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

Friday, March 16, 2018

36 Years of Inequality or: How I Learn to Stop Worrying and Love theProgressive 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:


Conclusions

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.

References:

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

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Appendix

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



Unlike
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.

References

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".

Statistics

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).

Conclusions

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.

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

References

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.


Results




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.

Conclusions

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.

Caveats

* 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%).

References:

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.



Datasource:
www.tax-index.org
data.worldbank.org
www.cepii.fr
freedomhouse.org



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.

Wednesday, August 16, 2017

How to Reduce Inequality in Indonesia

This piece is a more technical appendix of what I’ve wrote in Birokreasi about inequality. You can read the article here http://birokreasi.com/2017/08/rusak/.

In that article, I mentioned the research of Martinez-Vazquez, et al. (2014) concerning what fiscal policies (i.e., taxation and expenditure policies) that can reduce inequality.

The aforementioned study measures inequality by using Gini ratio, in which the closer it is to zero, the more equal society is. Martinez-Vazquez and co. found that progressive taxes such as corporate and personal income tax can reduce Gini ratio. Therefore, the better a country collects income taxes, the better she is in reducing inequality. It is also understandable as progressive taxation levies higher rate for entity with higher income.

On the other hand, Martinez-Vazquez, et al. found that regressive taxation (such as Value Added Tax) will increase inequality. It is understandable because in regressive taxation, the tax burden is similar across the population, regardless of their levels of income. Meaning that a billionaire and a farmer will pay the same amount of taxes when they purchase similar goods/services.

With regards to expenditure policy, generally it is found that the more a government dedicate their revenue to fund social expenditure, housing, education, and healthcare, the better she is in reducing inequality. As middle and lower income households are provided better access towards such needs by the government, not only they have better living standards, but they will also have more disposable income.

I am not interested in testing whether their findings are similar in the case of Indonesia. (My friend, Mikhail Nugroho Adi Setiawan, replicated Martinez-Vazquez, et al.’s study in his junior thesis. He found that their conclusions also hold for ASEAN countries.)

What I am interested to find out is how those policies play out during the periods after they were implemented. One of the tools for this is Vector Autoregression (VAR).

VAR models the how a variable (for instance, inequality) changes over time by the influence of its own lagged value (inequality in the previous year) and the past value of the variable that influences it (say, last year’s education expenditure). VAR modeling is also useful because it does not require as much a priori assumption about how the forces influencing a variable. Therefore, we can set aside econometric issues such as simultaneity and endogeneity. What is needed is a list of variables which can be hypothesized to affect each other within certain period of time.

The data used in this post is collected from IMF Government Financial Statistics, Laporan Keuangan Pemerintah Pusat and Anggaran Pendapatan dan Belanja Negara of various years, also Badan Pusat Statistik. The data I used in VAR modeling is from fiscal year 1990-2014.

To implement VAR, we must test how much lags are needed for the independent variable to affect dependent variable. For instance, increasing education expenditure for the current year may not directly reduce inequality for that year. The effect may be felt 2 or 3 years after the policy was implemented.

To obtain the best lags, we can subject the variables to a battery of tests. We can then select which one is the best based on several criteria. The usual criteria for lag selection test are, inter alia, final prediction error (FPE), Akaike’s information criterion (AIC), Schwarz’s Bayesian information criterion (SBIC), and the Hannan and Quinn information criterion (HQIC). Luckily, the command “varsoc” in statistical software STATA can do a comprehensive job.

After obtaining the appropriate lags, I subject all variables into VAR equations. However, reading VAR statistical output can be tedious.

To ease how we picture the relationship between fiscal policies and Gini ratio, I transform the VAR results into Impulse Response Function (IRF). In this case, fiscal policies such as corporate income tax (CIT), personal income tax (PIT), indirect taxes (INDIRECT), social expenditure (SOCIAL), housing expenditure (HOUSE), education expenditure (EDU), and healthcare expenditure (HEALTH) can be seen as the “impulse”. That is, they trigger a response in Gini ratio in the form of ups (worsening inequality) and downs (reducing inequality) in the periods that follow. To better illustrate how IRF works, imagine you throw a stone into a pond. The stone is the impulse, and the waves that show on the surface are the responses.

So, in terms of taxation policy, the IRF results are as follow:





In general, we can expect personal income tax to reduce Gini ratio in the next year after policy implementation. On the contrary, increasing VAT in the current year will lead to an increase in Gini ratio in the next year.



Interestingly, I found that an increase in corporate income tax may even worsen the inequality. There may be several causes. First, following the finding of Martinez-Vazquez, et al., the more open the economy, the higher share of the CIT that would fall on labor income, making this corporate income tax less progressive (Martinez-Vazquez, et al., 2014:110). Secondly, as in the case of Indonesia, our corporate income tax is not progressive to begin with. Currently, our corporate income tax rate is flat at 25%. (It started to be a flat tax since fiscal 2009, with the issuance of Law Number 36 Year 2008 concerning Income Tax, in which the corporate tax rate changed from progressive into single 28% rate). This may be the case to redesign our corporate income tax rate to be progressive again.

On the public spending side, the results are as follows:





An increased spending in healthcare and social expenditure will lead to a decrease in Gini ratio at least two years after implementation. An increase in education expenditure, however, increases Gini ratio one year after implementation, yet reduces it afterward in year two. Similarly, an increase in housing expenditure will increase Gini ratio in the first year.







It can be theorized that housing expenditure (at least in Indonesia) generally benefits middle-to-higher class instead of the poorest part of the population. In other words, it only enables middle to high class – who already have a steady stream of income – to accumulate more wealth. But this conjecture may be wrong. Thus, a further study is needed.

Conclusion: to reduce inequality, we need to better redistribute income via personal income tax. We also want to increase healthcare and social spending. A more progressive corporate income tax and a better-targeted housing policy are also needed if we want to reduce inequality.

(H/T to Mikhail for providing the necessary dataset.)

Reference:

Martínez-Vazquez, Jorge, Violeta Vulovic, and Blanca Moreno Dodson. 2014. “The Impact of Tax and Expenditure Policies on Income Distribution: Evidence from a Large Panel of Countries.” Hacienda Pública Española 200 (2012): October, 6th. 2014

Wednesday, September 28, 2016

Many Happy Birthday, N

‘Tis the time of the year when I hate to write. Not because it is a tedious task, but because this day is the day the siren of our old age rings again. I hate birthday because I don’t want to grow old. I don’t want you either to grow old. I hope you won’t get old. I want you to be forever young. You know, I always imagine you as a sempiternal doe, running around in the steppes. You are fascinatingly beautiful. So free and blissful, even death dares not conquer. That, my love, is my first and foremost prayer.

My second prayer is for you to be able to forgive me. I, for lack of better word, am nothing but thorns in your wilderness. I give you nothing but wounds. Yet you always give me grace and compassion. I took you – took us – for granted. I stopped listening to small things. I was clumsy and forgetful as ever. I harangued you even when you want me to just shut up. Yet you were always patient. Yet I did those mistakes again and again.

You offered me salvation. I put you on the cross instead.

I have nothing to give you as a birthday present but an apology for all these clashes I caused during this year.

Speaking of our little fights, here’s an equation I remember:
They say this is the key for lasting relationship. But what does it mean? It means that it would be better for a couple to argue on small problem and fix it as soon as possible than to keep the resentment build up. Of course, a relationship without a fight is more desirable. But it is impossible. After all, maybe it is true what Publius Terentius Afer once wrote in his comedic play: “Amantium irae amoris integratio est.” Fights are what bring lovers together.

So my next prayer is that I wish we can still argue about kittens and puppies and today’s millenials for years ahead, rather than become strangers again. Because I love you, and won’t cease to do so even when time and space divide us. I won’t cease, unless you wish for it.

Oh, and I hope we can travel somewhere. Of course you’ll be paying. Last year I wish you’d be richer than I am, and now you are. I’ll promise this time I won’t prefer staying on bed.

Live long. Live happily. And be prosper.

Amatus es. Ego semper amabo te.

Your kitten,


A


PS: Just this afternoon I saw a culinary travelogue on AFC. There's this bakpao vendor in Malaysia whose mom is still working in the kitchen, making the dough. She's 83. She's been working on the shop since 70 years ago. Yet she chooses to work there simply because she doesn't want to stay idle. I always imagine that how you'd be like 70 years from now.

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