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EU-SILC and the potential for synthetic panel estimates

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  • Brian Colgan

    (Vrije Universiteit)

Abstract

In the absence of panel data, researchers have devised alternative methods for estimating synthetic poverty dynamics using repeated cross section surveys. These methods are not only salient in the absence of panel data, but also in contexts where there are concerns over the quality of panel data and/or the panel data are of insufficient length to analyse medium- to long-term mobility trends. Both of these issues afflict the longitudinal element of the European Survey on Income and Living Conditions (EU-SILC) (Hérault and Jenkins, J Econ Inequ 17(1):51–76, 2019). Using the longitudinal element of EU-SILC, this paper assesses the accuracy of the synthetic panel approach put forth by Dang and Lanjouw (2021). For most conventional poverty lines, the DL approach is found to be highly accurate when the true $$\rho $$ ρ is known. Similar to Hérault and Jenkins (J Econ Inequ 17(1):51–76, 2019) the pseudo-panel approach for estimating $$\rho $$ ρ is found to be highly sensitive to cohort definition. The longitudinal element of EU-SILC, however, offers a unique route for overcoming this shortcoming.

Suggested Citation

  • Brian Colgan, 2023. "EU-SILC and the potential for synthetic panel estimates," Empirical Economics, Springer, vol. 64(3), pages 1247-1280, March.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:3:d:10.1007_s00181-022-02277-7
    DOI: 10.1007/s00181-022-02277-7
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    References listed on IDEAS

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    1. Hai‐Anh H. Dang & Elena Ianchovichina, 2018. "Welfare Dynamics With Synthetic Panels: The Case of the Arab World In Transition," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(s1), pages 114-144, October.
    2. 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.
    3. Van Kerm, Philippe & Pi Alperin, Maria Noel, 2013. "Inequality, growth and mobility: The intertemporal distribution of income in European countries 2003–2007," Economic Modelling, Elsevier, vol. 35(C), pages 931-939.
    4. Verbeek, Marno & Vella, Francis, 2005. "Estimating dynamic models from repeated cross-sections," Journal of Econometrics, Elsevier, vol. 127(1), pages 83-102, July.
    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. 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.
    7. Francisca Antman & David J. McKenzie, 2007. "Earnings Mobility and Measurement Error: A Pseudo-Panel Approach," Economic Development and Cultural Change, University of Chicago Press, vol. 56(1), pages 125-161, October.
    8. 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.
    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. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    11. Hai-Anh H. Dang & Andrew L. Dabalen, 2019. "Is Poverty in Africa Mostly Chronic or Transient? Evidence from Synthetic Panel Data," Journal of Development Studies, Taylor & Francis Journals, vol. 55(7), pages 1527-1547, July.
    12. Stephen Howes & Jean Olson Lanjouw, 1998. "Does Sample Design Matter For Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
    13. repec:ese:ukhlsp:2015-16 is not listed on IDEAS
    14. Perez, Victor, 2015. "Moving in and out of poverty in Mexico: What can we learn from pseudo-panel methods?," ISER Working Paper Series 2015-16, Institute for Social and Economic Research.
    15. P. Jenkins, Stephen, 2010. "The British Household Panel Survey and its income data," ISER Working Paper Series 2010-33, Institute for Social and Economic Research.
    16. Amable, Bruno, 2003. "The Diversity of Modern Capitalism," OUP Catalogue, Oxford University Press, number 9780199261147.
    17. 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.
    18. Guillermo Cruces & Peter Lanjouw & Leonardo Lucchetti & Elizaveta Perova & Renos Vakis & Mariana Viollaz, 2015. "Estimating poverty transitions using repeated cross-sections: a three-country validation exercise," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(2), pages 161-179, June.
    19. Kaminska, Olena & Iacovou, Maria & Levy, Horacio, 2012. "Using EU-SILC data for cross-national analysis: strengths, problems and recommendations," ISER Working Paper Series 2012-03, Institute for Social and Economic Research.
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    More about this item

    Keywords

    Synthetic panel; Pseudo-panel; Poverty; Poverty dynamics; EU-SILC;
    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

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