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Is Predicted Data a Viable Alternative to Real Data?

Author

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  • Tomoki Fujii
  • Roy van der Weide

Abstract

It is costly to collect the household- and individual-level data that underlie official estimates of poverty and health. For this reason, developing countries often do not have the budget to update estimates of poverty and health regularly, even though these estimates are most needed there. One way to reduce the financial burden is to substitute some of the real data with predicted data by means of double sampling, where the expensive outcome variable is collected for a subsample and its predictors for all. This study finds that double sampling yields only modest reductions in financial costs when imposing a statistical precision constraint in a wide range of realistic empirical settings. There are circumstances in which the gains can be more substantial, but these denote the exception rather than the rule. The recommendation is to rely on real data whenever there is a need for new data and to use prediction estimators to leverage existing data.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:wbecrv:v:34:y:2020:i:2:p:485-508.
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    File URL: http://hdl.handle.net/10.1093/wber/lhz007
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    Cited by:

    1. Diana K. L. Ngo & Luc Christiaensen, 2019. "The Performance Of A Consumption Augmented Asset Index In Ranking Households And Identifying The Poor," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(4), pages 804-833, December.
    2. van der Weide, Roy & Blankespoor, Brian & Elbers, Chris & Lanjouw, Peter, 2024. "How accurate is a poverty map based on remote sensing data? An application to Malawi," Journal of Development Economics, Elsevier, vol. 171(C).
    3. Pape,Utz Johann, 2021. "Measuring Poverty Rapidly Using Within-Survey Imputations," Policy Research Working Paper Series 9530, The World Bank.
    4. García-Suaza, Andres & Varela, Daniela, 2024. "Nightlight, landcover and buildings: understanding intracity socioeconomic differences," Documentos de Trabajo 21025, Universidad del Rosario.
    5. Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikas Paul & Subhashis Banerjee, 2017. "On monitoring development indicators using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Jun 2018.

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