Specific Objectives of the Project:
1. To conduct a study and provide data analysis on effects of severity of the pandemic measured in terms of county level health outcomes (deaths, deaths, hospitalizations) by day on the mobility and social distancing behavior across counties in California, and how mobility and social distancing changes when county level mitigation strategies and stay at home mandates are implemented using an econometric model and unique policy county level data merged with data on daily county level mobility measures (via a data sharing agreement between U C Berkeley and Unacast).
2. To develop an econometric model to measure the effects of the severity of the pandemic, the mobility changes, and the mandate effects on business revenues and rate of closures by county over time, investigating such patterns for different types of business.
3. Distinguish such patterns of effects by the county rural classification, and also as a function of availability of hospitals using a unique data set of hospital locations by county and characteristics of hospitals in terms of number of beds, trauma centers, and number of recent closures of hospitals, especially in rural counties.

Summary of Research to Date:
Results are preliminary. They rely on mobility data collected from Unacast by county by day, and the county level and city level extensive mandate and policy implementation data we collected. We also used womply business data by week, for less representative samples than county level data (we only realized this after we had access to the data). In addition, we define counties according to population, rural definition, and merge hospital census data with hospital characteristics and capacity into our analysis.

Objective 1: On average, mandates have a significant effect on mobility and encounters with other individuals. The measures we use are average distance traveled, distance to non-essential businesses, and the number of encounters (based on the number of cell phones in a radius to a certain cell phone by day) in the data. All three distance measures drop significantly even before the first policies are implemented, in all counties (first implementers and also for the later implementing counties) The incremental effect of mandates is a proportion of the initial drop.

Objective 2: There is a significant exit of businesses in the data during covid, resulting in tremendous sample selection that does not allow us to look at the effect on business revenues, as the businesses that survive in the sample during covid are the ones that are different from the ones that exit, so we do not have comparable samples throughout as the policies are implemented. Second, we find that almost all business sectors suffer the exit of small businesses. The effect is long-lasting and we do not have data long enough to investigate recovery. We plan to do so in a future project.
Objective 3: we did not have enough coverage of small business data in rural areas to investigate the differential effect of the pandemic and policies to investigate exit intensity. We do not find differential effects in mobility and human encounter responses to the mandates between rural and nonrural counties. We did not have enough power
(sample size) to distinguish the effect on mobility due to hospital capacity in rural versus non-rural counties.