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).
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.
