Good Identification, Meet Good Data

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.

Keywords: causal inference, data quality, research methods, survey design