Showing papers by "Deon Filmer published in 1998"

Estimating Wealth Effects without Expenditure Data or Tears: With an Application to Educational Enrollments in States of India

Abstract: The relationship between household wealth and educational enrollment of children can be estimated without expenditure data. A method for doing so - which uses an index based on household asset ownership indicators - is proposed and defended in this paper. In India, children from the wealthiest households are over 30 percentage points more likely to be in school than those from the poorest households, although this gap varies considerably across states. To estimate the relationship between household wealth and the probability that a child (aged 6 to 14) is enrolled in school, Filmer and Pritchett use National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993. In developing their estimate Filmer and Pritchett had to overcome a methodological difficulty: The NFHS, modeled closely on the Demographic and Health Surveys, measures neither household income nor consumption expenditures. As a proxy for long-run household wealth, they constructed a linear asset index from a set of asset indicators, using principal components analysis to derive the weights. This asset index is robust, produces internally coherent results, and provides a close correspondence with data on state domestic product and on state level poverty rates. They validate the asset index using data on consumption spending and asset ownership from Indonesia, Nepal, and Pakistan. The asset index has reasonable coherence with current consumption expenditures and, more importantly, works as well as - or better than - traditional expenditure-based measures in predicting enrollment status. The authors find that on average a child from a wealthy household (in the top 20 percent on the asset index developed for this analysis) is 31 percent more likely to be enrolled in school than a child from a poor household (in the bottom 40 percent). This paper - a product of Poverty and Human Resources, Development Research Group - is part of a larger effort in the group to inform educational policy. The study was funded by the Bank`s Research Support Budget under the research project Educational Enrollment and Dropout (RPO 682-11).

Estimating Wealth Effects without Expenditure Data--or Tears: An Application to Educational Enrollments in States of India. Policy Research Working Papers No. 1994.

Abstract: Using data from India, we estimate the relationship between household wealth and children’s school enrollment. We proxy wealth by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights. In Indian data this index is robust to the assets included, and produces internally coherent results. State-level results correspond well to independent data on per capita output and poverty. To validate the method and to show that the asset index predicts enrollments as accurately as expenditures, or more so, we use data sets from Indonesia, Pakistan, and Nepal that contain information on both expenditures and assets. The results show large, variable wealth gaps in children’s enrollment across Indian states. On average a “rich” child is 31 percentage points more likely to be enrolled than a “poor” child, but this gap varies from only 4.6 percentage points in Kerala to 38.2 in Uttar Pradesh and 42.6 in Bihar.

Health policy in poor countries : weak links in the chain

Abstract: The authors show how the recent empirical and theoretical literature on health policy sheds light on the disappointing experience with the implementation of primary health care. They emphasize the evidence on two weak links between government spending on health and improvements in health status. First, the capability of developing country governments to provide effective services varies widely -- so health spending, even on the"right"services, may lead to little actual provision of services. Second, the net impact of government provision of health services depends on the severity of market failures. Evidence suggests these are the least severe for relatively inexpensive curative services, which often absorb the bulk of primary health care budgets. Government policy in health can more usefully focus directly on mitigating market failures in traditional public health activities and, in more developed settings, failures in the markets for risk mitigation. Addressing poverty requires consideration of a much broader set of policies which may -- or may not -- include provision of health services.

An Application to Educational Enrollments in States of India

Abstract: We use the National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993 to estimate the relationship between household wealth and the probability a child (aged 6 to 14) is enrolled in school. A methodological difficulty to overcome is that the NFHS, modeled closely on the Demographic and Health Surveys (DHS), measures neither household income nor consumption expenditures. As a proxy for long-run household wealth we construct a linear index from a set of asset indicators using principal components analysis to derive the weights. This "asset index" is robust, produces internally coherent results, and provides a close correspondence with State Domestic Product (SDP) and poverty rates data. We validate the asset index using data from Indonesia, Pakistan and Nepal which contain data on both consumption expenditures and asset ownership. The asset index has reasonable coherence with current consumption expenditures and most importantly, works as well, or better, than traditional expenditure based measures in predicting enrollment status. When the asset index is applied to the Indian data the results show large, and variable, wealth gaps in the enrollment of children across states of India. While on average across India a rich (top 20 percent of the asset index) child is 31 percentage points more likely to be enrolled than a poor child (bottom 40 percent), this wealth gap varies from only 4.6 in Kerala, to 38.2 in Uttar Pradesh and 42.6 percentage points in Bihar. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.