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New Algorithm to Estimate Inequality Measures in Cross-Survey Imputation : An Attemptto Correct the Underestimation of Extreme Values

Author

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  • Betti,Gianni
  • Molini,Vasco
  • Mori,Lorenzo

Abstract

This paper contributes to the debateon ways to improve the calculation of inequality measures in developing countries experiencing severe budget constraints.Linear regression-based survey-to-survey imputation techniques are most frequently discussed in the literature.These are effective at estimating predictions of poverty indicators but are much less accurate with inequalityindicators. To demonstrate this limited accuracy, the first part of the paper discusses several simulations usingMoroccan Household Budget Surveys and Labor Force Surveys. The paper proposes a method for overcoming these limitationsbased on an algorithm that minimizes the sum of the squared difference between a certain number of direct estimates ofan index and its empirical version obtained from the predicted values. Indeed, when comparing the estimatedresults with those directly estimated from the original sample, the bias is negligible. Furthermore, the inequalityindices for the years for which there are only model estimates, rather than direct information on expenditures,seem to be consistent with Moroccan economic trends.

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  • Betti,Gianni & Molini,Vasco & Mori,Lorenzo, 2022. "New Algorithm to Estimate Inequality Measures in Cross-Survey Imputation : An Attemptto Correct the Underestimation of Extreme Values," Policy Research Working Paper Series 10013, The World Bank.
    • Handle: RePEc:wbk:wbrwps:10013
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    References listed on IDEAS

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    1. Chris Elbers & Peter F. Lanjouw & Johan A. Mistiaen & Berk Özler & Ken Simler, 2004. "On the Unequal Inequality of Poor Communities," The World Bank Economic Review, World Bank, vol. 18(3), pages 401-421.
    2. Gabriel Demombynes & Johannes G. Hoogeveen, 2007. "Growth, Inequality and Simulated Poverty Paths for Tanzania, 1992--2002," Journal of African Economies, Centre for the Study of African Economies, vol. 16(4), pages 596-628, August.
    3. Gianni Betti & Ruzhdie Bici & Laura Neri & Thomas Pave Sohnesen & Ledia Thomo, 2018. "Local Poverty and Inequality in Albania," Eastern European Economics, Taylor & Francis Journals, vol. 56(3), pages 223-245, May.
    4. 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.
    5. Corral Rodas,Paul Andres & Kastelic,Kristen Himelein & Mcgee,Kevin Robert & Molina,Isabel, 2021. "A Map of the Poor or a Poor Map ?," Policy Research Working Paper Series 9620, The World Bank.
    6. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
    7. Jose Cuesta & Gabriel Lara Ibarra, 2017. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, March.
    8. Caroline Krafft & Ragui Assaad & Hanan Nazier & Racha Ramadan & Atiyeh Vahidmanesh & Sami Zouari, 2019. "Estimating poverty and inequality in the absence of consumption data: an application to the Middle East and North Africa," Middle East Development Journal, Taylor & Francis Journals, vol. 11(1), pages 1-29, January.
    9. Hai‐Anh Dang & Dean Jolliffe & Calogero Carletto, 2019. "Data Gaps, Data Incomparability, And Data Imputation: A Review Of Poverty Measurement Methods For Data‐Scarce Environments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 757-797, July.
    10. Kusum W. Ketkar & Suhas L. Ketkar, 1987. "Socio-Demographic Dynamics and Household Demand," Eastern Economic Journal, Eastern Economic Association, vol. 13(1), pages 55-62, Jan-Mar.
    11. Paul Corral & Kristen Himelein & Kevin McGee & Isabel Molina, 2021. "A Map of the Poor or a Poor Map?," Mathematics, MDPI, vol. 9(21), pages 1-40, November.
    12. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    13. Christian Schluter, 2012. "On the problem of inference for inequality measures for heavy‐tailed distributions," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 125-153, February.
    14. Ray Chambers & Nikos Tzavidis, 2006. "M-quantile models for small area estimation," Biometrika, Biometrika Trust, vol. 93(2), pages 255-268, June.
    15. Astrid Mathiassen, 2013. "Testing Prediction Performance of Poverty Models: Empirical Evidence from U ganda," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(1), pages 91-112, March.
    16. Mohamed Douidich & Abdeljaouad Ezzrari & Roy Van der Weide & Paolo Verme, 2016. "Estimating Quarterly Poverty Rates Using Labor Force Surveys: A Primer," The World Bank Economic Review, World Bank, vol. 30(3), pages 475-500.
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