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Interlinkages between Growth, Distribution and Re-Distribution in Emerging Market Economies Vandana Bhaskaran* and Manasa Venkatesh** Abstract: We analyse the simultaneous relationship between growth, distribution and redistribution. Three issues emerge: the difficulty in establishing definitive causation between two sets of variables, the complex linkages between growth, distribution and re-distribution, and identifying the best form of re-distribution. We investigate these issues in Emerging Market Economies during 1980–2010, and consider the time dimension in our analysis. Keywords: Market income inequality, Net income inequality, Growth, Re-distribution, Emerging Market Economies, System GMM *Final Year Masters student in Economics at the Indian Institute of Technology Madras. The author can be contacted at [email protected] ** Final Year Masters student in Economics at the Indian Institute of Technology Madras. The author can be contacted at [email protected] Acknowledgements: We thank Dr. Suresh Babu for his guidance and useful comments 1. INTRODUCTION Economists and political leaders alike have been sounding alarm bells over rising income inequality across economies, their fear only worsened by the 2008 financial crisis. While many analysts still hold that economic weakness caused by income inequality led to the crisis, the issue of the relationship between economic growth and inequality has been heavily discussed and severely contested. Although the crisis saw the return of the questions of distribution at the centre of economic discussion and analysis, a large portion of this discussion has been marred by polemical arguments. Empirical evidence has not been so divisive and has mostly been ambiguous, panel specific and inconclusive. While the concern about distribution was principal in the analysis of classical economists, early modern economic theory was preoccupied with growth and the factors contributing to it. The distributional aspects of economic prosperity came into prominence in modern economic thought with Kuznets (1955) and Kaldor (1955). It is with Kuznets’ seminal work that we see the distributional question being looked into with more rigour. Kuznets analysed the distribution of income among different social groups and hypothesised that in an economy, in the early stages of development, the income inequality first increases and then decreases as the economy grows further. This relationship was stylised as the ‘inverted U-shape’. The influence of Kuznets and of the growth–inequality literature grew in the 1980s and 1990s with most theoretical works emphasizing growth as ‘a rising tide that lifts all boats’. Our interest in inequality is not just moral. At an intrinsic level, it is possible that inequality of income and wealth have possibilities of reducing economic growth. This involves looking at inequality at a functional level. At the level of simple correlation, more inequality seems to be associated with less sustained growth (Berg & Ostry, 2011b). Higher inequality might cause social and political instability, discouraging investments and leading to lower growth. Most empirical studies estimating the growth–inequality relationship looked into this functional aspect and these results, at best, have been mixed. Studies have estimated positive, negative and insignificant relations between the two. The addition of the re-distribution variable to the above relationship complicates the issue further. This has been a fairly recent addition in the empirical literature. Empirical arguments have swung both ways with regards to re-distribution as well. While some re-distribution policies are growth enhancing, most seem to impact growth negatively. This strand of thought has its roots in Okun’s (1975) leaky bucket concept which indicated that re-distribution undermined growth because of the transactions costs involved in the process of re-distribution. Our preliminary understanding of the literature brought up three issues that need further examination in the growth–inequality–re-distribution relationship. First, there is a difficulty in establishing causality between any two sets of variables. Second, the interlinkages between the variables seem to be bi-directional, calling for joint empirical estimation of the variables. Lastly, while studies have recognised normatively the type of re-distribution that works best, positive recognition seems lacking. This paper looks to address the above highlighted issues in the context of Emerging Market Economies (EMEs) over the 1980–2010 period. The rest of the paper is organised as follows: Section two examines and summarises the existing theoretical and empirical literature. Section three looks at the Emerging Market 1 economies (EMEs) in particular by mapping growth trends amongst the EMEs onto the inequality patterns using the Gini coefficient. It also derives some stylised facts about re-distribution trends in the EMEs. Section four discusses data and methodology while section five elaborates on the results and empirical estimation. We take the empirical results forward in section six by looking at policy evidence and outlining effective policy options. Section seven concludes. 2. LITERATURE REVIEW 2.1 Growth and Inequality There has been some consensus among the theoretical works that look to examine the growth–inequality relationship. The first formalised relationship between inequality and growth dates back to 1955, when Kuznets popularised the inverted U-shaped model, by drawing on three developed nations: the United States, England and Germany. He writes that his observations, ‘justify a tentative impression of constancy in the relative distribution of income before taxes, followed by some narrowing of relative income inequality after the First World War, or earlier’ (Kuznets, 1955). Kuznets himself acknowledges that the relationship is ‘tentative’, thus prompting a number of subsequent studies which test for the relationship empirically using different structural models. While the general stance in theoretical literature has been that inequality hurts growth, the empirical evidence since Kuznets (1955) has mostly been mixed and not conclusive of a trade-off between growth and inequality. To understand historically the nature of inequality over the stages of economic growth, it is important to look at the development trajectory of an economy. As the economy moves from agriculture to industry to services, the incomes of the households involved increase steadily. Those who move linearly across the sectors experience an increase in their income which gives rise to an increase in the economy’s overall level of inequality. The relationship is thus positive at the earlier stages of development. As the economy continues to transition, the relationship becomes negative, as iconized in the Kuznets curve. Early studies such as those by Perotti (Alesina & Perotti, 1996) and Barro (2000) empirically validate this proposition with their crosscountry studies. However, the non-linear structure of the model was lost as papers in the 1990s estimated a one-way monotonic effect of inequality on growth. Alesina and Rodrick (1994) document a negative impact of income and wealth inequality on subsequent growth. Persson and Tabellini (1994) use a measure of equality instead and arrive at the same result for nine developed nations. Perotti’s (1996) work also substantiates this negative effect argument. Ostry et al (2014) examine the inequality–growth relation in a historical context by looking at cross-country data. Their empirical findings point to a linear negative relationship between inequality and growth. Banerjee and Duflo (2003), however, contest the imposed linear structure and reinstate the non-linear form of Kuznets. Changes in inequality (in any direction) are associated with lower future growth rates (Duflo & Banerjee, 2003). But, Deninger and Squire (1998) use longitudinal data to disprove Kuznets curve. ‘The work on this issue is flawed not only because it relies on questionable data but, more importantly, because this intrinsically inter-temporal relationship has usually been tested using cross-country data’ (Deininger & Squire, 1998). Hence, the Kuznets hypothesis remains a fairly contested one. 2 The diverse results emerge from the fact that inequality affects growth via different channels, both economic and political. We identify five main channels in the literature: savings rate channel, the credit market channel, human capital channel, macroeconomic balances and social and political channel. The savings rate channel, as illustrated by economists since Keynes, hinges on the premise that higher incomes lead to higher marginal propensities to save thereby leading to higher savings and investments in an economy. Understood this way, this channel would argue that greater inequality leads to higher growth levels, i.e. there is a positive effect of inequality on growth. The high marginal propensity to save of these high-income individuals translates into greater investments. Additionally, concentration of income becomes important to facilitate bulky investments. This line of argument, to suggest that more unequal countries grow faster, was advanced by Kaldor (1955). Banerjee and Duflo (2003) suggest that the relationship between investments and income is not straightforward. Rather, the cost of inequality will depend on the mean income of the economy, that is, if everyone has that minimum income level to afford reasonable investments, inequality will have no consequence on growth. Under the credit market channel, imperfections in the credit market restrict access to credit and thereby reduce people’s ability to borrow. ‘It typically reflects asymmetric information and, in developing countries, it might point to the limitations of existing legal institutions’ (Barro, 2000). Any investment opportunity depends solely on the individual household’s existing and possible future income. But limited availability of credit to poorer households implies less capital availability to undertake both entrepreneurial and human capital investments. Perotti (1992) uses loan-to-value ratio as a proxy for credit availability and proves that greater credit availability has a significant positive impact on growth rate. Hence, in this particular channel, inequality has a negative effect on growth. The human capital investment channel operates alongside physical capital investment. ‘Higher dispersion can incite people to put forth additional effort or to invest in their human capital, as the rewards of this additional effort are higher compared to the situation in an egalitarian society’ (Thewissen, 2013). While inequality prompts growth in this channel, the human capital aspect could also play out as a negative effect. Wilkinson and Pickett (2009) point out that unequal countries lack good social indicators. This is because, as discussed, a large section of the population is credit constrained to make investments in education and human capital formation, which provide high rates of return. A deeper look into these factors suggests an augmented Solow model, where growth is a function of physical and human capital investments, wherein the role of human capital investment is quite ambiguous. The financial crisis of 2008 had an inequality argument as well. High inequality means high shareholder power and increasing share capital of income. This can lead to structural imbalances in the economy, prompting a financial crisis and thus, interrupting growth. While the economic reasons have been elaborated, and shown to be positive and negative, political factors are at play as well. Inequality, measured as a ratio of mean to median income, directs votes in favour of redistribution through the political process. This has its roots in the famous Median Voter Theorem: higher the ratio of mean to median income, the more likely a median preference for re-distributive 3 policies. As most policies are either transfers or taxes, they tend to distort the economic decision making. Taken this way, one would assume the relationship between inequality and growth to be negative, transmitted through the political economy channel. However, developments in recent literature have taken up government spending on productive processes, like education, health and insurance, as non-distortionary re-distributive processes that are non-detrimental to growth. The effects of such re-distributive policies on growth have been examined further in the later sections of this paper. An extension of the political economy channel addresses the concerns of social unrest that may arise in highly unequal societies. Such political instabilities have been witnessed across continents in the recent past. It is widely acknowledged that political stability is essential for prolonged economic growth. High levels of inequality lead to instability, which may prove to be a major hindrance to investment and capital flows, leading to falling output levels. This result holds regardless of the political nature of a society, across democratic and non-democratic states. Leaders favour stability as it breeds higher growth. This higher growth in turn has potential to bring down existing inequality through the trickle-down phenomenon, as illustrated in the literature (Alesina & Perotti, 1996). These five channels interact in complex ways at any given point of time and are specific to countries. Although there exist many theories examining this vital relationship, it is imperative to understand that the above mentioned channels are not mutually exclusive; they interact and the net effects of inequality on growth and vice-versa tend to be ambiguous. Consequently, their theoretical validity has been undercut by empirical irregularities in estimation. Barro (2000) contends that while inequality has negative impact on growth in poor countries, the savings rate channel dominates the other channels and inequality has a positive impact on growth in the richer or more developed countries. However, this finding has since been challenged by Assa (2012) who finds a negative relationship between inequality and growth for developing countries but does not find any significant relationship between the two for developed countries. Ostry et al, meanwhile, find a negative relationship between growth and inequality, independent of the level of economic development (Ostry, Berg, & Tsangarides, 2014). This paper looks to empirically establish this relation for the newly christened group of EMEs in the hope to derive new results for the extensively studied question. A closer look at these factors provides us with a clear distinction between the nature of variables engendering a positive relationship between inequality and growth, and those that cause a reverse effect. Manuel et al (2011) observe that most of the positive variables such as savings rate and capital investments are economic factors, which materialise in a relatively short-medium term. On the other side, the negative effects are associated with political factors and change of institutions. Such changes take a relatively longer time to come into effect. Thus, the time dimension needs to be factored in while assessing the relationship. However, a few studies (Perotti, 1996; Alesina and Rodrik 1994) reveal that inequality impedes growth both in the medium and long run. Thus, our paper tests for this relationship in both the short and long run. The use of GMM in the bulk of later literature suggests the recognition of simultaneity between the two variables. Starting with any initial distribution of wealth, both inequality and the growth rate must, on average, go down over time, with the consequence that in the long run there is no inequality and no growth (Banerjee & Duflo, 2003). While this explains the association or 4 correlation, the literature has been meticulous to identify the reverse linkage, that is, the effect of growth on inequality. It has been long argued that the benefits of growth will automatically trickle down to the poor in the form of increased production activities in the economy. The solution to tackle inequality lies in increasing growth. However, such a model did not hold up to empirical testing. Alesina and Rodrick (1994) show that there is no normative significance attached to the process of growth. This led to the distinction between the concepts of growth and development, what is required to tackle inequality is development and not growth per se. Most papers have also consciously made the difference between income inequality and wealth inequality. However, when it comes to empirical testing, income inequality is often used as a proxy for wealth inequality. But Deininger and Squire (1998) show that this receives little empirical support since the correlation between the Gini coefficients for initial distribution of land and income is relatively low at 0.39. Furthermore, the initial income Gini loses significance in the presence of region dummies. It suggests that region-specific characteristics which may, but need not, include income inequality, could be at the root of the relationship observed in much of the literature (Deininger & Squire, 1998). This is not the case with the initial land Gini, which is used a proxy for wealth inequality. Land Gini is significantly associated with lower growth rate even when region dummies are added, particularly in developing nations. While growth has been the standard variable linked to inequality, the persistence of this growth process is equally important. Berg and Ostry (2011a) capture this aspect of growth and observe that countries that enjoy longer growth spells are more equal. Ostry et al (2014) corroborate their earlier findings; they associate lower inequality with faster and longer periods of growth. This relationship also finds itself working the reverse way, where increased inequality will shorten the growth rate. Over longer horizons, reduced inequality and sustained growth may thus be two sides of the same coin (Berg & Ostry, 2011a). Inequality also seems to be associated with shorter growth spells. Berg and Ostry estimated that a 10 percentile decrease in inequality increased the expected length of the growth spell by 50 percent (Berg & Ostry, 2011b). Another term that is to be noted in Kuznets’ remark is ‘inequality before taxes’. The distinction between gross inequality (also known as market inequality) and net inequality (inequality after re-distribution efforts) has been neglected until very recently. Market inequality is understood in terms of pre-tax, pre-transfer disposable income while net inequality is taken as post-tax, post-transfer disposable income. Thus, differences in the conclusions are a result of myriad possibilities of structuring the relationship, in terms of the variables used. The specification of the nature of the relationship, namely simultaneity and non-linear effects, affects the results. The results are found to be highly cohort and country dependent. 2.2 Inequality and Re-distribution As discussed in the previous section, one of the channels through which inequality affects growth is through the political channel of the median voter theorem. When mean income exceeds the median income, the majority will favour re-distribution of market allotted outcomes. That is, by theoretical conventions, there ought to be a strong positive association between inequality and re-distribution, particularly in a democratic setting. The type of state, thus, determines the extent of re-distribution policy and hence the net inequality in the economy. Different welfare systems 5 and different social policies lead to various outcomes in changes of income inequality (Caminada, Goudswaard, & Wang , 2012). However, the most common fiscal instruments across all nations are taxes and subsidies. The tax–subsidy mix is modelled on progressivity1 grounds and hence automatically counters any rise in market inequality. Empirically, Immervoll and Richardson (2011) find evidence that tax-benefit system have become less re-distributive among the working population in OECD countries since the mid-1990s. However, Caminada et al (2012) do not conform to these findings. The income dimension of the policy instruments needs attention. Government expenditure on education and labour force benefit particularly the lower income tail, whereas capital income tax and other wealth tax are aimed at the top tail. In empirical studies, such policy instruments are captured through variables such as government transfers, social policy expenditures and more recently, the difference between market and net inequality. While the most direct way of testing the theory would be to relate income (wealth) inequality to measures of re-distributive policies, such a line of attack is problematic due to different countries pursuing different re-distributive policies in different periods of time (Alesina & Rodrick , 1994). Thus, as with growth and inequality, empirical results on the effect of re-distributive taxation on the long-run distribution of income have been mixed. Empirical studies challenging the conventional theory have emerged. Benabou (2000) does not agree with the median voter theorem and takes countries like the United States and the Scandinavian countries as evidence. The United States is the most unequal but less re-distributive, whereas Scandinavian countries are most equal and most re-distributive. He finds a similar contrast among the developing nations as well, wherein public education and health are more egalitarian in Asia than in Latin American countries. Thus, he contests that economies eventually reach two steady state equilibriums, namely high inequality–low re-distribution and low inequality–high re-distribution. This negative correlation again has its roots in political argument. Efficient re-distributions meet with a wide consensus in a fairly homogenous society but face strong opposition in an unequal one (Benabou, 2000). Similarly, Kenworthy and McCall (2008) study eight countries over the 1980s and 1990s and conclude that countries with high market inequality are less re-distributive. However, Ferreira (1999) normatively recognises the necessity of certain kinds of government-funded social insurances as re-distributive measures. In an economy where the distribution of households tilts towards poorer ones, the aggregate production potential would be highly under-utilised. This would call for re-distribution of income-generating opportunities. Benabou (2000) concurs with this argument by stating that state expenditures play an important role in re-distribution. He maintains that government intervention in such cases will be positive and not distortionary. The literature also hints at reverse causality. Becker and Tomes (1979) with their exclusive focus on returns to education in a multi-period model conclude that re-distributive policies, particularly taxes, reduce investment on children and therefore increase inequality in subsequent periods. Hence, re-distribution in the ‘n’th period will affect the inequality dynamics in the (n+1)th and subsequent periods. Unemployment allowances, while reducing income inequality 1 Progressivity is measured using the Kakwani Index (1977). Progressivity is the measure of distribution of tax and benefit. A tax or benefit which distributes more to the poor is more progressive. However, if it is distributed in proportion to pre-tax/benefit income, such a system has zero progressivity. 6 in the current period, will influence behaviours in the labour market, which will have a long-term effect. One possibility is dis-incentivising people from actively looking for jobs. Thus, similar to the growth–inequality relationship, the sign and direction of causation between these two variables are quite ambiguous in empirical literature. This is because, over longer time periods, trends in inequality and re-distribution result from a complex pattern of income and policy changes affecting all income groups (Immervoll, 2011). 2.3 Re-distribution and Growth The notion of growth can be equated to efficiency and that of re-distribution to equity. Okun (1975) articulated the relationship between the two as essentially one of a trade-off. The money must be carried from the rich to the poor in a leaky bucket. ‘Some of it will simply disappear in the transit; the poor will not receive all the money that is taken from the rich’ (Okun, 1975). This ‘leaky bucket’ captures the transaction cost involved in re-distribution which reduces the overall size of the pie while restructuring it. Okun’s ‘leaky bucket’ concept looked at a negative relation between re-distribution and growth. Okun and subsequent literature postulated that re-distributive efforts like transfers decreased the incentives to work and invest thereby altering the decision-making process and reducing growth and increasing deadweight losses. For Rodrik (1997), higher levels of inequality divert resources away from productive purposes to bargaining over ‘distribution of rents’ or ‘burdens of negative shock’. This decreases efficiency in an economy. Rodrik refers to it as negative re-distributive effects of inequality, i.e. distribution of resources from productive to unproductive sectors. Apart from this indirect consequence, re-distribution also has a direct negative effect on growth by distorting investment initiatives and altering agent behaviours. Alesina and Rodrick (1994) bring out the policy choices the government faces. To benefit the capitalists, the government should maximise its growth rate. However, to cater to the worker section of the society, the government must choose a growth rate that falls short of the maximum attainable since taxes are set above the optimal rate in this case. Similarly, the case of unemployment benefits inhibiting tendencies to actively look for a job was discussed in the previous section. The literature identifies two categories of re-distribution measures – some that are proequality and pro-growth (like fiscal spending on health, education and social insurance) and some that may lead to a fall in efficiency in the economy (like taxes and transfers). Arguments that point to the negative relation between growth and inequality usually offer, as explanations, the possible distortionary effects of re-distributive policies to explain causation. Most studies look at the effect of the re-distribution policy, whether distortionary or productive, to ultimately establish a normative judgement. If the effect is distortionary, then the net effect on growth is negative but if the effect is productive, that is if re-distributive policies allow accumulation of human capital and productive resources, then the net effect on growth is positive. Romer and Romer (2010) verify the negative effect of re-distribution on growth through tax policy changes. A tax increase of one percent of GDP lowers GDP by about three percent. An important part of that effect appears to be due to the pro-cyclical behaviour of investment (Romer & Romer , 2010). But not all empirical testing supports this theoretical convention. Lindert (2004) shows that growth in strongly re-distributive states has been relatively high. The positive association may arise from provision of social securities and insurance, which provide a risk-free 7 environment to develop. Some studies have also looked at drawing distinctions between wealth and income taxes. They estimate that while wealth tax increases growth in the long run, income tax is damaging and reduces growth in the same time horizon (Gruener, 1995). The divergences in empirics could be largely attributed to the different re-distribution mechanisms taken in such studies. Literature has popularly focussed on human capital investments, commonly primary school education. Other indicators have been urbanisation and infant mortality rates, and credit market imperfections (Galor and Zeira, 1989). Under the credit market imperfections channel, re-distribution of assets from the rich to the poor, if nondistortionary, has the capacity to raise average return on investment across the economy (Barro, 2000). Deininger and Squire (1998) emphasise on initial wealth endowments for investments to have a positive impact through their inclusion of land inequality as one of their regressors. ‘A policy conclusion that emerges directly from this discussion is that accumulation of new assets is likely to be a more effective way of reducing poverty than efforts to re-distribute existing assets’ (Deininger & Squire, 1998). But they caution that such attempts at asset re-distribution should not lead to lower levels of investments. Thus, certain re-distribution efforts could be more successful in countering inequality while sustaining growth. For example, Caminada et al (2012) identify cash transfers to have twice the re-distributive effect vis-à-vis household taxes in their study of 20 Luxembourg Income Study (LIS) countries. However, one cannot ignore the simultaneity in this relationship as well. Similar to redistribution impacting economic growth, economic growth also influences the need and demand for re-distribution. We allude to Wagner’s hypothesis of ‘increasing state activity’ to justify this converse relationship. In a loose sense, Wagner’s law points to a positive long run co-movement between government expenditures and economic growth (Zaghini & Lamartina, 2008). One, higher growth rates warrant increased government intervention to ensure political stability and develop infrastructure. Two, growth, by enhancing the reserve pool, creates possibilities for government transfers and provisions. The focus of this paper is on the latter aspect where growth facilitates the process of development through re-distribution. The scan of literature shows that considerable number of studies has focused on growth– inequality and growth–re-distribution relationship. The relationship between inequality and redistribution has seldom received the same attention. 3. INEQUALITY TRENDS IN EMERGING MARKET ECONOMIES The EMEs have been gaining prominence because of their growing role in the world economy. They have also been involved in the shaping of post-crisis global governance and the global economic growth is being strongly influenced by them. With their role expected to increase further in the future, it is important to understand the socio-economic dynamic at play here. While the sustained and strong economic growth in emerging economies has helped to counter absolute poverty, relative poverty levels continue to remain high, and have in fact shown an upward trend with a few exceptions like Brazil and Peru. We observe the correlation between market and net inequality to be close to 0.8 in our overall sample. This is consistent with Solt’s (2009) remark about developing countries having very low re-distribution levels. There has, however, been a gap in assessing these issues in the empirical literature. 8 Table 1: Decadal trends in market and net inequality and relative re-distribution in the EME panel 1985 Asia China 32.14438 Indonesia 52.6867 India 47.81893 Malaysia 50.60266 Philippines 54.85576 Russia 26.79766 Singapore 43.43512 Thailand 55.78826 Turkey 54.81611 Srilanka 43.90325 Pakistan 35.11723 Latin America Argentina 42.7503 Brazil 56.35445 Chile 51.39601 Colombia 52.93122 Ecuador 45.63401 Mexico 46.29375 Panama 50.09767 Peru 58.96486 Venezuela 43.68849 Europe Bulgaria 24.49099 Czech Rep 28.52422 Poland 29.50551 Hungary 32.25562 Africa & Middle East Egypt 35.38181 Jordon 50.92313 Morocco 45.556 Nigeria 46.17098 South Africa 53.70662 Market Inequality 1995 2005 % Change 1985 Net Inequality 1995 2005 % Change 1985 Relative Redistribution 1995 2005 % change 41.49858 72.08476 50.61657 48.86698 52.24481 44.85794 44.04957 50.07322 48.08705 41.44741 41.49088 49.81767 57.9182 49.96781 50.14609 49.02141 48.06327 42.31578 48.80146 46.54956 43.06209 44.16637 54.98098 9.929448 4.493787 -0.902256 -10.63581 79.35624 -2.577038 -12.52378 -15.08052 -1.915953 25.76838 27.11935 38.57069 47.52211 48.79143 47.13076 22.87644 40.5212 54.74971 51.86775 41.77795 32.95884 40.93068 46.33251 50.32106 46.46028 47.97787 38.46317 41.04251 60.67517 46.49364 43.10284 40.88086 49.31079 52.93949 49.33821 47.1876 50.64748 41.12357 40.86192 51.80508 44.12898 46.70348 44.06342 81.82878 37.25314 3.8216 -3.287101 7.461621 79.76385 0.840841 -5.378345 -14.9202 11.78978 33.69227 15.63267 26.79235 4.147774 3.579326 14.08238 14.63269 6.708655 1.8616 5.378636 4.840883 6.146243 1.368481 35.72497 0.583819 4.924998 8.167218 14.2556 6.826534 -21.1729 3.313599 -3.994045 1.470251 1.017474 8.596104 1.260003 5.899745 -3.317075 14.43868 3.435725 -6.154771 5.199997 -8.456133 0.233096 -93.49136 -67.91583 -69.62218 64.8284 -123.5548 -1.325867 -48.78668 -430.6174 -3.32127 -274.6816 -96.20751 44.73841 56.09085 51.63856 50.24972 50.77369 49.07821 52.25449 52.00716 42.66989 44.98963 53.26287 50.08922 51.16141 49.93905 48.17646 50.44473 51.05519 41.64335 5.23815 -5.485955 -2.542587 -3.343601 9.433846 4.066871 0.692772 -13.41421 -4.681172 40.22464 51.71463 49.65599 49.35327 44.88771 44.71236 46.74719 56.10587 42.14678 43.32096 52.10211 50.27586 49.48624 48.96075 47.59202 50.75689 52.15572 41.23357 43.91739 49.23182 48.666 50.49713 49.09079 46.02081 49.12176 50.43813 39.87384 9.180306 -4.800986 -1.993692 2.317716 9.363549 2.926368 5.0796 -10.10186 -5.392928 5.907937 8.233274 3.385514 6.759624 1.635408 3.415999 6.6879 4.848646 3.528859 3.168313 7.111208 2.638929 1.519366 3.570629 3.028191 2.865978 -0.285651 3.366105 2.383307 7.568215 2.841366 1.298388 1.698594 4.474494 2.622622 1.208628 4.249219 -59.65924 -8.0777 -16.07282 -80.79201 3.863667 30.9864 -60.78557 -75.07288 20.4134 29.28996 36.07225 40.14778 45.1156 32.43057 38.87033 42.48744 41.54138 32.41837 36.27133 43.99836 28.78803 20.34982 19.99282 25.99518 22.17787 26.77681 23.25906 28.31808 30.15887 28.90618 25.99878 29.64626 27.25365 42.04636 16.90893 8.580255 10.86747 -35.72938 30.04063 29.90934 35.52092 33.11407 10.71484 14.04524 11.8972 29.4654 30.22347 154.0386 22.88672 31.2434 33.15203 34.39396 10.08391 34.63161 46.01992 39.67899 51.16749 55.24856 34.31466 47.99558 41.43057 46.33655 61.54904 -3.016094 -5.748964 -9.055734 0.358597 14.60235 34.87304 49.58502 42.51394 44.79916 53.71029 36.70036 43.44884 37.01989 50.12056 53.91211 33.18794 47.43387 38.70284 42.50862 58.18109 -4.832109 -4.338302 -8.964342 -5.1129 8.323922 1.437928 2.627719 6.677632 2.971178 -0.006841 -5.973598 5.586887 6.701532 2.046092 2.418993 3.283495 128.349 1.170341 -55.46172 6.583849 -1.40443 8.261138 178.0425 5.471978 - Source: Authors’ calculations based on SWIID database The table (Table 1) tabulates changes in mean market inequality, net inequality and relative re-distribution between 1980 and 2010. In Asia, we see inconsistent trends among the countries, with market inequality rising in a few countries and falling in some others. China experiences the highest change in market inequality over the two decades. Most studies point to rising regional inequality linked to increasing globalisation and integration of its urban areas. ‘Uneven distribution of domestic capital, FDI and trade account for almost 50 percent of the total regional inequality’ (Wan, 2007). India also observes strong inequality trends arising from spatial factors. Like China, India is witnessing rising inequality between its urban and rural incomes. Datt and Ravallion (2002) cite India’s lagging agricultural sector, inflation and education as possible reasons for higher inequality. In both these countries, re-distribution efforts have not matched rising market inequality. Relative re-distribution has, in fact, reduced over the years, placing the net inequality on the same level as market inequality. These trends can be seen in figure 1. Sri Lanka, Singapore and Malaysia exhibit a constant trend in their inequality values. However, Sri Lanka’s net inequality is higher than market inequality in the 1990s and 2000s. The experience of Thailand and Philippines has been the same. In Russia, the market and net inequality move together while re-distribution has been stable over the period. 9 Latin America also displays heterogeneity in its inequality and re-distribution trends across its countries. Brazil and Peru have reduced their relative inequality levels, even as absolute inequality levels continue to remain high. Ferreira et al (2006) identify four structural and policy changes which have been the reason for reducing inequality in Brazil. These are declining returns to education, urban–rural income convergence, social assistance transfers and declining racial inequality. Among all these countries, the difference between net and market inequality has been insignificant. The re-distribution has also been at a relatively low level in relation to other country groups. Africa has been recognised as the world’s most inequitable region. In 2010, six of the 10 countries with the most unequal income distribution in the world were in sub-Saharan Africa, specifically in Southern Africa (African Development Bank, 2012). Africa, however, is not highly represented in our sample. South Africa2 has been historically characterised by high levels of income inequality. Its inequality values are the highest recorded in our sample but it has shown increasing absolute levels, albeit, at low levels since 2007. Re-distributive efforts have also been increasing in Nigeria since the 2000s. Meanwhile, relative re-distributive trends in Morocco have been at constant levels and this is reflected by the near overlap of market and net inequality trends in figure 4. Emerging Europe provides an interesting contrast to the trends observed in the other three region cohorts. The absolute values of inequality, both market and net, are among the lowest while the re-distribution values are much higher. However, these countries have also been facing increasing inequality levels. 2 The change in relative redistribution for Africa has not been calculated since its initial redistributive value is negative. 10 PHILIPPINES 75 65 55 45 35 25 15 75 65 55 45 35 25 15 MALAYSIA 75 65 55 45 35 25 15 THAILAND 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 INDIA 75 65 55 45 35 25 15 75 65 55 45 35 25 15 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2010 2007 2004 2001 1998 1995 1992 1989 1986 1983 1980 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 PAKISTAN 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 2007 2004 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 INDONESIA 2001 CHINA 1998 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 1995 1992 1989 1986 1983 75 65 55 45 35 25 15 1980 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 TRENDS IN MARKET INEQUALITY AND NET INEQUALITY ASIA (Figure 1) SRILANKA SINGAPORE TURKEY RUSSIA 11 75 65 55 45 35 25 15 PERU 75 65 55 45 35 25 15 75 65 55 45 35 25 15 ECUADOR ARGENTINA 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 VENEZUELA 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 75 65 55 45 35 25 15 75 65 55 45 35 25 15 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 CHILE 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 BRAZIL 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 LATIN AMERICA (Figure 2) PANAMA COLOMBIA MEXICO 12 75 65 55 45 35 25 15 75 65 55 45 35 25 15 75 65 55 45 35 25 15 MOROCCO NIGERIA 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 CZECH REPUBLIC 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 EGYPT 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 POLAND 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 75 65 55 45 35 25 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 EUROPE (Figure 3) 75 65 55 45 35 25 15 HUNGARY BULGARIA AFRICA & MIDDLE EAST (Figure 4) 75 65 55 45 35 25 15 JORDAN SOUTH AFRICA 13 4. DATA AND METHODOLOGY The empirical verification of this model has been largely fixated on OECD nations. This has been motivated by the fact that these countries have well documented datasets. But the need for a similar analysis in the context of developing nations was emphasised in the previous section. This paper, being one such attempt, looks at a panel of 29 EMEs as listed by the IMF. An important improvement in this field has been the launching of the SWIID database. The database provides Gini indices of gross income inequality and net income inequality on a comparable and wider scale3. The gross income inequality is the distribution of income from market outcome, while the net income inequality is the inequality post-government intervention in the form of taxes and subsidies4. A progressive tax-subsidy mix ought to reduce the value of the net inequality Gini vis-à-vis the market inequality Gini. The Gini index ranges from 0 to 100, where 100 refers to a point where a single unit is the recipient of all incomes. We use income Ginis from the SWIID database since the separation of gross and net inequality, based on taxes and subsidies, allows us to capture the re-distribution value in these economies, in addition to inequality values. Re-distribution is calculated as the difference between market inequality and net inequality. This gives us the absolute measure of re-distribution. In our analysis, we use the relative measure of re-distribution which calculates the value in relation to corresponding market inequality. Another important variable required to estimate the simultaneity is the growth of an economy. The relationship between growth and inequality is analysed via the economic channels of savings rate and human capital investment. The savings rate channel is proxied by investments, and education is used for human capital investment. The education variable is captured by primary and tertiary enrollment ratios separately. Secondary education could not be added due to lack of sufficient data points. Population has been an important factor in growth models since Solow, and hence our model factors in this variable. Other macroeconomic variables include inflation and trade openness. The sources for all these variables are World Development Index and Penn table 7.1 and table 8.0 To look at how the dynamics of the relationship change over time, we consider a long and a short panel. Long panel includes data points from 1980 to 2010. Short panel is taken from 1999 to 2008. This is to avoid the years of Asian Financial Crisis in 1997–1998 and the Global Financial Crisis post-2008. We choose annual data in case of both the datasets. The number of countries reduces to 20 when we include the control variables due to scarcity of data with respect to these variables. Our baseline models, nevertheless, use 29 countries. Our analysis extends further to look at the effective re-distributive policies to counter inequality. Here, the impact of tax-subsidy mix, government expenditure on health and education and inflation are assessed on net inequality. The tax-subsidy mix is denoted by the re-distribution variable. Hence, we don’t add income tax or any subsidy variable in the model. The fact that redistribution does not capture in-kind provisioning justifies the discrete inclusion of government 3 We use the SWIID 4.0. It is to be noted that the net inequality in the SWIID database does not include indirect taxes or in-kind government provision. 4 14 expenditure on health and education5. Inflation is added since it acts as a regressive tax and thus erodes the purchasing power. Our re-distributive model has been limited by lack of data. Insufficient data points for government agricultural expenditure and other taxes such as capital gain or indirect taxes do not allow us to look at these re-distributive aspects. These countries also lack data on the sources of household income ruling out an assessment of cash transfers and allowances. The simultaneity among growth, inequality and re-distribution, and the interdependence across years requires a dynamic panel modelling. Hence, we use the system GMM method to estimate the equations, where the instruments for the endogenous variables are derived from within the model itself. The variables are instrumented using their first differences and lags. While papers in this area have looked at both the difference and system GMM, we rely on system GMM. The specification of the model becomes extremely important because different estimation techniques yield different results. Manuel et al (2011) find a positive relationship between growth and inequality using first difference; the same relationship turns negative in middle and low income countries when system GMM is applied. Our reasons for using system GMM over difference GMM are as follows. First, system GMM, by introducing more instruments in its system of equations, has higher efficiency compared to the difference GMM model (Roodman, 2009). Second, first difference GMM exploits only within-country variation and does not account for cross-country variations. System GMM, on the other hand, exploits both timeseries and cross-country variation in data. 5. ESTIMATION AND RESULTS6 The objective of our empirical research is threefold. One, to model the relationship between the three variables simultaneously. Two, to factor in the time dimension by looking at the relationship in the long run and short run. Three, to identify effective re-distributive policies. We consider three baseline models, each taking the variables of interest as the dependent variable. We extend two of these baseline models, by including controls, to consider the relationship among the variables simultaneously and to estimate effective re-distributive policies. Table 2 shows that higher market inequality has led to greater re-distribution in these EMEs both in the short and long run. While growth and re-distribution exhibit a positive relationship in the long panel, it is negative in the short run, implying that the idea of growth dominates in the short run and that development (brought about by re-distribution) is a long run objective. Alesina and Rodrick (1994) allude to this by stating that re-distribution could lead to less-than-optimal growth rate. But the short-run results could be purely indicative of the decade under consideration. Considering these countries have experienced high growth rates in 1999–2008, the negative relationship points to the low or reduced re-distribution efforts during this phase. In both cases, high levels of initial income lead to high levels of re-distribution. 5 We have included input variables to assess the impact of re-distributive efforts on inequality. A better way would be to look at the output variables since huge inefficiencies in transferring resources would mean that the actual benefit (output) could be less than the intended benefit (input). 6 For all results presented, standard tests for the validity of the instruments and AR (1) and AR (2) are satisfied. Hansen J test and Sargan test provide favourable results. 15 Table 2: Baseline regression 1: Re-distribution on market inequality and GDP growth rate Dependent Variable :Re-distribution Variables Long Panel Short Panel (1980–2010) (1999–2008) Market inequality 0.036*** (0.0083) 0.537** (0.0220) GDP growth rate 0.106*** (0.0153) –0.062** (0.0275) Log(Initial per capita GDP) 1.774*** (0.0679) 4.978*** (0.1013) Constant –10.204*** (0.6362) –35.412*** (1.3720) Number of Observations 787 289 Note: System GMM estimation where robust standard errors are in the bracket. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent levels respectively. The following table (Table 3) looks at the relationship between growth rate and inequality and re-distribution. We find that market inequality is insignificant in the long run but has a high negative impact on growth in the short run. On the other hand, re-distribution exhibits an exact opposite behaviour. It is positive and significant in the short run and insignificant in the long run. A combined analysis of these two variables helps us explain this behaviour. A deeper look into the inequality–re-distribution data (Table 1) across the sample reveals that re-distribution efforts were relatively high in the 1980s and early 1990s. This led to lower market inequality in subsequent years in these two decades. This effect dominates the long panel results where redistribution is seen to be significant and market inequality is insignificant. However, there is a marked decline in the relative re-distribution in the 2000s, leading to higher market inequality levels. This is reflected in the short sample. 16 Table 3: Baseline regression 2: GDP growth rate on Market Inequality and Re-distribution Dependent Variable : Per Capita GDP Growth Rate Variables Long Panel Short Panel (1980–2010) (1999–2008) Market inequality –0.019 (0.0160) –0.101*** (0.0320) Re-distribution 0.260* (0.0138) 0.032 (0.0203) Log(Initial per capita GDP) –1.014*** (0.1287) –0.715*** (0.2145) Constant 10.838*** (1.183) 13.879*** (2.1238) Number of Observations 787 289 Note: System GMM estimation where robust standard errors are in the bracket. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent levels respectively. Table 4: Baseline regression 3: Market inequality on GDP growth rate and re-distribution Dependent Variable : Variables Market Income Inequality Long Panel Short Panel (1980–2010) (1999–2008) Per capita GDP growth rate –0.716*** (0.0107) –0.155*** (0.0241) Re-distribution 0.027*** (0.0005) –0.033** (0.0149) Log(Initial per capita GDP) 0.552*** (0.0480) –0.151 (0.1159) Constant 43.128*** (0.0368) 49.157*** (0.8740) Number of Observations 787 289 Note: System GMM estimation where robust standard errors are in the bracket. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent levels respectively. 17 Our last baseline (Table 4) estimates the effect of growth and re-distribution on market inequality. The impact of growth on inequality is consistent across time horizons. Higher growth rate implies lower inequality. Meanwhile, the effect of re-distribution is ambiguous. While redistribution lowers income inequality in the short run, the effect is inconclusive in the long run. The long-run relationship could merely be a reflection of the sample characteristic. Redistribution values have been observed to be negative in certain Latin American and Asian economies in late 1990s; implying regressive re-distribution. In this case, re-distribution leads to higher market inequality in subsequent periods. 5.1 Growth–Distribution–Re-distribution This section empirically verifies the simultaneity among all three variables. The variables are standard in accordance with the existing literature and the model specification follows Ostry et al (2014). However, we have used market inequality instead of net inequality. We observe that market inequality has a significant negative impact on growth in the long run (Table 5.1) but it is insignificant in the short run (Table 5.2). Our model finds no trade-off between re-distribution and growth. One unit increase in re-distribution, in fact, increases growth by 0.04 percent. This implication is time invariant. This is in contrast to Ostry et al (2014) who find the re-distribution variable to be insignificant. We reason that the use of net inequality in their model could have subsumed the effect of re-distribution, thereby rendering it insignificant. Furthermore, we find evidence to support the savings rate and human capital channels outlined in the literature. Investment and returns to education, both primary and tertiary, are significant and positive in the long run. The relationship extends to the short run as well with the exception of returns to primary education. Investment in physical capital seems to have a dominant effect on growth vis-à-vis human capital. Inflation as a regressive tax has a significantly detrimental effect on growth. We also tested for the non-linear relationship as hypothesised by Kuznets by adding a higher-order market inequality term. However, the second-order market inequality variable proved to be insignificant7. 7 The non-linear relationship was tested out only for the long sample. The short sample is too small a size to account for the structural changes underlying the Kuznets hypothesis. 18 Table 5.1: The effect of inequality and re-distribution on growth from 1980–2010: Extending the second baseline Dependent Variable: Per Capita GDP growth Variables (1) (2) (3) Log(Initial per capita GDP) –1.310*** (0.1809) –0.881*** (0.3059) –0.713** (0.3219) Market Inequality –0.049** (0.0230) –0.687** (0.0268) –0.067** (0.0271) Re-distribution 0.036** (0.0149) 0.052*** (0.0161) 0.047*** (0.0162) Investment 0.105*** (0.0199) 0.133*** (0.0235) 0.116*** (0.0253) Log(Population) 0.629*** (0.1878) 0.745*** (0.2270) Primary Education 0.017 (0.01744) 0.031* (0.0177) Tertiary Education 0.044*** (0.0143) 0.033** (0.0156) Inflation –0.002*** (0.0005) Trade Openness 0.006 (0.0059) Constant 11.839*** (2.1107) –5.362 (5.3331) –9.822 (6.1726) Number of Observations 504 430 430 Note: System GMM estimation where robust standard errors are in the bracket. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent levels respectively. 19 Table 5.2: The effect of inequality and re-distribution on growth from 1999–2008: Extending the second baseline8 Dependent Variable: GDP growth rate Variables (1) (2) (3) Log(Initial per capita GDP) –1.036*** (0.3393) –0.817* (0.4312) –0.773* (0.4352) Market Inequality –0.029 (0.0378) 0.030 (0.0462) 0.030 (0.0490) Re-distribution 0.061*** (0.0197) 0.051** (0.0208) 0.050** (0.0211) Investment 0.175*** (0.0320) 0.243*** (0.0366) 0.232*** (0.0435) Log(Population) 0.645*** (0.1768) 0.720*** (0.2114) Primary Education 0.006 (0.02134) 0.007 (0.0243) Tertiary Education 0.090*** (0.01455) 0.087*** (0.0154) Inflation –0.003 (0.0228) Trade Openness 0.004 (0.0072) Constant Number of Observations 8.7651** (4.1113) 199 –12.7432* (6.5477) 170 –14.50** (6.970) 168 Note: System GMM estimation where robust standard errors are in the bracket. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent levels respectively. 8 The shorter panel suffered from auto-correlation due to persistence in trends. We have used higher-order lags to correct for AR (1). 20 5.2. Re-distribution mechanisms Table 6: Effective re-distribution policies: Extending the third baseline. Dependent Variable: Net Income Inequality Variables Short Panel ((1999–2008) Re-distribution –0.565*** (0.0153) Log(Initial per capita GDP) 2.991 (0.2227) Per capita GDP growth –0.240*** (0.0458) Government Expenditure on education (% of GDP) 0.110 (0.1332) Government Expenditure on health (% of GDP) –1.322*** (0.0991) Log(population) 2.002*** (0.1366) Lag(Inflation) –0.059*** (0.0136) Democracy 8.179** (0.3895) Unemployment 0.376*** (0.0278) Constant –13.209*** (3.6038) Number of observations 181 Note: System GMM estimation where robust standard errors are in the bracket. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent levels respectively. 21 Table 6 shows the differential impact of various re-distributive measures on inequality. We have taken government expenditure on health and education, re-distribution and inflation as re-distributive measures. Re-distribution variable, as defined by Frederick Solt (2009), captures the tax-subsidy mix. This justifies the inclusion of government health and education expenditures as discrete variables. Demographic and economic factors are controlled by inclusion of growth rates, population, unemployment rates and a democracy dummy. The lack of availability of data severely hindered our focus on other re-distributive variables like government agriculture expenditure, direct cash transfers and wealth taxes, and restricted our analysis on different components of household income. Our analysis reveals that a one unit increase in a progressive tax-subsidy mix decreases inequality by 0.5 units. Other government re-distributive policies like human capital investment seem to be more efficient. We observe this empirically from the effect of government expenditure on health. A one unit increase in government spending on health decreases inequality by 1.3 units, which is almost double the impact of tax-based re-distribution. We cannot, however, infer the same for government spending on education, which is seen to be insignificant. This could be due to the larger gestation period associated with returns on education which cannot be analysed within our small time frame. Returns to education could prove to be significant in the long run. Our study presently does not account for that. We can nonetheless extend the argument to contest that certain re-distributive policies could indeed be both pro-growth and pro-equity. 6. POLICY EVIDENCE This section takes our empirical results forward by deriving insights from existing successful policies. Measuring the impact of fiscal policies on re-distribution involves comparing the incomes before and after the taxes and transfers. That is, the vertical distance between market income inequality and net income inequality shows the extent of fiscal re-distributive measures. Among the emerging market economies, the role of fiscal policy has not been very assertive with the exception of the European countries. ‘While average tax ratios for advanced economies exceed 30 percent of GDP, tax ratios in developing economies (excluding emerging Europe) generally fall in the range of 15–20 percent of GDP’ (IMF, 2014). Lower tax receipts translate to lower welfare and social security spending across countries. The re-distribution trends as observed in our panel (Figure 5) concur with the existing evidence. The emerging European nations have consistently shown positive and high re-distributive efforts. Simpler tax codes with fewer exemptions and tax brackets have worked in favour of these economies. These economies also emphasise social sector contributions with greater incidence on employers. Data suggests that there are no uniform policies that these four countries 9 have adopted, however they have each independently succeeded in reaching an optimal tax-subsidytransfer mix10. Moreover, we can extend the argument to contest a neighbourhood effect. The 9 Bulgaria, Czech Republic, Hungary and Poland The European Commission (2009) states that while Hungary, Bulgaria and Czech Republic have reduced their marginal income tax rates, Poland is the only country to have increased it. It also reports the introduction of a flat 10 percent personal income tax (PIT) rate in Bulgaria and a flat 15 percent PIT in Czech Republic. However, Hungary 10 22 countries have gained from the positive externality arising from proximity to high-income developed countries, in addition to being part of the European Union. A closer look at the social protection and welfare spending policies of the panel also reveals vast divergences between the different sub-groups11. Increased spending on education and health boosts social mobility. Our results about effective re-distributive policies (Table 6) correspond with this theory. While we find both re-distribution (tax and subsidies) and spending on health to be highly significant, the health coefficient is twice that of re-distribution. Amongst the other emerging nations, we see Brazil steadily displaying substantial redistribution efforts. The Bolsa Família federal assistance program launched in 2006 employed conditional cash transfers to improve education outcomes. Incremental transfers are made per child based on the level of education attained by the child. The scheme covers approximately 2.5 percent of government expenditure in Brazil. The success of Brazil’s inequality reduction lies in its social security system, which is the second-highest component of household income. In fact, among the Latin American countries, the highest transfer of income to the elderly is in Brazil. ‘One key policy variable is the progressive differentiation of social security adjustments, which means that higher income groups receive lower income gains’ (Neri, 2012). Figure 5: Composition of tax revenue and social spending (as a percentage of GDP in 2011) 35 30 25 20 15 10 5 0 Tax Revenue Property Corporate Income 35 30 25 20 15 10 5 0 Indirect Social Spending Education Health Social Protection Afr Source: IMF (2014), EUROSTAT and authors’ calculations Progressivity of tax indices also contributes considerably towards proportionality of redistribution. Using Kakwani index as a measure of progressivity and including it in the redistribution equation to look at tax progressivity and its impact on income inequality could be an extension of this paper. There has been an increased burden on fiscal policy to reorient towards re-distribution. It is important to remember that re-distribution has a critical effect on growth (Table 5.1 and 5.2). It becomes essential then that re-distributive policies are consistent with the overall macroeconomic policy objectives of the economy. Likewise, effective re-distributive policy needs complementary mix of direct and indirect instruments. The success of re-distributive policies in Europe can be attributed to this. Institutional factors also play a vital role. Reducing the ineffectiveness of redistribution hinges on the ability to make transfers with fewer leakages; increases in tax revenues and Poland continue to maintain two tax brackets. Hungary follows 18 percent and 36 percent tax rates whereas Poland has 18 percent and 32 percent with the incomes brackets being significantly different. 11 Asia-Pacific, Latin America, Europe, Middle-East and Africa. 23 should be effectually translated into increased spending. Human capital expenditures have shown to enhance efficiency and therefore, have a greater impact on income inequality in the long run. Increasing access to education and health care services, as evidenced by our paper, works to equitise opportunity in an efficient manner. 7. CONCLUSION Studies on the relationship between growth and inequality have not made the distinction between gross and net inequality. Gross inequality is the outcome of market distribution and net inequality is post-government re-distribution efforts. Our paper recognises this difference and classifies the growth–inequality relationship as a three-variable model: growth, distribution and re-distribution. We focus on the simultaneous relationship among these variables and their changing dynamics over time. Our results show that, controlling for re-distributive transfers, inequality has a significant detrimental effect on growth in the long run. The strand of literature that adheres to this standpoint suggests growth in itself as a measure to reduce inequality. Growth acts as ‘a rising tide that lifts all boats’. However, this idea of trickle down has received very little empirical support. This brings in the notion of re-distribution wherein market outcomes need to be reallocated to reduce the net inequality in the economy. While growth allows for more re-distributive efforts, the effect of re-distribution on growth is ambiguous. Okun (1975) contests that such interventions may be distortive and will eventually harm growth. However, our data finds no trade-off between re-distribution and growth both in the long run and short run. In fact, we show that re-distribution reduces inequality and accelerates growth, particularly in the long run. We go beyond the tax-subsidy nature of the re-distribution variable and look at other policy instruments like government expenditure on health and education, and their impact on reducing inequality. The government expenditure on health proves to be significant in reducing inequality, but our model fails to capture the education effect. This is because we have restricted our analysis to a period of 10 years which is too short to capture the long-run returns to education. The lack of consensus in empirical literature raises a lot of questions. Empirical estimation encounters several difficulties. In cross-country regressions, data comparability over time and countries may be difficult to achieve. Panel data estimations make several assumptions. In this case, re-distribution policies and social protection spending assume similar design across countries. Besides, the results and interpretations are panel and data specific, hindered by the paucity of data for the emerging economies. 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