Resident marine sportfishing effort in the United States varied non-monotonically with COVID policy stringency.

Governments responded to the Covid-19 pandemic with different policies to curtail the spread of the virus. We show how sportfishing levels are related to the stringency of Covid-19 policies. Specifically, we relate the total number of resident sportfishing trips taken each month in each of 16 U.S. states to a state-level index of COVID policy stringency. We model the number of recreational fishing trips taken in each state-month using a fixed effect Poisson regression model with state-specific seasonality and time trends. We estimate separate models for different fishing modes, and find that for fishing trips taken on private boats the number of trips may have increased by approximately 20% at moderate levels of stringency, while at high levels of stringency like those experienced in many states in March and April of 2020, trips may have stayed constant or declined by 10-20%. Similar inverse-U shaped relationships between trips and stringency are found for fishing trips from the shore and from charter boats.

Measuring the spatial distribution of health rankings in the United States.

We rank counties in the United States with respect to population health. We utilize the five observable county health variables used to construct the University of Wisconsin Population Health Institute's County Health Rankings (CHRs). Our method relies on a Bayesian factor analysis model that estimates data-driven weights for our rankings, incorporates county population sizes into the level of rank uncertainty, and allows for spillovers of health stock across county lines. We find that demographic and economic variation explains a large portion of the variation in health rankings. We address the importance of uncertainty caused by imputation of missing data and show that there is a substantial quantity of uncertainty in rankings throughout the rank distribution. Analyzing the health of counties both within and across state lines shows notable degrees of disparity in county health. While we find some disagreement between the ranks of our model and the CHRs, we show that there is additional information gained by utilizing the rankings produced by both methods.