Running head: Taxes and Inequality

Taxes and Inequality. Does higher progressivity of income taxation lead to less inequality?

A Thesis

Presented to the Faculty

of ISM University of

Management and Economics

in Partial Fulfillment of the

Requirements for the Degree of

Master of Financial Economics

by

Žilvinas Šilėnas

May 2015

Taxes and Inequality 1

Abstract

The paper explores theoretical and empirical evicence on whether progressivity of

personal income taxation lead to less income inequality. The paper demonstrates that

there is a lot of theoretical evidence, which question the simplistic notion that more

progressivity lead to less inequality and vice versa. Paper provides comprehensive review

of the spectrum of possible causal links between progressivity and inequality. Then the

paper develops an empirical indicator to measure progressivity of personal income tax. It

is used to estimate empirical relations between progressivity and income inequality in 27

European Union countries for the period of 2007-2013. The paper finds a weak

relationship between a country being a flat-tax country and smaller reduction of income

inequality measured by at-risk-of-poverty rate (but not with GINI inequality). However a

statistical test finds no meaningful relationship between progressivity and reduction of at-

risk-of-poverty income inequality. The paper concludes that it is impossible to find clear

and unambiguous relationship between progressivity of income tax and income

inequality. It suggests expanding the scope of research and including analysis of effects of

transfer systems.

Taxes and Inequality 2

Contents Introduction ...... 5 Literature review ...... 7 Terms and definitions used in this paper ...... 7 What is income inequality? ...... 7 What is a progressive PIT? ...... 8 Why would a more progressive income tax reduce indicators of inequality? ...... 11 The composition of widely used indicators of inequality...... 11 Why do societies engage in redistribution and reduction of inequality ...... 15 Review of academic literature ...... 20 Relationship between inequality and progressive taxation ...... 20 The effect of progressivity of tax system on inequality ...... 26 Is less progressivity related to more inequality? ...... 31 Other important factors and caveats ...... 33 Conclusion and directions for empirical research ...... 35 Methodology ...... 37 Methodology for comparison between flat and progressive PIT ...... 37 Methodology for testing the relationship between progressivity and inequality...... 38 Data ...... 38 Construction of progressivity indicator of personal income tax (PIPIT) ...... 43 Approach 1 – Linear approximation ...... 44 Approach 2 - Logarithmic approximation ...... 51 Approach 3 - GINI coefficient for PIT ...... 56 Empirical research and discussion ...... 67 Do progressive PIT systems reduce inequality more than flat PIT systems? ...... 67 Investigation of GINI data ...... 68 Investigation of at-risk-of-poverty (AROP) data ...... 73 Does higher progressivity reduce income inequality more? Comparison of countries with progressive PIT systems ...... 79 Conclusions ...... 83 APPENDIX ...... 90

Taxes and Inequality 3

List of figures Figure 1 Overview of most relevant findings in economic literature reviewed regarding causality, effect, and direction of the relationship between inequality and redistribution (progressivity) ...... 36 Figure 2 Illustration of personal income tax (PIT) rates at different levels of income ...... 43 Figure 3 Results of R squared for PIPITLIN of 2012 data ...... 46 Figure 4 Indicators of progressivity of PIT rates for EU countries with a progressive PIT rate system (in ascending order) using linear estimations ...... 47 Figure 5 Indicators of progressivity of PIT rates for EU countries with a progressive PIT rate system. Arranged from most progressivity to least progressivity according to PIPITLIN values ...... 48 Figure 6 Changes in PIPITLIN 2012 -2014 and factors behind the changes...... 49 Figure 7 Detailed illustration of PIT brackets, rates and PIPITLIN values for select countries 51 Figure 8 PIPITLOG vales using logarithmic estimation for years 2012 and 2014* ...... 52 Figure 9 PIPITLOG values using logarithmic approximation for France, Luxembourg and Portugal; 2012 and 2014 ...... 53 Figure 10 Comparisons of changes in PIT structures in Malta and Slovenia ...... 54 Figure 11 Comparison of Linear and Logarithmic PIPIT. Minimum, maximum values, standard deviation ...... 54 Figure 12 PIPITLOG values using logarithmic estimation for years 2012 and 2014 ...... 55 Figure 13 Conceptual representation of GINI coefficient for PIT ...... 57 Figure 14 Conceptual representation of calculating area between income brackets and EIPITR ...... 58 Figure 15 PIPITGINI area with EIPITR of 300.000 Euro ...... 61 Figure 16 PIPITGINI area with EIPITR 100.000 Euro ...... 61 Figure 17 Values of PIPITGINI with EIPITR of 300.000 Euro and 100.000 Euro (in ascending order) ...... 62 Figure 18 Percentage difference between PIPITGINI with EIPITR of 300.000 Euro and PIPITGINI with 100.000 Euro* ...... 63 Figure 19 Difference in SOR score and income bracket gap between 300.000 EUR and top PIT bracket ...... 64 Figure 20 Difference in SOR score and top PIT rate ...... 64 Figure 21 Examination of changes in PIT structure and capture of the effects by PIPITGINI .. 65 Figure 22 Difference between Eurostat and Euromod data for AROP and GINI values for countries with progressive and flat taxes ...... 68 Figure 23 GINI Income inequality measured before-taxes, after-taxes, and after-taxes & transfers ...... 69 Figure 24 Average reduction of GINI income inequality by direct taxes in countries with flat and progressive PIT systems. Comparison of average GINI inequality before-taxes and after- taxes (but before transfers). Reduction measured in percent and absolute value...... 72 Figure 25 Summary of distribution of flat PIT countries throught terciles of GINI inequality of income before-taxes and after-taxes** ...... 73 Figure 26 AROP Income inequality measured before-taxes, after-taxes, and after-taxes & transfers ...... 74 Figure 27 Average AROP reduction across countries for income before-tax and after-tax. Flat PIT and progressive PIT systems ...... 75 Figure 28 Summary of distribution of flat PIT countries through terciles of AROP inequality of income before-taxes and after-taxes** ...... 76 Taxes and Inequality 4

Figure 29 Distribution of AROP inequality reduction (percent) across terciles. Shaded values indicate countries with flat PIT. Larger values mean larger reduction of AROP inequality ... 79 Figure 30 Regression results of progressivity and reduction of inequality ...... 80 Figure 31 Regression results of progressivity and reduction of inequality for entire sample 2010-2012 ...... 81 Figure 32 Regression results of only AROP reduction and progressivity indicator PIPITGINI 82 Figure 33 Visualization of correlation between AROP and PIPITGINI. Entire sample 2010- 2012...... 82 Figure 34 Movement of flat PIT countries across terciles of countries ranked by GINI income inequality 2007-2010. The number represents GINI value of individual country, letter „F“ indicates country has a flat PIT system ...... 90 Figure 35 Movement of flat PIT countries across terciles of countries ranked by GINI income inequality 2011-2013. The number represents GINI value of individual country, letter „F“ indicates country has a flat PIT system ...... 91 Figure 36 Movement of flat PIT countries across terciles of countries ranked by AROP income inequality 2007-2010. The number represents AROP value of individual country, letter „F“ indicates country has a flat PIT system ...... 92 Figure 37 Movement of flat PIT countries across terciles of countries ranked by AROP income inequality 2011-2013. The number represents AROP value of individual country, letter „F“ indicates country has a flat PIT system ...... 93

Taxes and Inequality 5

Introduction

In recent years income inequality has become a widely discussed topic. The discussions started with the “99% vs 1%” rhetoric and culminated with Piketty’s “Capital in the Twenty-

First Century”. Increasing numbers of people, politicians and academics feel that inequality is a negative development per se and that something needs to be done about it. Various measures to combat inequality are proposed, most of them concentrating on redistribution.

Primarily, taking from “1%” and giving to the “99%”.

Progressive taxation has always been a proposed measure to reduce income inequality.

Progressive taxation used to dominate the second part of 20th century as the main type of taxation of income (and still does). However ever since Alvin Rabushka’s work on proposing the flat tax (Rabushka & Hall) in 1990-ies and the corresponding adoption of flat1 tax by former Soviet economies, the flat tax has been receiving both: criticism and praise. One of the biggest criticisms levied against the flat tax is its alleged failure to reduce income inequality. We can still see this debate every year in Lithuania, where opponents of flat tax propose introducing a progressive tax and their most prominent arguments center around the idea that the progressive income tax will reduce inequality.

This work is dedicated to answer the question whether more progressive personal income tax

(PIT) systems reduce income inequality. The question needs two approaches. First, does progressive PIT reduces income inequality more than flat PIT? Second, does more progressive PIT reduces income inequality more than less progressive PIT? These two question are related: if there is no discernable difference in reduction of income inequality between flat and progressive PIT, then any statistical relationship between higher

1 Some authors insist that proportional PIT rate should not be called “flat”, and that for a tax to qualify as “flat” a country has to have a proportional PIT and CIT (corporate income tax) of equal size. However in this work proportional and flat will be used interchangeably. Taxes and Inequality 6 progressivity and larger reductions in inequality may just be a coincidence, or a manifestation of other factors at play. In other words, if higher progressivity of PIT is a cause for higher reductions of inequality we should be expecting to observe this effect in both areas: comparing reduction of inequality under progressive and flat PIT systems as well as comparing among PIT systems with different progressivity.

The answers to these questions are significant in theoretical and practical terms. If there appears to be no clear relationship between higher progressivity and larger reduction of inequality, then one may question whether the calls for even higher taxes on the rich can be justified on the grounds of reduction of inequality. Since absence of this link in very counter- intuitive, in practice it is not the lay-people, but economists and decision makers, who are the primary audience of this research. In theoretical terms, the findings of no relationship between progressive taxation should encourage economists to re-examine their reasons behind the support of progressive taxation as well as to divert attention of measures other than taxation for reduction of income inequality.

This research has two distinct parts. The first part (Literature review) shows that that academic literature offers nearly all imaginable relationships (and directions of relationships) between progressivity and inequality. The second part (methodology and empirical research) constructs an indicator to measure progressivity of PIT. This paper goes at great lengths to illustrate and justify its selection of indicator, showing flaws and shortcomings of linear or logarithmic approximations of progressivity. Then it proposes a new indicator, constructed on the principles of GINI coefficient. The paper puts this indicator through rigorous test to show that it correctly estimates progressivity where linear and logarithmic approximations fail.

Finally the paper compares reductions of income inequality between more progressive and less progressive PIT systems. First it compares reductions of income inequality by flat and Taxes and Inequality 7 progressive PIT systems. Then it uses a regression analysis to test whether higher progressivity is related to higher reductions in income inequality.

Conclusions, discussions and suggestions are presented afterwards.

Literature review

This part defines terms used in this paper, explains what economic theory postulates on relationship between inequality and progressivity of taxes, and reviews relevant literature in this field. All this builds a foundation for creating methodology of this research as well as a direction of research.

Terms and definitions used in this paper

What is income inequality?

There are different types of economic inequality that could be measured. First, Inequality of income, which focuses on the inter-personal distribution of income, capturing how individual or household incomes are distributed across the population at a point in time. Inequality of wealth would focus on the distribution of wealth across individuals or households; it reflects differences in savings as well as inheritance. Lifetime inequality would focus on measuring inequality in income of individuals over their lifetime, rather than for a single year. (IMF

Staff Report, 2014).

The difference between inequality if wealth and inequality of income is significant and profound. Even though both capture a snapshot of a particular period, inequality of income could be compared to measurement of flow, whilst inequality of wealth could be compared to stock. It is quite possible to have high income and low wealth (e.g. high earning individual with a lot of debt or a frivolous lifestyle). Alternatively it also possible to have high wealth and low income, e.g. a retired person owning real estate, wealth (e.g. gold), and no debt. Taxes and Inequality 8

This distinction is often lost in political debates where the focus falls on income rather than wealth. In majority of the countries income rather than wealth is subjected to higher taxation.

The probable reasons are numerous but we can mention a few practical ones. First one might have wealth but not a liquid income to pay tax (not all wealth produces income). Second taxing of wealth requires constant re-evaluation of property in monetary terms, whilst income is usually expressed in monetary terms. Third, taxation of wealth can be interpreted as double taxation, since wealth was accumulated from the already-taxed income (although this argument does not prevent taxing revenue from interest, capital gains of dividends).

Therefore when it comes to fiscal policy and arguments about inequality, the debate tends to concentrate on inequality of income rather than inequality if wealth. Thus inequality of wealth at a given period of time is not the focus of this research. Lifetime income inequality would be a fascinating topic but it suffers from lack of data. Especially if one wanted to make meaningful cross-country comparisons.

What is a progressive PIT?

In very broad terms progressive income taxation is a set of tax rates, which tax income at higher rates as income rises. This results in increase in taxes as a percentage of income as income increases (Hagonian, 2011). This is in contrast with per-capita, proportional2 and regressive tax systems. However this does not take into account that some proportional PIT systems only tax income above a certain threshold or exemption (which makes them technically progressive). Another approach would define progressive tax as a system where a

2 Proportional PIT systems are often also referred to as “flat”. The two term will be used interchangeably in this paper. Taxes and Inequality 9 taxpayer with a higher income should pay at least as much rate of tax as a taxpayer with a lower income (Ju & Moreno-Ternero, 2007).

However when it comes to levels or degrees of progressivity, e.g. how to determine which of the two PIT systems is more progressive, there is some ambiguity. Jakobson (Jakobsson,

1976) lists a set of four criteria: (1) Average rate progression (the derivative of the tax rate with respect to income before-tax); (2) Marginal rate progression (the derivative of the marginal tax rate with respect to income before-tax); (3) Liability progression (elasticity of tax liability with respect to income before-tax); (4) Residual income progression (elasticity of income after-tax with respect to income before-tax). They are compatible with the principles mentioned above, but provide a more thorough approach.

The four approaches differ in two fundamental ways. (1) and (2) are estimating progressivity based on a derivative of tax rates applied to income. While (3)and (4) are estimating progressivity on a derivative of income left after-tax. We could broadly classify the two approaches as ex ante and the last two as ex post. Ex ante approach estimates the progressivity by estimating tax rates. Ex post estimates progressivity by estimating after-tax income. We shall use this distinction throughout the paper. Another way to illustrate the difference between ex ante and ex post is in the following way. Ex ante approach estimates the intentions behind the PIT system, while ex post estimates the effects.

A lot of authors seem to prefer a more ex post rather than ex ante approach especially when it comes to measurement of progressivity of PIT systems. In part because ex post approach allows to incorporate exemptions, deductions and other irregularities of the PIT system.

Classification of progressivity of the tax systems can be found in paper by Duncan and Peter

(Duncan & Peter, 2012) where they conclude that there are three groups of methods for measuring progressivity: (1) top statutory rates of personal income tax (or what is classified Taxes and Inequality 10 as ex ante in this paper); (2) effective-inequality based measures of progressivity (or what would be classified as ex post in this paper); and (3) structural progressivity measures – changes in average and marginal tax rates along the income distribution (falling more into ex ante) category.

The three mentioned these definitions concentrate on the progression of the tax tariff when income rises. More precisely – on the sign of the progression. Negative sign would indicate regressive taxation, zero sign indicates a proportional tax system, and a positive sign indicates a progressive system. This approach allows to distinguish between these systems and to conclude that a progressive tax system has higher progressivity than regressive or proportional PIT system3 (simply because progressive PIT would be more than zero).

However just knowing the sign does not allow to distinguish between progressivity of two progressive PIT systems. Especially if they have very dissimilar elements, e.g. number of PIT brackets, rates etc.

This problem is partially addressed by other measurements, e.g. Lorenz criterion. It evaluates the effects of two particular redistribution measures in the framework of the Lorenz curve and

GINI coefficient. Out of the two particular measures of redistribution the one that lays closer to the line of absolute equality is said to be more redistributive. What it essentially measures is how closely to the line of equal income distribution the tax system pushes post-tax income distribution. This approach could be classified as ex post in this paper.

A similar approach of measuring the progressivity of tax system ex post rather than ex ante is found in other works. This research paper (Verbist & Figari, 2013) measure progressivity

3 If we do not narrow our focus to mere technicalities, we could still question, which system is more progressive: a progressive PIT system even progression of tariff to 40% top rate, or a proportional 40% PIT system with a significant non-taxable minimum Taxes and Inequality 11 essentially by how much inequality was reduced after all taxes, tax credits and exemptions are applied.

However using the two last approaches offered (Lorenz criterion and inequality reduction offered by Verbist) can be applied not only to progressive but to any PIT system. It is then possible to conceive that using these indicators of progressivity certain particular flat PIT system would appear more progressive than certain progressive PIT system. This could happen if, a progressive tax system had a sizeable non-income deductions (e.g. for new vehicles, tuition etc.), which benefit high-income individuals. The conclusions of such test would, of course, raise profound questions on what PIT progressivity really is. If according to

Lorenz Criterion some flat PIT systems appear more progressive (e.g. reducing inequality more than progressive PIT systems) can we really call progressive PIT systems

“progressive”? Should the public policy debate concentrate on the PIT rates rather than the end result? Nonetheless for further purposes of this paper we are going to use a conventional classifications for progressive and flat PIT systems (more detailed definition can be found in a chapter on methodology).

Why would a more progressive income tax reduce indicators of inequality?

This section of the paper explains that the current indicators of income inequality are calculated in such a way, that we should expect a reduction of income inequality if a progressive PIT system were applied to pre-tax incomes.

The composition of widely used indicators of inequality

The most widely used indicators of inequality are constructed in such a way that progressive income tax (at least in theory) would seem to be reducing the income inequality. All the three main measurements – GINI coefficient, S80/S20 (quintile ratio), and at-risk-of-poverty

(AROP) rates evaluate the distribution of yearly income of a population of a country. These Taxes and Inequality 12 indices explicitly do not take into account differences in population structure, effort by individual people and many other factors. If distribution of income is the only factor determining inequality then measures that reducing this distribution (such as progressive income taxation) could be seen as appropriate and thus chosen by the decision makers.

The point to be stressed is that if progressive taxation is thought to be reducing income inequality (regardless of whether it actually does), it will be enacted regardless its actual efficacy. The situation is further complicated by the fact that countries cannot really compare ceteris paribus what their income inequality would be if they changed from progressive to flat PIT taxation or vice versa.

GINI coefficient

The Gini coefficient is defined as the relationship between cumulative shares of the population arranged according to the level of equalized disposable income, and the cumulative share of the equalized total disposable income received by population (Eurostat).

Graphically GINI coefficient essentially measures the area between the Lorenz curve and the hypothetical line of absolute income equality. The larger the area, the higher the GINI coefficient, the higher the income inequality of a country4.

GINI coefficient is constructed in such a way that reducing the incomes of higher income groups would result in a lower GINI coefficient (this is exactly what progressive PIT does).

The mechanism is very simple: under a progressive PIT system the higher income groups would lose relatively more income than the lower income groups, and this would result in a

4 Technically speaking GINI coefficient is a ratio between the area (bound by line of absolute income equality and Lorenz curve) and the area under the line of absolute income equality Taxes and Inequality 13 more equal income distribution. An important note is that this reduction in GINI coefficient under progressive taxation would happen even without transfer payments.

S80/S20

The S80/S20 also known as the quintile ratio. It is the ratio of total income received by the 20

% of the population with the highest income (top quintile) to that received by the 20 % of the population with the lowest income (Eurostat). The higher the ratio, the more income inequality we have.

Given a progressive tax system, where the rich experience higher tax rates than the poor, the income of the rich will be reduced relatively more than the income of the poor. Thus given a progressive PIT system, the pre-tax s80/s20 ratio should be higher than the post-tax s80/s20 ratio. It is important to note that progressive taxation would reduce the ratio even if there were no transfer payments to the poor. If transfers were targeted specifically to the poor the ratio would decrease even more.

At-risk-of-poverty rate

At-risk-of-poverty rate (AROP) is the share of people with an equalized disposable income below the at-risk-of-poverty threshold, which is set at 60 % of the national median equalized disposable income after social transfers. (Eurostat)

The main idea behind the at-risk-of-poverty rate calculations is to measure, what proportion of population lives below a certain relative income threshold, usually 60% of median equalized income (although 40%, 50%, 70% have also been used in the past). By measuring and comparing at-risk-of-poverty rates at three stages - before-taxes, after-taxes but before transfers, and after transfers - we can estimate the effectiveness of measures taken to reduce income inequality. Taxes and Inequality 14

In theory if one were to compare the effects of progressive and proportionate income tax, on

AROP of a simulated population, one would notice that less people fall below the threshold of relative poverty of after-tax incomes with a progressive tax rate. This is true of any rate of progressivity or for any average tax burden. From this mathematical feature a theoretical policy advice is derived for reduction of relative poverty – progressive taxation.

Conclusion

This brief review of the three most popular indicators of income inequality demonstrates the indicators of inequality are constructed in such a way, that progressive taxation does seem like a logical measure, especially if one supposes that reduction of income inequality is a worthy political goal.

The choice of the indicators for further research is driven by their prevalence in public debate. AROP in Lithuania has incorrectly been associated with absolute rather than relative poverty and is widely publicized and discussed. GINI is widely used in international comparisons and data is widely available (even though non-economists have hard time understanding what exactly it measures). Finally the S80/S20 is easiest for people and politicians to understand, in addition it is mostly (and correctly) associated with income inequality per se.

While each of the indicators measure income inequality, each of them has specific caveats and features. S80/S20 excludes analysis of the middle quintiles and focusses only on the bottom and top quintiles. AROP, being based on median rather than average income excludes impact on extremely high incomes. Finally GINI is most inclusive in term of data, but least understood by politicians and the public. The three indicators give not only different numerical values on income inequality, but even different rankings of inequality in countries.

This is why this research would benefits from the use all of the indicators in its estimates of Taxes and Inequality 15 income inequality. However due to lack of data for S80/S20 before and after-tax this indicator will not be further used in this study.

Why do societies engage in redistribution and reduction of inequality

We have determined what we mean by progressive PIT and income inequality. However we have to briefly address a question why societies do engage in reduction of income inequality.

While the breadth of this topic surely falls outside the scope of this paper, some insights would be useful. They could at least partially explain why progressive PIT rates are so prevalent even if their actual effect in reducing income inequality can be questioned.

The reasons why societies engage in redistribution of income and reduction of income inequality is closely connected to normative statements, ethics and values. One cluster of reasons found more in the realm of philosophy and abstract reasoning claims that it is ethical to engage in some sort of redistribution of income and / or wealth. Another approach takes the utilitarian route and claims that more equal societies are better, more cohesive, and more peaceful. Ethical and utilitarian approaches advocate redistribution even though they differ in the justification of it.

In the same time redistribution through taxation is a consequence of a political process rather than the result of an academic debate. If majority of voters or, more importantly, political decision makers believe that redistribution or reduction of inequality is a worthy political objective, it will happen. Political decision-making is not a justification, but an explanation of the phenomenon. Similar logic works when applied to progressive taxation.

In the same time, even the academic debate in economics about the advantages and disadvantages of redistribution is not without its normative statements. Even the utilitarian approach to income redistribution relies on certain assumptions that do not pass a strict scrutiny from the point of view of fundamental assumptions of neoclassical economic theory. Taxes and Inequality 16

Therefore we shall briefly examine these assumptions in relation to justification of redistribution and progressive PIT.

Maximization of utility of society as a policy objective

The school of thought of utilitarian social welfare assumes that it is the responsibility and the mandate of the government to maximize the utility of the society. Maximization of utility of society is different from maximization of income. Maximization of income would imply that for example the government should be concerned with maximum GDP. Maximization of utility of society would imply that the government should be concerned with maximization of happiness (expressed in measurements of utility derived from income) of the society. This utilitarian perspective permeates and chimes with previously described indicators of inequality. A good summary of this type of thinking is given in (Bakija, 2013).

There is much more depth behind these positions and the philosophical analysis of this position is not an objective of this paper. In the same time the identification of this paradigm is very important, because this way of thinking is often used to justify redistribution of income. We could assume that this type of justification sometimes is just a pretext for governments to justify progressive income taxation (or even taxation per se). In the same time we cannot discount that many politicians or technocrats genuinely believe that maximization of utility of society is worthy societal goal for which they have mandate from the electorate.

However this utilitarian thinking is a normative rather than a positive concept. One would be very hard pressed to objectively prove that maximization of society’s utility is an objective and unquestionable virtue. Yet this concept is so popular among the government, politicians, and even academia, that it is rarely questioned. We might have a discussion regarding the Taxes and Inequality 17 efficiency of redistributive mechanism, or the extent to which redistribution should happen, but not whether redistribution should happen at all.

Assumptions about marginal utilities of income at different levels of income

Assumptions about the utility functions of people is another crucial part of the redistributive policies. To put it simply, redistributive thinking assumes that $1 is worth less to a rich person than to a poor one. Therefore taking $1 from the rich man and giving it to the poor man would result in overall improvement in overall welfare of the society. In other words the utility lost by removal of $1 from a rich man would be more than adequately compensated by the gain in utility to the poorer recipient of $1.

This general thinking is widely accepted and arguing against it or questioning it far beyond the scope of this paper. In the same time one should remember that this type of thinking is riddled with assumptions and caveats. The assumption that $1 is worth less to the rich person in terms of utility than to a poor one is problematic. More precisely, while the principle of diminishing marginal utility can be applied to income, the conclusion that taking $1 from a rich person and giving it a poor one would result in increase in utility of society - is problematic.

First, strictly speaking we cannot objectively compare utility functions of different people. It might be safe to assume that ceteris paribus $1 is worth less to the same individual when he earns $100.000 compared to the situation when he earns $10.000 (even though we could also ask what time periods are involved, what opportunities are present etc.). But in no way is it possible to transpose this mechanism to two different individuals: one earning $100.000 and another $10.000. As Bakija himself admits:

„We cannot scientifically estimate how levels of utility differ across people, <...> So to make a utilitarian social welfare analysis operational, we need to make some assumptions about the nature of each person’s utility function that are potentially testable (e.g., the curvature of each individual’s the utility function), and some assumptions that not empirically testable (e.g., how the level of utility Taxes and Inequality 18 compares across individuals). Alternatively, we might think of assumptions about the latter as ethical judgments about how much each person’s utility should count when adding up social welfare.“ (Bakija, 2013, p. 2)

An important point captured here in relation to assumptions is that a situation where resources are transferred from the rich to the poor is considered „ethical“. This notion resonates in many levels of government and decision makers. Although why it is assumed to be ethical or why absence of such transfer could be considered „unethical“ is not self-evident from the point of view of science of economics (and is beyond the scope of this paper).

Nonetheless many researchers e.g. Saez (Dianom, Diamond, & Saez, 2011) insist that high income individuals should be subject to high and rising marginal PIT rates. Low income families should be encouraged to work with subsidies, which should then be removed and replaced high implicit marginal tax rates.

The second problem with the paradigm that taking a dollar from a rich person and giving it to a poor one would result in increase of welfare is that it ignores the S-shaped income-utility function. S-shaped income-utility function is a fundamental contribution of Prospect Theory to understanding human beings (Kahneman & Tversky, 1979). Essentially it means that the impact on person’s utility depends not only to changes in income, but also on whether income is added or taken away. For the same individual the loss of utility from losing $100 is greater than the gain of utility of getting $100.

Even if we were to assume that all humans have identical utility functions for income (and thus violate the fundamental assumption described in previous paragraphs), justification of taking $1 from the rich individual and giving it to the poor on the grounds that it increases the overall utility of the society, we are ignoring that a loss of $1 has a larger impact on utility than a gain of $1. In order to maintain that a transfer increases the overall utility of society, we have to make additional extreme assumptions about the curvatures of individual utility functions, which would weaken this position even further. We would essentially have to Taxes and Inequality 19 assume that: a) after a certain income threshold people value additional income so little, that losing that income would not result in sizeable (or any) reductions of utility; and b) the redistributive tax is applied in such a way that most of it (or all of it) falls above the income threshold described in previous point (a). All of this is highly unrealistic.

Finally, a real policy discussion does not revolve about transfers of $1 from the rich to the poor. The real policy discussion centers about whether and how much richer people should contribute in taxes compared to less affluent (not all tax income goes to social transfers to the poor). Even under the flat income tax (e.g. 10%) a person with $100.000 contributes ten times more than a person with a $10.000 income. With progressive taxation the gap between contributions is even larger. To make an argument, that under this distribution of tax burden the richer lose less utility than the poor, would require further and even more extreme assumptions about the concavity of the income-utility function of rich people. In practical terms this would mean that after a certain level of income negative marginal utility (disutility) of losing income via taxation is negligible or nil (similar to an argument in previous paragraph). However, if that were true we would not observe huge resources being invested into tax optimization, tax evasion or other phenomena, which we do observe in reality (see later paragraphs).

If we extend this line of thought to progressive income taxation, i.e. where a person with

$100.000 income pays $20.000 in taxes, compared to person with $10.000 income paying

$1.000 in taxes, we need even more extreme assumptions to justify progressive taxation on the grounds of increasing utility of society.

At this point we can make these conclusion. While this is by no means a full analysis of the current economic thought about redistribution, one would have to agree that normative and ethical positions are very important in justification of redistribution and progressive taxation. Taxes and Inequality 20

Especially when we consider how many fundamental assumption have to be ignored or bent in order to justify redistribution on supposedly objective grounds. Perhaps it is no surprise that inequality is said affect many aspects of life, for example some researchers (Aizenman &

Jinjarak) argue that even sovereign spreads are affected by GINI inequality5. Others propose extending progressive rates even to inheritance tax (Farhi & Werning, 2007). Yet this views is not shared by all. Using the same utilitarian steady state social welfare criterion some estimate that optimal tax for US is 17,2% flat PIT (Conesa & Krueger, 2006). Similarly others (Slemrod & Yitzhaki, 1983) estimate that a flat tax of 20%-25% would be revenue- neutral compared to the progressive PIT system in US.

Review of academic literature

Relationship between inequality and progressive taxation

In the previous portions of the paper we identified theoretical reasons why a relationship between inequality and progressive taxation might exist. This portion will review academic literature regarding the details of the relationship: the existence of relationship between inequality and progressive taxation, direction of the relationship, and causality between the two. Here we shall also determine specific mechanisms or paths for this relationship to occur.

Path No. 1: inequality sows the seeds of redistribution

Meltzer-Richard hypothesis

An interesting insight about the causal link between inequality and redistribution is offered in the Meltzer–Richard hypothesis. It states the inequality itself will generate political forces for more redistribution. This is explained through the so-called median-voter hypothesis. To put it very simply, voters living in unequal societies (where the majority of the population is poor

5 Although the proposed mechanism is via smaller tax base, and less tax revenue rather than inequality per se Taxes and Inequality 21 and the minority is rich) will call for income redistribution, because they would see redistribution as a measure to improve their well-being. The politicians would react to this political sentiment and enact it through legislation.

This explanation is powerful because it takes into account the actual causal mechanism of redistribution – the political decision making. It explains why (or rather how) redistribution happens rather than just assuming that it does.

Nonetheless other authors express their doubts about this relationship “the pure political- economy hypothesis of excessive redistributive pressure emanating from the poor, whether through the ballot box or the street, does not fare well” (Benabou, 1996). Bakija seconds this by noting that given increases in inequality we should had seen increases in PIT progressivity, but we did not (Slemrod & Bakija, 2000).

In addition there are many countries with similar income distribution, and a widely different size of government spending (Borge & Rattso, 2004). To put it simply: if redistribution is caused only by inequality itself, why do we have disparities among countries. Although this criticism is only partially correct; Richard-Meltzer hypothesis does not claim that the sole reason for redistribution is inequality.

Second problem is that for the causality of Richard – Meltzer mechanism to work the voters would have to be very rational, and organized. They would have to overcome the problem of collective action described my Mancur Olson half a century ago (Olson, 1965). According to

Olson small groups who are to lose significant amount of welfare are more likely to organize and overcome larger groups who are to gain smaller amount of welfare. This means that if there ever were a threat of a draconian redistributive tax imposed on the rich (the so-called

1%) this dynamic would definitely come into play. Taxes and Inequality 22

Also under these assumptions, the rational median voter supposedly votes for politicians who favor redistribution. Even though by the virtue of being rational the voter should have in mind that politicians do not always fulfil their election promises.

Regardless of theoretical or empirical problems with existence of Richard – Meltzer relationship, it is widely accepted. Furthermore the evidence to the contrary is treated not as evidence against the existence of this relationship but as a deviation from the norm. The deviation itself is called –“malapportionment (Ardanaz & Scartascini, 2011). These researchers argue that empirical observations of countries with high inequality and low government revenues (as percent of GDP) can be explained by “malapportionment”. The government’s failure to listen to the demands of people.

Regardless of empirical basis of Richard – Meltzer hypothesis, two important considerations must be made. When talking about redistribution the actual paper does not estimate the progressivity of income tax tariffs. Instead it relies on revenues from personal income tax to

GDP as a proxy indicator. Strictly speaking using Richard – Meltzer hypothesis we could claim that inequality causes redistribution, but not that inequality causes progressive PIT systems to appear.

An interesting side-note is that even Piketty himself differentiates between progressive PIT and progressive corporate income tax. According to Piketty it is the latter that provides significant long-lasting effects. While the former just produces “level effect on earnings through labor supply responses” (Piketty & Saez, 2001, p. 19). In other works he repeats the insight arguing that it was changes in corporate, not personal income taxes that are responsible for decrease in progressivity (Piketty & Saez, 2007). This could indicate that even among the economist favoring redistribution corporate and capital taxes, not PIT, are Taxes and Inequality 23 their main concern. Finally other factor like differences in savings rates can also be seen as contributing to inequality (Saez & Zuchman).

“Keeping up with the Joneses”

„Keeping up with the Joneses“ way of thinking about peoples’ utility function can also explain how inequality would create its own incentives to redistribute. While the origins of the term are hard to trace, the conventional explanation is that people care not only about their absolute income, but about income relative to their neighbors, peers. To put it more elaborately „individuals consider relative income in addition to absolute income when evaluating their own utility. <...> relative income affects utility in a two-sided manner, meaning that individuals care about the incomes of those above them (the Joneses) and those below them (the Smiths)“ (Daly & Wilson, 2006).

The utility function in which you start getting disutility if your neighbors live better than yourself can lead to thinking that reducing the wealth of your neighbors is good for you.

This approach is somewhat in line with the previously described Richard – Meltzer hypothesis. But instead of the pitting masses of „absolutely poor” against “absolutely rich“ here we have a conflict between neighbors of different level of affluence (especially if we are talking about more developed countries).

Likewise some authors (Stracca & al-Novaihi, 2005, p. 18) propose that a progressive tax schedule would modify the externalities stemming from the „Keeping up with the Joneses“ utility function. For the purposes of our discussion the interesting point is not whether this is empirically correct, but rather that highly redistributive, progressive taxation is seen as a correct intervention by the government. If such views are wide-spread, that would explain why a society that lives in this paradigm would enable (or at least not resist) highly progressive taxation. Taxes and Inequality 24

Similar thinking can lead people to call for more redistribution (possibly also in form of higher progressivity) even if their own wealth or income could be reduced as a result. One of the fundamental insights confirmed by behavioral and experimental economics is the capacity of people to sacrifice their own wealth if the losses to wealth of other were even higher. The behavior of people in the game of the „Dictator“(List, 2007) shows that people are willing to pay to reduce other people’s income if the reductions are higher for others than for themselves.

We should also not exclude a scenario under which people supporting more progressive taxation think that their own income would not be taxed by a higher progressive tariff. This is well reflected in some opinion polls. For example a representative opinion poll by RAIT in

20096 reported that more than half of respondents supported the idea of introducing progressive income taxation in Lithuania. Another representative poll by “Spinter” asked if people would want a progressive income tax applied to their own salary; 68% of respondents disagreed7. This shows is that people’s perception towards redistribution changes significantly if their own personal income is concerned. It also allows us to speculate that people might be a combination of rational agents under the “Keeping up with the Joneses” framework (demanding higher or progressive taxation thinking it would hurt others more) and agents with significant asymmetry of information vis-à-vis the government (regarding the actual progressive PIT rates that would be imposed by the government heeding the demands for more redistribution).

6 http://www.delfi.lt/verslas/verslas/gyventojai-linke-pritarti-progresiniu-mokesciu-ivedimui.d?id=25403955 7 http://www.15min.lt/verslas/naujiena/finansai/tyrimas-moketi-progresiniu-mokesciu-nenori-68-proc- gyventoju-662-151195 Taxes and Inequality 25

“Undeserved” income

Behavioral economics also sheds another insight. People are more willing to tax if they believe that income was earned unjustly or effortlessly. According to (Zizzo & Oswald,

2001) in the modified „Dictator“ game, „disadvantaged“ subjects appear to target undeservedly earned money substantially more than they do other money (Zizzo & Oswald,

2001, p. 16). Drawing an insight into reality this would mean that people of lower income are much more supportive of high taxation on income or wealth, if they think that income or wealth is „undeserved“. Of course „undeserved„ is a subjective term, which depends on the personal interpretation (e.g. are capital gains “deserved” or “undeserved”?) But this allows us to speculate that the perception of “undeserved income” is behind at least some taxation targeted at very specific income or institutions (e.g. capital gains tax, financial transactions tax etc.)

We should also not exclude an impact of political rhetoric on people’s views on taxation.

When looking for justification to increases taxes (or introduction of new taxes) politicians are keen to invoke the notion that some types of wealth or income are “unjust” and are therefore viable subject to taxation. Especially if people do not quite comprehend the nature of said wealth or income (e.g. capital gains). The interesting question however is the direction of this relationship: does the political rhetoric influences peoples’ thinking or are politician simply exploiting the already existing notions of “justice” and “fairness” among the population.

Path No. 2: can redistribution create inequality?

Many authors researched accept the general line of argument that inequality creates political incentives for higher redistribution (and most likely higher taxes or more progressive taxes).

However some other authors (Borge & Rattso, 2004) while accepting the existence of the relationship go further and claim the direction of the causality is opposite (Sinn, 1996). Sinn argues that high redistribution in terms of social insurance stimulates risk taking, which, if Taxes and Inequality 26 successful, is expressed as income inequality. In other words, redistribution via taxation creates suboptimal state-run social insurance systems, which provide too much insurance for too low of a price (or even insures from events that no private insurer would). Therefore risk- takers are subsidized via cheaper insurance feel safe and engage in risk taking activities. In the same time the risk-takers keep the earnings from their risk-taking activities if successful, thus increasing income inequality.

However how does this argument comply with a realistic empirical observation that higher income (or richer, successful) individuals also pay more tax? Effects of this type of redistribution on Lifetime income inequality would be very useful for future study.

The effect of progressivity of tax system on inequality

The previous chapter surveyed the existence of the causal relationship between inequality and redistribution (as a proxy for progressivity). This chapter will concentrate on reviewing the literature on the details of this relationship. Do increases in progressivity cause reductions in inequality, and vice versa?

Is more progressivity related to less inequality?

Findings of extensive research by (Duncan & Peter, 2012) conclude that in general higher personal income tax rates reduce inequality. However they distinguish between observed and true inequalities. Changes in observed inequality represents the change in GINI coefficient between gross and net incomes, which shows the effect of higher personal income tax rates.

In the same time true inequality (measured by consumption rather than income) shows much less of an effect. The authors conclude that in countries with weak democratic institutions higher personal income tax rates lead to tax evasion by the rich, and in turn has much weaker effect on real reduction on inequality. This insight echoes with the theory of

“malapportionment” (Ardanaz & Scartascini, 2011). Also interestingly enough (Duncan & Taxes and Inequality 27

Peter, 2012) apply their conclusions to the so-called “flat tax reform” in post-Soviet countries, arguing that shifting to flat tax (and less progressivity) may not necessarily lead to increased inequality (or may even lead to less inequality) given high level of tax evasion

(Duncan & Peter, p. 35). Similar effects are observed by (Gorodnichenko, Martinez-Vazquez,

& Peter) after examination of Russia’s transition to a flat tax system. Tax compliance and tax revenue increased drastically in large part because of the adoption of flat tax. Finally as

(Slemrod & Yitzaki, 2002) put it “The possibilities for evasion and the difficulties of administration have always shaped tax systems. Until recently, formal analysis of taxation largely ignored these realities.” This means that lowering progressivity or adopting a flat tax has benefits for revenue collection and prevention of tax avoidance. The effects are sizeable and can be significant in affecting inequality dynamics in a way that traditional approach does not predict, e.g. reducing inequality rather than increasing it. To illustrate the magnitude of the problem of tax evasion some researchers (Chetty, 2009) argue that tax evasion can even be classified as a separate economic activity.

The progressivity – inequality relationship or at least its empirical manifestations can be explained by a different mechanism. Some researchers argue that lowering of marginal income tax for high-earners (people in upper deciles), would induce those people to work more, convert their previous non-cash (and therefore untaxed) benefits into cash benefits

(Lindsey, 1990). All of this would manifest as statistically rising incomes and signal increases in income inequality. But the correct causal link would be between longer hours worked and rising incomes rather than a simple effect of reduction of top marginal income tax rates. Finally, if income inequality is generated by some people choosing to work longer hours, such inequality is hardly lamentable or need to be corrected by taxation.

Similar findings are presented by Prescott (Prescott, 2004) who makes essentially the same argument, but from an opposite approach. He argues that rising hours worked by Americans Taxes and Inequality 28 can be explained by falling marginal tax rates, which incentivized people to work more.

While such development (people working more) is a positive development technically it leads to increases in income equality. The incentives would be even more powerful if people with higher incomes were more sensitive to reductions in marginal tax rates. And there is ample evidence that they are more sensitive, even in Europe (Kleven & Schultz).

However the same paper that references Lindsey’s hypothesis (Karoly, 1993) argues that there is no evidence for a link between reduction of marginal tax rates and longer hours worked or other changes in behavior (although the paper (Karoly) contends that the data is hard to judge conclusively). Nonetheless the paper concludes that while more progressivity in tax systems may be justified on other grounds, there is little chance to address the fundamental factors affecting the pre-tax inequality of income. Especially since other fundamental factors such family composition and wage structure are playing a significant part.

Whether people change their behavior according to changes in progressivity is fundamentally a question of elasticity (Saez, Slemrod, & Giertz, 2012). High elasticity would imply that under increased progressivity people at high income levels would engage in evasive behavior: reduced labor supply, increased charitable contributions or mortgage interest payments

(assuming these are tax deductible), increased expenditures for tax professionals etc. Low (or zero) elasticity would imply that there would be no significant response. Paper (Saez,

Slemrod, & Giertz) concludes that most assumptions of elasticities range from 0,12 to 0,40.

This may seem as rather low value of elasticity (especially if we are used to seeing values of price elasticity of demand), but in fact are quite significant.

The same paper cites work by Kleven and Schultz (Kleven & Schultz), about insights of these elasticities in Denmark. First, elasticities for capital income are larger than for labor income. Taxes and Inequality 29

Second, elasticities for negative income (deductions, negative capital income) are larger than for positive income. Third, elasticities for the self-employed are larger than for employees.

Fourth, elasticities are monotonically increasing in income level and are two to three times larger in the top quintile of the distribution than in the bottom quintile of the distribution.

These findings confirm our earlier insights. Most importantly, people at higher income levels would to lengths to reduce their taxes and thus reduce the effects of increased progressivity of

PIT on inequality.

The observation that elasticities for negative income are higher syncs with the insights from the S-shaped income-utility function. It offers yet another explanation why the effect of increased progressivity of taxes may have conflicting effects on observed inequality of income. First, if increases in progressivity lead to tax evasion, then it possible to observe countries with high progressivity and relatively high inequality. Second, if reduced progressivity forces people to convert non-cash benefits to cash benefits, work more etc., then the reduction of taxes may reveal actual pre-tax income inequality (which was deliberately hidden by non-cash benefits in the presence of high taxes). If left to over-simplistic interpretation this observation could produce confusing policy recommendations. By producing increased income inequality statistic it could even lead to erroneous conclusions that reduction of progressivity of taxation increases inequality when in reality this inequality existed all along.

Another important insight is offered by Kesselman (Kesselman & Cheung, 2004). He argues that many studies naively assume that the tax burden falls on the individual taxpayer. The paper further argues that this assumption may overstate the efficacy of nominally progressive taxes on reduction of inequality. The reason why this happens is that highly skilled and desirable employees can shift increases in taxes onto their clients or employers. To put it simply, if taxes rates were increased such individual could demand for the increase of his pre- Taxes and Inequality 30 tax compensation from his employer in order to maintain his target after-tax income. If this happens, then as the paper argues “reported pre-tax distribution will not accurately measure the distribution of market incomes that would arise in the absence of the personal tax; market incomes of higher earners would in fact be lower without the tax shifting.” (Kesselman &

Cheung, p. 739). With such labor force one could observe that increases in progressivity of tax contributed to increasing of pre-tax inequality in society.

In addition given certain circumstances one could argue that high progressivity of taxes artificially inflates pre-tax incomes thus creating an illusion of significant pre-tax inequality, which is then supposedly reduced by progressive taxation. Higher pre-tax inequality creates justification for additional increases in taxes in order to combat pre-tax inequality. But the mechanism is exactly the opposite – it is progressive taxation, which creates significant increases in pre-tax inequality. It could be speculated that it is precisely the progressive PIT system that inflates the pre-tax income inequality observed in some countries.

Of course even if no personal income taxation were present, we would definitely observe income inequality. But this income inequality would be more similar to today’s post-tax inequality rather than pre-tax inequality. All this leads to a speculation that getting rid of PIT taxes could reduce income inequality rather than increase it. It would be very interesting to compare income inequality in society without PIT tax and reductions in inequality achieved by current PIT systems.

Finally other researchers (William & Hubbard, 2002) suggest that increase in progressivity of the tax schedule decreases job search and probability of getting a higher-paid job. If that were true then it would be a negative development to society, as it would discourage productive behavior by individuals. Taxes and Inequality 31

Is less progressivity related to more inequality?

While this causality is essentially is the same as discussed before, we should note that the statement “increases in progressivity leads to reductions inequality” is not the same as

“reductions in progressivity lead to increases in inequality”. The effects of changes in progressivity of a tax system on inequality may be asymmetric and direction-dependent.

Of course there is a great body of evidence arguing that less progressive taxation would lead to more inequality. The period after Thatcher and Reagan reforms and 1990’s in general are widely characterized a period of decreasing taxation and increasing inequality. Statements based on graphs plotting average tax global burden (which supposedly is falling since the

1990-ies) and global inequality (which is rising since 1990-ies) have been widely used as evidence of detriment of reduced taxation, as well as a policy prescription to increase taxes.

Even some authoritative institutions (e.g. IMF) argue that reductions in the generosity of benefits and less progressive taxation have decreased the redistributive impact of fiscal policy since the mid-1990s (IMF Staff Report, 2014). However quite different views are expressed by (Slemrod, 1992) who argues that changes in the income taxation had no effect on increases in inequality in the 1990’s.

Yet different interpretations on precisely the same period are presented by Feenberg and

Poterba (Feenberg & Poterba, 1993), who argue that reductions of marginal tax rates in 1986 reduced the incentive for the households to engage in tax avoidance activities, and more income being reported. It would suggest that reductions of marginal rates produce increases in observed income inequality rather than actual one (this echoes what we mentioned in previous chapter).

But even in such authoritative papers it is hard to untangle the effects of taxes from effects of benefits. Also, if we look at the data rather than IMF authors’ interpretation we see that while Taxes and Inequality 32 income increased constantly throughout the period, market income (pre-tax and pre-benefit income) GINI coefficient increased from 0,40 to 0,45 (12,5%) while disposable income

(after-taxes and after benefits) GINI increased from 0,27 to 0,30 (11,1%). In fact one could derive the opposite conclusion: the market income inequality increased slightly more than inequality after-taxes and transfers. Which could mean that the observed growth in global disposable income inequality was driven not by 1990-ies tax and benefits reforms, but by fundamental changes in the world economy. 1990-ies experienced the fall of communism, opening of global free trade, modernization of China and many other factors what could account for changes in global distribution of income. Research from Asia (Sivadasan &

Slemrod, 2008) argue that reductions in taxes and double taxation in India has produced increases in managerial wages (and thus wage inequality). If similar developments happened in other countries it is possible that global reductions in taxes produced some increases in wages and thus income inequality, but one could hardly argue that this is a negative development.

Some (Immerwolf & Richardson) argue the opposite - even though tax-benefit systems have become more redistributive since the 1980s income inequality kept on rising.

Some empirical evidence does suggest that there is a connection between larger revenues

(measured in tax revenues as percent of GDP) and less inequality (measured in average GINI coefficient). Gerry (Gerry & Mickiewicz, 2008) shows negative correlation between the two variables among post-Soviet countries in transition. In the same time two aspects have to be addressed, especially since other variables are involved. The sample from which the relationship is derived comprises of two distinct groups: Central Asia (and Caucasus) and

Central Europe. The former has low tax revenue to GDP and high inequality; the latter has higher tax to GDP ratio and lower inequality. Thus instead of observing a correlation between tax revenue and inequality we might just be observing characteristics of two distinct regions. Taxes and Inequality 33

Furthermore the authors touch on the progressivity of tax issue, observing that flat income tax countries such as Estonia and Latvia are associated with higher tax revenues. Another valuable observation rests in the Gerry and Mickiewicz observation that institutions, education, level of democracy are also important factors in explaining inequality.

An interesting insight is given by (Golosov, Maziero, & Guido), who argue that unemployment could be significantly lowered by a regressive income tax, which would reduce friction in labor markets. We could extrapolate that reduced number of unemployed people would lower inequality (assuming income from work is higher than unemployment benefits)

Another important observation in line of this work is that higher redistribution does not automatically imply more progressive taxation. Meltzer (Meltzer, 1983) himself makes it very clear that the tax system in his model is linear (proportionate) and that lump sum government spending is a good proxy for the scale of redistribution. Therefore the dynamic addressed in his research is whether more unequal societies would want more government spending. The question what the funds should be spent on, or how the tax burden should be shared among various income groups is not addressed explicitly. In connection to our paper it would be incorrect to state that Melzer’s work precisely implies that more unequal societies ask for more progressive taxes.

Other important factors and caveats

Like we mentioned before taxes are not the only means to reduce inequality. Evidence from

24 developed countries unambiguously show that: (1) Direct income taxes and transfers have decreased inequality in advanced economies by an average of one-third; (2) The redistributive impact of transfers accounts for about two-thirds of the decrease in the GINI

(IMF Staff Report, 2014, p. 15). Estimates show that in some European countries, transfers Taxes and Inequality 34 have nearly twice the effect in reducing inequality compared to taxes. EU average data shows taxes and transfers having equal effects in reducing inequality (Atkinson & Marlier, p. 355).

This means that the size of the tax or even its progressivity form just a piece of the whole picture. Discrepancies, e.g. why we observe countries with similar degree of redistribution (or similar progressivity of tax) but different levels of inequality may be explained by the system of transfers: how efficiently and how targeted the transfers are. It is quite likely possible to reduce inequality without changing taxes but by improving transfers. Others argue that corruption also contributes to income inequality (Gupta, Davoodi, & Rosa, 2002), which is certainly possible: misappropriation or misallocation of funds means that tax revenue collected does not reach the intended recipients.

In the same time the idea that redistributive taxation takes from the rich and gives to the poor is not necessarily how redistribution happens in real life. Perfect profile of redistribution – from the richest to the poorest – is not what modern welfare state achieves. Arguably large portions of public spending are not targeted at all (Radu, 2012). It is not inconceivable that a sizeable portion of tax revenues are not spent on increasing the incomes of the poorest (thus reducing inequality) but on infrastructure, subsidies and other factors having nothing to do with reduction of inequality. Some authors (Engel, Galetovic, & Raddatz, 1999) show that in some countries (e.g. Chile) pre-tax and post-tax income inequality remains essentially unchanged and even progressive taxation did not solve that. While others (Gregorio & Lee,

2002) argue that equal distribution of education have high effects on more equal distribution of income in long-term.

There is also an approach described by Kuznets curve, which says that as countries develop their inequality rises and then falls. To put it simply, that inequality is an inevitable side- effect of development. Taxes and Inequality 35

The analysis of distribution of transfer benefits falls outside the scope of this research, but how the transfers are targeted has a huge impact when measuring the inequality of disposable incomes.

Conclusion and directions for empirical research

The analysis of economic theory and existing research suggests this approach. First, tendencies to redistribute income via political decision making are very strong. Regardless of actual impact on inequality it is very likely that redistribution will happen (having in mind, of course, that redistribution is not the only reason for taxation). Second, progressive taxation is likely to reduce inequality, at the very least through a simple fact that indicators for inequality are constructed in such a way, that if you remove a relatively larger portion of income from higher income groups than from lower income groups inequality indicators will decrease.

Third, the important question and test for efficacy of progressive PIT is whether it reduces inequality more than flat PIT. If it does not, then it is a strong indicator that even if we found that higher levels of progressivity in progressive PIT countries are associated with lower levels of inequality, other more important mechanisms are at play: transfers, size of redistribution (or size of government, which is not the same as progressivity).

While theoretically progressive taxation should reduce inequality more than proportional taxation there are couple of effects that might distort this supposedly clear relationship. First, progressive taxation might increase incentives to either evade tax (e.g. by hiding income, changing cash income into non-cash benefits), which would reduce reported (or statistical) income inequality but not actual inequality. Conversely in countries with higher propensity to evade taxes the effects of smaller (or less progressive tax) might not increase inequality as much as it would be predicted from a theoretical perspective. Taxes and Inequality 36

In addition and as mentioned before, many countries have tax deductions and exemptions, which, given their idiosyncrasy are impossible to include in meaningfully comparable model.

While prevailing economic thinking would suggest that progressive taxation should be more successful in reducing inequality, Figure 1 shows that we could expect nearly all possible outcomes, form finding no relationship between progressivity to finding that progressivity is associated with higher levels of inequality.

Figure 1 Overview of most relevant findings in economic literature reviewed regarding causality, effect, and direction of the relationship between inequality and redistribution (progressivity)

Relation Authors, Direction of Mechanism Caveats, exceptions between paper causality progressivity and inequality? Yes. Negative (Duncan & Progressivity Higher taxes on If tax evasion is relationship. Peter, 2012) reduces higher income present real effects of Higher inequality earners reduce high taxes on reducing progressivity their income inequality may be is correlated more than that much lower. to lower of lower income Conversely, reducing inequality earners. taxes (of progressivity) might not have such negative effects on inequality as one could expect. Yes. High (Sinn, Redistribution State run and Lifetime income redistribution 1996) causes subsidized social inequality data needed (progressivity) inequality insurance to test factual is correlated schemes create observation with higher too many inequality incentives to engage in risk taking and earn high incomes. Yes. (Meltzer, Inequality Inequality and Meltzer explicitly Inequality 1983) causes more pressures via mentions that tax causes to redistribution median voter system he is using is undertake causes linear (proportional) more politicians to redistributive undertake more policies redistributive policies. Taxes and Inequality 37

Yes. Positive. (Ardanaz & Inequality Inequality Might not happen if Higher Scartascini, causes higher causes demand political elites are able inequality 2011) redistribution for more to ignore or obstruct should create (more redistribution the demands of incentives for progressivity) (progressivity) citizens. more redistribution (and progressivity) Yes. Increases (Kesselman Increased Increases in Observed in one type in tax & Cheung, taxes taxes (or of studies. Other progressivity 2004) (progressivity) progressivity) studies yield different increases pre- cause causes result. Only applicable tax inequality increases in individuals to to highly mobile, pre-tax shift the tax demanded individuals inequality burden onto who are able to shift their employers increases in taxes on thus increasing employers pre-tax inequality.

Methodology

Methodology for comparison between flat and progressive PIT

As we mentioned above, an integral part of examination of the relationship between progressivity and inequality will a simple examination of whether there is a discernable difference between how much income inequality is reduced in progressive and flat PIT countries. For this we shall compared reduction on income inequality measured as percent reduction of GINI and AROP income inequality in flat and progressive PIT countries. We shall compare average reductions in years 2007-2013 for GINI and AROP seperately. The test will look for consistent results across the years; if the results show that across different years it is impossible to tell which PIT system reduces income inequality more we shall come to the conclusion that there is no discernable difference between the effects of the two systems (for more detailed description refer to Question No. 1. Do progressive PIT systems reduce inequality more than flat PIT systems?). If the results are consistent we shall move to Taxes and Inequality 38 the second stage of investigation – looking for relationships between progressivity and inequality. The fact that we are using two different indicators for inequality – GINI and

AROP – increased the chance that at least one of them will show consistent results.

Methodology for testing the relationship between progressivity and inequality

Like we mentioned before, there is no simple way to measure which of the two progressive

PIT systems is more progressive. For that we shall have to construct a Progressivity Indicator of PIT system (PIPIT). Once we have the indicator we can run a regression between PIPIT and indicator for reduction of inequality. As mentioned above we shall run this test on the indicator of inequality (GINI or AROP), which shows consistent larger reductions in inequality for progressive PIT systems. A formal design of the test can be found in chapter

Question No. 2. Does higher progressivity reduce income inequality more? Comparison of countries with progressive PIT systems.

Data

Choice of countries

We have chosen EU countries for our study: the countries are of relatively similar level of development (compared to global deviations in development) and there is a high probability that the statistical data is comparable across countries due to identical standards and definitions. The source of the data for PIT rates "Taxes in Europe" database (European

Commission) which provides extensive and updated report on tax system of each country.

Taxes and Inequality 39

Figure 2 illustrates PIT rates for European countries with progressive PIT systems.

We have excluded several countries due to data issues. PIT in Germany is not set for each income bracket as is the usual practice in most of the countries; instead it is calculated for each income bracket individually according to a formula and subject to various exemptions.

Therefore while Germany does appear to have a progressive tax system we did not feel that its inclusion and calculations would be compatible with the rest of the sample. Denmark is excluded due to the complexity of state and municipal taxes that de facto comprise their PIT system. Data for Sweden suffers from a similar problem. The PIT (as we usually tend to understand it) has only two rates: 20% and 25% at levels if Income of 42500 EUR and 64700

EUR respectively. There is also a municipal tax averaging of 32%. In addition there is an approximate 1300 Euro non-taxable minimum. PIT schedule for Sweden is approximated from this data. Of course we are not including countries with flat tax systems into this sample, for flat tax countries do not have progressivity of PIT (if we exclude the non-taxable minimum, which we do exclude).

The complexity and uniqueness of the tax systems of different countries further illustrate the difficulty in correctly gaging the progressivity of tax system in each country vis a vis other countries. This perhaps explains why in political and even economic discussion there is so much confusion as to which country’s tax system is an example worth following. In addition this complexity (especially if we include deductions and tax credits) illustrates the political nature behind PIT systems.

Another issue derailing precise calculations is that certain PIT rates are changed mid-year or even couple of times per year. In quite a number of instances we are forced to allow inaccuracies e.g. if the new PIT rate enters mid-year, we assume the whole year is subject to that PIT rate. Taxes and Inequality 40

Not including deductions, tax credits

From the point of view of data inputs, deductions are an important part on any tax system.

One type of deductions is the minimal income allowance (or non-taxable minimum), which is not taxed with PIT. This feature, technically speaking, makes nearly all the so-called „flat tax“ PIT regimes in Europe into de facto progressive regimes. Omitting non-taxable minimums from our calculation distorts the indicator of progressivity, which we are constructing. However we have two important justifications for this.

First, if we want to follow the estimation of the PIT system in the ex-ante route (i.e. by judging how progressive the rates are) we are forced to ignore idiosyncratic and incomparable systems of deductions. Because calculating how much total tax people pay on their total tax would give us average rate of tax, which in turn would not tell us anything about progressivity.

Second, the myriad of deductions depend on different factors in different countries: marital status, number of children, employment status etc. Even if we tried to include all (or most) of deduction we would inevitable be forced to make assumptions about our “typical” taxpayer.

However important information would still be lost.

In addition these types of allowances are not seen as a defining feature of a progressive tax system, especially when it comes to policy discussions. This is an important consideration because one of the theorized causal links between income inequality and progressivity of taxes. Calls for higher progressivity and political pressure usually concentrate on increasing the progressivity by imposing marginally higher taxes on higher income, not by providing more deduction. If deductions are not perceived as a major component of a progressive tax system then their occurrence would be coincidental rather than causal. Taxes and Inequality 41

Another type of deductions are tax credits granted for some type of consumer spending.

Depending on the number and amount of these deductions granted, they can have a significant impact on implicit tax paid by the taxpayers. However their inclusion into PIPIT would also be problematic. Most importantly the amount of deductions an individual taxpayer gets depends on the actual incurred expenditure. People with similar incomes but different spending patterns would experience different implicit tax rates. Without data on consumer spending comparison would become impossible. In the same time it is worth mentioning that these types of deductions are rightly criticized for reducing progressivity of the tax system. Certain tax rebates (e.g. on new automobiles) are possible only for relatively well-off individuals, and not for the poorest ones for whom the progressive tax system is supposedly targeting.

Not all income is taxed with PIT systems

While concentrating on comparisons of PIT systems we have to admit that some of the taxable income in certain countries fall different rates and regimes. In this paper we are making an assumption that most people’s income consists of wages taxed with PIT rates.

However this excludes dividends, capitals gains and other similar income that is sometimes taxed with different regimes and rates. The significance of this omission depends on the development of personal capital market in country. Or more simply what proportion on income of people come from non-wage payments, e.g. dividends.

Finally, as we mentioned before we will be focusing on effects of progressivity of tax on pre- tax and post-tax income inequality. Therefore we are excluding tax rates applied on transfer payments and their effects. Taxes and Inequality 42

Data for inequality

Data for income inequality will come from the Euromod database, which provides extensive data for income inequality before tax and after tax for EU countries for past several years.

The possible issues with the data are addressed in the empirical part of the study (see

Investigation of GINI data and Investigation of at-risk-of-poverty (AROP) data)

Taxes and Inequality 43

Figure 2 Illustration of personal income tax (PIT) rates at different levels of income

Illustration of personal income tax (PIT) rates at different levels of income. Select EU countries with progressive PIT rates

60 Spain Sweden Malta Italy Belgium Austria

50 PIT rates PIT

Greece

40 Netherlands France

Cyprus 30 Poland Ireland

20

UK

10

Finland Portugal

0

0

30.000 10.000 20.000 40.000 50.000 60.000 70.000 80.000 90.000 100.000

Belgium Austria Cyprus Finland Levels of income France Greece Ireland Italy Luxembourg Malta Netherlands Poland Portugal Spain Sweden UK

Construction of progressivity indicator of personal income tax (PIPIT)

Like we discussed before it is possible to classify different methods of measurement of progressivity of income tax into two broad groups. First an ex ante method, which attempts to define progressivity by looking at tax rates at different levels of income. This method can be classified further by how tax rates are at different levels of income are determined, e.g. Taxes and Inequality 44 whether we are comparing nominal tax rates or implicit (or effective) tax rates at every level of income.

A second broad group determines progressivity by looking at the effects of taxes. The ex post approach compares how different tax systems reduce original (pre-tax) income inequality. A tax system that produces larger levels of reduction of income inequality (determined by comparing inequality before-tax and after-tax) is said to be more progressive.

In this research we are going to use a variant of the ex-ante approach. From the data of tax rates at different levels of income we are going to construct an indicator of income tax progressivity in each country at a given year. This is no trivial task, because these estimates will be essential for our empirical tests. Because PIPIT is subjective, not widely discussed or agreed upon concept, we shall devote a lot of attention how various approaches work, what problems they have and why we have finally chosen the approach we have chosen.

Approach 1 – Linear approximation (PIPITLIN)

First approach is to construct the linear progressivity indicator of personal income tax

(PIPITLIN) by linear estimations of the slope of PIT rates relative to income levels. Steeper slopes mean faster increases in PIT rates as incomes increases, and is a possible approximator for progressivity of progressive PIT. We take the simplest linear expression of y = mx + b and convert it to an expression of linear regression

y y y y TAXc = C0 + Cc *INCOMEc + εc

y y Where TAXc represents a PIT rate of country c at year y; INCOMEc is a corresponding income level (the highest value of income bracket), in country c at year y. In this correlation

y coefficient Cc would represent PIPITLIN for a country c at year y. Taxes and Inequality 45

However there are a couple of aspect to consider when using this method. While linear estimations are very widely used, one could argue that (at least graphically) PIT rates resemble not linear estimates but quadratic or logarithmic ones (see Figure 2). This happens for two reasons. First certain countries apply a zero tax rate to certain low incomes (instead of or in addition to a non-taxable minimum). This produces relatively steep slopes in transitioning from zero to medium incomes. Second, the progressive tax cannot increase up to

100% of income because such rates are unfeasible and past the optimal point on the Laffer curve; with high marginal taxes people start evading taxes or divert time into leisure (recall discussion in literature review). Therefore in high incomes the steepness of the slope starts do decrease. These are the two factors do give the impression of logarithmic rather than linear relationship between tax rates and income.

What attention should we pay to the constant term C0? In linear estimation setting the constant term to zero would imply that zero values of independent variable correspond to zero values of dependent variable. In PIT rates technically this is quite often the case. First, individuals with no income are not taxed by income tax (however, depending in the PIT rate system in some countries even the first dollar is taxed). Second there quite often exists a minimal income allowance for which no tax is levied upon, however in our construction of

PIPIT we decided against inclusion of this allowance. Therefore in our estimations of

PIPITLIN the constant term is not set to zero in order to allow for a better fit of a regression; in the same time, no particular attention is given to it or its values.

Linear estimation of progressivity of PIT system is in line with the approach used by (Duncan

& Peter, 2012, p. 19), where they use average rate progression as means to determine whether the PIT system of individual country is progressive, proportional or regressive. A positive

APR indicates a progressive system; APR=0 is a sign of a proportional PIT rate, and negative

APR shows a regressive PIT system. Obviously the countries that we have identified as Taxes and Inequality 46

having progressive PIT systems will have a positive slope (and PIPITLIN >0), whilst the countries in Europe with a proportional tax system will have slope (and PIPITLIN) equal to zero. Here we see the positive outcome of choosing to ignore the non-taxable minimum in the countries with the flat tax; had we done so, even the countries with a flat tax system would have PIPITLIN > 0 and we would have to conclude that no truly proportional PIT rate system exists in Europe.

A linear approximation seems to have a relatively high R squared (see Figure 3. Which gives us an indication that simple linear approximation represents the progressivity of tax system relatively well. However this high values may also be a product of a small sample size, since most of the countries observed have on average 4-5 brackets, which means only 4-5 points to derive linear approximation from.

Figure 3 Results of R squared for PIPITLIN of 2012 data

Country R squared Austria 0,874261 Belgium 0,919557 Cyprus 0,980299 Finland 0,922806 France 0,996919 Greece 0,987926 Ireland 1 Italy 0,872939 Luxemburg 0,993068 Malta 0,999026 Netherlands 0,939612 Poland 0,714433 Portugal 0,929138 Slovenia 0,995228 Spain 0,979354 Sweden 0,732338 UK 0,775801

Figure 4 and

Taxes and Inequality 47

Figure 5 show the estimates of PIPITLIN for countries with progressive taxation for years

2012 and 2014. The very small values of PIPITLIN although inconvenient are perfectly reasonable. We are plotting income measured in thousands and ten thousands vs percentage points; therefore the slopes are very small in absolute numbers. However is does allow to compare the PIPITLIN of various countries among themselves.

While countries with flat taxes are not included, it is quite obvious that if we applied the same rules (ignoring the non-taxable minimum), that PIPITLIN for them would be zero: they do not have any tax progression. Therefore their slope would equal zero.

Figure 4 Indicators of progressivity of PIT rates for EU countries with a progressive PIT rate system (in ascending order) using linear estimations

2012 2014 Less progressivity Spain 7,82E-05 UK 1,32995E-06 UK 0,000171 Spain 7,82E-05 Portugal 0,000208 Slovakia 0,000174 Italy 0,000272 Netherlands 0,000265 Poland 0,000279 Italy 0,000272 Netherlands 0,000314 France 0,000277 Greece 0,0004 Poland 0,000279 Finland 0,000425 Finland 0,000315 France 0,000559 Portugal 0,000362 Cyprus 0,000573 Slovenia 0,000412 Ireland 0,00061 Luxemburg 0,000452 Austria 0,000737 Greece 0,000471 Belgium 0,000744 Malta 0,000474 Sweden * 0,000802* Cyprus 0,000573 Luxemburg 0,000996 Ireland 0,00064 Slovenia 0,001594 Sweden * 0,000672 More progressivity Malta 0,001779 Austria 0,000676 * Constructed from combination of state, local taxes and non-taxable minimum income allowance of 12900 Swedish Krona

Taxes and Inequality 48

Figure 5 Indicators of progressivity of PIT rates for EU countries with a progressive PIT rate system. Arranged from most progressivity to least progressivity according to PIPITLIN values

PIPITLIN 2012 PIPITLIN 2014 Malta Belgium

Slovenia Austria

Luxemburg Sweden

Sweden Ireland Cyprus Belgium Malta Austria Greece Ireland Luxemburg Cyprus Slovenia France

Portugal Countries Countries Finland Finland Greece Poland

Netherlands France

Poland Italy

Italy Netherlads

Portugal Slovakia

UK Spain

Spain UK

0 0,0002 0,0004 0,0006 0,0008

0 0,0005 0,001 0,0015 0,002

PIPIT PIPIT

Test of robustness of PIPITLIN

In order to estimate the suitability of PIPITLIN to be used as a numeric value to express progressivity we have to run a couple of tests of robustness. First, how PIPITLIN reacts when additional data is added. Comparing 2014 and 2012 is useful since a number of countries introduced, removed or changed their PIT systems. Second, we need to compare what happened to PIT systems in the countries which show largest changes in PIPITLIN with what Taxes and Inequality 49

PIPITLIN actually shows. In other words we are going to test the ability of PIPITLIN to correctly estimate actual changes of the PIT systems.

First, Figure 6 shows that linear approximation is sensitive to even small changes in the PIT schedule especially to changes in top values. While this is to be expected from a linear approximation with relatively few data points, this is a first signal against the use of PIPITLIN.

Figure 6 Changes in PIPITLIN 2012 -2014 and factors behind the changes

Change of Reason for changes in PIPITLIN more than 50 % Austria -8% Belgium -8% Cyprus 0% Finland -26% France -50% * A new PIT bracket of 45% introduced for incomes above 151.200 EUR Greece 18% Ireland 5% Italy 0% Luxemburg -55% * A new PIT bracket of 40% introduced for incomes above 100.000 EUR Malta -73% * A new PIT bracket of 45% introduced for incomes above 151200 EUR Netherlands -16% Poland 0% Portugal 74% * Many changes in PIT brackets (see Figure 7) Slovenia -74% * A new PIT bracket of 50% introduced for incomes above 70907 EUR. As we well as some other changes in the lower brackets. Spain 0% Sweden -16% UK -99% * A new PIT bracket of 45% introduced for incomes above 151200 EUR

Second, a detailed examination shows that introducing an extra top PIT bracket with a progressively higher PIT rate actually reduces vales of PIPITLIN. But introduction of even higher PIT rates at higher income level is the definition of what progressive tax system is.

Why is PIPITLIN failing to estimate the changes in progressivity correctly? Taxes and Inequality 50

There are two issues at play here. First, as new PIT rates for very high income levels are introduced we receive a data point with large increases in X values (income brackets) and relatively small increases in Y values (tax rates). Combination of the two reduces the slope of our linear approximation of the PIT structure. Therefore we get lower PIPITLIN instead of higher ones (see Figure 7).

Second issue is also connected to the top brackets. If the new top tax rate were disproportionally higher than previous values, e.g. a “draconian” tax imposed, then the

PIPITLIN would increase rather than falling. However in no countries we surveyed such tax was introduced.

In three out of four problematic (see Figure 7) cases PIPITLIN failed to indicate the increases in progressivity of PIT system even in the same country, where only one data point was changed. All this means that linear approximation that we use PIPITLIN although reasonable is not an acceptable description of progressivity of PIT.

Taxes and Inequality 51

Figure 7 Detailed illustration of PIT brackets, rates and PIPITLIN values for select countries

PIPITLIN 2012 < PIPITLIN 2014 PIPITLIN 2012 < PIPITLIN 2014 PIT brackets 2012 and 2014 PIT brackets 2012 and 2014 France Luxembourg 60 60 50 50 40 40 30 30 20 20 Tax brackets Tax 10

Tax brackets Tax 10 0 0 0 50000 100000 150000 200000 -10 0 50000 100000 150000 Income levels Income levels

France 2012 Luxembourg 2012 France 2014 Luxembourg 2014 Linear (France 2012) Linear (Luxembourg 2012) Linear (France 2014) Linear (Luxembourg 2014)

PIPITLIN 2012 > PIPITLIN 2014 PIPITLIN 2012 < PIPITLIN 2014 PIT brackets 2012 and 2014 PIT brackets 2012 and 2014 Portugal Slovenia 60 60

40 40

20 20

Tax brackets Tax Tax brackets Tax 0 0 0 50000 100000 0 20000 40000 60000 80000 Income levels Income levels

Portugal 2012 Slovenia 2012 Portugal 2014 Slovenia 2014 Linear (Portugal 2012) Linear (Slovenia 2012) Linear (Portugal 2014) Linear (Slovenia 2014)

Approach 2 - Logarithmic approximation

Since linear approximation failed, could a logarithmic approximation be used instead? After all we mentioned that the PIT structures do seem resemble non-linear relationship between income and tax rates. This way our PIPITLOG would be estimated using a simple equation Taxes and Inequality 52 y=b*mx where y would represent PIT rates, X – income, and m would be the value of

PIPITLOG. This can be expressed in equation

^income PIT = b * PIPITLOG where higher values of PIPIT would represent higher progressivity.

The results of the estimation are presented in Figure 8 and Figure 12. The extremely low values are difficult to compare. However this is to be expected. Like in previous case there is a problem of scale: we are comparing percentage points (in tens) and income levels (in tens of thousands). In addition, we dealing with powers rather than multiplication.

Figure 8 PIPITLOG vales using logarithmic estimation for years 2012 and 2014*

2012 2014 Austria 1,0000501426 1,0000480709 Belgium 1,0000201498 1,0000185433 Cyprus 1,0000547351 1,0000547351 Finland 1,0000431823 1,0000807776 France 1,0000403014 1,0000173072 Greece 1,0000239734 1,0000153655 Ireland 1,0000203981 1,0000218856 Italy 1,0000083347 1,0000083347 Luxemburg 1,0000697664 1,0000275142 Malta 1,0001812405 1,0000394452 Netherlands 1,0000074749 1,0000060509 Poland 1,0000249841 1,0000249841 Portugal 1,0000074995 1,0000117836 Slovenia 1,0002368440 1,0000127990 Spain 1,0000020003 1,0000020003 Sweden 1,0000399149 1,0000399149 UK 1,0000050489 1,0000042120 Slovakia** 1,0000079776 * To make logarithmic estimation possible, data was altered. For countries which had a first income bracket taxed at 0% PIT, 0% was changed to 1%. ** Slovakia introduced progressive PIT in 2013.

How does the logarithmic estimation deal with the problems we encountered with linear estimation? Taxes and Inequality 53

Figure 10 shows the differences between the PIT structures of Malta in Slovenia, countries that showed biggest change in PIPITLOG (refer to Figure 12). In both countries PIPITLOG in 2014 was significantly lower (relative to other countries) compared to 2012. But the changes in the countries’ PIT structure was profoundly different. Malta made their PIT structure less progressive – in 2014 tax rate for those earning more than 14500 EUR was reduced. Slovenia on other hand made their PIT system more progressive, by introducing an extra PIT bracket of 50% for incomes larger than 70907 EUR. But the logarithmic PIPIT-

LOG decreases in both cases.

Recall Figure 7 where were examined select countries (France, Luxembourg, Portugal, and

Slovenia) that presented problems for linear PIPITLIN. With logarithmic PIPITLOG the problem persists. Even though France and Luxembourg made their PIT systems more progressive by introducing and extra PIT bracket for high incomes (refer to Figure 7) logarithmic PIPITLOG also shows reductions rather than increases.

Figure 9 PIPITLOG values using logarithmic approximation for France, Luxembourg and Portugal; 2012 and 2014

Portugal

Luxemburg Countries

France

1,0000200000 0,9999700000 0,9999800000 0,9999900000 1,0000000000 1,0000100000 1,0000300000 1,0000400000 1,0000500000 1,0000600000 1,0000700000 1,0000800000

2014 2012 PIPITLOG values

Taxes and Inequality 54

Figure 10 Comparisons of changes in PIT structures in Malta and Slovenia

Malta 2012 and 2014 Slovenia 2012 and 2014 40 60

35 50 30 40 25

20 30

PIT rates PIT rates PIT 15 20 10 10 5

0 0 0 20000 40000 60000 80000 0 20000 40000 60000 80000 Income levels Income levels

Malta 2012 Malta 2014 Slovenia 2012 Slovenia 2014

In addition to not solving the problems encountered previously, the logarithmic PIPIT also produces near-equal values for all countries (see Figure 11), especially if we ignore Slovenia in Malta in 2012 (Figure 12). This means that any meaningful statistical estimations later would be rendered near meaningless. We could apply another transformation to PIPITLOG values to accentuate the difference, but that would obscure the real situation even further.

Figure 11 Comparison of Linear and Logarithmic PIPIT. Minimum, maximum values, standard deviation

Linear PIPITLIN Logarithmic PIPITLOG 2012 2014 2012 2014 st.dev 0,00045913 0,000199666 st. dev 6,20836E-05 2,02427E-05 max 0,00177863 0,000684339 max 1,000236844 1,0000807776 min 7,81575E-05 9,68619E-07 min 1,000002000 1,0000020003

Taxes and Inequality 55

Figure 12 PIPITLOG values using logarithmic estimation for years 2012 and 2014

PIPITLOG values using logarithmic approximation Slovakia UK Sweden Spain Slovenia Portugal Poland Netherlands Malta Luxemburg

Countries Italy Ireland Greece France Finland Cyprus Belgium

Austria

1,0001000000 1,0002500000 0,9998500000 0,9999000000 0,9999500000 1,0000000000 1,0000500000 1,0001500000 1,0002000000 1,0003000000

PIPIT values 2014 2012

Evaluation of linear and logarithmic method

While there is some logic to use either linear PIPIT or logarithmic PIPIT for estimation of progressivity of PIT systems, both indicators have fundamental flaws. First they are very sensitive to changes in end values (e.g. top PIT rate at very high levels of income). Even if we decide calculate PIPITs for a set income (e.g. 300.000 Euro, which incidentally is the highest income bracket among the countries investigated), the problem would still persist.

Second they poorly represent situations where PIT starts at zero income. Because the both measure slope of a PIT system, country which starts taxing at for example 20% PIT on the first Euro, and then gradually moves to 40% PIT at 50.000 Euros, will have lower PIPIT Taxes and Inequality 56 score than a country that has a sizeable 0% PIT bracket and then progresses to tax income of

50.000 Euros at the same 40% PIT. A second country would get higher PIPIT scores denoting higher progressivity, even though a taxpayer in the first country would be paying higher PIT at any income bracket (up to 50.000). First country would have a higher average tax rate, higher tax at any income bracket but would still appear to have a less progressive

PIT system.

In authors opinion there is no way to remedy this; the author tried estimating slopes of PIT system of each separate income bracket, and then summing them up, averaging them, but similar results prevailed, which are not worthy to include in this paper. It does seem that derivatives (or slopes) are a poor choice to estimate irregular functions through the entire range of X values.

Approach 3 - GINI coefficient for PIT

Another approach to estimate progressivity of PIT comes from one of the indicators of measurement of inequality – GINI coefficient. Recall that PIT systems in Figure 2 resemble cumulative shares of distribution of income in GINI setting or the Lorenz curve. If it is possible to compare distribution of income with a line of absolute income equality, then it should also be possible to compare the tax rates of a progressive PIT system at every PIT bracket with a hypothetical situation where tax rates are increasing at an equal pace. We shall call this line EIPITR – Equally Increasing Personal Income Tax Rate. The difference between the PIT brackets and EIPITR would resemble a GINI coefficient flipped on its horizontal axis

(see Figure 13)

Taxes and Inequality 57

Figure 13 Conceptual representation of GINI coefficient for PIT

60

50

40

30

PIT rates, rates, PIT % 20

10

0 0 10.000 20.000 30.000 40.000 50.000 60.000 70.000 Income

PIT rates EIPITR

The practical problem is how to calculate the area between the two curves. Because EIPITR is essentially a straight line we can calculate area under EIPITR using simple integration.

However the irregularly shaped PIT structure is impossible to express in mathematical function and impossible to integrate. However simple geometry offers a solution. Recall that

PIT schedule is not as smooth line, but rather a set of punctuated steps each beginning at different income brackets. The area under the curve is easily calculated as a sum of rectangles

(SOR). The area comprised of PIT rates and corresponding income brackets less the area under EIPITR would be an indicator of tax progressivity (see Figure 14). In addition calculating the area under the PIT brackets in a geometrical method rather than integration would produce more precise results. Like we mentioned before - tax rates increase not in smooth gradual lines, but rather in jagged steps. If we used integration we would have to assume that PIT rates increase smoothly thus accumulating inaccuracies along the way.

Taxes and Inequality 58

Figure 14 Conceptual representation of calculating area between income brackets and EIPITR

However the concept has to be improved further. First different countries have tax brackets that describe vastly different incomes. Top income bracket for Spain in 2014 was 300.000, while for Malta it was only 19.500 (Eurostat). If we were only to calculate the area under tax brackets of each country’s PIT schedule we would underestimating huge taxes that Maltese would pay on the income of 300.000 Euro. To estimate each country’s progressivity in comparable manner we have to calculate it on equal income. Because we are comparing

European countries, we will do the calculations on income of 300.000 Euro.

Of course this assumption of equal income presents some problems. Due to differences in purchasing power parity and other factors identical amounts can be interpreted differently

(average wage is Western Europe seems like a very high one in Eastern Europe) and fall under different PIT rates. This is important because of the causal relationships that we discussed in previous chapters. If progressivity is driven by political will, subjective understanding of what denotes “high”, “low” or “average” income and what PIT rate to apply will differ from country to country. For example yearly income of 40.000 Euro would fall at the in mid-income and mid PIT rates for most of the Western European countries; while for Taxes and Inequality 59 example 40.000 Euro in Lithuania falls into the top decile of income distribution8. We shall address this question later in our robustness tests.

Calculation of GINI for PIT system

First we need to calculate the are under the curve of country‘s PIT schedule. If PIT could be reliably expressed as a function of income then the calculation would look like a simple integration (where x represents income).

300000 ∫ PIT(x)dx 0

However, for the reasons explained, approximating PIT schedule a as function, this is not feasible. Therefore in order to calculate the area under the PIT schedule we have to rely on simple geometric methods. We will calculate area under the curve as a simple sum of rectangles (SOR) formed by each tax bracket and corresponding tax rate (for better comprehension refer to Figure 14).

AREASOR = t0 X (b1-b0) + t1 X (b2-b1) +...+ tn X (bn – b3)

Where t0, t1 etc. represents different PIT rates, b1, b2 mark the intervals for different income brackets. Tn represents the top tax rate for each country and will be unique for each country.

Since we made an assumption for the maximum income (bn) to be 300.000, it will be the same for all countries. The area under the EITIPR curve can be calculated as a simple riangle.

Where tn is the top tax rate of an individual country and 300.000 is the maximum income.

AREAEPITR = (300.000 X tn) / 2

Subtracting the two we get the shaded area:

8 Therefore in most draft laws registered in 2012-2013 to introduce progressive PIT taxation in Lithuania the top income bracket in around 40.000 Euro. Taxes and Inequality 60

Area SOR - EIPITR = AREASOR - AREAEPITR

Or

Area SOR - EIPITR = [t0 X (b1-b0) + t1 X (b2-b1) +...+ tn X (bn – b3)] - [(300.000 X tn) / 2]

However we still need to do some equalization to estimate the area in one single comprehensible numbers. If we equalized the incomes we also have to equalize expressions of area into comparable numeric values. Taking a hint from the GINI coefficient we need to divide the shaded area by the total area: a product of income of 300.000 Euro and the maximum PIT rate of individual country. This is simply

PIPITGINI = Area SOR – EIPITR / 300.000 X tn

Or in a more generalized expression, where bn and tn are top income bracket and top tax rate respectively.

퐀퐑퐄퐀퐒퐎퐑 − (퐛퐧 × 퐭퐧) ÷ ퟐ 퐏퐈퐏퐈퐓퐆퐈퐍퐈 = 퐛퐧 × 퐭퐧

PIPITGINI of zero would indicate that tax rates are increasing evenly throughout the whole income range from 0 to 300.000 Euro. Higher values of PIPITGINI would indicate higher progressivity. Note that due to its construction the maximum value PIPITGINI can take is 0.5.

Test No. 1 for robustness of PIPITGINI. Differences in values by assuming different maximum incomes

To test if PIPITGINI is a robust indicator of progressivity we have to test how it behaves if some assumptions are changed. First, what happens if we change the assumption that maximum income is 300.000 Euro to 100.000? For graphical representation see Figure 15 and Figure 16 Taxes and Inequality 61

Figure 17 shows the values of PIPITGINI for countries assuming maximum income is 300.000 and 100.000 Euro. This distinction may be important. Due to the way we calculate PIPITGINI we expect the values calculated with assumption 300.000 Euro to be higher than those calculated with 100.000 Euro. However this might not a simple linear transformation where the 300.000 PIPITGINI values would simply be three times larger than 100.000 PIPITGINI values (see Figure 15 and Figure 16). Assuming income of 300.000 Euro inflates the values of PIPITGINI; however if this inflation is consistent then it should not be of major concern.

Figure 15 PIPITGINI area with EIPITR of 300.000 Euro Figure 16 PIPITGINI area with EIPITR 100.000 Euro

Figure 17 shows the results of quartile test. Even though the values of PIPITGINI are different, the ranking of the countries does not change dramatically across quartiles. Finally if we split the values into two halves rather than four parts, only one country – Portugal - would get into the first half (also note that Portugal was at the top of the third quartile).

All this shows that there is not much difference which PIPITGINI to use – countries are still consistently ranked according to progressivity. However given the fact that UK, Spain and Taxes and Inequality 62

Portugal have PIT brackets that extend above 100.000 Euro, the PIPITGINI calculated with the assumption of income of 300.000 Euro is more inclusive.

Figure 17 Values of PIPITGINI with EIPITR of 300.000 Euro and 100.000 Euro (in ascending order)

PIPITGINI at maximum income of 300.000 PIPITGINI at maximum income of * marks in which quartile the country falls 100.000 with EIPITR of 300.000 Euro UK* 0,3541739 Poland*11 0,108428 Poland*10 0,369476 Finland* 0,128933 Finland* 0,376311 UK* 0,16252 France* 0,391399 France* 0,174198 Spain** 0,391867 Austria** 0,18 Austria** 0,393333 Cyprus** 0,210997 Cyprus** 0,403666 Greece** 0,220444 Greece** 0,406815 Portugal*** 0,241039 Portugal*** 0,41368 Spain** 0,2506 Sweden*** 0,427008 Luxembourg**** 0,262124 Ireland*** 0,446667 Sweden*** 0,281024 Italy*** 0,447054 Ireland*** 0,34 Netherlands**** 0,452933 Italy*** 0,341163 Malta**** 0,455476 Netherlands**** 0,358799 Belgium**** 0,472187 Malta**** 0,366429 Slovenia**** 0,47514 Belgium**** 0,41656 Luxembourg**** 0,741221 Slovenia**** 0,425421

We also need to check whether the differences between the two indicators are biased to towards one of the main inputs – income brackets and tax rates. Figure 18 provides the percentage difference between the two indicators.

9 Correction. Should be 0,3002. Corrected in later parts of the paper. Error due to not converting British pounds to Euros 10 Correction. Calculations for Poland in both 2012 and 2014 are calculated in Polish zloty rather than Euro. The respective PIPITGINI value should be 0,4676. The values are changed in the main body of the work from hereon. 11 Correction. Calculations for Poland in both 2012 and 2014 are calculated in Polish zloty rather than Euro. The respective PIPITGINI value should be 0,428. The values are changed in the main body of the work from hereon. Taxes and Inequality 63

Figure 18 Percentage difference between PIPITGINI with EIPITR of 300.000 Euro and PIPITGINI with 100.000 Euro*

Austria 54,24% Belgium 11,78% Cyprus 47,73% Finland 65,74% France 55,49% Greece 45,81% Ireland 23,88% Italy 23,69% Luxemburg 64,64% Malta 19,55% Netherlands 20,78% Poland 70,65% Portugal 41,73% Slovenia 10,46% Spain 36,05% Sweden 34,19% UK 54,11% Maximum value 70,65% Minimum value 10,46% Standard deviation 0,19331 Average 40% * Percentage difference calculated on the PIPITGINI with EIPITR of 300.000 Euro basis

Recall Figure 15 and Figure 16 and notice that if we choose maximum income to be 300.000

Euro, the shaded area is much larger than that with the assumption of maximum income of

100.000 Euro. This happens because most top income brackets are well below 300.000 euro.

So the large portion of the shaded area comes from applying the top PIT rate for the income above the maximum bracket. Of course this applies to all countries, but the larger the difference between maximum PIT bracket of a country and 300.000 Euro the larger the

“inflation” of area.Refer to Figure 19. On X axis we plotting the percentage difference from

Figure 18. The Y axis is the difference between 300.000 Euro and the actual top income bracket of individual country. Even though there is a suggestion of trend, it is far from statistically significant to signal that the results of PIPITGINI estimated at maximum income of Taxes and Inequality 64

300.000 Euro are biased towards the size of individual country’s top PIT bracket. For consistency we perform a similar check in regards to the top PIT rate of an individual country. Results are presented in

Figure 20. Once again, a slight trend can be observed but statistical significance is not present. This allows us to conclude that PIPITGINI calculated on the income of 300.000 Euro is a reasonable estimation.

Figure 19 Difference in SOR score and income bracket gap between 300.000 EUR and top PIT bracket

SOR300K - SOR100K and 300.000 - top income bracket of an individual country 300.000 Slovenia Malta Belgium Ireland Luxemburg 250.000 Netherlands Sweden Cyprus Austria Italy France Finland Poland 200.000 Greece

150.000 Portugal UK

100.000

50.000 income income bracket, EUR

0 Spain 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00%

Difference Difference between 300.000 andTop PIT Difference in SOR score

Figure 20 Difference in SOR score and top PIT rate

SOR300K - SOR100K and top PIT rate of an individual country 60 Sweden Netherlands Spain 50 Belgium AustriaUK PortugalGreece Italy Slovenia Ireland France 40 Luxemburg Malta Cyprus Poland 30 Finland

20 Top PIT TopPIT rate, % 10

0 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% Difference in SOR score

Taxes and Inequality 65

Test No. 2 for robustness. Can PIPITGINI capture changes in PIT systems correctly?

Will PIPITGINI perform where PIPITLIN and PIPITLOG have failed. Recall that PIPITLIN and

PIPITLOG both failed to detect increases progressivity when a country introduced an extra PIT bracket for high income. Not only did they fail to detect this change, PIPITLIN and PIPITLOG produced smaller values, indicating smaller progressivity.

As we can see in Figure 21 nearly all the complex changes in PIT structure were correctly captured by PIPITGINI indicator. PIPITGINI correctly estimated even the cases that were troublesome for PIPITLIN and PIPITLOG. Nonetheless, the more complex changes where PIT structures were changing in both directions (e.g. lowering one PIT rate and introducing another one at higher PIT rates) were more difficult to capture. But such changes beg the question, how would an independent observer label such changes. Especially when whether the change makes the PIT system more progressive or less progressive depends on the income level of the person involved.

Figure 21 Examination of changes in PIT structure and capture of the effects by PIPITGINI

PIPITGINI values 2012 2014 Changes in PIT structure in 2014 Change compared to 2012 correctly captured by PIPITGINI? Country Austria 0,3933 0,4341 Increases in tax rates in mid-income Yes brackets. Increased progressivity of PIT structure Belgium 0,4722 0,4698 PIT rates the same but mid-income Yes brackets increased. Reduction in progressivity Cyprus 0,4037 0,4037 No change Yes (the value did not change) Finland 0,3763 0,3875 Extra PIT bracket and higher PIT rate Yes for incomes above 100.000 Euro. Increase in progressivity Taxes and Inequality 66

France 0,3914 0,3901 Extra PIT bracket for incomes above ambiguous 151200 Euros. Also some mid-income brackets increased. Ambiguous effect with tendency towards more progressivity Greece 0,4068 0,4468 Major overhaul of tax system, Yes reduction of number of PIT brackets, higher PIT rates start on lower incomes. Increase in progressivity. Ireland 0,4467 0,4440 Reduction of lower bracket PIT rate Yes by 1 p.p. Reduction of progressivity Italy 0,4471 0,4471 No change Yes

Luxembourg 0,4207 0,4250 An extra PIT rate introduced for Yes income above 100.000 Euros. However the new PIT rate only 1 p.p. higher than PIT on previous income bracket. Increase in progressivity Malta 0,4555 0,4323 Changes to couple of PIT brackets. Yes But now the top PIT rate of 35% comes in at significantly higher incomes that previously. Less progressive. Netherlands 0,4529 0,4565 Increased PIT on lowest income Yes bracket. More progressive Poland 0,3695 0,3695 No change Yes

Portugal 0,4137 0,4425 Multiple changes in various directions Yes of PIT rates and brackets. But higher PIT rates now come in at much lower income brackets. More progressive Slovenia 0,4751 0,5291 Multiple changes. Slight increase mid- Yes in income brackets retaining the same PIT rates. However a new PIT bracket introduced for higher incomes. More progressive Spain 0,3919 0,3919 No change Yes

Sweden 0,4270 0,4270 No change Yes

UK 0,354212 0,3970 Changes in both directions. 40% PIT ambiguous rate starts at a lower income level, but top income bracket is taxed less. Ambiguous effects.

12 Correction. Should be 0,3002. Error doe to not converting British pounds to Euros. Corrected in later parts of the paper Taxes and Inequality 67

While PIPITGINI is not a perfect indicator, it far better than the ones we constructed and tested earlier. Therefore we shall use PIPITGINI in for the main part of our work as an indicator of progressivity of the tax system.

In the same, PIPITGINI cannot estimate progressivity (or lack of progressivity) for flat PIT systems. Recall that the central mechanism of PIPITGINI is comparison of the actual (and irregular) increases in tax rate in progressive PIT system with a hypothetical situation where a progressivity increases steadily. Since in flat PIT systems there is no progression of nominal

PIT rate (recall that we are not taking into account exemptions and deductions), the PIPITGINI for flat PIT systems would be meaningless.

Empirical research and discussion

Question No. 1. Do progressive PIT systems reduce inequality more than flat PIT systems?

Our first question is relatively simple: do countries with progressive PIT systems reduce inequality more than countries with flat PIT systems. Here we shall compare reductions of inequality measured by GINI coefficient and at-risk-of-poverty-rate (AROP).

The data for GINI and AROP is taken from EUROMOD database. The reason for using this database rather than EUROSTAT is that publicly available EUROSTAT database only provides income inequality statistics for disposable income or inequality before transfers; it does not provide inequality data before-taxes. Since our investigation concentrates on investigation of impact of PIT systems, rather than effects of transfer systems, ability to compare inequality before and after-taxes is crucial. The EUROMOD data is compatible with

EUROSTAT data, although values are not identical (Jara & Laventi, 2014). On average Taxes and Inequality 68

EUROMOD indicators are 6% (not percentage points) lower than EUROSTAT data.

However this difference is consistent across countries (see Figure 22).

Figure 22 Difference between Eurostat and Euromod data for AROP and GINI values for countries with progressive and flat taxes

AROP progressive GINI progressive AROP flat GINI flat 9% 5,4% 5% 4,1% Source: (Jara & Laventi), authors calculations. For example “AROP progressive 9%” means that EUROMOD data on AROP was 9% lower on average than EUROSTAT data

Investigation of GINI data

Figure 23 shows that for any year in the period of 2007 to 2013 GINI income inequality for income after-taxes and transfers is lower in countries with progressive PIT than with flat PIT systems. This income is also disposable income, e.g. that people spend on consumption.

Considering that data for GINI inequality for disposable income is widely available, this may explain why flat PIT taxation is associated with higher levels of income inequality.

However if we look not at disposable income but at income after-taxes then the GINI levels are also consistently higher for flat PIT countries albeit by a very small fraction, not exceeding 1 p.p. (if GINI were expressed in percentage).

But countries with flat PIT have higher income inequality before-taxes. This would imply that income in those countries is distributed less equally to begin with. Does this reflect historical circumstances (considering that all flat PIT countries in EU are post-soviet states)?

Is it a manifestation of the Kuznets curve? Or is it simply a reflection of aging population

(retirees) entirely depending on state pensions? An insight could be offered by (Guvenen,

Kuruscu, & Ozkan, 2014), who argue that progressive taxes flatten the in after-tax incomes and thus reduce incentives to accumulate human capital, perform better, and this later flattens the pre-tax incomes (however they were comparing US an EU countries, not countries with Taxes and Inequality 69 progressive and flat taxes). The actual reasons are interesting for further study but fall outside the scope of this paper.

Another interesting observation is that there does not seem to be basis for idea that progressive PIT causes more pre-tax income inequality by driving up pre-tax wages because highly desirable, well paid professionals can simply pass the increase in tax onto clients and employers recall the argument by (Kesselman & Cheung). Either the insight is highly theoretical, or, alternatively the share of such professionals as a total of labor force is too small to detect its effects on macro level. Further investigation falls outside the scope of this paper.

Figure 23 GINI Income inequality measured before-taxes, after-taxes, and after-taxes & transfers

Income GINI at different stages 2007-2013

0,5 Prog - after taxes and 0,45

transfers

0,334

0,327 0,325

0,322 Prog - income

0,320

0,320

0,318

0,317

0,317

0,317

0,316

0,316 0,316

0,4 0,314 after taxes 0,35 Prog - Original 0,3 Income 0,25 Flat - after 0,2 taxes and Valueof GINI transfers 0,15 Flat - income 0,1 after taxes

0,05 Flat - Original Income 0 2007 2008 2009 2010 2011 2012 2013

Figure 24 shows that the effects of progressive and flat PIT systems on reduction of inequality is similar. Even more surprisingly, from 2009 to 2013 the flat PIT seems to be reducing GINI income inequality more than the progressive systems.

This is very counterintuitive. Like we discussed in previous parts (see The composition of widely used indicators of inequality) even without transfers, just by virtue of reducing higher Taxes and Inequality 70 income relatively more than lower ones, progressive PIT systems should reduce income inequality more than a flat PIT system.

Before making far-reaching insights we need to examine the data more closely, to check for outliers and abnormalities that could explain the unexpected result. Given that only 7 countries in EU have flat PIT systems (6 since 2013, when Slovakia introduced a second PIT bracket and rate making it a progressive system) average values are susceptible to outliers.

Figure 25 shows the summary of Figure 34 and Figure 35. It describes distribution of flat PIT countries across terciles (thirds) of countries ranked by their GINI score. The data reveals two distinct periods.

In 2007 and 2008 flat PIT countries were quite evenly spread out across terciles according to their before-tax GINI score. However their after-tax GINI score spread was already very uneven. 3 out of 9 most equal and 4 out of 9 most unequal countries in EU were countries with flat PIT system.

This means that in 2007-2008 the flat PIT system had two distinct and opposite effects on different set of countries. Flat PIT system pushed one set of countries towards a group of countries with relatively higher income inequality. And it pushed another set of countries towards a group of countries with relatively lower income inequality. Of course we have to not get confused – flat PIT reduced inequality in all countries with flat PIT. What we are implying that in some flat PIT countries the reductions in inequality were relatively smaller than in others.

However the 2009 – 2013 tells a different story. Flat PIT countries GINI score for before-tax income is disproportionally in bottom and top terciles. However after-tax figures show a considerable shift towards mid-tercile. In this period the flat PIT systems seems to be reducing inequality as well if not better than progressive PIT systems. Taxes and Inequality 71

Another important insight for interpreting Figure 25 comes from Figure 24. In 2007 and 2008 progressive PIT systems reduced inequality more than the flat systems. In 2009-2012 the trend is reversed – flat PIT systems seem to reduce inequality more (while the 2013 situation is caused by Slovakia moving from flat to progressive PIT system). This is consistent with how countries move across terciles. In periods when flat PIT system reduced inequality more, flat PIT countries are pushed towards the middle tercile. When progressive PIT system reduces inequality more it pushes flat PIT countries towards extremes (top and bottom terciles).

What this analysis has shown is that periods when flat PIT system seems to be reducing after- tax income inequality more than progressive systems cannot be explained by outliers.

However the change observed for year 2013 in Figure 24 shows that results are susceptible even to changes in one country (Slovakia adopting progressive PIT); but given that there are only 7 out of 27 countries with flat PIT rate, this is to be expected.

What this analysis does not explain however is why countries with flat PIT systems have higher income inequality before-tax. Nor does it shed any light whether such countries would reduce inequality more if they adopted progressive PIT systems. Interestingly enough before and after-tax GINI figures for 2013 and 2012 Slovakia are nearly identical (0,439 and 0,260 vs 0,439 and 0,261).

After investigation of GINI data we cannot say that we that countries with progressive PIT rates consistently reduce inequality more than countries with flat PIT rates. Actually in 4 out of 7 years investigated flat PIT showed larger reductions than progressive PIT.

Taxes and Inequality 72

Figure 24 Average reduction of GINI income inequality by direct taxes in countries with flat and progressive PIT systems. Comparison of average GINI inequality before-taxes and after-taxes (but before transfers). Reduction measured in percent and absolute value.

Reduction of GINI income inequality Reduction of GINI income inequality by by direct taxes, reduction in % direct taxes, reduction in absolute value

2013 2013 2012 2012 2011 2011

2010 2010 2009 2009 2008 2008 2007 2007 0,28 0,3 0,32 0,34 0,36 Reduction, % 0,14 0,16 0,18

Reduction, absolute value

year year flat PIT system progressive PIT system flat PIT system progressive PIT system

Note to Figure 24. For years 2013 percentage reduction of GINI score in flat PIT system is lower than that of progressive PIT system. But the reduction in absolute value seem slightly larger for absolute values. This is caused by different reference bases. Since flat PIT systems have higher original income inequality slightly larger reduction of inequality in absolutute terms are smaller in percentage terms when compared with countries with progressive PIT rates (which have lower before-tax income ineqality).

Taxes and Inequality 73

Figure 25 Summary of distribution of flat PIT countries throught terciles of GINI inequality of income before-taxes and after-taxes**

Year Number of flat PIT Number of flat PIT Number of flat PIT countries in bottom countries in middle countries in top tercile (lowest tercile (medium tercile (highest inequality) inequality) inequality) Before- After-tax Before-tax After-tax Before- After-tax tax tax 2007 2 3 3 1 3 4 2008 2 3 4 1 2 4 2009 3 3 1 1 4 4 2010 3 3 1 2 4 3 2011 3 3 1 2 4 3 2012 3 3 1 2 4 2 2013* 2* 2* 1 2 4 3 *In 2013 Slovakia adopted progressive PIT rate. Asterix (*) marks in which tercile Slovakia would have been given its GINI values had it not changes in progressive PIT system (assuming GINI values would had remained unchanged) **Explanation for easier comprehension. The 2007 row indicates that in 2007 27 EU countries were ranked according to their GINI score and divided into three groups of equal size (terciles), where bottom tercile indicates lower inequality and top tercile indicates more inequality. In 2007 GINI inequality before-tax 2 countries with flat PIT tax were in the bottom tercile, 3 in the middle one and 3 in the top tercile For GINI inequality after-tax the respective distribution was 3 in bottom, 1 in middle and 4 in top tercile.

Investigation of at-risk-of-poverty (AROP) data

After examining Income GINI data lets apply the same process to at-risk-of-poverty (AROP).

Just like GINI, AROP measures income inequality. Although it measures the same phenomenon – income inequality, since it uses a different methodology (see chapter At-risk- of-poverty , it yields slightly different results, which are very useful for the purposes of our investigation.

Figure 26 shows the average cross-country data for AROP divided among countries with flat and progressive PIT systems. Unlike with GINI data here it quite obvious that AROP after- taxes but before transfers is higher in countries with flat PIT systems. The same can be said about AROP after-taxes and transfers. Taxes and Inequality 74

An interesting observation comes from the fact that AROP after-taxes and transfers is higher than AROP just after-taxes (recall the analogous Figure 23, where for GINI this is not the case). This is a counterintuitive but legitimate observation13 in this framework. Transfer payments and pensions increase the equalized median income from which AROP is calculated thus increasing the AROP threshold. And since AROP does not measure the depth of AROP poverty it is possible to get a situation where transfer payments alleviate the depth of AROP poverty, but in the same time more people are technically fall below AROP line.

Why this is so consistent in this data warrants separate investigation, which unfortunately is outside the scope of this paper.

Figure 26 AROP Income inequality measured before-taxes, after-taxes, and after-taxes & transfers

AROP values at different stages of taxation

15,18

15,04

14,99

14,41

14,34

13,93

13,86

12,30

12,18

12,10

12,09

12,07

11,87

11,60 AROP AROP values

2007 2008 2009 2010 2011 2012 2013 Prog - after taxes and transfers Prog - income after taxes Prog - Original Income Flat - after taxes and transfers Flat - income after taxes Flat - Original Income

13 This is not directly comparable to AROP levels in EUROSTAT data Taxes and Inequality 75

We determined that after-tax AROP is consistently higher for countries with flat PIT system.

But we also observe that before-tax AROP is also consistently higher for flat PIT countries.

Like in case with GINI, the reasons for that warrant a separate investigation, outside the scope of this paper.

Figure 27 compares average AROP reduction in flat PIT and progressive PIT countries for the years 2007 to 2013. It is clear that progressive PIT system has been reducing AROP income inequality more than flat PIT system consistently, in all the years observed.

Figure 27 Average AROP reduction across countries for income before-tax and after-tax. Flat PIT and progressive PIT systems

Reduction of AROP income inequality Reduction of AROP income inequality by direct taxes in progressive and flat by direct taxes in progressive and flat PIT systems. Reduction in % PIT systems. Reduction in absolute value

2013 2013 2012 2012 2011 2011

2010

year 2010 year

2009 2009

2008 2008

2007 2007

0 0,2 0,4 0,6 0,8 0 10 20 30 Reduction, % Reduction, absolute value

flat PIT system progressive PIT system flat PIT system progressive PIT system

Average difference in AROP inequality reduction – 5,3 p.p.

We need to perform a similar examination of terciles that we performed of GINI data. Figure

36 and Figure 37 show the distribution of flat PIT countries across terciles, while Figure 28 Taxes and Inequality 76 shows the summary of results. This breakdown helps us address the issue whether the flat PIT countries simply have higher AROP inequality levels before-taxes (although this notion is partially dismissed by results in Figure 27).

In Figure 28 we can see three distinct periods. First, in 2007-2008 there is a movement from middle AROP inequality tercile to the top one. This means that before-tax AROP inequality in 2007-2008 was equally distributed across all terciles; in other words, flat PIT countries were likely to have similar distribution across terciles of pre-tax income inequality (also note

Figure 26 which shows near equal pre-tax inequality between flat PIT and progressive PIT countries). However in after-tax AROP flat PIT countries are much more likely to be found in the top tercile. This can be interpreted as an indicator that the flat PIT system was less effective in reducing after-tax AROP inequality than the progressive PIT systems.

A period of 2009 and 2010 is very different, it has no major movement across terciles. This would mean that in this period the flat PIT system was performing not dissimilarly from progressive PIT systems in terms of after-tax AROP inequality reduction. Also note Figure

27 which shows much smaller gaps in AROP reduction by flat and progressive PIT systems.

Finally the 2012-2013 period is again characterized by a more equal distribution across terciles for before-tax AROP and a more disproportionate concentration flat PIT countries in the tercile with higher after-tax AROP inequality.

Once again we need not to become confused. In all countries, regardless of PIT system, PIT reduced AROP inequality (as illustrated by Figure 26 and Figure 27). But this comparison of distribution shows two insights. First, in 2007-2008 and 2012-2013 relatively lower reductions of AROP poverty in flat PIT countries meant that after-tax inequality was relatively larger than in other countries.

Figure 28 Summary of distribution of flat PIT countries through terciles of AROP inequality of income before-taxes and after-taxes** Taxes and Inequality 77

Year Number of flat PIT Number of flat PIT Number of flat PIT countries in bottom countries in middle countries in top tercile (lowest tercile (medium tercile (highest inequality) inequality) inequality) Before- After-tax Before-tax After-tax Before- After-tax tax tax 2007 3 3 3 0 2 5 2008 3 3 3 0 2 5 2009 2 2 2 2 4 4 2010 2 3 2 1 4 4 2011 2 2 3 2 3 4 2012 2 2 3 1 3 5 2013* 1 1 3 1 3 5 *In 2013 Slovakia adopted progressive PIT rate. Asterix (*) marks in which tercile Slovakia would have been given its GINI values had it not changes in progressive PIT system (assuming GINI values would had remained unchanged) **For instance the 2007 row indicates that in 2007 27 EU countries were ranked according to their GINI score and divided into three groups of equal size (terciles), where bottom tercile indicates lower inequality and top tercile indicates more inequality. In 2007 GINI inequality before-tax 2 countries with flat PIT tax were in the bottom tercile, 3 in the middle one and 3 in the top tercile For GINI inequality after-tax the respective distribution was 3 in bottom, 1 in middle and 4 in top tercile.

Second (and this also applicable to GINI dataset) there are two distinct groups of flat PIT countries in regards to after-tax AROP inequality. First, countries in which AROP inequality is reduced significantly even with flat PIT systems (and even better than in some countries with progressive PIT system). And, second, countries in which flat PIT reduces after-tax

AROP inequality less than progressive systems. This indicates that there are some other significant factors at play, not just whether PIT system is flat or progressive.

The similar dynamics play out of we compare the distribution of countries across terciles by how much they reduce AROP inequality (refer to Figure 29). Flat PIT countries are on average twice more likely to cluster in bottom tercile (e.g. have lowest reduction of AROP inequality).

Two pieces of evidence allows us to conclude that progressive PIT system reduces AROP income inequality more than the flat PIT system. First, cross country average analysis shows Taxes and Inequality 78 that at any given year progressive PIT systems reduced after-tax AROP inequality on average by 5,3 p.p. more than flat PIT systems; moreover this reduction was consistent throughout the years analyzed.

Second the analysis of terciles shows that this result is not caused by an outlier (e.g. a flat PIT country, which drastically fails to reduce AROP inequality thus affecting the whole sample) but by a genuine clustering of flat PIT countries among the countries with highest after-tax income inequality. This is confirmed by similar finding by ranking countries by how much their PIT reduces AROP inequality (Figure 29).

Therefore we shall test the AROP data in the regression later on.

Taxes and Inequality 79

Figure 29 Distribution of AROP inequality reduction (percent) across terciles. Shaded values indicate countries with flat PIT. Larger values mean larger reduction of AROP inequality

2007 2008 2009 2010 2011 2012 2013

0,233 0,279 0,480 0,480 0,486 0,477 0,474 0,438 0,439 0,490 0,499 0,506 0,493 0,499 0,439 0,441 0,491 0,511 0,506 0,504 0,504 0,443 0,442 0,495 0,542 0,508 0,510 0,508 0,449 0,452 0,512 0,544 0,516 0,518 0,514 0,464 0,465 0,526 0,548 0,539 0,530 0,541 0,468 0,470 0,545 0,549 0,539 0,547 0,550 0,475 0,495 0,566 0,566 0,554 0,568 0,563 0,515 0,507 0,568 0,570 0,578 0,572 0,566 0,516 0,513 0,579 0,585 0,580 0,573 0,570 0,560 0,566 0,587 0,587 0,581 0,574 0,578 0,566 0,578 0,610 0,609 0,594 0,601 0,604 0,567 0,592 0,612 0,613 0,598 0,603 0,607 0,588 0,605 0,615 0,625 0,614 0,617 0,619 0,650 0,656 0,677 0,675 0,667 0,663 0,665 0,656 0,666 0,683 0,689 0,672 0,670 0,673 0,659 0,680 0,692 0,690 0,682 0,675 0,674 0,690 0,694 0,694 0,691 0,694 0,696 0,676 0,694 0,704 0,698 0,697 0,697 0,698 0,698 0,716 0,725 0,731 0,700 0,698 0,699 0,726 0,729 0,730 0,752 0,729 0,742 0,750 0,741 0,746 0,747 0,752 0,748 0,748 0,751 0,751 0,746 0,749 0,754 0,749 0,756 0,761 0,754 0,764 0,771 0,764 0,760 0,758 0,761 0,757 0,773 0,776 0,767 0,764 0,758 0,765 0,764 0,793 0,787 0,771 0,789 0,791 0,768 0,778 0,854 0,853 0,851 0,851 0,847 0,850 0,849

Question No. 2. Does higher progressivity reduce income inequality more?

Comparison of countries with progressive PIT systems

In previous chapter we were answering the question whether progressive PIT systems reduce income inequality more than flat PIT systems. This part analyzes whether more progressivity leads to larger reductions of inequality. This will be achieved by comparing AROP income Taxes and Inequality 80

reduction and the progressivity indicator of PIT systems we designed earlier - PIPITGINI. For this we just need to estimate a simple regression.

PIPITGINI = c0 + (c1)Reducti + (c2)Beforei + (c3)Afteri + ε

Where “Reduct” stands for AROP inequality reduction, estimated by percentage difference of before-tax and after-tax AROP values. For control we are adding variable “Before”, which signifies AROP income inequality before-tax; and After, which is AROP income inequality after-tax but before transfers. PIPITGINI denotes progressivity of progressive PIT systems. A year is indicated by “i”.

If our previous work is correct we should expect higher PIPITGINI values to positively correlate with higher Reduct values. This would signify that higher progressivity leads to larger reductions in AROP income inequality. We shall run the regressions for year 2010,

2011 and 2012.

Figure 30 Regression results of progressivity and reduction of inequality

Estimated values of coefficients for independent variables year Reduct Before After 2010 -0,63097 0,004374 -0,01634 2011 0,311553 -0,00724 0,012765 2012 -0,30838 3,24E-05 -0,00586 None of the estimates are significant at 5% or even 10% level

The results of the regression indicate that we there is no statistically significant relationship between progressivity of tax system and reduction of income inequality. The problem is not just the lack of statistical significance (which could be accounted for by a small sample).

Estimated coefficients display changes in signs (positive and negative). Higher progressivity would lead to more reduction in inequality in 2011; but to less reduction in inequality in 2010 Taxes and Inequality 81 and 2012. Of course, given extremely poor values of correlation coefficients and large standard errors even the significance of these sign changes is negligible.

How can the situation be remedied? If we reexamined the cross country data, we would notice we are looking at snapshots of individual countries at 3 given points in time (2010,

2011 and 2012). All the points across countries are not related to each other because they represent different countries (also we are dealing with numeric values and not rankings). If we ignored that data for individual country is related to each other (because inequality in individual country in any year has a strong causal and statistical relation to previous year) we could pool all the data for 2010-2012 and test whether increased sample size has any positive effects on the regression.

Figure 31 Regression results of progressivity and reduction of inequality for entire sample 2010-2012

Estimated values of coefficients for independent variables year Reduct Before After 2010-2012 -0,25431 -0,00028 -0,00454 sample None of the estimates are significant at 5% or even 10% level

This does not help the regression at all (see Figure 31). Results are insignificant and produce poor values for correlation coefficient.

Finally we are going to test the entire sample of 2010-2012 for just “Reduct” and “PIPITGINI” indicators (Figure 32). There we get somewhat statistically significant results, but still poor coefficients of correlation (R square = 0,06). If we visualize this relationship (Figure 33) this does not look a single trend but rather two separate clusters: one displaying positive and another displaying negative relationships between the variables. Moreover we keep on getting negative coefficients, which would suggest that higher progressivity is correlated with lower Taxes and Inequality 82 reductions in inequality. This goes completely against the theoretical basis that we have been building in the last chapter.

Figure 32 Regression results of only AROP reduction and progressivity indicator PIPITGINI

Estimated values of coefficients for independent variables year Reduct 2010-2012 -0,10724* sample significant at 10% level

Figure 33 Visualization of correlation between AROP and PIPITGINI. Entire sample 2010-2012

Scatter plot of AROP reduction and PIPITGINI indicators, full sample 2010-2012 0,9 0,8 0,7 0,6 0,5 0,4 0,3 AROP AROP reduction 0,2 0,1 0 0 0,1 0,2 0,3 0,4 0,5 PIPITGINI

Taxes and Inequality 83

Conclusions

This research produces a set multiple conclusions beneficial for examination of the topic further. First, it shows that regardless of efficacy of progressive PIT rates in reducing income inequality, progressive PIT rates are often justified on the grounds of ethics, values and other normative statements. The debate over which PIT system to have is highly connected to wider political debates over what society should look like, and how much should the government intervene into markets. Even the supposedly objective indicators that we use are laden with normative assumptions regarding these issues. It does not seem that the debate is going to be settled any time soon.

Second, at least in academic literature there is a whole spectrum of opinions regarding the effects that progressive PIT systems have on inequality. The paper shows the whole spectrum of possible effects of progressivity, some of them diametrically opposite to each other.

Increase in progressivity can reduce inequality, but it can also increase it. Reductions in progressivity can increase inequality but it can also reduce it. Add to the mix that we are dealing with two pairs of variables: pre-tax vs post-tax inequality and observed vs actual inequality and the simplistic narrative that PIT progressivity leads to less inequality falls apart. All this suggests that more investigative objective empirical work in this area should be done, and simplistic narratives – avoided.

Third, this paper produces a workable indicator to estimate the intended (ex-ante) progressivity of PIT system. It correctly estimates changes in PIT systems, and is an overall robust indicator of progressivity that can be used in other works. Additionally we get insights that estimates of progressivity derived from estimation of the slope of PIT system have many inherent problems and should not be used for meaningful estimates. Taxes and Inequality 84

Fourth, even a very simple empirical test on which PIT system – proportional or progressive

– reduces income inequality more is not as straight-forward as it seems. The results do show that progressive PIT systems reduce AROP inequality more than flat PIT systems. But we are not getting this result for GINI indicator. This echoes our prior theoretical insights that the relationship between progressivity and inequality is far from predictable. To determine whether this is a fluke of statistics we need to repeat these tests with more countries and more sets of data. In the same time comparable date is notoriously hard to find. Even for this work we had to rely on modeling of pre-tax income inequality rather than actual data, which most likely does not exist in single cross-country compatible database. Given the pre-occupation of governments with the issue this lack of data is rather strange.

Fifth, a formal statistical test on effects of progressivity on reduction of inequality fails to produce statistically meaningful results. Of course this can be explained by small a data sample. However the results showing different signs of coefficients in different years also suggests that the problem may be more than just a small sample of data. It may be an empirical illustration that many other and more important factors than PIT may be at play in reducing inequality. This work would definitely benefit from inclusion of transfers into the whole framework of thinking and calculations.

Does higher progressivity of income taxation lead to less inequality? This paper cannot confirm that neither by theoretical nor by empirical approach. This of course does not mean that more progressivity leads to more inequality. However demonstration of lack of empirical proof for such otherwise widely-accepted concept is very important. First it reduces the credibility of claims and policy proposals to reduce inequality simply through higher and more progressive taxation. Second, it puts into question whether such policy proposals are really intended to reduce inequality or to merely increase taxes (considering that the public is more sympathetic to policy goal of reduction of inequality then to mere increase in taxes). Taxes and Inequality 85

Third, it re-opens the debate and decouples reduction of inequality from increases (or higher progressivity) of taxation. This has a potential to de-politicize the issue and bring forward truly effective solutions.

Obviously this research could be improved. First, more and better data, especially on pre-tax inequality would make our insights more credible; expansion of geographical scope would also help. Second we need a system to correctly account for and estimate deductions and exemptions. Furthermore, a system that could reliably estimate progressivity and meaningfully include flat PIT countries would be an immense improvement. Finally incorporating all other taxes (e.g. taxes on personal non-wage income) and payments that essentially are taxes but are not called taxes (e.g. compulsory state-run health insurance schemes) would help us estimate both progressivity and tax burden.

A separate study need to be conducted on effectiveness of transfer payments and their role in reducing inequality. They might be the crucial missing piece of the puzzle which could explain why countries with similar PIT systems have such different figures for inequality of disposable income.

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APPENDIX

Figure 34 Movement of flat PIT countries across terciles of countries ranked by GINI income inequality 2007-2010. The number represents GINI value of individual country, letter „F“ indicates country has a flat PIT system

GINI GINI GINI GINI GINI GINI GINI inequalit inequalit GINI inequality inequalit inequality inequality inequality y on y on inequality on y on on Income on Income on Income Income Income on Income Income Income after - before- after -taxes before- after - before- after - before- taxes taxes 2010 2010 taxes taxes taxes 2008 taxes taxes 2009 2007 2007 2008 2009 0,400 F 0,257 0,377 F 0,242 0,378 F 0,245 0,400 F 0,257 0,410 F 0,264 0,418 0,265 0,417 F 0,267 0,410 F 0,263 0,434 0,267 F 0,419 F 0,270 F 0,419 0,269 0,431 0,266 0,435 0,269 0,429 0,277 0,431 0,270 0,435 0,269 F 0,439 F 0,272 0,434 0,277 0,432 0,275 0,439 0,277 0,441 0,277 0,436 0,277 0,434 0,276 F 0,439 F 0,277 0,453 0,288 0,438 F 0,289 0,438 F 0,287 0,453 0,287 F 0,453 0,295 0,452 0,293 F 0,451 0,292 F 0,453 0,296 F 0,462 0,300 F 0,452 0,295 0,451 0,294 F 0,465 0,298 0,476 0,307 F 0,458 0,296 F 0,457 0,298 0,475 0,307 F 0,480 0,307 0,461 0,303 0,461 0,299 0,480 0,308 0,480 0,312 0,472 0,308 0,472 0,307 F 0,482 0,311 0,481 0,313 F 0,473 0,309 F 0,475 0,311 0,482 0,313 0,482 0,315 0,481 0,324 F 0,481 0,326 0,482 0,315 0,488 0,320 F 0,481 0,327 0,481 0,326 0,488 0,318 0,491 F 0,324 0,483 0,331 0,483 0,327 0,492 F 0,326 0,492 0,325 0,490 0,331 F 0,486 F 0,328 0,492 0,326 0,500 F 0,329 0,490 F 0,333 0,489 0,330 0,501 0,328 0,509 0,330 F 0,492 0,334 0,490 0,331 0,506 F 0,332 F 0,511 F 0,343 0,495 0,350 0,494 F 0,348 F 0,512 F 0,345 F 0,512 0,348 0,499 F 0,353 0,500 0,349 F 0,512 0,350 F 0,515 0,349 0,504 F 0,365 0,507 F 0,361 F 0,514 0,351 0,517 0,352 F 0,516 0,368 F 0,516 0,369 0,517 0,352 0,521 0,359 0,519 F 0,371 0,520 0,371 0,522 0,359 0,526 F 0,365 0,524 0,373 0,524 F 0,371 0,527 0,366 F 0,539 0,365 0,551 F 0,383 0,525 F 0,395 0,524 0,394 F 0,539 F 0,378 F 0,541 0,396 F 0,542 F 0,395 0,551 F 0,378

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Figure 35 Movement of flat PIT countries across terciles of countries ranked by GINI income inequality 2011-2013. The number represents GINI value of individual country, letter „F“ indicates country has a flat PIT system

GINI GINI GINI GINI GINI GINI inequality inequality inequality inequality inequality inequality on Income on Income on Income on Income on Income on Income before- after -taxes before- after -taxes before- after -taxes taxes 2011 2011 taxes 2012 2012 taxes 2013 2013 0,400 F 0,260 0,400 F 0,260 0,400 0,261 0,410 0,263 0,411 F 0,266 0,408 F 0,267 0,431 F 0,265 0,436 0,269 0,436 0,272 0,436 0,272 0,436 0,272 0,438 0,272 F 0,439 0,278 F 0,439 0,276 0,439 0,275 0,444 F 0,279 0,444 F 0,282 0,444 0,285 0,453 0,290 0,453 0,286 0,453 F 0,288 F 0,453 0,295 F 0,453 0,294 F 0,454 0,298 F 0,468 0,303 F 0,466 0,302 F 0,466 0,302 0,477 0,307 0,476 0,308 0,476 0,304 F 0,480 0,307 F 0,480 0,308 F 0,480 0,307 0,480 0,312 0,480 0,312 0,480 0,311 0,481 0,313 0,481 0,313 0,481 0,313 0,483 0,319 0,483 0,320 0,483 0,320 0,488 0,323 0,488 0,324 0,488 0,324 0,489 0,326 0,490 0,326 0,489 0,325 0,492 F 0,333 0,492 F 0,333 0,492 F 0,334 0,500 F 0,334 0,499 F 0,335 0,499 F 0,337 0,509 F 0,335 0,509 F 0,336 0,509 F 0,342 F 0,509 0,340 F 0,509 0,341 F 0,509 0,342 F 0,514 0,345 F 0,513 0,345 F 0,513 0,345 F 0,516 0,350 F 0,513 0,348 F 0,515 0,348 0,517 0,352 0,514 0,352 0,517 0,350 0,518 0,358 0,517 0,354 0,518 0,362 0,526 0,368 0,526 0,357 0,526 0,365 F 0,539 F 0,371 F 0,538 F 0,375 F 0,538 F 0,378 0,551 F 0,386 0,551 F 0,389 0,551 F 0,393

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Figure 36 Movement of flat PIT countries across terciles of countries ranked by AROP income inequality 2007-2010. The number represents AROP value of individual country, letter „F“ indicates country has a flat PIT system

AROP AROP AROP AROP AROP inequalit inequalit inequalit inequalit inequalit AROP y on DPI y on y on y on AROP y on AROP inequality plus Income income Income inequality Income inequality on Income direct before- after- before- on income before- on income before- taxes taxes taxes taxes after-taxes taxes after-taxes taxes 2007 2007 2008 2008 2008 2008 2010 2010 22,6 4,0 22,6 4,1 23,0 4,2 22,9 4,2 26,6 6,1 26,6 6,3 28,2 7,1 28,4 7,2 27,7 F 8,1 28,0 F 8,0 29,9 F 7,9 29,9 F 8,0 F 28,2 8,3 F 29,4 8,4 30,7 8,1 30,7 8,2 29,4 F 8,6 29,7 8,4 31,7 F 9,2 31,4 F 8,5 29,8 8,9 F 29,7 8,8 31,7 9,2 F 31,8 9,4 F 30,1 F 8,9 30,3 8,9 F 32,0 9,4 32,0 9,5 30,8 9,8 31,0 F 9,0 F 32,3 9,7 F 32,2 10,0 F 31,7 9,8 F 31,9 F 9,1 32,9 10,1 32,9 F 10,0 F 31,8 10,1 F 32,2 9,8 33,7 F 10,2 33,9 10,2 F 32,1 10,3 32,6 9,9 34,3 11,0 34,3 10,7 32,6 11,6 33,1 11,1 F 34,7 11,3 34,6 10,9 33,1 11,8 F 33,1 11,7 34,9 11,3 F 34,8 12,0 33,1 12,3 F 33,2 11,9 F 36,3 11,5 F 35,9 12,3 33,2 12,3 33,3 12,8 36,4 12,3 36,3 12,7 F 33,7 14,7 33,4 14,8 36,7 13,1 36,6 13,4 33,8 14,9 34,5 15,2 37,1 F 13,9 36,9 13,7 34,6 15,0 34,9 15,6 37,3 14,2 37,1 F 13,9 34,6 16,5 34,9 15,7 37,4 15,4 37,3 14,6 35,0 F 16,8 35,2 F 16,1 37,6 15,5 37,3 15,3 35,7 F 17,8 35,7 F 17,2 37,7 16,2 F 37,5 16,0 36,0 18,6 36,0 F 18,6 F 38,2 F 17,7 37,6 F 16,9 37,0 F 18,8 37,0 18,7 F 40,0 F 18,2 F 40,2 F 17,4 37,5 19,0 37,6 18,9 F 40,1 F 18,2 F 40,5 F 17,9 37,6 F 19,8 38,4 20,3 41,3 19,3 40,8 F 18,2 F 39,4 19,8 F 39,5 F 20,6 F 42,0 F 19,3 F 41,3 19,0 F 40,8 F 21,6 F 41,9 F 21,2 42,8 21,0 43,2 20,0

Taxes and Inequality 93

Figure 37 Movement of flat PIT countries across terciles of countries ranked by AROP income inequality 2011-2013. The number represents AROP value of individual country, letter „F“ indicates country has a flat PIT system

AROP AROP inequality AROP AROP inequality AROP on Income inequality inequality AROP on Income inequality on before- on income on Income inequality on before- income after- taxes after-taxes before- income after- taxes 2011 taxes 2011 2012 2012 taxes 2013 taxes 2013 22,9 4,3 22,7 4,2 22,5 4,2 28,4 7,4 28,2 7,3 28,1 7,5 29,7 F 8,0 29,5 F 7,9 29,4 7,6 30,7 F 8,1 30,7 7,9 30,7 F 7,8 31,5 8,3 31,5 8,9 F 31,2 8,6 31,6 9,1 31,5 F 9,0 31,3 8,9 F 31,6 9,4 F 31,6 9,1 31,4 9,5 F 31,7 9,6 F 31,7 9,7 31,7 9,9 32,8 9,9 32,8 10,0 32,7 10,2 33,8 F 10,1 33,8 F 10,3 33,9 10,5 F 34,2 10,6 F 34,2 10,5 F 34,0 F 10,6 34,3 11,0 34,3 11,0 34,1 11,3 34,7 12,2 34,7 12,3 34,7 11,9 35,2 12,7 F 34,8 12,5 F 34,8 12,2 F 35,3 13,1 35,3 13,0 35,0 12,4 F 35,7 13,7 F 35,4 13,9 F 35,5 12,9 36,4 13,8 36,4 13,9 36,1 13,5 36,6 F 14,8 36,5 14,5 36,5 14,5 36,7 15,0 36,5 F 15,0 36,5 F 15,1 36,9 15,6 36,7 15,4 36,5 15,5 37,1 F 17,0 37,2 15,9 36,5 15,7 37,3 17,1 37,2 16,0 36,6 15,7 F 38,9 17,1 F 38,4 F 17,0 F 38,7 F 17,2 F 40,2 F 17,6 F 39,8 F 17,9 F 39,2 F 17,8 40,6 F 17,7 40,4 F 17,9 F 40,2 F 17,9 F 41,0 F 18,3 F 40,8 19,5 40,3 19,6 42,0 20,1 40,3 F 19,9 42,0 F 20,3