Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach

TL;DR

This paper evaluates the effectiveness of social distancing policies during COVID-19 in the US, showing they significantly reduced infection and death growth rates, with effects varying over time and across states with different demographics.

Contribution

It introduces a factor-based imputation model to estimate the impact of social distancing policies accounting for heterogeneities and provides empirical evidence of their effectiveness.

Findings

Social distancing policies significantly reduced COVID-19 growth rates.

Effects of policies increased over time, from weak to strong.

Higher effectiveness in states with higher income, education, and specific political and media demographics.

Abstract

We identify the effectiveness of social distancing policies in reducing the transmission of the COVID-19 spread. We build a model that measures the relative frequency and geographic distribution of the virus growth rate and provides hypothetical infection distribution in the states that enacted the social distancing policies, where we control time-varying, observed and unobserved, state-level heterogeneities. Using panel data on infection and deaths in all US states from February 20 to April 20, 2020, we find that stay-at-home orders and other types of social distancing policies significantly reduced the growth rate of infection and deaths. We show that the effects are time-varying and range from the weakest at the beginning of policy intervention to the strongest by the end of our sample period. We also found that social distancing policies were more effective in states with higher income, better education, more white people, more democratic voters, and higher CNN viewership.