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考虑选择性的养老院质量估计

Estimating Nursing Home Quality with Selection
Review of Economics and Statistics · 2022 · Andrew R. Olenski、Szymon Sacher

中文摘要

我们使用变分推断(VI)——一种来自机器学习文献的技术——来估计考虑选择性的基于死亡率的养老院质量贝叶斯模型。我们展示了如何使用VI快速且灵活地利用大型数据集估计高维经济模型。利用我们的机构质量估计值,我们考察了质量的相关因素,发现公共记分卡的相关性接近于零。随后我们表明,与既有文献相反,较高质量的养老院在疫情期间表现更好:质量增加一个标准差对应于COVID-19病例减少2.5%。

Abstract

Abstract 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.
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