Producing Health: Measuring Value Added of Nursing Homes

Liran Einav, Amy Finkelstein and Neale Mahoney

No 30228, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We develop a stylized model that allows us to estimate a value-added measure for nursing homes (“SNFs”) which accounts for patient selection both into and out of a SNF. We use the model, together with detailed data on the physical and mental health of about 6 million Medicare SNF patients between 2011 and 2016, to estimate the value added for about 14,000 distinct SNFs. We document substantial heterogeneity in value added. Nationwide, compared to a 10th percentile SNF, a 90th percentile SNF is able to discharge a patient at the same health level almost a week sooner, or one quarter of the median length of stay. Heterogeneity in value added within a market is almost as large as it is nationwide. Our results point to the potential for substantial gains through policies that encourage reallocation of patients to higher-quality SNFs within their market.

JEL-codes: I1 I11 L15 L8 L88 (search for similar items in EconPapers)
Date: 2022-08
New Economics Papers: this item is included in nep-hea
Note: AG EH IO PE PR
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.nber.org/papers/w30228.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:30228

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w30228

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().