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Estimating Quarterly Poverty Rates Using Labor Force Surveys: A Primer

Author

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  • Mohamed Douidich
  • Abdeljaouad Ezzrari
  • Roy Van der Weide
  • Paolo Verme

Abstract

This paper builds on the existing cross-survey imputation literature to provide up-to-date estimates of poverty when official estimates are deemed outdated. This is achieved by imputing household expenditure data into Labor Force Surveys (LFSs) with models that have been estimated using Household Expenditure Surveys (HESs). In an application to Morocco, where the latest official poverty rate is for 2007, estimates of poverty are obtained for all years (and quarters) between 2001 and 2009. It is found that the approach accurately reproduces the official poverty statistics for the two years these surveys are available. The imputation-based estimates furthermore reveal that poverty has consistently declined over the entire 2001–2009 period. This would suggest that poverty reduction in Morocco was not halted by the global financial crisis. While our focus is on head-count poverty, the method can be applied to any welfare indicator that is a function of household income or expenditure, such as the poverty gap or the Gini index of inequality.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:wbecrv:v:30:y:2016:i:3:p:475-500.
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    Cited by:

    1. 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.
    2. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.
    3. Diana K. L. Ngo & Luc Christiaensen, 2019. "The Performance Of A Consumption Augmented Asset Index In Ranking Households And Identifying The Poor," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(4), pages 804-833, December.
    4. Atamanov,Aziz & Tandon,Sharad Alan & Lopez-Acevedo,Gladys C. & Vergara Bahena,Mexico Alberto, 2020. "Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region : Data Gaps and Different Options to Address Them," Policy Research Working Paper Series 9259, The World Bank.
    5. 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.
    6. 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.
    7. F. Clementi & A. L. Dabalen & V. Molini & F. Schettino, 2017. "When the Centre Cannot Hold: Patterns of Polarization in Nigeria," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 608-632, December.
    8. Dang, Hai-Anh & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    9. Theresa Beltramo & Hai-Anh H. Dang & Ibrahima Sarr & Paolo Verme, 2020. "Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad," Working Papers 536, ECINEQ, Society for the Study of Economic Inequality.
    10. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    11. Verme, Paolo, 2020. "Which Model for Poverty Predictions?," GLO Discussion Paper Series 468, Global Labor Organization (GLO).
    12. 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.
    13. Dang,Hai-Anh H., 2018. "To impute or not to impute ? a review of alternative poverty estimation methods in the context of unavailable consumption data," Policy Research Working Paper Series 8403, The World Bank.
    14. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    15. Elena Ianchovichina & Lili Mottaghi & Shantayanan Devarajan, "undated". "Middle East and North Africa Economic Monitor, October 2015," World Bank Publications - Reports 22711, The World Bank Group.
    16. Hai-Anh H. Dang & Peter F. Lanjouw, 2023. "Regression-based imputation for poverty measurement in data-scarce settings," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 13, pages 141-150, Edward Elgar Publishing.
    17. Dang, Hai-Anh & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," GLO Discussion Paper Series 1392, Global Labor Organization (GLO).
    18. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
    19. Lain,Jonathan William & Schoch,Marta & Vishwanath,Tara, 2022. "Estimating a Poverty Trend for Nigeria between 2009 and 2019," Policy Research Working Paper Series 9974, The World Bank.
    20. Pape,Utz Johann & Parisotto,Luca, 2019. "Estimating Poverty in a Fragile Context -- The High Frequency Survey in South Sudan," Policy Research Working Paper Series 8722, The World Bank.
    21. Ahmed, Faizuddin & Dorji, Cheku & Takamatsu, Shinya & Yoshida, Nobuo, 2014. "Hybrid survey to improve the reliability of poverty statistics in a cost-effective manner," Policy Research Working Paper Series 6909, The World Bank.
    22. Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar & Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
    23. Newhouse, D. & Shivakumaran, S. & Takamatsu, S. & Yoshida, N., 2014. "How survey-to-survey imputation can fail," Policy Research Working Paper Series 6961, The World Bank.
    24. Gregorio Izquierdo Llanes & Antonio Salcedo Galiano, 2023. "Why does equivalization matter? An application to the monetary poverty in the sustainable development goals framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2575-2589, June.
    25. Sinha Roy,Sutirtha & Van Der Weide,Roy, 2022. "Poverty in India Has Declined over the Last Decade But Not As Much As Previously Thought," Policy Research Working Paper Series 9994, The World Bank.

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    More about this item

    JEL classification:

    • D6 - Microeconomics - - Welfare Economics
    • H53 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Welfare Programs
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies

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