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

这一跨学科研究项目将使用管理数据和尖端统计方法，在竞争激烈的劳动力和住房市场中，促进关于科学、技术、工程和数学(STEM)教师招聘、聘用和留用的动态知识。培养下一代科学家和工程师是美国面临的最紧迫的问题之一，而不断变化的全球劳动力市场对美国公立学校构成了双重挑战。随着对受过STEM高级培训的工人的需求增加，各级教育机构必须为越来越多的学生提供高质量和严格的STEM教育。要做到这一点，学校必须雇佣和留住一支受过高等教育、技能娴熟的科学和数学教师队伍。然而，需要改善美国STEM教育体系的市场力量，对学校聘用和留住STEM教师的能力构成了根本性的制约，因为学校难以与薪酬相对较高的私营部门劳动力市场竞争，以聘用和留住拥有强大STEM技能的个人担任教师。该项目的发现将有助于对劳动力市场的基本理论了解，特别是对STEM教师市场的重视，并将为旨在招聘和留住高效STEM教师的政策提供参考。调查人员将采用旧金山联合学区的数据，包括现任和前任教师以及旧金山联合学区教师职位的申请者。他们将把这些数据与美国人口普查局提供的行政记录联系起来，以了解美国学校在当今高科技经济中面临的人力资本挑战。这些匹配的数据将为高效STEM教师提供独特的机会，以更好地了解劳动力市场；检查基于生产率的劳动力市场分类流程；以及调查在工作分类过程中选择应聘者的作用。除了检查生产率与劳动力市场分类的关系，以及谁申请和不申请特定工作之外，这些数据还将有助于显示，在一系列后来的结果中，那些被录用和未被录用的人是否受到了不同的影响。调查人员还将深入了解教师离开教学后的去向，以及学校在留住高效STEM教师方面面临的潜在压力。该项目通过NSF跨学科行为和社会科学研究(IBSS)竞赛得到支持，NSF教育和人力资源局的项目也提供了额外支持。

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.