IDEAS home Printed from https://ideas.repec.org/p/zbw/glodps/538.html
   My bibliography  Save this paper

Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad

Author

Listed:
  • Beltramo, Theresa
  • Dang, Hai-Anh H.
  • Sarr, Ibrahima
  • Verme, Paolo

Abstract

Household consumption surveys do not typically cover refugee populations, and poverty estimates for refugees are rare. This paper tests the performance of cross-survey imputation methods to estimate poverty for a sample of refugees in Chad, by combining United Nations High Commissioner for Refugees survey and administrative data. The proposed method offers poverty estimates based on administrative data that fall within a 95 percent margin of poverty estimates based on survey consumption data. This result is robust to different poverty lines, sets of regressors, and modeling assumptions of the error term. The method outperforms common targeting methods, such as proxy means tests and the targeting method currently used by humanitarian organizations in Chad.

Suggested Citation

  • Beltramo, Theresa & Dang, Hai-Anh H. & Sarr, Ibrahima & Verme, Paolo, 2020. "Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad," GLO Discussion Paper Series 538, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:538
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/216898/1/GLO-DP-0538.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gerard J. van den Berg & Pia R. Pinger & Johannes Schoch, 2016. "Instrumental Variable Estimation of the Causal Effect of Hunger Early in Life on Health Later in Life," Economic Journal, Royal Economic Society, vol. 126(591), pages 465-506, March.
    2. David Coady & Margaret Grosh & John Hoddinott, 2004. "Targeting of Transfers in Developing Countries : Review of Lessons and Experience," World Bank Publications - Books, The World Bank Group, number 14902.
    3. 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.
    4. Altındağ, Onur & O'Connell, Stephen D. & Şaşmaz, Aytuğ & Balcıoğlu, Zeynep & Cadoni, Paola & Jerneck, Matilda & Foong, Aimee Kunze, 2021. "Targeting humanitarian aid using administrative data: Model design and validation," Journal of Development Economics, Elsevier, vol. 148(C).
    5. 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.
    6. 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.
    7. George J. Borjas & Joan Monras, 2017. "The labour market consequences of refugee supply shocks," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 32(91), pages 361-413.
    8. repec:ucp:ecdecc:doi:10.1086/697555 is not listed on IDEAS
    9. Verme, Paolo & Gigliarano, Chiara, 2019. "Optimal targeting under budget constraints in a humanitarian context," World Development, Elsevier, vol. 119(C), pages 224-233.
    10. World Bank, 2013. "Repbulic of Chad Poverty Notes : Dynamics of Poverty and Inequality following the Rise of the Oil Sector," World Bank Publications - Reports 19322, The World Bank Group.
    11. Paolo Verme & Chiara Gigliarano & Christina Wieser & Kerren Hedlund & Marc Petzoldt & Marco Santacroce, 2016. "The Welfare of Syrian Refugees," World Bank Publications - Books, The World Bank Group, number 23228.
    12. Dang,Hai-Anh H. & Lanjouw,Peter F. & Dang,Hai-Anh H. & Lanjouw,Peter F., 2015. "Poverty dynamics in India between 2004 and 2012 : insights from longitudinal analysis using synthetic panel data," Policy Research Working Paper Series 7270, The World Bank.
    13. 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.
    14. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    15. Dang, Hai-Anh H. & Verme, Paolo, 2019. "Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity?," GLO Discussion Paper Series 429, Global Labor Organization (GLO).
    16. Kathleen Beegle & Luc Christiaensen & Andrew Dabalen & Isis Gaddis, 2016. "Poverty in a Rising Africa," World Bank Publications - Books, The World Bank Group, number 22575.
    17. 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.
    18. Astrid Mathiassen, 2009. "A model based approach for predicting annual poverty rates without expenditure data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(2), pages 117-135, June.
    19. 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.
    20. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Balanced Variable Addition In Linear Models," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1183-1200, September.
    21. Skoufias, Emmanuel & Davis, Benjamin & de la Vega, Sergio, 2001. "Targeting the Poor in Mexico: An Evaluation of the Selection of Households into PROGRESA," World Development, Elsevier, vol. 29(10), pages 1769-1784, October.
    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. Pape, Utz & Verme, Paolo, 2023. "Measuring Poverty in Forced Displacement Contexts," GLO Discussion Paper Series 1245, Global Labor Organization (GLO).
    2. 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.
    3. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    4. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, The World Bank.
    5. Della Guardia, Anne & Lake, Milli & Schnitzer, Pascale, 2022. "Selective inclusion in cash transfer programs: Unintended consequences for social cohesion," World Development, Elsevier, vol. 157(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. Dang,Hai-Anh H. & Verme,Paolo, 2019. "Estimating Poverty for Refugee Populations : Can Cross-Survey Imputation Methods Substitute for Data Scarcity ?," Policy Research Working Paper Series 9076, The World Bank.
    2. 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," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    3. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    4. 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.
    5. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, 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. Dang, Hai-Anh & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    8. 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.
    9. 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.
    10. 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.
    11. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    12. 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.
    13. 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.
    14. Paolo Verme, 2020. "Which Model for Poverty Predictions?," Working Papers 521, ECINEQ, Society for the Study of Economic Inequality.
    15. Sarr, Ibrahima & Dang, Hai-Anh & Gutierrez, Carlos Santiago Guzman & Beltramo, Theresa & Verme, Paolo, 2024. "Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia," IZA Discussion Papers 17036, Institute of Labor Economics (IZA).
    16. Betti, Gianni & Molini, Vasco & Mori, Lorenzo, 2024. "An attempt to correct the underestimation of inequality measures in cross-survey imputation through generalized additive models for location, scale and shape," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    17. Dang, Hai-Anh H. & Serajuddin, Umar, 2020. "Tracking the sustainable development goals: Emerging measurement challenges and further reflections," World Development, Elsevier, vol. 127(C).
    18. 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.
    19. François Bourguignon & A. Hector Moreno M., 2020. "On synthetic income panels," Working Papers halshs-01988068, HAL.
    20. 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.

    More about this item

    Keywords

    Refugees; Forced displacement; Targeting; Poverty; Chad;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • 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
    • O20 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:glodps:538. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/glabode.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.