Modeling STEM Workforce Dynamics Using Microsimulation & Multi-State Life Tables

描述(由申请人提供)：提高科学、技术、工程和数学(STEM)领域中未被充分代表的少数群体的职业动态知识，对于维持和增加科学劳动力至关重要。少数群体是人口中迅速增长的一部分，在科学劳动大军中的代表性不足。目前，它们代表着一种未得到充分利用的资源。增加少数群体在科学领域的存在的一个重要部分是增加他们在学术STEM劳动力中的代表性。为了确定哪些计划和政策在维持和增加学术STEM劳动力方面是或将是最有效的，重要的是了解学术和非学术STEM劳动力的进入和保留的动态。该项目将使用SPACE(复杂事件的随机总体分析)来估计多状态寿命表函数。制作的生命表模型将总结组成学术STEM劳动力的亚群体的就业动态，并将根据美国劳动力人口结构的变化预测对未来学术STEM工人的培训需求。所使用的方法还将产生生命表估计的可信区间。置信度区间将允许假设检验回答一些问题，如种族/民族差异是否在职业生涯早期观察到并持续存在，或者这些差异是否随着时间的推移而积累。此外，空间使用微观模拟程序，将为代表不足的少数群体(作为一个整体或单独)产生关于科技、经济和经济管理部门劳动力动态的有用的综合数据。

DESCRIPTION (provided by applicant): Improved knowledge of career dynamics for underrepresented minorities in science, technology, engineering, and mathematics (STEM) fields is critical to sustaining and increasing the scientific workforce. Minorities are a rapidly growing segment of the population and are underrepresented in the scientific workforce. Currently, they represent an underused resource. An important part of increasing the presence of minorities in the science fields is to increase their representation among the academic STEM workforce. In order to identify which programs and policies are or would be the most effective in sustaining and increasing the academic STEM workforce, it is important to understand the dynamics of entry into and retention in both the academic and non-academic STEM workforce. The project will use SPACE (Stochastic Population Analysis for Complex Events) to estimate multi-state life table functions. The life table models produced will summarize the employment dynamics for subpopulations comprising the academic STEM workforce and will project the need for training of future academic STEM workers in light of the changing demographic composition of the U.S. workforce. The methods used will also produce confidence intervals around life table estimates. Confidence intervals will allow hypothesis testing to answer questions such as whether race/ethnic differences are observed early in careers and persist or whether these differences accumulate over time. In addition, SPACE uses microsimulation procedures that will produce useful synthetic data on STEM workforce dynamics for underrepresented minority groups (as a whole or separately).