IDEAS home Printed from https://ideas.repec.org/a/oup/wbecrv/v30y2016i3p475-500..html
   My bibliography  Save this article

Estimating Quarterly Poverty Rates Using Labor Force Surveys: A Primer

Author

Listed:
  • 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cross-survey imputation literature to provide up-to-date estimates of poverty when official estimates are deemed outdated.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.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/wber/lhv062
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    3. repec:pri:rpdevs:deaton_price_trends_in_india_version_3_jan_08_all.pdf is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. 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.
    4. Ligon, Ethan & Christiaensen, Luc & Sohnesen, Thomas P, 2020. "Should Consumption Sub-Aggregates be Used to Measure Poverty?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9b9929jh, Department of Agricultural & Resource Economics, UC Berkeley.
    5. 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.
    6. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    7. 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).
    8. Lovaton Davila, Rodrigo & McCarthy, Aine Seitz & Gondwe, Dorothy & Kirdruang, Phatta & Sharma, Uttam, 2022. "Water, walls, and bicycles: wealth index composition using census microdata," Journal of Demographic Economics, Cambridge University Press, vol. 88(1), pages 79-120, March.
    9. Carlo Azzarri & Elizabeth Cross, 2016. "Improved Spatially Disaggregated Livestock Measures for Uganda," The Review of Regional Studies, Southern Regional Science Association, vol. 46(1), pages 37-73, Winter.
    10. World Bank, 2015. "Tanzania Poverty Assessment," World Bank Publications - Reports 21871, The World Bank Group.
    11. Anh Thu Quang Pham & Pundarik Mukhopadhaya & Ha Vu, 2020. "Targeting Administrative Regions for Multidimensional Poverty Alleviation: A Study on Vietnam," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(1), pages 143-189, July.
    12. 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.
    13. Alfani, Federica & Dabalen, Andrew & Fisker, Peter & Molini, Vasco, 2015. "Can we measure resilience ? a proposed method and evidence from countries in the Sahel," Policy Research Working Paper Series 7170, The World Bank.
    14. Pape,Utz Johann, 2021. "Measuring Poverty Rapidly Using Within-Survey Imputations," Policy Research Working Paper Series 9530, The World Bank.
    15. Diana Chiliquinga & Gaurav Datt, 2016. "Changing Betas or Changing X’s? Evolution of Income and Poverty in Ecuador, 2001-12," Monash Economics Working Papers 14-16, Monash University, Department of Economics.
    16. Dang, Hai-Anh H & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    17. Li, Qing & Yu, Shuai & Échevin, Damien & Fan, Min, 2022. "Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    18. Javier Sierra & Victoria Muriel-Patino & Fernando Rodríguez-López, 2024. "A comprehensive framework for understanding microfinance performance evaluation methods," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    19. Floyd Mwansa, 2023. "Measuring Distribution of Wealth in Zambia Using Census Micro Data: An Application of Principal Component Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 13(3), pages 126-140, May.
    20. World Bank, "undated". "Africa's Pulse, October 2013 : An Analysis of Issues Shaping Africa's Economic Future," World Bank Publications - Reports 20237, The World Bank Group.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:wbecrv:v:30:y:2016:i:3:p:475-500.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/wrldbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.