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Estimating quarterly poverty rates using labor force surveys : a primer

Author

Listed:
  • Douidich, Mohamed
  • Ezzrari, Abdeljaouad
  • Van der Weide, Roy
  • Verme, Paolo

Abstract

The paper shows how Labor Force Surveys can be used effectively to estimate poverty rates using Household Expenditure Surveys and cross-survey imputation methods. With only two rounds of Household Expenditure Survey data for Morocco (2001 and 2007), the paper estimates quarterly poverty rates for the period 2001-2010 by imputing household expenditures into the Labor Force Surveys. The results are encouraging. The methodology is able to accurately reproduce official poverty statistics by combining current Labor Force Surveys with previous period Household Expenditure Surveys, and vice versa. Although the 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. The newly produced time-series of poverty rates can help researchers and policy makers to: (a) study the determinants of poverty reduction or use poverty as an explanatory factor in cross-section and panel models; (b) forecast poverty rates based on a time-series model fitted to the data; and (c) explore the linkages between labor market conditions and poverty and simulate the effects of policy reforms or economic shocks. This is a promising research agenda that can expand significantly the tool-kit of the welfare economist.

Suggested Citation

  • Douidich, Mohamed & Ezzrari, Abdeljaouad & Van der Weide, Roy & Verme, Paolo, 2013. "Estimating quarterly poverty rates using labor force surveys : a primer," Policy Research Working Paper Series 6466, The World Bank.
  • Handle: RePEc:wbk:wbrwps:6466
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    References listed on IDEAS

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    1. 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.
    2. Natalia Evgenevna Antonova, 2007. "The international scientific conference «Economic Cooperation between the Russian Far East and the Asia-Pacific Region Countries»," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 2, pages 177-182.
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    Citations

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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. 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.
    4. 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.
    5. Atamanov, Aziz & Tandon, Sharad & Lopez-Acevedo, Gladys & 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," IZA Discussion Papers 13363, Institute of Labor Economics (IZA).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Other Operational Studies 24410, The World Bank.
    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. Elena Ianchovichina & Lili Mottaghi & Shantayanan Devarajan, "undated". "Middle East and North Africa Economic Monitor, October 2015," World Bank Other Operational Studies 22711, The World Bank.
    14. Utz Pape & Luca Parisotto, 2019. "Estimating Poverty in a Fragile Context – The High Frequency Survey in South Sudan," HiCN Working Papers 305, Households in Conflict Network.
    15. 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.

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    More about this item

    Keywords

    Rural Poverty Reduction; Regional Economic Development; Achieving Shared Growth; Poverty Monitoring&Analysis;
    All these keywords.

    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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