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The impact of CON regulation on hospital efficiency

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Abstract

In this paper we propose an empirically implementable measure of aggregate-level efficiency along the lines of Debreu’s (1951) coefficient of resource utilization but restricted to the production side. The efficiency measure is based on directional distance functions, which allows the overall measure of efficiency to be decomposed into measures of technical and “structural” efficiency. The latter measure, which captures inefficiencies associated with the organization of production within an industry, is further decomposed into measures of scale and mix efficiency. The measures developed in the paper are illustrated using U.S. hospital data. The illustration sheds light on the efficacy of certificate of need (CON) regulations.

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Notes

  1. Over time other payers followed Medicare’s lead in shifting from FFS to PPS in an effort to contain costs. The Balanced Budget Act of 1997 furthered the transition from FFS to PPS.

  2. See Färe and Grosskopf [11] for an excellent exposition of directional distance functions; in particular, see essay 3 for a discussion of aggregation issues.

  3. Given the ability of resources to move across particular industries or sectors of the economy, Debreu’s first source of inefficiency—underemployment of resources—does not apply at the industry/sectoral level.

  4. Farrell [8] argued that the same approach he proposed for measuring the efficiency of firms (in our case, hospitals) could also be applied to measuring the efficiency of industries. Noting, however, that it may be difficult to find comparable cross-country data for performing industry efficiency analyses, he suggested “…a very satisfactory way of getting around this [data] problem: that is, by comparing an industry’s performance with the efficient production function derived from its own constituent firms” [6, p. 262]. Farrell termed this “technical efficiency” measure “structural efficiency,” and argued that it measured how well an industry performed relative to its best firms, which was a matter of firms being of scale and technically efficient and output being optimally allocated across producers. We borrow Farrell’s term, “structural efficiency,” but separate it from technical efficiency, so that it is more in line with Debreu’s [7] notion of “efficiency of organization.” Thus, our decomposition of overall inefficiency into technical and structural components (with the latter have mix and scale sub-components) is consistent with Debreu’s original decomposition of the CRU.

  5. As defined by Farrell [8], output served as the weight. However, this approach is restricted to the very limited case of a single-output technology.

  6. Many hospital regulations—including the certificate of need (CON) program which is the focus of our empirical illustration—and a considerable amount of hospital revenue are determined at the state level, making this an appropriate level of aggregation.

  7. This aggregation of firm level technology to form industry level technology is formally presented in the next subsection of the paper.

  8. CRS is defined here as the minimum of the average cost curve; i.e., the most productive scale size which is the typical measure of scale efficiency from an economics perspective.

  9. This decomposition is analogous to the decomposition of firm level efficiency measures into its constituents.

  10. The 14 states that have discontinued CON regulation are Arizona, California, Colorado, Idaho, Indiana, Kansas, Minnesota, New Mexico, North Dakota, Pennsylvania, South Dakota, Texas, Utah, and Wyoming.

  11. As pointed out by an anonymous referee, there are many ways to measure the scale and size of complex organizations such as hospitals (see [33] for an excellent treatment of this issue).

  12. See O’Neill et al. [34] for a systematic analysis of hospital efficiency studies, including the specifications of inputs and outputs used in these studies.

  13. There is no graph for structural inefficiency over time since it is derived by the sum of the mix and the scale efficiencies.

  14. We do not use the Malmquist approach since it is based on the DEA approach and is radial in nature, we cannot aggregate results from the Malmquist as we can in the distance function by year.

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Acknowledgements

We wish to thank Professor Jan Clement (Department of Health Administration, Virginia Commonwealth University) for supplying the case mix indices for the hospitals in this paper and to the editor of the journal and anonymous referees for their suggestions that improved this paper.

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Authors and Affiliations

  1. Department of Economics, University of Arkansas, Fayetteville, AR, USA

    Gary D. Ferrier

  2. CNRS-LEM and IÉSEG School of Management, Catholic University of Lille, Lille, France

    Hervé Leleu

  3. Department of Health Policy and Public Health, University of the Sciences in Philadelphia, Philadelphia, PA, USA

    Vivian G. Valdmanis

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  1. Gary D. Ferrier
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Correspondence to Vivian G. Valdmanis.

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Ferrier, G.D., Leleu, H. & Valdmanis, V.G. The impact of CON regulation on hospital efficiency. Health Care Manag Sci 13, 84–100 (2010). https://doi.org/10.1007/s10729-009-9113-z

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