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Does it pay to be poor? Testing for systematically underreported GNI estimates

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  • Andrew Kerner

    (University of Michigan)

  • Morten Jerven

    (Norwegian University of Life Sciences)

  • Alison Beatty

    (University of Michigan)

Abstract

Coordinating aid distribution to the poorest countries requires identifying which countries are poor. In practice this has meant sorting countries into developmental cohorts on the basis of macroeconomic data, with countries in poorer cohorts gaining access to more and more concessional aid programs. To the extent that governments can influence their macroeconomic data, some, especially those in aid-dependent countries, may prefer to report data that sorts them into lower development cohorts. We term such behavior “aid-seeking data management.” The possibility of data management has substantial implications for aid distribution, and for the use of macroeconomic data in social scientific settings. We look for evidence of aid-seeking data management in the distribution of GNI per capita data around the eligibility threshold for the World Bank’s International Development Association (IDA). Because macroeconomic data are subject to frequent ex post revisions, we separately analyze the heavily revised data available for download from the World Bank’s World Development Indicators and the substantially less revised data that we gleaned from back issues of print edition of World Bank Atlas. We find that the less revised GNI per capita data display patterns that are consistent with aid-seeking data management among aid-dependent countries, and only among aid-dependent countries. This finding is robust to a variety of model specifications, but somewhat sensitive to the exclusion of individual countries from the sample. We find no such evidence in the currently downloadable data, suggesting that whatever biases aid-seeking data management may have generated in early data releases are largely and perhaps entirely wiped away in ex post revisions.

Suggested Citation

  • Andrew Kerner & Morten Jerven & Alison Beatty, 2017. "Does it pay to be poor? Testing for systematically underreported GNI estimates," The Review of International Organizations, Springer, vol. 12(1), pages 1-38, March.
  • Handle: RePEc:spr:revint:v:12:y:2017:i:1:d:10.1007_s11558-015-9239-3
    DOI: 10.1007/s11558-015-9239-3
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    Cited by:

    1. Axel Dreher & Sarah Langlotz, 2020. "Aid and growth: New evidence using an excludable instrument," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(3), pages 1162-1198, August.
    2. Manuel Oechslin & Elias Steiner, 2022. "Statistical capacity and corrupt bureaucracies," The Review of International Organizations, Springer, vol. 17(1), pages 143-174, January.
    3. Carrie B Dolan & McKinley Saunders & Ariel BenYishay, 2020. "Childhood health and the changing distribution of foreign aid: Evidence from Nigeria's transition to lower-middle-income status," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-13, November.
    4. Christopher Gandrud & Mark Hallerberg, 2016. "Interpreting Fiscal Accounting Rules in the European Union," CESifo Working Paper Series 6228, CESifo.
    5. Takaaki Masaki & Bradley C. Parks, 2020. "When do performance assessments influence policy behavior? Micro-evidence from the 2014 Reform Efforts Survey," The Review of International Organizations, Springer, vol. 15(2), pages 371-408, April.
    6. Kerner, Andrew & Crabtree, Charles, 2018. "The Political Economy of Data Production," SocArXiv qsxae, Center for Open Science.

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    World Bank; Data; Foreign Aid;
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