Download Interlinkages between Growth, Distribution and Re

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. This paper must be viewed in light of the above
addressed limitations.
We, nonetheless, highlight the rising inequality and falling or near constant re-distribution
levels in most of the EMEs. The net inequality levels in the country closely follow the gross
inequality levels. This warrants a relook at the current re-distribution mechanisms and their
progressivity to counter inequality effectively while not compromising on growth.
24
REFERENCES
Alesina, A., & Perotti, R. (1996). Income distribution, political instability and investment.
European Economic Review, 1203-1228.
Alesina, A., & Rodrick , D. (1994). Redistributive Politics and Economic Growth. The Quarterly
Journal of Economics, 109(2), 465-490.
Assa, J. (2012). Inequality and Growth Re-Examined. Technology and Investment, 1-6.
African Development Bank. (2012). Income Inequality in Africa.
Barro, R. J. (2000). Inequality and growth in a panel of Countries. Journal of Economic Growth,
5-32.
Becker, Gary S., and Nigel Tomes. An Equilibrium Theory of the Distribution of Income and
Intergenerational Mobility. Journal of Political Economy. 87 (1979), 1153-1189.
Benabou, R. (2000). Uneqaul Socieites: Income distribution and the Social Contract. The
American Economic Review, 90(1), 96-129.
Berg, A. G., & Ostry, J. D. (2011a). Inequality and Unsustainable Growth: Two Sides of the
Same Coin? IMF Staff Discussion Note.
Berg, A. G., & Ostry, J. D. (2011b). Equity and Efficiency. Finance and Development.
Caminada, K., Goudswaard, K., & Wang , C. (2012). Disentangling Income Inequality and the
Redistributive Effect of Taxes and Transfers in 20 LIS Countries Over Time. LIS Working
Paper Series No 581.
Datt, G., & Ravallion, M. (2002). Is India's Economic Growth Leaving the Poor Behind? Journal
of Economic Perspectives. 16(3).89-108.
Deininger, K., & Squire, L. (1998). New ways of looking at old issues: Inequality and Growth.
Journal of Development Economics, 57, 259–287.
Duflo, E., & Banerjee, A. (2003). Inequality and Growth: What Can the Data Say. Journal of
Economic Growth, 8(3), 267–99.
European Commission. (2009). Taxation trends in the European Union.
Ferreira, F. H. (1999). Inequality and Economic Performance: A Brief Overview to Theories of
Growth and Distribution. Washington, D.C: World Bank.
Francisco H.G. Ferreira, P. G. (2006). The Rise and Fall of Brazilian Inequality: 1981-2004.
World Bank Policy Research Working Paper 3867.
Gruener, H. P. (1995). Redistributive Policy, Inequality and Growth. Journal of Economics, 1-23.
Halter, D., Oechslin, M., and J. Zweimüller. (2010). Inequality and Growth: The Neglected Time
Dimension. CEPR Discussion Paper No. 8033 (Centre for Economic Policy Research).
IMF. (2014). Fiscal Policy and Income Inequality. Washington, D.C: International Monetary
Fund.
25
Immervoll, H. & Richardson, L. (2011). Redistribution Policy and Inequality Reduction in OECD
Countries: What Has Changed in Two Decades? OECD Social, Employment and
Migration Working Papers 122.
Kakwani, N. C. (1977). Measurement of Progressivity: An International Comparison.
The Economic Journal. 71-80
Kaldor, N. (1955-56). Alternative Theories of Distribution. The Review of Economic Studies,
23(2), 83-100
Kenworthy, L., & McCall, L. (2008). Inequality, public opinion and redistribution. SocioEconomic Review, 6(1), 35-68.
Kuznets, S. (1955). Economic Growth and Income Inequality. The American Economic Review,
15.
Lindert, P.H. (2004). Growing Public: Social Spending and Economic Growth Since the
Eighteenth Century. (Cambridge, U.K.: University Press).
Neri, M. (2012). Declining income inequality in Brazil. In R. Traub-Merz (Ed.), Redistribution
for Growth? Income inequality and EconomicRrecovery (pp. 57-65). Shanghai: Friedrich
Ebert Stiftung.
Okun, A. (1975). Equality and Efficiency: The Big Trade-Off. Washington DC: Brookings
Institution Press.
Ostry, J. D., Berg, A., & Tsangarides, C. G. (2014). Redistribution, Inequality, and Growth.
International Monetary Fund.
Perotti, R. (1992). Fiscal Policy, Income Distribution, and Growth. NBER Discussion Paper
Series 636.
Perotti, R., 1996. Growth, Income Distribution, and Democracy: What the Data Say. Journal of
Economic Growth. Vol. 1(2), pp. 149–87.
Persson, T., & Tabellini, G. (1994). Is Inequality Harmful for Growth? The American Economic
Review. 84(3), 600-621.
Rodrik, D. (1997). Where did All the Growth Go?: External Shocks, Social Conflict and Growth
Collapses. Cambridge: Kennedy School Harvard University.
Romer, C. D., & Romer , D. H. (2010). The Macroeconomic Effects of Tax Changes:Estimates
Based on a New Measure of Fiscal Shocks. American Economic Review, 100, 763-801.
Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in
Stata. The Stata Journal, 86-136.
Solt, F., (2009).Standardizing the World Income Inequality Database. Social Science Quarterly,
Vol. 90(2), pp. 231–42.
Thewissen, S. (2013). Is it the income distribution or redistribution that affects growth? SocioEconomic Review, 1-27.
26
Wan, G. (2007). Understanding regional poverty and inequality trends in China: Methodological
issues and empirical findings. Review of Income and wealth, 53(1), 25-34.
Zaghini, A., & Lamartina, S. (2008). Increasing Public Expenditures: Wagner's Law in OECD
Countries. Italy: European Central Bank.
Wilkinson, R., and K. Pickett. (2009).The Spirit Level: Why Greater Equality Makes Societies
Stronger (New York: Bloomsbury Press).
27