Chalfin, AaronDanieli, OrenHillis, AndrewJelveh, ZubinLuca, MichaelLudwig, JensMullainathan, Sendhil2016-11-222016Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 124–127.0065-812Xhttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29429082Economists 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.en-UShuman capitalselection and staffingdata and data setsmathematical methodsJournal Article2016-11-2210.1257/aer.p20161029