revenue as % of GDP in year 2008-2017 is as follows:

Interestingly, this suggests that if companies actually consider GDP as the main factor to create subsidiary in a country, there is a tax base spillover to Indonesia. Meaning that Indonesia actually gains from tax avoidance.

After I examine the data, it turns out that Indonesia's tax rate is lower than the rest of the world if weighted by GDP. This is understandable as small GDP countries with zero tax rates such as British Virgin Island matters less in this scenario than, for instance, Germany (whose tax rate approximately 30% is higher than Indonesia and whose GDP is much bigger than Indonesia as well.)

On the other hand, if a company seeks to establish a subsidiary in low tax jurisdictions near Indonesia (i.e. assuming it also wants to exploit agglomeration effect or to shorten supply chains), then Indonesia loses tax base approximately 3-5% of GDP.

Translating the above result into Rupiah, this is how much Indonesia loses (gains) the tax revenue:

This is a very crude estimation due to severe data limitation. Maybe I will update this if I have the time to collect more and better data. Maybe.


Crivelli E, De Mooij R, Keen M. 2016. Base erosion, profit shifting and developing countries. FinanzArchiv: Public Finance Analysis 72(3): 268–301. https://doi.org/10.1628/001522116X14646834385460

Cobham A, Janský P. 2018. Global Distribution of Revenue Loss from Corporate Tax Avoidance: Re-estimation and Country Results. Journal of International Development. UNU-WIDER.

Gravelle J G. 2013. Tax havens: international tax avoidance and evasion. Washington, DC. http://fas.org/sgp/crs/misc/R40623.pdf. Accessed 25 June 2018

OECD. 2015. Measuring and Monitoring BEPS, Action 11 - 2015 Final Report. Paris: Organisation for Economic Co-operation and Development.

Data is from World Development Indicator of World Bank; World Economics Outlook and International Finance Statistics of IMF; tax rate data is from KPMG Tax Rate Table, with some additional research for small jurisdictions; Badan Pusat Statistik; and APBN of Indonesia.

Monday, May 28, 2018

Inequality and Tax Evasion in Indonesia

Do rich people evade more taxes, compared to the less wealthy? This pose a problem for some scholars (and government). If the richest understated their income in their tax records – and disproportionately so compared to other taxpayers – the study of inequality which often based on tax records may be inaccurate. Inequality may actually be worse than what appears in Gini ratio.

In answering this question, Annette Alstadsæter, Niels Johannesen, and Gabriel Zucman (2017) conduct a detailed study by using data from Scandinavian countries. Scandinavians are arguably one of the places where the data quality is excellent. They are able to collect data from randomized audit results, provided by Scandinavian tax authorities (because the transparency policy there also grants easier access).

Despite the trove of quality data, they still theorize that audit can potentially fail to capture sophisticated tax evasion scheme done by the rich. So it is still possible that the resulting income in the tax assessment is understated. Thus, they combine the audit data with leaked data from offshore financial institutions (Swiss Leaks and Panama Papers). They also supplement it with information from recent tax amnesties (again, provided by Scandinavian tax authorities).

Based on Swiss Leaks, Panama Papers, and amnesties, Alstadsæter, et al. found that the top 0.01% richest households evade about 25% of the taxes they owe by concealing assets and investment income abroad. Adding the result from the tax evasion detected in random audits, the total evasion in the top 0.01% reaches 25-30%, versus 3% on average in the population.

The State of Inequality in Indonesia
In 2017, Indonesian Statistic Agency (Badan Pusat Statistik/BPS) reported that the Indonesia's Gini index is 39.1.  But you've probably heard/read the Oxfam-INFID paper in 2017. Combining the data from Credit Suisse Global Wealth Databook 2016 and the list of richest people in Forbes, the paper shows shows how the 4 richest Indonesians have more wealth ($25bn) than the combined wealth of 100 million Indonesians in the bottom 40% ($24bn). The same report also suggest that Indonesia is the 6th most unequal countries in the world, after Russia, Denmark, India, United States, and Thailand.

By using the similar data from Credit Suisse Global Wealth Databook (year 2017), I found out that the inequality in Indonesia is worse than official Gini index calculated by BPS.

74.8% of Indonesian wealth is owned by the top 10% richest. If we "zoom" it in further, 60% of wealth owned by the top 10% population is owned only by the top 1% richest. In other words, the top 1% richest owned almost half of total Indonesian wealth.
45.4% of total wealth to be precise.

If this distribution is correct, then the Gini ratio should be twice as worse at 83.7, instead of 39.1 as reported by Badan Pusat Statistik.*)

If we were to measure the average wealth of each population decile, the inequality is more pronounced.

The top 10% richest are, on average, 7.31 times more wealthy than the average Indonesian. Meanwhile the top 1% richest are, on average, 44 times more wealthy than average Indonesian. We can go further: top 20% are 287 times richer than the bottom 20%; top 10% and top 1% are 748 and 4,540 times richer than the bottom 10%, respectively. Look at how the average wealth of bottom 10% population did not even show up in the graph above. This is indeed staggering.

I compare the analysis by looking at the distribution of saving accounts in Indonesia. In this regard, saving accounts could be construed as a proxy of wealth (at least, financial wealth), similar to the HSBC data contained in the Swiss leaks used by Alstadsæter, et al. Indonesian Deposit Insurance Agency (Lembaga Penjamin Simpanan/LPS) keeps track of account ownership that are subsequently grouped based on the amount of money in those saving accounts. This is the source of the saving data.

In the LPS data, the state of inequality in Indonesia still looks astoundingly bleak. For instance, in 2017, 98.06% of saving accounts in Indonesia contain less than 100 million Rupiah in amount. But those 98.06% cumulatively only own 14.6% of total deposit in Indonesia.

In contrast, only 0.04% of saving accounts that have more than 5 billion Rupiah in it. But these 0.04% cumulatively own 46.17% of total savings.

To make it easier to imagine: if we scale down Indonesians into 10,000 people, then the richest 4 persons have 3 times more money in their bank accounts than the other 9,800 combined.

I then calculate the average amount in each saving group. Below is the result:

Note how similar this is to the distribution based on Credit Suisse data. The average amount in the lowest group barely show up in the graph at all, despite accounting for 98% of account ownership in Indonesia. The top group (account with > 5 billion Rupiah) on average contains 2,268 times more money than the average Indonesian saving account. This is an obscene picture of inequality.

Tax Evasion

Now we go back to the original question: do rich people evade more taxes? If I had a hand on the data as good as Alstadsæter, et al., combined with a more detailed distribution of wealth in Indonesia, I can get a more precise estimate.

In the case of Indonesian data, however, I face several roadblocks.

First, the distributional data in the World Inequality Database for Indonesia is inadequate. It does not have a detailed distribution for each percentile/decile. That being said, I have to make do with the Credit Suisse data. However, Credit Suisse does not report the range in each decile nor the standard deviations. I have to reconstruct the distribution points based on the assumption that the Indonesian wealth has uniform distribution, so it is efficient to compute its L-statistics to find out the range in each decile.

Second, because of the obvious confidentiality reason, I don't have the full amnesty data, only its statistics. Since the statistics are anonymized and aggregated (meaning I don't know who owns what and how much), I cannot cross them with the names in Swiss Leaks or Panama Papers. Similarly, I also do not have the data from tax audit to cross with the amnesty/leaked data.**)

Despite such limitations, I try my best to follow the method as outlined by Alstadsæter, et al. in their appendix. This is the finding of Alstadsæter, et al. in Scandinavian countries:

Using the available statistics and reconstructed distribution points, this is what I find in Indonesia:

In both cases, we can see that the probability of the rich to use tax amnesty is high, and the richer you are the more likely you use tax amnesty. Nonetheless, in Scandinavia, the probability for 90-95 percentile is quite close to 0%, while the probability for Indonesians in comparable wealth band (91-93 percentile) is twice that number. Admittedly, even the intragroup probability in the 90-100 percentile (top 10% richest) of Indonesia is in stark contrast compared to Scandinavia. You can see that in Indonesia it is skewed exponentially (i.e. increase very quickly) to the very richest compared to the much more steady, linear-like of Scandinavia.

If we consider tax amnesty as a sign of the previously undetected tax-related wrongdoings – be it evading, avoiding, or simply filing the tax return not in correct, complete, and clear manner – then the richest Indonesians disproportionately commit more wrongdoings compared to the general population. The top 1% richest of Indonesia is 16 more likely to use amnesty compared to average Indonesian (and 201 times more likely compared to the bottom 10%). This is after considering the fact that tax amnesty is basically open to all, and that there is no special treatment for the rich.

Unfortunately, this is the furthest I can do with the data. Given better access, it may be possible to gauge the post-amnesty compliance as outlined by Alstadsæter, et al., or by using macrodata similar to James Alm's study in post-amnesty Colorado. It is also interesting to combine audit data, leaked data, and amnesty data to paint a complete picture of tax evasion and inequality in Indonesia. But, alas.

In the end, it's likely that rich Indonesians have the means and opportunities to concoct tax-evading schemes. And being richer granted even more means and opportunities. "In the little game of tax demagogy," Piketty once wrote, "the weakest seldom come out winners."


Alstadsæter, A, N Johannesen and G Zucman (2017), “Tax evasion and inequality”, NBER Working Paper 23772. Appendix available at http://gabriel-zucman.eu/leaks/

Alstadsæter, A, N Johannesen and G Zucman (2018), “Who owns the wealth in tax havens? Macro evidence and implications for global inequality”, Journal of Public Economics, forthcoming.

Piketty, T (2016), "Chronicles: On Our Troubled Times", Penguin

Oxfam - INFID. (2017), "Towards a More Equal Indonesia", Oxfam International

Lembaga Penjamin Simpanan - Distribusi Simpanan Bank Umum Desember 2017
Badan Pusat Statistik
World Inequality Database, available at WID.world
Direktorat Jenderal Pajak
Credit Suisse (2017) Global Wealth Databook 2017. Credit Suisse Research Institute

*) Different methodologies could result in different Gini ratios for the same country. For instance, Badan Pusat Statistik use consumption data based on household surveys, while Credit Suisse combined household surveys with SUR and upward-adjustment based on available financial wealth data. Different bracketing could also affect Gini calculation. If you rank the population in decile (1-10) or percentile (1-100) or by 40-40-20 (poorest 40%-middle 40%-richest 20%), the resulting Gini ratios can be very different. If the wealth range in each distribution point is different, Gini can also be different; etc.

**) To my knowledge, Indonesia has yet to implement nationwide randomized audit program. The national audit program for individual taxpayers is focused on prominent people, corporate owners, and entrepreneurs. The good: rich Indonesians are given more scrutiny. The downside: there is not much information regarding the tax compliance of middle-to-low income class. Even if I have the audit data, it may not be representative to infer from these focused audit targets as samples of the general Indonesian population.

Friday, March 16, 2018

36 Years of Inequality or: How I Learn to Stop Worrying and Love the Progressive Income Tax

This post continues from the previous post. In the previous post, I wrote that one of the possible remedies for inequality is progressive taxation. Thomas Piketty advocates an extreme form of progressive income tax, up to 80% tax rate for the ultra-rich.

How could it be justified for Indonesia? Credit Suisse's Global Wealth Report 2017 shows that the top 1% richest Indonesians own 45.4% of national wealth, and top 10% richest own 74.8% of national wealth. If this does not seems obscene to you, take a look at the chart here:

The chart above shows Material Power Index (MPI), one of inequality measure developed by Winters (2013). By measuring the wealth of top 40 richest and divide it by per capita GDP, MPI approximates the relative economic imbalances between the ultra rich and the rest of us.

The MPI chart shows that the Indonesian ultra rich are about 500.000-600.000 times richer than average Indonesian. If we can scale the wealth down, theirs (on average) are worth a unit of apartment. The rest of us are (on average) worth a cup of instant coffee. Not even a cup of Starbucks.

Such obscenity probably warrants a more drastic measure, as what Piketty recommends. Although, income tax rate of 80% for the ultra rich is most likely not feasible politically.

The question, then, is whether a more progressive income tax may lead to a reduction in inequality if any, despite not being as high of a rate as what Piketty recommends.

Given that question, it is interesting to see how Indonesia fares.

On Personal Income Tax and Measurement of Its Progressivity

The first caution to be understood is that Indonesia does not have progressive corporate income tax since 2008. Added to that complication are certain rules such as turnover-based single-rate tax (final income tax according to Government Regulation no. 46 Year 2016) and the 50% discount for corporate taxpayer whose turnover does not exceed 50 billion rupiah (under Article 31E of Income Tax Law).

Secondly, the ultimate beneficiary of corporate profit is not the corporation itself, but the people associated with it: workers, managements, creditors, and shareholders – in the form of bonuses, interests, dividends, and/or capital gains. (Hence the economic double taxation that occurs when we tax dividends.) So, even though corporation could consume parts of its profit to expand, it could not be construed that corporation “enjoys” the profit in the traditional sense.

Additionally, in the event of liquidation, the excess value of the company will be distributed to those aforementioned people. Whereas, when a person dies, she can bequeath her excess value in the form of inheritance or estate. Both forms of distribution may indeed sustain or even increase inequality, but such effect can only manifest in personal level instead of corporate.

In light of those difficulties, it is more appropriate to measure the progressivity of personal income tax instead. There are several metrics to measure progressivity of an income tax regime. For example, the top statutory tax rate and the rate in each income bracket could be used roughly (at a glance) as indicators of how progressive the income tax is. US, whose top personal income tax rate of 39.6% applies for taxable income of more than $415,000, could be seen as less progressive than Australia, whose top personal income tax rate of 45% applies for taxable income of more than AUD 180,000.

A more refined measure is outlined by, inter alia, Benabou (2002) and Sabirianova Peter, et al. (2010), in which the latter will be used here. Sabirianova Peter, et al. (2010)'s methodology is as follows: first, we obtain per capita GDP of a country in that year. Next, we create a 100-level income distribution, ranging from 4-400% of the aforesaid per capita GDP. This results in 100 different gross income levels. Then, we employ the relevant tax schedule, i.e. apply the standard deduction for single taxpayer and appropriate tax rate. This will result in the tax liability for that particular income level. Finally, we regress the tax liability to the gross income to obtain the regression coefficient. Such coefficient, called ARP (average rate progression) is our progressivity measure of personal income tax.

If the ARP is zero/statistically insignificant, then the personal income tax is neutral. If the ARP positive and statistically significant, that means the personal income tax is progressive. Conversely, negative and statistically significant ARP means the personal income tax is regressive.

Here how the ARP of Indonesia looks like for the years 1981-2016:

The Effect on Inequality

If we graph Gini ratio and our income tax progressivity measure ARP, it would look like the chart below. I plot ARP on secondary axis because the difference in value range (ARP range from 0.01-0.03 while Gini range from 0.3-0.4) makes it visually difficult to see their fluctuations.

We can visually suspect that in the last 36 years (1981-2016) there seems to be a relationship between Gini and progressivity of income tax.

To gauge the effect of progressive personal income tax to inequality, I follow the methodology of Duncan and Sabirianova Peter (2012). I use Instrumental Variable (IV) estimation using ARP as the independent variable and Gini ratio as the dependent variable.

Here ARP is assumed to be endogenous, either due to simultaneity problem (see Slemrod and Bakija 2000) or by the existence of confounding variable. If we use OLS (Ordinary Least Squared), the estimates will be biased. So, I estimate ARP using several IVs, such as inflation, interest rate spread, exchange rate depreciation against USD, population, Freedom House index of civil liberties and political rights, and GDP per capita. Some variables are omitted (such as religion and corruption) because they are either irrelevant in non-cross country comparison or having insufficient data.

Based on the IV estimation of the data from 1981-2016, ARP (progressivity of personal income tax) is negatively correlated with Gini ratio (p-value: 0.013; 95% CI: -2.814528 to -0.33558101). To paraphrase: it is likely that the more progressive personal income tax, the lower income inequality will be. To make it easier to understand the result, I presented it in the graph form below:


We can see that as the value of progressivity get bigger (x-axis), Gini ratio is declining (y-axis), i.e. the more progressive personal income tax is, the lower the Gini ratio is. So, it may be the case that progressive personal income tax can reduce income inequality in Indonesia

How should Indonesian government proceed? I personally think that Indonesian government is somewhat ambivalent in this matter. You want to reduce inequality, which necessitates a transfer of wealth by means of taxation. But on the other hand, you do not want to upset the capital owners; the ones which – as the r > g theory suggests – exacerbate inequality.

80% tax rate for the ultra rich is good, if what Indonesian government aims is drastic reduction in inequality. The second best alternative is to gradually increase tax rate, to make it more progressive, and then hoping that inequality does not creep up faster than our tax reform.


Benabou, Roland. 2002. "Tax and Education Policy in a Heterogeneous Agent Economy: What Levels of Redistribution Maximize Growth and Efficiency?" Econometrica 70, 481–517

Husain, Ishrat and Ishac Diwan [eds]. 1990. Dealing with the Debt Crisis. Washington, DC : The World Bank.

Sabirianova Peter, Klara, Steve Buttrick, and Denvil Duncan. 2010. “Global Reform of Personal Income Taxation, 1981-2005: Evidence from 189 Countries.” National Tax Journal, 63(3): 447–478.

Duncan, Denvil and Klara Sabirianova Peter. 2012. Unequal Inequalities: Do Progressive Taxes Reduce Income Inequality? IZA Discussion Papers, No. 6910

Sargan, J. D. 1958. The Estimation of Economic Relationships Using Instrumental Variables. Econometrica 26: 393-415.

Solt, Frederick. 2016. “The Standardized World Income Inequality Database.” Social Science Quarterly 97(5):1267-1281.

Slemrod, Joel B. and Jon Bakija. 2000. Does Growing Inequality Reduce Tax Progressivity? Should it? NBER Working Paper No. w7576. Available at SSRN: https://ssrn.com/abstract=220033

Winters, Jeffrey A. 2013. Oligarchy and Democracy in Indonesia. Indonesia (96), 11-33. doi:10.5728/indonesia.96.0099

Wooldridge, Jeffrey M. 1995. “Score Diagnostics for Linear Models Estimated by Two Stage Least Squares”, in Advances in Econometrics and Quantitative Economics: Essays in Honor of Professor C. R. Rao, ed. G. S. Maddala, P. C. B. Phillips, and T. N. Srinivasan, 66-87. Oxford: Blackwell.

Data Source:
MPI: author's calculation based on Winters (2013)
Gini ratio: Solt (2016), Badan Pusat Statistik
Progressivity (ARP): author's calculation based on Sabirianova Peter, et al. (2010)
Civil liberties and political rights: Freedom House
Interest rate spread (interest on loans minus interest on deposits): Husain and Diwan (1990), World Development Indicator
Inflation (end of period): IMF World Economic Outlook
Per capita GDP (t-1, logged): IMF World Economic Outlook
Population (total, logged): IMF World Economic Outlook
Exchange Rate (Currency annual depreciation rate with respect to USD, end of period): IMF International Finance Statistics



ARP is correctly identified as endogenous using Woolridge (1995) robust regression-based test (p = 0.0040). However there may be misspecification in the equation, based on the result of Sargan (1958) test of overidentifying restriction (p = 0.0224).

Further, I wish to re-calculate the confidence intervals assuming weak instrumental variable. The results, using conditional likelihood ratio (CLR), Anderson-Rubin, Wald, and Lagrange’s K and J tests, are as follow:

Tuesday, February 20, 2018

r > g and Inequality in Indonesia

In his controversial book, “Capital in the Twenty-First Century”, Thomas Piketty outlines the apparent cause of inequality in the world. He espouses the notion of “r > g”, i.e. as long as the rate of return from capital (r) is bigger than the GDP growth (g), most of economic growth is accumulated by capital owners. This, Piketty says, worsens the inequality.

Even though Piketty vehemently denies that he is a Marxist, the concept of capital accumulation is indeed best elaborated by Karl Marx. In Marxist lens, it could be argued that “r > g” would naturally follow as long as capital owners appropriate surplus value and further accumulate wealth by valorisation of fictitious capitals. (Whether this condition can be dismantled by introducing wealth tax/extreme progressive taxation for the rich or by seizing the means of production, is where Piketty and Marx depart from each other.)

I'm not gonna explore further about Piketty vis-à-vis Marx. I am more interested in knowing whether Piketty’s “r > g” actually hold, especially in Indonesian context.

In a 2017 paper titled “The Rate of Return on Everything, 1870-2015”, Òscar Jordà and co. provided interesting tool to empirically probe this theory. In this short blog post, I try to apply their method with some modification. I modify some of Jordà, et al. specifications due to the unavailability of the data here in Indonesia. However, I would try to be as faithful as possible to their methodology.

Variable Construction

Jordà, et al. define r as the weighted average real returns (inflation-adjusted) of risky investments (housing and equities) and safe investments (government bonds and bills). The general equation is r = rhousing x whousing + requities x wequities + rbonds x wbonds. This method has its limits, however. Income from dividends, interests from private debts, or alienation of immovable property cannot be captured here.

For the rate of return on housing, Jordà, et al. uses price-to-rent ratio. I use Residential Properties Price Index (RPPI) instead. RPPI a composite index of housing price from 16 cities in Indonesia. As a caveat, RPPI also cannot capture rent income as other source of returns for house owners, as well as depreciation and maintenance costs that may reduce the returns of housing investment.

For the housing weight, the paper uses stock of housing wealth scaled to GDP. The absence of such data in Indonesia compels me to use real estate contributions towards GDP in lieu of housing stocks. This choice is actually problematic as it severely discounted weight because big portions of Indonesian housing wealth aren’t captured, although inequality in housing (or generally, land) is evident here. For instance, Indonesian land Gini coefficient shows increasing trend as shown by graph below. (A land Gini of 0 means everyone have the same amount of land; a Gini of 1 means every land in Indonesian is owned by 1 person).

As an important note, the paper by Jordà finds that housing comprises significant portion of investments in many countries.

For the rate of return on equities, I use the growth of IHSG (Jakarta Composite Index) as it is representative of stock market returns. The limit for this, of course is in its inability to capture private/unlisted stock values. Similar to Jordà, et al., I use market capitalization scaled to GDP as weighting.

For the rate of return on safe investments, I use the 10-Year Indonesian government bond yield. Again, following Jordà, et al., I use public debt to GDP ratio as weighting.

There are still other types of financial instruments that should actually be included for any discussion about inequality. For instance, a report by OJK (Financial Service Authority) in Q2 2017 shows that 48 conglomeration group own 66.96% of total financial assets in the financial service system (SJK). Further, according to the report by LPS (Deposit Guarantee Body) on October 2017, 56.87% of total deposit in banks are owned by only 0.11% of bank account. 98.07% of bank accounts own just 14.03% total deposits.

Regardless of the limitation, I shall proceed with Jordà's paper. All the above-mentioned variables are entered into the general equation specified before, resulting in the variable r. For g, I use real GDP growth. The inequality measure I use is Gini ratio. If r - g is positive, i.e. r > g, we would observe an increase in Gini ratio and hence an increase in inequalities. In other words, we want to know if changes in Gini ratio trail the value of the excess of r over g.

Results and Discussions

Jordà and co. which manage to collect data spanning hundreds of years, I only have 10 data points for Indonesia. There is not much degree of freedom here, so it may not be appropriate to use inferential statistics. (It would lead to bias). Nevertheless, by visual inspection it seems that whenever r minus g is positive, change in Gini ratio is also positive. In other words, when r > g, Gini ratio for that year is also increasing from the previous year. And vice versa.

I further add a trendline of those two variables (r minus g and changes in Gini ratio) and find out that they show similar trends. Thus, Piketty’s theory of r > g as the cause of economic inequality may be true here in Indonesia.
For additional comparison, here is how the net worth growth of 40 richest Indonesian (based on Forbes), compared with growth of per capita GDP of Indonesian people.

If we calculate r-g based on the difference between growth in 40 richest Indonesians' net worth and GDP growth, then compare it to changes in Gini, it would look like this:

So, what could be done in the light of such fact? The ultimate solution is like what Marx said: if means of production or the capitals are owned by all, then the returns from those capitals are appropriated by all. However, in a nation still haunted by the invoked spectre of the dead PKI party, proletariat revolution is politically hard to implement. It may as well be impossible.

A practical application of such non-capitalistic underlying principle actually exists: a co-operative. To be quite simplistic, there are no shareholders to appropriate the surplus value in a co-operative. Co-operative thus poses no agency problem. It also reduces the incentive to take aggressive/speculative profit-taking behaviors. So basically, if you manage to scale up this type of economy into the size of a nation, r will pretty much the same with g. (Some Marxists will see co-operative as insufficiently revolutionary, though. But still.)

What are the other options? Generally, as long as r < g, society would be getting more equal. So, financial crisis and war could theoretically be the great leveler. But we all do not want that. Thus, reducing the excess of r over g via super-progressive income tax combined with wealth tax, is one of the option to lessen the inequality. But this one is also politically hard to implement – in Indonesia or anywhere else.


Durand, Cédric. 2017. Fictitious Capital: How Finance Is Appropriating Our Future. Verso Book

Jordà, Òscar, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M. Taylor. 2017. “The Rate of Return on Everything, 1870–2015” Federal Reserve Bank of San Francisco Working Paper 2017-25. https://doi.org/10.24148/wp2017-25.

Piketty, Thomas. 2014. Capital in the Twenty-First Century. Harvard University Press

Scheidel, Walter. 2017. The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century. Princeton University Press

The data for RPPI is from Global Property Guide. IHSG and market cap data are from IDX. Indonesian government 10-Y bond yield is from Bank of Indonesia. Debt-to-GDP and real GDP growth data are from IMF. Real estate as portion to GDP, inflation, land Gini ratio, and GDP current price data are from BPS. I also use the reports from LPS and OJK.

Tuesday, December 19, 2017

Tax Attractiveness Is Not Attractive: A Lesson from Asia-Pacific Countries

During the last few months, many analysts were busy scrutinizing the tax cut plan proposed by Donald Trump and his cohorts at Republican Party. While the Wall Streets awaits in excitement, economists are generally wary.

The logic of tax incentive is simple yet deceptive. You as the government sacrifice some revenue from the foregone tax. You hope that the new investments will not only take over your duty (to boost the economy, to reduce the unemployment, etc.) but you will also reap rewards by taxing them in the future eventually.

The problem, however, is that this logic does not always hold in real life. Tax incentive induces aggressive tax planning, which can further distorts the economy and cause negative spillover to other countries (Oxfam, 2016).

Tax cuts also hinders the income redistribution efforts by government, which will widen the inequality. A study by Tax Policy Center, for instance, estimated that 82.8% of the benefit of the tax bill agreed to by House and Senate Republicans would go to the top 1% income. On the other hand, 53.4% of American households would see a tax increase. The costs of tax incentive may be too excessive. An estimation by Center on Budget and Policy Priorities projected that the same Republican tax bill would cost about $2.2 trillion over the first decade.

But what about the benefit? The benefit of tax break is often insignificant (see the latest and detailed study by Bartik 2017). A thorough analysis by Jason Furman and Lawrence Summers on Project Syndicates also highlights the many flaws of the alleged benefits of tax cuts. In one episode of Last Week Tonight, John Oliver even went as far to suggest – using Missouri-Kansas tax war as example – that it is more fiscally responsible to buy some people Ferraris and told them to ride around a bonfire made of burning money. Doing this inanity is 20 million dollar less costly than giving tax incentives, since the benefit of Missouri and Kansas tax break is essentially 0. (His rhetoric was extreme, but he was on point.)

Other studies such as Klemm and van Parys (2009), and Van Parys and James (2010) show that that there is no effect of tax incentives on total investment or economic growth. If there's any benefit, it appears to be modest and may not justify the forgone tax revenue (Chai and Goyal, 2008).

Even entrepreneurs only give tax incentive a lesser consideration, compared to other factors, when they are planning to invest/open a business. This is reinforced by the result of a study by UNIDO (2011). The UNIDO study shows that incentive ranks on the bottom of consideration factors. Incentive is also given lesser importance over time.

In fact, a lot of investors would still invest even if there is no tax incentive. The figures below show how many investors who'd still invest, according to studies in various countries compiled by Sebastian (2013).

Sebastian (2013) further argues that tax incentives do not create much jobs. Column (2) above gives the redundancy ratio, the percentage of investors who would have invested even without the tax incentives. Column (3) shows the percentage of jobs created by these marginal investors. Most of the numbers there above are negative, showing that tax incentives are often useless.

Measuring Tax Incentives Using Tax Attractiveness Index

Rather than measuring the effect of tax incentives to job creation (which requires a more "micro" data, whose collection is time consuming), I choose to go the "macro" way by analyzing the effect of tax incentives on FDI. Also, rather than using corporate tax rate, I use Tax Attractiveness Index (TAI) which is a rather comprehensive measure of tax incentives. I opt to use TAI because tax incentives can take many forms. Besides, many different forms of investment incentives is tax-related, but not generally included in the list of types of tax incentives, such as liberal safe harbors in transfer pricing rules, provisions that facilitate aggressive tax planning, and even tacit forms of lax tax enforcement (Zolt, 2015).

TAI covers various tax concessions beyond mere tax rate cut. Developed by Keller and Schanz (2013), TAI indexes anti-avoidance rules, CFC (Controlled Foreign Corporation) rules, corporate income tax rate, depreciation, membership in EU, group taxation regime, incentive for holding companies, loss carryback, loss carryforward, intellectual property/patent box regime, personal income tax rate, research and development incentives, taxation of capital gains, taxation of dividends received, thin capitalization rules, transfer pricing rules, tax treaty network, withholding tax rate of dividends, withholding tax rate of interest, and withholding tax rate of royalties.

Under the TAI framework, the less a country tax the income and regulate the taxes, the more attractive she is to attract FDI (Foreign Direct Investment), and vice versa. Tax havens have high TAI scores.

Since it can be argued that TAI is a more comprehensive measure of tax incentives than tax rates, I am interested in testing the assumption of “more incentive = more FDI” using TAI as the proxy. In this case, we can model TAI as the “cause” that leads to the increase of FDI inflow.

I estimate the result using panel data of 16 Asian-Pacific countries (Australia, Bangladesh, China, Hong Kong, India, Indonesia, Japan, South Korea, Malaysia, New Zealand, Pakistan, the Philippines, Singapore, Thailand, Taiwan, and Vietnam) from year 2007-2016. The choice of countries is conveniently made considering the availability of Tax Attractiveness Index data while still taking into account the possibility of agglomeration effect.

The data is taken from tax-index.org and Freedom House. Macroeconomic variables are taken from World Bank's World Data Indicator, except for Taiwan, which is taken from Asian Development Bank's Statistical Database System.

As usual, if you’re only interested in the results, proceed to "Results and Discussions".


I use the estimation based on Walsh and Yu (2010). The equation is:

Where where y denotes inward FDI as a share of GDP, X is the vector of macroeconomic and institutional variables, μ represents the time-invariant country-specific effects, ν is the error term. The macroeconomic factors included here are:

- openness (OPEN) which is export plus import scaled by GDP. The more active a country in international trade, the more it attracts investment
- real effective exchange rate (REER), to control the strength of domestic currency
- inflation (INFL), calculated as 3-year trailing average, to account for the reluctance to invest in high inlfation country
- GDP growth (GDPGROW) and logged GDP per capita (GDPPC)

The institutional factor included is Tax Attractiveness Index (TAI), which is our variable of interest. I also include Freedom House (FREE) to account for political stability.

All the variables involve can actually influence each others. For example, TAI may influence FDI, but FDI may influence tax policies which are reflected in TAI – the so called simultaneity. Simultaneity may cause ordinary least squares regression to be biased. To mitigate this bias, I employ Generalized Method of Moments (GMM). GMM is dynamic panel data technique proposed by Arellano and Bond (1991) that able to controls for simultaneity, unobserved country-specific effects, autocorrelation, as well as endogeneity. (Endogeneity means the variables are affected by other things outside the model.)

GMM transforms the equation above into first-differenced below

removes the time-invariant country-specific effects (μ), as it doesn’t change over time.

However, the original GMM (difference GMM) performs poorly if the dependent variable is close to random walk. Random walks happen when an apparent upward/downward trend is actually random. An oft-cited example of random walk phenomenon is stock price. I thus employ system-GMM developed by Blundell and Bond (1998) that is robust against random walk which may happen in FDI trend. The Stata module for system-GMM is provided by Roodman (2009).

There are other econometrics issues. First, my samples are too small*) to be able to handle lagged level of those variables and their difference as instruments. To reduce the instrument counts, I use Principle Component Analysis following, inter alia, Kapetanios and Marcellino (2010). To further account for the presence of heteroskedasticity and autocorrelation, the resulting standard error estimates are also made robust.

Results and Discussions

Tax Attractiveness Index is not statistically significant to FDI inflow (p-values = 0.438). Real exchange rate, openness, and freedom are statistically signifcant and positively correlated with FDI (all p-values < 0.05).

I repeat the calculation, this time using full instruments. The result remains similar: TAI is not statistically significant to FDI inflow (p-values = 0.513).


This study means that giving tax incentives, be it reducing tax rate and/or offering preferential tax regime, may not that significant in attracting FDI inflow. This finding may confirm that tax incentives are the of least concerns for the investors (at least within the context of this study).

This blogpost is just one of many studies that offer counter-narrative about the efficacy of tax incentives. So why does a country offer excessive tax incentives, or even going so far as to engage in tax competition with her neighbors? That is actually quite the puzzle. So whenever there's an argument for tax cut to attract investment, it must be taken with (lots of) grains of salt.


*) Kaiser-Meyer-Olkin score of sampling adequacy is 0.649, indicating mediocre samples but sufficient for factorability.


Arellano, M. and S. Bond. 1991. Some Tests of Specification for Panel Data: Monte Carlo Evidence and An Application to Employment Equations. The Review of Economic Studies 58: 277-97.

Bartik, T. J. 2017. A New Panel Database on Business Incentives for Economic Development Offered by State and Local Governments in the United States. Upjohn Research

Blundell, R., and S. Bond. 1998. Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics 87: 115-143

Chai, J. and R. Goyal. 2008. Tax Concessions and Foreign Direct Investment in the Eastern Caribbean Currency Union. International Monetary Fund Working Paper no. WP/08/257

Kapetanios, G., M. Marcellino. 2010. Factor-GMM Estimation with Large Sets of Possibly Weak Instruments. Computational Statistics & Data Analysis 54(11): 2655-2675

Keller, S. and D. Schanz. 2013. Measuring Tax Attractiveness across Countries. arqus - Working Paper No. 143.

Klemm, A. and S. Van Parys. 2009. Empirical Evidence on the Effects of Tax Incentives. International Monetary Fund Working Paper no. WP/09/136

Oxfam. 2016. Tax Battles: The dangerous Global Race to the Bottom on Corporate Tax. Oxfam Policy Paper, 12 December 2016

Roodman, D. 2009. How to Do xtabond2: An Introduction to "Difference" and "System" GMM in Stata. Stata Journal 9(1): 86-136

Sebastian, J. 2014. Effectiveness of Tax and Non-Tax Incentives in Promoting Investments – Evidence and Policy Implications. Investment Climate Advisory Services Policy Paper, The World Bank Group, Washington, DC

UNIDO. 2011. Africa Investor Report: Towards Evidence-Based Investment Promotion Strategies. United Nations: United Nations Industrial Development Organizations

Van Parys, S. and S. James. 2010. "The Effectiveness of Tax Incentives in Attracting Investment: Panel Data Evidence from the CFA Franc Zone". International Tax and Public Finance 17(4)

Walsh, J. and J. Yu. 2010. Determinants of Foreign Direct Investment: A Sectoral and Institutional Approach. IMF Working Paper no. WP/10/187

Zolt, E. 2015. Tax Incentives: Protecting the Tax Base. United Nations, Paper for Workshop on Tax Incentives and Base Protection New York, 23-24 April 2015

Wednesday, November 22, 2017

Do ASEAN Countries Compete on Their Taxation Policies? A Nash Game Approach

Government often compete on their tax rates to attract investment. One of the issues in tax competition studies is how to measure the extent of such competition. Do countries only compete on their statutory tax rates (i.e. the tax rates based on the tax regulation)? At face value, this seems to be the case.

It turns out measuring tax competition based on statutory tax rates is misleading. Because the effective tax rates – the tax you actually pay divided by taxable income – most likely be lower than the statutory rates. If two companies get different tax incentives, they will have different effective tax rates. In a nutshell, statutory tax rates are too narrow to measure various tax concessions a government may offer.

Alternative measures were then developed. Put forward by Devereux and Griffith (1999), Effective Average Tax Rate (EATR) and Effective Marginal Tax Rate (EMTR) are two such measures that are widely used. However, as Altshuler and Goodspeed (2002) noted, EATR/EMTR are more likely only valid within firm-specific/project-specific scope. Altshuler and Goodspeed themselves use corporate tax revenue divided by GDP as their tax rates measure. This also has limitations that it is too wide, because it assumes that tax collection/tax enforcement efforts are ceteris paribus, and that the cyclical effect of economy is negligible.

Tax Attractiveness Index

Tax Attractiveness Index (TAI) was developed by Keller and Schanz (2013), and further updated by Dinkel, Keller, and Schanz (2016). TAI covered 20 different components of real-world tax systems. Those 20 components are: Anti-avoidance rules, CFC (Controlled Foreign Corporation) rules, corporate income tax rate, depreciation, membership in EU, group taxation regime, incentive for holding companies, loss carryback, loss carryforward, intellectual property/patent box regime, personal income tax rate, research and development incentives, taxation of capital gains, taxation of dividends received, thin capitalization rules, transfer pricing rules, tax treaty network, withholding tax rate of dividends, withholding tax rate of interest, and withholding tax rate of royalties.

TAI codes and calculates all of the above-mentioned features to yield an index of 0 to 1. Under the TAI framework, the less a country tax the income and regulate the taxes, the more attractive she is. So a country with 0% tax rate for any income – personal or corporate – and no taxation rules as specified above, will have TAI score of 1. On the other hand, a country which tax any income with 100% rate, and has strict taxation rules as specified above, will have TAI score of 0. Most tax havens have the score of 0,9.

TAI is not too narrow as statutory tax rate, and not too wide as tax revenue per GDP measure. (It still has limitations, see “Caveat” below*.) But to my knowledge, there hasn’t any study using TAI as a measure in the tax competition setting. So this will be first.

ASEAN countries included in this blogpost are Indonesia, Malaysia, Singapore, the Philippines, Thailand, and Vietnam. The period covered is 2007-2016, based on the availability of Tax Attractiveness Index in www.tax-index.org. Data for the control variables are taken from World Bank and Freedom House.

What follows is a wonkish preambule about the statistics. If you're interested in the results, proceeds to "Results" and "Conclusions."

Specifying the Nash Game

I use a spatial autoregressive model to estimate Nash game between jurisdiction, in this case ASEAN countries. This technique is widely used to estimate strategic interactions (Duncan and Gerrish 2014; Devereux et al. 2008; Brueckner 2003; Altshuler and Goodspeed 2002). Nash game here meaning country A influences country B and C’s taxation policies, but is also influenced by B and C’s taxation policies, simultaneously. The choice of neighbor is based on geographical distance (using CEPII data).

I employ instrumental variable – 2SLS (Two-Staged Least Squares**), with tax attractiveness index of the neighbors as the instrumented variable. The distance from neighbors is then used to compute the weighting matrix. The fitted values for the neighbor’s TAI are then used as instruments in the second-stage regression.

The equations are as follow:

where τ is the Tax Attractiveness Index (coded TAX), indexed by country and year, W is the weight matrix, and ρ is the coefficient of tax competition. The lower equation is the first-stage regression used to estimate the predicted weighted neighbor TAI. Here α is a vector of parameters on the instruments, γ is a vector of parameters on the exogenous regressors, and υit is the first stage error term. Wτ j≠i,t is the predicted weighted tax rates derived from the first stage regression.

The vector X is a standard set of controls used in the literature. I follow Duncan and Gerrish (2014) to use logged per capita GDP (GDP), inflation rate (INF), trade openness in constant prices (imports plus exports divided by GDP, coded as OPN), government expenditures as a percent of GDP (EXP), the Freedom House index of political freedom (FRE), the proportion of the population both over 65 (OLD) and under 15 (YOU), and the size of the rural population (RUR), μi and λt are country and year fixed effects, respectively. Neighbors’ variables will have the letter N- before the coding.

Kleibergen and Paap Lagrange multiplier test suggests that the instrumental variables are rightly identified***. Additionally, the Hansen J test of over-identifying restrictions does not reject the null hypothesis of a well-identified model. Therefore the instruments seem reasonably well suited for this purpose. Standard errors are made robust even in the presence of heteroscedasticity and autocorrelation.


NTAX (neighbors’ Tax Attractiveness Index) is statistically significant at 5%. Interestingly, the negative coefficient on NTAX suggests that an ASEAN country adopts stricter taxation policy when her neighbors adopt looser policy. And the opposite is also true. An ASEAN country adopts looser tax policy when her neighbors get stricter. Following Duncan and Gerrish (2014), while a priori expectation is a positive coefficient, it theoretically can take on any value between −1 and 1, which indicates strategic substitutes and complements, respectively. In this case, what happens is strategic substitutes.


The empirical findings in the tax literature seem to converge on values ∈ (0, 1], i.e. strategic complements, which is when countries compete to offer low tax rates or looser taxation policy as a response – the so-called race to the bottom. However, studies by Brueckner (2003), Altshuler and Goodspeed (2002) also find negative responses as what happens here.

What happens here? Either one of these: an ASEAN country opportunistically offering more taxation incentives to attract investment as her neighbors tighten their tax policies. Or, a country enacts stricter taxation rules to mitigate tax avoidance. This serves as a response to neighbors offering lax taxation rules. It's not a tax competition in the traditional, race to the bottom sense. This warrants further study.


* The limitation of TAI is that it assumes that a multinational corporation (MNC) exploits taxation rules holistically instead of partially. In reality, this is not the case. Let me give you an example. Country A offers R&D incentives, country B offers no withholding tax on interest income, and country C offers low corporate income tax rates. Suppose TAI of A is better than B and C. By TAI’s assumption, an MNC will prefer to locate in A. This is not what happens in real life. Most likely this will happen:
The manufacturing will be located in country C. The research division will be located in country A. The loan for making the factory in country C will be funneled by a special purpose vehicle in country B.

In other words, it is not accurate to assume that those 20 components of TAI should be given equal weights. Tax planning strategy of an MNC will utilize the tax rules of different countries, for different purposes. This is also what happens in "treaty shopping"/"treaty abuse" cases.

** The usage of 2SLS in small sample statistics is justified by Bollen (1996). Monte Carlo experiments also shows that 2SLS performs well in small sample, see Islam (1998). For similar usage of IV estimation in small sample, see Acemoglu, Johnson, and Robinson (2001)

*** The result of Kleibergen-Paap Lagrange Multiplier (LM) indicates a rather weak instrument (only significant at 10% instead of 5%).


Acemoglu, D., Johnson, S., and Robinson, J. A.. 2001. The Colonial Origins of Comparative Development: An Empirical Investigation. The American Economic Review, Vol. 91, No. 5 (Dec., 2001), pp. 1369-1401

Altshuler, R. and Goodspeed, T. J. 2002. Follow the Leader? Evidence on European and U.S. Tax Competition. Mimeo

Brueckner, J. K. 2003. Strategic Interaction among Governments: An Overview of Empirical Studies. International Regional Science Review 26 No. 2, pp. 175-188

Bollen, K. A. 1996. “An Alternative Two Stage Least Squares (2SLS) Estimator for Latent Variable Equations”. Psychometrika, March 1996, Volume 61, Issue 1, pp 109–121

Devereux, M. P. and Griffith, R. 1999. The Taxation of Discrete Investment Choices. The Institute for Fiscal Studies. Working Paper Series No. W98/16

Devereux, M. P., Lockwood, B. and Redoano, M. 2008. Do Countries Compete over Corporate Tax Rates? Journal of Public Economics 92, pp. 1210-1235

Dinkel, A., Keller, S., and Schanz, D.. 2016. Tax Attractiveness and the Location of German-controlled Subsidiaries. Review of Managerial Science, pp 1-47

Duncan, D., and Gerrish, E. 2014. Personal Income Tax Mimicry: Evidence from International Panel Data. Int Tax Public Finance (2014) 21, pp. 119–152

Islam, N. 1998. Small Sample Performance of Dynamic Panel Data Estimators: A Monte Carlo Study on the Basis of Growth Data. Emory University Department of Economics Working Paper

Keller, S. and Schanz, D.. 2013. Measuring Tax Attractiveness across Countries. arqus-Working Paper No. 143.


Wednesday, September 27, 2017

On 26

"Lord of my love, to whom in vassalage
Thy merit hath my duty strongly knit,
To thee I send this written embassage,
To witness duty, not to show my wit:
Duty so great, which wit so poor as mine
May make seem bare, in wanting words to show it..."
(William Shakespeare - Sonnet XXVI)

Reading Shakespeare's 26th sonnet feels like a kindred spirit to me. He, as I do, felt an agony, that having a "wit so poor" makes his poetry seem bare, for his lord of his love is so great that there are no adequate "words to show it."

Verbiage and profundity are of no use. So, let me speak about your 26th year in a parlance that I am familiar with. For, as Pablo Neruda also said in his 26th sonnet, "...in every pure landscape, the earth has imitated you."

You may think 26 as a mundane number, unwarranted for further peroration. But behind its unremarkable facade, lies interesting things.

I can speak of 26 as the atomic number of iron. Seems trite, indeed, as iron is the most common element of the earth. But lest you forget, iron is the backbone of mankind and its civilization – in our machines, in our houses, in our weapons. It is also there as the core of the earth, and it is also there in blood that runs in our veins.

26 is also the number of letters of Latin alphabets, without which there will be very little ideas to communicate.

26 is, quite simply, what moves our world. And so when you think that this year is just another year, you may miss its importance to your life.

In fact, 26 in itself is not just a boring number. It is the only solution for Fermat's sandwich theorem. Named after the great mathematician, Pierre Fermat, who was the first to prove that 26 is the only number between a perfect square number (25, which is 5^2) and a cube (27, which is 3^3).

You can count from 1 to infinity, and you would find that 26 – and only 26 – is uniquely sandwiched between a square and a cube. And so is this year may not like any other in your life.

You know, N, things are actually getting a bit more interesting if you mix numbers with the words of God. YHWH – the name of biblical God – for instance, turns into 26 when you assign it using gematria. (Yod is 10, Waw is 6, and He is 5 each. So together 10 + 5 + 6 + 5 makes 26.)

But I like this one better: if you search at Greek Strong's Concordance, the 26th entry is ἀγάπη – agape, which means love.

So may your 26th birthday be full of Godly blessing, and be full of love.

Happy birthday, N.

With love, A.

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