FIGURE 4—

Relationship Between Prisons or Jail Presence by Governance Type and Either COVID-19 Outbreak Delay, Inverse Hyperbolic Sine (IHS)-Transformed Cases, or IHS-Transformed Deaths: United States, 2020

Note. CI = confidence interval. We use ordinary least squares regression to estimate the relationship between prisons or jail presence by governance type (a binary indicator equal to 1 if the county has a jail, state prison, or federal prison) and either COVID-19 outbreak delay (days since cases exceeded 1 per 100 000 population in the county), IHS-transformed cases, or IHS-transformed deaths.²² Column 1 describes the treatment variable of interest (a binary indicator for whether the county has a jail, a state prison, or a federal prison). Column 2 describes the outcome variable of interest. The points and spikes represent the estimated effect size and 95% confidence interval, whereas the last column states these effect sizes and confidence intervals in numbers. We include state-level fixed effects to account for state policy and economic factors that may be associated with COVID-19 spread. We control for presence of a meat processor within the county, days since cases exceeded 1 per 100 000 population, logged population, population density, urban–rural classification dummies, population share commuting by public transit, population share older than 75 years, population share living in a nursing home, average temperature February to April, logged median household income, the social capital index value, and 2018 midterm Republican vote share.