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

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No AccessPolicy Research Working Papers21 Jun 2013Estimating Wealth Effects without Expenditure Data or Tears: With an Application to Educational Enrollments in States of IndiaAuthors/Editors: Deon FilmerDeon Filmerhttps://doi.org/10.1596/1813-9450-1994SectionsAboutPDF (0.2 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract:October 1998 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). Deon Filmer may be contacted at [email protected] Previous bookNext book FiguresReferencesRecommendedDetailsCited BySocioeconomic determinants of Schistosoma mansoni infection using multiple correspondence analysis among rural western Kenyan communities: Evidence from a household-based studyPLOS ONE, Vol.16, No.623 June 2021From Participation To Repurchase: Low Income Households And Micro‐insuranceJournal of Risk and Insurance, Vol.87, No.328 June 2019Aflatoxin exposure in pregnant women of mixed status of human immunodeficiency virus infection and rate of gestational weight gain: a Ugandan cohort studyTropical Medicine & International Health, Vol.25, No.926 July 2020Prevalence and Associated Factors of Hypertension Subtypes Among the Adult Population in Nepal: Evidence from Demographic and Health Survey DataOsong Public Health and Research Perspectives, Vol.10, No.6Prevalence and factors associated with hypertension among adults in rural Sylhet district of Bangladesh: a cross-sectional studyBMJ Open, Vol.9, No.1028 October 2019Trends in educational and wealth inequalities in adult tobacco use in Nepal 2001–2016: secondary data analyses of four Demographic and Health SurveysBMJ Open, Vol.9, No.96 September 2019Le rôle de l'environnement dans les maladies diarrhéiques chez l'enfant : l'apport des méthodes mixtesCahiers québécois de démographie, Vol.48, No.111 November 2020Assessing changes in social determinants of health inequalities in South Africa : a decomposition analysisInternational Journal for Equity in Health, Vol.17, No.111 December 2018Child Malnutrition in Indonesia: Can Education, Sanitation and Healthcare Augment the Role of Income?Journal of International Development, Vol.30, No.525 March 2018Financial Inclusion and Welfare in South Africa: Is there a Gender Gap?Journal of African Development, Vol.18, No.2The Link between Health Condition Costs and Standard of Living Through Out-of-Pocket Health Expenditures and Labour Market Outcomes in Turkey: A Structural Equation ModellingSSRN Electronic JournalTime trends in socio-economic inequalities in stunting prevalence: analyses of repeated national surveysPublic Health Nutrition, Vol.18, No.1218 December 2014Constructing Common Factors from Continuous and Categorical DataEconometric Reviews, Vol.34, No.6-1017 December 2014Household living arrangements and transition to sexual debut among young people in GhanaSex Education, Vol.15, No.126 June 2014Factors associated with inappropriate comple