Estimating Hospital Quality with Quasi-Experimental Data

64 Pages Posted: 16 Feb 2018

See all articles by Peter Hull

Peter Hull

Microsoft Corporation - Microsoft Research New England; University of Chicago

Date Written: February 5, 2018


Non-random sorting can bias observational measures of institutional quality and distort quality-based polices. I develop alternative quasi-experimental approaches to quality estimation that accommodate nonlinear causal effects, institutional specialization, and unobserved selection-on-gains. I use this framework to compute empirical Bayes posteriors of the quality of 4,821 U.S. hospitals, combining estimates from ambulance referral quasi-experiments with predictions from observational risk-adjustment models. Higher-spending, higher-volume, and privately-owned hospitals are of higher quality, and most healthcare markets exhibit positive Roy selection-on-gains. I then simulate Medicare reimbursement and consumer guidance programs based on different hospital quality measures. Higher-spending providers tend to see moderately larger performance-linked subsidies when quality posteriors replace conventional rankings, while teaching hospitals are reimbursed relatively less. Admissions policy simulations highlight limitations of consumer guidance programs in settings with unobserved Roy selection: redirecting patients to top-ranked hospitals may worsen expected survival when based on observational rankings, while quasi-experimental rankings appear to generate modest gains.

Keywords: hospital quality, instrumental variables, Roy selection

JEL Classification: C26, C36, I11, I18, L15

Suggested Citation

Hull, Peter, Estimating Hospital Quality with Quasi-Experimental Data (February 5, 2018). Available at SSRN: or

Peter Hull (Contact Author)

Microsoft Corporation - Microsoft Research New England ( email )

One Memorial Drive, 14th Floor
Cambridge, MA 02142
United States

HOME PAGE: http://

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States


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