IDEAS home Printed from https://ideas.repec.org/p/wbk/wbrwps/9530.html

Measuring Poverty Rapidly Using Within-Survey Imputations

Author

Listed:
  • Pape,Utz Johann

Abstract

Poverty is an indicator of paramount importance for gauging the socioeconomic well-being of a population. Especially during or after a shock, poverty estimates are invaluable for assessing the severity of the impact and for identifying which parts of the population were most affected. The measurement of consumption-based monetary poverty, however, has traditionally been very time consuming. A household consumption questionnaire usually includes more than 200 items, including food and nonfood items, often requiring more than two hours to administer. This paper proposes a new methodology that combines an innovative questionnaire design with standard imputation techniques. It substantially shortens the time required to administer a household consumption questionnaire to less than 60 minutes by imputing deliberately absent consumption values for items that are not explicitly asked. The proposed methodology makes it possible to derive poverty estimates without compromising the credibility of the resulting estimate, and it performs considerably better than alternative approaches based on reduced consumption aggregates and cross-survey imputations. This new methodology is particularly useful in fragile states given the significant risks associated with lengthy interviews, as well as to rapidly assess the impact of a shock or of a project. It can also be useful to reduce enumerator and respondent fatigue, or to mitigate the problem of high nonresponse rates.

Suggested Citation

  • Pape,Utz Johann, 2021. "Measuring Poverty Rapidly Using Within-Survey Imputations," Policy Research Working Paper Series 9530, The World Bank.
  • Handle: RePEc:wbk:wbrwps:9530
    as

    Download full text from publisher

    File URL: http://documents.worldbank.org/curated/en/900741611846381624/pdf/Measuring-Poverty-Rapidly-Using-Within-Survey-Imputations.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    2. Fiedler, John L. & Mwangi, Dena M., 2016. "Improving household consumption and expenditure surveys’ food consumption metrics: Developing a strategic approach to the unfinished agenda," IFPRI discussion papers 1570, International Food Policy Research Institute (IFPRI).
    3. Jean Olson Lanjouw & Peter Lanjouw, 2001. "How to Compare Apples And Oranges: Poverty Measurement Based on Different Definitions of Consumption," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 47(1), pages 25-42, March.
    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. repec:wbk:wbpubs:22575 is not listed on IDEAS
    6. Baird, Sarah & Hamory, Joan & Miguel, Edward, 2008. "Tracking, Attrition and Data Quality in the Kenyan Life Panel Survey Round 1 (KLPS-1)," Center for International and Development Economics Research, Working Paper Series qt3cw7p1hx, Center for International and Development Economics Research, Institute for Business and Economic Research, UC Berkeley.
    7. Schräpler, Jörg-Peter & Schupp, Jürgen & Wagner, Gert G., 2010. "Changing from PAPI to CAPI: Introducing CAPI in a Longitudinal Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 26(2), pages 239-269.
    8. Tomoki Fujii & Roy van der Weide, 2020. "Is Predicted Data a Viable Alternative to Real Data?," The World Bank Economic Review, World Bank, vol. 34(2), pages 485-508.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dang, Hai-Anh H & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, IZA Network @ LISER.
    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. McCandless, Erin, 2025. "Inclusion and social contracts in Tunisia: Navigating the complexities of political and socio-economic transformation," World Development, Elsevier, vol. 188(C).
    4. 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).

    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. 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).
    2. Luc Christiaensen & Ethan Ligon & Thomas Pave Sohnesen, 2022. "Consumption Subaggregates Should Not Be Used to Measure Poverty," The World Bank Economic Review, World Bank, vol. 36(2), pages 413-432.
    3. 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.
    4. Hassine, Nadia Belhaj, 2014. "Economic inequality in the Arab region," Policy Research Working Paper Series 6911, The World Bank.
    5. Hassine, Nadia Belhaj, 2015. "Economic Inequality in the Arab Region," World Development, Elsevier, vol. 66(C), pages 532-556.
    6. Dang, Hai-Anh H. & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," GLO Discussion Paper Series 904, Global Labor Organization (GLO).
    7. Corral Rodas, Paul Andres & Ham, Andres & Lanjouw, Peter & Lucchetti, Leonardo Ramiro & Stemmler, Henry, 2025. "Stress Testing Survey to Survey Imputation: Understanding When Poverty Predictions Can Fail," Policy Research Working Paper Series 11192, The World Bank.
    8. Dean Jolliffe & Espen Beer Prydz, 2016. "Estimating international poverty lines from comparable national thresholds," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(2), pages 185-198, June.
    9. 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.
    10. 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.
    11. Luisa Natali & Marta Moratti, 2012. "Measuring Household Welfare: Short versus long consumption modules," Papers inwopa671, Innocenti Working Papers.
    12. Hai‐Anh H. Dang & Talip Kilic & Kseniya Abanokova & Calogero Carletto, 2025. "Poverty Imputation in Contexts Without Consumption Data: A Revisit With Further Refinements," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 71(1), February.
    13. World Bank, 2015. "Tanzania Poverty Assessment," World Bank Publications - Reports 21871, The World Bank Group.
    14. Backiny-Yetna, Prospère & Steele, Diane & Yacoubou Djima, Ismael, 2017. "The impact of household food consumption data collection methods on poverty and inequality measures in Niger," Food Policy, Elsevier, vol. 72(C), pages 7-19.
    15. 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.
    16. Shinya Takamatsu & Nobuo Yoshida & Rakesh Ramasubbaiah & Freeha Fatima, 2021. "Rapid Consumption Method and Poverty and Inequality Estimation in South Sudan revisited," Global Poverty Monitoring Technical Note Series 18, The World Bank.
    17. Yoshida, Nobuo & Kawashima, Yusaku & Takamatsu, Shinya, 2026. "Survey-to-Survey Poverty Monitoring under Economic Shocks : The Role of Proxies and Flexible Nonlinear Learners," Policy Research Working Paper Series 11359, The World Bank.
    18. 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.
    19. 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.
    20. Pape,Utz Johann & Wollburg,Philip Randolph, 2019. "Estimation of Poverty in Somalia Using Innovative Methodologies," Policy Research Working Paper Series 8735, The World Bank.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:wbk:wbrwps:9530. 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: Roula I. Yazigi (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.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.