The leaky pipeline of diverse race and ethnicity representation in academic science and technology training in the United States, 2003-2019

Abstract

Introduction: Diverse race and ethnicity representation remains lacking in science and technology (S&T) careers in the United States (US). Due to systematic barriers across S&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S&T training in the US.

Methods: We analyzed US S&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S&T transition points: bachelor to doctorate degrees (2003-2019) and doctorate degrees to postdoctoral positions (2010-2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression.

Results: For 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95% confidence interval [CI] 0.81-0.92; RR 0.85, 95% CI 0.81-0.89; and RR 0.82, 95% CI 0.77-0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95% CI 0.66-0.78; Asian men RR 0.73, 95% CI 0.70-0.77)]. We observed that Black men (RR 0.60, 95% CI 0.51-0.69) and Black women (RR 0.56, 95% CI 0.49-0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02).

Conclusion: We quantified diverse race and ethnicity representation in contemporary US S&T training and found that Black men and women experienced the most consistent loss in representation across the S&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities.

Fig 1. Race and ethnicity representation in the US science and technology pipeline, 2003–2019.

Panel A–Race and ethnicity representation among US women in bachelor, doctorate, postdoctoral positions in 2019 versus US Census estimates; Panel B–Race and ethnicity representation among US men in bachelor, doctorate, postdoctoral positions in 2019, versus US Census estimates; Panel C–Ratios of doctorate to bachelor representation by race and ethnicity for US women, 2003–2019, all p-trend > 0.05; Panel D–Ratios of doctorate to bachelor representation by race and ethnicity for US men, 2003–2019, NHW men p-trend = 0.004 (ratio change per year 0.004 [95% CI 0.002,0.007]), all other p-trend > 0.05; Panel E—Ratios of postdoctoral to doctorate representation by race and ethnicity for US women, 2010–2019, NHW women p-trend = 0.007 (ratio change per year 0.006 [95% CI 0.002, 0.01]), all p-trend > 0.05; Black women p-trend = 0.02 (ratio change per year -0.01 [95% CI -0.02, -0.002]), all other p-trend > 0.05; Panel F–Ratios of postdoctoral to doctorate representation by race and ethnicity for US men, 2010–2019, all p-trend > 0.05. For panels A-B, US Census estimates refer to population-level estimates from the 2019 American Community Survey, all sexes combined. For panels C-F, ratios are obtained by dividing the representation of the race and ethnicity group in the subsequent stage by representation in the previous stage. A ratio less than 1 suggests loss of representation. Native Hawaiian and Pacific Islanders not visualized to due to incomplete data availability. AI/AN groups are not visualized due to representation <0.5%. Abbreviations: CI, confidence interval; NHW, Non-Hispanic White.