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

使用微观模拟对 STEM 劳动力动态进行建模

• 批准号: 8187336
• 负责人: Marlene Ann Lee
• 金额: $ 16.59万
• 依托单位: POPULATION REFERENCE BUREAU
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-26 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8187336
• 关键词: Academic Training; American; Asians; Characteristics; Complex; Confidence Intervals; Data; Databases; Employment; Engineering; Ethnic Origin; Event; Feedback; Female; Funding; Future; Gender; Goals; Government; Health; Immigrant; Individual; Industry; Information Systems; Investments; Knowledge; Labor Forces; Life; Life Table Estimate; Life Table Models; Life Tables; Light; Mathematics; Measures; Methods; Minority; Minority Groups; Modeling; Occupations; Outcome; Performance; Policies; Population; Population Analysis; Population Group; Principal Investigator; Procedures; Race; Recording of previous events; Research; Resources; Retirement; Sample Size; Science; Solutions; Technology; Testing; Time; Training; Translational Research; Underrepresented Minority; Work; base; career; ethnic difference; improved; knowledge base; life history; longitudinal database; male; programs; racial and ethnic; skills; technological innovation; tool; web site; women employment

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). PUBLIC HEALTH RELEVANCE: The STEM workforce is an important part of maintaining the knowledge base, skill level, and technological innovation necessary to research and translation of research into practical solutions to health problems. The proposed research provides useful tools for targeting investment of government funds in the training of STEM workers that contribute to improvement of American's health.

描述（由申请人提供）：提高在科学、技术、工程和数学（STEM）领域未被充分代表的少数民族的职业动态知识对维持和增加科学劳动力至关重要。少数民族是人口中迅速增长的一部分，但在科学工作队伍中代表性不足。目前，它们是一种未被充分利用的资源。增加少数民族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。为了确定哪些项目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，重要的是要了解学术和非学术STEM劳动力的进入和保留动态。该项目将使用SPACE（复杂事件随机种群分析）来估计多状态生命表函数。生成的生命表模型将总结由学术STEM劳动力组成的亚群体的就业动态，并根据美国劳动力人口构成的变化，预测未来学术STEM工人的培训需求。所使用的方法还将产生生命表估计值周围的置信区间。置信区间将允许假设检验来回答诸如种族/民族差异是否在职业生涯早期被观察到并持续存在，或者这些差异是否随着时间的推移而积累等问题。此外，SPACE还使用微观模拟程序，为代表性不足的少数群体（整体或单独）生成有关STEM劳动力动态的有用合成数据。