On the Unequal Inequality of Poor Communities

On the Unequal Inequality of Poor Communities Chris Elbers, Peter F. Lanjouw, Johan A. Mistiaen, Berk O¨ zler, and Ken Simler Public Disclosure Authorized Communities differ in important ways in their needs, capacities, and circumstances. Because central governments are not able to discern these differences fully, they seek to achieve their policy objectives by relying on decentralized mechanisms that use local information. Household and individual characteristics within communities can also vary substantially. A growing body of theoretical literature suggests that inequality within communities can influence policy outcomes in ways that are either harmful or helpful, depending on the circumstances. Until recently, empirical investigations into the impact of inequality have been held back by a lack of systematic evidence on community-level inequality. This study uses household survey and population census data to estimate per capita consumption inequality within communities in three developing economies. It finds that communities vary markedly in their degree of inequality. It also shows that there should be no presumption that inequality is less severe in poor communities. The kind of community-level inequality estimates generated here can be used in designing and evaluating decentralized antipoverty programs. Public Disclosure Authorized Governments commonly implement decentralized antipoverty programs that are designed to distribute assets or cash to individuals or households. Usually, the central government distributes antipoverty funds to communities, which then decide how to allocate the funds. One example is social fund projects, a type of community-based development initiative in which poor communities identify projects, apply for funding, and design, implement, and manage their projects (Mansuri and Rao 2004).1 These initiatives intend to improve poverty Chris Elbers is a professor at the Vrije (Free) University Amsterdam; his e-mail address is celbers@ feweb.vu.nl. Peter F. Lanjouw is a lead economist in the Development Research Group at the World Bank; his e-mail address is [email protected]. Johan A. Mistiaen is an economist/statistician in Public Disclosure Authorized the Development Data Group at the World Bank; his e-mail address is [email protected]. Berk O¨ zler is an economist in the Development Research Group at the World Bank; his e-mail address is [email protected]. Ken Simler is a research fellow at the International Food Policy Research Institute; his e-mail address is [email protected]. The authors are grateful to Francois Bourguignon, Francisco Ferreira, Emanuela Galasso, Ravi Kanbur, Jenny Lanjouw, Vijayendra Rao, and Martin Ravallion for comments and helpful discussions. They would also like to thank the journal editor and three anonymous referees for guidance. 1. Mansuri and Rao (2004) distinguish community-based development from community-driven development, popularized by the World Bank, which refers to projects in which communities have direct control over key decisions as well as management of investment funds. Community-based development can be thought of as a broader term that accommodates but is not restricted to the World Bank’s community-driven development concept. THE WORLD BANK ECONOMIC REVIEW, VOL. 18, NO. 3, Ó The International Bank for Reconstruction and Development / THE WORLD BANK 2004; all rights reserved. doi:10.1093/wber/lhh046 18:401–421 Public Disclosure Authorized 401 402 THE WORLD BANK ECONOMIC REVIEW, VOL. 18, NO. 3 targeting and project implementation by using local information and inviting local participation. In practice, however, these potential benefits of local involve- ment may be outweighed by the possibility of resources being captured by local elites.2 In a review of the community-based development approach, Mansuri and Rao (2003) argue that although potential gains are large, there are also important risks inherent in the basic precepts of the approach. Uncertainty about the ultimate impact of such programs implies that a blanket application of a given approach in all communities may not be approp- riate. Again, Mansuri and Rao (2004) caution against the wholesale scaling up of best practices identified in a few pilot settings, because the success of such pilot projects might depend crucially on local conditions that are not found elsewhere. Still, large projects such as a countrywide cash transfer or social fund program cannot take into account the full range of local characteristics that could possibly affect project performance. Hence, policymakers must confront the challenge of designing schemes that take critical local information into account but are not prohibitively costly to implement. Governments have traditionally dealt with this problem by categorizing communities by easily observable characteristics and adapting schemes for each group. Lacking local-level data on poverty, government programs may draw on proxy indicators—believed to be correlated with local poverty conditions—to determine the eligibility of communities for various projects. But despite emerging theoretical analysis and empirical evidence that local inequality may also affect local development outcomes, such information has rarely made its way into program design. One reason is that estimates of local inequality have not been widely available until recently.3 Another is that inequality may not be considered of primary importance when the target of an intervention is a small, poor community in a developing economy. The natural assumption is that where livelihoods are at the subsistence level there is little likelihood that well-being would vary much across households and individuals. This article addresses both these issues. Applying a newly developed metho- dology, it estimates local-level welfare outcomes using the detailed information available from household surveys and the large-scale representation of the population census for Ecuador, Madagascar, and Mozambique. These techni- ques can be used to derive meaningful estimates of income or expenditure inequality for small areas for many countries, using readily available data. The article examines the importance of local-level inequality by decomposing national inequality in each country into a within-community and between- community component. This decomposition exercise produces a summary sta- 2. A vivid illustration of elite capture problems in practice and a theoretical treatment of this issue are provided in Platteau and Gaspart (2003). 3. McKenzie (2003) provides a recent attempt to proxy local inequality on the basis of easily observed correlates of household income. Elbers and others 403 tistic that masks significant heterogeneity in inequality across communities. The article provides additional evidence that this heterogeneity in inequality is evident even among poor rural communities. It demonstrates that information on local inequality can help program implementers further categorize communities after conditioning on local poverty and type of area. I. HOW C AN L OCAL I NEQUALITY A FFECT W ELFARE O UTCOMES? Mansuri and Rao (2004) present a comprehensive overview of the theoretical and empirical literature on the relationship between local inequality and development outcomes. Two critical issues emerge. How does inequality within a community influence the targeting impact of a particular project? How does local inequality affect collective action within communities? Recent theoretical analysis suggests that inequality may affect targeting outcomes of social fund projects or antipoverty transfer schemes by reducing the relative power of the intended beneficiaries (Galasso and Ravallion forthcoming; Bardhan and Mookherjee 1999). In such cases, the advantage of such decentralized approaches to make use of better community-level information about priorities and the characteristics of residents could be offset by the possibility that the local governing body is controlled by elites, who may have different objectives than the poor within their communities. Although the predictions from this theoretical work are ambiguous, limited empirical evidence shows that both the pros and the cons of decentralized decisionmaking are at work in various countries. Alderman (2002) finds that communities in Albania were able to improve targeting by using information unavailable to the central government. By contrast, Galasso and Ravallion (forthcoming) find that high levels of local inequality (as measured by land- holding) were associated with worse targeting performance under the Food for Education program in villages in Bangladesh. A detailed case study of the small north Indian village of Palanpur from the late 1950s through the early 1990s shows how local elites appropriated public resources and opportunities that were to be made available to the whole community (Dre` ze and others 1998). The study documents the introduction of 18 types of government-provided programs into the village, including a public works road-building program, free schooling, free basic health care, old-age pensions, a fair-price shop, and a farmer cooperative. The sobering diagnosis is that most of these programs were nonfunctional, particularly programs that had a redistributive component. Dre` ze and others argue that a key explanation for this dispiriting record is that village institutions were dominated by privileged groups and that only programs that enjoyed their backing were allowed to succeed. Dre` ze and others (1998,

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