Teacher applicant hiring and teacher performance: Evidence from DC public schools

By Brian A. Jacob, Jonah E. Rockoff, Eric S. Taylor, Benjamin A Lindy, Rachel Rosen. Journal of Public Economics, 2016

120 citations recorded by the source corpus.

Selecting more effective teachers among job applicants during the hiring process could be a highly cost-effective means of improving educational quality, but there is little research that links information gathered during the hiring process to subsequent teacher performance. We study the relationship among applicant characteristics, hiring outcomes, and teacher performance in the Washington DC Public Schools (DCPS). We take advantage of detailed data on a multi-stage application process, which includes written assessments, a personal interview, and sample lessons, as well as the annual evaluations of all DCPS teachers, based on multiple criteria. We identify a number of background characteristics (e.g., undergraduate GPA) as well as screening measures (e.g., applicant performance on a mock teaching lesson) that strongly predict teacher effectiveness. Interestingly, we find that these measures are only weakly, if at all, associated with the likelihood of being hired, suggesting considerable scope for improving teacher quality through the hiring process.

Subfields: Accounting, Education

View Paper: https://www.semanticscholar.org/paper/18374f54a2f6920f21ae6ee8612660c17c7576b7

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Citations in the corpus

Machine Learning: An Applied Econometric Approach (2017, background): “The decision of which teacher to hire, however, requires a prediction: the use of information available at time of hiring to forecast individual teacher quality (Kane and Staiger 2008; Dobbie 2011; Jacob et al. 2016).”