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Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross-Sections

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  • Dang, Hai-Anh H.
  • Lanjouw, Peter F.

Abstract

Panel data are rarely available for developing countries. Departing from traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty mobility at the cohort level, we develop a procedure that works with as few as two survey rounds and produces point estimates of transitions along the welfare distribution at the more disaggregated household level. Validation using Monte Carlo simulations and real cross-sectional and actual panel survey data-from several countries, spanning different income levels and geographical regions-perform well under various deviations from model assumptions. The method could also inform investigation of other welfare outcome dynamics.

Suggested Citation

  • Dang, Hai-Anh H. & Lanjouw, Peter F., 2022. "Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross-Sections," GLO Discussion Paper Series 1213, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:1213
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    1. Verbeek, Marno & Vella, Francis, 2005. "Estimating dynamic models from repeated cross-sections," Journal of Econometrics, Elsevier, vol. 127(1), pages 83-102, July.
    2. Guell, Maia & Hu, Luojia, 2006. "Estimating the probability of leaving unemployment using uncompleted spells from repeated cross-section data," Journal of Econometrics, Elsevier, vol. 133(1), pages 307-341, July.
    3. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
    4. Verbeek, Marno & Nijman, Theo, 1992. "Can Cohort Data Be Treated as Genuine Panel Data?," Empirical Economics, Springer, vol. 17(1), pages 9-23.
    5. Jenkins, Stephen P., 2011. "Changing Fortunes: Income Mobility and Poverty Dynamics in Britain," OUP Catalogue, Oxford University Press, number 9780199226436, Decembrie.
    6. François Bourguignon & A. Hector Moreno M., 2020. "On synthetic income panels," Working Papers halshs-01988068, HAL.
    7. Serajuddin,Umar & Uematsu,Hiroki & Wieser,Christina & Yoshida,Nobuo & Dabalen,Andrew L., 2015. "Data deprivation : another deprivation to end," Policy Research Working Paper Series 7252, The World Bank.
    8. Verbeek, Marno & Nijman, Theo, 1993. "Minimum MSE estimation of a regression model with fixed effects from a series of cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 125-136, September.
    9. Nicolas Hérault & Stephen P. Jenkins, 2019. "How valid are synthetic panel estimates of poverty dynamics?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(1), pages 51-76, March.
    10. Nayoung Lee & Geert Ridder & John Strauss, 2017. "Estimation of Poverty Transition Matrices with Noisy Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 37-55, January.
    11. Propper, Carol & Rees, Hedley & Green, Katherine, 2001. "The Demand for Private Medical Insurance in the UK: A Cohort Analysis," Economic Journal, Royal Economic Society, vol. 111(471), pages 180-200, May.
    12. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    13. Dolores Collado, M., 1997. "Estimating dynamic models from time series of independent cross-sections," Journal of Econometrics, Elsevier, vol. 82(1), pages 37-62.
    14. Alkire, Sabina & Foster, James, 2011. "Counting and multidimensional poverty measurement," Journal of Public Economics, Elsevier, vol. 95(7), pages 476-487.
    15. Niny Khor & John Pencavel, 2006. "Income mobility of individuals in China and the United States," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 14(3), pages 417-458, July.
    16. Inoue, Atsushi, 2008. "Efficient estimation and inference in linear pseudo-panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 449-466, January.
    17. Chaudhuri, Shubham & Ravallion, Martin, 1994. "How well do static indicators identify the chronically poor?," Journal of Public Economics, Elsevier, vol. 53(3), pages 367-394, March.
    18. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    19. Artūras Juodis, 2018. "Pseudo Panel Data Models With Cohort Interactive Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 47-61, January.
    20. Bourguignon, Francois & Goh, Chor-ching & Kim, Dae Il, 2004. "Estimating individual vulnerability to poverty with pseudo-panel data," Policy Research Working Paper Series 3375, The World Bank.
    21. Gibson, John, 2001. "Measuring chronic poverty without a panel," Journal of Development Economics, Elsevier, vol. 65(2), pages 243-266, August.
    22. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Can cohort data be treated as genuine panel data?," Other publications TiSEM d4eada8f-b91c-4fe7-a58c-7, Tilburg University, School of Economics and Management.
    23. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Balanced Variable Addition In Linear Models," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1183-1200, September.
    24. Wojciech Kopczuk & Emmanuel Saez & Jae Song, 2010. "Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 91-128.
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    More about this item

    Keywords

    transitory and chronic poverty; income mobility; consumption; cross sections; synthetic panels; household surveys;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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

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