World Development Vol. 31, No. 8, pp. 1421–1441, 2003
Ó 2003 Elsevier Ltd. All rights reserved
Printed in Great Britain
0305-750X/03/$ - see front matter
www.elsevier.com/locate/worlddev
doi:10.1016/S0305-750X(03)00098-6
Bridging the Economic Divide Within Countries:
A Scorecard on the Performance of Regional
Policies in Reducing Regional Income Disparities
RAJA SHANKAR
Massachusetts Institute of Technology, Cambridge, MA, USA
and
ANWAR SHAH *
The World Bank, Washington, DC, USA
Summary. — Regional inequalities represent a continuing development challenge in most countries.
There is a presumption in development economics that decentralized fiscal arrangements would
lead to ever-widening regional inequalities. This paper provides an empirical test of this hypothesis.
We conclude that regional development policies have failed in almost all countries, federal and
unitary alike. Still, federal countries do better in restraining regional inequalities, because of the
greater political risk these disparities pose for such countries. Our findings also suggest that
countries experiencing divergence tend to focus on interventionist policies, while those experiencing
convergence have taken a hands-off approach to regional development and instead focus on
promoting an economic union by removing barriers to factor mobility and ensuring minimum
standards in basic services across the country.
Ó 2003 Elsevier Ltd. All rights reserved.
Key words — regional inequality, regional disparity, income inequality, decentralization,
convergence, divergence
1. INTRODUCTION
Regional inequalities represent an everpresent development challenge in most countries, especially those with large geographic
areas under their jurisdictions. Globalization
heightens these challenges as it places a premium on skills. With globalization, skills rather
than the resource base of regions determine
their competitiveness. Skilled workers gain at
the expense of unskilled ones. As typically rich
regions also have better educated and better
skilled labor, the gulf between rich and poor
regions widens. Large regional disparities represent serious threats as the inability of the
state to deal with such inequities creates potential for disunity and, in extreme cases, for
disintegration. While the policy challenges in
reducing regional disparities are large, the division of powers in the federation curtails federal flexibility in the choice of instruments.
Central governments in unitary states are, on
the other hand, relatively unconstrained in their
choice of appropriate policies and instruments.
Under these circumstances, there is a presumption in development economics that a
decentralized fiscal constitution would lead to
ever-widening regional inequalities. This paper
* The
authors are grateful to the four anonymous reviewers of this journal for helpful comments. We are also
grateful to the following individuals for their valuable
help in obtaining regional GDP data and other information used in this study: David Rosenblatt, Ritva S.
Reinikka, Alberto Valdes, Viet Tuan Dinh, Hanid Mukhtar, Sayyora Umarova, Fahretin Yagci, Joachim Von
Amsberg, Magda Adriani, Princess Ventura, Xiofan
Liu, Cornelia Giurescu, Timothy E. Heleniak, Mariana
Urbiola, Mariusz Safin, and Joven Balbosa. Final revision accepted: 20 February 2003.
1421
1422
WORLD DEVELOPMENT
attempts to provide an empirical test of this
hypothesis.
The paper is organized as follows. Section 2
provides an introductory overview of various
measures of regional inequality that are available from the literature. Section 3 provides estimation of inequality measures for a sample of
eight industrial 1 and 18 developing countries.
These countries are further subgrouped in federal and unitary countries for analytical purposes. For a smaller subset of 14 countries,
Section 4 presents evidence on historical trends
in regional disparities in per capita income. 2
Section 5 provides a summary scorecard on
national policies for regional development.
2. MEASURES OF REGIONAL
INEQUALITY
Interest in income inequality has led to the
development of several ways of measuring it.
Two types of measures are of interest in this
paper––static and dynamic. Static measures
provide a snapshot of these inequalities at a
point of time whereas the dynamic measures
capture historical trends. These are described
below in turn:
(a) Static measures of regional inequality
The measurement of regional disparities is an
arduous task and no single statistical measure is
able to capture its myriad dimensions. Recognizing these difficulties, this paper has applied a
variety of measures to highlight various dimensions of these inequalities. The selected
measures are briefly described in the following
paragraphs.
(i) Maximum to minimum ratio (MMR)
A comparison of the per capita GRDP (gross
regional domestic product) of the region with
the highest income to the region with the lowest
income (minimum per capita GRDP) provides
a measure of the range of these disparities. If
this measure is small (close to 1), then it would
mean that the different regions have relatively
equal incomes. If this measure is large, then the
interpretation is more problematic, as it does
not tell us if the high ratio is due to substantial
variation in the distribution of per capita
GDRPs or the presence of outliers. Nevertheless, maximum to minimum ratio (MMR)
provides a quick, easy to comprehend, and
politically powerful measure of regional income
inequality.
(ii) Coefficient of variation
The coefficient of variation (CV) is one of the
most widely used measures of regional inequality in the literature. 3 The CV is a measure
of dispersion around the mean. This dispersion
can be calculated in a few different ways. Several authors have used the standard deviation
of the logarithm of real per capita GRDP. 4 In
this study, however, the CV as defined below
attempts to capture the dispersion of per capita
GRDP. This measure is standardized and can
be used to make comparisons between countries and over time (especially if GRDP data
are only available in current prices). In the
following analysis, the CV is calculated in two
ways. The first, the ‘‘simple coefficient of variation’’ is an unweighted measure as given
below:
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
P ðyi yu Þ2
CVu ¼
i
yu
N
;
ð1Þ
where yi is the GRDP per capita of state i, N is
the number of regions and yu is the mean per
capita GRDP. yu is computed as the mean of
the per capita GRDPs without weighting them
by population as follows:
yu ¼
1 X
yi :
N i
ð2Þ
This is slightly different from WilliamsonÕs
(1965, p. 11) formula for the unweighted CV,
where the mean GRDP per capita, y , is taken as
the national mean per capita GRDP. The
Williamson measure is not appropriate in this
application as it uses a weighted measure for
the denominator and an unweighted one for the
numerator. CVu varies from 0 for perfect inequality (equal per capita GRDP for the difpffiffiffiffiffiffiffiffiffiffiffiffi
ferent regions) to N 1 for perfect inequality
(only one region has all the GDP). Although
this measure can be used for comparisons of
regional disparities in countries across time, it is
problematic for comparisons between countries
because the inequality value is sensitive to the
number of regions.
This problem is somewhat overcome by the
‘‘weighted coefficient of variation,’’ where each
regional deviation is weighted by its share in the
national population. This measure is calculated
as given below:
BRIDGING THE ECONOMIC DIVIDE
CVw ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
P
y Þ2 pPi
i ðyi y
;
ð3Þ
yi is the GRDP per capita of region i, and y is
the national mean per capita GDP. P is the
national population and pi is the population of
state
i. CVw varies from 0 for perfect equality to
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðP pi Þ=pi for perfect inequality where region i has all the GDP. This is better than CVu
for crosscountry comparison as the measure of
inequality depends not on the number of regions but on the population proportion of the
regions.
(iii) Relative mean deviation (Rw )
Following Kakwani (1980, 1990) and Williamson (1965, p. 16) we also compute the relative mean deviation of per capita GRDP as
follows:
P
jyi y j pPi
;
ð4Þ
Rw ¼ i
y
yi is the GRDP per capita of region i, and y is
the national mean GRDP per capita. P is the
national population and pi is the population of
state i. Rw is a measure weighted by population
proportions of the regions. As CV is computed
by squaring differences, it could be unnecessarily sensitive to outliers. Rw , which avoids this
problem can thus be used to check the CV results. Rw varies from 0 for perfect equality to 2
for perfect inequality. Kakwani (1980) divides
Rw by 2 to get his measure of relative mean
deviation, as it gives the desirable property of
the measure becoming equal to 1 for perfect
inequality. But, as we use Rw only to check our
CV results for outlier effects, we do not follow
Kakwani (1980) in this regard.
(iv) Gini index (G)
The Gini index like the CV is widely used in
the inequality literature. 5 Following Kakwani
(1980), we compute the unweighted Gini index
as follows:
n X
n
X
1
1
jyi yj j;
ð5Þ
Gu ¼
2yu nðn 1Þ i
j
yi and yj are the GRDPs per capita of region i
and j respectively. n is the number of regions,
and yu is the unweighted mean of the per capita
GRDPs. Gu varies from 0 for perfect equality
to 1 for perfect inequality. The Gini index thus
measured is the arithmetic average of nðn 1Þ
differences of per capita GRDPs, taken as ab-
1423
solute values divided by the maximum possible
value of this average, 2yu .
The weighted Gini index, which weights each
difference of per capita GRDPs by respective
population proportions is calculated as shown
below:
X
n X
n
1
pi pj
jyi yj j 2 ;
ð6Þ
Gw ¼
2y
P
i
j
y is the national mean per capita GDP. pi and pj
are the populations of regions i and j respectively. P is the national population, and n the
number of regions. Gw varies from 0 for perfect
equality to 1 ðpi =P Þ for perfect inequality. If
pi is small compared to P , i.e., if the region with
a small proportion of the population produced
all the GDP then the value for perfect inequality would approach 1.
(v) Theil index (T )
A final measure of inequality used in this
paper is an information or entropy measure of
inequality. Following Theil (1967), it is computed as follows:
X
xi
T ¼
;
ð7Þ
xi log
qi
i
xi is the GDP share of region i of region i and
qi is the population share of region i. For equal
per capita GRDPs, i.e., with GRDPs proportional to regional populations, T takes a value
of 0. For a case where region i produces the
entire GDP, T becomes logðP =pi Þ, where P is
total population of the country, and pi is the
population of state i. Note here that as the
population share of region i goes down, T rises
if region i produces the entire GDP.
(b) Dynamic concepts of regional inequality
While a snapshot view of regional income
disparities is illuminating, a longer-term perspective is more helpful in ascertaining the impact of public policies. This requires developing
a time profile of static measures and discerning
whether these inequalities appear to diminish
(the so-called convergence hypothesis) or accentuate (divergence hypothesis) over time. A
strong convergence hypothesis suggests that
equality in factor productivity and income
levels will be achieved regardless of initial
conditions provided diffusion and adoption of
technological change is unrestrained. A weak
convergence hypothesis, on the other hand,
1424
WORLD DEVELOPMENT
requires competitive market structures to send
the right signals for allocation of productive
factors (see Boldrin, 2001). Under the weak
convergence hypothesis, differences in technology alone do not explain the differences in
factor productivity. Lack of competitive price
signals such as those observed with regional
incentives and subsidies, infant industry protection, barriers to trade etc. may perpetuate
regional differences in factor productivity and
income.
At the conceptual level, regional convergence
is assured under perfect competition, constant
returns to scale with no external effects and free
and cost-less mobility of factors across relatively homogeneous (with respect to resource
endowment, topography, composition of population, human capital, political and legal
environment, informal culture, etc.) regions
within the nation state. This requires that political units are commensurate with reasonably
large geographic areas with reasonably diverse
endowments so that regional income differentials are attributable to policy and institutional
considerations rather than simply to irreversible acts of nature. For example, one should not
expect to have convergence among three completely heterogeneous regions comprising solely
desert, mountainous and arable lands.
Regional convergence becomes more difficult
to achieve under increasing returns to scale and
with externalities of investment and growth.
Strong nonconvergence (divergence) hypothesis
places a greater emphasis on path dependency
(initial conditions matter), increasing returns to
scale, and externalities of investment as sources
of differences in factor productivity and growth
(see Krugman, 1991; Romer, 1990). Realization
of increasing returns to scale and/or agglomeration economies under perfect mobility in one
of the regions and not others would accentuate
regional divergence. Divergence would also
happen if factors are either unable (due to impediments) or unwilling (say age and ethnicity
consideration) to move. Under strong divergence hypothesis, inequality in levels of income
and resource endowments will prevent convergence in regional growth rates. Under a weak
divergence hypothesis, attainment of a minimum threshold of physical and knowledge
capital in the leading regions is necessary for
persistence in the divergence of growth paths.
Thus some regions that attain the minimum
thresholds in these factors may form ‘‘clubs’’ or
‘‘growth poles’’ and may grow faster than
others and achieve ‘‘club convergence.’’ Public
policies to break this regional concentration of
powers may have tradeoffs between national
growth and overcoming regional inequalities
(for a discussion of this issue in the European
Union context, please see Boldrin, 2001). In
federal countries regional inequalities are likely
to be given significant importance in evaluating
any tradeoffs that may be observed. Section 4
provides empirical evidence on the outcome of
the choices on these tradeoffs that are made in
various countries.
Two statistical concepts are helpful in looking at the dynamics of regional inequalities.
First, a reduction in the dispersion of regional
income over time is termed as r convergence. In
this paper, we analyze the trends in the different
inequality measures discussed in the previous
subsection to look for evidence of convergence. 6 Section 4 provides empirical estimates
on r convergence for 16 countries. Second, any
catching up in incomes by relatively poorer
regions through faster growth is called b convergence (see Barro & Sala-i-Martin, 1995, p.
383). For this analysis, also in Section 4, we
have estimated a growth equation based on a
log-linear approximation around the steady
state of the Solow growth model. This is similar
to the one suggested in Barro and Sala-i-Martin
(1992, 1995). The model is as follows:
1
yi;t
1 ebT
ln
¼ ai lnðyi;tT Þ þ ui;t ;
T
yi;tT
T
ð8Þ
where yi;t is the per capita GRDP of the ith
region at time t, yi;tT is the per capita GRDP in
the ith region at the beginning of the time interval under study and ui is an independently
distributed error term. For our purposes, we
rearranged Eqn. (8) and estimated the following convergence equation: 7
lnðyi;t Þ lnðyi;tT Þ ¼ ai þ b lnðyi;tT Þ þ ui;t ;
ð9Þ
r convergence and b convergence analyses may
not always give similar results. For instance,
there could be a significant decrease in r convergence (divergence), with a change in the
ranks of the regions. A poorer region could
grow so fast that it could overshoot on the
richer side, leading to greater overall dispersion. b convergence analysis could capture this
divergence as convergence, or it may show an
absence of either. Therefore, it is important to
BRIDGING THE ECONOMIC DIVIDE
do both types of analysis to understand what is
really happening.
3. REGIONAL DISPARITIES––A
CROSSCOUNTRY SNAPSHOT
The data on regional GRDPs are available
only for a surprisingly small number of developing countries. We could find such data for eight
industrial and 17 developing countries. In the
following, measures of regional income inequality
are presented separately for industrial and nonindustrial countries. The experiences of federal
and unitary countries are also compared. Before
we proceed, we want to make two brief points.
First, we have deliberately used per capita GRDP
as a basis for all measures on regional inequality.
This indicator which measures the value added in
a region better captures the true economic potential of a region than an alternate indicator of
‘‘overall regional per capita income’’ as the latter
indicator includes net transfers. 8 The superiority
of this indicator becomes obvious later, when we
examine the effects of centralization vs. decentralization on regional inequality as we focus on
the impacts of alternative policies on the productive capacity of a region rather than their
overall redistributive results. We are not trying to
see if centralization can lead to more redistribution, which it probably might. What we are trying
to see is whether a strong center has led to increases in the productive capacity of the poorer
regions. Second, our analysis is, of course, as
good as the data are. The data have been collected
with care from various reliable sources, both from
within the World Bank, and individual country
statistical publications. 9 It must be recognized,
however, that even with the best of care, the data
from developing countries are always subject to
well-recognized limitations including imperfect
accounting of uneven regional price index variations in the measurement of GRDP. Where we
had GRDP figures only in current values, we
assumed that price index variations were identical
across regions in a country. Whether this introduces any bias, it is difficult to tell. There is no
reason, ex-ante, to assume that there is any relationship between inflation and regional income in
any country. Regions that grow faster may have
higher inflation. If the richer regions grow faster
with higher inflation, we may overestimate divergence if we assume a common price index
change. On the other hand, if poorer regions grow
faster, we may overestimate convergence.
1425
(a) Industrial countries
Table 1 presents our calculations of the different measures of regional inequality of per
capita GRDP in seven industrial countries––
four federal and three unitary. For Germany,
the inequality measures are presented for the
unified Germany as well as for the states of
former West Germany. As expected, former
West Germany is less unequal than unified
Germany.
By most measures of inequality, the federal
countries have lower levels of regional disparity
than the unitary countries in our sample. This is
especially true for the population-weighted
measures, which are more appropriate for crosscountry comparisons. Figure 1 shows the
weighted measures of regional inequality for developed countries. The figure is ordered in descending order of the weighted CV. Except for the
United Kingdom, the federal countries in our
sample have lower values for all the inequality
measures.
Canada and the United States have the
lowest levels of inequality on almost all measures. Further the weighted values are smaller
than unweighted ones, showing that the provinces or states with extreme per capita GRDPs
are generally those with smaller populations.
They are followed by the United Kingdom, and
then former West Germany and Spain. France
and Italy have the highest regional disparities.
The weighted and unweighted values of inequality for the United Kingdom and Italy are
similar, showing that regions at different points
of the income distribution have similar populations. As expected, unified Germany is more
unequal than just the states of former West
Germany. The weighted values are lower as in
the case of Canada and the United States.
Spain and FranceÕs weighted inequality measures are higher than the unweighted ones. This
means that regions with extreme per capita
GRDPs have larger populations in these
countries. The relative mean deviation, which
we use to check for a few extreme deviations, is
significantly different only for the United
Kingdom and former West Germany.
(b) Nonindustrial countries
Table 2 presents our calculations of the different measures of regional inequality in 18
developing countries––five federal and 13 unitary. 10 In general, on all the measures of inequality, the developing countries are much
0.032
0.037
0.015
0.126
0.145
0.083
0.096
0.152
0.085
0.206
0.243
0.123
0.267
0.264
0.178
0.178
0.262
0.177
1995–97
1995–97
1995–97
United States
Germany
Former West Germany
Spain
Unitary
France
Italy
United Kingdom
2.039
2.228
1.794
0.067
0.068
0.039
0.122
0.076
0.118
0.118
0.113
0.090
0.191
0.128
0.111
0.123
0.127
0.097
0.197
0.140
0.189
0.137
0.137
0.122
0.262
0.207
0.210
0.201
0.195
0.162
0.341
0.241
0.189
1.838
1.718
1.927
3.048
2.033
1.866
1997
1998
1997
1995–97
1995–97
1995–97
Federal
Canada
Weighted Gini
index (Gw )
Unweighted
Gini index
(Gu )
Relative mean
deviation (Rw )
Weighted coefficient
of variation (CVw )
Simple coefficient
of variation (CVu )
Max/Min
(MMR)
Year
Country
Table 1. Regional disparities with industrial countries
0.008
0.006
0.007
0.027
0.010
0.022
WORLD DEVELOPMENT
Theil index
(T )
1426
more unequal than the developed countries.
Except for Pakistan and Romania, all the developing countries are more unequal than the
most unequal developed country in our sample,
Italy. On average, if one takes the weighted CV,
developing countries are two to six times more
unequal than the developed countries. There
are two plausible explanations––an imperfect
government and an imperfect market one––for
higher regional inequality in developing countries. The imperfect government explanation is
that developing countries have a greater focus
on growth, which might force them to concentrate their developmental efforts on fewer
regions due to investment and capability constraints. With increased development, increased
capabilities and larger resources allow them to
focus on more backward regions. The imperfect
market explanation is that developing country
markets are less developed and there are greater
interregional barriers (institutional, infrastructural and cultural) to trade and factor movements. This might hamper convergence. As
countries become more developed, market imperfections are removed and easier factor and
goods movements leads to convergence and
lower regional inequality. These explanations
are in conformity with WilliamsonÕs (1965)
‘‘inverted U’’ hypothesis. 11
As in the case of developed countries, unitary
developing countries are, in general, more unequal than federal developing countries. Figure
2 presents the weighted measures of inequality
for developing countries for 1997 or the latest
available year before 1997. The countries are
presented in descending order of the weighted
CV. The countries with the highest measures of
inequality are large unitary countries––Vietnam, Thailand, China and Indonesia. Inequalities in federal and smaller unitary countries are
considerably lower.
Pakistan, Romania, Chile, Brazil and Indonesia have lower values for the weighted measures than the unweighted ones, signifying that
regions with extreme values of per capita
GRDPs have smaller populations. On the other
hand, the higher values for the weighted measures signify that India, Sri Lanka, Mexico,
China and Thailand have regions with larger
populations at the extremes of the per capita
GRDP distribution. Uzbekistan, Philippines,
Russia, South Africa and Vietnam have relatively more equal distribution of the populations in the regions at different points of the per
capita GRDP distribution. The relative mean
deviation, which we use to check for a few ex-
BRIDGING THE ECONOMIC DIVIDE
1427
France (1995-97)
Italy (1995-97)
Theil index
Spain (1995-97)
Weighted gini index (Gw)
Former West
Germany (1995-97)
Relative mean Deviation (Rw)
Weighted coefficient of variation
(CVw)
United Kingdom
(1995-97)
Canada (1998)
United States (1997)
0.000
0.050
0.100
0.150
0.200
0.250
0.300
Inequality measure
Figure 1. Regional disparities within industrial countries.
treme deviations, is significantly different for
Pakistan, Russia, Indonesia, Philippines, Uzbekistan and Vietnam.
(c) Federal vs. unitary countries
We have seen above that, in general, federal
countries are less unequal in terms of per capita
GRDP than unitary countries. In this subsection, we look at this relationship between
decentralization and regional inequality. We
regressed three weighted measures of inequality
on a dummy variable representing whether a
country is unitary or federal. 12 The value of
the dummy is and 1 if the country is unitary
and 0 if the country is federal. For each measure of inequality we carried out three regressions––the first just on the unitary dummy, the
second with the natural logarithm of population as the control variable, and the third with
an additional control variable––a dummy with
value 1 if a country is developing and 0 if a
country is developed. The measures of inequality used in the regression were from 1997
or the latest year available before 1997.
We obtained positive coefficients on the
unitary dummy in all the regressions, signifying
that unitary countries tend to be more unequal.
But, except in the case of weighted CV as the
dependent variable, the R-square value was too
small or the coefficients were not statistically
significant. Table 3 shows the results of re-
gressions with weighted CV as the dependent
variable. Regression 1 has the natural logarithm of the country population as a control
variable while regression 2 has an additional
control variable––a dummy for whether the
country is developed or not. In both the regressions, the coefficient on the unitary dummy
is positive and statistically significant at the 5%
level with the two-tailed test. This provides
some evidence for the proposition that centralization leads to greater regional disparities.
The coefficient on the log of population also
has a positive sign and is statistically significant
at the 5% level with the two-tailed test. This is
not surprising as, ceteris paribus, we would
expect countries with larger populations to
have greater regional inequalities. As expected,
in regression 2, the developing country dummy
has a positive coefficient significant at the 1%
level with the two-tailed test. 13
We also computed the Spearman rank correlations of the weighted inequality measures
with the unitary dummy. We computed the
rank correlations in two different ways. First,
we took just assigned an average rank of 5.5 to
all the nine federal countries and 15.5 to all the
12 unitary countries. In the second case, we
ranked the countries within each group (federal
or unitary) according to the level of decentralization. The level of decentralization was
measured as the proportion of subnational
government expenditures to that of total
Max/Min
(MMR)
Simple coefficient of
variation (CVu )
Weighted coefficient
of variation (CVw )
Relative mean
deviation (Rw )
Unweighted
Gini index (Gu )
Weighted Gini
index (Gw )
Theil index (T )
Federal
Brazil
India
1997
1997
7.567
3.811
0.563
0.387
0.468
0.414
0.409
0.334
0.334
0.226
0.267
0.227
0.116
0.082
Mexico
1997
1998
5.793
5.874
0.473
0.469
0.571
0.566
0.422
0.421
0.253
0.251
0.301
0.300
0.136
0.134
Pakistan
1997
1998
1.514
1.516
0.186
0.183
0.150
0.141
0.094
0.095
0.113
0.114
0.072
0.069
0.009
0.008
Russia
1997
21.307
0.625
0.645
0.387
0.283
0.280
0.153
Unitary
Chile
1994
5.696
0.486
0.334
0.243
0.267
0.165
0.052
China
1997
1998
1999
11.625
12.183
12.507
0.692
0.709
0.730
0.924
0.952
0.987
0.666
0.679
0.694
0.351
0.357
0.365
0.250
0.254
0.264
0.111
0.115
0.125
Indonesia
1997
1998
11.048
11.436
0.827
0.832
0.716
0.722
0.401
0.416
0.378
0.381
0.274
0.277
0.176
0.178
Nepal
1996
1.440
0.157
Philippines
1997
1998
6.653
6.760
0.530
0.536
0.532
0.537
0.367
0.369
0.307
0.311
0.261
0.262
0.123
0.125
Poland
Romania
Sri Lanka
South
Africa
Thailand
Uganda
1996
1996
1995
1994
2.031
1.783
3.362
7.038
0.206
0.189
0.394
0.621
0.174
0.452
0.639
0.132
0.397
0.558
0.106
0.230
0.352
0.090
0.249
0.341
0.012
0.101
0.195
1997
1997–98
8.273
1.760
0.797
0.274
0.925
0.745
0.438
0.442
0.351
Uzbekistan
1997
1998
1999
3.047
2.991
2.779
0.353
0.321
0.304
0.355
0.320
0.301
0.238
0.218
0.206
0.155
0.147
0.142
0.170
0.159
0.152
0.054
0.046
0.041
Vietnam
1997
24.746
1.067
0.996
0.596
0.372
0.410
0.306
WORLD DEVELOPMENT
Year
1428
Table 2. Regional disparities within nonindustrial countries
Country
BRIDGING THE ECONOMIC DIVIDE
1429
Figure 2. Regional disparities within nonindustrial countries.
Table 3. Regression resultsa
Independent variable
Dependent variable: weighted CV
(Regression 1)
Dependent variable: weighted CV
(Regression 2)
)1.86
0.048
0.28
0.016
0.12
0.022
)1.54
0.054
0.2
0.044
0.09
0.036
0.28
0.008
22
0.567
Intercept
Unitary dummy
log(population)
Developing dummy
# of observations
R-square
22
0.344
a
p-Values in italics.
Significant at 10% level.
**
Significant at 5% level.
***
Significant at 1% level (all two-tailed tests).
*
expenditures. Government expenditure data
were taken from the International Monetary
Fund (IMF) Government Finance Statistics.
Table 4 presents the results of our calculations. The first row presents the rank correlations of the 22 countries in our sample with
ranking according to only whether a country is
federal or unitary. The second row shows the
results when the ranking taking into account
the level of decentralization in each country. In
this case, our sample contains only 15 countries 14 as we did not have government expenditure data for the other six countries. All the
correlation values are positive. These values
Table 4. Spearman rank correlation
Federal/unitary
Federal/unitary and
decentralization
Weighted
CV
Weighted
Gini
index
Theil
index
0.458
0.382
0.423
0.371
0.458
0.411
may not be large, but they do question the
conventional wisdom from the literature (Musgrave, 1959; Oates, 1972) that a centralized
1430
WORLD DEVELOPMENT
form of government is better for the reduction
of inequalities between jurisdictions.
We conclude that significant regional inequalities persist in many developing countries.
By most measures of regional inequality, developing countries are two to six times more
unequal than developed countries. Similarly,
the unitary countries are more unequal than
federal countries. This challenges the widespread assumption that centralized countries
are better at equalizing economic differences
among regions. On the contrary, the federal
countries have a better record at ensuring regional equity.
4. REGIONAL INCOME DISPARITIES
AND CONVERGENCE
In this section, the time trends of inequality
for 16 different countries are presented to discern the degree of convergence. 15 Two of these
countries are developed countries, Canada and
the United States, and the rest are developing
countries. Of the developing countries, five are
federal countries and nine are unitary countries.
(a) Regional disparity trends in federal countries
As part of the r convergence analysis, Figures 3–5 show regional inequality trends in the
federal countries over the time-period 1980–99.
Figure 3 plots the trends in the weighted CV,
Figure 4 the weighted Gini index, and Figure 5
the Theil index. Trends for different countries
are plotted over different subperiods depending
on data availability. Table 5 presents evidence
on b convergence in the federal countries. This
table contains the results of basic regressions of
the growth rate of per capita GRDP on the
logarithm of initial GRDP. The United States,
Canada and Pakistan have the lowest levels of
regional inequality followed by India, Brazil,
Mexico and Russia respectively.
The United States has seen a decline in its
regional inequality measures over 1990–94 after
which they have stabilized. The b convergence
analysis in Table 5 shows a negative and statistically significant coefficient on the initial per
capita GRDP variable. CanadaÕs inequality has
remained more or less constant during 1994–98,
which can be seen in both the r convergence
and b convergence analyses. Pakistan 16 has seen
a decline in inequality over 1990–98, with a
relatively sharp drop in 1994. We did not run a
b convergence regression for Pakistan as it has
only four regions. PakistanÕs level of inequality
is comparable to that of Canada and the
United States. The low levels of regional inequality are consistent with these countries
being at an advanced stage of economic development, 17 and with their having few barriers
to interregional trade and factor mobility. Pa-
0.7
Weighted coefficient of variation
0.6
0.5
0.4
0.3
0.2
0.1
0
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Brazil
Canada
India
Mexico
Pakistan
Russia
United States
Figure 3. Regional disparity trends in federal countries (weighted CV).
BRIDGING THE ECONOMIC DIVIDE
1431
0.35
0.3
Weighted gini index
0.25
0.2
0.15
0.1
0.05
0
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Brazil
Canada
India
Mexico
Pakistan
Russia
United States
Figure 4. Regional disparity trends in federal countries (weighted Gini index).
0.18
0.16
0.14
Theil index
0.12
0.1
0.08
0.06
0.04
0.02
0
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Brazil
Canada
India
Mexico
Pakistan
Russia
United States
Figure 5. Regional disparity trends in federal countries (Theil index).
kistanÕs low level of regional inequality, uncharacteristic of developing countries, may be
surprising, but it could be explained by the fact
that Pakistan has a relatively low number of
provinces and that the two richest provinces
have over 80% of the countryÕs population.
1432
WORLD DEVELOPMENT
Table 5. Beta-convergence results in federal countries
Country
United States
Canada
Pakistan
India
Brazil
Mexico
Russia
**
Time period
Independent variable
Beta
p-Value
R-Square
# of
regions
1990–97
1994–98
log(1990 per capita GRDP)
log(1994 per capita GRDP)
)0.297
)0.019
0.000
0.751
0.462
0.012
50
11
1980–97
1994–97
1993–98
1994–97
log(1980 per capita GRDP)
log(1994 per capita GRDP)
log(1993 per capita GRDP)
log(1994 per capita GRDP)
0.283
)0.02
)0.01
0.01
0.04
0.566
0.73
0.824
0.307
0.013
0.004
0.001
14
27
32
79
Significant at 5% level.
Significant at 1% level.
***
India 18 has seen a constant rise in regional
disparity during 1980–96, followed by a slight
drop in 1997. Regional inequality dramatically
increased in 1992 after the liberalization reforms started. Its level of inequality has risen
from about twice that of the United States in
1990 to more than three times in 1997. Our
findings, which show that India has not witnessed both r divergence and b divergence, are
consistent with other authors who have looked
at regional inequalities in India. Bajpai and
Sachs (1996) find evidence of divergence of
state domestic products during 1971–93. Cashin and Sahay (1996) also find a widening in
the dispersion of the net domestic products of
Indian states during 1971–91. Das and Barua
(1996), Nagaraj et al. (2000), Rao, Shand, and
Kalirajan (1999), and Yagci (1999) also find
similar evidence of divergence in India. Some of
these authors have also found evidence against
b convergence in India. Rao et al. (1999) find
that the growth of SDP per capita is positively
related to initial levels for various subperiods
beginning in 1965 and ending in 1994–95. Yagci
(1999) reports that higher income states have
grown faster since 1980 compared to lower income states. Bajpai and Sachs (1996) also find
evidence of a weak positive relationship between initial state domestic product and the
economic growth rate during 1971–93. But,
Cashin and Sahay (1996) report evidence of
weak b convergence. They also point out that
the speed of convergence, 1.5% per year, is
slower than that of regional convergence in
industrial economies (Australia, United States,
Canada and Japan), whose rate of convergence
has been 2%. In fact, the speed of convergence
is slower than between OECD countries, which
is surprising as one would expect faster convergence within national borders.
There could be several reasons for increasing
regional inequality in India. One explanation
could be that India is in an early stage of development and therefore is on the wrong side of
the ‘‘inverted U’’ pattern of regional inequalities (Williamson, 1965). Another reason could
be the relatively high barriers to interstate trade
in India. A third reason could be the perverse
nature of the central governmentÕs regional
development policies and the intergovernmental transfer system (Shankar & Shah, 2003).
Brazil saw a rise in inequality in the 1980s, a
slight fall in the early 1990s, and then a slight
rise during 1995–97. Its inequality has been
around three to four times that of the United
States. More or less, the level of regional inequality has remained stable within a relatively
narrow band. The weighted CV has fluctuated
between a high of 0.51 and a low of 0.45 during
this period. The weighted Gini index has varied
between 0.29 and 0.26, while the Theil index
has stayed between 0.14 and 0.11. The b convergence analysis also shows no convergence
during 1994–97. 19
MexicoÕs regional inequality is around five
times that of the United States and has remained more or less stable in the 1990s. There
is a slight dip in 1995, which was the year when
the Mexican economy contracted 6.2%, the
worst recession since the Great Depression.
Mexico is the most centralized of the federal
countries in our sample. The state governmentsÕ
are largely dependent on central transfers for
their revenues. Transfers to states from revenue
sharing were almost six times as large as statesÕ
own revenues in 1996 (Giugale, Nguyen, Rojas,
& Webb, 2000, p. 19). This highly centralized
nature of the Mexican federation may be one of
the reasons for its high level of regional inequality (Shankar & Shah, 2003).
BRIDGING THE ECONOMIC DIVIDE
Russia has a dramatic rise in inequality
during 1994–97, with its 1997 weighted CV
being 50% more than its 1994 value. During
this period the Russian economy has undergone major structural transformation. It has
undergone a long period of recession during
1990–97 when it had a positive growth rate of
0.9%. The increasing regional inequality may
be due to the complex political and economic
changes taking place in Russia, especially with
economic power being concentrated in Moscow
and a few other regions. Intriguingly, the b
convergence results are not significant for
Russia. This is explained the fact that several
Russian regions have changed ranks (just a
couple of so places in most cases), even while
the overall dispersion has increased, with the
places that changed ranks often contributing to
such dispersion. So in the regression analysis,
this rank change cancels out the divergence
effect of more dispersion, and one sees no statistically significant result.
(b) Regional disparity trends in unitary
countries
Figures 6–8 show regional inequality trends
in the unitary countries during 1978–99. Figure
6 plots the trends in the weighted CV, Figure 7
the weighted Gini index, and Figure 8 the Theil
index. Trends for different countries are plotted
over different subperiods depending on data
1433
availability. Table 6 presents evidence on b
convergence in the unitary countries. This table
contains the results of basic regressions of the
growth rate of per capita GRDP on the logarithm of initial GRDP.
The smaller unitary countries in our sample
have the lowest levels of regional inequality.
Romania, Sri Lanka, Uzbekistan and Chile
have relatively low levels of inequality, while
Philippines, China, Indonesia and Thailand
have high levels of inequality. In general, except
for the smaller countries, the levels of inequality in these countries are significantly higher
than in the federal countries (Figures 6–8).
Romania, which has the lowest level of regional inequality has seen it rise during 1993–
96. Its inequality, measured by the weighted
CV, is around a third more than that of the
United States. Another transition country,
Uzbekistan, has seen its regional inequality decrease during 1996–99. Its weighted CV is
around two and a half times more than that of
the United States and Canada. Uzbekistan is
a highly centralized country with 14 regions
whose leaders can be sacked by the countryÕs
President. The most important activity happens
in a sparsely populated region, Navoi, where
most gold and uranium is mined. As gold is one
of its major exports, this could account for
some of UzbekistanÕs regional inequality.
Chile has seen a decline in its level of regional
inequality during 1987–94. Its weighted CV has
Table 6. Beta convergence results in unitary countries
Country
Romania
Uzbekistan
Chile
Sri Lanka
Vietnam
Philippines
Indonesia
China
Thailand
*
Time
period
1993–96
1996–99
1987–94
1990–95
1990–97
1982–86
1986–90
1990–94
1994–98
1983–92
1993–98
1978–90
1990–95
1993–99
1991–97
Significant at 10% level.
Significant at 5% level.
***
Significant at 1% level.
**
Independent variable
log(1993 per capita
log(1996 per capita
log(1987 per capita
log(1990 per capita
log(1990 per capita
log(1982 per capita
log(1986 per capita
log(1990 per capita
log(1994 per capita
log(1983 per capita
log(1993 per capita
log(1978 per capita
log(1990 per capita
log(1993 per capita
log(1991 per capita
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
GRDP)
Beta
p-Value
R-Square
# of
regions
)0.014
)0.058
)0.155
0.095
)0.068
)0.103
0.063
)0.048
0.002
)0.164
)0.03
)0.229
0.011
0.052
)0.014
0.936
0.373
0.068
0.619
0.652
0.023
0.499
0.439
0.959
0
0.356
0
0.882
0.273
0.821
0.001
0.067
0.271
0.053
0.004
0.388
0.04
0.051
0
0.505
0.034
0.393
0
0.044
0.011
8
14
13
7
52
13
13
14
15
27
27
28
29
29
7
1434
WORLD DEVELOPMENT
1.8
1.6
1.4
Weighted cv
1.2
1
0.8
0.6
0.4
0.2
0
1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Chile
Romania
China
Sri Lanka
China (new)
Thailand
Indonesia
Uzbekistan
Philippines
Vietnam
Figure 6. Regional disparity trends in unitary countries (weighted coefficient variation).
0.5
0.45
0.4
Weighted gini index
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Chile
Romania
China
Sri Lanka
China (new)
Thailand
Indonesia
Uzbekistan
Philippines
Vietnam
Figure 7. Regional disparity trends in unitary countries (weighted Gini index).
been around three times that of the United
States. The decline may be because ChileÕs development stage is on the right side of the
‘‘inverted U.’’ Moreover market liberalization
in Chile may be causing convergence. Economic activity is heavily concentrated in the
central region. Centralizing trends however
appear to have stopped, as a result of the
mining boom in the north and the economic
dynamism achieved in the extreme south by
salmon breeding, tourism and large-scale
methanol production. Tourism and export ag-
BRIDGING THE ECONOMIC DIVIDE
1435
0.45
0.4
0.35
Theil index
0.3
0.25
0.2
0.15
0.1
0.05
0
1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Year
Chile
China
China (new)
Indonesia
Philippines
Romania
Sri Lanka
Thailand
Uzbekistan
Vietnam
Figure 8. Regional disparity trends in unitary countries (Theil index).
riculture are strong engines of growth in the
center-north, while forestry, tourism, fruit
production and traditional agriculture are important to the center-south regions. Further,
the traditionally strong centralist bias is being
reversed through a gradual devolution of power
to the municipalities, regional offices of ministries and public agencies, and other regional
organizations (EIU, Country Profile).
Although Sri Lanka has a lower level of inequality among the unitary countries, it is more
unequal than India. Its weighted CV is almost
four times that of the United States. Sri Lanka
saw a decline in regional inequality in 1991, after
which it has steadily increased. Inadequate infrastructure development in rural areas, the
concentration of industry close to the main ports
and the airport, and the poor performance of the
agricultural sector have led to an unequal distribution of the benefits of economic growth
between regions. Because of its proximity to the
port and airport, Western province is home to
85% of industry and generates over 40% of
GDP, while predominantly agricultural areas
such as Northwestern and Uva provinces remain
backward due to the stagnation in coconut and
domestic agriculture (EIU, Country Profile).
Vietnam has seen a dramatic rise in regional
inequality in the 1990s. The weighted CV has
almost doubled during this period. There was a
big spurt in inequality over 1990–92, after
which it increase at a slower pace till 1995,
1995–97 has seen another big increase. In 1990,
VietnamÕs weighted CV was more than three
times as that of the United States. In 1997, the
weighted CV became more than eight times
that of the United States. VietnamÕs economy
has grown strongly during the 1990s after it
started on a policy of economic liberalization in
the late 1980s. Its performance since 1989 was
close to that of China. Since economic reform
began in 1986, Ho Chi Minh City and the
nearby provinces (especially Dong Nai and
Binh Duong) have consolidated their position
as the countryÕs industrial heartland, although
the Hanoi-Haiphong area has grown equally
quickly over the past few years. Left behind are
the mountainous areas of the north and most of
the north-central coastal provinces, which have
traditionally been the poorest parts of the
country. The widening regional inequality is
counterbalanced to a limited extent by the
budget, which raises revenue mainly in the
wealthier areas but spreads the expenditure (on
infrastructure, health, and education) more
widely.
There is no major discernible trend in regional inequality in The Philippines according
to the weighted measures during 1982–98. Inequality increased in 1983, then declined till
about 1986, after which it increased till 1989.
After 1989 there was a decline till about 1995,
1436
WORLD DEVELOPMENT
and then there has been an increase during
1995–98. The weighted CV of The Philippines
has varied from about three and a half times
that of the United States in 1990 to about four
and a half times in 1997. The weighted measures, which were higher than the unweighted
ones in the 1980s have fallen below the unweighted measures in the 1990s. This might
mean that most of the bigger population regions have tended to converge nearer the mean
per capita GRDP, while the smaller population
regions have moved to the extremes. Further,
the relative mean deviation is significantly different from the weighted CV, which would
mean that there are a few extreme outliers.
The figures for overall GDP of P2.42trn
(US$85.7bn) and for GDP per head of P32,961
(US$1,166) in 1997 conceal a wide disparity in
wealth between different regions of the country.
The National Capital Region (NCR), the region centered on Manila, accounts for 14% of
the population and produces one-third of
GDP. Its per capita GRDP is well over double
the national average. Only two other regions––
South Tagalog and Cordillera Administrative
Region––have GRDP per head that is above
the national average, while four register around
half that figure and the four autonomous
provinces in Mindanao only one-third. This
reflects the concentration of manufacturing
activities in the Manila area. Growth points
however have been developing in other regions,
where industrial parks have been the focus for
much investment, both domestic and foreign, in
recent years. 20
Indonesia has seen a constant drop in regional inequality since 1983. The drop was
more pronounced till about 1992, after which
the decline slowed. With the 1997 economic
crisis, regional inequality increased slightly in
1997 and 1998. In 1983, Indonesia was almost
four times as unequal as India, in 1985, its
weighted CV was almost twice as unequal as
Brazil, and in 1990, it was five times as unequal
as the United States. In 1997, IndonesiaÕs
weighted CV was three-fourths more than that
of India, around 55% more than that of Brazil
and six times that of the United States. In other
words, Indonesia has improved its position
relative to India and Brazil, but in the 1990s its
rate of convergence has been slower than that
of the United States.
Akita and Lukman (1995) also find a constant decline in the weighted CV during 1975–
92. When they exclude the mining sector,
however, no trend is discernible in regional in-
equality. They conclude that the decreasing
weighted CV could be attributed to the decline
of the mining sectorsÕs share in GDP. Akita and
Lukman also find that the contribution of the
tertiary sector to regional inequality has declined during this period while that of the secondary sector has increased. Indonesia has
averaged an annual growth rate of 6% over
1970–96.
Asra (1989) estimates the Gini index for regional inequality for expenditure for 1969–81.
He finds that if one adjusts the standard inequality measures for the differential impact of
inflation on different expenditure groups, regional inequality in Indonesia has increased
during 1969–76 and then declined during 1976–
81.
Garcia and Soelistianingsih (1998) find evidence for both r convergence and b convergence for per capita GRDP during 1975–93.
But they also find that regions at the top and
bottom of the distribution in 1983 remained at
the top and bottom of the distribution in 1993.
Our dataset also shows the same result for
1983–98––the top and bottom regions remained
the same.
Regional inequality in China fell over 1978–
90 after which there was an increase till 1993.
Inequality fell again during 1993–96 and then
increased till 1999. The weighted CV for China
has varied from around five times that of the
United States in 1990 to around eight times in
1999. Its weighted CV has been higher than
almost all other countries in our sample. Only
Thailand in the early to mid-1990s and Vietnam in the late 1990s has had a higher value for
the weighted CV.
On other weighted measures of inequality––
the weighted Gini index and the Theil index––
China has performed better than Indonesia,
Thailand, Vietnam and the Philippines. This is
intriguing especially as the weighted CV for
China is higher than the unweighted CV, while
the weighted Gini index is lower than the unweighted Gini index. This could be due to the
presence of provinces with larger populations at
the lower end of the distribution. In fact almost
70% of the population is in provinces with per
capita GRDP below the national average. The
median per capita GRDP is significantly lower
than the mean per capita GRDP. For example,
the national mean GRDPs per capita in current
prices for 1978 and 1999 are Rmb 357 (US$210)
and Rmb 7242 (US$826) respectively. The corresponding population weighted medians are
Rmb 313 (US$184) and Rmb 5400 (US$621)
BRIDGING THE ECONOMIC DIVIDE
respectively. This would skew the estimates of
weighted CV, which is also evident from the fact
that the relative mean deviation is significantly
lower than the weighted CV.
Several authors have looked at regional inequality in China. Jian et al. (1996) have found
that the economies of Chinese provinces converged somewhat during 1952–65, then diverged during the cultural revolution years of
1966–77. After 1977 till about 1990, during the
reform period, they find statistically significant
evidence for b convergence. Raiser (1998) also
finds evidence of b convergence after 1978 till
about 1990. Both studies find that the economies of the Chinese provinces diverge after
1990. A look at r convergence yields a similar
picture. Jian et al., find that the standard deviation of log of real per capita GRDP fell
slightly during 1952–65, increased from 1966–
77 and fell significantly during 1977–90. The
period after 1990 has seen an increase in the
dispersion of per capita GRDP. Raiser finds
similar results during 1978–92 using the CV.
Jian et al. (1996) suggest that there were two
forces at work during 1952–65, a government
induced bias against agricultural regions causing divergence, and unidentified forces pushing
toward convergence leading to slight overall
convergence. During 1966–77, the autarkic
years of the cultural revolution led to divergence, while the market reforms after 1978 led
to convergence. But, Jian Sachs and Warner,
point out that almost all of the convergence is
due to a narrowing of inequality among the
coastal provinces, rather than a narrowing of
inequality between the coastal and interior
provinces or a narrowing of inequality in the
interior provinces. Raiser (1998) finds similar
results. The divergence between the coast and
the interior is in large part due to the special
economic privileges (in tax policy and trade
policy) granted to the coastal regions as part of
the economic reforms. The contradiction between the poor, inaccessible and inhospitable
terrain of most of the interior and the more
fertile coastal deltas and plains has been exacerbated by these reforms. The coastal areas
have been far more able to achieve rapid
growth, while the interior provinces have been
left behind.
In our sample, Thailand has the highest level
of regional inequality. Its level of inequality has
slightly declined during 1991–97. The weighted
CV has varied from seven times that of the
United States in 1991 to about a little more
than seven and a half times in 1997. The
1437
weighted measures have been greater than the
unweighted measures throughout this period,
signifying that large population regions are at
the extreme ends of the distribution.
Although rapid growth rates have led to a
steady rise in real income levels per head over the
last 30 years, there are alarming disparities in
national wealth distribution. Per capita GRDP
in the northeast is only 52% of the national
average and just over 20% of that in the Bangkok Metropolitan Area. Recent governments
have responded by pushing economic decentralization, generally with disappointing results.
This could be due to the strong role of the center
in regional development. Since 1993 about 65%
of promoted investment has gone outside
Bangkok, but most only reached the Eastern
Seaboard Industrial Zone, an hour or so from
the city. The Board of Investment (BOI) responded in 1997 by setting up the countryÕs first
special economic zones (SEZs) in 13 of the
poorest provinces, but there had been little response by early 1999 because of a perception of
poor infrastructure in these areas. Tax incentives will be targeted at 21 industries located in
the zones. Specialist free-trade zones are also
being established for more technologically advanced industries. The Eighth Five-Year Development Plan incorporates plans for
decentralization, and the old zones system seems
likely to be replaced by more localized incentives. 21
5. REGIONAL INEQUALITIES AND
CONVERGENCE––A SCORECARD ON
NATIONAL POLICIES FOR REGIONAL
DEVELOPMENT
The empirical analysis presented in the earlier
sections is summarized in Table 7. The following conclusions emerge from this analysis.
Regional development policies have failed in
almost all countries––federal and unitary
alike––to reduce regional inequalities. 22 Of the
10 countries listed in Table 7 with high or substantial degree of regional inequalities, only one
country (Thailand) has experienced convergence
in per capita GRDPs. Federal countries, however, do better in restraining regional inequalities. This is perhaps due to three broad reasons.
First, widening regional disparity poses a greater
political risk in federal countries. In such countries inequalities beyond a threshold may lead to
calls for separation by both the richest and the
poorest regions. While the poorest regions may
1438
WORLD DEVELOPMENT
Table 7. Regional inequalities and convergence: a summary viewa
Country
Federal [F]/
unitary [U]
Degree of regional
inequality
Trends:
sigma convergence [C]/
divergence [D]/
stable [S]
Trends:
beta convergence [C]/
divergence [D]/
stable [S]
Vietnam
Thailand
U
U
High
High
D (90–97)
C (91–97)
S) (90–97)
S) (91–97)
China
U
High
C (78–90)
D (90–93)
C (93–96)
D (96–99)
C (78–80)
S+ (90–95)
S+ (93–99)
Indonesia
U
High
C (83–95)
D (95–98)
C (83–92)
S) (93–98)
Russia
Mexico
F
F
High
Substantial
D (94–97)
S (93–98)
S+ (94–97)
S) (93–98)
Philippines
U
Substantial
C (82–86)
D (86–89)
C (89–95)
D (95–98)
C (82–86)
S+ (86–90)
S) (90–94)
S+ (94–98)
Brazil
F
Substantial
D (85–89)
C (89–94)
D (94–97)
S) (94–97)
Sri Lanka
India
Uzbekistan
Chile
France
Italy
Spain
Former
West
Germany
United
Kingdom
Romania
Pakistan
Canada
United
States
U
F
U
U
U
U
U
F
Substantial
Moderate to substantial
Moderate
Moderate
Moderate
Moderate
Low
Low
D (90–95)
D (80–97)
C (96–99)
C (87–94)
S+ (90–95)
D (80–97)
S) (96–99)
C (87–94)
U
Low
U
F
F
F
Low
Low
Low
Low
D (93–96)
C (90–98)
S (94–98)
C (90–97)
S) (93–96)
S) (94–98)
C (90–97)
a
In the beta convergence column a + ()) sign after S denotes a positive (negative) coefficient on the log of initial
GDP, but this coefficient is not statistically significant at the 10% level. ‘‘High’’ signifies countries with weighted CVs
greater than 0.6 during all or most of the years included in our analysis. ‘‘Substantial’’ includes countries with
weighted CVs from 0.4 to 0.6. ‘‘Moderate’’ includes countries with weighted CVs from 0.25 to 0.4. ‘‘Low’’ includes
countries with weighted CVs less than 0.25.
consider such inequalities as a manifestation of
regional injustice, the richest regions may view a
union with the poorest regions possibly holding
them back in their drive to prosperity in the long
run. It is true that such secessionist pressures
exist even in centralized unitary countries (for
example, Indonesia), however, the presence of
relatively autonomous political entities at the
federal level could increase the power of local
secessionist forces in the face of large disparities.
Centralized countries can also suppress such
tendencies using force rather than regional policies of balanced development. A case in point is
China, where a central government driven by
BRIDGING THE ECONOMIC DIVIDE
growth concerns has focused on fewer regions
for development, in contrast to federal India,
where the central government has had to invest
in a more regionally dispersed manner due to
regional political pressures.
Second, national political parties have to
compete politically in the regions in federal
countries, therefore, they may have to focus on
a more equal development of the regions.
Third, and perhaps the most important, relatively autonomous regional governments accountable to the local electorate have greater
compulsions to follow developmental policies
as compared to a more remote center. As a
related argument, competition among regional
governments may also lead to greater regional
equality.
The table further provides the following
classification of countries by the degree of
convergence.
––Countries experiencing regional income divergence: Vietnam, China in the 1990s, Indonesia post-1993, Russia, The Philippines in
late 1980s and late 1990s, Brazil, Sri Lanka,
India and Romania.
––Countries experiencing no significant change
in regional income variations: Mexico and
Canada.
––Countries experiencing regional income
convergence: Thailand, China till early
1990s, Indonesia till early 1990s, The Philip-
1439
pines in early 1980s and early 1990s, Uzbekistan, Chile, Pakistan and The United States.
Regional development outcomes observed
here provide a revealing look at the impact of
regional development policies. 23 For example,
countries experiencing divergence by and large
focus on interventionist policies for regional
development. On the other hand, countries
experiencing convergence have had a hands-off
approach to regional development policies and
instead focused on policies to promote a common economic union through removal to barriers to factor mobility and ensuring minimum
standards of basic services across the country.
For example, regional income convergence in
Chile is largely attributable to economic liberalization and removing distortions in the
economy so that regions could discover their
own comparative advantage in the economic
union. In Pakistan and United States such
convergence is attributable to greater factor
mobility rather than region specific policies. On
the other hand, in a country such as China with
a more centralized top-down structure, barriers
to factor mobility might have hampered reductions in regional disparity between coastal
and interior regions in the 1990s. This leads us
to conclude that, paradoxically, creation of a
level playing field is more helpful to disadvantaged regions than following paternalistic protectionist policies.
NOTES
1. This number includes both the former West Germany and unified Germany.
2. Wherever we mention regional income, we refer to
the gross regional domestic product, unless otherwise
specified.
3. Some examples include Akita and Lukman (1995),
Decressin (1999), Dev (2000), Lyons (1991), Nagaraj,
Varoudakis, and Veganzones (2000), Raiser (1998),
Sacks (1999), Tsui (1996), and Williamson (1965).
4. Bajpai and Sachs (1996), Cashin and Sahay (1996),
Garcia and Soelistianingsih (1998), and Jian, Sachs, and
Warner (1996).
5. Tsui (1996) and Yao and Liu (1998).
6. Usually r convergence is measured by looking at
changes in CV or standard deviation of over time. For
the latter, authors such as Bajpai and Sachs (1996) have
taken the standard deviation of the logs of per capita
GRDPs. We look at trends in all our inequality
measures, which as expected, are consistent with each
other.
7. We do not include any control variables to explore
conditional convergence as this is beyond the scope of
this paper. Conditional convergence analysis would
require substantial data on relevant variables for each
country. Further, the ‘‘conditions’’ in the conditional
convergence could vary country by country. For example, convergence may be hampered by structural differences in regional economies, human capital differences,
interregional taxation, the interregional transfer structure, differences in political structure and so on. Therefore, a fruitful analysis of these ‘‘conditions’’ within any
country would require a separate paper, which is one
reason most empirical papers on regional convergence
focus on a single country.
1440
WORLD DEVELOPMENT
8. Except for Nepal and Uganda as mentioned in the
next footnote. We did not have GRDP figures for these
countries.
9. Details of the data sources and regional disparity
trends are available from the authors upon request.
10. The computations for Uganda are based on regional household expenditure data. The computations
for Nepal are based on household income data. Therefore
we do not use computations from these two countries for
comparisons with other countries in Figure 2.
11. This is analogous to but different from KuznetsÕ
(1955) ‘‘inverted U’’ hypothesis, which pertains to
overall income inequality and not regional inequality.
12. All the countries in our sample, except Uganda,
Nepal and Poland, were used in the regression analysis.
We could not obtain data to calculate the weighted
measures of inequality for these countries.
13. We have to be careful in interpreting the results of
these regressions as the number of data points was limited. The consistent positive sign on the unitary dummy
does however question the conventional wisdom of
centralization being good at reducing regional inequality.
14. The countries included are the United States,
Canada, India, West Germany, Brazil, Mexico, Spain,
Russia, Italy, France, Chile, United Kingdom, Thailand,
Romania and Indonesia.
15. We present trends only in the measures weighted
by population in the interests of space. We discuss
differences between weighted and unweighted measures
for any country if these differences have interesting
implications. We do have detailed calculations of
unweighted measures as well as weighted ones by
country and year, which interested readers can obtain
from the authors upon request.
16. The GRDP figures for Pakistan actually span the
fiscal year, which is spread over parts of two calendar
years. But as the greater part of the fiscal year falls in the
earlier calendar year (for example, the greater part of
1990–91 falls in 1990), we use the earlier calendar year
(1990 for 1990–91) in showing the computations for
Pakistan in the figures and the tables. This is done for
purposes of convenience only.
17. This is consistent with WilliamsonÕs (1965) ‘‘inverted U’’ thesis that inequalities widen in the early
development stages, while mature development produces
divergence.
18. As in the case of Pakistan, the GRDP figures for
India actually span the fiscal year, which is spread over
parts of two calendar years. But as the greater part of the
fiscal year falls in the earlier calendar year (for example,
the greater part of 1990–91 falls in 1990), we use the
earlier calendar year (1990 for 1990–91) in showing the
computations for India in the graphs and the tables. This
is done for purposes of convenience only.
19. We could not do b convergence analysis for years
prior to 1994 due to the fact that Brazil has changed its
currency four times during this period and seen astronomical levels of inflation. We did not have the
appropriate deflators. On the other hand, the r convergence calculations are unitless and do not depend on the
unit of currency, as long as it is the same across regions.
Of course, we have had to assume no differences among
regions in price index changes.
20. Data and information taken from the EIUÕs
Philippines Country Profile.
21. Data and information taken from the EIUÕs
Thailand Country Profile.
22. It is possible that regional inequalities might have
been worse in the absence of these policies. It is beyond
the scope of this paper to test counterfactual scenarios,
which in any case would have to be done on a countryby-country basis. What we are saying here is not that
countriesÕ regional policies have no effect, but that they
have not in most cases, reduced regional inequalities.
23. Shankar and Shah (2003) delve deeper into the
design of such policies and their impacts.
REFERENCES
Akita, T., & Lukman, R. A. (1995). Interregional
inequalities in Indonesia: a sectoral decomposition
analysis for 1975–92. Bulletin of Indonesian Economic
Studies, 31(2), 61–81.
Asra, A. (1989). Inequality trends in Indonesia, 1969–
1981: a re-examination. Bulletin of Indonesian Economic Studies, 25(2), 100–110.
Bajpai, N., & Sachs, J. D. (1996). Trends in inter-state
inequalities of income in India. Development Dis-
cussion Paper no. 528. Harvard Institute for International Development, Cambridge, MA.
Barro, R. J., & Sala-i-Martin, X. (1992). Convergence.
Journal of Political Economy, 100, 223–251.
Barro, R. J., & Sala-i-Martin, X. (1995). Economic
growth. New York, NY: McGraw-Hill.
Boldrin, M. (2001). Inequality and convergence: reconsidering European regional policies. Economic Policy, 16(32), 205–253.
BRIDGING THE ECONOMIC DIVIDE
Cashin, P., & Sahay, R. (1996). Regional economic
growth and convergence in India. Finance & Development, 33(March), 49–52.
Das, S. K., & Barua, A. (1996). Regional inequalities,
economic growth and liberalisation: a study of the
Indian economy. The Journal of Development Studies
UK, 32(February), 364–390.
Decressin, J. (1999). Regional income redistribution and
risk sharing: how does Italy compare in Europe?
IMF Working Paper WP/99/123. International Monetary Fund, Washington, DC.
Dev, S. M. (2000). Economic reforms, poverty, income
distribution and employment. Economic and Political
Weekly, 35(10), 823–835.
Economist Intelligence Unit (various issues). Country
profiles.
Garcia, J. G., & Soelistianingsih, L. (1998). Why do
differences in provincial incomes persist in Indonesia? Bulletin of Indonesian Economic Studies, 34(1),
95–120.
Giugale, M. M., Nguyen, V., Rojas, F., & Webb, S. B.
(2000). Overview. In M. M. Giugale, & S. B. Webb
(Eds.), Achievements and challenges of fiscal decentralization––lessons from Mexico. Washington, DC:
The World Bank.
International Monetary Fund (various issues) Government Financial Statistics. Washington, DC: IMF.
Jian, T., Sachs, J., & Warner, A. M. (1996). Trends in
regional inequality in China. Working Paper 5412.
Cambridge, MA: National Bureau of Economic
Research.
Kakwani, N. C. (1980). Inequality and poverty––
methods of estimation and policy applications.
The World Bank. New York: Oxford University
Press.
Kakwani, N. C. (1990). Large sample distribution of
several inequality measures––with application to C^ote
dÕIvore. Washington, DC: The World Bank.
Krugman, P. (1991). Increasing returns and economic
geography. Journal of Political Economy, 99(3), 483–
499.
Kuznets, S. (1955). Economic growth and income
inequality. American Economic Review, 45, 1–28.
1441
Lyons, T. P. (1991). Interprovincial disparities in China:
output and consumption, 1952–87. Economic Development and Cultural Change, 39(April), 471–506.
Musgrave, R. (1959). The theory of public finance––A
study in public economy. New York: McGraw-Hill.
Nagaraj, R., Varoudakis, A., & Veganzones, M. A.
(2000). Long run growth trends and convergence
across Indian states. Journal of International Development, 12(1), 45–70.
Oates, W. E. (1972). Fiscal federalism. New York:
Harcourt Brace Jovanovich.
Raiser, M. (1998). Subsidizing inequality: economic
reforms, fiscal transfers and convergence across
Chinese provinces. The Journal of Development
Studies, 34(3), 1–26.
Rao, M. G., Shand, R. T., & Kalirajan, K. P. (1999).
Convergence of income across Indian States. Economic and Political Weekly, 34(March 27).
Romer, P. (1990). Endogenous technical change. Journal
of Political Economy, 98, S71–S102.
Sacks, M. P. (1999). Regional inequality and branch
employment in Russia between 1990 and 1995. PostCommunist Economies, 11(2), 149–159.
Shankar, R., & Shah, A. (2003). Regional development
policies in India: helping regions or building transfer
dependencies? Processed (in draft). Washington, DC:
The World Bank.
Theil, H. (1967). Economics and information theory.
Amsterdam, Netherlands: North-Holland Publishing
Company.
Tsui, K. (1996). Economic reform and interprovincial
inequalities in China. Journal of Development Economics, 50(August), 353–368.
Yagci, F. (1999). Narrowing inter-state disparities agenda
for fiscal and sector reforms. Washington, DC: The
World Bank.
Williamson, J. G. (1965). Regional inequality and
process of national development: a description of
the patterns. Economic Development and Cultural
Change, XIII(4, Part II), 2–84.
Yao, S., & Liu, J. (1998). Economic reforms and
regional segmentation in rural China. Regional
Studies, 32(8), 735–746.