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

Principal Investigator(s): View help for Principal Investigator(s) Aaron Chalfin; Oren Danieli; Andrew Hillis; Zubin Jelveh; Michael Luca; Jens Ludwig; Sendhil Mullainathan

Version: View help for Version V1

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P2016_1029_data			10/12/2019 11:02:AM
LICENSE.txt	text/plain	14.6 KB	10/12/2019 07:02:AM

Project Citation:

Chalfin, Aaron, Danieli, Oren, Hillis, Andrew, Jelveh, Zubin, Luca, Michael, Ludwig, Jens, and Mullainathan, Sendhil. Replication data for: Productivity and Selection of Human Capital with Machine Learning. Nashville, TN: American Economic Association [publisher], 2016. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113448V1

Project Description

Summary: 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.

Scope of Project

JEL Classification:
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      I11 Analysis of Health Care Markets
      H75 State and Local Government: Health; Education; Welfare; Public Pensions
      H76 State and Local Government: Other Expenditure Categories
      J24 Human Capital; Skills; Occupational Choice; Labor Productivity
      J45 Public Sector Labor Markets

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V1 [2019-10-12]

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