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Knowledge in Development Note: Poverty Traps

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Poverty Traps (2009)

Under certain conditions, an economy, region or household can find itself in a “poverty trap.” This is a stable equilibrium but at a low level of wealth and output, and it cannot get out of this low-level equilibrium (sometimes called a “low-level attractor”) without a potentially large injection of external assistance. Similarly, with a sufficiently large negative shock, the economy or individual might fall into this low-level equilibrium. For an individual this may mean destitution.

If such poverty traps exist then they have striking implications for development policy. For one thing, small amounts of external aid may do little, but a large expansion of aid may well catapult large numbers of people out of poverty.[1] For another, poverty traps at the individual or household level imply potentially large economic returns from social protection policies to assure that people do not fall into the low-level attractor.

What does current theory and evidence suggest about these important ideas?

Growth and distribution

Theories of economic growth incorporating credit-market failure suggest that high inequality reduces an economy’s aggregate efficiency and (hence) growth rate.[2] The market failure is typically attributed to information asymmetries—that lenders are poorly informed about borrowers. The key analytic feature of such models is a suitably nonlinear relationship between an individual’s initial wealth and future wealth; this relationship is sometimes called the “recursion diagram.” The economic rationale for a nonlinear recursion diagram is that the credit market failure leaves unexploited opportunities for investment in physical and human capital and that there are diminishing marginal products of capital. Then higher current inequality implies lower future mean wealth at a given value of current mean wealth.[3] By the same token, such credit constraints also imply that unambiguously higher current poverty incidence—defined by any poverty line up to the minimum level of initial wealth needed to not be liquidity constrained in investment—yields lower growth at a given level of mean current wealth.[4] High poverty can thus constrain growth.

Other arguments suggest the initial incidence of poverty is a relevant parameter in explaining why some economies grow faster than others. If there is a subsistence consumption requirement then higher poverty incidence (failure to meet the subsistence requirement) can imply lower growth.[5] Another example can be found in the theories that have postulated impatience for consumption (high time preference rates possibly associated with low life expectancy) and hence low savings and investment rates by the poor.[6]

Yet another example is found by considering how work productivity is likely to be affected by past nutritional and health status. Only when past nutritional intakes have been high enough (above basal metabolic rate) will it be possible to do any work, but diminishing returns to work will set in later.[7]

This type of argument can be broadened to include other aspects of child development that have lasting impacts on learning ability and earnings as an adult.  By implication, having a larger share of the population who grew up in poverty will have a lasting negative impact on an economy’s aggregate output.

There are also theoretical arguments involving market and institutional development. While past theories have often taken credit-market failures to be exogenous, poverty may well be a deeper causative factor in financial development (as well as an outcome of the lack of financial development). For example, given fixed cost of lending (both for each loan and for setting up the lending institution), liquidity constraints can readily emerge as the norm in very poor societies.

The above arguments need not imply a poverty trap in the sense of a “low-level attractor.” Essentially, a poverty trap also requires that the recursion diagram has what is called a “low-level non-convexity,” whereby a minimum level of current wealth is essential before any positive level of future wealth can be reached. In theory, in poor countries, the nutritional requirements for work can generate such dynamics.[9] Such a model predicts that a large exogenous income gain may be needed to attain a permanently higher income and that seemingly similar aggregate shocks can have dissimilar outcomes.[10

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What does the evidence suggest?

Cross-country regressions for GDP growth rates have almost invariably found a significant negative coefficient on initial GDP once one controls for initial conditions. A subset of the literature has used inequality as one such initial condition. Support for the view that higher initial inequality impedes growth has been reported in a number of papers.[11]  However, not all the evidence has been supportive.  (World Bank researchers are found on both sides of this debate.) The main reason why some studies have been less supportive appears to be that they have allowed for latent country-level effects in growth rates; this type of specification has some theoretical advantages but probably has weak power for detecting the true relationship given the noise in the data on aggregate growth rates and other key variables.[13]

The aspect of initial distribution that has received almost all the attention in the empirical literature is inequality, as typically measured by the Gini index of income or consumption inequality. (Wealth inequality is arguably more relevant though this has rarely been used due to data limitations.[14]) The popularity of the Gini index appears to owe more to its availability in secondary data compilations than any intrinsic relevance to the economic arguments.[15] At the time of writing (July 2009) The only papers in which a poverty measure was used as a regressor for aggregate growth across countries have been by World Bank researchers who have found evidence that a higher initial poverty rate retards growth.[16] The significance of the Gini index in past studies may reflect an omitted variable bias, given that one expects that inequality will be highly correlated with poverty at a given mean.

There are also issues about the relevant control variables when studying the effect of initial distribution on growth. The specification choices in past work testing for effects of initial distribution have lacked clear justification in terms of the theories predicting such effects. Consider three popular predictors of growth, namely human development, the investment share, and financial development. On the first, basic schooling and health attainments (often significant in growth regressions) are arguably one of the channels linking initial distribution to growth.[17] Turning to the second, one of the most robust predictors of growth rates is the share of investment in GDP; yet arguably one of the main channels through which distribution affects growth is via aggregate investment and this is one of the channels identified in the theoretical literature. Finally, consider private credit (as a share of GDP), which has been used as a measure of “financial sector development” in explaining growth and poverty reduction.[18] The theories discussed above based on borrowing constraints suggest that the aggregate flow of credit in the economy depends on the initial distribution.

Another set of specification issues concerns interaction effects. While liquidity constraints stemming from credit-market failures imply that the growth rate depends on the extent of inequality in the initial distribution, theory also suggests that there will be an interaction effect between the initial mean and inequality.[19] However, the more relevant interaction effect may well be that between poverty and inequality.[20]

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Some of the literature has focused instead on testing the assumptions of these theories. At least some of the theoretical models of poverty traps appear to be hard to reconcile with the aggregate data; that appears to be the case with poverty traps that might arise from low savings (high time preference rates) in poor countries.[21

There are also testable implications for micro data. An implication of a number of the models based on credit-market failures is that individual income or wealth at one date should be an increasing function of its own past value with a slope that declines as income rises (giving a “concave function”). This can be tested on micro panel data. One study by World Bank researchers provided supportive evidence in panel data for Hungary and Russia while another study found the same thing using panel data for China.[22] These studies did not, however, find the properties in the empirical income dynamics that would be needed for a poverty trap. The low-level non-convexity described above was not in evidence, although prevailing institutions may well have kept incomes above the critical floor.

There is also evidence of nonlinear wealth effects on new business start-ups in developing countries, though with little sign of a non-convexity at low levels due to lumpiness in capital requirements.[23]  Similarly, another study found no sign of non-convexities in production at low levels among Mexican microenterprises.[24]  However, micro studies have found evidence of wealth-differentiated behaviors in addressing risk in rural Zimbabwe and Kenya that are consistent with the idea of poverty traps.[25]

Micro-empirical support for the claim that there are efficiency costs of poor nutrition and health care for children in poor families has come from a number of studies. In a recent example, an impact evaluation by World Bank researchers of a conditional cash transfer scheme in Nicaragua found that randomly assigned cash transfers to poor families improved the cognitive outcomes of children through higher intakes of nutrition-rich foods and better health care.[26]  This echoes a number of findings on the benefits to disadvantaged children of efforts to compensate for family poverty.[27

While the evidence for dynamic poverty traps is mixed, it is also possible to have what can be termed “geographic poverty traps,” whereby (social and economic) characteristics the place of residence inhibit prospects of escaping poverty. The data requirements for testing this argument are rather special. Bank researchers used a six-year panel of farm-household data for rural southern China found that indicators of geographic capital had divergent impacts on consumption growth at the micro level, controlling for household characteristics.[28] Living in a poor area appears to lower the productivity of a farm-household’s own investments, which reduces the growth rate, given restrictions on capital mobility. Indeed, this study’s results suggest that there are areas in rural China that were so poor that the consumptions of some households living in them were falling even while otherwise identical households living in better-off areas enjoyed rising consumption. The geographic effects are strong enough to imply poverty traps.

While the theories and evidence reviewed above point to inequality and/or poverty as the relevant parameters of the initial distribution, yet another strand of the literature has pointed to various reasons why the size of a country’s middle class can matter to the fortunes of those not (yet) so lucky to be middle class. It has been argued that a larger middle class promotes economic growth, such as by fostering entrepreneurship, shifting the composition of consumer demand, and making it more politically feasible to attain policy reforms and institutional changes conducive to growth.[29] One study by a World Bank researcher (at the time) found evidence that a larger income share controlled by the middle three quintiles promotes economic growth.[30

So we have three contenders for the distributional parameter most relevant to growth: inequality, poverty, and the size of the middle class. The fact that very few encompassing tests are found in the literature, and that these different measures of distribution are not independent, leaves one in doubt about what aspect of distribution really matters. There are some difficult attribution problems. As already noted, when the initial value of mean income is included in a growth regression alongside initial inequality, but initial poverty is an excluded but relevant variable, the inequality measure may pick up the effect of poverty rather than inequality per se. Similarly, the main way the middle class expands in a developing country is probably through poverty reduction, so it is unclear whether it is a high incidence of poverty or a small middle class that impedes growth. Also, a relative concept of the “middle class,” such as the income share of middle quintiles, will probably be highly correlated with a relative inequality measure, clouding the interpretation.

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A comprehensive encompassing test by a World Bank researcher—whereby the various contenders are allowed to “fight it out” in the same regression model of economic growth—found that it was poverty not inequality or the size of the middle class that matters to growth.[31] That does not mean that initial inequality is unimportant. Rather, what it suggests is that the main way inequality matters is via its bearing on the initial incidence of poverty. Lower inequality among the non-poor, leaving the incidence of absolute poverty unchanged, would not bring a longer-term payoff in terms of growth and poverty reduction. And in the minority of cases in which high inequality comes with low absolute poverty at a given mean, it does not imply worse longer-term prospects for growth and poverty reduction. A larger middle class—by developing-country (but not Western) standards—makes growth more poverty-reducing. But this effect is largely attributable to the lower poverty rate associated with a larger middle class.

What about the reverse link from growth to poverty? This has received far more attention and is the subject of another Knowledge in Development Note, on “Equitable Growth.” The consensus in the literature is that higher growth rates tend to yield more rapid rates of absolute poverty reduction.[32]  This is implied by another common finding in the literature, namely that growth in developing countries tends to be distribution-neutral on average, meaning that changes in inequality are roughly orthogonal to growth rates in the mean.[33] Distribution-neutrality in the growth process implies that the changes in any standard measure of absolute poverty (meaning that the poverty line is fixed in real terms) will be negatively correlated with growth rates in the mean.

There is also evidence that inequality matters to how much a given growth rate reduces poverty.[34]  Intuitively, in high inequality countries the poor will tend to have a lower share of the gains from growth. At minimum inequality, growth has its maximum effect on poverty (in expectation) while the elasticity reaches zero at maximum inequality.

Recent evidence has suggested that the more relevant factor in explaining why a given rate of growth has diverse impacts on poverty may well be the initial level of poverty, rather than inequality per se.[35] The (absolute) growth elasticity of poverty reduction tends to be lower in countries with a higher initial poverty rate.

“Poverty constraints”

The jury is still out on the empirical importance of poverty traps. But the research on this topic has revealed many ways in which poverty itself can constrain prospects of escaping poverty.

There are three distinct consequences of being a poor country for subsequent progress against poverty. The usual neoclassical convergence effect entails that countries with a lower initial mean, and so (typically) a higher poverty rate, grow faster and (hence) enjoy faster poverty reduction than otherwise similar countries. Against this, there is an adverse direct effect of poverty on growth, such that countries with a higher initial incidence of poverty tend to experience a lower rate of growth, controlling for the initial mean (as well as other controls). Additionally a high poverty rate makes it harder to achieve a given proportionate impact on poverty through growth in the mean. (By the same token, the poverty impact of economic contraction tends to be smaller in countries with a higher poverty rate.)

The two “poverty effects” work against the mean convergence effect, leaving little or no correlation between the initial incidence of poverty and the subsequent rate of progress against poverty.  In terms of the pace of poverty reduction, the “advantage of backwardness” for countries starting with a low capital endowment (given diminishing returns to aggregate capital) is largely wiped out by the high level of poverty that tends to accompany a low initial mean. This dynamic “disadvantage of poverty” appears to exist independently of other factors impeding growth and poverty reduction, such as human underdevelopment and policy distortions.

Knowing more about the “reduced form” empirical relationship between growth, poverty reduction and the parameters of the initial distribution will not, of course, resolve the policy issues at stake. The policy implications depend on why countries starting out with a higher incidence of poverty tend to face worse growth prospects and enjoy less poverty reduction from a given rate of growth. The initial level of poverty may well be picking up other factors, such as the distribution of human and physical capital; indeed, the underlying theories point more to “wealth poverty” than consumption or income poverty. Standard control variables such as schooling, life-expectancy and the price of investment goods do not appear to “knock out” the effect of poverty, either on growth or poverty reduction at a given rate of growth.

However, there is a pressing need in future work to better understand these deeper constraints faced by poor countries, and poor people, in their efforts to become less poor.

Contact: Martin Ravallion, mravallion@worldbank.org, 202-473-6859

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Notes  

Most World Bank research documents cited in this summary are available through the World Bank’s research archives at http://econ.worldbank.org/docsearch or the Bankwide archives at http://www-wds.worldbank.org/.

1.  See, for example, Sachs (2005).

J. Sachs. 2005. Investing in Development: A Practical Plan to Achieve the Millennium Development Goals. Millennium Project, United Nations, New York.

2.  There are a number of surveys including Perotti (1996), Hoff (1996), Aghion et al. (1999), Bardhan et al. (2000), Banerjee and Duflo (2003), Azariadis (2006) and World Bank (2006, Chapter 5). Borrowing constraints are not the only way that inequality can matter to growth. Another class of models is based on the idea that high inequality restricts efficiency-enhancing cooperation, such that key public goods are underprovided or efficiency-enhancing policy reforms are blocked (Bardhan et al., 2000). Other models argue that high inequality leads democratic governments to implement distortionary redistributive policies, as in Alesina and Rodrik (1994).

R. Perotti. 1996. “Growth, Income Distribution and Democracy: What the Data Say.” Journal of Economic Growth 1(2): 149–87.

K. Hoff. 1996. “Market Failures and the Distribution of Wealth: A Perspective from the Economics of Information.” Politics and Society 24(4): 411–32.

P. Aghion, E. Caroli, and C. Garcia-Penalosa. 1999. “Inequality and Economic Growth: The Perspectives of the New Growth Theories.” Journal of Economic Literature 37(4): 1615–60.

P. Bardhan, S. Bowles, and H. Ginitis. 2000. “Wealth Inequality, Wealth Constraints and Economic Performance.” In Handbook of Income Distribution ,Volume 1, ed. A.B. Atkinson and F. Bourguignon. Amsterdam: North-Holland.

A. Banerjee and E. Duflo. 2003. “Inequality and Growth: What Can the Data Say?” Journal of Economic Growth 8(3): 267–99.

C. Azariadis. 2006. “The Theory of Poverty Traps: What Have we Learned?” In Poverty Traps, ed. S. Bowles, S. Durlauf, and K. Hoff. Princeton: Princeton University Press.

World Bank. 2006. World Development Report: Equity and Development, Chapter 5. Oxford University Press.

P. Bardhan, S. Bowles, and H. Ginitis. 2000. “Wealth Inequality, Wealth Constraints and Economic Performance.” In Handbook of Income Distribution, Volume 1, ed. A.B. Atkinson and F. Bourguignon. Amsterdam: North-Holland.

A. Alesina and D. Rodrik. 1994. “Distributive Politics and Economic Growth.” Quarterly Journal of Economics 108: 465–90.

3.  Models with such features include Galor and Zeira (1993), Benabou (1996), Aghion and Bolton (1997) and Banerjee and Duflo (2003).

O. Galor and J. Zeira. 1993. “Income Distribution and Macroeconomics.” Review of Economic Studies 60(1): 35–52.

R. Benabou. 1996. “Inequality and Growth.” In National Bureau of Economic Research Macroeconomics Annual, ed. B. Bernanke and J. Rotemberg. Cambridge: MIT Press.

P. Aghion and P. Bolton. 1997. “A Theory of Trickle-Down Growth and Development.” Review of Economic Studies 64: 151–72.

A. Banerjee and E. Duflo. 2003. “Inequality and Growth: What Can the Data Say?” Journal of Economic Growth 8(3): 267–99.

4.  Ravallion (2009) provides a more formal demonstration.

M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

5.  This is shown by Lopez and Serven (2009).

H. Lopez and L. Servén. 2009. “Too Poor to Grow.” Policy Research Working Paper 5012, World Bank, Washington, DC.

6.  See, for example, Azariadis (2006).

C. Azariadis. 2006. “The Theory of Poverty Traps: What Have we Learned?” In Poverty Traps, ed. S. Bowles, S. Durlauf, and K. Hoff. Princeton: Princeton University Press.

7.  See the model in Dasguta and Ray (1986).

P. Dasgupta and D. Ray. 1986. “Inequality as a Determinant of Malnutrition and Unemployment.” Economic Journal 96: 1011–34.

8.  See Cunha and Heckman (2007).

F. Cunha and J. Heckman. 2007. “The Technology of Skill Formation.” American Economic Review (Papers and Proceedings) 97(2): 31–47.

9.  As illustrated by the model of Dasgupta and Ray (1986).

P. Dasgupta and D. Ray. 1986. “Inequality as a Determinant of Malnutrition and Unemployment.” Economic Journal 96: 1011–34.

10.  Growth models with such features are also discussed in Day (1992) and Azariades (2006) among others.

R. H. Day. 1992. “Complex Economic Dynamics: Obvious in History, Generic in Theory, Elusive in Data.” Journal of Applied Econometrics 7: S9–S23.

C. Azariadis. 2006. “The Theory of Poverty Traps: What Have we Learned?” In Poverty Traps, ed. S. Bowles, S. Durlauf, and K. Hoff. Princeton: Princeton University Press.

11.  See Alesina and Rodrik (1994), Persson and Tabellini (1994), Birdsall, Ross, and Sabot (1995), Clarke (1995), Perotti (1996), Deininger and Squire (1998) and Knowles (2005).

Alesina and D. Rodrik. 1994. “Distributive Politics and Economic Growth.” Quarterly Journal of Economics 108: 465–90.

T. Persson and G. Tabellini. 1994. “Is Inequality Harmful for Growth?” American Economic Review 84: 600–21.

N. Birdsall, D. Ross, and R. Sabot. 1995. “Inequality and Growth Reconsidered: Lessons from East Asia.” World Bank Economic Review 9(3): 477–508.

G.R.G. Clarke. 1995. “More Evidence on Income Distribution and Growth.” Journal of Development Economics 47: 403–28.

R. Perotti. 1996. “Growth, Income Distribution and Democracy: What the Data Say.” Journal of Economic Growth 1(2): 149–87.

K. Deininger and L. Squire. 1998. “New Ways of Looking at Old Issues: Inequality and Growth.” Journal of Development Economics 57(2): 259–87.

S. Knowles. 2005. “Inequality and Economic Growth: The Empirical Relationship Reconsidered in the Light of Comparable Data.” Journal of Development Studies 41(1): 135–59.

12.  Also see Li and Zou (1999), Barro (2000), and Forbes (2000).

H. Li and H. Zou. 1998. “Income Inequality is not Harmful to Growth: Theory and Evidence.” Review of Development Economics 2(3): 318–34.

R. Barro. 2000. “Inequality and Growth in a Panel of Countries.” Journal of Economic Growth 5(1): 5–32.

K. J. Forbes. 2000. “A Reassessment of the Relationship Between Inequality and Growth.” American Economic Review 90(4): 869–87.

13.  See the Monte Carlo simulations found in Hauk and Wacziarg (2009) and the tests in Ravallion (2009).

W. R. Hauk and R. Wacziarg. 2009. “A Monte Carlo Study of Growth Regressions.” Journal of Economic Growth 14(2): 103–147.

M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

14.  An exception is Ravallion (1998), who studies the effect of geographic differences in the distribution of wealth on growth in China.

M. Ravallion. 1998. “Does Aggregation Hide the Harmful Effects of Inequality on Growth?” Economics Letters 61(1): 73–77.

15.  The compilation of Gini indices from secondary sources (and not using consistent assumptions) in Deininger and Squire (1996) led to almost all the tests in the literature since that paper was published.

K. Deininger and L. Squire. 1996. “A New Data Set Measuring Income Inequality.” World Bank Economic Review 10: 565–91.

16.  See Lopez and Servén (2009) and Ravallion (2009).

H. Lopez and L. Servén. 2009. “Too Poor to Grow.” Policy Research Working Paper 5012, World Bank, Washington, DC.

M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

17.  Indeed, that is the link in the original Galor and Zeria (1993) model. More recently, Gutiérrez and Tanaka (2009) show how high initial inequality in a developing country can yield a political-economy equilibrium in which there is little or no public investment in basic schooling; the poorest families send their kids to work, and the richest turn to private schooling.

O. Galor and J. Zeira. 1993. “Income Distribution and Macroeconomics.” Review of Economic Studies 60(1): 35–52.

C. Gutiérrez and R. Tanaka. 2009. “Inequality and Education Decisions in Developing Countries.” Journal of Economic Inequality 7: 55–81.

18.  See Beck et al. (2007).

T. Beck, A. Demirguc-Kunt, and R. Levine. 2007. “Finance, Inequality and the Poor.” Journal of Economic Growth 12: 27–49.

19.  See Banerjee and Duflo (2003).

A. Banerjee and E. Duflo. 2003. “Inequality and Growth: What Can the Data Say?” Journal of Economic Growth 8(3): 267–99.

20.  As argued by Ravallion (2009).

M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

21.  See Kraay and Raddatz (2007).

A. Kraay and C. Raddatz. 2007. “Poverty Traps, Aid and Growth.” Journal of Development Economics 82(2): 315–47.

22.  See Lokshin and Ravallion (2004) and Jalan and Ravallion (2004).

M. Lokshin and M. Ravallion. 2004. “Household Income Dynamics in Two Transition Economies.” Studies in Nonlinear Dynamics and Econometrics 8(3).

J. Jalan and M. Ravallion. 2004. “Household Income Dynamics in Rural China.” In Insurance Against Poverty, ed. S. Dercon. Oxford University Press.

23.  See Mesnard and Ravallion (2006).

A. Mesnard and M. Ravallion. 2006. “The Wealth Effect on New Business Startups in a Developing Economy.” Economica 73: 367–92.

24.  See McKenzie and Woodruff (2006).

D. McKenzie and C. Woodruff. 2006. “Do Entry Costs Provide and Empirical Basis for Poverty Traps? Evidence from Mexican Microenterprises.” Economic Development and Cultural Change 55(1): 3–42.

25.  See Hoddinott (2006) and Barrett et al. (2006).

J. Hoddinott. 2006. “Shocks and their Consequences Across and Within Households in Rural Zimbabwe.” Journal of Development Studies 42(2): 301–21.

C. Barrett, P. Phiri Marenya, J. McPeak, B. Minten, F. Murithi, W. Oluoch-Kosura, F. Place, J. Claude Randrianarisoa, J. Rasambainarivo, and J. Wangila. 2006. “Welfare Dynamics in Rural Kenya and Madagascar.” Journal of Development Studies 42(2): 248–77.

26.  See Macours et al. (2008).

K. Macours, N. Schady, and R. Vakis. 2008. “Cash Transfers, Behavioral Changes and Cognitive Development in Early Childhood.” Policy Research Working Paper 4759, World Bank, Washington DC.

27.  See Currie (2001).

J. Currie. 2001. “Early Childhood Development Programs.” Journal of Economic Perspectives 15(2): 213–38.

28.  See Jalan and Ravallion (2002).

J. Jalan and M. Ravallion. 2002. “Geographic Poverty Traps? A Micro Model of Consumption Growth in Rural China?” Journal of Applied Econometrics 17: 329–46.

29.  Analyses of the role of the middle class in promoting entrepreneurship and growth include Acemoglu and Zilibotti (1997) and Doepke and Zilibotti (2005). Middle-class demand for higher quality goods plays a role in the model of Murphy et al. (1989). Birdsall et al. (2000) conjecture that support from the middle class is crucial to reform. Sridharan (2004) describes the role of the Indian middle class in promoting reform.

D. Acemoglu and F. Zilibotti. 1997. “Was Prometheus Unbound by Chance?” Journal of Political Economy 105(4): 709–51.

M. Doepke and F. Zilibotti. 2005. “Social Class and the Spirit of Capitalism.” Journal of the European Economic Association 3(2-3): 516–24.

K. Murphy, A. Schleifer, and R. Vishny. 1989. “Industrialization and the Big Push.” Journal of Political Economy 97(5): 1003–26.

N. Birdsall, C. Graham, and S. Pettinato. 2000. “Stuck in the Tunnel: Is Globalization Muddling the Middle Class?” Center on Social and Economic Dynamics, Working Paper 14, Brookings Institution, Washington, DC.

E. Sridharan. 2004. “The Growth and Sectoral Composition of India’s Middle Class: Its Impact on the Politics of Economic Liberalization.” India Review 3(4): 405–28.

30.  See Easterly (2001).

W. Easterly. 2001. “The Middle Class Consensus and Economic Development.” Journal of Economic Growth 6(4): 317–35.

31.  See Ravallion (2009).

M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

32.  See World Bank (1990, 2000), Ravallion (2001, 2007), Fields (2001) and Kraay (2006). Also see the review of the arguments and this point in Ferreira and Ravallion (2009).

World Bank. 1990. World Development Report: Poverty. Oxford University Press.

World Bank. 2000. World Development Report: Attacking Poverty. Oxford University Press.

M. Ravallion. 2001. “Growth, Inequality and Poverty: Looking Beyond Averages.” World Development 29(11): 1803–15.

M. Ravallion. 2007. “Inequality is Bad for the Poor.” In Inequality and Poverty Re-Examined, ed. J. Micklewright and S. Jenkins. Oxford University Press.

G. Fields. 2001. Distribution and Development. New York: Russell Sage Foundation.

A. Kraay. 2006. “When is Growth Pro-Poor? Evidence from a Panel of Countries.” Journal of Development Economics 80: 198–227.

F. Ferreira and M. Ravallion. 2009. “Poverty and Inequality: The Global Context.” In The Oxford Handbook of Economic Inequality, ed. W. Salverda, B. Nolan, and T. Smeeding. Oxford University Press.

33.  See Ferreira and Ravallion (2009) for an overview of the literature on this point.

F. Ferreira and M. Ravallion. 2009. “Poverty and Inequality: The Global Context.” In The Oxford Handbook of Economic Inequality, ed. W. Salverda, B. Nolan, and T. Smeeding. Oxford University Press.

34.  See Ravallion (2007), World Bank (2000, 2006), Bourguignon (2003).

M. Ravallion. 2007. “Inequality is Bad for the Poor.” In Inequality and Poverty Re-Examined, ed. J. Micklewright and S. Jenkins. Oxford University Press.

World Bank. 2000. World Development Report: Attacking Poverty. Oxford University Press.

World Bank. 2006. World Development Report: Equity and Development. Oxford University Press.

F. Bourguignon. 2003. “The Growth Elasticity of Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods.” In Inequality and Growth: Theory and Policy Implications, ed. T. Eicher and S. Turnovsky. Cambridge: MIT Press.

35.  See M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

36.  See Ravallion (2009).

M. Ravallion. 2009. “Why Don’t We See Poverty Convergence?” Policy Research Working Paper 4974, World Bank, Washington, DC.

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