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Growth and Inequality:
Issues and Policy Implications
CESifo Conference Centre, Munich
18-20 May 2001
Growth and Inequality: Evidence from Transitional
Economies in the 1990s
by
Oleksiy Ivaschenko
Growth and Inequality: Evidence from Transitional Economies in the 1990s
by Oleksiy Ivaschenko*
Ph.D. in Economics student
Gothenburg School of Economics and Commercial Law
Department of Economics
first version (February 2001)
Abstract
Transitional economies of Eastern Europe and the former Soviet Union experienced a
dramatic economic decline and an increase in income inequality in the 1990s. In this paper we
investigate, using panel data for 24 transitional countries, what explains unprecedented changes
in income distribution.
Our major finding is a significant relationship between income inequality and the level of
economic development. There is some evidence that while being negative for some transitional
countries, this relationship is positive for others. There was also found support for a normal
(rather than inverted) U-shaped relationship between income inequality and economic
development for the transitional region as a whole.
We have found inflation to have a strong positive impact on income inequality, while
unemployment was not detected to influence the distribution of income. The evidence suggests
that deindustrialization contributed to rising income inequality in the transitional region. An
increasing private sector is revealed to have inequality-generating effect, while government
involvement in the economy does not seem to affect income distribution. We have also found
that population’s aging had a significant positive impact on income inequality, thus implicitly
suggesting that elderly people were amongst those to lose from transition.
Keywords: Economic development, income distribution, transitional economies, panel data
JEL classification: D31, O1, O5
__________________________
*Correspondence: Department of Economics, Göteborg University, Box 640, SE 405 30, Göteborg, Sweden;
e-mail: [email protected]
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I. Introduction
The relationship between income inequality and economic growth has attracted much
attention in the economic literature. In his seminal work Kuznets (1955) argued for an
inverted U-shaped relationship between inequality and economic development, the socalled Kuznets’ hypothesis. It has been tested many times since then in studies of both
industrialized and developing countries. The impact of economic development on income
inequality, however, remains ambiguous. Paukert (1973) and Ahluwalia (1976) in crosssectional studies for developing countries found support for an inverted U-shaped
relationship between income inequality and economic development, while most of recent
research does not find economic development to affect income distribution (e.g., Anand
and Kanbur, 1993; Ravallion, 1995). Even if it is found to be significant in univariate
regressions of income distribution on economic development, the parameter estimate on
income loses its strength and may even reverse sign (Deininger and Squire, 1998) when
other explanatory factors or country-specific dummies are introduced.
A lack of sufficient area coverage and time series data up to now ruled out the
possibility to study an inter-temporal relationship between income inequality and
economic development for transitional economies. We undertake this attempt here based
on panel data covering 24 transitional countries of Eastern Europe and the former Soviet
Union from 1989 to 1998.
The transitional region has witnessed an unprecedented increase in income inequality
over the past decade, and in this paper we try to investigate why inequality has increased
so rapidly in such a short period. A striking economic decline at the start of transition
might have been one explanation of rising inequality, and in this paper we investigate
whether this has been the case. However, even if found significant in explaining
inequality, the general level of economic development
may not be a satisfactory
explanation of changes in inequality. There have been profound region-specific economic
and demographic changes during the transition that might have had a strong impact on
income distribution. Therefore, in this paper we do not constrain ourselves to
investigating only the relationship between income inequality and economic decline, but
go a step further and try to identify more specific determinants of changes in income
distribution.
2
We use panel data estimation methods to control for unobservable country-specific
effects that result in missing-variable bias in cross-sectional studies.
In Section II we present some evidence on the evolution of income inequality and
economic growth during the transition. Section III discusses potential determinants of
rising inequality in transitional countries with a reference to existing literature. Section IV
describes the data used in the empirical analysis. Section V is devoted to model
specification and estimation technique. Section VI describes the regression results. In
Section VII we examine the robustness of the results. Section VIII concludes and presents
some policy implications of our findings.
II. Growth and Inequality During Transition
Transitional economies witnessed an unprecedented economic decline and an
increase in income inequality over a short period, and there appears to be a strong
relationship between economic decline and inequality dynamics. Figure 1 shows the
relationship between the change in income inequality (in percentage points) and the rate
of real GDP growth (in per cent). It clearly suggests that better growth performers
experienced much smaller increases in income inequality.
Figure 1. The dynamics of income inequality and GDP growth in transitional
economies, 1989-1998
Average annual change in Gini
index (percentage points)
4,50
4,00
A RM
3,50
3,00
GEO
RUS
2,50
KGZ
M DA
2,00
TJK
TKM
A ZE
B GR
LVA
ROM
KA Z
UKR
EST
LTU
-8,00
-6,00
CRI
M KD
B LR
-10,00
1,50
CZE
-4,00
UZB
-2,00
SVK
P OL
SVN
HUN
1,00
0,50
0,00
0,00
2,00
Average annual grow th in real GDP (per cent)
Source: Authors’ estimation using income inequality data presented in Table A in the Appendix, and real
GDP growth data from TransMONEE2000 database, UNICEF, Florence.
There is a substantial variation in regional performance, with transitional economies
of Eastern Europe (EE) doing much better than the former Soviet Union (FSU) countries.
3
However, there are significant differences within these two broad groups of countries as
well.
Although quite illustrative, inequality and growth dynamics presented in Figure 1
may be quite misleading as it does not fully reflect what was happening at different stages
within this period. For instance, given the evidence presented in Figure 1 one may
mistakenly conclude that Poland (POL) and Slovenia (SVN) were growing all the time
over the 1990s while other countries were declining, and that there was a uniform trend
for inequality to increase during the period. Therefore, in Table 1 below we present the
evidence on the evolution of inequality and poverty over two periods - before and after
1994.
Table 1. The dynamics of income inequality and GDP growth in transitional economies
Region/
Country
Population
(mid-1997,
mln.)
Real GDP
index
(1989=100)
at the
bottom of
decline
(year)
Real GDP
index in
1998
(1989=100)
Average
annual
growth rate
of real GDP
(percent)
1990-1993
Average
annual
growth rate
of real GDP
(percent)
1994-1998
Average
annual change
in Gini index
(percentage
points)
1990-1993
Average
annual change
in Gini index
(percentage
points)
1994-1998
1
2
3
4
5
6
7
8
I. Former Soviet
Union
a) Baltic states
Estonia
Latvia
Lithuania
b) Western CIS
Belarus
Moldova
Russia
Ukraine
c) Caucasus
Armenia
Azerbaijan
Georgia
d) Central Asia
Kazakhstan
Kyrgyzstan
Tajikistan
Turkmenistan
Uzbekistan
II. Central
Eastern Europe
Czech Republic
Hungary
Poland
Slovak Republic
290.33
7.64
1.46
2.47
3.71
211.58
10.22
3.98
146.94
50.44
17.02
3.79
7.84
5.39
54.09
15.33
4.61
5.95
4.64
23.56
64.49
10.30
10.16
38.65
5.38
50.39(95)
39.19(96)
41.99(97)
83.36(95)
54.25
71.51
75.70
59.30
79.53
50.56
77.75
32.00
55.89
36.61
40.93
41.68
49.40
31.70
59.33
61.20
60.30
41.90
43.75
89.50
-10.30
-9.58
-9.50
-12.22
-7.03
-8.04
-4.99
-10.90
-7.07
-9.20
-15.13
-17.09
-10.23
-18.06
-6.43
-5.90
-8.36
-10.56
-4.29
-3.05
-3.67
4.43
4.42
3.20
5.68
-5.53
-0.57
-8.65
-4.46
-8.42
1.96
6.35
-3.30
2.84
-3.61
-3.97
-1.89
-3.16
-9.44
0.39
3.01
2.53
3.95
1.13
2.51
1.60
2.53
0.67
8.42
8.42
2.22
0.90
6.02
1.05
0.90
-0.19
0.15
-0.36
1.02
-0.21
4.40
3.83
4.96
-0.40
-0.40
1.95
0.77
4.05
1.04
-
84.58(92)
81.89(93)
82.21(91)
74.97(93)
101.71
94.90
95.20
117.15
99.60
-4.41
-3.74
-4.53
-3.11
-6.26
4.21
2.31
3.25
6.76
4.52
0.74
0.55
0.32
1.67
0.41
0.76
1.52
0.52
0.06
0.92
60.76(94)
51.04(95)
64.83(94)
62.69(95)
31.63(93)
41.86(95)
24.60(94)
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III. South
Eastern Europe
Albania
Bulgaria
Romania
IV. Former
Yugoslavia
Croatia
Yugoslavia, FR
Macedonia, FYR
Slovenia
34.16
3.30
8.31
22.55
60.36(92)
63.69(97)
74.99(92)
78.13
86.40
65.90
82.08
-7.04
-8.46
-6.68
-5.98
2.02
6.11
-2.02
1.97
1.30
1.86
0.74
0.85
0.67
1.02
22.48
4.67
10.60
2.00
1.99
59.54(93)
40.60(93)
67.99(95)
82.04(92)
75.85
77.70
50.30
71.50
103.90
-9.09
-10.12
-14.85
-7.48
-3.92
4.28
6.10
5.97
0.41
4.64
1.53
0.35
4.56
0.33
0.89
-0.11
3.40
-4.51
0.70
-0.01
Source: Authors’ calculations using income inequality data presented in Table A in the Appendix, and real
GDP growth and population data from TransMONEE2000 database, UNICEF Innocenti Research Center,
Florence.
Note: All regional averages are unweighted averages. The former Yugoslavia also includes BosniaHerzegovina, but since no data for it are available it is not included in the table. “-“ in Column 3 means that
by the end of 1998 a country under consideration continued to decline.
Several major conclusions emerge out of Table 1. First, no single country escaped
economic decline and an increase in income inequality at the start of transition (Columns
5 and 7). Second, after the sharp economic decline in the initial period most of the
countries started to recover at some later stage (Columns 3 and 6). In general, Eastern
Europe and the Baltic states have been showing positive growth since 1993-1995, while
the Commonwealth of Independent States (CIS) countries started to grow later or
continued to decline as of 1998. It is worth noting that the fall in real GDP and the
increase in inequality were much smaller in Central Eastern Europe (CEE) as compared
to other countries. Third, although the surge in inequality seems to slow down after 1993
(compare Columns 7 and 8) for most of the countries, and for a handful of economies
inequality has even decreased, there does not seem to be a universal relationship between
income inequality and growth trends after 1993. For instance, while economic recovery in
Estonia and Lithuania were followed by decreasing inequality, in CEE countries
inequality was rising, although at very modest rates, despite a growing economy
(Columns 6 and 8). This suggests that economic growth per se, especially at the later
stage of transition, may not adequately, if at all, explain inequality dynamics. Thus, in
what follows we discuss potential determinants of income distribution in transitional
countries. This discussion serves as a basis for the choice of variables used later in the
empirical analysis. Most of the factors that we consider are those commonly found in the
literature on the determinants of cross-country inequality, while others are those specific
5
circumstances that we anticipate to be influential in explaining the pattern of inequality in
the transitional region.
III. Potential Determinants of Rising Inequality in Transitional Countries
There is a vast literature on the determinants of income inequality considering both
individual-level factors (e.g., increasing returns to skills) and general economic
conditions (e.g., GDP per capita, unemployment). In this paper we focus on the latter,
although the former might be equally important.
The main factors that we anticipate to have affected income inequality in transitional
countries are: the level of economic development (measured by per capita GDP),
macroeconomic conditions (inflation, unemployment), the government involvement in
the economy (general government consumption as a percentage of GDP), structural
factors (the size of the private sector, the share of industrial sector in total output), and
demographic shifts (a share of population aged 60 years and older).
That economic development may affect income inequality was first suggested by
Kuznets (1955), who argued that there was an inverted U-shaped relationship between
inequality and per capita income. As mentioned in the introduction, many attempts to
identify a link between income inequality and the level of economic development have
been undertaken since then, with most of the recent literature failing to find a significant
relationship. However, a striking economic decline in EE and the FSU countries over the
last decade may suggest its likely significant implications for income distribution. The
evidence presented in the previous section renders a strong support for anticipating a
negative relationship between income inequality and economic development for
transitional countries, implying that income inequality increases during recessions and
decreases during recoveries.
Inflation may have had a strong redistributional impact through its effect on
individuals whose nominal incomes were not adjusted proportionally to the increase in
prices, mostly state sector employees, pensioners and beneficiaries of various social
benefits. That would be an argument for a positive relationship between income
inequality and inflation. However, inflation may also have an equalizing impact on
income distribution through a progressive tax system that pushes wage earners into higher
6
tax brackets during high inflation, thus implying less inequality in disposable income.
These effects may well counterbalance each other. In a study of the determinants of
inequality for OECD countries (Gustafsson and Johansson, 1999) inflation was not found
to be significant in explaining inequality. That may not be the case for transitional
economies, however, as most of them experienced a sharp rise in inflation at the start of
transition. In our sample a mean annual inflation is 263 per cent, and a maximum is 9,750
per cent (Turkmenistan, 1993).
Destruction of the old economic system and significant structural changes during the
transition caused a strong rise in unemployment across transitional countries. In many of
them unemployment grew from virtually zero to 10-15 per cent of the labor force even
when measured by the number of officially registered unemployed, which may
significantly understate the real level of unemployment. This is not to say that many
people are registered employed while not getting paid. Unemployment is likely to affect
mostly those in the lower percentile of income distribution. Milanovic (1998) indicates
that unemployment increased most amongst women and young people, while those with
higher education (and thus, presumably, higher incomes) were among those to suffer the
least. A negative impact of unemployment on income distribution has been confirmed in
a number of studies for industrialized countries (Gustafsson and Palmer, 1997; Weil,
1984), and we anticipate it to have inequality-increasing impact for transitional
economies as well.
In times of economic hardship and increasing unemployment government-financed
projects (e.g., construction) may provide a source of employment and income (with lowskilled labor probably benefiting the most) and thus serve as a buffer to widening income
inequality. The size of the public sector is found to reduce inequality in cross-country
studies by Stack (1978) and Boyd (1988). As an indicator of the government’s role in the
economy we use general government consumption (including state sector wages and
services) as a percentage of GDP.
Another mechanism for the government to influence income distribution is through
social security transfers. They are found to have a negative impact on inequality in a
cross-country study of both advanced and developing countries by Milanovic (1994), but
appear insignificant in a study of OECD countries by Gustafsson and Johansson (1999).
We were not able to collect enough data on the evolution of the size of social security
7
transfers across countries over the transition period to perform a meaningful estimation,
so in this paper we restrict ourselves to considering the role of the government in income
distribution through public consumption.
Centrally planned economies were dominated, although to a various extent, by the
state enterprises where wages were administratively set. The overwhelming
predominance of the state sector in EE and the FSU economies is widely viewed as a
main reason for low income inequality in the region before transition, with a level of
inequality broadly considered as being lower than in the rest of the world (Atkinson and
Micklewright, 1992). The process of transition brought about a massive expansion of the
private sector1 and, hence, a share of the private sector employment. This is very likely to
increase income inequality due to larger disparities in the private sector earnings as
compared to the state sector earnings. The impact of the private sector expansion on
income distribution, however, does not seem to have attracted sufficient attention in the
literature on the determinants of income inequality, probably because of the poor data on
this indicator for developing countries. In view of a sharply increasing private sector in
transitional economies it is particularly interesting to shed some light on its role in the
evolution of income distribution in the region.
Economic transformation not only involved an expansion of the private sector, but
also led to profound changes in sectoral composition of the economy. There is a clear
trend for the industrial sector to shrink, while the evidence on the agricultural sector is
mixed - in some countries its relative importance has declined, while in others it has
increased2. It is very likely that declining industrial sector employment might have an
inequality-increasing impact. As a majority of industrial sector jobs require a very
specific set of skills that might be difficult to apply somewhere else, it would be difficult
for unemployed factory worker to find a comparable remuneration, meaning a lower
crossbar on the income distribution ladder. Also, wage differentials outside of largely
remaining state-owned industrial sector, for instance in services, are very likely to be
much larger. A negative effect of industrial sector employment on income inequality is
confirmed in studies by Gustafsson and Johansson (1999) and Levy and Murnane (1992).
1
In many transitional countries the share of private sector in total economy has increased from barely
existing to 50-60 %.
2
Diminishing relative importance of industrial and/or agricultural sector was at least partly offset by
growing relative importance of services sector, which in many countries has increased 1,5 - 2 times.
8
Demographic shifts may influence income inequality as they imply changes in
households’ earners’ composition (earners vs non-earners) and thus household’s total and
per capita income. There is a uniform tendency toward aging populations in transitional
countries during the 1990s. The share of pensioners in the total population is likely to
have a negative effect on inequality if retired people leave relatively high-paid labor force
and join the middle income class, which is probably indeed a case in industrialized
countries. An inequality decrease may also come from a more equal distribution of
pensions as compared to earnings. However, if a majority of elderly people have low
incomes, which is generally true for the transitional region, an increasing share of elderly
in total population would imply greater inequality. The results of studies on the impact of
demographic changes on income inequality are mixed. Gustafsson and Palmer (1997) and
Jenkins (1995) do not find demographic factors to influence income inequality in,
respectively, Sweden and the UK. In Gustafsson and Johansson (1999) a share of children
in total population was revealed to have positive effect on inequality, while a percentage
of population aged 65 and older was insignificant. A positive impact of a percentage of
population aged under 14 is confirmed by Milanovic (1994) in a cross-section study
involving 80 countries (both industrialized and developing) for a period from early to
mid-1980s. Whether demographic changes have affected income distribution in the
transitional region remains to be seen.
The data used in the regression analysis and their sources are described in detail
below. A simple correlation analysis was also performed to see whether the choice of
explanatory variables is justified.
IV. The Data
The time-series data on income inequality across transitional countries used to
construct a panel of inequality estimates are drawn mostly from UNU/WIDER-UNDP
World Income Inequality Database (WIID) (Version 1.0, 12 September 2000), which
represents the latest and the most extensive data on inequality for both developed and
developing countries available up to date. About a half of the WIID database is formed by
K. Deininger and L. Squire’ 1996 database. However, for the transitional region most of
the data in the database come from UNICEF/IRC TransMonee2000 Project, Florence.
9
The assembled panel of inequality estimates is augmented by a few observations from
Milanovic (1998) (mostly for 1989) and inequality estimates based on the latest
household surveys conducted by the World Bank (2000) (mostly for 19983).
To minimize problems with data comparability across countries and over time we
require inequality data to be based on the same living standard indicator (disposable or
gross income), have the same sample and enumeration unit, be drawn from nationallyrepresentative surveys, and, whenever possible, come from one source. Income inequality
is measured by the Gini coefficient with individuals representing the unit of analysis. The
coefficients are calculated based on household per capita income. A detailed description
of the constructed panel of inequality estimates and data sources are presented in Table A
in the Appendix.
The assembled data are far from perfect, however, as not all of the above
requirements could always be met. The resulting inequality measures are still subject to
potential measurement error problems, and the results thus have to be interpreted with
caution. The use of panel data and panel data estimation methods (to be discussed), may,
however, help diminish some problems with data consistency. The country-specific
intercepts in the fixed effects model setting absorb, among all unobservable
characteristics, the differences in inequality definition across countries (Deininger and
Squire, 1998). Nevertheless, while panel data estimation helps address some of the data
problems, it cannot remedy all data limitations. The assembled panel of inequality
estimates, however, represents perhaps the most consistent and extensive coverage
available for transitional countries up to now. It consists of about 150 observations
covering 25 countries in transition4 from 1989 to 1998 (3 values are for 1999).
We now turn to the definitions and the sources of data used to explain income
inequality.
The level of economic development is represented by PPP-adjusted GDP per capita
(expressed in USD). The data come from the World Bank World Development Indicators
(WDI) 2000 database.
Inflation is measured as the annual per cent change in the consumer price index (CPI)
(end-year). As CPI based inflation is not available for all countries in our sample for 1989
3
As there are only 5 observations for 1999, in the estimation they were used as 1998 values.
10
and 1990, the GDP deflator inflation is taken for those years instead. Finally, for Croatia
(1989), Macedonia, FYR (1989, 1990), and Slovenia (1989), as neither of the mentioned
above indexes could be obtained, inflation is measured by the food price index (a subindex of the CPI). All inflation data are drawn from the World Bank World Development
Indicators (WDI) 2000 database.
Unemployment represents a share of labor force (in percentage points) that is without
work but available for and seeking employment. However, unemployment data for
transitional countries may substantially understate the actual scope of unemployment as
they are mostly based on the number of officially registered unemployed. Unofficial
estimates indicate significantly higher unemployment for many countries. However, as no
better alternative is available, official estimates are used in most cases. Unemployment
data are taken from the EBRD Transition Report 2000, which provides further reference
on the origination of the data for each country.
General government consumption, expressed as a fraction of GDP (in percentage
points), refers to all current spending for purchases of goods and services (including
wages and salaries). It also includes most expenditures on national defense and security,
but excludes government military expenditures that are part of government capital
formation. As such, government consumption represents a good measure of the
government’s involvement in the economy. The data on government consumption come
from the World Bank WDI 2000 database.
Industrial employment represents a share of industry in total employment (in
percentage points). The data are taken from the EBRD Transition Report 2000. We have
been able to get 105 observations covering 24 countries (no data for Yugoslavia, FR). As
other explanatory variables contain more observations, the use of industrial employment
as a variable in model estimation would substantially reduce a number of observations on
other variables. As our sample is relatively small, we consider that inappropriate.
Therefore, we use a share of industry value added in GDP (defined below) as a proxy for
industrial sector employment. This provides us with 140 observations (no data for
Yugoslavia, FR, and also for 6 countries in 1989).
4
Due to the lack of the data on various explanatory variables for Yugoslavia, FR, the estimation is
performed for 24 countries.
11
Industry value added, expressed as a percentage of GDP, is the net output of a sector
after adding up all outputs and subtracting intermediate inputs. It corresponds to ISIC
divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It is calculated
without making deductions for depreciation of fabricated assets or depletion and
degradation of natural resources. The data come from the World Bank WDI 2000
database.
Private sector employment represents a number of people employed in the private
sector as a percentage of total employment. The data availability, however, represents a
severe constraint here, as practically no data prior to 1993-1994 exist. We have managed
to collect only about 50 observations using IMF country reports, which is not enough for
any meaningful estimation. Thus, in our regression analysis we use a share of the private
sector in GDP as a proxy for a share of the private sector employment. As there was
found a high correlation between the size of the private sector and the private sector
employment in our sample (for those observations that are available), and in view of no
better alternative, such a proxy may be justifiable. These data and their more extensive
description are available from the EBRD Transition Report 2000.
We view a percentage of population aged over 60 (in per cent) as a proxy for a share
of pensioners in total population. In studies of industrialised countries a threshold level of
65 years is commonly used to construct this indicator. However, as in EE and the FSU
countries the average retirement age is 55 years for women and 60 years for men, we
consider a percentage of population aged over 60 as a more appropriate indicator to use.
The data on this indicator come from TransMonee2000 Database. As the data referred to
there are beginning-of-year elderly population, we use the data on beginning-of-year
population in period t+1 as end-of-year population in period t.
Table B in the Appendix provides the descriptive statistics of the data used in the
regression analysis.
V. Model Specification and Estimation
The primary interest of our study is to explain the change in income inequality in
transitional economies, and we thus estimate income inequality as a function of various
potential explanatory variables presented below. The model specification is:
12
GINI(it) = αi + β 0*GDPPC(it) + β 1*GDPPC_S(it) + β 2*INFL(it) + β 3*UNEMP(it) + β 4*CONSG(it) +
β 6*INDVA(it) + β 7*PRIVS(it) + β 8*SH60(it) + (it); (1)
i = 1, …, N; t = 1, …, T;
where i represents country index, t denotes time period, αi is a country-specific intercept,
GINI is the Gini coefficient of income inequality, GDPPC is PPP-adjusted GDP per
capita (USD), GDPPC_S is its squared value (a squared value of the natural log of
GDPPC is used for the estimation), INFL is annual inflation as measured by the year-toyear change in the consumer price index, UNEMP is a share of unemployed in total labor
force, CONSG is general government consumption as a percentage of GDP, INDVA is
industry value added as a percentage of GDP, PRIVS is the private sector share in GDP
(in per cent), SH60 is a percentage of population aged 60 years and older, and (it) is an
error term. The assumption on (it) is that (it) ~ IID( 0, σ e 2 ). All variables except
UNEMP enter the regressions in the natural log form. The natural log of (1+INFL/100) is
used for INFL variable in the estimations.
A squared value of GDPPC (expressed in the natural log form) is included into the
regression to test whether the relationship between income inequality and per capita GDP
changes over time.
A body of growth literature suggests that economic growth and inflation can be
negatively related. However, Berg et al. (1999) reveal no contemporaneous effect of the
inflation level on the index of real output in transition economies, and a low correlation
coefficient between inflation and per capita GDP in our sample also points out that
including both variables in the model may not be inappropriate. The presence of
unemployment alongside with per capita GDP also does not represent a collinearity
problem.
We estimate equation (1) using an assembled panel for 24 transitional countries
covering a period from 1989 to 1998.
The use of panel data produces several well known advantages. The most important
is that it allows us to control for unobservable time-invariant country-specific effects that
result in missing-variable bias, an often encountered (and yielding misleading results)
problem when cross-section data are used. This problem is recognized in Bourguignon
13
and Morrison (1997), Bruno et al. (1995), Deininger and Squire (1998), Forbes (2000),
Ravallion (1995), and other studies.
To control for unobservable country-specific characteristics we introduce countryspecific intercepts in the fixed effects model setting. The addition of fixed effects to the
model also help alleviate potential heteroscedasticity problem stemming from possible
differences across countries (Green, 1997). However, the fixed effects model is chosen
mostly because the main goal of our study is to investigate what caused substantial
changes in income inequality over time rather than to explain variation in inequality
across countries. Here the use of the fixed effect estimator, which is also called “within”
estimator, appears to be very appropriate, as it allows to focus on how within-country
characteristics are related to within-country inequality. There might be one more
substantial reason to prefer fixed effects model to the random effects one. A crucial
assumption for the random effects model is that country-specific terms ( α i ) are
uncorrelated with the other explanatory variables, which might be a too strong
assumption, especially for transitional countries. Its violation makes random effects
estimators inconsistent (Green, 1997). The use of the fixed effects model avoids this
problem as individual effects are allowed to be correlated with the other regressors. The
fixed effects estimation technique is not perfect either. The random effects estimates are
more efficient given that all necessary assumptions are satisfied. Also, the fixed effects
model is very costly in terms of lost degrees of freedom, which may represent a particular
problem for such a relatively small sample as ours. However, we prefer the fixed effects
model as its advantages seem to outweigh its weaknesses given our data and research
purposes.
The panel that we estimate is unbalanced as a number of time series observations
differs across countries. However, assuming that observations are missing randomly,
consistency of the fixed effects estimator is not affected.
The fixed effects model is estimated with OLS, which, given the assumed properties
of (it), is the best linear unbiased estimator (Hsiao, 1986). The estimation is performed
14
in deviations from the group means5. The within-groups estimator is identical to the least
squares dummy variable estimator obtained if a dummy variable is included for each
country (as in the original formulation of equation (1)), but the resulting R2 is lower
(Green, 1997).
VI. The Regression Results
We first estimate a “short” version of equation (1) with the log of per capita GDP
(GDPPC) and its squared value (GDPPC_S) being the only explanatory variables. This
represents a classical specification to test for Kuznets’ hypothesis. However, most of the
up to date attempts to confirm or reject an inverted U-shaped relationship between
income inequality and economic development used cross-sectional regressions, which
might be conceptually incorrect as Kuznets’ hypothesis is about the intertemporal
relationship between inequality and per capita income. Therefore, if one wants to see
whether inequality changes with economic development, longitudinal data are needed
(Deininger and Squire, 1998). Here the use of a panel provides an obvious advantage over
a single time series or purely cross-sectional data as it allows to study the relationship
between income inequality and economic development over time for a set of countries.
However, as our data cover a relatively short period of time, it might be wrong to make
some strong inferences about the validity of Kuznets’ hypothesis that assumes a long run
relationship between inequality and per capita GDP. To undermine or support Kuznets’
hypothesis is, however, not the main purpose of our study, but rather a by-product of,
perhaps a bit more ambitious, attempt to identify more specific factors behind changing
inequality. While economic growth represents a good aggregate measure of the
economy’s health as its reflects the outcome of multiple complex processes taking place
at all levels of the economy, it alone does not seem a satisfactory explanation of the
inequality pattern. The estimation of the “full” equation (1) thus introduces other
5
Specifying the original formulation of equation (1) as:
of deviations from the group means becomes: ( yit t;
yit = α i + β´ X it + εit , the formulation in terms
yi ) = β´( X it - X i ) + ( εit - εi ), where yi = / yit /
X i = / X it / t; εi = / εit / t.
15
potential explanatory forces into play. The regression results from estimating both “short”
and “complete” modifications of equation (1) are reported in Table 26.
Table 2. Fixed-effects Estimates from the Regression of the Gini Coefficient on Selected
Explanatory Variables: Transitional Economies of EE and the FSU
Explanatory Variable
Short Model
Full Model
1
2
3
Intercept
-
-
GDPPC
-4.785***
(1.401)
0.255***
(0.083)
-
Number of countries
24
- 3.602***
(1.106)
0.207***
(0.065)
0.037***
(0.011)
-0.001
(0.003)
-0.021
(0.033)
-0.232***
(0.066)
0.071***
(0.023)
1.186***
(0.273)
24
Number of observations
139
133
GDPPC_S
INFL
UNEMP
-
CONSG
-
INDVA
-
PRIVS
-
SH60
-
2
R adj.
0.377
0.690
F-value
36.771
31.820
Estimated turning point (PPPadjusted GDP per capita, USD)
11802
6058
Note: All variables except UNEMP are in the natural log form. Standard errors are presented in
parentheses. The model is estimated in deviations from the group means. The Yule-Walker (iterated)
method was used to correct for serial correlation and heteroskedasticity.
*- significance at 10% level; **- significance at 5% level; ***- significance at 1% level; (two-tailed tests).
The regression results suggest a strong negative relationship between income
inequality and per capita GDP. That income inequality might increase during recessions
was confirmed in a number of studies of the United States (Meier, 1973; Metcalf, 1969;
Thurow, 1970). As economic decline in EE and the FSU countries reached an
unprecedented scale, it is perhaps not surprising to expect profound changes in the
6
To detect outliers and influential cases we have conducted influence diagnostics such as the studentized
residuals, the “hat” matrix, the COVRATIO statistic, DFFITS and DFBETAS (Belsley, Kuh, and Welsch,
16
distribution of income. The parameter estimates on GDPPC and GDPPC_S indicate a
normal U-shaped relationship between income inequality and the level of economic
development, meaning that inequality first declines and then increases with rising per
capita GDP7.
Inflation was found to have a positive effect on income distribution. That inflation
was associated with significant distributional costs in the transitional region was also
confirmed in the recent study by the World Bank (2000). The estimation of the pooled
data, however, does not take into consideration that inflation might have a very
differential impact on income distribution across countries as well as over different
periods.
Unemployment does not seem to have an impact on inequality. There might be
several reasons for unemployment to have low explanatory power. A likely inequalityincreasing effect of growing unemployment can be counterbalanced by increasing flow of
unemployment benefits and decreasing fertility (Gustafsson and Johansson, 1999). For
transitional countries, it is unlikely that unemployment benefits may adequately
compensate for the loss of income from work. However, a sharply declining birth rate in
the transitional region during the 1990s might have had inequality-decreasing impact. The
quality of the data on unemployment may also be a simple and quite probable explanation
for not detecting unemployment to have any substantial impact on income distribution.
These data, as was mentioned before, are mostly based on official unemployment records,
which may several times underestimate the actual scope of unemployment. Thus, their
use in the estimation may induce a substantial downward bias on the parameter estimate
on unemployment.
We do not find the government involvement in the economy to have a strong
influence on income distribution, although the sign of the parameter estimate is as
anticipated. In all but four countries of the FSU (not including the Baltic states) the share
of government consumption in GDP decreased between 1989 and 1998, while in EE
countries the share of government consumption over the same period was either stable (6
countries) or increasing (7 countries including Baltic states). Although the government
1980; Bollen and Jackman, 1985). We then deleted those observations that were detected influential by at
least 3 tests. These observations turned to be Moldova (1990), Poland (1990) and Tajikistan (1998).
7
The support for such an “inverted Kuznets’ curve” is found in a study by Fields and Jakubson (1994) for a
small sample of developing countries.
17
involvement in the economy could have a role to play in some countries, the estimation of
the pooled data does not support its significance in explaining the changes in the
distribution of income for the region as a whole.
The parameter estimate on INDVA supports our hypothesis of the shrinking
industrial sector to have inequality-increasing impact, as was found in other studies (e.g.,
Levy and Murnane, 1992). The coefficient suggests that 1 per cent decline in the share of
industrial sector leads to a 0.23 per cent increase in income inequality. The relative
importance of industrial sector in transitional countries dropped dramatically over the last
decade. The share of industry in total output has declined in the region on average by 25
per cent from 1989 to 1998, and in several countries the drop was even more profound.
For instance, the share of industrial sector declined from 52% in 1989 to 32% (or by 38
per cent) ten years later in Poland, 58% to 33% in Slovak Republic, 59% to 25% in
Bulgaria, and 50% to 35% in Russia during the same period. The parameter estimate on
INDVA implies that in Poland, for example, the Gini coefficient would increase due to
this factor by 2.2 percentage points over the period, thus explaining about a quarter of the
total increase in income inequality. Thus, there seems to be no doubt that
deindustrialization had strong adverse impact on income distribution in the transitional
economies.
There seems to be a strong positive relationship between income inequality and the
size of the private sector. However, while the estimate on PRIVS is statistically very
significant, its magnitude is not large. It takes a 10 per cent increase in the share of
private sector to provoke a 0.71 per cent rise in income inequality. Nevertheless, as
transitional countries witnessed a rapid growth of the private sector (and in the FSU
countries the private sector hardly existed at all at the start of the transition), an increasing
private sector employment could be a significant reason for increasing income inequality.
However, the inequality-increasing effect of the private sector expansion is to evaporate
as the size of the private sector stabilizes. In the longer term, it may well be that the buildup of the efficient private sector may even cause income inequality to decrease through
increasing labor force participation.
Demographic shifts such as the population’s aging are revealed to have a strong
positive impact on income distribution in transitional countries. This finding is in contrast
to many studies for industrialized countries (Jenkins, 1995; Gustafsson and Johansson,
18
1999), which do not find population’s aging to affect income distribution. That might not
be surprising, though, considering that social safety nets in developed countries are much
better developed, and, what is much more important, funded, than those in the transitional
region. As pensions do not compensate for the loss of income from work an increasing
number of elderly in family’s composition is supposed to hit, in the first place, lowincome families as it represents an increasing burden for working family members. Also,
as elderly people often live alone, a decline in real income would directly put them on the
bottom of income distribution. The parameter estimate on SH60 suggests that 1 per cent
increase in the percentage of elderly in total population induced a 1.19 per cent increase
in income inequality. As in many countries of the region a share of elderly in population
grew about 10-15 per cent (although expressed in percentage points the change does not
seem that dramatic) over the ten-year period, such an estimate would imply a nonnegligible effect. For instance, in Latvia, the share of elderly in total population (the
highest in the FSU region) increased by 2.71 percentage points, or 15.44 per cent from
1989 to 1998, implying the increase in inequality by 4.13 percentage Gini points, which
accounts for more than one third of the total increase in inequality. In general,
population’s aging in the FSU region over the 1990s was more profound than in EE
countries, and that may be one of the reasons for a sharper increase in income inequality
in the former as compared to the later.
VII. Sensitivity Analysis
In this section we investigate the robustness of our findings by performing several
robustness tests. We first check whether our results are sensitive to the definition of the
dependent variable. Although most of the Gini coefficients in the data set are based on
disposable income, there are a few ones nested on gross income (see Table A in the
Appendix), and we test whether the exclusion of the latter from the estimation affects the
results presented in Table 2. A few Gini indexes in our sample come from other sources
than the main data series (WIDER database) used (see Table A in the Appendix), and we
19
thus also examine the sensitivity of our findings to the omission of these “auxiliary”
observations8. The resulting parameter estimates are reported in Table 3.
Table 3. Fixed-effects Estimates from the Regression of the Gini Coefficient on Selected
Explanatory Variables: Transitional Economies of EE and the FSU; Robustness
to the Definition of the Gini coefficient and the Choice of the Data Series
Explanatory Variable
1
Full Model
Full Model
Disposable Income Ginis only
The WIDER database Ginis only
2
3
Intercept
-
-
GDPPC
- 2.508**
(1.142)
0.144**
(0.067)
0.037***
(0.014)
-0.002
(0.003)
-0.042
(0.051)
-0.250***
(0.075)
0.085***
(0.026)
1.118***
(0.286)
21
- 4.281***
(1.242)
0.248***
(0.073)
0.026**
(0.011)
-0.002
(0.003)
-0.018
(0.033)
-0.224***
(0.069)
0.075***
(0.025)
1.394***
(0.313)
24
115
118
R adj.
0.706
0.685
F-value
34.457
31.454
6046
5598
GDPPC_S
INFL
UNEMP
CONSG
INDVA
PRIVS
SH60
Number of countries
Number of observations
2
Estimated turning point (PPPadjusted GDP per capita, USD)
Note: All variables except UNEMP are in the natural log form. Standard errors are presented in
parentheses. The model is estimated in deviations from the group means. The Yule-Walker (iterated)
method was used to correct for serial correlation and heteroskedasticity.
*- significance at 10% level; **- significance at 5% level; ***- significance at 1% level; (two-tailed tests).
As compared to the results reported in Table 2, the use of disposable income-based
Gini coefficients somewhat lowers the magnitude and significance of the parameter
estimates on GDPPC and GDPPC_S (Column 2), whereas employing the data series on
income inequality derived from the WIDER database adds strength to the detected
8
Unfortunately, we cannot test the robustness of our findings to alternative measures of inequality as the
data on Gini coefficients are the only available.
20
relationship between inequality and the level of economic development9 (Column 3).
However, the coefficients on other variables remain largely the same.
We also verify to what extent the results could be driven by observations for a
particular time period. The parameter estimates reported in Table 2 were found to be
fairly robust to the removal of any single period from the estimation. We note, however,
that the significance of the parameter estimates on GDPPC and GDPPC_S declines when
observations for 1990 are removed from the estimation (with parameter estimates on
other variables in the “full” model not affected), and that the coefficient on inflation turns
to be insignificant when the model is estimated without data for 199310.
As the countries in our sample, despite being collectively referred to as transitional
economies, differ widely in their levels of development and growth experiences during
the transition, it is necessary to investigate the robustness of results to regional coverage.
In view of countries’ institutional characteristics and macroeconomic performance during
the transition, we estimate the model separately for the FSU and EE countries11. The
results are presented in Table 4.
Table 4. Fixed-effects Estimates from the Regression of the Gini Coefficient on Selected
Explanatory Variables: Transitional Economies of EE versus the FSU
Explanatory Variable
Re-specified
Model
Re-specified
Model
FSU countries
EE countries
4
5
-
-
-
1.249
(2.665)
-0.051
(0.152)
-0.005
(0.019)
0.005
(0.004)
-0.242***
(0.089)
-
0.351***
(0.096)
-
0.047***
(0.016)
0.002
(0.006)
-0.006
(0.018)
0.006
(0.004)
Full Model
Full Model
FSU countries
EE countries
2
3
Intercept
-
GDPPC
-2.716*
(1.518)
0.149
(0.091)
0.049***
(0.016)
0.004
(0.006)
1
GDPPC_S
INFL
UNEMP
9
We may not conclude from here on the quality of different data series, though, as the changes in the
parameter estimates could be driven by exclusion of the observations for particular countries and/or time
periods.
10
The decline in the significance of the parameter estimate on INFL can probably be explained by the fact
that for most of the countries in the transitional region 1993 was the year of hyperinflation (for 15 countries
in our sample in 1993 the average inflation was 1,114 per cent, with the largest for Turkmenistan reaching
9,750 per cent), and thus the elimination of this year from the estimation is likely to substantially
underestimate the impact of inflation on the distribution of income.
11
We do not argue that such a division of the countries into sub-samples is perfect as the countries within
EE and the FSU regions are not homogenous either, but it seems to be a natural choice in many respects.
21
CONSG
-0.011
(0.046)
-0.293***
(0.105)
0.026
(0.040)
1.032**
(0.423)
15
-0.053
(0.056)
-0.110
(0.097)
0.087**
(0.038)
0.764**
(0.311)
9
-0.015
(0.047)
-0.274**
(0.106)
0.018
(0.040)
1.131**
(0.423)
15
-0.057
(0.054)
-0.123
(0.089)
0.080**
(0.032)
0.759**
(0.306)
9
68
65
68
65
R adj.
0.709
0.738
0.700
0.741
F-value
21.023
22.929
23.063
26.635
-
-
-
-
INDVA
PRIVS
SH60
Number of countries
Number of observations
2
Estimated turning point
(PPP-adjusted GDP per
capita, USD)
Note: All variables except UNEMP are in the natural log form. Standard errors are presented in
parentheses. The model is estimated in deviations from the group means. The Yule-Walker (iterated)
method was used to correct for serial correlation and heteroskedasticity.
*- significance at 10% level; **- significance at 5% level; ***- significance at 1% level; (two-tailed tests).
A number of interesting observations could be made based on the results presented in
Table 4. First, when the estimation is performed separately for the FSU and EE countries,
a U-shaped relationship between income inequality and the level of economic
development collapses. That result, however, could partly be caused by relatively small
sub-samples’ sizes. It is worth noting, however, that the signs of the parameter estimates
on GDPPC and GDPPC_S are different for two group of countries, probably suggesting
that inequality-development relationship may be distinct in these regions. The estimation
of the re-specified model (we exclude GDPPC_S to test for the linear relationship
between inequality and per capita GDP) also seems to confirm this finding. The
parameter estimate on GDPPC is positive for the FSU region, but is negative for EE
countries (see Columns 4 and 5). The regression results suggest that inflation and
deindustrialization had significant impact on inequality in the FSU countries but not in
EE, while an increasing private sector had inequality-increasing effect exclusively in EE
countries. However, as regional samples are significantly reduced as compared to the
whole sample, these results have to be treated with some caution. The lesson that,
nevertheless, can certainly be drawn, is that countries’ heterogeneity makes it hardly
visible to fully explain the changes in income inequality based on a single equation and
using pooled data.
22
VIII. Conclusion
In this paper we have tried to identify factors that caused a dramatic increase in
income inequality in the transitional countries of EE and the FSU over the 1990s. The
empirical analysis is performed using panel data estimation methods. The panel data
combines time-series and cross-sectional dimensions thus making possible to study why
inequality has changed over time across a group of countries.
We have found support for economic decline to increase income inequality in the
transitional region as a whole. However, there seems to be the evidence that the
relationship between income inequality and economic development can depend on a
country’s level of development. This suggests that while economic recovery-promoting
policies may certainly have equalizing effect on income distribution for some counties,
there might be, at least in the short-run, a trade-off between economic growth and income
inequality for others. However, in the long-term perspective, low initial inequality may be
potentially beneficial for subsequent long-run growth (Birdsall, 1995; Deininger and
Squire, 1998) and poverty alleviation.
The regression results indicate a normal U-shaped relationship between income
inequality and the level of development for the transitional region as a whole. This
relationship, nevertheless, does not seem to hold when the estimation is performed
separately for EE and the FSU countries. The results are, however, based on a relatively
short period and thus may apply specifically to the region and circumstances under
consideration. It may well be that the found relationship(s) between income inequality
and economic development could change with time. The data quality and quantity also
have to be kept in mind when interpreting the results.
Although undoubtedly important, the relationship between income inequality and
general level of economic development does not represent the main focus of our study.
We have also searched for more specific determinants of changing income distribution in
the transitional region.
We have found inflation to have significant implications for income inequality. In the
FSU countries, where inflation was much higher than in the rest of the transitional region,
there seems to be a strong evidence for higher inflation to have inequality-increasing
23
impact. Thus, macroeconomic stabilization in the transitional region may not only foster
economic recovery, but also be beneficial in terms of distributional outcomes.
Unemployment and the degree of government involvement in the economy were not
revealed to affect income distribution in the transitional countries, while the declining
industrial sector seems to have inequality-increasing effect. A strong relationship between
income inequality and the size of the industrial sector is especially evident for the FSU
countries.
A rapidly growing private sector in transitional countries appears to have contributed
to an increase in income inequality. However, as it was argued before, privatization may
have hefty longer-term rewards by creating new jobs and fostering economic growth.
Ultimately, it is better to be unequally rich than equally poor.
Finally, we have also looked at the role of demographic changes in income
distribution. A share of population aged 60 years and older was found to have a strong
positive effect on income inequality. This implicitly suggests that elderly people have
been amongst those to lose from transition. Thus, policy measures aimed on the
strengthening of social safety nets and improved targeting can be vitally important for the
transitional region.
Our findings have to be considered with caution, however, as the data quality is
quite shaky. They have to be viewed as a speculation on what might have caused swings
in income distribution rather than an affirmative statement on what have caused
inequality to vary. We have also far from exhausted all possible determinants of the
observed changes in income inequality. Although some factors have been found relevant
in explaining inequality dynamics, there might be even more important others.
The avenue of research undertaken in this paper, however, appears promising as it
may reveal other forces influencing income distribution that are specific to transitional
countries. The better data may also reinforce (or weaken) the results presented here.
24
1998
1999
1998
1999
1997
1996
1989
1989
1989
1989
1989
1989
1989
1990
1989
1989
1989
1989
Latvia
Lithuania
b) Western CIS
Belarus
Moldova
Russia
Ukraine
c) Caucasus
Armenia
Azerbaijan
Georgia
d) Central Asia
Kazakhstan
Kyrgyz Republic
1997
1998
1999
1997
1999
1998
3
End
period
2
Start
period
1
I. Former Soviet
Union
a) Baltic states
Estonia
Region/
Country
4
5
6
4
4
6
6
5
4
6
5
9
4
No.
of
obs.
59.00
43.00
51.86
35.00
47.00
29.103
31.203
32.00
47.00
26.00
42.00
34,00
32.10
36.97
Gini
index
(end
period)
6
26.90
31.703
31.303
25.80
23.80
22.80
26.403
22.50
22.50
23.00
Gini
index
(start
period)
5
55.30 (93)
35.00 (96)
62.14 (95)
44.00 (95)
58.71 (96)
32.00 (99)
47.00 (98)
26.00 (99)
42.00 (97)
35.04 (94)
32.60 (97)
36.97 (98)
7
Max.
value
(year)
Table A. Description of the Data on Inequality Used in the Analysis*
Appendix
Disposable Income
(94, 96), Gross
Income
Disposable Income
Disposable Income
Gross Income
Disposable Income
Disposable Income
Disposable
Income, Gross
Income (89,90)
Disposable Income
(89,98), Gross
Income
Disposable Income
Disposable Income
Disposable Income
Disposable Income
8
Income
definition1
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
9
Area/Population
Coverage2
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH
Household
(HH)
10
Sample
unit
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
(1989), Person
HH per capita
HH per capita
11
Reference unit
WIID, BM (for 1993),
WIID, WB (for 1996)
WIID
WIID, WB (for 1998)
WIID
WIID, WB (for 1999)
WIID, BM (for 1989),
WB (for 1998)
WIID, BM (for 1989)
WIID, BM (for 1993),
WB (for 1997)
25
WIID, BM (for 1989),
The World Bank (WB),
2000 (for 1999)
UNU/WIDER-UNDP
World Income Inequality
Database (WIID), 2000;
Branko Milanovic (BM),
1998 (for 1989)
WIID, BM (for 1989)
12
Source
1998
1997
1989
1990
1989
1989
Macedonia, FYR
Slovenia
6
5
4
8
10
8
9
8
9
9
4
4
4
32.22
21.50
25.10
31.88
23.24
24.47
36.65
25.00
33.30
27.52
30.00
34.59
27.64
25.30
32.00
23.36
41.00
33.00
31.603
30.603
19.36
21.41
25.05
18.06
47.00
31.803
36.94 (96)
25.05 (93)
33.30 (98)
45.57 (93)
31.18 (95)
34.78 (96)
28.14 (96)
25.30 (98)
34.20 (97)
24.83 (96)
41.00 (98)
33.30 (93)
47.00 (90)
Disposable Income
Disposable Income
Disposable Income
Disposable Income
Disposable
Income, Gross
Income (89,91)
Disposable Income
Disposable Income
Disposable Income
Disposable Income
Disposable Income
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
All/All
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
HH per capita
WIID
WIID, BM (for 1989),
WB (for 1998)
WIID
WIID
WIID, WB (for 1998)
WIID
WIID
WIID
WIID, WB (for 1998)
WIID
WIID, WB (for 1998)
WIID
WIID, WB (for 1999)
WB (for 1997)
26
* - UNU/WIDER-UNDP World Income Inequality Database, Version 1.0, 12 September 2000 provides further reference on the source and estimation
methodology for each data point drawn from this database. The data by Branko Milanovic are from the Appendix 4 “The Original Income Distribution Statistics”
of his book Income, Inequality and Poverty during the Transition form Planned to Market Economy (1998). The data by the World Bank are taken from the
Appendix D “Poverty and Inequality Tables” of the book Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia (2000).
1 - Disposable income is equal to gross income minus direct personal taxes. Gross income consists of earnings from labor, cash social transfers, self-employment
income, other income (gifts, income from property) and in-kind consumption (for instance, vegetables grown on a household’s plot of land). The difference
between gross and disposable income for the countries in the sample is assumed to be small (especially for the earlier period) as in most of the FSU and EE
countries the gross income already excludes payroll taxes, and personal income taxes are small. For an extensive discussion of possible data biases see Milanovic
(1998). The Gini coefficients for Romania and Macedonia, FYR are based on money income, which does not include in-kind consumption. Two data points
(Azerbaijan,1999 and Turkmenistan,1998) are consumption-based Gini coefficients as no alternatives are available; they are provided mainly for illustrative
purposes.
2 - The data coming from Family Budget Surveys (FBS) (mostly 1989 data in our sample) are not completely representative and may underestimate inequality as
FBS excluded pensioner-headed households and households headed by the unemployed.
3 - The Gini index for 1988 is used in the absence of 1989 data.
1997
1993
1998
1997
1989
1989
1997
1998
1998
1997
1989
1989
1989
1989
Romania
IV. Former Yugoslavia
Croatia
Yugoslavia, FR
1998
1994
1989
1989
Turkmenistan
Uzbekistan
II. Central Eastern
Europe (CEE)
Czech Republic
Hungary
Poland
Slovak Rep.
III. South Eastern
Europe (SEE)
Bulgaria
1999
1989
Tajikistan
(93, 97), Gross
Income
Disposable Income
(1999), Gross
Income
Gross Income
Gross Income
27
Table B. Descriptive Statistics of the Data Used in the Analysis
Variable
GINI
GDPPC
INFL
UNEMP
CONSG
INDVA
Year
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
Number of
observations
(countries)
22
17
9
8
15
13
14
14
16
15
22
9
15
8
15
13
14
14
16
14
22
17
9
8
15
13
14
14
16
15
22
17
9
8
15
13
14
14
16
15
22
17
9
8
15
13
14
15
16
15
16
17
9
8
15
Mean
25.88
27.16
22.26
25.70
30.98
32.53
33.03
35.72
34.35
36.24
7090.71
6185.79
7233.11
7101.86
5802.78
5818.15
6144.52
6373.06
5837.81
6117.79
174.43
67.85
158.08
515.11
1113.85
378.91
50.85
39.75
58.56
31.79
1.72
3.10
7.27
7.98
8.07
10.02
12.37
11.66
10.03
9.61
16.51
18.46
19.06
19.91
19.73
17.51
16.68
15.57
17.02
18.34
45.31
39.71
45.04
42.09
39.30
Standard
Deviation
4.52
5.35
2.89
7.28
8.84
10.24
10.82
11.04
8.16
9.54
2764.68
2650.83
2366.67
2337.76
2725.44
2643.60
2759.52
2831.14
3087.23
3461.11
435.73
156.32
111.73
948.39
2477.27
628.33
64.50
79.42
143.47
64.80
4.94
6.53
4.37
5.36
5.99
7.51
8.07
8.81
8.14
3.96
5.88
4.64
3.95
3.84
4.02
3.86
4.66
5.04
5.09
6.32
9.39
12.24
7.77
4.98
8.59
Minimum
18.01
17.80
17.96
18.62
19.68
20.81
20.00
24.22
23.36
25.00
2138.65
2310.41
5091.90
4982.60
2157.20
1732.60
1896.20
2015.30
2052.70
1040.90
-2.25
3.10
32.20
9.10
18.20
9.70
7.20
-0.60
2.60
-7.60
0.00
0.00
0.00
0.20
0.20
0.40
2.70
2.80
1.50
2.10
3.45
10.22
11.43
14.28
12.34
11.27
10.51
9.11
9.15
9.10
32.56
7.95
33.21
34.71
31.79
Maximum
32.22
34.90
26.70
41.23
55.30
60.60
62.14
59.72
51.86
59.00
13170.79
12368.00
10973.00
10714.00
11428.00
11414.00
12426.00
13021.00
12930.00
14293.00
1303.30
540.41
338.90
2730.00
9750.00
1885.00
244.00
310.80
578.60
251.30
22.60
23.60
13.20
15.30
16.40
30.00
35.60
38.80
36.00
14.10
24.69
25.58
23.53
25.58
25.01
22.91
24.11
24.09
27.40
26.30
59.39
59.14
60.12
50.91
67.44
28
PRIVS
SH60
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
13
14
14
16
14
22
17
9
8
15
13
14
14
16
15
22
17
9
8
15
13
14
14
16
15
36.38
34.29
32.19
31.82
34.97
13.18
14.71
24.44
26.25
32.33
45.38
50.71
56.43
59.36
55.00
13.88
12.47
17.40
17.40
14.84
15.33
16.51
16.65
16.88
16.21
5.23
4.27
6.97
7.33
5.42
6.28
9.10
13.33
9.91
15.22
14.36
15.67
15.74
13.89
18.32
4.41
4.32
1.77
1.78
4.94
4.19
3.29
3.07
3.46
5.19
26.43
27.42
15.33
15.79
26.72
5.00
5.00
10.00
10.00
10.00
20.00
15.00
15.00
20.00
20.00
6.17
6.25
14.89
15.01
6.16
6.28
8.98
9.90
8.15
5.81
46.28
42.74
44.31
44.58
46.09
30.00
40.00
50.00
45.00
55.00
65.00
70.00
75.00
75.00
85.00
19.15
18.66
19.90
20.43
20.75
21.12
21.35
21.51
21.59
20.98
Source: Assembled panel data
Key:
GDPPC = GDP per capita, PPP-adjusted (USD);
INFL = inflation rate, measured by the year-on-year change in CPI (per cent);
UNEMP = share of unemployed in total labor force (per cent);
CONSG = government consumption as share of GDP (per cent);
INDVA = industry value added as percentage of GDP;
PRIVS = private sector share in GDP (per cent);
SH60 = share of population aged 60 years and older in total population;
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