Estimating Nursing Home Quality with Selection

44 Pages Posted: 21 Apr 2022 Last revised: 5 Aug 2023

Date Written: March 10, 2022


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 high dimensional 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.

Funding Information: Olenski gratefully acknowledges support from the National Science Foundation Graduate Research Fellowship.

Declaration of Interests: None to declare.

Keywords: snf, nursing homes, covid, medicaid, variational inference

JEL Classification: I11, I18, L15

Suggested Citation

Olenski, Andrew and Sacher, Szymon, Estimating Nursing Home Quality with Selection (March 10, 2022). Available at SSRN: or

Andrew Olenski

Lehigh University

621 Taylor St
Bethlehem, PA 18015
United States

Szymon Sacher (Contact Author)

Stanford University ( email )

655 Knight Way
Palo Alto, CA 94304
United States


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