Screening and Recruiting Talent At Teacher Colleges Using Pre-College Academic Achievement

Abstract

This paper studies screening and recruiting policies that use pre-college academic achievement to restrict or incentivize entry to teacher-colleges. Using historical records of college entrance exam scores since 1967 and linking them to administrative data on the population of teachers in Chile, we first document a robust positive and concave relationship between pre-college academic achievement and several short and long run measures of teacher productivity. We then assess the effectiveness of two recent policies that used pre-college achievement to recruit or screen out students entering teacher-colleges. Using a regression discontinuity de-sign based on the government’s recruitment efforts, we evaluate the effectiveness of targeted scholarships at shifting career choices of high achieving students as well as the effect on the overall stock of teachers predicted effectiveness. We then assess a screening policy that forced teacher colleges to exclude below-average applicants. We quantify the policy effectiveness by retroactively simulating the rule and evaluating its success at screening out low performing teachers. Comparing this benchmark policy rule to a series of data-driven alternatives, we find that even simple screening policies can identify a significant portion of ex-post low performing teachers. In both policies studied, screening out low performing students is more effective than targeting recruitment efforts to only very high achieving students. Taken together, these findings suggest that the combination of better administrative data and flexible prediction methods can be used to implement practical screening and recruiting policies in some contexts and allow for better targeting of investments in future teachers.

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Prediction Plot

Article
  • Coauthors: Sebastian Gallegos, Franco Calle, Mohit Karnani
  • Published: Industrial Relations Section Working Paper Series (November 2019, No. 636)
  • Date: 2019
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