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Measuring poverty dynamics with synthetic panels based on cross-sections

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

Abstract

Panel data conventionally underpin the analysis of poverty mobility over time. However, such data are not readily available for most developing countries. Far more common are the"snap-shots"of welfare captured by cross-section surveys. This paper proposes a method to construct synthetic panel data from cross sections which can provide point estimates of poverty mobility. In contrast to traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty at the cohort level, the proposed method can be applied to settings with as few as two survey rounds and also permits investigation at the more disaggregated household level. The procedure is implemented using cross-section survey data from several countries, spanning different income levels and geographical regions. Estimates fall within the 95 percent confidence interval -- or even one standard error in many cases -- of those based on actual panel data. The method is not only restricted to studying poverty mobility but can also accommodate investigation of other welfare outcome dynamics.

Suggested Citation

  • Dang,Hai-Anh H. & Lanjouw,Peter F., 2013. "Measuring poverty dynamics with synthetic panels based on cross-sections," Policy Research Working Paper Series 6504, The World Bank.
  • Handle: RePEc:wbk:wbrwps:6504
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    1. Steven Mcintosh, 2006. "Further Analysis of the Returns to Academic and Vocational Qualifications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(2), pages 225-251, April.
    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. Lorenzo Cappellari & Stephen P. Jenkins, 2006. "Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation," Stata Journal, StataCorp LP, vol. 6(2), pages 156-189, June.
    4. 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.
    5. Verbeek, Marno & Nijman, Theo, 1992. "Can Cohort Data Be Treated as Genuine Panel Data?," Empirical Economics, Springer, vol. 17(1), pages 9-23.
    6. Richard Blundell & Alan Duncan & Costas Meghir, 1998. "Estimating Labor Supply Responses Using Tax Reforms," Econometrica, Econometric Society, vol. 66(4), pages 827-862, July.
    7. McKenzie, D.J.David J., 2004. "Asymptotic theory for heterogeneous dynamic pseudo-panels," Journal of Econometrics, Elsevier, vol. 120(2), pages 235-262, June.
    8. Claudio A. Agostini & Philip H. Brown, 2010. "Local Distributional Effects Of Government Cash Transfers In Chile," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(2), pages 366-388, June.
    9. Deaton, Angus & Paxson, Christina, 1994. "Intertemporal Choice and Inequality," Journal of Political Economy, University of Chicago Press, vol. 102(3), pages 437-467, June.
    10. Mullahy, John, 2011. "Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models," Working Papers 201135, Geary Institute, University College Dublin.
    11. Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2002. "Micro-level estimation of welfare," Policy Research Working Paper Series 2911, The World Bank.
    12. Elbers, Chris & Fujii, Tomoki & Lanjouw, Peter & Ozler, Berk & Yin, Wesley, 2007. "Poverty alleviation through geographic targeting: How much does disaggregation help?," Journal of Development Economics, Elsevier, vol. 83(1), pages 198-213, May.
    13. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    14. 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.
    15. Demombynes, Gabriel & Ozler, Berk, 2005. "Crime and local inequality in South Africa," Journal of Development Economics, Elsevier, vol. 76(2), pages 265-292, April.
    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. Bierbaum, Mira & Gassmann, Franziska, 2012. "Chronic and transitory poverty in the Kyrgyz Republic: What can synthetic panels tell us?," MERIT Working Papers 2012-064, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    18. Demirguc-Kunt, Asli & Klapper, Leora F. & Panos, Georgios A., 2009. "Entrepreneurship in post-conflict transition : the role of informality and access to finance," Policy Research Working Paper Series 4935, The World Bank.
    19. Andrews,Donald W. K. & Stock,James H. (ed.), 2005. "Identification and Inference for Econometric Models," Cambridge Books, Cambridge University Press, number 9780521844413.
    20. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    21. Paul J. Devereux, 2007. "Small-sample bias in synthetic cohort models of labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 839-848.
    22. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    23. James Banks & Richard Blundell & Agar Brugiavini, 2001. "Risk Pooling, Precautionary Saving and Consumption Growth," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 68(4), pages 757-779.
    24. Friedman, Lynn & Wall, Melanie, 2005. "Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression," The American Statistician, American Statistical Association, vol. 59, pages 127-136, May.
    25. Dang, Hai-Anh & Lanjouw, Peter & Luoto, Jill & McKenzie, David, 2014. "Using repeated cross-sections to explore movements into and out of poverty," Journal of Development Economics, Elsevier, vol. 107(C), pages 112-128.
    26. 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.
    27. John Mullahy, 2011. "Marginal Effects in Multivariate Probit and Kindred Discrete and Count Outcome Models, with Applications in Health Economics," NBER Working Papers 17588, National Bureau of Economic Research, Inc.
    28. 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.
    29. Cruces, Guillermo & Lanjouw, Peter & Lucchetti, Leonardo & Perova, Elizaveta & Vakis, Renos & Viollaz, Mariana, 2011. "Intra-generational mobility and repeated cross-sections : a three-country validation exercise," Policy Research Working Paper Series 5916, The World Bank.
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