The Determinants of the Differential Exposure to COVID-19 in New York City and Their Evolution Over Time

In this paper, we explore different channels to explain the disparities in COVID-19 incidence across New York City neighborhoods. To do so, we estimate several regression models to assess the statistical relevance of different variables such as neighborhood characteristics and occupations. Our results suggest occupations are crucial for explaining the observed patterns, with those with a high degree of human interaction being more likely to be exposed to the virus. Moreover, after controlling for occupations, commuting patterns no longer play a significant role. The relevance of occupations is robust to the inclusion of demographics, with some of them, such as income or the share of Asians, having no statistical significance. On the other hand, racial disparities still persist for Blacks and Hispanics compared to Whites, although their magnitudes are economically small. Additionally, we perform the same analysis over a time window to evaluate how different channels interact with the progression of the pandemic, as well as with the health policies that have been set in place. While the coefficient magnitudes of many occupations and demographics decrease over time, we find evidence consistent with higher intra-household contagion as days go by. Moreover, our findings also suggest a selection on testing, whereby those residents in worse conditions are more likely to get tested, with such selection decreasing over time as tests become more widely available.