Underrepresented Minorities in Academic Science

In the United States, underrepresented minorities (URM) with doctorates in Science and Engineering make up less than 8% of all full time faculty employed in institutions of higher learning, resulting in what the National Academy of Science has recognized to be a significant national policy crisis and “waste” of human capital. While underrepresentation is a significant issue, the “story” extends well beyond these numbers, as researchers of underrepresented populations in science have noted. Studies specific to minorities in the academy point to evidence of exclusion, and qualitatively different experiences in the work environment. Questions of how faculty are attracted to and function in minority-serving institutions versus other universities and colleges have also been explored, as have been issues in the ability to gain positions in the most competitive research institutions. Embedded in faculty professional development is the ability to form, access, participate, and effectively benefit from career-relevant professional networks. While attention to electronic networks are increasing, we refer to the larger and more general networks in which faculty participate in regard to teaching, research, and overall career development.  This paper addresses the issue of social networks for underrepresented minorities (URM) as compared to other majority groups in academic science, across all levels of academic institutions. We ask: how does the structure of URM networks vary across institutional types? Do underrepresented minorities who are employed in minority-serving institutions (HBCU’s, HSI’s and other institutions) develop different types of professional networks that advance their careers and productivity? Specifically, we assess distinctions in the structure, characteristics, resources and exchange that occur within these networks.  Data are drawn from a 2011 NSF-funded large national stratified random sample of faculty in all levels of Carnegie foundation-ranked institutions in four disciplines of science and engineering. The resulting data include responses from more than 3500 faculty from hundreds of institutions, including an oversampling of underrepresented minorities allowing for statistical analysis that has not been possible in other studies. Data analysis includes detailed descriptive statistics as well as a series of explanatory models to test the how institution and race matters in distinctions in core network variables.