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To impute or not to impute, and how? A review of alternative poverty estimation methods in the context of unavailable consumption data

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  • Hai-Anh H. Dang

    (World Bank, USA)

Abstract

There is an increasingly stronger demand for more frequent and accurate poverty estimates, despite the oftentimes unavailable household consumption data. We review imputation methods that have been employed to provide poverty estimates in such data-scarce contexts. These range from estimates on a non-monetary basis, estimates for specific project targeting or tracking trends at the national level, to estimates at a more disaggregated level, and estimates of poverty dynamics. We provide a concise synthesis, which serves as an introduction to the literature. We focus on intuition and practical insights that highlight existing methods’ nuanced differences rather than their technical aspects.

Suggested Citation

  • Hai-Anh H. Dang, 2019. "To impute or not to impute, and how? A review of alternative poverty estimation methods in the context of unavailable consumption data," Working Papers 507, ECINEQ, Society for the Study of Economic Inequality.
  • Handle: RePEc:inq:inqwps:ecineq2019-507
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    File URL: http://www.ecineq.org/milano/WP/ECINEQ2019-507.pdf
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    References listed on IDEAS

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    1. 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.
    2. Angrist, Joshua D. & Krueger, Alan B., 1999. "Empirical strategies in labor economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 23, pages 1277-1366, Elsevier.
    3. Brown, Caitlin & Ravallion, Martin & van de Walle, Dominique, 2018. "A poor means test? Econometric targeting in Africa," Journal of Development Economics, Elsevier, vol. 134(C), pages 109-124.
    4. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
    5. Balcazar,Carlos Felipe & Dang,Hai-Anh H. & Malasquez Carbonel,Eduardo Alonso & Olivieri,Sergio Daniel & Pico,Julieth, 2018. "Welfare dynamics in Colombia : results from synthetic panels," Policy Research Working Paper Series 8441, The World Bank.
    6. Hai‐Anh H. Dang & Peter F. Lanjouw, 2017. "Welfare Dynamics Measurement: Two Definitions of a Vulnerability Line and Their Empirical Application," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 633-660, December.
    7. Hai-Anh H. Dang & Peter F. Lanjouw & Umar Serajuddin, 2017. "Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country," Oxford Economic Papers, Oxford University Press, vol. 69(4), pages 939-962.
    8. Katharine G. Abraham & John Haltiwanger & Kristin Sandusky & James R. Spletzer, 2013. "Exploring Differences in Employment between Household and Establishment Data," Journal of Labor Economics, University of Chicago Press, vol. 31(S1), pages 129-172.
    9. Nguyen Viet, Cuong, 2011. "Poverty projection using a small area estimation method: Evidence from Vietnam," Journal of Comparative Economics, Elsevier, vol. 39(3), pages 368-382, September.
    10. Hai-Anh H. Dang & Peter F. Lanjouw, 2018. "Poverty Dynamics in India between 2004 and 2012: Insights from Longitudinal Analysis Using Synthetic Panel Data," Economic Development and Cultural Change, University of Chicago Press, vol. 67(1), pages 131-170.
    11. David Stifel & Luc Christiaensen, 2007. "Tracking Poverty Over Time in the Absence of Comparable Consumption Data," The World Bank Economic Review, World Bank, vol. 21(2), pages 317-341, June.
    12. Ravallion, Martin, 2016. "The Economics of Poverty: History, Measurement, and Policy," OUP Catalogue, Oxford University Press, number 9780190212773.
    13. 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.
    14. Bilton, Penny & Jones, Geoff & Ganesh, Siva & Haslett, Steve, 2017. "Classification trees for poverty mapping," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 53-66.
    15. Christopher B. Barrett, 2005. "Rural poverty dynamics: development policy implications," Agricultural Economics, International Association of Agricultural Economists, vol. 32(s1), pages 45-60, January.
    16. Joseph Deutsch & Jacques Silber & Guanghua Wan, 2017. "Curbing One’s Consumption and the Impoverishment Process: The Case of Western Asia," Research on Economic Inequality, in: Research on Economic Inequality, volume 25, pages 137-159, Emerald Group Publishing Limited.
    17. Angus Deaton & Valerie Kozel, 2005. "Data and Dogma: The Great Indian Poverty Debate," The World Bank Research Observer, World Bank, vol. 20(2), pages 177-199.
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    Cited by:

    1. Dang, Hai-Anh & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    poverty; imputation; consumption; wealth index; synthetic panels; household survey.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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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