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Good identification, meet good data

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  • Dillon, Andrew
  • Karlan, Dean
  • Udry, Christopher
  • Zinman, Jonathan

Abstract

Causal inference lies at the heart of social science, and the 2019 Nobel Prize in Economics highlights the value of randomized variation for identifying causal effects and mechanisms. But causal inference cannot rely on randomized variation alone; it also requires good data. Yet the data-generating process has received less consideration from economists. We provide a simple framework to clarify how research inputs affect data quality and discuss several such inputs, including interviewer selection and training, survey design, and investments in linking across multiple data sources. More investment in research on the data quality production function would considerably improve casual inference generally, and poverty alleviation specifically.

Suggested Citation

  • Dillon, Andrew & Karlan, Dean & Udry, Christopher & Zinman, Jonathan, 2020. "Good identification, meet good data," World Development, Elsevier, vol. 127(C).
  • Handle: RePEc:eee:wdevel:v:127:y:2020:i:c:s0305750x19304450
    DOI: 10.1016/j.worlddev.2019.104796
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    References listed on IDEAS

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    Cited by:

    1. Dillon, Andrew & Mensah, Edouard, 2024. "Respondent biases in agricultural household surveys," Journal of Development Economics, Elsevier, vol. 166(C).
    2. Masselus, Lise & Fiala, Nathan, 2024. "Whom to ask? Testing respondent effects in household surveys," Journal of Development Economics, Elsevier, vol. 168(C).
    3. Zezza,Alberto & Mcgee,Kevin Robert & Wollburg,Philip Randolph & Assefa,Thomas Woldu & Gourlay,Sydney, 2022. "From Necessity to Opportunity : Lessons for Integrating Phone and In-Person Data Collectionfor Agricultural Statistics in a Post-Pandemic World," Policy Research Working Paper Series 10168, The World Bank.
    4. Fiala, Nathan & Masselus, Lise, 2022. "Whom to ask? Testing respondent effects in household surveys," Ruhr Economic Papers 935, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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