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

这个跨学科的研究项目将使用行政数据和尖端的统计方法，以推进科学，技术，工程和数学（STEM）教师招聘，雇用和保留在竞争激烈的劳动力和住房市场中的动态知识。 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，不断变化的全球劳动力市场对美国公立学校提出了双重挑战。 随着对接受STEM高级培训的工人需求的增加，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，学校必须雇用和留住一批受过高等教育、技能熟练的科学和数学教师。 同样的市场力量，需要改善美国的STEM教育系统对学校的能力，雇用和留住STEM教师的根本限制，但是，因为学校努力与相对高薪的私营部门劳动力市场竞争，雇用和留住具有强大的STEM技能的个人作为教师。 该项目的研究结果将有助于对劳动力市场的基本理论理解，特别强调STEM教师市场，并将为旨在招聘和留住高效STEM教师的政策提供信息。 调查结果将积极传播给学区内外的政策制定者和从业人员。调查人员将使用弗朗西斯科联合学区（SFUSD）关于现任和前任教师以及SFUSD教学职位申请者的数据。 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。 这些匹配的数据将提供独特的机会，以更好地了解高效STEM教师的劳动力市场;检查基于生产率的劳动力市场排序过程;并调查在工作排序过程中选择申请人的作用。 除了研究生产率如何与劳动力市场排序相关，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那些被雇用和未被雇用的人是否会在一系列后续结果中受到不同的影响。 调查人员还将深入了解教师离开教学岗位后的去向，以及学校在留住高效的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，并得到NSF教育和人力资源局项目的额外支持。

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.