Good identification, meet good data

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

*Corresponding author for this work

Research output: Contribution to journalComment/debate

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.

Original languageEnglish (US)
Article number104796
JournalWorld Development
Volume127
DOIs
StatePublished - Mar 2020

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ASJC Scopus subject areas

  • Geography, Planning and Development
  • Development
  • Sociology and Political Science
  • Economics and Econometrics

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