Expense preference behavior and management “outsourcing”: a comparison of adopters and non-adopters of contract management in U.S. hospitals


Specialized managerial expertise, coupled with the threat of non-renewal should improve efficiency in firms that opt for contract management arrangements. To examine this we apply a generalized version of tests for expense preference behavior to U.S. hospitals in the 1990s. Extending prior literature, we create a quasi-experimental design for a comparison of adopters and non-adopters of contracts using propensity score methods. We generate the distribution of ‘expense preference’ parameters for all contract adopters in both the pre- and post-adoption states, and for a matched control group of non-adopters over the same period. Our results show that contract adoption leads to reduced expense preference behavior, but that this result depends critically on the input being examined.

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Fig. 1


  1. 1.

    Hospital care may be the only industry where information on contract management is collected on a regular basis; however, trade reports suggest that its use is widespread in service industries, government contracting, and among non-profit organizations. A recent government report surveys the use of comprehensive contract-management services as an intermediate step towards privatization, especially in construction projects (CETS 2000). The closest analogy to hospitals occurs in education where 15–20% of all charter schools use for-profit EMOs (Education Management Companies—the equivalent terminology for contract-management firms) for comprehensive management (Molnar et al. 2001).

  2. 2.

    Furthermore, the trade/practitioner literature views contract management as means by which boards can weaken the community oversight over the management function by contracting with an outside corporate entity not bound by the hospital’s charter, a matter which has been tested in the courts (Kimberly and Rosenzweig 1998).

  3. 3.

    U L and U K are realizations of the state-dependent utility, under labor-preference and capital preference, respectively. State dependent postscripts are suppressed for notational convenience. U E is independent of state, while E i  =  W i . Adding probabilities p L and p K = 1 −  p L to each state will not affect the basic result.

  4. 4.

    The banking literature has similarly treated labor as the more abundant input which can be more easily manipulated by the expense-preferring manager; see Hannan and Mavinga (1980) and Awh and Primeax (1985).

  5. 5.

    A full theory of incentives under contract-management remains to be developed. However there is substantial and relevant literature on managerial incentives in nonprofit organizations. For instance, Roomkin and Weisbrod (1999) and Brickley and Van Horn (2002) discuss limitations placed on hospital boards by community-oriented charters and member organizations. This implies weakened board oversight, leading to management entrenchment, a problem shared by other industries when boards are said to be “weak” (e.g., Denis et al. 1997). The principle-agent literature has dealt extensively with the issue of pay-for-performance contracts when executive compensation is tied to stock options, dividends and the like. This tends to less relevant to an industry dominated by non-profits and community oversight.

  6. 6.

    An alternative strain in the literature considers the problem of overall firm inefficiency along the firm’s production function or cost function for given level of inputs. Therein, the stochastic frontier (SF) specifies a truncated or half-normal error term for observations that lie below the least-cost frontier, in addition to the usual (normal) regression error. (e.g., Fare et al. 1994). Skinner (1994) notes that SF is sensitive to the distributional assumptions made, and may lead to biased inefficiency scores if errors are skewed. Moreover, SF does not provide a direct measure of allocative inefficiency due to varying input proportions, which is of main interest to us. In a related review, Dor (1994b) notes that panel data yield more robust estimates of cost differences among nursing homes or hospitals. In this analysis we track longitudinal changes in inefficiency, as well as input-specific behavior.

  7. 7.

    Dor et al. (1997) and Mester (1989) constrain the expense-preference for the non-preferred input to be positive (z k  > 0). Here we demonstrate that an increase in the preferred input requires a concomitant decrease in the non-preferred input. Although constraints on z k can be imposed in our estimation framework using a penalty function, in practice this was not necessary since convergence of the unconstrained nonlinear system of equations occurred at the permissible range.

  8. 8.

    This is akin to multiple cause-multiple indicator (MIMIC) models. See Van Vliet and Van Praag (1987).

  9. 9.

    Note that by emphasizing preferences for a given input, the expense preference model essentially assigns behavioral motivation for allocative inefficiency. Other models, such as Data Envelopment Analysis (DEA) focus on technical efficiency, namely maximizing output given the level of inputs employed. However, DEA is essentially a linear programming algorithm that does not take into account random error and statistical noise, thus producing inefficient estimates. While the non-parametric estimation in DEA is an attractive property in some applications where explicit functional form is not of interest and hence restrictive, structure is required for the problem at hand, namely identifying changes in unobservable preferences due to adoption of contracts.

  10. 10.

    Labor costs are defined as the sum of total facility payroll expenses and total facility employee benefits. Unfortunately the survey does not provide a breakdown of expenses by type of labor; this category covers registered nurses, licensed practical nurses, and administrative staff. Depreciation plus interest accounted for 8% of total expenses in 1993, the latest year in this study for which the AHA data reported these items specifically.

  11. 11.

    The AHA conversion is based on adjusted patient days = actual inpatient days · (total patient revenue/inpatient revenue). (AHA Annual Survey Database).

  12. 12.

    Another strain in the literature has focused on estimating the multi-output cost function of the hospital, and related marginal costs (e.g., Vita 1990; Dor and Farley 1996; Carey 1997). In this paper, we are not interested in output-specific costs per se, but it may be the case that inefficiency varies by output category or that preferences for inputs vary. As far as we can tell, the problem of output-specific inefficiency has not yet been treated in the econometrics literature. In our case, obtaining estimates of output-specific z’s would require having data on the share of each input within each output category. Such data are unavailable.

  13. 13.

    The issue of monopsonistic labor markets for nurses has been the subject of some debate in the empirical literature, yet evidence on this point is inconclusive. For instance Staiger et al. (1999) find some evidence in favor of monopsony, in the form of inelastic supply for nurses. Conversely, Hirsch and Schumacher (1995) found no support for the presence of monopsony power in the nursing labor market. Due to this ambiguity we implemented a Hausman test for endogeneity of the wages in each of the four samples. The test resulted in our failure to obtain a rejection of the null hypothesis of no endogeneity in any of the four cases.

  14. 14.

    For instance see, Mobley and Magnussen (2002). Recently the trade literature has begun to focus on the problem of nurse ‘shortage’ in hospitals, suggesting that hospitals tend to under-employ nurses (Green and Nordhaus-Bike 1998). However this concern is limited to certain high-end specialties of registered nurses, and does not seem to apply to licensed practical nurses, nurse-aides and the like. Moreover, even for registered nurses as a whole the national trend has been that of increased employment in hospitals during most of the period observed in our data (Buerhaus and Staiger 1999).

  15. 15.

    This can also be referred to as a ‘difference-in-difference’ estimator. See Angrist and Krueger (1999).

  16. 16.

    At the Federal level, special bonus payments are available to physicians located in Health Professional Shortage Areas (HPSAs). A variety of similar state-level programs are also available. See Shugarman and Farley (2003).


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Corresponding author

Correspondence to Kathleen Carey.

Additional information

Earlier versions were presented at the NBER Summer Institute, the BU/Harvard/MIT health economics workshop 2002, and the American Economic Association annual meetings, 2003. The authors are grateful for comment received. Avi Dor acknowledges financial support from the Presidential Research Initiative grant at Case Western Reserve University. The views expressed here are those of the authors and do not necessarily represent the Department of Veterans Affairs or NBER.

Appendix: Propensity scores calculation

Appendix: Propensity scores calculation

Propensity score methods are commonly used in observational studies to replicate the benefits of a controlled experimental trial in which two similar groups are compared, but only one group receives a treatment. A propensity score is defined as the probability of assignment to a treatment, given the distribution of known covariates, just prior to intervention. It is used to balance the distribution of covariates between a treatment group and a control group in cases where a random draw from the general population results in a poor match (Rosenbaum and Rubin 1983, 1984; Imbens 2000) thereby alleviating the bias due to systematic differences between the treated and comparison groups (Dehejia and Wahba 2002). The most common algorithm for converting propensity scores into non-experimental matching criteria is to stratify the unit scalar into quintiles or similar groupings (Rosenbaum and Rubin 1985; Rosenbaum 1987). Dehejia and Wahba (1999) demonstrate that results are robust with respect to percentile definitions within the propensity score.

A random drawing of non-contract hospitals failed to create a reliable control for hospitals identified as contract-adopters, because the sampling strategy involves drawing all hospitals that adopted contract management across years, and because those hospitals differ in profile from internally managed hospitals. More specifically, a much higher proportion of contract-managed hospitals are rural (see Table 2). These hospitals are also more likely to be government-affiliated hospitals, less likely to be not-for-profit, are lower in case-mix index, and have longer lengths of stay. To account for these differences we used propensity scores to reduce the bias in drawing the comparison group.

Propensity scores, or conditional probabilities of adopting contract management, were estimated for each hospital using logistic regression, pooling contract-adopters with all non-contract hospitals in the database. We performed two regressions corresponding to the ‘pre’ and ‘post’ periods. The first regression included all available observations for the adoption years 1993–1998 and the second included those for adoption years 1992–1996. We incorporated all variables used to explain variation in cost as covariates in the logit regression. Table A1 shows the results of these regressions. Although results of these regressions are tangential to our main question, a number of results are worth noting: What matters in the distinction of contract adopters is ownership form and locality. Contract management is on the order of two times as likely to be found among hospitals with government control, non-profit status, or rural location.

Table A1 Propensity score model (logit)

Stratification proceeded by dividing the propensity scores from the combined groups into quintiles. Stratification also took into account the distribution of contract-managed hospitals over time. Table A2 reports stratum boundaries for propensity scores and sampling distributions by quintile-year cells. Counts pertain to hospitals in the contract-managed group. The control groups were finally drawn randomly within propensity score quintile-year cells (without replacement). For purposes of cost function estimation we produced two control groups each containing three times the number of contract adopters that matched the adopter groups’ distributions. Cost function estimation pertained to two years prior and two year after the year of adoption.

Table A2 Propensity score distributions by quintile and year

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Carey, K., Dor, A. Expense preference behavior and management “outsourcing”: a comparison of adopters and non-adopters of contract management in U.S. hospitals. J Prod Anal 29, 61–75 (2008). https://doi.org/10.1007/s11123-007-0065-3

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  • Expense preference
  • Hospitals
  • Contract management

JEL Classifications

  • D24
  • L24