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Measuring efficiency gains from hospital mergers.


The impact of mergers on the technical efficiency of hospitals is a matter of considerable interest to hospital managers, regulators and the public. Tests of the effects of mergers on hospital efficiency have generally relied on comparison of performance ratios for pre-and post-merger time periods. These methods do not provide a global measure of the impact of mergers on efficiency and give little insight into the causes of changes in efficiency. This study used a global, non-parametric Data Envelopment Analysis (DEA) to test whether there were changes in efficiency associated with hospital mergers. The results indicated that there no detectible improvements in efficiency in the first year after the merger but that efficiency improved significantly in the second year after the merger.


Despite the recent spate of hospital mergers, there is little consensus about the extent of merger-related efficiency gains or losses. Hospital industry representatives have argued that hospital mergers would improve service coordination, reduce excess capacity and increase hospitals' ability to accept risk-based payment. Antitrust enforcement agencies such as the Department of Justice and Federal Trade Commission are rather skeptical of the claim that clinical consolidation is a source of efficiency and fear that higher market concentration may cause higher market prices; see Gaynor and Haas-Wilson (1999) for a summary of these issues.

Health services literature ascribes the benefits for merger to one of the following two categories. First, mergers help attain the requisite investment and management base necessary to acquire costly services or attract increasingly specialized technical staff (Alexander, Halpern & Lee 1996). Second, mergers help consolidating services to achieve efficiency and reduce over-bedding and staffing in highly restricted markets (Levitz & Brooke 1985; Schwartz & Joskow 1980; Sinay 1998; Sinay & Campbell 2002; Wicks et al. 1998).

Evaluation of efficiency gains in the case of hospital mergers is an important issue because antitrust enforcement agencies generally weigh efficiency claims from hospital mergers against any adverse competitive effects from market consolidation (Varney 1995). On the one hand, efficiency gains from mergers may initiate significant cost reductions. Therefore, in the end, patients potentially benefit from lower market prices. On the other hand, market consolidation brought about by mergers may reduce competition and induce higher market prices. Mergers would be desirable if the efficiency induced cost reduction effect dominates the price margin effect so that there is a net decrease in market prices paid by consumers of hospital services.

In this study, the technical efficiencies of hospitals were evaluated. That is, to what extent a hospital produced the maximum output combinations with a given input combination, or alternatively, to what extent a hospital has produced a given combination of outputs with the minimum input combination was considered. Data envelopment analysis (DEA) was particularly attractive in this context. It provided an efficiency score for each hospital such that an efficient hospital received a score of one while an inefficient hospital received a score less than one. Efficiency scores are a product of choices involving both the combination and quantities of input and output variables.

DEA scores, using a two-stage approach (Bardhan, Cooper & Kumbhakar 1998), over time for a sample of merged and non-merged hospitals were computed. If a merger improved the technical efficiency, then the merged hospital was more likely to be classified as efficient. A test sample of merger hospitals and a control sample of non-merger hospitals were constructed. The relative efficiencies of the hospitals in the two samples over a two-year period after the merger were evaluated. A censored regression method is applied to detect whether the status of being a consolidated hospital led to a greater efficiency score. To avoid spurious results, control variables that have been shown to have effects on efficiency scores in prior studies were included in the censored regression. Hospital mergers led to an efficiency gain if the coefficient of the merger status variable showed a positive association between efficiency scores and mergers.


Two recent studies, Harris, Ozgen and Ozcan (2000) and Ferrier and Valdmanis (2004) applied DEA methods to analyze the effects of a merger on hospital efficiency. Harris, Ozgen and Ozcan (2000) found scale efficiency to be the dominant source of efficiency improvements, but did not find improvements in technical efficiency one year after a merger. Ferrier and Valdmanis (2004) used methods very similar to those used in this study to evaluate the effects of hospital mergers. They compared efficiency scores one year before, the year of and one year after the merger using matched pairs of hospitals. They also found no significant change in technical efficiency in the year after a hospital merger. However, disruptions associated with consolidations likely take more than a single year to resolve themselves and improvements in technical efficiency can only then emerge. For example, Dranove and Lindrooth (2003), evaluating cost savings, did not identify savings until at least two years after a merger. In this research, the DEA technique was used, but the efficiency analysis was extended to two years beyond the merger date. The sample of hospitals was larger than had been used in prior studies and a longer test period than had generally been used in previous studies was employed. The Tobit regression method, that is more appropriate when analyzing the censored data that the DEA procedure produces, was employed in this study.

DEA efficiency measures with six inputs and four outputs for 1992-1997 were computed. Hospitals consolidated 1994-1995 were treated as the test group. The Tobit regression results indicated that there was no detectible improvement in technical efficiency in the first year after the merger, but the efficiency improved significantly in the second year after the merger.


Prior research in hospital mergers has focused on analyzing changes in ratios of costs or resource usage for pre- and post-merger hospitals (Sinay 1998; Sinay & Campbell 2002; Dranove & Lindrooth 2003). While such an approach can provide insights into the effects of mergers on a number of parameters that impact efficiency, it cannot answer the global question about whether the resulting merged institution was more efficient than were its independent components. This is really the question regulators must address.

To make such an assessment, a global measure of efficiency is required. Two widely used measures of global efficiency are stochastic frontier analysis and data envelopment analysis (DEA). Because stochastic frontier analysis allows for only one output and because the price data necessary to estimate stochastic cost frontiers was not available, the DEA method was chosen to measure hospital efficiencies.

DEA is a non-parametric approach that uses linear programming to estimate efficiency scores for individual entities in a sample relative to all of the other entities in that same sample set. In DEA, the individual entities for which efficiency scores are calculated are called decision-making units or DMUs. DEA is particularly attractive in this context because it provides a global measure of efficiency and because it allows for multiple outputs. The advantages and limitations of the DEA techniques are well described in the literature (see, for example, Banker, Conrad & Strauss 1986; Bowlin et al. 1985; Seiford & Thrall 1990).

Researchers have long recognized the power of DEA in evaluating efficiency in health care institutions. Sherman (1984) studied the technical efficiency of a group of teaching hospitals. Kooreman (1994) analyzed the technical efficiency of Dutch nursing homes with respect to the use of labor. Using DEA, Valdmanis (1990) found public non-profit hospitals to be more efficient than private non-profit hospitals. Ozcan, Luke and Haksever (1992) used DEA to show that government and non-profit hospitals were indistinguishable from one another regarding their percentages of inefficient scores. Using DEA, White and Ozcan (1996) studied the effect of church- ownership on hospital efficiency, using a sample of California hospitals. As mentioned earlier, both Harris, Ozgen and Ozcan (2000) and Ferrier and Valdmanis (2004) used DEA to study the effects of hospital mergers on hospital efficiency.

While DEA as been widely used to measure hospital and healthcare efficiency, there are limitations associated with the technique that should be disclosed. Because DEA relies on extreme points to create the efficient frontier, measurement error may distort the efficiency scores yielding only a very small number of DMUs classified as efficient relative to the size of the sample pool.

DEA measures relative efficiency not absolute efficiency. This is both a strength, since no production function need be specified, and a weakness, since the efficiency scores do not generalize beyond the sample of DMUs used or the period in which the efficiency scores are computed.

Finally, as a nonparametric procedure, statistical hypothesis tests are difficult. Since DEA scores are constrained to be between 0 and 1, their distribution is censored. Using the DEA scores as dependent variables in an OLS regression will result in biased estimates. Thus alternative procedures such as Tobit, that are designed to evaluate censored data, must be used.

Because of the size of the sample of hospitals, the fact that control hospitals were incorporated in the sample and the Tobit procedure was used to evaluate the DEA scores, these limitations were largely mitigated in this research.

In this study, measures of efficiency (DEA efficiency scores) for each hospital in the sample set were developed. The second stage of the analysis involved the explanation of cross-sectional differences in the efficiencies. This approach was chosen, because several recent papers (Avkiran 1999; Sathye 2001; Worthington 2001) have used DEA in this manner to examine mergers in the banking industry. Like hospitals, banks are highly regulated, have been significantly impacted by changing regulatory and financial climate and been subjects of numerous mergers.

Efficiency Measurement

DEA assumes every DMU has access to common production technologies. Using non-parametric (linear programming) methods, it constructs a production frontier such that DMUs lying on the frontier are deemed efficient and assigned an efficiency score of one. Those DMUs that do not lie on the efficient frontier (and actually lie within the efficiency frontier envelope) are termed inefficient and their relative efficiency scores can be calculated.

Because the concern in this study was with technical rather than scale efficiencies, the variable returns to scale (VRS) version of DEA was used. Because hospitals generally have more control over reducing excess input than augmenting output (White & Ozcan 1996), only input-oriented efficiency scores were discussed. Banker, Charnes and Cooper (BCC) (1984) formulated a linear programming model to measure pure technical efficiency based on an input-oriented VRS envelopment surface model for a sample of (n + 1) DMUs. Consider a decision-making unit "0" with input vector x0 and output vector y0. There are n other decision-making units whose input and output data vectors are available, denoted by [x.sub.j] and [y.sub.j], respectively, for j = 1, 2 ..., n. To determine the efficiency of the "0" decision-making unit with respect to VRS envelopment surface (often referred to as envelopment model, see Ali, Lerme & Seiford [1995]), the following minimization problem is constructed:


The solution of the problem is denoted by {[[lambda].sup.*.sub.j]}. The corresponding value [[theta].sup.*.0] yields the efficiency score for DMU "0". If [[theta].sup.*.sub.0] = 1, then the DMU is said to be efficient. When [[theta].sup.*.sub.0] < 1, the unit is inefficient. In this model, {[[lambda].sup.*.sub.j} indicates how efficiency may be improved.

Note that the n DMUs included in the model comprise the reference set. The efficiency of the DMU being evaluated (DMU "0") is measured relative to that of all the other DMUs in the reference set, under the restriction that all DMUs are on or below the efficiency frontier (Bjurek, Hjalmarsson & Forsund 1990).


According to the American Hospital Association (AHA), a hospital merger is a combination of previously independent hospitals formed by either the dissolution of one hospital and its absorption by another or the creation of a new hospital from the dissolution of all participating hospitals. Since most mergers occurred in short-term hospitals, defined as hospitals with stays of less than thirty days, this study focused on those hospitals. In 1992, 5,619 short-term hospitals existed in the United States. From this population the AHA drew a random sample of 500 short-term hospitals that were included in the AHA Survey for every year in the 1992-1997 period. To be included in the AHA sample, a hospital had to have no merger activity in the 1992, 1993, 1996 and 1997 survey years. The test window was defined as the years 1994 and 1995.

The test sample was constructed from those hospitals that had merger activity in the test window years. Of the sample of 500 hospitals, there were 166 hospitals (77 in 1994 and 89 in 1995) that were involved in mergers in the test period. These 166 hospitals constituted a preliminary test sample. The balance of the hospitals constituted a preliminary control sample. The final numbers of test and control hospitals were determined by additional data availability for the variables listed in Exhibit 1.



FTE physicians and dentists
FTE registered nurses
FTE licensed practical & vocational nurses
FTE other personnel
Number of beds
Number of high technology services (a)


Inpatient days (b)
Outpatient visits (c)
FTE trainees
Stanby services (d)


(a) This includes cardiac catheterization labs, open heart surgery,
extracorporeal shock wave lithotripters, megavoltage radiation therapy,
magnetic resonance imaging (MRI), organ transplant and certified trauma

(b) An inpatient day is defined as one patient registered at the
hospital at 12:00 midnight.

(c) Outpatient visits are defined as all visits to hospital emergency
and outpatient facilities that occurred during a day.

(d) Standby service is measured by the ratio of the difference between
the number of beds and the average daily census to the squared root of
the average daily census where the average daily census is defined as
the average number of inpatients, excluding newborns, receiving care
each day.

INput and OUTput variables

To obtain the DEA efficiency scores in this study, six input and four output variables were collected for each DMU. These were obtained from American Hospital Association Annual Surveys for the years 1992-1997. The survey includes information for individual hospitals on facilities, services, staffing, finance and administration.

Exhibit 1 summarizes the inputs and outputs used to measure technical efficiency. While Ozcan (1993) found hospital DEA scores to be relatively stable across different input/output combinations, the choices of inputs and outputs do impact the final scores. However, the ranking of the DMUs based on the efficiency scores is less sensitive to these choices than are the scores themselves.

There is no established consensus on how to accurately measure the outputs of hospital production (Biorn & Magnussen 2002). A variety of options exist for both input and output variables (see, for example, Magnussen [1996]). Hospitals produce a wide variety of both primary and auxiliary services, the effects of which must be captured in the output proxies. In this study, inpatient days and outpatient visits were used as proxies for primary services and medical training and standby services were used to proxy for auxiliary services; see Olesen and Petersen (2002) for a more extensive discussion of hospital outputs and a discussion of using diagnosis related groups (DRGs) as an output measure. These data were not available for this study.

While outpatient visits is relatively straightforward, inpatient days is more problematic. Inpatient days can be viewed as either an input or an output depending on the perspective of the researcher. Using inpatient days as a measure of outputs presumes that two hospitals with identical patients would use the same number of days to provide treatment. Therefore, differences in inpatients days was presumed to measure the difficulty of treatment and ignored the fact that hospital revenues are a function of the number of treatment days.

Butler (1995) classified hospital output into four categories including teaching and research. Dalman-Matarrodona and Puig-Junoy (1997) incorporated the number of resident physicians as one of the eight output variables. The number of full-time equivalent trainees, including medical and dental residents and interns and other trainees, employed by the hospital, was chosen to measure medical training provided by a hospital which was a proxy for the output of teaching and research.

The last output was standby services. As noted by Cowing, Holtmann and Powers (1984), hospitals also must provide capacity in excess of that required to meet normal anticipated patient loads. This service is called standby service and allows hospitals to meet the demands of disaster and epidemics that may affect the communities they serve. Cowing et al. (1984) adopted a queuing model to provide an analytical measure for standby service. In this study, standby services were calculated using this queuing model and incorporated it into the efficiency analysis.

The inputs to the DEA model are straightforward. Two variables, the number of beds and the number of high technology services available, were used to capture capital in the hospitals' production. Labor inputs were measured by four variables, fulltime equivalent (FTE) physicians and dentists, FTE registered nurses, FTE licensed practical and vocational nurses, and FTE other personnel. FTEs were computed by counting each full time employee as 1 FTE and each part time employee as 0.5 FTE.

In this study, efficiency was measured for four different time periods, one year and two years before a merger and one year and two years after a merger. The initial AHA sample included 500 randomly chosen hospitals. In the first year after the merger, 93 of those had one or more input or output variables missing and could not be used. Of the remaining 407 hospitals, 77 merged with other hospitals in the sample during that year. Consequently, the test sample included 330 hospitals, 77 of which were the result of mergers. Eighty-nine (89) additional mergers took place in year 2. Since neither the hospitals that merged in year 1 or year 2 could be used as controls for year 2, and 79 did not have all of the required data, the sample for the year 2 analysis was reduced to 255 control and merged hospitals. The cases are reconciled in Exhibit 2.

DEA efficiency scores were derived for each hospital two years before, one year before and one year after and two years after a merger. Summary data for the DEA scores are presented in Exhibit 3. In the data for one year before merger, the percentages for non-merged hospitals classified as efficient was 35% and for merged hospitals it was 39%. For two years before the merger, the percentages were 25% and 26%, respectively. As a rough observation, note that there was no substantial difference between the percentage of non-merged and merged hospitals that were classified as efficient in the years prior to the merger year. Since the efficiency scores were measured within years and were calculated relative to all other hospitals within a single year, the difference in percentage between the two groups is what is important, rather than the absolute size of the percentage. Conditions that affected all hospitals without regard to merger status may have caused a change in efficiency from year to year.

In the data for one year after merger, 20% of non-merged hospitals and 37% merged hospitals were classified as efficient. In the data of two year after merger, 17% non-merged hospitals and over 64% of merged hospitals were classified as efficient. Consistent with the discussion immediately above, the important observation here is that there was a striking difference between the merged and non-merged samples after the merger occurred. These data are summarized in Exhibit 4.

Explanations for Technical efficiency

The DEA efficiency scores became dependent variables in a series of cross-sectional regressions used to explain differences in hospital efficiency and to test whether mergers impacted efficiency. Because efficiency scores above an upper limit are all assigned a single value of 1, the distribution of scores was censored and not normal. Consequently, a censored regression model, the Tobit model, was adopted rather than using ordinary least squares (Maddala 1983).

Our variable definitions are summarized below. Observations were obtained for various publicly available data sources as well as from the AHA survey data discussed above. Since a standard Tobit model requires the dependent variable to be greater than zero, the DEA efficiency scores were transformed into inefficiency scores to satisfy this condition. MERGER was the independent variable of interest and the other nine variables were control variables.

BED was included to capture the positive effect of returns to scale on technical efficiency. Efficiency differences have been found between governmental, private not-for-profit and privately-owned for-profit hospitals. Two dummy variables, GNFP and PNFP, were used to analyze the effects of the ownership. Three region dummy variables, NE, MW, and SOUTH captured possible regional differences of hospital performance.

Teaching hospitals generally have high patient volume allowing them to improve efficiency in a learning-by-doing way. However, they are generally large and heavily staffed, which may negatively impact efficiency. The effect of TEACH on technical efficiency was, therefore, undetermined. Accreditation was a proxy for the quality of a hospital's service. As a consequence, ACCRE was hypothesized to have a positive effect on technical efficiency.

Hirschman-Herndahl index (HHI) was calculated by summing the squares of each hospital's market share in its market area (which was defined as standard metropolitan statistical areas (MSA) for urban hospitals and as counties for rural hospitals). A higher HHI index implied less market competition. More market competition was hypothesized to be associated with improved efficiency.

The primary beneficiary group for Medicare is the elderly. More elderly patients have two potentially opposite impacts on hospital efficiency. Elderly patients tend to have longer inpatient stays, therefore, making a positive impact on one of the specified output variables, inpatient days (Garber, Fuchs & Silverman 1984; Becker & Sloan 1983). On the other hand, elderly patients often need constant medical attention. Therefore, more medical staff is required, which could have a negative impact on hospital efficiency. The impact of percentage of Medicare inpatient days on efficiency would depend upon the relative importance of each of these two factors.

There is evidence that prepayment arrangements in HMOs encourage physicians and hospitals to use resources more efficiently (Hillman 1987; Chilingerian 1995). The hypothesis was that the presence of an HMO would be positively associated with technical efficiency. Finally, it was hypothesized that those hospitals that had community health services assessments would be more efficient than those did not.

Tobit Model Estimation Results

Exhibit 5 presents the results of the two-year after merger analysis. To conduct the analysis, the 1996 and 1997 DEA scores were applied to hospitals consolidated in 1994 and 1995, respectively. For the non-merger hospitals, the average of 1996 and 1997 DEA scores were used. Exhibit 5 shows that merger status was positively and significantly associated with a hospital's technical efficiency. Hospitals engaged in merger were significantly more efficient two years after the merger than were their non-merging cohorts. This result provides justification for the recent spate of hospital mergers that were supported by claims of improved efficiency.

Being located in the south was positively and significantly related to a hospital's efficiency, while the percentage of Medicare inpatient days was negatively and significantly related to efficiency. This last result supports the argument that the negative impact of the elderly patients on efficiency dominates. None of the other control variables was statistically significant.

A similar method was applied to the one-year after merger analysis. It was found that none of the independent variables (including merger status) was significant. This result supports the contention that it takes time before the merged hospitals can redeploy the resources necessary to capture the benefits of the merger (Dranove & Lindrooth 2003). This is an important result for regulators to keep in mind as they assess the impact of mergers on hospital efficiency.

Similar methods were also applied to periods one and two years before a merger. In neither period was MERGER statistically significant, although the sign was consistent with the merging hospitals actually being less efficient than their non-merging counterparts. This is consistent with our contention that there was no significant difference between the efficiency of the two groups of hospitals in the pre-merger periods. In fact, Brooks and Jones (1997) argued that hospital mergers were not driven by considerations of market power or efficiency as much as by the existence of specific merger opportunities.


The research question addressed in this paper is whether hospital mergers result in increased technical efficiency for the merged hospitals. Using data from the American Hospital Association Annual Survey and a two-stage approach to calculating and analyzing technical efficiency, it was demonstrated that by the end of the second year after a merger, the merged hospitals had a significantly higher level of efficiency relative to their non-merging cohorts than before the merger. However, like previous studies by Harris, Ozgen and Ozcan (2000) and Ferrier and Valdmanis (2004) our research found no significant difference in the first year after the merger. The results of our study support the speculation in Ferrier and Valdamis (2004: 1,097) that the lack of a significant improvement in technical efficiency in the first year after merger "... may be attributed to not fully realizing technical efficiencies in the first year of the merger ...". The efficiency improvements began in the second year after the merger. It is for future research to determine how long these gains continue thereafter.

This result indicates that hospital mergers satisfy one of the major conditions--improved efficiency--imposed by various federal regulatory bodies charged with oversight of healthcare and antitrust regulations. It is likely that the improvement in efficiency was achieved through the elimination of duplicate services, with consequent realized personnel or capital savings.

Nevertheless, a merger increases the hospital's market power and the reduced competition discourages hospitals from passing on the saving to the patients. Thus, while increased efficiency allows the hospitals to reduce prices charged (which would directly benefit patients), the reduced competition allows the hospitals to do otherwise. For the community, there will likely be job losses and inconvenience costs resulting from service consolidations. These social losses can be recouped only when the efficiency gains are transferred to the patients. In other words, efficiency gains through mergers are desirable. However, the government may need to undertake measures to provide for the transfer of some of these gains to the patients through price reductions.

The limitations of this study leave several questions unanswered. Most importantly, this study examined only questions of technical efficiency. Because it did not address price efficiency, it does not offer insight into the question of whether the benefits of improved efficiency are passed along to consumers. The study used only the DEA approach for measuring technical efficiency. While it was argued that DEA is the most appropriate measure for the data used, alternative efficiency measures such as stochastic frontier analysis could also be used to measure technical efficiency. Finally, this study was limited to one, six-year period. Further study is necessary to observe whether the results documented here sustain themselves over different time periods.

This paper benefited from helpful discussions and comments from the editor and several reviewers. The authors are solely responsible for any remaining omissions and commissions. No seniority in the co-authorship is assigned. Neither the University of Texas at San Antonio nor GE Employer Reinsurance Corp. necessarily agrees with the statements contained in this paper.


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James E. Groff

University of Texas at San Antonio

Donald Lien

University of Texas at San Antonio

Jiwei Su

GE Employer Reinsurance Corp

Address for correspondence: James E. Groff, Department of Accounting, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249 USA,

Disposition of 1-year 2-years
Hospitals in the out out
Original AHA Sample test test

Sample Hospitals 330 255
(Includes hospitals
resulting from mergers)

Cumulative mergers 77 166
(Hospitals lost
due to mergers)

Hospitals lost due to 93 79
missing data items

Total hospitals in the 500 500
AHA supplied set


 Mean Median Minimum Maximum

 Non-merged 0.75 0.80 0.10 1.00
 Merged 0.72 0.83 0.08 1.00

 Non-merged 0.80 0.80 0.33 1.00
 Merged 0.86 0.83 0.48 1.00

 Non-merged 0.84 0.92 0.81 1.00
 Merged 0.96 0.99 0.28 1.00

Percentage of Sample Classified as Efficient
By Merger Class

 2-Years 1-Year 1-Year 2-Years
 Before Before After After

Non-merged 25 35 20 17
Merged 26 39 37 64

Variables for Tobit Regression Models

Dependent Variable:
Inefficiency score = (1 / efficiency score) - 1

Independent Variables:

MERGER = Merger status: 1 if a hospital engaged in a merger
 in 1994 or 1995, 0 otherwise

BED = Bed size: the number of staffed beds

GNFP = Government owned not-for-profit: 1 if a hospital
 is government owned not-for-profit, 0 otherwise

PNFP = Private owned not-for-profit: 1 if a hospital is
 private owned not-for-profit, = 0 otherwise

NE = Northeast region: 1 if a hospital is located in
 the Northeast region, 0 otherwise

MW = Midwest region: 1 if a hospital is located in
 the Midwest region, 0 otherwise

SOUTH = South region: 1 if a hospital is located in the
 South region, 0 otherwise

TEACH = Teaching status: 1 for a teaching hospital,
 0 otherwise

ACCRE = Accreditation status: 1 if a hospital is
 accredited, 0 otherwise

HMO = HMO status: 1 if a hospital has written
 contracts with an HMO, 0 otherwise

MEDICARE = Role of Medicare: the percentage of Medicare
 impatient days in hospital total impatient days

HHI = Market concentration: the Hirchsman-Herndahl index,
 calculated by summing the squares of each hospital's
 market share in its market area

COMMU = Community status: 1 if a hospital works with local
 community to assess its health service, 0 otherwise

Tobit Model Estimation Results
 Pr >
 Parameter Standard [chi [chi
Variables Estimate (a) Error square] square]

VIERGER -0.315 ** 0.144 4.784 0.029
SOUTH -0.248 0.125 3.923 0.048
MEDICARE 0.371 ** 0.179 3.321 0.038
HHI 0.125 0.044 3.342 0.068


(a) Dependent variable is defined as inefficiency, thus a
negative coefficient indicates a positive association with

** Indicates statistically significant at the p [less than
or equal to] 0.05 level.

None of the other control variables was significant at the
p [less than or equal to] 0.10 level.
COPYRIGHT 2007 isRHFM Ltd. Towson, MD. All rights reserved.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2007 Gale, Cengage Learning. All rights reserved.

Article Details
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Author:Groff, James E.; Lien, Donald; Su, Jiwei
Publication:Research in Healthcare Financial Management
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2007
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