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
Lyrics by OpenAI ChatGPT
Verse 1:
In a nursing home, where the old folks stay
There’s a quality that’s hard to measure
But Olenski and Sacher found a way
To estimate it with a Bayesian treasure
Chorus:
Estimating nursing home quality,
Accounting for selection,
With a mortality-based model,
It’s a statistical sensation
Verse 2:
They examined the correlates of quality,
And found that public report cards were poor
But higher quality homes did well in the pandemic,
With fewer Covid cases, oh what a score!
Chorus:
Estimating nursing home quality,
Accounting for selection,
With a mortality-based model,
It’s a statistical sensation
Bridge:
A 10% increase in Medicaid reimbursement,
Raises quality, and improves survival,
It’s cost-effective, and a life-saver,
Thanks to Olenski and Sacher.
Chorus:
Estimating nursing home quality,
Accounting for selection,
With a mortality-based model,
It’s a statistical sensation."