This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2025
  4. Program
  5. Rationing in Healthcare
« Back to Results

Rationing in Healthcare

Paper Session

Sunday, Jan. 5, 2025 1:00 PM - 3:00 PM (PST)

Hilton San Francisco Union Square, Union Square 11
Hosted By: Econometric Society
  • Chair: Paulo Somaini, Stanford University

Market Design in Single-Payer Healthcare: Evidence from a GP Allocation System

Daniel Waldinger
,
New York University
Ingrid Huitfeldt
,
BI Norwegian Business School
Victoria Ray Marone
,
University of Texas-Austin

Abstract

Many centralized assignment systems seek to not only provide good matches for participants’ current needs, but also to accommodate changing preferences and circumstances. We study the problem of designing such a mechanism in the context of Norway’s system for dynamically allocating patients to general practitioners (GPs). We provide direct evidence of misallocation under the current system––patients sitting on waitlists for each others’ GPs, but who cannot trade––and propose an alternative mechanism that adapts the Top-Trading Cycles (TTC) algorithm to a dynamic environment. Because of patients’ dynamic incentives, dynamic TTC raises novel incentive and distributional concerns relative to the static case. We then estimate a structural model of switching behavior and GP choice and empirically evaluate how this mechanism would perform relative to the status quo. While introducing TTC would on average reduce waiting times and increase patient welfare—with especially large benefits for young and female patients—patients endowed with undesirable GPs would be harmed. Adjustments to the priority system can avoid harming this group while preserving most of the gains from TTC.

Wait Times for Surgery in the U.S.: Measurement and Allocative Efficiency in Private Insurance

Pierre Bodéré
,
New York University
Michael Dickstein
,
New York University
Guillaume Frechette
,
New York University

Abstract

In healthcare systems across the world, limited capacity implies that patients must wait to access surgical care. To evaluate the efficiency and equity consequences of rationing care via queues, however, requires comprehensive measurement of the length of these waits for multiple treatments, patient types, and insurance generosities. We employ machine learning models trained on a large claims dataset of U.S. patients with employer-sponsored insurance to measure wait times as the delay between (a) the moment our models can confidently classify a patient as in need of surgery and (b) the day of the surgery. We use this novel measure to study the distribution of wait times for roughly one million patients across many common surgeries. We find that men wait less than women, while older patients and patients with comorbidities wait longer, suggestive of potential medical inefficiencies. Similarly, we show that health insurance design affects surgical wait times in ways that may not coincide with the value of care. Using an instrument based on weekly congestion in patients' insurance plan, we find that delays have adverse effects on recovery across a breadth of medical outcomes. Patients who wait a month more are 3.1% more likely to be readmitted to a hospital, spend 5.9% more, and are prescribed 6.6% more opioids in the six months following a surgery. Combining this empirical design with recent machine learning tools to recover heterogeneous effects, we quantify the medical allocative efficiency of surgical wait-lists. Applying our estimates to a subset of the surgeries that patients undergo, we find that reassigning patient priorities in the queue could substantially reduce hospital spending.

Endogenous Priority in Centralized Matching Markets: The Design of the Heart Transplant Waitlist

Kurt Sweat
,
Stanford University

Abstract

Centralized matching markets that prioritize specific participants to achieve certain policy goals are common in practice, but priority is often assigned using endogenous characteristics of participants. In the heart transplant waitlist in the United States, the treatment that a patient receives is used to assign waitlist priority. Policymakers recently changed the prioritization in an attempt to reduce waitlist mortality by assigning higher priority to patients receiving specific treatments previously associated with high waitlist mortality. First, I document a significant response to waitlist incentives in treatments given and transplants that take place. Then, I develop and estimate a structural model of treatment and transplant choices to evaluate the effect of the policy change on patients’ outcomes and doctors’ decisions. I find three main results from my model. First, there is little change in aggregate survival, and the effect of the change has been mainly redistributive. Second, the change has effectively targeted patients with lower untransplanted survival, with these patients receiving higher expected survival under the current design. Third, the effect on survival is largely driven by changes in the decision to accept/decline offers for transplants rather than directly due to a change in treatment decisions. The policy implications suggest that future designs of the waitlist should disincentivize declining offers for transplants.

Discussant(s)
Anna Russo
,
Massachusetts Institute of Technology
JEL Classifications
  • I1 - Health