Szymon Sacher

We use variational inference (VI), a technique from the machine learning literature, to estimate a mortality-based Bayesian model of nursing home quality accounting for selection. We demonstrate how one can use VI to quickly and flexibly estimate a highdimensional economic model with large datasets. Using our facility quality estimates, we examine the correlates of quality and find that public report cards have near-zero correlation. We then show that in contrast to prior literature, higher quality nursing homes fared better during the pandemic: a one standard deviation increase in quality corresponds to 2.5% fewer Covid-19 cases

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as \data" in a downstream econometric model. We establish theoretical arguments for why this two-step strategy leads to biased inference in empirically plausible settings. More constructively, we propose a one-step strategy for valid inference that uses the upstream and downstream models jointly. The one-step strategy (i) substantially reduces bias in simulations; (ii) has quantitatively important effects in a leading application using CEO time-use data; and (iii) can be readily adapted by applied researchers.

To what extent do mass media outlets influence viewers’ trust in scientific evidence and compliance with behavior recommended by scientific experts? Exploiting the U.S. lockdown period of the COVID-19 pandemic in early 2020, we analyze a large longitudinal database that combines daily stay-at-home behavior from approximately 8 million mobile phones and local viewership of cable news networks. Early in the pandemic, several of Fox News’ hosts downplayed the severity of the pandemic and the risks associated with the transmission of the virus. A combination of regression analysis and a natural experiment finds that a 10% increase in viewership of Fox News in a zip code causes a 0.76-percentage-point reduction in compliance with stay-at-home behavior. The results imply a media persuasion rate that is larger than typical advertising persuasion rates on consumer behavior. Similar analyses using viewership of MSNBC and CNN, which supported lockdown measures, were inconclusive but suggested a smaller, positive effect on compliance with social distancing regulations.