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Chapter 10. How Pro-Poor and Inclusive Is China’s Growth? A Cross-Country Perspective

Author(s):
Anoop Singh, Malhar Nabar, and Papa N'Diaye
Published Date:
November 2013
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Author(s)
Ravi Balakrishnan, Chad Steinberg and Murtaza Syed 

Poverty in China has fallen dramatically over the last 35 years of reform and opening up. However, inequality has increased since the early 1990s, dampening the impact of China’s growth on poverty reduction and making it among the most unequal large economies in the world. This chapter finds that although China’s recent period of growth has remained strongly pro-poor, it has been less inclusive compared with other regions. Based on cross-country experiences, policies that could broaden the benefits of growth in China include a fairer fiscal tax and expenditure system, higher public spending on health and education, enhanced social safety nets including conditional cash transfers, greater assistance for vulnerable workers, reforms to increase labor income, and improvements to financial access. As a beneficial by-product, many of these policies would also facilitate the rebalancing of China’s growth model by rationalizing saving, boosting household incomes, and unleashing consumption.

Introduction

Income inequality has risen across much of the world since 1990. The academic literature attributes this rise to three main factors: globalization, skill-biased technological change, and the decreasing bargaining power of workers. In addition, country-specific features may also be at play, including diminished social spending, barriers to labor mobility, and geographic disparities. The 2008–09 global financial crisis and recent social turmoil in different parts of the world have heightened awareness of the potential impact of rising inequality on economic and social stability and the sustainability of growth. Such concerns have not bypassed China, where policymakers are placing high priority on finding ways to arrest rising inequality and share the fruits of growth more equitably.

This chapter quantifies the degree to which China’s recent growth has been pro-poor and inclusive, and discusses what factors drive these outcomes and which policies could be considered to help make growth more broad-based and equitable. The main findings are that China’s impressive growth has translated into a dramatic reduction in poverty. However, inequality has increased sharply since 1990, dampening the impact of growth on poverty reduction. As a result, relative to other regions and in contrast to its own experience of equitable growth in the 1980s, China’s recent period of growth has been less inclusive. To rein in this trend, opportunities are available for policy measures to broaden the benefits of growth, notably enhanced spending on health and education, stronger social safety nets, labor market interventions, and financial inclusion. Many of these policies would also help rebalance the Chinese economy toward household and private consumption.

This chapter is organized as follows: The next section motivates the research by comparing recent trends in poverty and inequality in China with those in other regions of the world. The two subsequent sections propose ways to quantify how pro-poor and inclusive growth is in any economy and use a regression approach to assess China’s performance on these metrics relative to its peers. The final section draws lessons from international experiences in poverty-reduction initiatives and proposes potential policy interventions for broadening the benefits of growth in China.

Poverty, Inequality, and Growth: China in an International Context

Since 1990, growth in China and most Asian economies has been robust and higher on average than in other emerging regions. In turn, this growth has translated into significant reductions in poverty (Table 10.1).

Table 10.1People Living on Less Than $1.25 Per Day
Percent of PopulationNumber (millions)Percent of World TotalNumber (millions)Percent of World Total
1990200819902008
Europe and Central Asia2<19<120
Latin America and The Caribbean126533373
Middle East and North Africa6313191
Sub-Saharan Africa57482901538630
Asia55251,5448185566
China60136833617313
India47334332339531
Rest of Asia58314272228722
Total43221,9091,290
Source: World Bank, PovcalNet database.Note: At 2005 purchasing-power-parity prices.
Source: World Bank, PovcalNet database.Note: At 2005 purchasing-power-parity prices.

When China’s reforms began, it was one of the poorest countries in the world. In 1981, 84 percent of its population lived on less than $1.25 a day, the fifth-largest poverty incidence in the world. By 2008, this proportion had fallen to 13 percent, well below the developing-country average (Figure 10.1). Nevertheless, China’s large population means that it still remains home to almost 175 million people who live in extreme poverty.

Figure 10.1Asia: Change in Poverty Headcount since 1990

Source: World Bank; and IMF staff calculations.

Note: At 2005 purchasing-power-parity prices. In parentheses, the latest available year and corresponding poverty headcount ratios at $1.25 and $2 per day, respectively.

Poverty in China fell fastest during the early 1980s and mid-1990s, spurred by rural reforms and low initial inequality. With a relatively equal allocation of land—through land use rights rather than ownership—agricultural growth unleashed by the agricultural and rural economic reforms of the early 1980s translated into rapid poverty reduction. High access to health and education opportunities also ensured that the subsequent nonfarm growth in both rural and urban areas was poverty reducing.

Since the early 1990s, however, the nature of poverty in China has been changing. Growth in the agricultural sector has slowed and the benefits of agrarian reforms have started to dissipate. These changes have resulted in slower growth in rural employment and incomes, as well as a rise in urban poverty, partly reflecting large-scale migration from rural areas.

Most striking, inequality has increased sharply (Figure 10.2). According to the World Bank, China’s Gini index increased from 29 percent in 1981 to more than 42 percent in 2005, a level higher than that of the United States.1 Notwithstanding a downtick since 2009, official estimates report a Gini index of more than 47 in 2012.

Figure 10.2China: Trends in Poverty and Inequality

Source: World Bank, World Development Indicators.

Rising disparities have been characterized by increases in rural-urban inequality and regional inequality. In China, the rural-urban income gap has increased significantly since 1998, reaching a ratio of more than 3:1, which is high by international standards (Figure 10.3). For most other Asian economies, the ratio falls between 1.3 and 1.8 (Eastwood and Lipton, 2004).

Figure 10.3China: Rural and Urban per Capita Incomes

Source: National Bureau of Statistics.

At the same time, the historically slower pace of income growth in central and western regions compared with China’s eastern coast has widened income gaps among regions. The coastal regions, China’s export centers, have provided more opportunities for nonagricultural employment and income. This regional inequality was partly the result of geographical advantages but was compounded by preferential policies, as well as persistent disparities in human capital and infrastructure (Fan, Kanbur, and Zhang, 2009). Since the mid-2000s, this trend has reversed somewhat, as the result of supportive government policies in the inland and western parts of China.

From a cross-country perspective, the rise in inequality has been an almost global phenomenon since 1990, with the exception of parts of Latin America and parts of the Middle East and North Africa (Figure 10.4). The rise has been especially pronounced in parts of Asia, including China (Figure 10.5). For China and other economies in the region, this rising inequity is in sharp contrast to the previous three-decade record of equitable growth in Japan, the newly industrialized economies (NIEs), the members of the Association of Southeast Asian Nations, and China itself (Figure 10.6). At the same time, even as the size and purchasing power of China’s middle class has grown, its share of overall income has fallen while that of the richest quintile has increased. By contrast, in Latin America and in the Middle East and North Africa, the share of the richest quintile has fallen. Earlier work (IMF, 2006) attributes the rise in inequality around the world to skill-biased technological change and the transition from agriculture to industry for lower-income Asian economies (consistent with the Kuznets hypothesis).2

Figure 10.4Emerging Economies: Change in Gini Index since 1990

Source: CEIC Data; World Bank, PovcalNet database; WIDER income inequality database; Milanovic, 2010; national authorities; and IMF staff calculations.

Note: ASEAN-5 = five largest economies in the Association of Southeast Asian Nations (Indonesia, Malaysia, the Philippines, Thailand, and Vietnam); NIE = newly industrialized economies.

Figure 10.5Asia: Change in Gini Index since 1990

Source: World Bank; national authorities; and IMF staff calculations.

Note: In parentheses, the latest available year and corresponding Gini coefficients.

Figure 10.6Asia: Change in Gini Index before 1990

Source: Milanovic, 2010; and IMF staff calculations.

Note: In parentheses, the time period and end-value for the Gini coefficients.

How Pro-Poor is China’s Growth?

Going beyond these stylized facts, regression analysis can be used to quantify how pro-poor and inclusive China’s growth is relative to other emerging regions.3 There are various ways to interpret what it means for growth to be inclusive and pro-poor. This chapter follows the Ravallion and Chen (2003) approach and defines growth as pro-poor simply if it reduces poverty. Inclusive growth, in contrast, is defined as growth that is not associated with an increase in inequality, following Rauniyar and Kanbur (2010). More specifically, this chapter defines growth as inclusive when it is not associated with a reduction in the share of the bottom quintile of the income distribution.

To examine the relationship between poverty reduction and growth, the following regression is estimated:

in which Pi,t is the poverty headcount below the $2 per day line in country i at time t, Yi is a country dummy, yi,t is per capita income in country i at time t’, GINIi,t is the Gini coefficient in country i at time t, and ρd is a set of decade dummies. Because the equation is in logs, β gives the impact of income growth on poverty reduction, and δ gives the impact of a change in the Gini coefficient. Both β and δ are allowed to vary across countries and decades.

To estimate the fixed effects, a set of benchmark countries is needed. Because the main interest of this analysis is to compare China with the rest of Asia and Latin America, all countries falling in other emerging and developing regions—notably, Europe and Central Asia, the Middle East and North Africa, and sub-Saharan Africa—are included in the benchmark category. An instrumental variables approach is used to take account of endogeneity bias and potential measurement error in the income variable. In particular, lags of real per capita income as measured in the Penn World Tables are used to instrument the household-survey-based average income variable.

The regression analysis presented in Table 10.2 suggests that for all countries in the sample, growth is in general pro-poor, with growth leading to significant declines in poverty across all economies and time periods. Specifically, a 1 percent increase in real per capita income leads to about a 2 percent decline in the poverty headcount (column 1). However, a 1 percent increase in the Gini coefficient almost directly offsets the beneficial impact on poverty reduction of the same increase in income. This finding is consistent with other work that suggests that the incidence of extreme poverty in China would have fallen to less than 5 percent had inequality not increased after 1990 (ADB, 2012).

Table 10.2Pro-Poor Growth Regressions1
(1)(2)(3)(4)(5)
VariablesPPPPP
Log of mean household income (y)–2.146***–8.205***–2.627***–3.406***–10.536***
(0.262)(1.079)(0.300)(0.428)(1.232)
EAP × y1.258**1.138*
(0.616)(0.644)
South Asia × y–0.1591.177**
(1.202)(0.584)
LAC × y1.294***0.653
(0.502)(0.506)
China × y1.1492.046***
(0.712)(0.743)
India × y1.889***2.178***
(0.675)(0.559)
Brazil × y1.220***0.640
(0.436)(0.409)
Indonesia × y1.957***2.432***
(0.433)(0.464)
Log of Gini index2.258***–5.838***2.277***2.003***–7.799***
(0.463)(1.205)(0.450)(0.499)(1.502)
Ninety (1990s decade dummy)–0.7430.0740.054
(0.536)(0.075)(0.075)
Noughty (2000s decade dummy)0.6940.262**0.142
(0.647)(0.112)(0.099)
Ninety × y0.193*
(0.110)
Noughty × y–0.067
(0.126)
Income-Gini interaction1.723***2.035***
(0.267)(0.317)
Observations579579579579579
R-squared0.5580.6540.5580.4610.591
Number of clusters9898989898
ModelFE IVFE IVFE IVFE IVFE IV
Source: Authors’ calculations.

Dependent variable is the log of poverty headcount below the $2 line. Robust standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.

Source: Authors’ calculations.

Dependent variable is the log of poverty headcount below the $2 line. Robust standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.

Moreover, inequality interacts with income, meaning that a higher level of inequality tends to reduce the impact of income growth on poverty reduction (column 2). As an illustration, an increase in the Gini coefficient of about 25 percent (as in urban China from 1995 to 2005) reduces the impact of a 1 percent increase in income to about a 1½ percent decline in the poverty head-count from 2 percent in the base case. The implication of this result is that past rises in inequality are likely to reduce the future impact of income growth on poverty, even if the level of inequality remains constant. In addition, the impact of growth on poverty reduction is found to be somewhat lower during the 1990s, possibly as a result of a change in the nature of growth (column 3).

The relationship, however, varies across regions and economies (columns 4 and 5 and Figure 10.7). In particular, in East Asia and Latin America, income growth has a significantly lower impact on poverty than it does in the Middle East and North Africa, Eastern Europe and Central Asia, and sub-Saharan Africa, the baseline economies. The impact is particularly weak in India and Indonesia, where it is significantly less than the impact of an equivalent reduction in the Gini coefficient. For China, the impact is as strong as in the baseline economies, suggesting that growth has been highly pro-poor.

Figure 10.7Income Elasticity of Poverty Reduction1

(impact on poverty headcount, in percent, of 1 percent increase in per-capita income)

Source: World Bank, PovcalNet, Penn World Tables; and staff calculations.

1 The black bars represent countries for which the estimated income elasticity of poverty reduction is significantly different to that of the baseline countries.

2 EAP includes Cambodia, Malaysia, Philippines, Thailand, and Vietnam.

How Inclusive is China’s Growth?

As a second step, the analysis follows Dollar and Kraay (2002) and looks at the relationship between per capita income and the income of a broader definition of “the poor”—the income of the bottom quintile of the income distribution. If the income of the poor tends to rise in the same proportion as average incomes—that is, income growth is not associated with a decrease in the income share of the bottom quintile—then growth would be considered inclusive. Specifically, the following panel regression is estimated:

in which yp1i,t is the per capita income of the bottom quintile of the income distribution in country i at time t, θi is a country dummy, yi,t is per capita income in country i at time t, and ηd is a set of decade dummies. The term λ—which is allowed to vary across country and decade—is the elasticity of growth in income of the bottom quintile with respect to growth in average income. This equation can be rewritten as follows:

in which Q1i,t is the bottom quintile share of the income distribution in country i at time t. As equation (10.3) shows, if λ is less than 1, income growth is associated with a decrease in the income share of the bottom quintile: that is, growth is not inclusive. Equation 10.3 is the model we estimate. Given that much of the ongoing debate on inclusiveness has not just focused on the poorest fifth of society being left behind, but the richest fifth doing particularly well, the analysis also estimates a similar relationship for income in the top quintile. As with the pro-poor regressions, we use an instrumental variables approach to take account of endogeniety bias and potential measurement error in the income variable.

The results are shown in Table 10.3. If all observations are simply pooled, the familiar Dollar-Kraay result is obtained—that average incomes of the poorest fifth of society rise proportionately with per capita income (column 1), something which also holds for the richest fifth (column 4). However, once the analysis instruments for the income variable (columns 2 and 5), then the income of the bottom quintile rises significantly less than proportionately with average income, and the income of the top quintile rises significantly more than proportionately with average income.

Table 10.3Inclusive Growth Regressions1
(1)(2)(3)(4)(5)(6)
VariablesInQ1InQ1InQ1InQ5InQ5InQ5
Log of mean household–0.025–0.142**–0.0970.040*0.119***0.060
income (y)
(0.043)(0.061)(0.126)(0.023)(0.034)(0.061)
EAP × y0.126–0.142
(0.180)(0.101)
NIEs × y–0.430***0.098
(0.126)(0.062)
LAC × y0.133–0.068
(0.186)(0.080)
South Asia × y–0.480***0.390**
(0.178)(0.185)
China × y–0.2040.138**
(0.128)(0.062)
Brazil × y0.469***–0.260***
(0.126)(0.063)
India × y0.320–0.224
(0.305)(0.146)
Indonesia × y0.049–0.030
(0.133)(0.069)
Observations661633633661633633
R- squared0.001–0.0270.0170.021–0.0190.064
ModelFEFE IVFE IVFEFE IVFE IV
Number of clusters107105105107105105
Source: Authors’ calculations.

Dependent variable is the log share of the income distribution of the bottom/top quintile. Robust standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.

Source: Authors’ calculations.

Dependent variable is the log share of the income distribution of the bottom/top quintile. Robust standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.

Moreover, these elasticities vary significantly across regions and countries (columns 3 and 6). For the bottom quintile, the elasticity is significantly less than 1 for China, as well as for the NIEs and South Asia (excluding India), whereas for Brazil, it is significantly greater than 1 (Figure 10.8).4 Turning to the top quintile, the results are the mirror image of those for the bottom quintile, except for the NIEs (Figure 10.9). The elasticity is significantly greater than 1 for China, the NIEs, and South Asia (excluding India), and significantly less than 1 for Brazil (column 6). In sum, the results suggest that growth has generally not been inclusive in China, nor in the NIEs and South Asia (excluding India), whereas it has been inclusive in Brazil.5

Figure 10.8Degree of Inclusiveness of Growth1

(impact on income of the bottom quintile, in percent, of a 1 percent increase in per-capita income)

Source: World Bank Development Indicators; and IMF staff estimates.

1 The black bars represent countries for which the estimated degree of inclusiveness is significantly different from one.

2 EAP includes Cambodia, Malaysia, Philippines, Thailand, and Vietnam.

Figure 10.9Degree of Inclusiveness of Growth1

(impact on income of the top quintile, in percent, of a 1 percent increase in per-capita income)

Source: World Bank Development Indicators; and IMF staff estimates.

1 The black bars represent countries for which the estimated degree of inclusiveness is significantly different from one.

2 EAP includes Cambodia, Malaysia, Philippines, Thailand, and Vietnam.

Finally, using the regression estimates, Table 10.4 investigates the importance of growth to the welfare of the poor. It constructs measures of pro-poor and inclusive growth for Brazil, China, India, Indonesia, Mexico, and the Russian Federation for recent decades. The table shows that although the income elasticities of poverty and income of the bottom quintile vary significantly across economies, per capita income growth remains a key driver of income of the poorest fifth of society. Some of the more specific results include the following:

Table 10.4Pro-Poor and Inclusive Growth Measures
[1]

Elasticity of Poverty w.r.t. Income Growth1
[2]

Degree of Inclusiveness1
[3]

Income Growth2
[4]

Change in Gini
[5]=[1] × [3] + 2 × [4]

Predicted Change in Poverty (%)
[6] = [2] × [3]

Predicted Change in Bottom Fifth Income (%)
China 1980s−3.40.78454−17759
China 1990s−3.40.78836−22761
China 2000s−3.40.712322−37786
Brazil 1980s−2.21.4245−4333
Brazil 1990s−2.21.45−3−187
Brazil 2000s−2.21.434−9−9247
India 1990s−1.51.010−1−1710
India 2000s−1.51.0269−2126
Indonesia 1990s−1.41.015−5−3115
Indonesia 2000s−141.09023−8490
Mexico 1990s−2.11.0−17−331−17
Mexico 2000s−2.11.041−4−9441
Russia 1990s−3.41.0−47−26110−47
Russia 2000s−3.41.09212−28992
Source: World Bank, PovcalNet; Penn World Tables; and IMF staff calculations.

Set equal to the value for the baseline countries when the null of a significant difference cannot be rejected.

As proxied by 100 times the change in the log over the corresponding period.

Source: World Bank, PovcalNet; Penn World Tables; and IMF staff calculations.

Set equal to the value for the baseline countries when the null of a significant difference cannot be rejected.

As proxied by 100 times the change in the log over the corresponding period.

  • Inequality has widened in China, in contrast with Brazil and Mexico. Still, China has experienced greater poverty reduction because of its larger growth in average income.

  • The importance of average income growth is reinforced when looking at trends in Indonesia and Russia. For both economies in the first decade of the 2000s relative to the 1990s, poverty reduction was much greater despite worsening inequality, because growth was much higher.

  • A similar story emerges when looking at measures of inclusive growth. For example, although growth has been only half as inclusive in China as compared with Brazil, the income of the poorest fifth of society has increased by relatively more in China because average income growth has been much stronger.

Toward More Inclusive Growth in China: Some Lessons from International Experience

This section discusses policies that could reduce inequality and increase inclusiveness, based on the regression results and international experience.6 It is by no means exhaustive and the multiple factors behind rising inequality suggest that a set of mutually reinforcing policies will likely be needed, and that the necessary mix will vary from country to country.

In China, boosting household incomes would appear to be a key priority for addressing inequality. Higher incomes could be achieved, for instance, by decreasing existing subsidies to capital in the form of artificially low input costs, promoting services and agriculture that tend to be more labor intensive, continuing to raise minimum wages, and raising the returns to household savings through interest rate reform and financial development.

Fiscal Policy

Increasing government spending on education and health. The relatively low amount of public spending on education and health as a share of GDP in China points to an important potential role for fiscal policy in strengthening inclusiveness (Figure 10.10). Increased spending is particularly important in the face of rising skill premiums and increasing returns to human capital. Between 1988 and 2003, wage returns to one additional year of schooling increased in China to 11 percent from 4 percent (Zhang and others, 2005) and disparities in educational attainment beyond primary school have also emerged.

Figure 10.10Labor Market Institutions and Inclusiveness

Source: Botero and others, 2004; World Bank, Doing Business; and IMF staff estimates.

Note: EAP = East Asia and Pacific; LAC = Latin America and the Caribbean; NIEs = newly industrialized economies; SA = South Asia. The light gray diamonds represent countries for which the estimated degree of inclusiveness is significantly different from one.

1 Measures the protection of labor and employment laws as the average of alternative employment contracts, the cost of increasing hours worked, the cost of firing workers, and dismissal procedures.

Tax and spending effort. In addition, adjusting the level and structure of taxes and spending may have a part to play. In Organization for Economic Cooperation and Development (OECD) countries, taxes and transfer policies have also been estimated to reduce inequality by about a quarter (OECD, 2012). In sharp contrast, the redistributive impact of fiscal policy in developing economies is severely restricted by lower overall levels of both taxes and transfers—whereas average tax ratios for advanced economies exceed 30 percent of GDP, ratios in Asia and the Pacific are only about half that level and among the lowest in developing regions (Bastagli, Coady, and Gupta, 2012). At about 20 percent of GDP, China’s tax ratio is on the low side. Partly as a result, social spending is also substantially lower in developing economies, at about 8 percent of GDP compared with 15 percent of GDP in advanced economies, with lower transfers and health spending explaining most of the difference. Again, China is an even greater outlier on the low side. Public expenditures on health, pensions, and other forms of social protection only amount to 5.7 percent of GDP in China. On average, economies at similar levels of development spend more than twice as much.

Tax and spending structure. Greater reliance on less progressive tax and spending instruments adds to the problem. In Asia and the Pacific, indirect taxes account for half of tax revenue, compared with less than one-third in advanced economies. In China, the situation is even more extreme, with taxes on incomes and profits making up only about one-quarter of total tax revenue. Broadening the tax base and improving the progressivity of some taxes could also be considered in China.7 Meanwhile, participation in social insurance schemes remains limited in many developing countries like China (particularly in rural areas), and expenditure on social assistance programs is often low and poorly targeted. According to the Asian Development Bank (ADB), about one-third of the poor population in China do not have access to any social programs. Thus, reliance on targeted social expenditures aimed at vulnerable households, including for health and education, could be increased. Conditional cash transfer programs are being increasingly used in low-income emerging economies. Brazil and Mexico have two of the largest schemes (in the former, Bolsa Familia covers about 25 percent of the population) with transfers contingent on requirements such as children’s school attendance or vaccination records. Both are considered to have been successful, with the Mexican program being associated with a 10 percent reduction in poverty within two years of its introduction. In Asia, the Philippines introduced a conditional cash transfer program in 2008 (the 4Ps) to help redirect resources toward socially desirable programs in a well-targeted way. By 2012, it was budgeted to reach 60 percent of the poor. In India, the recently launched unique identity scheme holds significant promise in ensuring better targeting of social schemes and allowing the vulnerable to access the welfare system.

Intergovernmental fiscal arrangements. In China, the existing system of intergovernmental fiscal relations may be compounding the problem. Fiscal decentralization is much higher in China than in OECD and middle-income countries, particularly on the spending side. More than half of all expenditure, including social spending, takes place at the subprovincial level. The result has been that poor villages cannot afford to provide good services, and poor households cannot afford the high private costs of basic public services. With regard to public spending on education, large differences in per capita allocations are found across provinces. Overall public spending per capita in the richest province is almost 50 times that in the poorest (ADB, 2012), although the go-west policy in place since 2000 has helped to narrow some of these gaps.

Other social safety nets. In China, the World Bank estimates that only about 30 percent of the labor force contributed to a pension scheme in 2008, with participation especially limited among migrants. This proportion compares to an average coverage rate of 60 percent in OECD countries (OECD, 2009). As well as increasing inclusiveness, enhancing such safety nets would also reduce precautionary motives to save, thereby increasing consumption and facilitating the needed rebalancing of China’s economy. Ensuring the sustainability of these welfare programs will be important, however, given China’s aging population. A key question about such policies is their fiscal cost. The Bolsa Familia program in Brazil only costs 0.4 percent of GDP, and recent IMF work on China (Barnett and Brooks, 2010) argues that a minimum social safety net can be provided at low cost, with more comprehensive nets funded by broadening the tax base and increasing some taxes, along with reallocating existing spending. In addition, some policies may have no fiscal cost, such as unemployment insurance schemes in which employees and employers contribute to individual accounts. Regarding education, in many cases the challenge is to improve quality. Expanding pension provision could entail costs, but not necessarily if benefits are provided on a defined contribution basis and contribution rates are increased.

Labor Market Policies

Labor share of income. Across most of the OECD as well as Asia, the past two decades have seen a decline in the income share of labor and a rise in that of capital—in China, the labor share fell from an estimated 50 percent during the early 1990s to about 40 percent by the middle of the first decade of the 2000s. This disproportion contributes to inequality, because capital income tends to be less evenly distributed than income from basic wage labor. It is partly the result of technological change that the return to capital has risen and the employment elasticity of growth has declined; according to the ADB, between 1991 and 2011, the employment elasticity of growth fell from 0.44 to 0.28 in China. In the case of China, this has been exacerbated by an artificially low cost of capital. The historically large pool of surplus labor in rural areas has also reduced the bargaining power of workers, contributing to holding wages low relative to productivity.

Bargaining power of workers. Academic work also links rising inequality to the weaker bargaining power of workers (Levy and Temin, 2007). Indeed, addressing labor market duality and the use of minimum wages are being increasingly advocated across the world to support the income of low-earning workers. In this vein, China’s February 2013 announcement of a 35-point plan to tackle income inequality includes a provision to raise minimum wages to at least 40 percent of average salaries by 2015 across most regions. These effects seem to matter empirically. Inclusive growth is positively associated with the degree of employment protection and minimum wage levels (Figure 10.10). Although recent increases have made minimum wage rates in China relatively favorable compared with other emerging regions, employment protection appears weak, possibly reflecting the predicament of migrant workers.

Labor market impediments. In China, additional labor market interventions could be considered. In particular, restrictions on rural-urban migration under the hukou system—whereby workers without urban registrations have difficulty accessing housing, social services, and social security—have limited opportunities for the relatively poor rural population. Anecdotal evidence suggests that practical difficulties continue to complicate selling or mortgaging rural land, compounding the problem. In addition, worker training and skills upgrading would help make growth more employment friendly.

Financial Access

A burgeoning literature demonstrates that financial development not only promotes economic growth but also helps apportion it more evenly. According to some estimates, for the lowest quintile, the benefits of financial development are split roughly equally between those associated with faster growth and those from greater income equality (Beck, Demirgüç-Kunt, and Levine, 2007). Financial market imperfections—such as asymmetric information and costs associated with transactions and contract enforcement—hit poor and small-scale entrepreneurs hardest because they typically lack collateral, credit histories, and connections. These deficiencies prevent capital from flowing to poor individuals, even if they have projects with high prospective returns, thereby reducing the efficiency of capital allocation and aggravating inequality. By addressing these imperfections and creating enabling conditions for financial markets and instruments to develop—such as insurance products that facilitate adjustment to shocks—governments can not only spur growth but also help ensure that it is distributed more evenly.

How does China currently fare on financial development compared with its peers? There is appreciable disparity across Asia (Figure 10.11). Financial deepening—a measure of the level of financial services, typically proxied by broad-money-to-GDP—is positively associated with per capita income, and is elevated in China, reflecting high domestic saving and strong external inflows.

Figure 10.11Financial Deepening, Latest Year Available

Source: World Bank Development Indicators; and IMF staff estimates.

However, deepening by itself may not translate into financial services being broadly available across firms and households, making “access to finance” equally important as financial deepening. Across the globe, high-income countries tend, on average, to have almost 12 times more bank branches and 30 times more automated teller machines for every 100,000 adults than do low-income countries. Indeed, lack of access to finance is a major impediment in many parts of Asia, including in China, where more than half the population and a significant proportion of small and medium enterprises lack access to the formal financial system (IFC, 2010).

Moreover, there is evidence that access to finance is positively correlated with relative success in reducing poverty incidence and ensuring equity across Asian economies (Beck, Demirgüç-Kunt, and Levine, 2007). For China, several empirical studies suggest that uneven access to financial services has contributed to inequality. Zhang and others (2003) find that after controlling for other factors—such as provincial infrastructure, institutional transition in rural areas, and degree of international integration—differential financial development and urban biases in lending have contributed significantly to the rise in China’s urban-rural income disparity since the late 1980s. Financial development—measured as total rural loans to rural GDP—has been found to contribute significantly to reducing rural inequality in China (Liang, 2008). Tellingly, three of the poorest provinces in China—Tibet, Yunnan, and Sichuan—have more than 50 unbanked counties.

How might the Chinese government address the twin goals of supporting growth and reducing inequality? International experience provides some direction. First is ensuring macroeconomic stability as financial systems are liberalized, and particularly as they are opened up to the rest of the world, because financial shocks typically hit the poor hardest (see Chapter 13 for a discussion of a road map designed for China). Second is identifying and removing impediments to financial access without directing particular outcomes. Expanding credit availability by promoting rural finance, extending micro-credit, promoting credit information sharing, and developing venture capital markets should significantly expand credit availability (Beck and Demirgüç-Kunt, 2006). Third, because poverty is often relatively higher in rural areas, is ensuring that regulations—such as loan classification criteria and capital requirements—do not discriminate against the provision of finance to the rural poor, including the agricultural sector. Fourth is bolstering the legal environment and financial market infrastructure, including property rights and contract enforceability. For instance, well-defined processes for securing collateral in the event of default can encourage banks to lend more to small and medium enterprises, and developing capital markets can help broaden the channels for financial access. Fifth is promoting regulatory policies that foster transparency and competition among financial institutions (Levine, 2011), rather than those that channel credit to politically favored ends (see, e.g., Barth and others, 2009).

In addition to arresting the rising tide of inequality in China, many of the policies discussed in this chapter have the potential to rationalize savings and boost household incomes, reducing the bias toward capital and large corporates, and unleashing consumption. In this way, these measures would have the positive side effect of facilitating the needed rebalancing of China’s growth model toward households, workers, and consumption.

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The Gini index is a commonly used measure of the extent to which the distribution of income or consumption expenditure within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Jaumotte, Lall, and Papageorgiou (2008) also argue that skill-biased technological progress is a key driver of rising inequality.

For the econometric analysis, the main sources of data are the latest versions of the PovcalNet database (updated in July 2012) and the Penn World Tables. PovcalNet was chosen because major efforts have been made to make its inequality and poverty data comparable across surveys and countries: It draws on 700 household surveys and 120 countries (econ.worldbank.org/povcalnet). Household survey data for the NIEs are added to this, resulting in an unbalanced panel between 1971 and 2010, with the sample skewed toward the latter part of the period.

Although the elasticity for China is not significantly different from that of the baseline at the 10 percent level (column 3 of Table 10.3), further χ2 tests show that it is significantly different from 1 at the 1 percent level.

One important caveat is that Brazil entered the 1990s with a relatively higher level of inequality.

In the Asian context, evidence indicates that the labor share of income, public education spending, years of schooling, industry employment, and financial reform significantly increase the degree of inclusiveness (Balakrishnan, Steinberg, and Syed, 2013).

The Asian Development Bank notes that only 11 types of personal income are subject to taxation. Moreover, while some of these are taxed at progressive rates (wages and salaries), others are taxed at a flat rate (such as income from personal services, royalties, and rental and lease income).

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