The U.S. economy has been the world’s largest for over half a century, largely fueled by its unparalleled technological capacity and advancement. There is an increased concern about the production of skilled science, technology, engineering, and mathematics (STEM) research workers in U.S. While there is a general concern about the level of production, there are specific issues with the diversity of the science workforce. Recent reports for NIH, the National Academies, and academic publications (Augustine, 2007; Working Group on Diversity in the Biomedical Research Workforce , 2012; Ginther et. Al, 2012; Ghaffarzadegan et. al, forthcoming) have described the fact that among scientists there are patterns which lead to shortages by race and ethnicity, as well as continuing gaps between international and domestic workers.

Fixing this situation requires an understanding of the career development process for research scientists, as well as the impact that public policies can have upon individual and group outcomes. The objective of this study is to investigate the key issues and the dynamics of STEM postdoctoral and researcher training and to explain job search behavior in the job market. Macroeconomic methods are widely used to model the job search behavior, and in this model we use search theory and job matching (Mortensen and Pissarides, 1994; Mortensen and Pissarides; 2003; Haan et. al, 2001) to develop an Agent Based Model focusing on the job search behavior of STEM postdoctoral associates and screening procedures employers use during the job search.

The Agent Based Modeling (ABM) methodology is used in the literature to model job matching. The agents in this model are the employment positions, PhDs, and postdocs. Each agent interacts with one another in determining job opportunities and pay scales, and negotiating arrangements for employment.  Employment positions in the model are academic positions (postdoc and faculty positions) and non-academic (industry and government positions). Each employment position has characteristics such as reputation, field, benefits, work environment, travel frequency, future career options, citizenship, and potential employees should fulfill required skill set.

Results from this study are useful because systems perspectives (Sterman, 2000; Maroulis, 2010) allow us to run scenarios examining the impact of particular policy changes. Given recent attempts to diversity the workforce have not been as successful as the government would like (Ginther et. Al, 2012) we model the impact of changes in NIH and NSF awards (e.g., reducing the length of time post-doctoral researchers can serve in positions) on the diversification of the workforce.  Moreover, by visualization of networks and geographic spread of job matching, using ABM models allows for the connection of detailed data on personal characteristics to networks of jobs - as well as allowing for the natural variation of labor demand across geographic space