IBSS-L: Recruiting, Hiring, and Retaining Math and Science Teachers

Stanford University, Stanford CA

Investigators

Abstract

This interdisciplinary research project will use administrative data and cutting-edge statistical methods to advance knowledge on the dynamics of science, technology, engineering and mathematics (STEM) teacher recruitment, hiring, and retention in the midst of competitive labor and housing markets. Training the next generation of scientists and engineers is one of the most pressing problems the nation faces, and the changing global labor market presents a two-sided challenge to U.S. public schools. As the demand for workers with advanced training in STEM increases, educational institutions at all levels must provide high-quality and rigorous STEM education to an ever-larger number of students. To do so, schools must hire and retain a corps of highly educated and skilled science and mathematics teachers. The same market forces that necessitate improvements in the American STEM education system place fundamental constraints on schools' abilities to hire and retain STEM teachers, however, because schools struggle to compete with the relatively high-paying private sector labor market to hire and retain individuals with strong STEM skills as teachers. The findings from this project will contribute to basic theoretical understanding of labor markets, with special emphasis placed on the market for STEM teachers, and it will inform policy aimed at recruiting and retaining highly effective STEM teachers. Results will be actively disseminated to policy makers and practitioners in school districts and beyond. The investigators will employ data from the San Francisco Unified School District (SFUSD) about current and former teachers as well as applicants to SFUSD teaching positions. They will link these data with administrative records available from the U.S. Census Bureau in order to understand the human capital challenges facing U.S. schools in today's high-tech economy. These matched data will provide unique opportunities to better understand the labor market for highly effective STEM teachers; to examine productivity-based labor market sorting processes; and to investigate the role of selection into the applicant pool in the job-sorting process. In addition to examining how productivity is related to labor market sorting, and who does and does not apply for particular jobs, that data will help show whether those who are and are not hired for a given job are differentially affected across a range of later outcomes. The investigators also will provide an in-depth picture of where teachers go when they leave teaching and the potential pressures that schools face in retaining highly effective STEM teachers. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition with additional support from programs in the NSF Directorate for Education and Human Resources.