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How good a map ? Putting small area estimation to the test

Author

Listed:
  • Demombynes, Gabriel
  • Elbers, Chris
  • Lanjouw, Jean O.
  • Lanjouw, Peter
Abstract
The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

Suggested Citation

  • Demombynes, Gabriel & Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2007. "How good a map ? Putting small area estimation to the test," Policy Research Working Paper Series 4155, The World Bank.
    • Handle: RePEc:wbk:wbrwps:4155
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    Cited by:

    1. Corral Rodas,Paul Andres & Molina,Isabel & Nguyen,Minh Cong, 2020. "Pull Your Small Area Estimates up by the Bootstraps," Policy Research Working Paper Series 9256, The World Bank.
    2. Mark Schreiner, 2015. "A Comparison of Two Simple, Low-Cost Ways for Local, Pro-Poor Organizations to Measure the Poverty of Their Participants," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 537-569, November.
    3. Jesse Naidoo, 2009. "Finite-Sample Bias and Inconsistency in the Estimation of Poverty Maps," SALDRU Working Papers 36, Southern Africa Labour and Development Research Unit, University of Cape Town.
    4. Deepa Narayan & Lant Pritchett & Soumya Kapoor, 2009. "Moving Out of Poverty : Volume 2. Success from the Bottom Up," World Bank Publications - Books, The World Bank Group, number 11838.
    5. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and Predicting Household Expenditures and Income Distributions," MAGKS Papers on Economics 201147, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    6. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    7. Piyush Kant Rai & Sarla Pareek & Hemlata Joshi, 2017. "Met And Unmet Need For Contraception: Small Area Estimation For Rajasthan State Of India," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 329-360, June.
    8. Sumonkanti Das & Ray Chambers, 2017. "Robust mean‐squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1137-1161, October.
    9. Ferre, Celine & Ferreira, Francisco H.G. & Lanjouw, Peter, 2010. "Is there a metropolitan bias ? the inverse relationship between poverty and city size in selected developing countries," Policy Research Working Paper Series 5508, The World Bank.
    10. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    11. Modrego, Félix & Berdegué, Julio A., 2015. "A Large-Scale Mapping of Territorial Development Dynamics in Latin America," World Development, Elsevier, vol. 73(C), pages 11-31.
    12. Fiifi Amoako Johnson & Sabu S. Padmadas & Hukum Chandra & Zoe Matthews & Nyovani J. Madise, 2012. "Estimating unmet need for contraception by district within Ghana: An application of small-area estimation techniques," Population Studies, Taylor & Francis Journals, vol. 66(2), pages 105-122, July.
    13. Lang, Corey & Barrett, Christopher B. & Naschold, Felix, 2013. "Targeting Maps: An Asset-Based Approach to Geographic Targeting," World Development, Elsevier, vol. 41(C), pages 232-244.
    14. Rai Piyush Kant & Pareek Sarla & Joshi Hemlata, 2017. "Met and Unmet Need for Contraception: Small Area Estimation for Rajasthan State of India," Statistics in Transition New Series, Statistics Poland, vol. 18(2), pages 329-360, June.
    15. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    16. Tarozzi, Alessandro, 2011. "Can census data alone signal heterogeneity in the estimation of poverty maps?," Journal of Development Economics, Elsevier, vol. 95(2), pages 170-185, July.
    17. Lanjouw, P. & Marra, M.R., 2018. "Urban poverty across the spectrum of Vietnam’s towns and cities," World Development, Elsevier, vol. 110(C), pages 295-306.
    18. Escobal, J. & Ponce, C., 2011. "Spatial Patterns of Growth and Poverty Changes in Peru (1993 – 2005)," Working papers 078, Rimisp Latin American Center for Rural Development.

    More about this item

    Keywords

    Small Area Estimation Poverty Mapping; Rural Poverty Reduction; Science Education; Scientific Research&Science Parks; Population Policies;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies

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