Association for Public Policy Analysis & Management

This hybrid model explores the complex dynamics of scientific research workforce supply and demand through a combination of system dynamics modeling and agent based modeling. It provides insights into the effectiveness of government policies on attracting and retaining researchers in the labor market. The model forecasts how current and potential policy changes effect scientific workforce supply and demand dynamics. Specifically, it examines the behavioral and social sciences research (BSSR) segment of the STEM workforce in the U.S.. The individualized, detailed capabilities of agent based modeling (ABM) are combined with decision theory at the individual level to examine supply and demand concepts. The model observes the number of PhD graduates who enter the BSSR workforce as well as their pursuit of research positions in academic institutions, industry, or government agencies. Workforce demand is represented using an economics-based system dynamics model. The use of ABM allows PhD job seekers to develop emergent behaviors while the simulator controls the dynamics of the job market conditions. Hiring preferences of employers also inform this model.

Inter-individual heterogeneity is introduced into the model by assigning PhD job seekers’ characteristics, such as career goals and life events, through input data. BSS PhD graduates who choose to enter the scientific workforce evaluate three career options, including applying for an academic job, pursuing non-academic jobs in government or industry, or remaining unemployed. The ABM also considers each agent’s personal life including transitions into being married and having children. Factors influencing their decisions include salary and proximity to where they are currently living. The demographic characteristics of the researchers initially populating the model are primarily informed by analysis of NSF’s Survey of Doctoral Recipients (SDR) data. These empirical factors are also based on the results of an earlier study conducted by Hur et al. (Hur, Andalib, Maurer, Ghaffarzadegan, & Hawley, 2017). NSF-SDR data from 1993 through 2013 are analyzed to determine the employment trends represented in the model. Publicly available data such as United States Census data, Centers for Disease Control and Prevention data, and United Nations Educational, Scientific and Cultural Organization Institute for Statistics data are also used. The model interface encourages users to engage in interactive simulations to explore various policy scenarios through parameter adjustments that reflect fluctuations in government research funding levels. Users can also predict how policy changes might impact the science workforce composition.