The DOI matches its Crossref record.
Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 25
- Agrawal, 1996, India Economic Information Year-Book
- Ashenfelter, 1994, Estimates of the Economic Returns to Schooling From a New Sample of Twins, American Economic Review, 84, 1157
- Behrman, 1999, Household Income and Child Schooling in Vietnam, World Bank Economic Review, 13, 211, 10.1093/wber/13.2.211 · DOI · Paper page
- Bonilla-Chacin, 1999, Life and Death Among the Poorest
- Deaton, 1997, The Analysis of Household Surveys, 10.1596/0-8018-5254-4 · DOI · Paper page
- Deaton, 1980, Economics and Consumer Behavior, 10.1017/CBO9780511805653 · DOI · Paper page
- Deaton, 1999, Guidelines for Constructing Consumption Aggregates for Welfare Analysis
- Dreze, 1997, Widowhood and Poverty in Rural India: Some Inferences From Household Survey Data, Journal of Development Economics, 54, 217, 10.1016/S0304-3878(97)00041-2 · DOI · Paper page
- Filmer, 2000, The Structure of Social Disparities in Education: Gender and Wealth, 10.1596/1813-9450-2268 · DOI
- Filmer, 1998, Estimating Wealth Effects Without Expenditure Data—Or Tears: With an Application to Educational Enrollments in States of India
- Filmer, 1999, The Effect of Household Wealth on Educational Attainment: Evidence From 35 Countries, Population and Development Review, 25, 85, 10.1111/j.1728-4457.1999.00085.x · DOI · Paper page
- Filmer, 1999, Determinants of Education Enrollment in India: Child, Household, Village and State Effects, Journal of Educational Planning and Administration, 13, 135
- Grosh, 1998, The World Bank’s Living Standards Measurement Study Household Surveys, Journal of Economic Perspectives, 12, 187, 10.1257/jep.12.1.187 · DOI
- Gwatkin, 2000, Socio-Economic Differences in Health, Nutrition, and Population
- Haque, 1998, A Poverty Profile for India 1993-94
- Jalan, 1998, Transient Poverty in Postreform Rural China, Journal of Comparative Economics, 26, 338, 10.1006/jcec.1998.1526 · doi:10.1006/jcec.1998.1526
- Jalan, 1995, Poverty and Household Size, Economic Journal, 105, 1415, 10.2307/2235108 · DOI · Paper page
- Lindeman, 1980, Introduction to Bivariate and Multivariate Analysis
- Maddala, 1988, Introduction to Econometrics
- Montgomery, 2000, Measuring Living Standards With Proxy Variables, Demography, 27, 155, 10.2307/2648118 · DOI · Paper page
- Patrinos, 1997, Differences in Education and Earnings Across Ethnic Groups in Guatemala, Quarterly Review of Economics and Finance, 37, 809, 10.1016/S1062-9769(97)90005-3 · doi:10.1016/s1062-9769(97)90005-3
- Skoufias, 1999, Krismon and Its Impact on Household Welfare: Preliminary Evidence From Household Panel Data From the 100 Village Survey in Indonesia
- StataCorp, 1999, Stata Statistical Software: Release 6.0
- Stecklov, 1999, Trends in Equity in Child Survival in Developing Countries: A Illustrative Example Using Ugandan Data
- 1998, Reducing Poverty in India: Options for More Effective Public Services
Cited by
Developmental potential in the first 5 years for children in developing countries
Grantham-McGregor, S., Cheung, Y. B., Cueto, S., Glewwe, P., Richter, L., & Strupp, B. (2007) · The Lancet · doi:10.1016/s0140-6736(07)60032-4 · Cited by 3,630
Kotloff, K. L., Nataro, J. P., Blackwelder, W. C., Nasrin, D., Farag, T. H., Panchalingam, S., Wu, Y., Sow, S. O., Sur, D., Breiman, R. F., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60844-2 · Cited by 3,560
The Economic Lives of the Poor
Banerjee, A. V., & Duflo, E. (2007) · Journal of Economic Perspectives · doi:10.1257/jep.21.1.141 · Cited by 1,703
Combining satellite imagery and machine learning to predict poverty
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016) · Science · doi:10.1126/science.aaf7894 · Cited by 1,664
Demographic and health surveys: a profile
Corsi, D. J., Neuman, M., Finlay, J. E., & Subramanian, S. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys184 · Cited by 1,178
Metadata
- Authors
- Filmer, D., & Pritchett, L. H.
- Venue
- Demography
- Publisher
- Duke University Press
- Published
- 2001-02-01
- Volume
- 38
- Issue
- 1
- Pages
- 115-132
- ISSN
- 0070-3370 (ISSN-L)
- 1533-7790
- OpenAlex
- W3122664795
- Cited by
- 5,238
- References
- 0
- FWCI
- 166.62
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. https://app.cochat.ai/papers/10.1353/dem.2001.0003
- The paper
- Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. Demography. https://doi.org/10.1353/dem.2001.0003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
