The differential impact of COVID-19 across demographic groups: Evidence from NYC – Milena Almagro and Angelo Orane-Hutchinson, Department of Economics, New York University

 

A priori, COVID-19 is a disease that does not discriminate across different demographic groups or locations. In practice, not only are there differences across locations but there is extensive evidence that some demographic groups are severely more affected than others. For example, in the US African Americans are being hit the hardest by the pandemic, which creates an additional source of worry to this already vulnerable socio-economic group.

To shed some light on the mechanisms, we use data on the share of positives across neighborhoods for New York City provided by DOH, which releases (almost) daily updates. We have collected these data updates across different days, which not only allows us to explain cross-sectional results, but also the time-varying importance of different channels. We combine this test data with demographic data provided by the American Community Survey (ACS), also at the zip code level.

We present evidence of an occupation mechanism in explaining different rates of COVID-19 incidence across income, race, age, and other socio-demographic groups. We argue that part of these demographic disparities can be explained due to differences in occupations and their exposure to the illness.

Occupations are a key channel for explaining the differential impact of the COVID-19 outbreak across locations and demographic groups within a city.

We show that after controlling for occupations, commuting patterns and healthcare controls are not significant. Our results also indicate that other demographics such as gender, income, and age become not significant, suggesting the effects across demographics groups can be partially explained by correlation between demographics and occupations.

Moreover, we show that the effect of occupations on positive tests are heterogeneous and arguably related to their rates of human interaction.

We find the strongest positive effect on share of positive tests in the share of workers on Transportation, Industrial, Natural resources, Construction, and Non essential – Professional.

These results align with the fact that the previous occupations tend to involve more human interaction. Covid and Inequality