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

Tuesday, December 19, 2017

Tax Attractiveness Is Not Attractive: A Lesson from Asia-PacificCountries

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


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