Forthcoming in World Development
Are Economic Liberalization and Equality Compatible? Evidence from South Asia
Udaya R. Wagle
School of Public Affairs and Administration
Western Michigan University
Kalamazoo, MI 49008
Phone (269) 387-8934; Fax: (269) 387-8935
Udaya.wagle@umich.edu
ABSTRACT
Global studies of liberalization and inequality have produced divergent findings arguably because of highly
heterogeneous contexts across countries and regions. This paper focusing on the South Asian experience with
more homogeneous contexts finds that liberalization efforts and inequality grew in the region between 1980
and 2003. Data support a mutually reinforcing positive relationship in the region suggesting that liberalization
helps increase inequality, which in turn serves as a precondition to liberalize. Because economically unequal
countries tend to liberalize more intensely, possibly hurting the poor, a lesson for concerned policymakers is to
introduce policies to incrementally advance economic openness.
Keywords: Economic liberalization; Economic inequality, Comparative analysis; Panel data; South Asia
1. OVERVIEW
Economic liberalization has been sine qua non to economic growth and prosperity in today’s global epoch. It
has been a much-favored strategy among governments in developing countries seeking to harness the economic
opportunities provided by the global market. International financial institutions advocate it as a strategy to help
developing countries accomplish more efficient economic management. But policymakers in developing
countries also find reasons to be hesitant of the liberalization path suspecting unintended negative
consequences.
Economic inequality is one area in which the effect of liberalization has been highly controversial. Partly, this
has to do with a dearth of consistent data, especially in case of developing countries. Even in advanced
developed countries on which more consistent, time series data are available, however, it has been difficult to
extract a conspicuous universal trend.1 Part of the reason is extreme heterogeneity of culture and history, policy
efforts to liberalize, and experiences with economic growth and structures. No two countries, for example, can
have exactly identical inequality outcomes despite comparable degrees of liberalization. Because countries
within a region tend to manifest homogenous cultural and historical contexts, regional level analyses are needed
to derive more conclusive findings.
A host of factors feed into economic inequality, the process of one’s integration into the global economy
being just one. Given the unprecedented progress toward the integration of poor, developing economies into the
global economic system, it has been enormously important to disentangle the effect of liberalization from the
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total effect that goes into creating the type and magnitude of inequality that exists today. Similarly, while most
studies examine the role of economic liberalization on inequality, how the latter contributes to the former also
deserves a due treatment. Any evidence of bidirectional relationship would suggest the existence of a selfperpetuating cycle.
This paper investigates the relationship between economic liberalization and inequality in South Asia. Using
data from different sources covering 1980 to 2003, the motivation is to quantify the degree of liberalization and
inequality in each of the major countries in the region—Bangladesh, India, Nepal, Pakistan, and Sri Lanka—
and examine how, if any, the former affects the latter. Because of the potential mutually reinforcing dynamics,
however, I also intend to test for a bidirectional relationship between the two in the region. My expectation is
that the greater cultural and historical similarities across the countries will help meaningfully disentangle the
relationship using time series analysis.
This paper is organized as follows. Next section focuses on the broader context of liberalization and
inequality with section three providing an overview of the social and political contexts of the region. Data and
variables are described in section four. Sections five and six highlight the degree of liberalization and
inequality, while section seven estimates models and presents results. Findings are discussed in section eight
and the final section concludes with directions for future research.
2. THE LIBERALIZATION AND INEQUALITY NEXUS
No country can remain untouched today by the universal march toward ‘global village,’ perceived to provide
unprecedented opportunities. From education and health to technology, integration into the global village offers
countries with benefits that are impossible without it (Sen 2002). Given its comprehensive nature, however, the
everyday, all-inclusive concept of globalization is neither quantifiable enough nor highly relevant for studies of
economic inequality. Countries can be integrated culturally, economically, environmentally, politically, and so
on with profound implications on the relationships with other countries in the world as well as on the domestic
order of management (Kearney/Foreign Policy, 2005; Scrase, Holden, and Baum, 2003). But what really
matters in terms of economic order of society is the economic integration. Some look at economic integration
from ‘dependence’ standpoint in which global economic hegemons are finding an alternative strategy to
maintain their colonial relationships with developing countries (Alderson and Nielsen, 2003; Dos Santos, 1970;
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Goldsmith, 1996). Others see it as an opportunity for developing countries to accelerate their sluggish
economic growth, more efficiently manage their economies, and improve the lot of the masses (Firebaugh,
2003b; Masson, 2001; World Bank, 2005a). Regardless of the form of integration, however, it is the economic
liberalization policies that drive the process of integration necessarily producing economic implications for the
countries involved.
Studies from the World Bank (Dollar and Kraay, 2001a; Pigato, Farah, Jun, Martin, Murrell, and Srinivasan,
1997; World Bank, 2005a), International Monetary Fund (Masson, 2001), and other international financial
institutions show that countries more deeply integrated into the global system have performed better in
economic growth and development. Neoclassical arguments that international trade and reduced public
spending lead to higher economic growth is widely embraced as economies look for comparative advantage,
economies of scale, diversification, and technological innovation. The case of East Asian countries spearheaded
by Singapore, for example, shows that rapid economic growth likely follows efforts to liberalize the economy.
Given the affirmative role of liberalization on boosting economic growth, what has been highly contested is
how the benefit of growth is to be distributed in society. Neoclassical economics suggests that expanding
exports enable entrepreneurs and workers to realize increased earnings and the government to realize increased
tax base and foreign currency. The multiplier effect in turn percolates in the economy, thus making everyone
better off. Many studies find supporting evidence with income rising for both rich and poor and absolute
poverty declining worldwide (Dollar, 2004; Dollar and Kraay, 2001a, 2001b; Firebaugh, 2003a; Masson, 2001;
Pigato et al., 1997; World Bank, 2005a). But with the assumption that economic equality is preferred over
economic inequality as a socially desirable goal,2 whether economic liberalization exacerbates, alleviates, or
does not alter inequality is difficult to answer.
Ideally, increased economic activities prompted by liberalization will help expand employment and increase
earnings, which then would reduce inequality. The classic Stopler-Samuelson (1941) hypothesis also suggests
that the poor with abundant labor power in developing countries are likely to gain from international trade.
While globalization may also accentuate the Mathew effect3 and create both winners and losers with the latter
knocking the door of the state for support, one can hope that economies will be more capable to cope with
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different kinds of economic shocks and to develop a welfare state that reduces inequality (Amis, 1994; EspingAndersen, 1990, 1996; Mahler, 2004).
Critics are not fully satisfied, however. The notion of fiscal discipline, for example, may disallow
governments to increase state spending and introduce progressive tax structures. As Goldberg and Pavcnik
(2004) succinctly summarize, liberalization may escalate earnings inequality by increasing ‘skills premium’
and wages and by expanding the informal sector.4 Although whether or not presence of firms owned by
foreigners increases wages is debatable, te Velde and Morrissey’s (2003) work in Africa shows that it allows
‘rent-sharing’ among the skilled workers thus increasing inequality.
Studies have provided highly divergent findings on the effect of liberalization on inequality. For example,
Firebaugh (2003b, 2003c), O’Rourke (2001), and Sala-i-Martin (2002) find global economic inequality to have
fallen, whereas Alderson and Nielsen (2003), Bourguignon and Morrison (2002), Milanovic (2005a), and Wade
(2004) find it to have risen. And yet, Dollar (2004, 2005) and Dollar and Kraay (2001b) find no clear crossnational pattern, whereas Lindert and Williamson (2001) find inequality to have risen but argue that it is not
due to liberalization. This divergence of findings has to do with more than what the raw data suggest. First, data
especially on developing countries are subject to large measurement errors, warranting a high degree of
unreliability. Second, inequality data including Gini index and population share of resources are highly
incomparable across countries as the ‘basis of inequality’ can be income, consumption, expenditure, and
wealth, coupled by the ‘unit of analysis’ that can be people, households, or countries.5 Third, extremely
heterogeneous contexts across regions and countries lead to estimates that are highly unreliable with large
margins of error. Fourth, the concepts of within and between country inequality and more recently inequality
across individuals worldwide6 greatly affect the findings as globalization may make rich richer and poor poorer
in a country and yet help mitigate inter-country disparities.7 Comparative analyses, as Ravallion (2003, 2004)
accurately asserts, need to ensure comparability of data across countries and over time.
3. WHY SOUTH ASIA?
It is this last set of methodological incongruities that indicate that more focused studies like this are needed to
disentangle the relationship between liberalization and inequality. Data on South Asian countries are more
comparable especially when they are based on similar ‘basis of inequality’ and ‘unit of analysis.’ This region
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also manifests more homogeneous historical and cultural contexts with increasingly dissimilar policy
experiences and inequality outcomes, thus leading to more conclusive findings.
South Asia is one of the underdeveloped and yet economically important regions in the world. The economic
performance of the five countries in South Asia is uniformly unsatisfactory with average per capita gross
domestic product (GDP) of US$523 in 2003, with the lowest of $241 (Nepal) and highest of $921 (Sri Lanka).8
The entire region brings a history of adopting import substitution policies up until the mid 1980s and all
countries in the region have a track record of liberalization that started in mid or late 1980s, with the exception
of Sri Lanka, which embraced the liberalization path in the 1970s. As Pigato et al. (1997) noted in the second
half of the 1990s, for example, South Asia remained one of the least integrated regions in the world. While the
move in the region to liberalize economies did effect faster growth (Ahluwalia and Williamson, 2003), in none
of the countries has the liberalization effort been so massive that would go at par with the neighboring country
China. Despite not so ‘encouraging’ signs of economic openness, however, advocates of global capitalism view
this region to be strategically important. In addition to providing cheap labor needed to produce goods for
export, its massive population (1.4 billion as of 2003) has the potential to create enormous demand to absorb
goods from other countries and regions (Vicziany, 2004).
South Asia has also remained one of the highly politically unstable regions in the world. With exception of
India, countries in this region are far from practicing ‘representative democracy.’ Ethnic tensions and human
rights abuses are widespread throughout the region and, since its inception, Freedom House (2005) has
consistently ranked all five countries as “partly free” with some exceptions especially for India between 1991
and 1998.9 Corruption has plagued the entire subcontinent with all countries being at or above the 91st rank out
of 146 countries included in the Transparency International’s Annual Report (2004).10
These economic and political realities, along with other social and cultural similarities including in health,
education, and civic activism, make South Asia a highly appropriate setting to study the relationship between
liberalization and inequality. While this region may have some idiosyncratic properties and experiences, their
appropriate contextualization allows reasonable confidence that findings from this longitudinal analysis will
provide a consistent direction for the entire region as well as for the individual countries included.
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4. HYPOTHESIS AND DATA
(a) Operational issues
Liberalization represents a broad set of policy attempts to open up the economy for integration into the world
systems. But this analysis concentrates on the economic aspect of liberalization. While an array of indicators is
potentially useful to measure the degree of liberalization, studies typically focus on foreign direct investment
(FDI), export, and import as its chief indicators (Alderson and Nielsen, 2002, 2003; Firebaugh, 2003b; Kentor,
2003; Mahler, 2004; Pigato et al., 1997; Wade, 2004). This analysis captures a more accurate degree of
liberalization by using a broader set of indicators including FDI, external debt, debt service, export, and import.
Given that policy efforts to liberalize an economy include laws, tariffs, tax structures, and foreign loans, one
can argue that the indicators used here mostly focus on the outcome aspect of liberalization. At the same time,
however, outcome measures can accurately assess the degree of policy efforts to liberalize, as the former
largely follow the latter.
Where as FDI outflow has been used as an indicator of liberalization in advanced countries (Alderson and
Nielsen, 2002; Mahler, 2004), its inflow has enormous relevance in developing countries. Because low-income
countries desperately need external financing to boost economic activities, how much FDI a particular country
has attracted and absorbed can be a reliable indicator of liberalization. Given that countries have some degree
of FDI, however, how efficient a takeoff the private sector can achieve largely depends on the availability and
quality of development infrastructure. It is the role of the government to prepare the economy with the needed
infrastructure. Governments will need tremendous public sector spending, a major part of which in developing
settings comes from external funding; hence the relevance of external debt to measuring a country’s
preparedness with investment finance. Moreover, the role of external debt is crucial together with debt service
indicating that larger the size of external debt and debt service, the more economically integrated will be the
countries (Alderson and Nielsen, 2003). Additionally, increase in import and especially export enables
countries to harness the opportunities available from the global market (Mander 1996). More import and export
would thus be indicative of a country’s deeper degree of integration (Irwin, 2005).
Economic inequality, on the other hand, is a state in which economic resources are not distributed equally in
society. Although its multifaceted character makes it difficult to come up with a single measure, it is important
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to quantify the degree of inequality for a meaningful analysis. I use consumption data as the basis of inequality,
as their availability is consistent across the countries included11 with the individual level of analysis. This is
noteworthy, however, that the use of estimates based on consumption and individual level data may in many
cases attenuate the extent of inequality within countries. Similar effect can be at play as the consumption data
account for government transfer especially in the form of food aid, which is a chief source of consumption for
the poor.12
Where as Gini index (coefficient), Theil index, coefficient of variation, percentile distribution, mean-median
differential, and poverty gap are different ways to gauge inequality, Gini index is by far the most widely
adopted indicator (Alderson and Nielsen, 2002; Bourguignon and Morrison, 2002; Korzeniewicz and Smith,
2000; Mahler, 2004; Sala-i-Martin, 2002). Since these indicators are not exactly identical, using multiple
indicators can improve the measurement accuracy. To maintain theoretical consistency and operational
feasibility, I use Gini index, ratio of consumption for the top to bottom quintile, and poverty incidence at one
dollar a day of income as the indicators of inequality.13 Theoretically, Gini and 80/20 ratio of consumption are
helpful to measure inequality at different levels of distribution. The former indicates the deviation of the entire
distribution from the state of perfect equality whereas the latter indicates the variation at the two moderate
extremes.14 Poverty incidence, moreover, captures absolute inequality, indicating that, given the small variation
in GDP per capita across countries in the region, countries with larger ratios have higher inequality than those
with smaller ratios. Although this is not a precise measure of inequality, the estimates over time indicate
people’s mobility from the lower economic rung in and out of absolute poverty.
(b) Hypotheses
Studies have thus far operationalized unidirectional relationships specifying the effect of liberalization on
inequality. While it is the policy context in individual countries that largely determines liberalization efforts and
outcomes, economic forces may bolster or obstruct this process. Interplay of economic interests giving rise to
inequality, for example, feed into policy preferences thus influencing policy decisions regarding whether and
how rapidly to liberalize. This paper looks at the bidirectional relationships hypothesizing that liberalization
and inequality mutually reinforce each other. This, in other words, is to argue that not only does liberalization
widen the gap between the rich and the poor within countries, more unequal countries are also more likely to
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liberalize faster thus creating a vicious cycle. This vicious cycle may make it difficult for a country to reduce
inequality.
To further enrich the understanding of this relationship with specific mechanisms, I seek to test other
hypotheses involving respective indicators. Since the indicators of liberalization positively contribute to its
measurement and since liberalization is hypothesized to positively affect inequality, I expect these indicators to
be positively associated with inequality. Similarly, I expect positive relationship between liberalization and the
indicators of inequality.
Although liberalization and inequality are endogenous concepts, their relationships cannot be meaningfully
disentangled without controlling for other variables that directly influence them. Time and country identities
are two of such variables on which policies and outcomes largely depend. Other potentially important variables
include GDP, population growth, urban population, and inflation.
(c) Data
I use time series data from the World Development Indicators (WDI) compiled by the World Bank (2005b).
While the dataset includes a comprehensive array of indicators, almost all of the five countries included have
missing values especially for the 1960s and 1970s. For this reason, I use data beginning with the 1980s. This
timeframe is also relevant given its ability to cover the decades marking a major turn in economic policies in
South Asia. While formal opening up of the economy did not begin in every country precisely in 1980, it is
important to capture the trend after as well as prior to the inception of liberalization efforts.
Despite significant stride on generating country estimates on various measures of economic inequality, the
WDI does not include their consistent estimates.15 I draw Gini and consumption ratio data from the World
Income Inequality Database (Version 2.0a) developed by WIDER (2005).16 Since the WIDER takes raw data
from different sources and performs appropriate methodological transformations to increase their validity and
consistency,17 it provides estimates that are of high methodological rigor and relevance. Regardless of the
source of data, however, it is noteworthy that these are mostly survey estimates rather than actual figures, with
important implications on the accuracy of results.
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5. THE DEGREE OF LIBERALIZATION
Table 1 summarizes the estimates of the liberalization indicators for each country in South Asia. It suggests that
the level of net inflow of FDI remained a little over one percent or less of GDP, with averages well below one
percent for all countries and time periods—1980s, 1990s, and 2000-2003. Sri Lanka, the leading country on
FDI, however, attracted highly fluctuating amounts of foreign capital especially prior to 1998, after which the
figures stayed just over one percent. Because of the size of India with over three quarters of the population in
South Asia, FDI appears to have grown more rapidly from the population average standpoint, signifying the
impact for the average person from the region, than from the country average standpoint. Although the overall
size of the economy may inversely affect FDI when expressed as percent of GDP, the extent of FDI in South
Asia being figuratively close to one half of a percent is far below that of China, where FDI culminated at over
six percent in 1993 and settled just below four percent after 1999.
(Insert Table 1 here)
In spite of the relatively low levels of FDI, external debt steadily increased in the region. Starting at little over
six percent in the 1980s, average external debt in these countries reached over 40 percent, with the lowest in
India (22%) and highest in Sri Lanka (65%). South Asians appeared to have been less indebted from the
population standpoint than from the country standpoint apparently because of the smaller external debt in India.
Despite the small size of FDI, however, South Asia had relatively higher external debt averaging well over 40
percent on a per capita basis for 2000-2003 compared to 14 percent in China.18
Estimates of debt service paint a slightly different picture. While Pakistan and Sri Lanka already had
relatively higher proportion of the gross national income (GNI) going to debt service by the 1980s, country
estimates in South Asia range between one and six percent of GNI. Consistent with the level of FDI,
Bangladesh and Nepal had less than two percent devoted to debt service. Although sizable variance exists in the
region, its debt service averaged at less than three percent is quite comparable with that of China in 2003.
From the international trade standpoint, South Asia remained relatively closed throughout the 1980s, after
which both export and import steadily increased. Liberalized in the 1970s, Sri Lanka’s trade level was
substantially higher throughout the period. Despite much emphasis on export-orientated manufacturing, almost
all of South Asia imported more than its export and yet managed to speed up export more rapidly than its
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import. The comparatively lower trade levels of Bangladesh and especially India give the impression that an
average person from the region traded internationally much less than an average country, when expressed as
percent of GDP. Comparatively, China managed trade deficit better with its import being slightly lower than its
export throughout the period.
To assess the degree of overall liberalization efforts in each country and changes over time, I created
liberalization indices using factor analysis. Principal component factor analysis showed that all five indicators
supported the common factor, liberalization, thus proving their relevance in accurately measuring it.19 The
resulting indices summarized in Table 2 indicate that while a considerable variation exists across countries,20
the region in aggregate made a steady progress toward integration into the global economic system.21 As the
average indices suggest, the state of liberalization in the region leaped between 42 and 61 percentage points
between the 1980s and 2000-2003 depending on country or population averages. Amidst the progress of
different magnitudes toward liberalization in different countries, Bangladesh and Pakistan made considerably
slower progress than did India, Nepal, and Sri Lanka, the last with already a quite high degree of liberalization
by the 1980s. Starting with a relatively better off position in the 1980s, Pakistan tended to slow down the
process compared to the progress made by India and especially Nepal. Although smaller economies often find
their policy efforts making faster quantitative progresses, India’s progress begun in the late 1980s epitomizes
one of enormous transformation. Because of this progress in India, an average person from this region appears
to make a slightly bigger leap than that suggested by the country averages.
(Insert Table 2 here)
6. THE DEGREE OF INEQUALITY
The three measures summarized in Table 3 suggest that the degree of inequality varied widely in South Asia
both across countries and over time. Gini index shows that while the estimates for most of the countries and
time periods fell between 0.26 and 0.37, countries diverged in trend over time. For example, it did not change
much in Bangladesh, India, and Pakistan and yet dramatically increased in Nepal and considerably declined in
Sri Lanka. Gini index slightly increased in South Asia during the 1990s and 2000-2003 by both country and
population averages. The change was more conspicuous, however, in case of population average during 20002003 indicating that an average person from the region could expect to see Gini index rise faster during this
10
period compared to the 1990s. While China lacks comparable estimates for the 1980s and 1990s, South Asia
has registered somewhat lower Gini index than China.22
(Insert Table 3 here)
Consumption ratio between the top and bottom quintile, too, slightly increased in South Asia using the
country average and remained virtually unchanged using the population average. While the consumption
differential slightly increased in Bangladesh during this period, it rapidly accelerated in Nepal,23 and slightly
decreased in Pakistan and Sri Lanka. In India, by contrast, it did not change at all. Of those recording an
increasing consumption differential between the 1980s and 2000-2003, the gain in consumption share for the
top 20 percent in Bangladesh was five percent which occurred at the expense of the middle classes, where as
such gains in Nepal and India were 15 and four percents respectively, which happened at the expense of all
other classes. 24 A five percent decline in the consumption share for the top quintile took place in Pakistan and
Sri Lanka each thus increasing the consumption share for all other classes. In a comparative perspective,
disparity in the share of consumption between the rich and the poor was lower in South Asia than in China.25
The proportion of population with incomes exceeding the absolute poverty line of one dollar greatly varied
across time periods and countries. Over 40 percent of the population in South Asia was categorized as poor
during the 1980s, which declined by 11 percent by the end of the period.26 Amidst this progress in the region,
over a third of the population was still poor in Bangladesh and India, compared to a quarter in Nepal and
slightly less than a fifth in Pakistan. Pakistan’s progress was clearly the most impressive in the region, bringing
the country from the most poverty-stricken position in the 1980s to the second least poverty-stricken at the end
of the period. This figure was in single digit in Sri Lanka throughout the period although its progress of the
1990s was reversed by 2000-2003. A 17 percent poverty incidence in China (World Bank 2005a) suggests that
poverty situation may have been more daunting in South Asia.
As with the degree of liberalization, I used factor analysis to test the relevance of each of the three indicators
in terms of their commonality and predicted factor scores with usefulness to identify the degree of inequality.
This process, however, was complicated as the estimates included missing values for a number of years, which
were completed by meticulously inter- and extra-polating where applicable.27 The resulting factor scores28
summarized in Table 4 indicate a relatively wide variation in inequality in the region. Nepal’s experience
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epitomizing the highest degree of inequality in South Asia during 2000-2003 was strikingly different from that
of Sri Lanka with the lowest degree of inequality.29 Nepal with identical position in the 1980s scored 37 percent
higher on inequality than India by 2000-2003.
(Insert Table 4 here)
These estimates capture the commendable stride that Pakistan and Sri Lanka made in reducing inequality.
From the most highly unequal country in the region, Pakistan drastically lowered inequality with most of the
progress attained during the 1990s. Even more progressive has been the change in Sri Lanka, where most of the
progress was seen during 2000-2003. From the most highly equal status in the 1980s, Bangladesh demonstrated
a regressive move up until the mid-1990s, after which inequality tended to level off.
These individual country experiences paint a blurry picture of inequality in South Asia. While inequality
increased up until the mid-1990s for all other countries than Sri Lanka, what can be said about the entire region
depends mostly on the methodology adopted. The region as a whole showed a linear increase in inequality
following the population average and an increasing inequality succeeded by some leveling off following the
country average. Although absolute comparisons are prohibitive,30 the increase in both country and population
averages suggest, contrary to the findings of many large scale studies (Bourguignon and Morrison, 2002;
Dollar, 2004; Dollar and Kraay, 2001a; Lindert and Williamson, 2001; Sala-i-Martin, 2002) that the within
country inequality grew in this region.
7. MODELS AND RESULTS
The above discussion suggests that the process of liberalization accelerated in the region, together with
increasing inequality in Bangladesh, India, and Nepal and decreasing inequality in Pakistan and Sri Lanka.
Plotting the marginal distribution of liberalization and inequality, Figure 1 depicts that inequality increased in
South Asia especially at the initial levels of liberalization and leveled off and declined after liberalization
reached some point. It is precisely the role of Pakistan and Sri Lanka with relatively higher levels of
liberalization and yet lower inequalities that led to this potentially curvilinear relationship. A somewhat
anomalous in this graph is the marginal distribution for Nepal. As shown by the dotted slope, the curvilinear
relationship holds, perhaps with a smaller level of liberalization serving as the cutoff point, even after excluding
these potentially outlying data points for Nepal.
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(Insert Figure 1 here)
This curvilinear relationship may not have captured the true relationship in South Asia, however, since
inequality and especially liberalization have tended to drastically change over time. A given level of FDI and
external debt, two of the indicators of liberalization, for example, may not have the same effect on inequality
between the 1980s, 1990s, and beyond because of the massive differences in policy efforts to augment or
contain liberalization. By charting the average levels of liberalization and inequality over time, Figure 2
provides some relevant information useful to assess the dynamic nature of the relationship. The growing trend
on both variables suggests that, while population and country averages differ in magnitudes, liberalization and
inequality increased over time especially after 1990. This shows the possibility of a linearly positive
relationship when time factor is considered. But because inequality and liberalization are functions of a
multitude of factors including socioeconomic contexts as well as political realities in specific countries and
international political economy forces, a true form of the relationship cannot be identified without controlling
for them. As Dani (2005) provocatively argues, for example, a partial ‘equilibrium’ analysis does not provide
an adequate basis for definitive conclusion especially in studies involving the impact of particular policy tools.
Although controlling for all theoretically relevant factors may be operationally infeasible especially given data
constraints, the use of panel data techniques as well as other appropriate techniques incorporating the effects of
country specific contextual variables can lead to more promising results.
(Insert Figure 2 here)
Heeding these methodological issues, I estimated four different fixed effects regressions and four other three
stage least square (3SLS) regressions to examine how liberalization and inequality are related, together with
other appropriate control variables including GDP per capita, population growth, urban population, and
inflation. Reported in Tables 4 and 5 are the results of fixed effects regressions of the following generic form:
yit = α + β xit + γwit + ui + eit …
…
…
(1)
where, y is the dependent variable, x is the vector of explanatory variables of interest, w is the vector of control
variables, u and e are the error terms, and i and t are the country and year.
13
Models FE I and FE II (Table 5) estimate the effects of liberalization on inequality. The former estimates
the ‘net’ effect of liberalization on inequality as suggested by their aggregate scores, whereas the latter
including each of the five indicators of liberalization as separate explanatory variables provides estimates for
the mechanisms leading to such net effect. While I also estimated the parallel random effects regressions, the
fixed effects regressions provided more consistent and equally efficient estimates suggesting the use of the
fixed effects estimates for the given data.31 This was an indication that controlling for the country specific
factors including political environment and history of policy efforts that tend to be specific to countries was
more relevant in the region than controlling for any potential time specific factors including the changing
international political economy forces such as the global market and lending situations.
(Insert Table 5 here)
Where as the FE II model does a better job at accounting for the variation in inequality with relatively high
R2 estimate, the FE I model provides a more conspicuous effect of liberalization with one single estimate.32 As
the composition of the model gets more specific, however, more of the effects of explanatory variables used for
controlling purposes tend to be insignificant.33 Clearly, both models report a significant effect of liberalization
on inequality, with FE I detecting a positive net effect of liberalization and FE II detecting positive effects of
external debt and import. More specifically, FE II suggests an increase in inequality in South Asia given
increase in external debt and import.
Of the control variables, while the coefficient on GDP per capita is consistently negative and significant
across two models, other coefficients are not highly consistent. Obviously, part of this inconsistency arises from
the form that the liberalization variable takes. When liberalization is used as a composite of export, import,
external debt, FDI, and debt service, for example, inflation has negative coefficient and the coefficients on
population growth and urban population are positive. When liberalization is operationalized using its indicators,
on the other hand, none of the coefficients appear to be significant. Results from FE II, however, may be more
accurate given its ability to incorporate more detailed specification, thereby controlling for a more complex set
of correlations among the explanatory variables. Yet, results generally suggest that economically more
prosperous countries in South Asia can expect to see decreasing inequality. The curvilinear negative
relationship between GDP and inequality suggesting that inequality declines nonlinearly with increase in
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income does not align with the oft-cited Kuznets (1955) inverted-U hypothesis for South Asia. Nevertheless,
the relatively homogeneous economic performance with GDP figures less than $920 and a little over two
decades of time frame may not be consistent with such expectation. Central to this hypothesis is also the
economic transformation from the agricultural to the manufacturing sector, which this analysis supports
through the positive effects of population growth and especially urban population. Since economic
transformation and urbanization often go hand in hand thus perhaps contributing to increasing inequality
between rural and urban areas (Lipton, 1976; Wei and Wu, 2001), findings partly corroborate the argument that
increased opportunities that may concentrate on the urban areas accelerate earnings inequality. While the
economically better off are assumed to find ways to transfer any increased burden to those in the lower rung of
society through business and other rent-seeking mechanisms, some indication of the negative effect of inflation
on inequality suggests otherwise. Because inflation hurts those who cannot meaningfully participate in the
labor market, greater inflation may compel governments to introduce policies benefiting the poor or the lower
middle class, perhaps at the expense of the rich.
Models FE III and FE IV (Table 6) constitute an attempt to identify if—and how—inequality affects
liberalization. Just like the previous models, FE III uses the inequality scores as the explanatory variable of
interest, where as FE IV incorporates all three of its indicators. Here too, I estimated the random effects
versions of the model and found that the estimates provided by the fixed effects regressions are more consistent
and yet equally efficient.34 This supports the argument for relevance of the country specific effects that need to
be incorporated. It is true that international factors with massive march toward globalization and changing
lending requirements are relevant in this region as elsewhere. Yet, results do not support these time specific
changes perhaps indicating that the domestic political and historical factors may have made more important
differences.
The FE III model shows that the inequality score has highly significant, positive coefficient, reaffirming that
the net effect of inequality on liberalization is in fact positive. The coefficient on Gini index is relatively
significant and positive (FE IV), indicating that the degree of liberalization increases as a country gets more
unequal with a large Gini index.
(Insert Table 6 here)
15
Some of the control variables have consistent effects on liberalization. The consistently positive effect of
GDP supports that higher levels of development or economic prosperity are conducive to introducing
liberalization policies, as the country would be both capable and willing to pursue liberalization further. The
curvilinear relationship suggests that the size of the effect would attenuate once a certain level of GDP is
achieved. The consistently positive coefficient on population growth suggests that liberalization favors cheap
labor thereby inducing countries with more rapidly growing population to liberalize faster. The form of the
dependent variable matters here, too, just like in the previous case with the use of the composite scores
providing a larger number of significant coefficients. But there is some indication that growing urban
population is conducive to liberalization as its positive effect suggests. Additionally, the positive effect of
inflation may be justified given that business elites and policymakers seek to undervalue currency thus
encouraging exports and discouraging imports.
In sum, results thus far suggest that liberalization and inequality may have mutually reinforcing, positive
relationships. But the regressors and the error terms may have been correlated causing simultaneity bias. The
results reported in Table 7 are based on the following simultaneous equations model estimated using the 3SLS
technique:
yit = β 0 + β 1 xit + β 2 wit + u it
…
…
…
(2)
xit = γ 0 + γ 1 yit + γ 2 wit + vit
…
…
…
(3)
where, y is the inequality measure, x is the liberalization measure, w is the vector of control variables including
country identification, u and v are error terms, and i and t are the countries and years. This generic form of the
model incorporates country effects, accounting for country specific policy and historical factors, and minimizes
the potential simultaneity bias.
The first two sets of the estimates reported in Table 7 are based on the unweighted and weighted models, in
which liberalization and inequality scores enter as endogenous variables. Since the data include countries as
large as India with over 76 percent of the total population and as small as Sri Lanka with just over one percent
of the population, estimates of the Weighted I (and also Weighted II) model use population weights to
accurately capture the trend for an average person from South Asia. While the power of the models to explain
16
the variations in inequality is not as strong, this may have more to do with a large measurement error that
persisted in estimating its values as well as the explanatory power of the variables. These models provide
largely consistent results showing that liberalization and inequality have simultaneous, positive, but yet
nonlinear relationships.
(Insert Table 7 here)
The last two sets of estimates reported in Table 7 detail the relationships between liberalization and inequality
by identifying how the indicators of liberalization and inequality affect each other. These two models account
for a larger proportion of the variation in inequality compared to the earlier two models but yet not as high as in
the case of liberalization. Also, these results assuming the time invariant nature of liberalization and inequality
somewhat differ from one model to another. While external debt and import have consistently positive
coefficients for inequality, coefficient on export is significant only in the Weighted II model and coefficients on
FDI and debt service are neither consistent nor significant. With regard to the relationships of the indicators of
inequality with liberalization, only the Unweighted II model provides a positive coefficient on Gini index with
other indicators showing consistent and yet insignificant coefficients.
These models also provide results with regard to the effects of country specific dummy variables. For
example, inequality is consistently greater in India than in Bangladesh (reference category), whereas in Sri
Lanka it is somewhat consistently lower. In the same vein, liberalization is consistently lower in India than in
Bangladesh and somewhat less consistently lower in Pakistan and it is consistently greater in Nepal and Sri
Lanka. There is also a distinction in terms of the role of other control variables. GDP per capita has consistently
negative coefficient for inequality and yet consistently positive coefficient for liberalization. Inflation has
somewhat consistent effect with negative coefficient for inequality and positive coefficient for liberalization.
While urban population has somewhat consistent, positive coefficient for liberalization, its coefficient is
consistently insignificant for inequality. Similarly, population growth has consistently insignificant effects on
inequality and yet consistently positive effect on liberalization as shown by the unweighted models.
8. DISCUSSIONS
Findings support that liberalization holds significant power to explain inequality within countries in South Asia
and that while there are variations in country experiences the net effect of liberalization on inequality is
17
positive. Although inconsistent with many studies, this is largely consistent with some (e.g., Alderson and
Nielsen, 2002; Milanovic, 2005b) suggesting that liberalization is helpful to explain increasing inequality.35
At a general level, the suggestion that liberalization positively affects inequality supports the argument that
international trade and external debt either exclusively benefit the rich, poignantly hurt the poor, or produce a
combination of the two. No doubt, competition, the chief motto of liberalization, is likely to reward those who
already possess the resources to mobilize to their interest. By creating winners and losers, liberalization is also
likely to increase costs to governments as under the fair game it needs to take care of the losers (Mahler, 2004)
and it is from this perspective that liberalization can be necessary but not a sufficient condition to improving the
overall living standard in societies (Graham 2004; Sen 2002). However, the findings do not appear to confirm
the neoclassical argument that the benefits accrued to the better off will necessarily trickle down to the poor.
But what mechanisms may have produced such positive net effects? As models FE II, Unweighted II, and
Weighted II indicate, external debt and import have positive effects on inequality. Since acquiring external debt
is the most sought after strategy for governments in developing countries so that their policy priorities can be
kept afloat, the finding that external debt accelerates inequality paints the picture in which the rich and not the
poor may have benefited from liberalization. Because external debt represents the loan that governments
acquire internationally, its positive effect on inequality clearly manifests the inefficiency attached to the usage
of such public resources. Moreover, government inefficiency and corruption that are almost a norm in these
countries provide a powerful explanation that external debt exacerbates inequality (Jeffrey, 2002; Li, Xu, and
Zou, 2000; Transparency International, 2005). An equally consistent explanation applies to the positive effect
of import on inequality. Increase in import occurs in developing countries largely as a result of the increased
capacity of the rich and the upper middle classes to consume foreign goods and services so that they can
demonstrate their distinguished social status (Sackrey and Schneider, 2002). Increasing import, therefore,
signifies that within country economic inequality has widened. But it is surprising to find that it is import, and
not export, that matters more to inequality.36 This does not support the oft-cited Stopler-Samuelson (1941)
hypothesis that countries with abundant unskilled and skilled labor would witness declining economic
inequality as a result of foreign trade. While the negative sign of the coefficient on export (Weighted II) would
be supportive of this hypothesis, this relationship does not hold consistently across different models. The
18
consistently insignificant coefficient on FDI may be as a result of its private nature thus rewarding foreign
investors and expatriates without directly altering the distribution of resources within the country.37 Similarly,
the insignificant effect of debt service may have heralded the fact that increasing amounts of short-term
government dues are not associated with directly rewarding any specific economic class, thus leaving the
distribution unchanged.
On a similar fashion, results include positive net effect of inequality suggesting that countries with higher
degrees of inequality may be more likely to liberalize.38 Up until the mid 1980s, all of South Asia remained
disenchanted by liberalization with the exception of Pakistan and especially Sri Lanka. While the island country
of Sri Lanka already had considerable degree of foreign trade, an increasing magnitude of inequality in
Pakistan may have contributed to its policy intent to liberalize the economy in the early 1980s. But what may
be at play with this positive relationship is the interest of the upper or upper middle classes holding the key
decision-making positions in developing countries to liberalize so that they could harness the opportunities
available in the global market.39 Because of the abundance of cheap labor in these countries, liberalization
would turn out to be a sure bet for these eminent economic players. Interests of these internal players coincide
with those of the external forces thus creating and perpetuating a cycle of foreign dependency (dos Santos,
1970).
Contemporary research does not focus on inequality as the potential precondition for liberalization, as
political economists and sociologists often conceive the former as a socially undesirable goal thus drawing
much of the attention to it. While the debate is not whether to liberalize in this inescapably global epoch,
understanding how much of the decision to liberalize is driven by the forces relating to inequality is
important.40 Capturing this simultaneous aspect of the relationships that the models estimated here support
contributes to the understanding of how liberalization and inequality operate in South Asia. Although inequality
in this analysis is indicated by Gini index, consumption ratio, and poverty incidence, the FE IV and
Unweighted II models suggest that Gini index has consistently positive effect on liberalization.41 It is logical to
find that increasing inequality in the distribution of resources positively affects liberalization, indicating that
countries with highly unequal distribution of resources tend to liberalize more rapidly. It is the notion of the
19
comparative advantage with cheap labor or the clientelistic culture embedded in society that motivates
decision-makers to open up the economy.
Moreover, none of the models supports that the consumption ratio and poverty incidence are related to
liberalization. A large segment of the population in poverty with very high consumption ratio is an indication of
the widespread illiteracy and unskilled labor. Countries with very high degree of presence of these
characteristics may be incapable of offering exportable products and drawing foreign investment and thus are
not prepared to meaningfully liberalize as indicated by their unimpressive progress on the state of the overall
human development (Mahbub ul Haq Human Development Centre, 2003). While the process of liberalization
arguably reduces poverty of the absolute nature (Dollar, 2004; Dollar and Kraay, 2001a, 2001b; Firebaugh,
2003c; Masson, 2001; Pigato et al., 1997; Sala-i-Martin, 2002; World Bank, 2005a), liberalization does not
depend on the state of the poorest sections of the population as indicated by high poverty incidence and high
consumption ratio.
The concept of liberalization is dynamic in nature, constantly evolving in this region just like everywhere
else. The political and economic pressures within the country as well as pressures from outside change if not
constantly grow over time. But, although the random effects regressions were not supported given data, the
fixed effects regressions as well as its equivalent counterpart 3SLS may have failed to capture this dynamics
thus rendering the estimates to be less efficient. The consistently insignificant coefficient of consumption ratio
and poverty incidence suggests that the position of the lower class relative to the rich may not matter to the
policy efforts to liberalize. But the results that poverty incidence and especially consumption ratio do not matter
and yet Gini index does to liberalization may have uncovered the role of the middle classes in supporting the
government policies. The government cannot pursue liberalization without the support of the middle classes in
a representative democracy especially with full public participation. Given that not all countries in South Asia
practice representative democracy, a more systematic examination is necessary to understand how policies get
made and carried out in these countries.
9. CONCLUSION
The five major countries in South Asia share many historical and cultural similarities. With a few exceptions,
they also had comparable degrees of economic openness and inequality at the beginning of the 1980s. By 2003,
20
however, there have emerged some diverging trends on inequality despite consistently deepening economic
openness, although at various magnitudes. That inequality has declined in more intensely liberalizing
economies including Pakistan and Sri Lanka where as it has accelerated in others suggests that inequality may
initially rise with liberalization and begin to recede once liberalization surpasses certain degree. The overall
time series data do not support this relationship, however, indicating that liberalization and inequality may
instead go hand in hand.42 Because inequality and especially liberalization evolve over time in this region as
elsewhere and yet partly depend on country-specific policy and other contextual factors, any attempt to uncover
the true nature of the relationship needs to incorporate the dynamic perspective. Reports from regressions with
ability to appropriately account for the dynamic and contextual factors show a mutually reinforcing positive net
relationship. Consistent with their marginal distribution, the effect of liberalization on inequality is nonlinearly
positive such that inequality may level off once liberalization reaches certain point. At the same time, the effect
of inequality on liberalization is linearly positive suggesting that economically unequal countries tend to
liberalize more intensely.
At a general level, the policy implication of this analysis is that a more organic decision to open up the
economy would be preferred with incremental process of liberalization that society can absorb, thus finding the
appropriate locus for the middle classes and especially the poor and integrating them in process. The specific
mechanisms through which liberalization and inequality positively affect each other can offer more specific
policy implications for South Asia. The finding with the positive contribution of the external debt suggests that
policymakers may need to refrain from mounting external borrowing especially given its inefficient use in these
countries with rampant corruption. Although the relatively inconsistent coefficients on import and especially
export register extreme caution, policymakers seeking to open up the economy need to introduce measures to
meaningfully integrate the poor thus providing them with adequate economic incentives. Also, even though the
actual benefit of FDI needs to be thoroughly understood in these countries, the finding that FDI does not affect
inequality may encourage policymakers to find ways to promote it.
What is excluded from this analysis, however, is the specific policy dynamics in individual countries, with
often influential, if not the determining, roles of the international players. Every country intends to take
advantage of the enormous opportunities offered by the global market. What policies are appropriate for
21
particular countries is not necessarily on their discretion, however. To receive the needed resources, for
example, these poor South Asian countries accept the terms and conditionalities imposed by the relevant
international financial institutions. Although the rules of international lending may change under renewed
initiatives especially targeted at these highly indebted poor countries, the substance that they ought to
demonstrate ‘fiscal discipline’ and ‘measurable progress’ does not. It is, therefore, on the point of rapid
liberalization that the internal and external economic players collude as both have only to gain out of the deal. It
is also on the point of revamping social and economic reforms that these same players collude and thus give
continuity to balancing the budget, even though these efforts are sometimes marred by considerable political
unrests.
The virtue of globalization per se is uncontested (Sen, 2002) but it is in the absence of effective social
policies (Bardhan, 2004) and hasty liberalization attempts that inequality and liberalization can positively
reinforce each other as is operational in South Asia. Given that inequality is found to fuel social discontents,
thus accelerating sociopolitical inequality (Alesina and Perotti, 1993), questions remain on its relevance in this
region and on the sociopolitical consequences the new liberalization and inequality landscape may have
brought.
Finally, the findings from this research are only indicative and not definitive. Further research is needed to
enhance the reliability of the measures of inequality. Although the WIDER (2005) strives to make the existing
estimates more comparable across countries and over time, especially when prudently used, more
comprehensive and more accurate cross-country datasets are required to conduct more definitive analyses.
1
Studies demonstrate consensus patterns of inequality when looking at individual countries. Harrison and Bluestone
(1990), for example, concluded that economic inequality in the United States took a U-turn with sharp increase especially
after the 1960s. O’Rourke (2001) also observed rising inequality in many OECD countries during the past few decades.
From cross-national standpoint, however, there does not appear to be a consistent global trend (Alderson and Nielsen,
2002; Firebaugh, 2003a; Milanovic, 2005a).
2
This is not universally the case, however.. For conservative leaders like Margaret Thatcher, for example, extreme
inequality is a matter of “glory” as it gives vent to “talent and abilities” (George, 1997).
3
Coined by Robert K. Merton in 1968 drawing from the passage of Gospel of Matthews, Matthew effect has seen its
application in many social science disciplines. As Wade (2004) utilizes, for example, the line “For unto every one that hath
shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath”
22
(XXV:29) implies that “The rich get richer, and the poor get poorer.”
4
Productivity is central to the neoclassical arguments advocating liberalization. Pigato et al. (1997) contend, for example,
that FDI, one of the most effective vehicles of liberalization, confers productivity gains by making production and
management practices more efficient. “Even where productivity improvements from FDI come at the expense of local
entrepreneurs, FDI tends to bring in ‘net’ productivity gains to the host country” (Pigato et al., 1997: 21). At the same
time, however, te Velde and Morrissey (2003) do not find firms owned by foreigners to necessarily increase efficiency.
5
Bias can also result from the use of exchange rate and purchasing power comparisons of income as the former overlooks
the relative prices of ‘non-tradables’ and the latter invokes ‘substitution bias’ (Dowrick and Akmal, 2005). See also
Weisbrot, Baker, Naiman, and Neta (2001) on the inappropriateness of the former and Milanovic (2005b) on the
inappropriateness of the latter.
6
Milanovic (2005a), for example, used data from household surveys to examine global inequality across individuals (or
households). While whether country-specific survey data can be aggregated is debatable, the finding that inequality
slightly decreased during the past two decades contradicted findings from other types of analyses showing inequality to
have either rapidly increased or rapidly decreased (Milanovic, 2005a).
7
In fact, it is the within country inequality on which evidence appears to have fallen short of providing any conclusive
finding owing largely to the substantial measurement errors and unreliable data.
8
These values are in 2000 constant dollars, which translate into an average per capita GDP purchasing power parity of
$2259, with the lowest of $1341 and highest of $3568 (World Bank, 2005b).
9
Every year, Freedom House (2005) assesses the state of freedom in over 200 countries and labels each country as either
“Free,” “Partly Free,” or “Not Free.” Each of two freedom factors—political rights and civil liberties—is assigned the
value between one and seven and then aggregated to come up with the final freedom score.
10
Based on country surveys, Transparency International (2004) produces perceived corruption index for each of the 146
countries included. Its 2004 Annual Report assigned 2.8 or lower index (out of the highest possible of 10) for each of these
countries from South Asia, placing them on the 91st most corrupt ranking (India) or higher.
11
Studies have used consumption and income data depending on data availability. Data are hard to come by on physical
capital such as land and even human capital such as education and health, which have important implications for
redistribution or accumulation of assets or human capital (Birdsall and Londono, 1997; Deninger and Squire, 1998). Apart
from data availability, there can be other considerations in choosing to use consumption versus income data. Unlike in
developed countries, using income as the basis of inequality in developing countries can be problematic. People have
consumption despite lacking income, which is typically the case especially in rural areas, making it very difficult to
accurately estimate household or individual income. Consumption, on the other hand, can be estimated based on people’s
direct responses to relevant questions and their lifestyles (WIDER, 2005).
12
Glewwe (1988), for example, found a significant effect of food aid on economic inequality in Sri Lanka, suggesting that
inequality would be much higher if people’s income or monetary expenses on consumption were used.
13
To assess the degree of inequality, possibilities also exist for using other social indicators including human development
index, human poverty index, education, and life expectancy. While just like consumption these indicators tend to attenuate
the level of inequality (Sutcliffe, 2004), some of these are not very relevant to assess the economic aspect of inequality and
the decision not use even those that may be appropriate has to do with a lack of consistent data.
23
14
The 90/10 ratio represents another variation of percentile distribution with a more radical tone as one is likely to find
larger differences from this approach than from the 80/20 approach used here (Sutcliffe 2004). In this analysis, however,
the decision to use the latter had to do with the availability of data.
15
More recent effort at the Bank has been to collect household survey data. While the Bank did include some inequality
estimates on the South Asian countries prior to conducting such surveys, data reliability and other methodological issues
including unit of analysis and bases of inequality pose validity threats to conducting comparative analyses (Ravallion,
2003, 2004).
16
In addition, I use some recent inequality estimates especially on Nepal and Pakistan from the WDI 2006 (World Bank
2006), which the WIDER lacked.
17
More specifically, the WIDER (2005) draws data from various sources including the World Bank and other surveys and
in case of consumption estimates, which are what I use in this analysis, recreates a consistent measure of consumption
values for households. It derives consumption estimates using national income accounts and makes appropriate allowance
for goods and services not bought in the market and finally makes appropriate equivalence scale adjustments to come up
with per capita estimates for people from households of different sizes (WIDER, 2005).
18
With comparable size of population, India’s external debt grew steadily even beyond 23% whereas China’s appears to
have stabilized at around 14%, after reaching over 17 percent in 1997.
19
Factor analysis finds commonality among the variables supplied and for each provides weight estimates useful in
predicting factor scores. These factor scores in turn can be used to ascertain the relative position of the countries on the
common variable under consideration. In this particular case, the five indicators together accounted for over 64 percent of
the commonality with a relatively large Eigenvalue (3.223 compared to 0.87 for the second factor) providing factor scores
that are quite realistic.
20
There is a caveat in terms of making absolute comparisons, however. Because these factor scores have a range of 0-1,
the variations between countries and over time hay have been slightly magnified. As a result, the values are only relative to
the estimates in this distribution and cannot be compared with any other estimates. The value of one, for example,
indicates its highest ranking in the distribution, not a perfect case for liberalization, where as the value of zero indicates the
lowest in the distribution, not a case with absolutely no liberalization. It is for this comparison across the countries in the
dataset that the factor scores were created from the overall factor analysis, not country specific analyses, which would
otherwise be more appropriate given the time series nature of data.
21
However, being globally integrated may be qualitatively different from being regionally integrated because of their
physical, political, and cultural proximity. Regional trade and cooperation, which these countries are actively seeking
through their regional association—the South Asian Association for Regional Cooperation (SAARC)—has helped
promote trade and investment across its member countries. From foreign currency standpoint too, exporting to or attracting
FDI from other nations may be strategically more important than doing so with a regional partner.
22
The World Bank (2005a) reports Gini index for China to be 44.73 in 2000. Because China’s figure is based on per capita
expenditure, absolute comparisons are prohibitive. At the same time, however, since the difference between consumption
and expenditure at the per capita level is not as large as the difference between consumption and income, some comparison
is justified.
23
Looking at the distribution of income, Guha-Khasnobis and Bari (2003) found similar patterns in Nepal with the highest
24
ratio of income share of the top to bottom deciles followed by Pakistan.
24
Calculations are available from the author.
25
The share of the top quintile on per capita expenditure is over 10 times larger than the share of those at the bottom
quintile in China (World Bank 2005a). Usual caution applies as in the case of Gini index earlier.
26
While both country and population averages are provided for these estimates, the latter are more appropriate in case of
the proportion of population below the poverty line.
27
Because these inequality data come from nationally representative surveys, estimates are available only for some years.
To derive reliable estimates for the years in a given country, I linearly interpolated the data thus leaving the overall trend
intact. In many cases, however, estimates were not available for a few years in or after 1980 and /or in or prior to 2003.
Specifically, Bangladesh lacked Gini index and 80/20 consumption estimates prior to 1983 and after 2000, India after
2000, Nepal prior to 1984, Pakistan prior to 1984 and after 2002, and Sri Lanka prior to 1986 and after 2000. Similarly, the
poverty incidence data which were drawn from the WDI 2005 (World Bank, 2005b) lacked estimates prior to 1980 and
after 2000 for Bangladesh, prior to 1984 for Nepal, prior to 1987 and after 2000 for India, prior to 1987 and after 2002 for
Pakistan, and prior to 1985 and after 2000 for Sri Lanka. In each case, I extrapolated the data by extending the actual value
of the closest estimate to the beginning or to the end. While this does not capture the true state of inequality for given year,
it systematically inputs values that are highly probable from conservative standards. Because values in the time series data
can change from one year to next only by marginal percentage points, this process leads to outcomes that are justifiably
realistic. Also, this should not heighten the chance of Type I error in hypothesis testing, as data are likely to provide
insignificant coefficients.
28
The principal component factor analysis showed that the three indicators together accounted for the commonality of 66
percent forming the common factor, inequality, with an Eigenvalue of 1.96 compared to 0.96 for the second factor.
29
Some important political economy and policy factors are associated with these two extreme cases, however. In Nepal,
for example, the increasing inequalities of the 1990s and beyond are attached to the rising remittances that certain
proportion of the households received from foreign employment thus rendering differential consumption capacities (CBS
2004). Similarly, government initiatives to import large amounts of food grains and sell at subsidized prices in the 1970s
and 1980s, which continued as food stamp in the 1980s and beyond, may have reduced inequality in consumption in Sri
Lanka (Glewwe, 1988).
30
See endnote 20 above.
31
The associated Hausman test results with large Chi-square statistics suggested rejection of the assumption that the
unobserved effects were uncorrelated with each of the explanatory variables.
32
It is interesting to note that the R2 for FE I is exceedingly small despite significance of all of the coefficients that are
estimated. This is no indication of the questionable validity of the model, however, because an OLS counterpart of the
fixed effects regression would yield an R2 value of 0.67. This explanation of relatively low R2 holds for all of the fixed
effects regressions estimated here.
33
Partly, this may be an indication that the data are heteroskedastic as is the case with all cross-country analyses or that
some fragility may be associated with the model due to the alternative model specification. Yet, I did not observe any
eminent sign of multicollinearity as the resulting regression coefficients were relatively uncorrelated. For this reason, and
for consistency purposes, I use unaltered specifications in all relevant models
25
34
See footnote 31 for relevant explanations.
35
While the former explicitly looks at OECD countries, the latter observes positive relationships in case of poor
developing countries—like those from South Asia—out of a range of countries included.
36
Only the RE II and Weighted II models detect significant effect of export and even the coefficient signs are opposite.
Partly, this uncovers the relevance of the time factor, with the general social and economic structures that are changing
within the country as well as the international political economy factors such as migration and remittance that efficiently
alter the distribution of resources in the country. More importantly, however, it highlights the complexity of the
relationships, thus invoking more careful analysis with comprehensive data.
37
The insignificance of the coefficient on FDI suggests rejection of the hypothesis that it is related to earnings inequality
as was suspected to be operational in Africa (te Velde and Morrissey, 2003). But a more direct test would be needed to
draw definitive conclusions.
38
Because this finding is inconsistent with other studies (Alesina and Perotti, 1993; Barro, 2000) suggesting that higher
inequality would retard growth and investment in low-income countries, how inequality affects growth and investment
may highly depend on the context and the particular policies that are in place.
39
Although historical attachments are used to explain why Latin America was integrated into the world systems well
before most countries in Africa and Asia, anecdotal evidence suggests that the decision of Latin American countries to
liberalize, at least in part, had to do with the bifurcation of the population with different capacities and interests. A high
degree of economic inequality that has historically persisted in Latin America suggests that the interests of the rich would
have been different from those of the poor who simply serve the rich to make their daily ends meet.
40
Korzeniewicz and Smith (2000), for example, argue that technocratic forces and elites seeking to restrict reforms and
advance their own interests make all the globalization and other economic reform decisions. Although Vicziany (2004)
points out that the South Asian middle classes are relatively weak in taking up economic and political power, the upper
and upper middle classes essentially dominate the decision-making environment.
41
The Weighted II model, however, did not provide consistent result with respect to the coefficient on Gini index.
Although this lack of significant relationship shown by this weighted model is enigmatic given Pakistan’s and especially
Sri Lanka’s progress in lowering Gini index and yet higher degree of liberalization, India’s massive population size may
have dwarfed these interesting dynamics for the overall region.
42
Herein lays the dilemma posed by the econometric approach focusing on the aggregate analysis and the case study
approach focusing on single units. Part of it, however, is attributable to the fragile reliability of the inequality data.
26
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Tables
Table 1
Mesures of Liberalization
(All figures are as percent of GDP except for Debt Service which are as percent of GNI)
Period
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Average
Average
(Country) (Population)
Net Inflow of FDI
1980s
0.014
0.043
0.017
0.329
0.752
0.231
0.079
1990s
0.129
0.395
0.114
0.882
1.213
0.547
0.436
2000-2003
0.267
0.692
0.036
0.689
1.148
0.567
0.646
External Debt
1980s
14.200
6.543
6.993
10.557
11.067
9.784
7.959
1990s
33.079
19.695
36.797
30.780
41.461
32.362
22.912
2000-2003
34.218
22.206
52.233
47.229
65.304
45.238
27.261
Debt Service
1980s
1.568
1.568
0.945
4.037
5.656
2.755
1.892
1990s
1.835
3.062
1.898
5.020
4.109
3.165
3.121
2000-2003
1.428
2.598
1.821
4.055
4.382
2.856
2.651
Export of Goods and Services
1980s
5.244
6.063
11.444
12.077
27.444
12.464
6.989
1990s
9.886
10.023
19.520
16.402
33.561
17.878
11.215
2000-2003
14.463
14.267
20.135
18.370
37.050
20.857
15.144
Import of Goods and Services
1980s
13.988
8.059
20.380
22.438
40.739
21.121
10.692
1990s
15.707
10.998
30.225
20.006
42.423
23.872
13.221
2000-2003
19.952
15.085
30.353
19.172
44.619
25.836
16.683
Table 2
Degree of Liberalization
Country
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Average (Country)
Average (Population)
1980s
0.303
0.270
0.337
0.463
0.713
0.417
0.301
1990s
2000-2003
0.391
0.444
0.402
0.467
0.519
0.555
0.595
0.594
0.823
0.901
0.546
0.592
0.431
0.486
31
Table 3
Mesures of Inequality
Period
Bangladesh
India
Nepal
Gini Index
1980s
0.268
0.312
1990s
0.321
0.311
2000-2003
0.319
0.360
Ratio of Consumption for Top to Bottom Quintile
1980s
3.784
4.603
1990s
4.833
4.540
2000-2003
4.600
4.781
Poverty Incidence at $1 a Day of Income
1980s
27.290
46.310
1990s
30.390
42.310
2000-2003
36.030
35.300
Pakistan
Sri Lanka
Average
Average
(Country) (Population)
0.300
0.426
0.472
0.326
0.320
0.306
0.341
0.332
0.276
0.305
0.328
0.347
0.309
0.315
0.351
4.336
7.633
9.100
6.330
5.584
4.333
5.351
5.070
4.045
4.726
5.256
5.572
4.618
4.703
4.546
39.720
39.130
24.100
49.630
40.830
17.000
9.390
5.190
7.600
32.417
29.406
24.006
41.504
38.418
32.795
Table 4
Degree of Inequality
Country
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Average (Country)
Average (Population)
1980s
0.277
0.459
0.455
0.631
0.475
0.459
0.457
1990s
2000-2003
0.445
0.445
0.502
0.596
0.779
0.966
0.492
0.374
0.422
0.287
0.528
0.532
0.501
0.560
32
Table 5
Panel Data Regressions of Inequality
(N=120)
FE I
1.0678 **
(0.2469)
Variables
Liberalization score
Export as percent of GDP
External Debt as percent of GDP
Net Inflow of FDI as percent of GDP
Import as percent of GDP
Debt Service as percent of GNI
GDP per capita
-0.0013
(0.0002)
0.1828
(0.0673)
0.0346
(0.0108)
-0.0090
(.0031)
Population Growth
Urban Population
Inflation
_Constant [(Inequality)2]
**
**
**
**
-0.7759 **
(0.2881)
0.0350
R-sq
FE II
-0.0111
(0.0067)
0.0071 **
(0.0014)
-0.0499
(0.0328)
0.0294 **
(0.0042)
0.0139
(0.0117)
-0.0012 **
(0.0002)
-0.0849
(0.0661)
0.0021
(0.0104)
-0.0040
(0.0026)
0.1982
(0.3140)
0.3023
Note: 1) Numbers in parentheses are standard errors
2) * p<0.05 ** p<0.01
Table 6
Panel Data Regressions of Liberalization
(N=120)
Variables
Inequality score
FE III
0.0729
(0.0322)
FE IV
*
Gini Index
Consumption Ratio
Poverty incidence at $1 a day of income
Log of GDP per capita
0.3359
(0.0329)
Population Growth
0.0946
(0.0210)
Urban Population
0.0127
(0.0037)
Inflation
0.0021
(0.0010)
_Constant [Liberalization]
-1.9920
(0.1565)
R-sq
0.2968
Note: 1) Numbers in parentheses are standard errors
2) * p<0.05 ** p<0.01
**
**
**
*
**
1.1602 *
(0.4496)
-0.0264
(0.0147)
-0.0004
(0.0007)
0.3385 **
(0.0347)
0.1015 **
(0.0208)
0.0077
(0.0041)
0.0017
(0.0010)
-2.1007 **
(0.2001)
0.4262
33
Table 7
Three Stage Least Square Regressions of Liberalization and Inequality
(N=120)
Variables
Inequality
Liberalization score
Unweighted I
Weighted I
2.0670 **
(0.5439)
1.5931
(0.7082)
0.2300 *
(0.1143)
-0.0056
(0.2766)
0.0552
(0.2068)
-0.2529
(0.2443)
-0.0014 **
(0.0003)
0.0441
(0.1120)
-0.0054
(0.0213)
-0.0082
(0.0045)
0.2572 **
(0.0914)
-0.0453
(0.2410)
0.2317
(0.1615)
-0.3085 *
(0.1432)
-0.0009
(0.0005)
-0.0619
(0.1025)
-0.0204
(0.0142)
-0.0087 *
(0.0043)
-0.1302
(0.5187)
0.6000
0.4484
(0.4188)
0.3828
0.3961
(0.2860)
0.7837
0.2268 **
(0.0197)
0.1987 **
(0.0239)
Net Inflow of FDI as percent of GDP
Import as percent of GDP
Debt Service as percent of GNI
Pakistan
Sri Lanka
GDP per capita
Population Growth
Urban Population
Inflation
_Constant [(Inequality)2]
R-sq
Liberalization
Inequality score
Gini Index
Consumption Ratio
Poverty incidence at $1 a day of income
Countries: India
-0.0156
(0.0055)
0.0026
(0.0011)
0.0148
(0.0265)
0.0380
(0.0036)
-0.0050
(0.0072)
0.3388
(0.0497)
0.0686
(0.1039)
0.1692
(0.0970)
-0.5070
(0.0809)
-0.0006
(0.0002)
-0.0994
(0.0483)
0.0014
(0.0073)
-0.0015
(0.0017)
-0.0074
(0.0060)
0.0078
(0.0013)
0.0167
(0.0295)
0.0292
(0.0038)
0.0197
(0.0105)
0.4218
(0.0634)
-0.1119
(0.1265)
0.3637
(0.1271)
-0.2450
(0.1220)
-0.0014
(0.0002)
-0.1190
(0.0612)
-0.0084
(0.0096)
-0.0049
(0.0024)
External Debt as percent of GDP
Nepal
Weighted II
*
Export as percent of GDP
Countries: India
Unweighted II
-0.1048
(0.0167)
Nepal
0.2199
(0.0325)
Pakistan
-0.1263
(0.0343)
Sri Lanka
0.1871
(0.0341)
Log of GDP per capita
0.3429
(0.0315)
Population Growth
0.0757
(0.0199)
Urban Population
0.0074
(0.0034)
Inflation
0.0023
(0.0010)
_Constant [Liberalization]
-1.9918
(0.1450)
R-sq
0.9485
Note : 1) Numbers in parentheses are standard errors
2) * p<0.05 ** p<0.01
**
**
**
**
**
**
*
*
**
-0.1039
(0.0143)
0.2100
(0.0349)
-0.0617
(0.0401)
0.1744
(0.0288)
0.2944
(0.0237)
0.0205
(0.0249)
0.0067
(0.0031)
0.0038
(0.0008)
-1.5886
(0.1656)
0.9394
**
**
**
**
*
**
**
1.0048
(0.4096)
-0.0137
(0.0134)
-0.0004
(0.0006)
-0.0816
(0.0177)
0.2162
(0.0416)
-0.0844
(0.0395)
0.1729
(0.0335)
0.3593
(0.0326)
0.0890
(0.0196)
0.0050
(0.0038)
0.0021
(0.0010)
-2.2046
(0.1825)
0.9596
**
**
**
**
*
**
*
**
*
**
**
**
**
-0.0156
(0.2242)
0.7703
*
**
**
*
**
**
**
*
**
0.7315
(0.4300)
-0.0122
(0.0140)
-0.0012
(0.0006)
-0.0664
(0.0150)
0.2064
(0.0433)
0.0173
(0.0446)
0.1387
(0.0280)
0.2812
(0.0302)
-0.0003
(0.0243)
0.0034
(0.0037)
0.0036
(0.0008)
-1.4494
(0.2136)
0.9548
**
**
**
**
**
**
34
Figures
1
NE
NE
NE
NE
NE
NE
NE
NE
.8
NE
Inequality
Slope with Nepal
.6
SR
.4
SR SR
SR
SR
SR
Slope without Nepal
SR
SR
SRSR
.2
.2
.4
.6
.8
1
Liberalization
Figure 1. Liberalization and Inequality
.7
.7
.6
Country Average
Country Average
.6
.5
.5
.4
.4
Population Average
Population Average
.3
.3
2005
2000
1995
1990
a. Liberalization
1985
1980
1980 1985
1990
1995
2000
2005
b. Inequality
Figure 2. Liberalization and Inequality Over Time
35