Asia Rising? Inequality and Patterns of Growth in Asia: Towards a Regional Policy Framework

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Taimur Khilji


(Persistent inequality, in addition to being ethically wrong, is politically risky and is likely to arrest development gains. In a continent that is now largely democratic, issues of equity will shape the future of governments. Rising levels of inequality across Asia underscore the regional dimension of the problem. Although currently lacking, a coherent regional response to address this issue is desperately needed for Asian countries to make a smooth development transition. If the transition from developing to developed is to be achieved by the least developed countries in Asia while keeping inequality in check, then the transition from rural to urban needs to be managed carefully. The policy focus needs to shift from pursuing merely growth to developing a more inclusive form of growth. This requires that the distribution of, as well as the contribution to growth be critically questioned).


Rising levels of income inequality in Asia is becoming a central public policy problem. Over the past two decades the level of income inequality, as measured by the Gini index has steadily increased in a number of Asian economies (Milanovic 2009). Across countries, inequality has also increased as measured by differences in per capita income over time. The emerging, and now more developed economies of Asia have grown at a much faster rate than the fourteen Least Developed Countries (LDCs) in Asia. While the policy focus has been on sustaining economic growth, reducing inequality has largely been off the development agenda (Mishra 2009; Jolly 2009). Asia is often projected as a fast growing region with China and India leading the way (Winters and Yusuf 2007).

There is good reason to see Asia through the lens of growing prosperity. The rapid economic successes of several East Asian countries, including South Korea, Singapore, Malaysia, Taiwan, and Thailand, were aptly deemed miracles (Stiglitz 1996).   The region’s fastest growing economies have reaped the benefits of globalization and made commendable strides in reducing poverty (Habito 2009). Through the 1990s and into this century, East Asian economies have come together to form complex trade networks within the region based on intra-regional trade in electronic parts and components, where the production process has been fragmented according to dynamic comparative advantage to provide almost 30 percent of world’s merchandize exports (ADB 2006; WTO 2008).  China and India, the two most populous countries in the world, continue to be the fastest growing within the region. From such a vantage point, Asia provides an exceptional example of economic achievement.

In sharp contrast, Asia comprises the largest number of poor on a single continent; the number is close to a billion (World Bank 2009). In 2008, the number of hungry was 500 million (FAO 2008). The fourteen LDCs of Asia have an average per capita income of just over $500 (UNDP 2005). A number of countries, including Afghanistan, Pakistan, Philippines and Indonesia, are faced with conflict while others, such as Sri Lanka, Nepal and Timor Leste, are just beginning to recover from it. The recent rise of domestic food prices has disproportionately affected the poor, especially where the poor are net consumers (Ivanic and  Martin  2008).  In  Pakistan,  for  example  the  number  of  food insecure people increased from 60 million to 77 million in 2007/2008. Most recently, the global financial crisis has not only affected those employed in the financial sector, but also those in the manufacturing and services industries (Chhibber et al. 2009). Current social protection and social security measures that help buffer the vulnerable population during times of crises have proved inadequate as they do not extend to those working in the in the informal sector (Baulch et al. 2008).

On the one hand, Asia has made unprecedented economic progress, but on the other it is still grappling with basic development issues. While growth has been consistent and strong, there are still large pockets of poverty. India, for example has the largest number of billionaires in Asia (Petras 2008), and is also home to over 450 million persons living below the $1.25 poverty line (World Bank 2008).  Both extremes must be considered to capture the dynamic nature of income inequality. The gap between the more developed and less developed economies has widened as has the gap between prosperous and less prosperous regions within countries. Also, the top 20 percent of the income distribution has steadily increased its share in total income, while the bottom 20 percent’s share has decreased (ADB 2007). Furthermore, for most countries in the region the Gini index values are not only high but have increased over time. It is also worth noting that inequality is not just limited to income, but is also prevalent in access to essential social services such as education and health (ADB 2007).

Current  development  literature  has  either  taken  up  inequality within  specific  countries  in  Asia  (such  as  China,  India,  and  a number of East Asian economies) or explored it at a global level. Although a comprehensive study of inequality in Asia was recently published by the Asian Development Bank (2007), the report focuses mainly on trends and patterns and fails to develop a regional policy framework to address this critical issue. This paper attempts to provide a comprehensive overview of inequality in Asia, and also to develop a regional policy framework.

This paper treats inequality separately from poverty. Inequality and poverty have historically been grouped together in development literature, which has led to the development of a common set of policies for countries. Addressing economic inequality demands a structural change in policy thinking and design—from devising ways to reduce absolute income poverty to addressing relative income level. This represents a shift from viewing inequality as part and parcel of a static income problem to treating it as a dynamic and relative issue. Poverty, and by association inequality, has traditionally been viewed in absolute and isolationist terms, where the focus has been limited to reducing the number of poor. The broader concept of inequality, however, requires exploration of the difference in levels (of income, of provision to public goods, of opportunity, etc.) between individuals and groups in society. It requires looking at society at large and identifying the sources of disparate outcomes, which tend to be social, geographical, political and economic in nature.

Is income inequality bad?

Economists have historically cast the inequality debate in terms of efficiency vs. equity, where inequality is to be tolerated and indeed accepted as a trade-off to efficiency and economic growth. Posner (2007) has argued that “income inequality is not bad in general when it does not involve any reduction in the incomes of a substantial fraction of the population.” His rationale is quite simple: in the event that incomes of the bottom quintile increase by two percent while incomes of those in the top quintile increase by ten percent, everyone is better off even though inequality has increased. Posner (2007) believes that as long as incomes increase across all groups, it is not significant whether one group’s average income is increasing at a faster rate. In a similar vein, inequality is viewed as irrelevant by some, and what seems to matter is absolute increase in income and not relative increase (Krueger 2002; Feldstein 1999). However, there is agreement amongst economists that extreme inequality is bad, as Posner concedes that extreme inequality “can be politically destabilizing.”

Psychological studies show that relative incomes do matter and that people care about where they stand in the social hierarchy (Graham and Felton 2005, Frank 2005). According to Milanovic, “national inequality [is] an issue—simply because people compare their own standard of living and make judgments whether these income differences [are] deserved or not.” Using time series data on views about income inequality and social policy preferences in the 1980s and 1990s, Kenworthy and Macall found that “Americans do tend to object to inequality and to believe government should act to redress it.” Kuznets, who famously argued that income inequality is a common feature of a developing economy, also noted, “it is only through contact that recognition and tension are created, one could argue that the reduction of physical misery associated with low income and consumption levels[…]permit[s] an increase rather than a diminution of political tensions [because] the political misery of the poor, the tension created by the observation of the much greater wealth of  other communities[…]may have only increased.” The difference in relative incomes has led development economists and political scientists to explore the link between income inequality and conflict (Cramer 2003; Piazza 2006; Stewart 2002). A study of 85 developing countries between 1973 and 1977 found inequality to be a significant predictor of political violence (Muller and Weede 1990). Income and wealth differentials based on ethnicity, religion, and other groupings have been the cause of social violence. In Sri Lanka, for example, income, wealth and employment differentials between the minority ethnic Tamils and the majority Sinhala population has been the sources of a 25-year-long conflict (Gunewardena 2009). In Malaysia, despite considerable improvements, the incomes of the Chinese Malays are almost twice as much as those of the Bumiputeras (Stewart 2005). In India, the stark difference in incomes based on caste and religion has led to communal violence (Kundu 2009).

In the neoclassical economics framework, inequality is bad if it is perceived as detrimental to economic progress. Thus conceived, the value (bad or good) of inequality is largely determined by whether it impedes conditions for sustaining growth. While extreme inequality is considered bad for economic growth, there is no consensus as to what constitutes this extreme. This is partly due the elusive nature of inequality manifests: development experience of how income inequality plays out and its impact on economic progress has been mixed (Mishra 2009). A high degree of inequality in country X may not immediately result in adverse social and economic outcomes, whereas a relatively lower degree of inequality in country Y may quickly escalate conflict, which in turn would hamper growth. In Nepal, for example, despite increases in GDP, increases in income inequality over a relatively short period (5-7 years) became a rallying point for the Maoist opposition party, and had in effect, helped fuel the insurgency (Murshed and Gates 2003; Khatiwada 2006). China, on the other hand has grown at an average of more than eight percent in the past two decades, despite a sharp rise in inequality over the same period.   Lately, vast differences in average incomes between rural and urban areas, and across provinces in China, is becoming apparent, and there is growing realization that future growth will be compromised due to the widening income disparities (UNDP 2005). In consequence, increasing inequality in China is now viewed by the national government as a limiting factor to growth. Reducing inequality is thus seen instrumental (and as a means) to sustaining growth.

In philosophical literature the concept of equality/inequality is couched in ethical terms.  Issues of equity, including a more equal distribution of income, are associated with ideals such as justice and fairness (Aristotle 1912 (1282); Berlin 1956; Rawls 1971). Social scientists and policymakers often forge a link between income inequality and unjust social and political practices. Inequality therefore emerges as a manifestation of unjust practices and bias policies (Rawls 1971, 1977; Reddy and Pogge 2002; Singer 2002). As such, reducing inequality is seen as an end in itself and an inherently worthwhile pursuit; its value is not derived based on whether it drives or limits growth.

In sum, extreme inequality is bad, both from an ethical and an economic point of view. Inequality in Asia has risen to unprecedented levels, posing a threat to economic growth. This presents a timely opportunity for ethical and economic thinking to come together to address the issue through public policy.

  1. Research Questions

What are the recent trends in growth, as measured by GDP and GDP per capita, for countries in Asia? What are the trends in inequality, both across and within countries, for the major economies? How have the different sectors of the economy grown over time? What is the status of regional inequality within countries? How can governments, multi- lateral organizations, and regional bodies work together to develop a framework for reducing inequality at the regional level?   What short and long-run policy options do governments have at their disposal to achieve a more equitable income distribution and a balanced growth trajectory?

  1. Hypotheses

Based on increasing levels of inequality across and within countries, this paper attempts to present the broad outlines of a regional policy framework for reducing inequality in Asia. Any serious attempt to reduce inequality will require decoupling policies and programmes geared at reducing the number of poor from those aimed at reducing inequality. While poverty has significantly reduced in Asia in terms of persons living below a-dollar-day, inequality remains not only persistent but has dramatically increased in the region, especially since the 1990s (ADB 2007; World Bank 2009). Given the secular rise in inequality, we must take a fresh look at inequality and consider it separately from poverty, both in theory and in practice. This paper will focus on the following hypotheses.

Growth in Asia has been driven by the expansion of manufacturing and services, at the expense of the rural sector. As most of the poor reside in and are employed in this sector, they are excluded from the growth engines of the economy. Growth of the economy is increasingly shouldered by a limited segment of society, specifically those employed in the manufacturing and services. Reductions in inequality would require broadening the economic base to include a greater proportion of the working population as well as measures that make growth a more inclusive enterprise where, over time, low income earners account for a greater share in Gross Domestic Product (GDP). Direct investments that help build the potential of semi-and unskilled workers to be productive members of society are urgently needed.  Finally, education provides the main means for building human capital and realizing productive potential.

The conventional approach to reducing inequality through increased and targeted government spending and so-called ‘redistribution,’ although useful in curbing poverty, is not a sustainable solution for decreasing inequality. While direct income transfers and social protection measures are necessary, especially in helping to lift persons out of poverty, they do not directly build the capacities and the productive assets of the poor. The fiscal expansionary policies (such as those instituted by a number of emerging economies in the aftermath of the recent global financial crisis)  focused  on  providing  employment,  building  infrastructure, and extending basic social services to the low income segment of the population, need to be complemented by deeper structural policy reform that develops human capital and works with the values of societies to reorient the current pattern of growth.  Income inequality is a regional issue as it continues to affect the majority of countries in Asia. The paper will, therefore, develop the broad outlines of a regional policy framework to address inequality.

  1. Methodology

In the analysis section of the paper, the focus is on capturing the  pattern  of  inequality  both  across  and  within  countries.  Given that rising inequality is not limited to a few economies in the region, but  rather  is  prevalent  in  a  several  Asian  economies,  a  regional lens  is  applied  in  capturing and  addressing the  rise  in  inequality. Both inequality across countries (as measured by respective country GDP per capitas over time) and within country inequality (as measured by respective Gini coefficient/Gini index values) are considered in the analysis. China and India, due to their population size, high growth rates, and increasing levels of inequality, are analyzed in greater depth than other countries. Due to lack of data gathering and standardization, the paper limits the country-level analyses to China, India, Philippines, Bangladesh and Vietnam.

The growth-inequality link is explored through a sectoral analysis of growth. A disaggregation of growth by sector (i.e. services, manufacturing and agriculture) is used to identify sectors that drive growth. Employment data for select countries in the region, disaggregated by sector, is used to look at the distribution of the labor force across sectors.

Based on the above analyses, the policy section attempts to develop a regional policy framework to address inequality.

  1. Analysis

Inequality between Countries

Data on Asia over the past three decades supports three broad trends. First, the majority of countries in Asia have reduced poverty in terms of persons living under the internationally defined dollar-a-day poverty threshold (Dollar 2004; World Bank 2008). East Asian countries, and more recently China, have been the major players in this respect (World Bank 1993; Dollar 2007). Second, the region as whole has achieved a high level of sustained economic growth. Asia has grown faster than any other region over the past 20 years (World Bank 2008). Finally, income disparities between countries in the region have widened and the distribution of income within countries has become more uneven (ADB 2007). While the first two trends are positive, the third—rising inequality across and within countries—poses a risk to future growth (Humphrey 2007). To better understand the potential causes of increasing inequality requires 1) a close look at the nature of the growth process that has accompanied the divergence in incomes and 2) an analysis of the structural shifts that have aided and abetted economic growth.

Figure 1 reflects the GDP per capita values of countries in Asia and the Pacific since 1985 and into the 2000s. The comparison, based on PPP adjusted GDP per capita also helps to assess whether Asian economies are displaying what economists commonly refer to as σ convergence, the claim that the dispersion in incomes across countries should decrease over time.

Figure 1: Growth Trajectory of Countries in Asia and Pacific, PPP US$, 1985-2000s

Real GDP per capita time trend (All of Asia)

1985       1990       1995       2000       2005 year

Time trend in chained PPP adjusted GDP per capita over the time period of 1985-2003 for all Asian countries: Afghanistan, Bangladesh, Bhutan, China, Fiji, Federated States of Micronesia, Hong Kong, Indonesia, India, Iran, Cambodia, Kiribati, ROK, Laos, Sri Lanka, Macao, Maldives, Mongolia, Malaysia, Nepal, Pakistan, Philippines, Palau, Papua New Guinea, North Korea, Singapore, Solomon Islands, Thailand, Tonga, Taiwan (China), Vietnam, Vanuatu, and Samoa.

Each line in Figure 1 corresponds to a particular country’s Purchasing Power Parity (PPP) adjusted GDP per capita value in US dollars over a period of approximately 20 years. The dispersion in 1985 GDP per capita values across countries is much less than in the 2000s, indicating a divergence over time in GDP per capita across countries. While the average per capita income of a number of countries grew at a similar rate, several economies appear to have undergone a growth spurt, in effect branching off on higher growth paths. As a consequence, disparities in average per capita GDP have increased between faster growing economies (including China, Hong Kong,  Malaysia, Singapore, South Korea, Philippines, Taiwan, Thailand, and Vietnam) and relatively slower growing economies (including Bangladesh, Cambodia, Laos, Nepal and Pakistan). Countries such as Mongolia and North Korea have experienced little or no growth over the period. Although China had per capita incomes as low as those of North Korea and Mongolia in the mid-1980s, spectacular growth in the 1990s and 2000s has propelled China away from these two poorer economies. Ultimately, these growth trends have led to clear winners and losers, reflected as increasing divergence in economic fortunes.

Applying a similar exercise to other sub-regions, including East Asia, South East Asia, and South Asia, also shows a pattern of divergence in GDP per capita over time (Figures A1-A3 in Annex 1).  The Pacific Island Countries (PICs) are an exception, as they do not conform to this general trend of divergence, but they do not show convergence either (Figure A4 in Annex 1).  What is noteworthy is that none of the PICs showed strong or sustained growth, and therefore no particular economies took off while leaving others behind. Instead, these countries seemed to share similar economic experiences and therefore while they have not exhibited the divergence seen in other regions.

Testing for β Convergence

According   to   Barro   and   Sala-i-Martin   (1991),   β convergence    occurs    when    initially    poorer    countries    catch up   to   initially   richer   countries  by   growing   at   a   higher   rate.

This exercise is not meant as a formal test of the theory behind β convergence, especially as it is unlikely that an economy like South Korea has the same steady state growth rates as an economy like Cambodia. It is instead employed merely to show that Asian economies have lacked a tendency to converge in terms of PPP adjusted per capita GDP.

Figure 2 plots the average annual growth rate in PPP adjusted GDP per capita between 1985 and 2003 on the y axis and the log of initial level of PPP adjusted GDP per capita in 1985 on the x axis (following Barro and Sala-i-Martin 1991). The red line also shows the linear relationship between the two variables from a linear regression of the log of the initial level GDP per capita (1985 value) on the growth rate of GDP per capita (over the period 1985-2003).  For β convergence to occur, there should be a negative relationship between the two variables.   Clearly, there is no evidence in favor of β convergence. If anything, Figure 2 shows that there is divergence.  The coefficient from the linear regression is positive. The initially richer countries appear to grow faster than the initially poorer countries, resulting in a widening gap in GDP per capita over time between the two sets of economies. Along with the previous analysis of simple GDP per capita trajectories of economies, this result paints a picture of rising inequality across the Asian region.

Figure 2: Testing for β convergence in all of Asia for the time period of 1985-2003

Testing for Beta Convergence (all of Asia)

There is a noticeable difference in the growth trajectory between high/middle income countries on the one hand and the low income and the least developed countries on the other. Figure 3 groups countries according to their respective income levels; by indexing the Gross National Income (GNI), the difference in growth over time becomes apparent. While the GNI of currently high/middle economies in the region grew 7.5 times over 1980-2005, the GNI of LDCs grew at less than half this rate. The low income countries grew slightly faster than the LDCs, but markedly slower than high income countries.

Figure 3: Rate of GNI growth, 1980-2005 (1980=100)

Source: World Development Indicators 2007, The World Bank

Assessing inequality across countries requires careful examination of specific policies that have led to a lag in economic growth for some and have sparked tremendous growth for others. However, as inequality between countries has been measured by average per capita income, it tells us very little about within country inequality. A relatively slower growing economy such as Mongolia may have a more even distribution of income across its population compared to its faster growing neighbor China where the income distribution is heavily skewed in favor of the upper deciles. Should Mongolia grow faster to catch up with other countries at the expense of greater inequality within its borders? Or should it grow at a slower rate, but maintain a more constant level of equality? Such questions are commonly posed and highlight the apparent trade-off between equity and growth. It is assumed that there exists an inverse relationship between growth and equality (Stigltiz 1996). This paper will argue that it is indeed possible to have faster growth with equity leading to less disparate growth between and within countries.

Inequality Within Countries

Income inequality within countries has also increased. Figure 4 depicts the percentage change as well as the final value of the Gini index for a select group of countries. All countries excepting Thailand record increases in income inequality. While Thailand reduced its Gini index value by slightly over four percent over the past fifteen years, it still has relatively high levels of inequality with a final Gini index value of 42. Economies showing high rates of Gini index increase include both relatively developed economies (i.e. Hong Kong, South Korea, Philippines and Singapore) and developing economies (China, Nepal, and Sri Lanka).

Contrary to Kuznet’s famous hypothesis (1955), increases in inequality are not limited to periods of development, but also occur after passing milestones of development.  Kuznet’s predicted that inequality should increase during periods of development, and ought to taper off and begin to decrease once a country achieves a certain level of development. Japan, South Korea, Singapore, and Taiwan for instance, should thus show a declining trend in Gini index values. This, however, is not the case, as increasing inequality seems to affect both developed and developing economies (Figure 4).

Figure 4: Levels and Changes in Gini Index, 1990s-2000s (percentage Gini points)

China (46.9) Sri Lanka (40.2)

Nepal  (47.3)

China, urban (33.3)

Hong Kong SAR (51.4)

China, rural   (36.3)

Philippines (46.1)

Singapore (48.1)

Korea (33.1)

Lao PDR (34.7)

Bangladesh (31.8)

Malaysia (49.2)

Taiwan POC (33.9)

New Zealand (33.7)

Japan (31.4)

Thailand (42.0)

17.5 %

Source: IMF 2006, data from World Bank PovcalNet, WIDER World Income Inequality Database 2008, OECD 2005

The Gini index has increased in several countries not covered in Figure 4; in India (from 32.2 in 1986 to 36.8 in 2004), in Indonesia (from 32.4 in 1984 to 36.3 in 2005), and in Vietnam (from 32.8 in 1993 to 37 in 2004). In the case of Pakistan it declined slightly (from 32.44 in 1985 to 30.6 in 2002). By and large, for most countries Asia, inequality has increased within countries, with relatively shaper increase in this divergence over the past decade and a half.

What has driven this increase in inequality within countries? Finding an answer to this question requires a careful analysis of the nature of growth; specifically, a sectoral analysis will allow us to assess how the three main sectors of the economy—services, manufacturing/ industry, and agriculture have grown relative to each other. In addition to examining disparities across sectors, regional differences in income within countries will also be described. Certain geographical locations (urban and coastal regions for instance), have experienced greater economic activity and economic growth, while other locations (rural and inland regions) have achieved lower levels (ADB 2007).

Sectoral Analysis of Growth

What is immediately noticeable and supported by sectoral growth data (Table 1) is that GDP growth has been largely driven by industry, manufacturing and the services sectors. The high growth rates that East Asia and South Asia have been able to sustain are derived from the growth of these sectors. While East Asia’s GDP has grown at an average of over eight percent per annum since 1990, its industry, manufacturing and services sectors have grown significantly faster. South Asia is similar in this respect. On the other hand, in both of the sub-regions the agricultural sector has grown at a much slower rate than the other three sectors and overall GDP growth. In effect, agricultural growth, by being markedly slow relative to other sectors, has tempered overall GDP growth.1 There is evidence suggesting that sectoral composition of growth impacts inequality independently of the rate of growth (Ravallion and Chen

2007). The slow growth of agriculture has contributed to increasing inequality in spite of the high average growth rate for economies as a whole.

Table 1: Sectoral Growth for Countries in Asia 1990-2005

Countries            GDP       Agriculture          Industry               Manufactur-      Services ing

1990-   2000-   1990-   2000-   1990-   2000-   1990-   2000-   1990-   2000-

2000       05           2000       05           2000       05           2000       05           2000       05

EastAsia and Pacific         8.5          8.4          3.4          3.7          11           9.4          10.8        9.8          8.1          8.7

Cambodia   7.1   8.9          3.9          5.7          14.3        14.2        18.6        14.1        7.1          8.2

China     10.6        9.6          4.1          3.9          13.7        10.9        12.7        11.1        10.2        10

Indonesia            4.2          4.7          2              3.4          5.2          3.9          6.7          5.2          4              6.2

K o r e a , Rep     5.8          4.6          1.6          -0.1        6              6.3          7.3          7              5.6          3.7

Lao PDR                6.5          6.2          4.8          2.8          11.1        12.1        11.7        10.4        6.6          6.7

Malaysia              7              4.8          0.3          3.4          8.6          4.6          9.5          5.2          7.3          5.3

Mongolia             2.7          5.8          3.7          0.1          2.3          7.5          9.7          5.5          0.2          7.8

P h i l i p – pines 3.3          4.7          1.7          3.9          3.5          3.3          3              4.3          4              6

Thailand               4.2          5.4          1              1.9          5.7          6.9          6.9          7.2          3.7          4.5

Vietnam               7.9          7.5          4.3          3.8          11.9        10.2        11.2        11.5        7.5          6.9

South Asia   5.6  6.5          3.1          2.4          6.1          7.2          6.6          7              7.1          7.8

Bangla- desh      4.8          5.4          2.9          2.5          7.3          7.3          7.2          6.7          4.5          5.6

India      6              7              3              2.5          6.3          7.5          7              6.9          8              8.5

Iran        3.1          5.8          3.2          5.5          2.6          7              5.1          10.2        3.8          5.1

Nepal    4.9          2.8          2.4          3.2          7.2          1.1          8.9          -0.6        6.4          2.8

Pakistan               3.8          4.8          4.4          2.3          4.1          6.5          3.8          9.1          4.4          5.4

Sri Lanka              5.3          4.2          1.8          0.7          6.9          3.3          8.1          2.9          5.7          5.8

Source: World Development Indicators, World Bank 2008

While manufacturing, industry and services are currently driving Asian growth, this was not historically the case. Over time, the value added as a share of GDP for industry, manufacturing and services has steadily increased for both East Asia and South Asia, while the relative contribution of agriculture towards GDP has declined (Table 2). The decline in agriculture’s value added has been significant: for East Asia its share in GDP shrunk three times, from 34.6 percent in 1970 to just 11.9 percent of GDP in 2008. In South Asia’s case the decline was more than 20 percentage points, from 41.5 percent to 18.6 percent over the same period.  Although the shift from being predominately agrarian to manufacturing and services oriented is in line with typical development trajectory, but has taken place over a relatively short period of time inAsia.2

The abrupt structural change, with a focus on the secondary and tertiary sectors has paid dividend in way of unprecedented growth. However, a significant proportion of the population has neither contributed to, nor has benefited from the increased growth.

Table 2: Value-added by Sector, 2008 (percentage of GDP)

Agriculture          Industry/Manufac- turing


East   Asia   and   1970     1990       2008       1970       1980       2008       1970       1980       2008 the Pacific

34.6        25.0        11.9        36.0        39.8        47.5        29.3        35.2        40.6

South Asia           41.5        29.1        18.6        21.0        26.1        28.6        37.1        44.7        53.4

Source: World Development Indicators, World Bank 2009

Each sector’s contribution to GDP over time underscores that many Asian economies are no longer agriculture-based. While the agriculture sector has grown at the slowest rate and has contributed the least towards GDP amongst the main sectors, it employs the majority of the labor force (Table 3). In South Asia, agriculture still accounts for almost half of total employment, whereas in East Asia over 40 percent of the employed are in agriculture. The emerging dynamic is clear: agriculture has come to contribute least to the GDP, employs considerably more persons than industry and services, and, thus, also pays relatively lower wages on average. On the other hand, industry and services contribute significantly more to the GDP than agriculture, employ significantly fewer persons, and consequently pay relatively higher wages on average. This dynamic over time has led to a widening disparity in average incomes between those employed in agriculture and those employed in more productive sectors (ADB 2007).

Table 3: Employment by Sector, 2008 (percentage)

Agricul- ture

Industry/Manufacturing               Services

East  Asia  and    41.1        21.7        36.4

the Pacific

South Asia           47.7        22.2        30.1

Source: ILO Kilm 6th Edition 2009

While there has been high and sustained economic growth, it has not led to significant increases in employment. There has been a general lack of absorption of labor by the more productive sectors, which is reflected by  the  low  employment elasticity of  growth  (Pasha  and  Palanivel 2004).3This phenomenon of high economic growth in conjunction with low employment growth has been termed “jobless growth.”4 In fact, the level of unemployment has increased over the past ten years in South Asia and Southeast Asia and the Pacific (Figure 4).5  In East Asia, the unemployment rate showed a slight decline from around 4.6 percent to 4.2 percent between 1998 and 2008 despite record levels of economic growth during the period.

Figure 5: Unemployment Rate 1998, 2008 (percentage)

Source: ILO Kilm statistical database 2009

The Geography of Growth

Increased industrial dynamism, over a sustained period, has not been without consequence. Growth has not only been labor saving, but has also had a geographical bias. Certain regions with better access to international trade through historical ties to world trade, better infrastructure, reduced transport costs, better management by the state, or regions specifically targeted for foreign investment by government policy, have benefited more from economic industrialization and liberalization than other regions. For example, the coastal regions in China are now many times more well off than the inland regions, the central area of Thailand including Bangkok is much wealthier than the north eastern and southern states of the country, and states like Maharashtra and Karnataka in India are much more economically and technically advanced than states like Bihar and Orissa.

The experiences of several high population countries in Asia show income differentials between geographical regions to be large and on the rise.  China and India, due to their enormous populations, account for the majority of the poor in Asia; in both countries the issue of rising regional inequality has come to the fore, and has been well documented.6

Even though the Chinese central government has recognized the urgent need to reduce these regional disparities through large government campaigns such as the Great Western Development project (Goodman 2004), regional inequality persists.  The Human Development Index, a well-being index composed of GDP, life expectancy and gross enrollment ratio, showed urban areas to be much better off than rural areas (UNDP 2005). Urban areas recorded an HDI of 0.816 (comparable to Turkey, Russia, and Brazil), while rural areas recorded an HDI of 0.685 (comparable to Namibia, Tajikistan, and Bhutan).

Figure 6 shows the different rates of GDP per capita increase over time across five provinces of China. As a member of the burgeoning coastal regions, Shanghai is noticeably ahead of the other provinces for which data is available. Another coastal province, Zhejiang, is also growing faster than other provinces.  Shandong province, a remnant of the heavily industrial state owned enterprise dominated region of the pre-reform era, has seen less growth and started at a lower level than the two coastal regions. Nonetheless, it is still ahead of the inland province of Hunan and the western province of Xinjiang. As suggested by Figure 6, the inland and western provinces have yet to really share the massive wealth gains of the other regions.

Factors that have contributed to such large gaps between provinces have been preferential policies that have shifted productive resources towards manufacturing, creating Special Economic Zones to encourage foreign investment, restrictions on labor and capital mobility, and urban biased policies (Yang 2002; Ravillion and Chen 2007).   In addition, regional differences have come to exist in other aspects of society including access to health, education, and government fiscal expenditure (UNDP China Human Development Report 2005). These disparities are believed to have reinforced income inequality across regions.

Figure 6: GDP per capita (in Chinese Yaun) growth in Chinese


GDP per capita Time Trend for Selected Provinces

1994       1996       1998       2000       2002

SHANGHAI gdp_per_capita         HUNAN gdp_per_capita XINJIANG gdp_per_capita         ZHEJIANG gdp_per_capita SHANDONG gdp_per_capita

Like China’s, India’s urban areas have experienced a disproportionately large share of the country’s growth, leading to rising income inequality.  In 1986, India had a Gini index of 32.2.  By 2004, the value of the index had increased fourteen percent to 36.8. The rural Gini has hovered around 29 and the urban Gini has fluctuated around 36 in recent years.  This lack of drastic movement of the rural and urban Gini cannot account for the increase in the overall Gini. It seems that the increase in the overall index is due to the increasing disparity between the rural and urban areas.

India’s regional inequalities have been driven mostly by inequalities in the agriculture and service sectors (Das and Barua 1996).   For example, not all states benefited equally from the sharp productivity gains of the Green Revolution. Also, only a few states have significantly changed the structure of their economy and driven India’s rise to global leadership in the information technology industry. Consequently, one observes trends such as the one depicted in Figure 7 (below), where states such as Karnataka and Maharashtra lead other states such as Bihar and UP.  However, urban centers such as Delhi are even wealthier than the relatively wealthy states of Karnataka and Maharashtra. Inequality across states has been further exacerbated by reductions in fiscal spending, skewed sectoral and geographical distribution of domestic and foreign direct investment (FDI), and the impact of trade liberalization on employment intensive sectors (Pal and Ghosh 2007).

Figure 7:   GDP per capita (in Rupee) across select Indian States 2001-2006

2001       2002       2003       2004       2005       2006

KARNATAKA gdp_per_capita     UTTARPRADESH gdp_per_capita BIHAR gdp_per_capita                MAHARASHTRA gdp_per_capita DELHI gdp_per_capita

In Philippines the National Capital Region (NCR) that encompasses Manila accounts for more than one third of the national economy and has a GDP per capita three times the national average. In contrast, the region of Mindanao has GDP per capita that was less than one quarter of the national average.  Since 2000, the incidence of poverty has been lowest in NCR with eleven percent of the population below the poverty line, whereas the poverty incidence has been consistently over 50 percent in the Mindanao and Visayas regions (ADB 2005).

The Philippines have relatively more complete and consistent data for the time period of 2001-2006. As shown in Figure 8, the metropolitan

Manila area has much higher per capita GDP compared to the other regions. It is the region that seems to have experienced the most growth in GDP over the early half of the 2000s.

Figure 8:   GDP per capita (in Philippine Peso) across regions in Philippines, 2001-20067

GDP per capita Time Trend for Selected States

2001       2002       2003       2004       2005       2006

METROMANILA gdp_per_capita               CORDILLERA gdp_per_capita MUSLIMMINDANAO gdp_per_capita ZAMBOANGAPENINSULA gdp_per_capita BICOL gdp_per_capita

Bangladesh is no exception to the general trend of regional disparity across Asian economies.  Dhaka exceeds the other regions of Bangladesh significantly in terms of GDP per capita, as shown in Figure

9. Chittagong and Khulna form the mid-level regions in terms of wealth and Barisal, Rajshahi, and Sylhet trail far behind.

Figure 9:  GDP per capita (in Taka) across regions in Bangladesh, 1996-20008

GDP per capita Time Trend for the Regions of Bangladesh

1996       1997       1998       1999       2000

BARISAL gdp_per_capita              CHITTAGONG gdp_per_capita DHAKA gdp_per_capita  KHULNA gdp_per_capita RAJSHAHI gdp_per_capita                SYLHET gdp_per_capita

Given the scarcity of regional data, average agricultural wages across districts has been used as a proxy for income (Mahmoud et al. 2008). While average wages had increased across all districts, inequality also increased over the period 1993-2004 as wages in some districts (Chittagong) grew much faster than in other regions (Rangpur). Maholoud et al. (2008) cite regional distribution of public spending skewed in favor of richer districts as one of the main sources perpetuating inequality over time.

With  the  Doi  Moi  economic  reforms  in  Vietnam,  particular regions of the country have been heavily promoted at the expense of others. While there was a general decline in inequality within rural and urban areas, the national rise in inequality is attributed to a rise in inequality between rural and urban areas (Glewwe et al. 2000; VASS 2007). Notable reforms took place with regard to land and agriculture (dismantling of agricultural communes), private sector (promoting business),  public sector (closing down  of  state-owned enterprises), trade (liberalization through reductions in import tariffs and duties), and investment (promoting domestic and foreign investment) (McCaig et al. 2009).   With real per capita expenditures growing at 133, 117, and 111 percent between 1993 and 2004 for South East, North East, and Red River Delta respectively, inequality between regions grew (McCraig et al. 2009). Since the South East and Red River Delta, containing Ho Chi Minh City and Hanoi, were already the two richest regions in 1993, their faster growth has widened the relative gap between the richest and poorest regions. These fast growing regions (around Ho Chi Minh City, Hanoi, and Haiphong) have led growth and have left the mountainous areas of the North, the North-central, and parts of the central highlands mired in relative poverty (Glewwe et al. 2000; McCaig et al. 2009).

Factors contributing to inequality are education disparities, ethnicity, uneven access to infrastructure, and low employment status of head of household (Van de Walle and Gunewardena 2001, Molini and Wan 2008). Importantly, education has been singled out as a key determinant, as it was consistently found to be higher amongst urban households and positively correlated with higher income (Nguyen et al. 2006).

  1. Developing  a  Regional  Policy  Framework  for  Addressing Inequality

Inequality emerges as a regional issue, prevalent in the majority of countries in Asia. It also coincides with the economic rise of Asia and has manifested itself most visibly in the faster growing economies of the region. Thus far, a regional level response has not been articulated to deal with rising inequality. Regional institutions such as ASEAN and SAARC have  focused on  regional  integration and  cooperation, culminating in trade and investment agreements (ASEAN 2007, SAARC 2009). However, the charters of these multi-national organizations, like that of the United Nations, are grounded in peace building and not necessarily in economic development. While the United Nations, the World Bank, and the Asian Development Bank have continued to highlight social and development concerns, none have developed a specific regional framework that squarely addresses rising inequality. 9 together to develop a framework similar to the Millennium Development Goals and the Climate Change agenda that addresses inequality. The approach should:

1) set specific country and regional level targets and metrics that can be monitored;

2) identify dominant values that promote equality;

3)  share and build on experiences of successful interventions; and

4) develop action oriented policies and programmes that attempt to achieve growth with equity.

The Millennium Development Goals that emerged out of the Millennium Declaration,10 where 189 member states pledged to eradicate poverty by 2015, was an initial step in creating the conditions necessary for global and regional cooperation on social issues. Although the goals in full are unlikely to be achieved by 2015, they are quantitative and time bound, with a roadmap/development framework that can be adapted to suit the specific context of a country.11    Similarly, regional level agreement on reducing inequality, conceptualized in the form quantitative, measurable and time-bound targets, would be a necessary first step in realizing the regional dimension of inequality.

The Gini index or the Gini coefficent is the commonly used measure for income inequality. At the national level, consumption/expenditure data are often used as proxies to measure wellbeing and inequality between regions and provinces (Slesnick 1994; Gradín et al. 1998; Anwar 2006). Ideally, household surveys such as the Living Standard Measurement Surveys12 used by the World Bank should be used to maintain consistency and accuracy of data across countries and within countries. Eventually, by building their statistical capacity, each country should be able to report disaggregated levels of inequality—between and within states/provinces, between rural and urban areas, and across the male and female population.

Often, income inequality is accompanied by other types of inequalities, for instance, a low income person is likely to suffer from inequalities in basic rights, education, health, social standing, etc. (Stewart and Langer 2007). Tracking the wider associations of inequality such as access to health and sanitation, education, transportation (infrastructure), and social capital should be included as part of a broad set of indicators. This exercise should help identify the complementarities income inequality may have with other forms of inequality.

For  a  sustainable solution to  the  issue  of  inequality, we  need to carefully analyze the prevalent value system of a society, and see whether it encourages a sense of equality between individuals. We need to identify and work with values that lead to greater equality. While promoting values such as individual achievement, the merits of cooperation must also be emphasized. Introducing changes to the national education curriculum can be a starting point, where collective effort is rewarded. On the playing field, fairness, team effort, talent and skill, should take precedence over winning. In the workplace, social responsibility and sharing of profits should underpin business strategy. In places of worship, similarities between religions should also be highlighted, encouraging acceptance and tolerance of other religions. By working within the value system of a society, behavioral shifts can alter our standard notions of success such that individual success can come to be more closely associated with public benefit rather than personal gain (Barr and Gilg 2006; Cárdenas 2009).

This may seem like a utopian vision.  However, current public policy on climate change and the environment is already making headway in creating conditions for a more equal society. The emphasis on environmental sustainability is encouraging societies to rethink individual consumption patterns (Jorgenson 2003; Mont and Plepysa 2007). It is questioning the notion of mass production of food items, and advocating a more local, community driven approach to farming (Rosset 2000; Hole et al. 2004). It is instilling the value of sharing through carpooling, public transportation and the use of renewable sources of energy (Kockelman 2008). It is checking wasteful behavior by making recycling an everyday activity. Finally, it is promoting together to act collectively to set norms  and standards.13   Many  of these behavioral shifts and their likely impact on what constituents the Zeitgeist go unnoticed. However, our inability to measure such change does not render it inconsequential. As cooperation, sharing, a collective sense of responsibility, and prudence become dominant values, gross differences in income are less likely to be tolerated. Such a normative stance can be developed and articulated at the regional level through partnerships between governments, international NGOs, and multi- lateral and regional bodies.

The main lesson emerging from the success of the Asian miracle economies was that they achieved “rapid and equitable growth.”14 The early development experiences (1960s -1980s) of Indonesia, Malaysia, Thailand, Taiwan, Singapore and Hong Kong were marked by little or no positive change in the Gini coefficient while sustaining high levels of per capita growth (World Bank 1993). According to the World Bank report, the key ingredients that led to balanced growth were 1) sound macro-economic policy, 2) gradual liberalization and export-led growth, and 3) and investments in human and physical capital. Moreover, despite the diminishing role of agriculture, the sector grew at a formidable rate with high levels of productivity.

The main engines of growth were “private domestic investment and rapidly growing human capital,” and the education policies instituted by these countries “focused on primary and secondary schools [generating] rapid increases in labor force skills.” 15  Rural incomes, especially of those employed in agriculture, were not taxed excessively.  Another commonly cited aspect of success was government intervention, which “was conducive to technology transfer.”16  With regard to maintaining equality,  or  rather  preventing  inequality  from  worsening,  South Korea, Japan and Taiwan carried out land reforms which led to greater productivity and savings for farmers, thereby increasing domestic demand. Redistribution of income contributed to overall political stability, and relatively stable housing prices eased the burden on the poor (Stiglitz 1996). Finally, Stiglitz believes that Thailand’s program to provide credit to the rural sector “seemed not only to have promoted equality but also to have yielded reasonably high economic returns.”17

In recent years, Thailand also dramatically reduced its military budget, from 20.6 percent of GDP in 1985 to 8.6 percent of GDP in 2001; at the same time, the Thai government increased spending on human development (Numnak 2006).

Although useful, the experiences of growth with equity may not be easily replicable. What may have worked in Taiwan in the 1970s may not work in India in the 2010s. As such, the sharing of experiences stands rather as a testament, contrary to popular belief, that growth and equity can complement one another. Conventional theory, including Kuznet’s hypothesis, has long supported the idea that inequality is necessary for growth and that the initial growth trajectory produces inequality.

The next and final step toward a regional stance on addressing inequality is for countries in the region to develop their own policies and programmes to achieve more equitable and inclusive development. The analysis section identified two common and interrelated concerns. First, the sectoral analysis of growth revealed that the agricultural sector lagged behind industry and manufacturing. Second, there is a growing rural-urban development gap, one manifestation of which has been a growing disparity in incomes between those employed in a rural setting and those working in the more lucrative and productive urban sectors. The two concerns are interconnected as agriculture is the mainstay of the rural economy. According to the World Bank (2008) poverty is a rural phenomenon: “75 percent of the developing world’s poor live in rural areas.”18 Moreover, “between 80 to 90 percent of the poor are rural in all the major countries of the [Asia and the Pacific].”19 The working poor of Asia are very much part of the agricultural economy, one that has been in steady decline over the past two to three decades (IFAD 2002). Faced with these alarming facts, a regional strategy to make growth more equitable would require a focus on rural areas in general, and on the agricultural sector in particular. As such, policies should consider the following:

Growth should take place in sectors in which the poor work: the majority of the poor in most Asian countries are engaged in rural through public investments in rural infrastructure and services. In particular, the focus must be on raising productivity of small farms and on promoting off-farm employment opportunities in rural areas (Pasha and Palanivel 2003).

Growth should occur in backward and marginalized areas where the poor live: poverty rates are generally much higher in backward areas, sometimes over twice the national average (World Bank 2008). Therefore, the development strategy has to focus on uplift of backward areas and removal of the basic obstacles to growth.

Growth should derive from the factors of production that the poor possess and enhance their capabilities: labor demand created during the process of growth should be concentrated on creating employment opportunities for unskilled and semi- skilled workers. Sectors characterized by high levels of labor- intensity should be encouraged by preferential allocations of credit and tax treatment. In addition, the ability of the poor to avail the emerging opportunities should be enhanced by greater investment in human development, especially in basic education and health services.

Growth must keep prices of goods and services consumed by the poor, like food, relatively low: high rates of growth may fail to achieve significant poverty alleviation if simultaneously the rate of inflation, especially in food prices, also rises (Palanivel 2008). Not only is there a need for supporting domestic food production but also for improving marketing arrangements of such items (Timmer 1991).

Thus, an inclusive and equitable growth strategy in the shorter to medium term horizon has to focus on sectors, areas, factors of production and items of consumption, which play a critical role in increasing the productivity and incomes of the rural population.

As the growth of economies is clearly driven by manufacturing, industry and services, skill development will play a key role in raising incomes, productivity and the capabilities of the working population at large in the longer run education (Te Velde and Xenogiani 2007; Kirkegaard 2007). Currently, the proportion of the population driving Asian growth is marginal compared to the region’s total population; over 50 percent of the labor force is employed in agriculture suggesting a narrow base responsible for growth.   Investment targeted towards high growth sectors reveals a skill-bias in favor of high-skilled workers, leaving out the majority of the population in Asia, especially the rural unskilled workers (Goldberg and Pavcnik 2007). Different levels of education have  been  known  to  manifest themselves as  differences in skill level among workers, and as a consequence account for the disparity in wages (Juhn, Murphy & Pierce 1993). Reaping the benefits of globalization, and simultaneously enlarging the economic base so that a greater proportion of the population contributes to long-term growth, requires a focus on education and skills development. Currently, most governments, particularly in South Asia, spend less than three percent of their GDP on education, and in some cases it is as low as two percent of GDP. Clearly, an increase in public expenditure on education is desirable, especially targeted towards the poor.

  1. Conclusion

Persistent inequality, in addition to being ethically wrong, is politically risky and is likely to arrest development gains. In a continent that is now largely democratic, issues of equity will shape the future of governments. Rising levels of inequality across Asia underscore the regional dimension of the problem. Although currently lacking, a coherent regional response to address this issue is desperately needed for Asian countries to make a smooth development transition.

If the transition from developing to developed is to be achieved by the least developed countries in Asia while keeping inequality in check, then the transition from rural to urban needs to be managed carefully. The policy focus needs to shift from pursuing merely growth to developing a more inclusive form of growth. This requires that the distribution of, as well as the contribution to growth be critically questioned.

While the role of agriculture is likely to diminish in the future, it still employs a significant proportion of the working population. By building the  potential and  capabilities of  individuals, especially of those employed in agriculture, the economic base can be broadened. This would allow for a greater proportion of society to increase their respective contribution toward growth and development. Under such an outcome, the benefits of growth are also likely to be shared more widely.

Finally, if disparities are to persist in the short term, then we should ultimately be driven by the Rawlsian maxim, where inequalities are permitted only if they are to the greatest benefit of the least-advantaged members of society.

(The author is solely responsible for the views expressed in this paper, and the opinions expressed in this paper do not necessarily reflect the views of the United Nations Development Programme. The author is also indebted to Dr. Hafiz Pasha for his guidence).


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Annex I:

Figures of Real per capita GDP Time Trends for Sub-regions in Asia

Figure A1: Time trend in chained PPP adjusted GDP per capita over the time period of 1985-2003 for East Asian countries:  China, South Korea, North Korea, and Mongolia Real GDP per capita time trend (East Asia)

1985       1990       1995       2000       2005

CHN rgdpch        KOR rgdpch

MNG rgdpch      PRK rgdpch

Figure A2:  Time trend in chained PPP adjusted GDP per capita over the time period of 1985-2003 for Southeast Asian countries:  Indonesia, Vietnam, Malaysia, Cambodia, Philippines, Singapore, Thailand, and Laos.

Real GDP per capita time trend (Southeast Asia)

1985       1990       1995       2000       2005

IDN rgdpch                       PHL rgdpch VNM rgdpch                      THA rgdpch MYS rgdpch                      LAO rgdpch

KHM rgdpch                      SGP rgdpch

Figure A3: Time trend in chained PPP adjusted GDP per capita over the time period of 1985-2003 for South Asian countries: Pakistan, Bangladesh, Nepal, Maldives, India, Sri Lanka, and Bhutan.

Real GDP per capita time trend (South Asia)

1985       1990       1995       2000       2005

PAK rgdpch                    IND rgdpch BGD rgdpch                    LKA rgdpch NPL rgdpch                    BTN rgdpch

MDV rgdpch

Figure A4: Time trend in chained PPP adjusted GDP per capita over the time period of 1985-2003 for Oceania: Papua New Guinea, Kiribati, Tonga, Fiji, Federated States of Micronesia, Solomon Islands, Vanuatu, Palau, and Samoa.

Real GDP per capita time trend (Oceania)

1985       1990       1995       2000       2005

PNG rgdpch        FJI rgdpch KIR rgdpch     FSM rgdpch PLW rgdpch               WSM rgdpch SLB rgdpch               TON rgdpch VUT rgdpch

Annex II: Notes on Data

The data used for the majority of the following analysis is the Gini coefficient/index database compiled by Branko Milanovic. It can be downloaded from and also used his book, Worlds Apart: Measuring International and Global Inequality, Princeton: Princeton University Press, 2005.  This database is the most encompassing database of world Gini coefficients/index values currently available. Created in 2004, it compiles and adapts three existing datasets: the Deininger-Squire dataset that covers the years 1990-1996, the UNU Wider dataset that covers the period 1950-1998, and the World Income Distribution dataset that covers the period 1985-2000.  For the Milanovic database, Milanovic only included Gini coefficients of the previous datasets that were compiled from nationally representative household-based surveys.20  And in an attempt to supplement the Milanovic database, the 2008/2009 CIA World Factbook and IMF Statistics were also used. However, note that these later figures may not have undergone the same rigorous filtering process as the Milanovic figures.

Additionally, the Gini coefficients for the rural and urban economies of particular countries were extracted from the UNU-WIDER WIID2 database.  Since Milanovic only extracted Gini coefficients from nationally representative surveys, he left out many useful Gini coefficients that were computed from either rural or urban representative household-based or individual-based surveys.   Consequently, returning to the original WIID2 database allowed rural and urban trends to be analyzed.  However, because the WIID2 database compiled data from a variety of sources, care was taken to only pick the Gini coefficients from the most credible sources. There was also a deliberate attempt to sacrifice number of data points in favor of consistency. For example, China offered numerous sources for rural and urban data. Nonetheless, only one source, Chotikapanich et al 2006, was used because it contained the relatively highest quality data and a relatively broad coverage of years. Using Gini coefficients from different sources sometimes significantly changes the patterns.

For Countries

Data limitations led to an analysis on a handful of countries in Asia:

China, India, Bangladesh, Philippines, and Vietnam.  Even then, the data quality varies by country.  For China, the 1997, 1998, 1999, 2000, and 2003

Statistical Yearbooks published by the National Bureau of Statistics of China were used to calculate the province level GDP per capita figures. All figures are in Yuan and are in current market prices.  Since province level GDP per capita figures are not reported by the Statistical Yearbooks, they are crudely calculated by dividing the province level GDP figures by the province level populations.

For India, the data are taken from the Directorate of Economics & Statistics of respective State Governments, the Central Statistical Organisation, and the 2001 Census of India. The state level GDP per capita figures are denominated in current price Rupees.  Also, the data for 2006 is based off a population projection conducted by the Census.  Since state level GDP per capita figures are not available, they are calculated crudely by dividing the state level GDP figures by the state level populations.

The Indonesian data are from Badan Pusat Statistik (BPS-Statistics Indonesia). The GDP per capita figures are denominated in Rupiah and are in constant 2000 prices. Again, since province level GDP per capita figures are not reported, they are calculated crudely by dividing the province level GDP figures by the province level populations.

The data for the Philippines was extracted from the National Statistical Coordination Board and are denominated in pesos at 1985 constant prices. Here, region level GDP per capita figures are reported and are therefore used in the analysis.

The data for Vietnam are collected from the General Statistics Office, Viet Nam Economy in the Years of Reform, Statistical Publishing House, Hanoi and are data from 2002.  The Vietnam figures are actually the gross regional product per capita as a ratio of the national average.  Other sources have also been used and have been cited accordingly.

Finally, the Bangladesh data are from the Bangladesh Bureau of Statistics and some are extracted from the March 2008 “A Strategy for Poverty Reduction in the lagging Regions of Bangladesh,” published by the General Economics Division.  The data here are GDP per capita at current market prices and are denominated in Taka.


1              The extent to which growth has been affected depends on the agricultural sector’s relative weight in country/regional GDP.

2              In  the  case of  much of Western Europe and United States the  process  of development was spread over a much longer period of time.

3              Employment elasticity of growth is the incremental increase in employment due to a one percent increase in GDP. Pasha and Palanivel (2004) calculated the employment elasticities of growth over time and across sectors for several Asian economies. Their results show that manufacturing and services sectors, especially since 1990, have shown a declining employment elasticity of growth.

4              UNDP-ILO Press Release, ROAP/07/04,  February 20, 2007, p1. Available at PR_format.pdf

5              The 2008 unemployment level reflect pre-crisis (global financial crisis) levels.

6              For China see Wei 1999, Sachs at el 1996, Kanbur and Xiaobo 2001, Yang 2002, UNDP 2005). For India see Datt and Ravallion 1990, Das and Barua 1996, Deaton and Dreze 2002, Chamarbagwala 2006)

7              GDP per capita time trends for the Metropolitan Manila area, Cordillera, Muslim Mindanao, the Zamboanga Peninsula, and Bicol.

8              Note that only two data points have been taken (1996 and 2000). More recent figures were not available.

9              Currently the advocacy focus of these three organizations has been at the regional level, with an emphasis on climate change (in the longer term) and the social and economic impact of the financial crisis (in the short-term).

10  Available at

11  Additional goals have been added by some countries: Mongolia added Human Rights and Democratic Governance as its 9th  goal, whereas Afghanistan added Human Security as its 9th goal. In addition, countries such as Thailand that have fared well have set new—MDG plus–targets for themselves.

12  For more information on Living Standard Measurement Surveys is available at

13  The Kyoto Protocol and the Global Climate Conference held in Copenhagen are examples.

14  The World Bank .(1993). The East Asian Miracle: Economic Growth and Public Policy, World Bank Policy Research Report, Oxford University Press, pp v

15  The World Bank. (1993). The East Asian Miracle: Economic Growth and Public Policy, World Bank Policy Research Report, Oxford University Press. pp 5.

16  Stiglitz, J. (1996) Some Lessons from the East Asian Miracle, The World Bank Research Observer, vol. 11, no. 2, pp. 151.

17  Ibid., pp. 168.

18  World Bank. (2008). World Development Report 2008. pp 45.

19  IFAD. (2003). Assessment of Rural Poverty: Asia and the Pacific, pp 3.

20  Note:  For some countries, the Gini coefficient was only available for a very limited set of years.  Consequently, a detailed analysis of the trend in income inequality in those countries cannot be conducted.  In addition, some countries were left out of the following analysis all together due to lack of data