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Replication data for: Productivity and Selection of Human Capital with Machine Learning

Resource Type
  • Chalfin, Aaron
  • Danieli, Oren
  • Hillis, Andrew
  • Jelveh, Zubin
  • Luca, Michael
  • Ludwig, Jens
  • Mullainathan, Sendhil
Publication Date
  • Abstract

    Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired--which requires not an estimate of a causal effect, but rather a prediction. We demonstrate that there can be large social welfare gains from using machine learning tools to predict worker productivity, using data from two important applications - police hiring and teacher tenure decisions.
  • Is supplement to
    DOI: 10.1257/aer.p20161029 (Text)
  • Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. American Economic Review, American Economic Review, 106, no. 5 (n.d.): 124–27.
    • ID: 10.1257/aer.p20161029 (DOI)

Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-12

Chalfin, Aaron; Danieli, Oren; Hillis, Andrew; Jelveh, Zubin; Luca, Michael et. al. (2016): Replication data for: Productivity and Selection of Human Capital with Machine Learning. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset.