Themes | Highlights | Team | Notes | Current Research Program | 2009 Publications
The research program on poverty and inequality has two main objectives. The first is to improve current data and methods of poverty and inequality analysis. The second is to better understand the economic and social processes determining the extent of poverty and inequality, assessing the effectiveness of specific policies in reducing poverty, and tracking the impact of the ongoing global economic crisis.
Since 2006 the central research themes of the poverty and inequality group have been laid out in two research programs. The Equity and Development program (2006-2009) builds on insights from the World Development Report 2006: Equity and Development.1 The program has aimed to add rigorous new thinking and evidence in two areas where the development report was more speculative, given limitations on the existing stock of knowledge: better data for describing inequity, and research on understanding and breaking poverty and inequality traps.
The team has been active on multiple fronts, producing new household-level survey data, monitoring poverty and inequality using household-level data (including the global poverty monitoring task, which produces the Bank’s official “$1.25 a day” poverty counts) and developing improved tools for the analysis of poverty and inequality. As the ongoing global economic crisis continues to evolve, efforts are also firmly focused on gauging its impact on distributional outcomes in the developing world.
In support of the World Bank’s role as a leader in the production of accurate and policy relevant micro-level data, the newest phase (Phase IV) of the long-standing Living Standards Measurement Study (LSMS) also launched in 2006, is an innovative new multi-year program of research aimed at improving the quality of household-level measurement of key concepts in poverty and policy analysis. Through methodological experiments, field validations, as well as reviews of existing knowledge, the program aims to provide sound advice for improving LSMS and other surveys in developing countries.
Raising the quality and availability of living standards data
Continued research to increase data availability and to improve survey methodologies is critical to ensuring the quality of data available to researchers. During the past year a multi-year effort began to collect panel data in a number of Sub-Saharan African countries with a particular emphasis on high-quality information on agricultural practices. A number of methodological experiments and studies have been undertaken during the past year to improve the measurement of core indicators in LSMS surveys such as consumption and income; develop methods for expanding policy areas that the LSMS surveys can cover; and improve the quality of the data generated either substantively or by improving its accuracy, relevance, and timeliness.
An investigation into the collection of data on access to, and use of, financial services by households in developing countries, highlights the importance of using screening questions and avoiding proxy respondents. A randomized experiment in Ghana to test whether the identity of the respondent and the inclusion of product-specific cues in questions affect the reported rates of household usage of financial services found that household heads are generally better placed to respond to such questions than randomly selected household informants. It is also found that precisely worded questions about specific financial products elicit more accurate responses than asking about respondent’s dealings with types of financial institutions.2
Investigations into methods for improved data collection also consider non-economic indicators of well-being. A survey of the literature on the mental health effects of conflict highlights the difficulty of collecting information on mental health from surveys because of a lack of validated mental health scales. There are also challenges in measuring individual exposure to conflict, and further issues related to making causal inferences from observed correlations. A study of mental health in post-conflict Bosnia and Herzegovina demonstrates how such difficulties might be overcome, by employing clinically validated mental health scales, and using administrative data to proxy conflict exposure. The study finds no evidence of a systematic association, across localities, between mental health outcomes and intensity of ethnic conflict.3
Subjective questions about an individual’s economic status do not always correspond closely to measures of economic welfare based on income or consumption. Diverse ideas among survey respondents about the meaning of “poverty” likely vary with individual and household characteristics. A study in Tajikistan added vignettes to a nationally representative survey of living standards and found that respondents do indeed hold diverse scales in assessing their welfare.4 However the study finds that there is little bias in either the economic gradient of subjective welfare or other commonly considered covariates. The study concludes that the use of subjective welfare questions is on firmer ground than detractors might often claim.
The structural transformation of India’s economy impacts rural and urban poverty
Developing country’s economic structures typically transition through stages of development that reflect evolving sources of comparative advantage. A fundamental question about this structural transformation in many developing countries is how this alters the level and distribution of incomes. In India policy makers and informed observers share the view that, despite impressive economic performance in recent years, the benefits of growth have not been equitably distributed.5
A study, building on a time series of consumption based on poverty measures in India, documents that economic growth in India has indeed reduced poverty during the past 50 years. However, the study finds no evidence to support the notion that poverty reduction has accelerated following the economic reforms that began in earnest in the early 1990s. The study also points to evidence of rising inequality. In contrast to the pre-reform period, the years following economic reform reveal that urban growth has brought significant gains to the rural poor, as well as to the urban poor.6
A second study asks about the accelerated diversification out of agriculture in rural areas in recent years, and how such structural changes have affected rural poverty in India. Between 1983 and 2004/05 the rural non-farm sector grew slowly, with some acceleration since the early 1990s. The rural poverty response to this process has been modest, mostly because much of the non-farm growth has been in low-return casual non-farm activities. The study predicts that continued growth in urban areas, particularly income growth in India’s small- and medium-sized towns, will further stimulate rural non-farm diversification and put upward pressure on agricultural wages in rural areas.7
|1.||World Bank. 2005. World Development Report 2006: Equity and Development. Washington, DC: World Bank.|
|2.||Cull, Robert, and Kinnon Scott. 2009. “Measuring Household Usage of Financial Services: Does it Matter How or Whom You Ask?” Policy Research Working Paper 5048, World Bank, Washington, DC.|
|3.||Do, Quy-Toan, and Lakshmi Iyer. 2009. “Mental Health in the Aftermath of Conflict.” Policy Research Working Paper 5132, World Bank, Washington, DC.|
|4.||Beegle, Kathleen, Kristen Himelein, and Martin Ravallion. 2009. “Frame-of-Reference Bias in Subjective Welfare Regressions.” Policy Research Working Paper 4904, World Bank, Washington, DC.|
|5.||Ravallion, Martin. 2009. “A Comparative Perspective on Poverty Reduction in Brazil, China and India.” Policy Research Working Paper 5080, World Bank, Washington, DC.|
|6.||Datt, Gaurav, and Martin Ravallion. 2009. “Has India’s Economic Growth Become More Pro-Poor in the Wake of Economic Reforms?” Policy Research Working Paper 5103, World Bank, Washington, DC.|
|7.||Lanjouw, Peter, and Rinku Murgai. 2009. “Decline, Agricultural Wages and Non-Farm Employment in Rural India: 1983-2004.” Agricultural Economics 40(2): 243–63.|