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This paper examines changes in occupational crowding of immigrant women in frontline industries in the United States during the onset of COVID-19, and we contextualize their experiences against the backdrop of broader race-based and gender-based occupational crowding. Building on the occupational crowding hypothesis, which suggests that marginalized workers are crowded in a small number of occupations to prop up wages of socially-privileged workers, we hypothesize that immigrant, Black, and Hispanic workers were shunted into frontline work to prop up the health of others during the pandemic. Our analysis of American Community Survey microdata indicates that immigrant workers, particularly immigrant women, were increasingly crowded in frontline work during the onset of the pandemic. We also find that US-born Black and Hispanic workers disproportionately faced COVID-19 exposure in their work, but were not increasingly crowded into frontline occupations following the onset of the pandemic. The paper also provides a rationale for considering the occupational crowding hypothesis along the dimensions of both wages and occupational health.

We investigate the value of a two-armed Bayesian response adaptive randomization (RAR) design to investigate early preterm birth rates of high versus low dose of docosahexaenoic acid during pregnancy. Unexpectedly, the COVID-19 pandemic forced recruitment to pause at 1100 participants rather than the planned 1355. The difference in power between number of participants at the pause and planned was 87% and 90% respectively. We decided to stop the study. This paper describes how the RAR was used to execute the study. The value of RAR in two-armed studies is quite high and their use in the future is promising.