The Influence of Competitors' Performance on Hospital Efficiency
- Presenter:
Chair: Kathleen Carey; Discussant: Katherine Ho Tue June 6, 2006 15:30-17:00 Room 313
Rationale: The focus to date of “peer effects” and “social interactions” has been on individuals. One important exception to this is the idea of “yardstick competition”-a regulatory scheme under which the performance of firms is compared in order to determine the appropriate level of prices and subsidies (see Schleifer, 1983).
In this paper we examine the role of “peer effects” on firm behavior. Specifically, we estimate the influence of a firm’s competitors’ efficiency levels on a firm’s own level of efficiency. In sports one often hears that a team “plays to the level of the competition;” we ask whether the same is true of hospitals. Given the evolving nature of competition in the hospital industry, this is an important question. .
Objectives: To determine the role of market interactions and peer effects in explaining variations in hospital efficiency.
Methodology: Hospital efficiency is measured by Farrell input-oriented technical efficiency scores whereby all inputs are proportionately scaled back until the observed levels of output are still just feasible. The input-orientation, rather than an output-orientation, is used because it is consistent with the goal of cost containment in the hospital industry. The efficiency scores are calculated using the linear programming approach of data envelopment analysis (DEA). The technical efficiency scores thus obtained were then used as the dependent variable in a regression model that seeks to explain variability in efficiency scores across hospitals. The focus of this second stage of the analysis is the effect of the average performance of peers on a hospital’s own performance. Thus the following regression was performed: (4) where Eff-i is the mean efficiency of hospital i’s peers and Xi includes a variety of other factor that might be expected to influence a hospital’s efficiency level. The other factors include the radius of a hospital’s market, the number of competitors within the radius, the HHI of each market, ownership status, network/alliance membership, teaching status, the share of Medicaid patients among all patients served by a hospital, and hospital size (proxied by number of beds).
Results: 38% of the variation in technical efficiency is explained by the model. The traditional measures of competition-the number of competitors and the Herfindahl-Hirschman Index-appear to have little effect on a hospital’s efficiency. Peer efficiency; however, does appear to influence how efficiently hospitals operate. A 10% increase in peers’ efficiency would result in a just over a 2% increase in a hospital’s own efficiency.
Other influences on efficiency include ownership form-both not-for-profit and public hospitals appear to be more efficient than their for-profit counterparts, teaching status-teaching hospitals are more efficient than non-teaching hospitals, and payer mix appears to matter as well-as the share of Medicaid patients increases, hospital efficiency declines. The regressions based on the radii from which 75% of a hospital’s patients are drawn. Qualitatively similar results were obtained when data based on the 90% radii were used instead.
