Hospital ownership and operating efficiency: Evidence from Taiwan

European Journal of Operational Research 159 (2004) 513–527 www.elsevier.com/locate/dsw

O.R. Applications

Hospital ownership and operating efficiency: Evidence from Taiwan Hsihui Chang b

a,b

, Mei-Ai Cheng c, Somnath Das

d,*

a School of Management, The University of Texas at Dallas, Dallas, TX, USA A. Gary Anderson Graduate School of Management, University of California at Riverside, Riverside, CA, USA c Department of Accounting Information, National Taipei College of Business, Taipei, Taiwan d College of Business Administration, University of Illinois at Chicago, MC:006, Chicago, IL 6067-7123, USA

Received 20 May 2002; accepted 30 May 2003 Available online 22 September 2003

Abstract This paper employs the non-parametric data envelopment analysis to document empirical evidence on the relationship between hospital ownership and operating efficiency using annual cross-sectional data on Taiwan hospitals over the period 1996–1997. Hospitals within the same category are compared on the basis of their relative efficiency. Conventional and data-envelopment-analysis-based test procedures are employed to test for efficiency differences between public and private hospitals. The statistical test results indicate that, in general, public hospitals are less efficient than private hospitals for both regional and district hospitals. Specifically, we provide evidence that private hospitals without intensive-care units outperform their public counterparts. Ó 2003 Elsevier B.V. All rights reserved. Keywords: Data envelopment analysis; Health care; Health services; Hospital ownership; National health insurance; Operating efficiency

1. Introduction In this paper, we empirically examine the relation between hospital ownership and operating efficiency. While numerous studies have attempted to estimate the differences in hospital efficiency as a function of ownership type over the last two de-

*

Corresponding author. Tel.: +1-312-996-4482; fax: +1-312996-4520. E-mail address: [email protected] (S. Das).

cades (Wilson and Jadlow, 1982; Cowing and Holtmann, 1983; Becker and Sloan, 1985; Grosskopf and Valdmanis, 1987; Valdmanis, 1990; Burgess and Wilson, 1996), the empirical results are inconclusive. Moreover, most prior studies use parametric estimation approaches to assess operational efficiency. In this paper, we use a non-parametric data envelopment analysis (DEA) approach developed by Charnes et al. (1978) and extended by Banker et al. (1984) to estimate the production frontier of hospitals. DEA avoids some of the pitfalls associated with using traditional parametric

0377-2217/$ - see front matter Ó 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0377-2217(03)00412-0

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methods for measuring efficiency. 1 Specifically, unlike traditional parametric estimation methods, DEA does not assume a particular functional form (e.g. translog) for the underlying production function. In recent times, there has been a growing trend towards the conversion of not-for-profit health care delivery systems into for-profit institutions in the US (Cutler and Horowitz, 2000; Kuttner, 1996). This trend has brought the investigation of differences in operating performance across different ownership types to the forefront. Most prior studies that examine differences in operating efficiency by ownership types have primarily used data from US health care providers. One of the key limitations of studies based on US data is the inability to control for payment driven incentives given the non-uniformity of reimbursements/payments across hospitals. In this paper, we depart from prior literature by examining differences in operational efficiency using data from an international context i.e., Taiwan. Our use of data from Taiwan health care providers is motivated by the implementation of the National Health Insurance (NHI) Program in March 1995. The implementation of this program resulted in health care becoming one of the largest and fastest growing industries in Taiwan. More fundamentally, the implementation of NHI resulted in a uniform payment system within a given class of homogeneous hospitals, thus ensuring a level playing field at least with respect to payment driven incentives. Thus, the data from hospitals in Taiwan provides a unique opportunity to re-examine the relationship between hospital operating efficiency and ownership patterns. Our investigation is also motivated by recent political and economic developments in Taiwan following the implementation of the NHI. In particular, there are two major ongoing policy debates among policy makers and the public at large: (a) concerns over the efficiency of health care

delivery and (b) debate over the privatization of public hospitals due to the increased budget deficits experienced by the Taiwan government in recent years. 2 To our knowledge there is no empirical evidence comparing the relationship between different ownership patterns and performance using data from hospitals in Taiwan. Finally, our analysis is motivated by the current worldwide trend towards privatization of state owned enterprises (SOE). Majumdar (1998a,b), for instance, has used DEA to examine differences in comparative efficiency between public and private firms in India. Megginson and Netter (2001) provide an extensive review of the literature on empirical studies examining the performance of public versus private enterprises. The evidence to date on the superior operating performance of private enterprises relative to publicly owned units is at best mixed. This paper addresses the ongoing debate by focusing on the health care industry in Taiwan and examining whether there are differences in performance based on ownership patterns. Using annual data reported by hospitals to the Taiwan Department of Health for years 1996 and 1997, we employ a non-parametric DEA methodology to evaluate the relation between hospital ownership and operating efficiency for regional and district hospitals in Taiwan. Our empirical results indicate that public hospitals are less efficient than private hospitals for both regional and district hospitals in each of the two years under study. This paper contributes to the existing literature on the relationship between hospital ownership and operating efficiency in the following ways. First, while earlier research has used DEA to examine hospital efficiency, no prior work has used the associated DEA-based statistical tests employed in this paper, to test for differences in efficiency across groups of hospitals. Second, given the inconclusive results of the impact of ownership

2

1 Parametric regression analysis, for example, focuses on average behavior and is confined to aggregate measures and cannot identify the sources and amounts of inefficiencies in each decision making unit (DMU).

Most public hospitals in Taiwan are primarily supported through government subsidies since they have been making losses from patient care services. Hence, some policy makers argue that better utilization of health care resources and alleviation of budgetary constraints can be best attained through privatization (United Daily, March 3, 2000; Taiwan Daily, April 16, 2002).

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on hospital operating performance, this study provides additional evidence. Third, by examining the operating performance of Taiwan hospitals, we provide evidence on the influence of ownership using data outside of the US. Hence, given the paucity of research using international data this paper provides evidence on the effect of ownership on hospital performance in an international context. Fourth, relative to prior studies, we use comparable units of homogeneous hospitals from Taiwan. As argued by Carter et al. (1997), one of the difficulties and complexities of determining differences in performance by ownership structure is in part due to the heterogeneity of hospital services. Our sample hospitals within a given hospital category are homogeneous operating units with a relatively similar production technology and patient case-mix. Fifth, our sample hospitals within a given hospital category are also reimbursed by the same payment system under the NHI program, and hence the incentive structures are also likely to be quite homogeneous within each hospital category. Finally, this paper contributes to the ongoing debate over the relative superiority in performance of private enterprises over publicly owned units. The remainder of this paper is structured as follows. Section 2 provides institutional background and review of related prior studies. Section 3 describes the data and variables. Section 4 presents and discusses the empirical results. Concluding remarks are drawn in Section 5.

2. Institutional background and review of prior research 2.1. Health care systems in Taiwan In Taiwan, medical care providers must be certified by the Department of Health on the basis of size (number of beds), facility and mission into four different categories: medical centers, regional hospitals, district hospitals, and local clinics. In comparison to hospitals, local clinics are relatively small and only provide ambulatory outpatient services. They do not provide hospitalization care services (Chang, 1998).

515

Before March 1995, 67% of the total population in Taiwan had been covered by the 13 health insurance schemes operated by the Central Trust of China and the Bureau of Labor Insurance. Seven of these insurance schemes were under financial deficits for many years. To reform the health care system, Taiwan government set up a planning committee to draft a universal health insurance plan called the National Health Insurance Program which consolidated all of the 13 health insurance schemes into a single system (Department of Health, 1997). This draft was passed by the congress of Taiwan in September 1994. It was then known as National Health Insurance Act 1994. The Executive Yuan decided to implement this universal health insurance program in March 1995. Since then, this NHI program covers essentially all people in Taiwan. Under NHI Act, the Bureau of National Health Insurance in the Department of Health has developed a uniform payment system for reimbursement of services rendered by each individual category of health care providers. This payment system is essentially on a fee-for-service basis, and will be modified toward a fee-per-case basis gradually in the near future. 2.2. Hospital ownership in Taiwan Hospital ownership in Taiwan can be broadly classified into two principal groups––government or publicly owned and privately owned. There were 16,645 public and private medical care institutions in Taiwan at the end of 1996. Public medical care institutions include 95 hospitals and 497 clinics. Private medical care institutions include 678 hospitals and 15,375 clinics (Department of Health, 1997, p. 18). While all medical facilities must be non-profit by law, most private hospitals are owned and controlled by physicians. These hospitals are managed to maximize profits similar to any other profit seeking organization. Since public hospitals are an operational unit of government funds, they typically do not have to assume the risk of profits or deficits. Thus, relative to private hospitals, public hospitals may not be as concerned about the operating efficiencies either before or after the implementation of the NHI

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program. Hence, they are likely to have lower operating efficiency relative to the private hospitals. 2.3. Related prior research In examining hospital efficiency, existing research commonly uses ownership differences to explain cross-sectional variations in performance. There is considerable prior work on the effects of ownership on hospital performance. 3 From a theoretical viewpoint, there have been several alternative theories that have been tested. The property rights theory, for example, is taken to explain performance differences between nonprofit (public) and for-profit (private) hospitals. Briefly stated, the property rights theory suggests that firm owners will maximize utility by choosing an optimal combination of firm wealth and nonpecuniary benefits. When property rights are attenuated, the price of non-pecuniary benefits is reduced thus leading to higher consumption and hence a reduction in efficiency. The attenuation of property rights with respect to the ownersÕ rights to profits reduces their incentives for monitoring performance. 4 Hence, it is commonly argued that efficiency in public hospitals will be lower than in private hospitals. Agency theory argues that because contracts cannot be costlessly written and enforced, managers will act in their own interest with serious implications for operational efficiency. Expense preference theory, an extension of agency theory, also suggests that managers maximize their own utility, leading to excess consumption of nonpecuniary benefits particularly among public hospitals. Within the broader (non-hospital specific) context of public versus private ownership, there has

3 Some recent work has focused on the impact of hospital ownership on the quality of service. However, overall the results indicate no significant differences in quality across ownership patterns. Baker et al. (2000) and Sloan (2000) provide an extensive survey of this research. 4 See Ahuja and Majumdar (1998) and Caves (1990) for a discussion of these issues in a more general non-hospital context.

been extensive debate using data from a variety of national settings. Boardman and Vining (1989) report that a substantial body of evidence from a variety of countries finds that privately owned firms do outperform publicly owned units. For example, Majumdar (1998a,b) examines differences in performance between public and private enterprises in the Indian context, and documents lower efficiency and higher slack for publicly owned enterprises. Empirical research in the health care industry has also provided mixed support for these theories. Some studies found public hospitals to be less efficient than private hospitals (Wilson and Jadlow, 1982; Cowing and Holtmann, 1983; Robinson and Luft, 1985), while others found public hospitals to be more efficient than private hospitals (Custer and Wilke, 1991; Menke, 1997). Cowing and Holtmann (1983) in a study of New York hospitals found that private proprietary hospitals had lower costs than non-profit hospitals. Similarly, Sharp and Register (1984) found differences in performance by ownership categories for a set of Oklahoma hospitals. In contrast, Grannemann et al. (1986) found that public hospitals had lower costs than not-for-profit private hospitals. More recently, Burgess and Wilson (1996) also found differences in technical efficiency across hospitals with different ownership patterns. Comparing three ownership forms of hospitals––private, non-profit, and public, Becker and Sloan (1985), for example, do not find any differences in hospital costs or profitability among different ownership patterns. Based on this evidence they conclude that there is no empirical support for the standard property rights theory. Some others have also found insignificant differences in efficiency between public and private hospitals (Vita, 1990; Grosskopf and Valdmanis, 1987). Thus, prior empirical work relating hospital ownership and performance has been inconclusive. In addition to the mixed empirical evidence on the validity of alternative theories, there are several alternative explanations that may contribute to the failure of property rights theory to explain differences in performance across hospitals characterized by different ownership patterns. For example, Pauly and Redisch (1973) argue that

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hospital doctors play a role in the functioning of hospitals and may behave as a cooperative to maximize their bonuses. Newhouse (1970), on the other hand, argues that hospital doctors attempt to maximize their prestige and the amount of health care they are able to provide. As a result, the lack of property rights arising in a public hospital may be mitigated. In addition, competition among hospitals may often limit the extent of inefficiency, even in the presence of attenuated property rights. In sum, neither existing economic theory nor the empirical results to date provide any clear evidence on whether or not there exist differences in operating performance amongst different ownership patterns. Also, to date the literature has not examined data from a setting in which a uniform payment system has been adopted. Moreover, very few, if any, of these investigations have used data from a setting other than the US to investigate the relationship between hospital ownership and operating performance.

3. Sample description and variable definitions 3.1. Description of data In order to guide its policy for the development of medical manpower and facilities, the Department of Health in Taiwan conducts an annual survey of all hospitals. As of the end of 1996, there were 773 hospitals in Taiwan, of which 578 hospitals were accredited. 5 Included in the survey are data items such as number of physicians, number of nurses, number of patient beds, number of ambulatory visits, and number of patient days. This survey constitutes one of the most reliable sources of annual operating data on Taiwan hospitals and is the primary source of data used in our analysis. 6 Specifically, we use data collected from

517

the 1996 and 1997 surveys of hospitals compiled by the Department of Health in Taiwan. The same survey questionnaire was used during both the sample years. 7 While all accredited hospitals are required to respond to the survey, only 497 and 488 hospitals provided complete information in 1996 and 1997, respectively. 8 Hospitals in Taiwan are classified into three basic levels depending upon their primary functions i.e.: medical centers, regional hospitals and district hospitals (Department of Health, Taiwan, 1997, pp. 18–19). Under the NHI Act, patients have to pay a higher co-payment if they receive services from a higher level of hospital (such as a regional hospital) without getting a referral from a lower level hospital (such as a district hospital). Further, the type of patients serviced (hospital output) varies by these different levels of hospitals. In 1996 (1997), among surveyed hospitals, 14 (16) are medical centers, 45 (45) are regional hospitals, and 474 (461) are district hospitals. Obviously, regional and district hospitals represent the largest category in the Taiwan hospital system. During 1996, they accounted for 91% of hospitals, 76% of beds, and 70% of patient days. In this paper, we therefore focus on regional and district hospitals. After eliminating hospitals with missing inputs or outputs values, the final sample in 1996 consists of 43 regional hospitals, of which 18 are public and 25 are private, and 440 district hospitals, of which 49 are public and 391, are private. In 1997, the final sample consists of 44 regional hospitals, of which 17 are public and 27 are private, and 429 district hospitals, of which 49 are public and 380 are private hospitals. 3.2. Measurement of hospital efficiency Efficiency can be measured as minimal consumption of inputs for a given level of outputs or the augmentation of outputs at a given level of input usage. In this study, we adopt the

5

The purpose of hospital accreditation is to upgrade the quality of medical care so as to lay a foundation for medical care at different levels (Department of Health, 1996). 6 The data were scrutinized or revised, as necessary, for consistency using government budget related information.

7 The raw questionnaire containing specific questions asked is available from the authors upon request. 8 Our sample is restricted to those hospitals that reported all the required data.

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H. Chang et al. / European Journal of Operational Research 159 (2004) 513–527

output-based efficiency measure. In general, most hospital management and health care providers anticipate demand and invest in inputs necessary to support the expected level of demand. Therefore, in the short run, it is hard for them to adjust input levels. Hence, from a practical standpoint, it is more realistic to assume that they would maximize output subject to the available inputs (capacity). This output-based approach measures how much output can be generated from a given level of inputs. Typically, ex post input–output data of individual organizations is used to map the production frontier using cross-sectional data. By definition, the production frontier is the efficient boundary of the production possibility set representing how much output is produced using a given level of inputs. Each firm (or observation) that is rated as efficient is used to define an efficient frontier, and firms less efficient are evaluated by comparison to a hypothetical firm that is on the frontier, and which has the same input or output mix as the firm being compared. The efficiency of the firm being compared is the ratio of the actual output level to that of the hypothetical firm. 9 In this paper, we use a non-parametric DEA approach developed by Charnes et al. (1978, hereafter CCR) and extended by Banker et al. (1984, hereafter BCC) to estimate the production frontier of Taiwan hospitals. 10 In particular, we use the BCC model to estimate the efficiency indices, since we cannot predetermine

9

The overall output measure of efficiency consists of two components––a technical and an allocative part. An organization is technically inefficient if it is not producing on the production frontier irrespective of relative prices. Even if the organization is operating on its production frontier, it may not be using the appropriate input mix, given relative input prices. This latter phenomenon is called allocative inefficiency. Since cost and price data are not available to us, we focus on the evaluation of technical efficiency. 10 An alternative would be to use the stochastic frontier estimation used by Zuckerman et al. (1994). The stochastic frontier estimation however requires data on input prices, and other co-variates, which are not available to us thus limiting our ability to use the approach. Moreover, DEA is complementary to the stochastic frontier estimation as an approach.

the return to scale characteristics of the underlying production function of our sample hospitals. The output-oriented efficiency measure using the BCC model of DEA can be represented as a programming problem as shown below. Let Yj ¼ ðy1j ; . . . yrj ; . . . yRj Þ P 0 and Xj ¼ ðx1j ; . . . xij ;...xIj ÞP0, j ¼ 1;...N , be the observed output and input vectors generated from an underlying production possibility set T ¼ fðX ; Y Þj outputs Y can be produced from inputs X g for a sample of N hospitals in the same level of hospitals in Taiwan. The efficiency score, which is the reciprocal of the inefficiency, h^j is obtained by solving the following BCC model of DEA: h^j ¼ Max h; s:t: X

kk yrk P hyrj

ð1:0Þ

8 r ¼ 1; . . . R;

ð1:1Þ

k

X

kk xik 6 xij

8 i ¼ 1; . . . I;

ð1:2Þ

k

X

kk ¼ 1;

ð1:3Þ

k

h; kk P 0:

ð1:4Þ

DEA approximates piecewise linear functions, where the approximations are determined endogenously to envelop the data tightly. A key advantage of the production correspondence implicit in the DEA specification used here is substitution within both inputs and outputs. This is important in the hospital sector as many hospitals often substitute within both inputs and outputs. For example, in the United States, registered nurses often substitute for many of the physician functions such as inoculations. Among others, Banker et al. (1986), Banker et al. (1989), Burgess and Wilson (1996), Grosskopf and Valdmanis (1987), Valdmanis (1990) and Chang (1998) have employed DEA to evaluate hospital efficiency. Banker et al. (1989) show this method to be particularly well suited for relative performance evaluation across a cross-section of health care organizations.

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3.3. Hospital inputs and outputs Prior research on hospital efficiency has used several measures of hospital inputs and outputs. A detailed review and discussion of the measurement of hospital inputs and outputs is provided in Tatchell (1983) and Banker et al. (1989). To date, there is no statistical technique to unambiguously determine inputs and outputs for measuring efficiency using DEA. Given the constraints of the available data, we consider four inputs and three outputs for the estimation of the DEA model. In particular, we use the following four inputs: (i) Number of patient beds (X1) which includes general beds, special treatment beds, psychiatric beds, chronic beds, tuberculosis beds, and leprosy beds; (ii) Number of physicians (X2) which includes physician and Chinese medicine doctors; (iii) Number of nurses (X3) which includes registered professional nurses and registered nurses; and (iv) Number of supporting medical personnel including ancillary service personnel (X4) which includes pharmacists, assistant pharmacists, medical technologist, medical technicians, medical radiological technologists, midwives and dietitians. The three outputs we consider are: (i) Number of patient days (Y1) which includes general care, acute and intensive care, and chronic care patient days; (ii) Number of clinic or outpatient visits (Y2) which includes ambulatory and emergency room visits; and (iii) Number of patients receiving surgery (Y3). An alternative output measure that is perhaps more relevant is the number of cases. This alternative output measure is important since hospitals can improve efficiency if they maintain the number of patient days, but increase the number of patients treated through either better capacity utilization or through a decrease in the length of stay, without sacrificing quality. However, our choice of using Ôpatient daysÕ instead of Ônumber of casesÕ as a measure of output is driven by two factors: (a) data on a case basis is not available for our sample hospitals, and (b) the reimbursements during our sample period were still on a fee-per-visit and not on a fee-per-case basis. To this extent, at least during our sample period,

519

hospital managers were unlikely to have had incentives with the potential to distort hospital outputs as measured by patient-days, clinical visits, or surgeries.

4. Empirical results 4.1. Overview of sample characteristics Our analysis in this paper is presented separately for the two sample years. This is done primarily to overcome the problems of pooling data across years, when in fact, there may have been changes in the production technology between the sample years. This is particularly important when measuring relative efficiency. Panels A and B of Table 1 provide descriptive statistics for inputs and outputs by ownership type for years 1996 and 1997, respectively. Comparing panels A and B of Table 1, we observe that the average sample public district hospitals uses more inputs, but also produces more outputs than their private counterparts in both the sample years. In contrast, the pattern with respect to the regional hospitals is less clear in either sample year, as some inputs are lower for public hospitals while others are higher relative to their private counterparts. A similar lack of clear pattern emerges for outputs of regional hospitals. Thus there is no prima facie evidence that public hospitals are using more inputs to produce fewer outputs in either regional or district hospitals. 4.2. Operating efficiency of hospitals across ownership types As discussed in Section 3.2, we estimate the relative operating efficiency of individual units using the DEA methodology. Table 2 reports the distribution of the relative efficiency scores by hospital category and ownership type. It can be seen form Table 2 that in 1996, the mean (median) efficiency scores for the sample public regional and district hospitals were 0.841 (0.852) and 0.568 (0.581) as compared to 0.932 (1.000) and 0.629 (0.598) for the corresponding category of private

520

Table 1 Descriptive statistics for inputs and outputs All hospitals Std. dev.

Public hospitals Median

N ¼ 43 Panel A: Year 1996 Regional hospitals Inputs Number of patient beds Number of doctors Number of nurses Number of medical supporting personnel including ancillary services Outputs Number of patient days Number of clinic and emergency visits Number of patients receiving surgery

Private hospitals Median

Mean

Std. dev.

Median

N ¼ 25

553 108 332 67

223 52 167 32

485 105 296 56

468 114 245 57

147 39 89 19

463 115 242 53

614 104 394 74

250 61 183 38

551 90 355 58

129,172 488,557 9954

62,597 303,840 7079

117,424 418,263 7875

100,851 354,418 6174

43,400 186,872 3663

88,047 313,672 5468

149,562 585,137 12,675

66,996 337,062 7731

125,145 485,234 10,158

N ¼ 49

N ¼ 391

89 10 33 7

111 11 43 9

48 5 16 4

241 26 68 17

178 14 39 9

190 23 63 15

70 7 29 6

81 9 42 8

42 4 14 4

16,738 86,177 1188

24,391 81,639 3117

8040 56,761 498

46,476 127,213 1709

42,269 81,667 1438

31,518 112,702 1352

13,011 81,035 1123

18,021 80,271 3263

6989 54,136 467

N ¼ 44 Panel B: Year 1997 Regional hospitals Inputs Number of patient beds Number of doctors Number of nurses Number of medical supporting personnel including ancillary services Outputs Number of patient days Number of clinic and emergency visits Number of patients receiving surgery

Std. dev.

N ¼ 18

N ¼ 440 District hospitals Inputs Number of patient beds Number of doctors Number of nurses Number of medical supporting personnel including ancillary services Outputs Number of patient days Number of clinic and emergency visits Number of patients receiving surgery

Mean

N ¼ 17

N ¼ 27

576 102 360 78

224 52 176 35

517 94 321 73

500 105 260 63

156 43 98 21

472 101 264 65

624 100 422 87

249 57 186 39

551 79 397 80

126,659 479,825 9299

63,700 293,315 6700

111,601 440,918 7002

93,729 346,583 5855

44,685 180,514 2667

80,241 256,375 5642

147,392 563,717 11,467

65,777 321,430 7566

133,806 486,580 10,111

H. Chang et al. / European Journal of Operational Research 159 (2004) 513–527

Mean

6440 54,461 476 16,666 75,468 3279 11,464 80,525 1156 27,642 103,143 1199 43,239 82,027 1488 44,246 129,663 1578 23,762 77,736 3128 15,208 86,137 1204

7332 59,603 531

43 4 14 5 73 8 37 8 68 7 28 8 193 20 65 18 151 14 41 9 229 24 74 19 48 5 16 6 100 10 40 9

N ¼ 49 N ¼ 429

86 9 33 9

District hospitals Inputs Number of patient beds Number of doctors Number of nurses Number of medical supporting personnel including ancillary services Outputs Number of patient days Number of clinic and emergency visits Number of patients receiving surgery

N ¼ 380

H. Chang et al. / European Journal of Operational Research 159 (2004) 513–527

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Table 2 Descriptive statistics of DEA efficiency scores Public hospitals

Private hospitals

18 0.841 0.153 0.852

25 0.932 0.115 1.000

N Mean Std. dev. Median

49 0.568 0.224 0.581

391 0.629 0.217 0.598

Panel B: Year 1997 Regional hospitals N Mean Std. dev. Median

17 0.837 0.152 0.874

27 0.911 0.103 0.931

49 0.584 0.250 0.552

380 0.676 0.215 0.662

Panel A: Year 1996 Regional hospitals N Mean Std. dev. Median District hospitals

District hospitals

N Mean Std. dev. Median

hospitals. In 1997, the mean (median) efficiency scores for the sample public regional and district hospitals were 0.837 (0.874) and 0.584 (0.552) as compared to 0.911 (0.931) and 0.676 (0.662) for their private counterparts respectively. These results suggest that there is greater efficiency among private hospitals relative to public hospitals for both regional as well as district hospitals. Further, the results are consistent across sample years, 1996 and 1997. 4.3. Conventional tests of differences across different ownership groups Next, we examine whether the observed differences in efficiency scores across the two ownership types are statistically significant. To test efficiency differences between public and private regional and district hospitals, we employ two conventional statistical tests, WelchÕs two sample means and Wilcoxon two sample tests. The null hypothesis being tested is that there are no differences in efficiency between the two groups of hospitals. Grosskopf and Valdmanis (1987) test a similar

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Table 3 Conventional tests of equality of efficiency between public and private hospitals Welch two sample test T -Stat. Panel A: Year 1996 Regional 2.12 hospitals District )2.06 hospitals Panel B: Year 1997 Regional 1.78 hospitals District )2.77 hospitals

Wilcoxon two sample test Pr > jT j

Z-Stat.

Pr > jZj

0.042

)2.25

0.024

0.044

)1.77

0.076

0.086

)1.80

0.071

0.008

)2.72

0.007

hypothesis using data from the 1982 American Hospital Association Survey of Hospitals. 11 The statistical test results of equality in efficiency between public and private hospitals using the two conventional tests are presented in Table 3. As can be seen from Table 3, the null hypothesis is rejected at 10% (or better) level of statistical significance in each hospital category (regional and district) as well as in each of the two sample years. Thus, while Table 2 provides evidence that private hospitals are more efficient than public hospitals, Table 3 provides evidence that the differences in efficiency are statistically significant. Together Tables 2 and 3 therefore provide evidence that over the two sample years, private hospitals are operationally more efficient than their public counterparts, for both regional and district hospitals. 4.4. DEA-based tests of differences across different ownership groups To further evaluate the validity of our conclusions, we also use the recently introduced DEAbased statistical tests (Banker, 1993). Given our use of DEA-based efficiency scores, these tests are more appropriate since they have been developed specifically to test for differences in DEA ineffi-

Table 4 DEA-based tests of equality of inefficiency between public and private hospitals DEA-based tests Texp -Stat. Panel A: Year 1996 Regional 2.48 hospitals District 1.35 hospitals Panel B: Year 1997 Regional 2.13 hospitals District 1.67 hospitals

Pr > F

Thn -Stat.

Pr > F

0.002

3.09

0.005

0.017

1.85

0.001

0.006

3.97

0.001

0.001

2.79

0.001

ciency scores. These DEA-based tests of inefficiency differences between two groups have been found to outperform conventional parametric tests in Monte Carlo simulation studies (Banker and Chang, 1995). Appendix A provide details on these DEA-based statistical tests. The two tests differ in their assumptions regarding the distribution of the underlying DEA inefficiency score being (a) exponential or (b) half-normal. The results from these tests are reported in Table 4. As before, the null hypothesis being tested is that there is no difference in inefficiency between the two groups of hospitals. As can be seen from Table 4, the null hypothesis is rejected at 1% (or better) level of statistical significance in each hospital category (regional and district) in both sample years. The results in Table 4 therefore corroborate the results in Table 3. Thus, while Table 2 indicates that private hospitals are more efficient than public hospitals, Tables 3 and 4 together provide evidence that the differences in efficiency are statistically significant. Together the results provided in Tables 2–4, indicate that over the two sample years, private hospitals are operationally more efficient than their public counterparts, for both regional and district hospitals. 4.5. Evaluation of alternative explanations

11 Grosskopf and Valdmanis (1987) used the non-parametric Mann–Whitney test to test their null hypothesis.

In this section we discuss various sensitivity analyses performed to evaluate the susceptibility

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Table 5 Tests of equality of efficiency between public and private district hospitals by intensity of competition Competition intensitya

Sample size

Mean efficiency

Conventional tests

DEA-based tests

Private

Public

Private

t-Stat.

Z-Stat.

Texp

Thn

Panel A: Year 1996 High 13 Low 36

206 185

0.573 0.566

0.622 0.636

0.74 1.71


)0.67 )1.68


1.26 1.35



1.45 1.73



Panel B: Year 1997 High 14 Low 35

167 213

0.542 0.600

0.665 0.685

1.93
1.81


)2.00

)1.99



1.75

1.64



2.72


2.79




Public









( / ) indicates significant at 10% (5%/1%) level. a A hospital is classified as ‘‘High’’ if its inverse of Herfindahl index (HI) is greater than the median inverse of HI (median ¼ 12.5 and 14.3 for years 1996 and 1997, respectively) and as ‘‘Low’’ otherwise.

of our results to alternative explanations. To the extent our classification of public versus private hospitals maps on to urban versus rural or vice versa, a possible explanation of the observed differences may be attributed to rural versus urban location of the hospitals. In other words, if public hospitals are generally in rural (urban) areas and private hospitals are generally in urban (rural) areas, then our tests of differences would not be representing differences in ownership, rather would represent differences in geographical location. The database of hospitals that we use does not identify hospitals by their geographic location in terms of urban versus rural, and hence we are unable to analyze the data by such a partition. However, the Department of Health (of Taiwan) does divide Taiwan into 17 medical care regions to balance the development of medical care resources in various areas. Since each area represents a submarket and has a different intensity of competition, we partitioned our sample district hospitals into two sub-groups: High and low intensity of competition. We measure intensity of competition using the inverse of the Herfindahl index (HI) constructed with the number of patient beds in each region. These results are reported in Table 5. With the exception of the high intensity group of hospitals in 1996 (panel A of Table 5), where we do not see any significant difference in efficiency between public and private hospitals, the remaining results are consistent with the results reported in Tables 2–4, thus indicating that the observed differences are attributable to differences in ownership.

The above sensitivity analysis relies on our use of the Herfindahl index in 17 regions. To investigate the validity of the Herfindahl index we examine whether regions differ along urban and rural dimensions, leading to differences in patient populations across hospitals operating in different regions. To do this we examined the provisions of the Medical Care Act of Taiwan. The regulatory provisions of the Act provide for the establishment or expansion of hospitals in regions with limited medical care resources in order to improve the accessibility of medical care services in regions that have a relative lack of resources. 12 Thus it is unlikely that there will be significant differences in patient population by urban and rural locations. Second, a single regional medical care coordination committee is set up in each medical care region by the local health authorities and other related organizations to coordinate matters concerning health and medical care services in the region. Evaluations by the Department of Health in Taiwan show that the standards of medical care manpower and facilities in each region have greatly improved since the implementation of NHI. In addition, we conducted field interviews with select officials at the Department of Health in Taiwan who indicated that differences exist primarily across regions. The officials argued that in Taiwan the differences between urban and rural sectors, particularly by population density, are not as stark as in many developed countries such as the

12

See, Department of Health (1997, p. 17).

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Table 6 Tests of equality of efficiency between public and private district hospitals by severity of illness Severity of illnessa

Sample size

Mean efficiency

Conventional tests

DEA-based tests

Private

Public

Private

t-Stat.

Z-Stat.

Texp

Thn

Panel A: Year 1996 None 16 High 5 Low 28

295 60 36

0.502 0.563 0.607

0.648 0.552 0.597

2.95


)0.11 )0.16

)2.59


0.11 0.03

1.84


1.09 1.01

3.13


1.18 1.15

Panel B: Year 1997 None High Low

295 54 31

0.564 0.457 0.617

0.701 0.566 0.629

2.08

1.17 0.18

)2.29

)0.91 )0.24

2.19


1.56 1.02

5.42


2.26
1.06

Public

16 5 28









( / ) indicates significant at 10% (5%/1%) level. a We use a proxy for the severity of illness as the ratio of ‘‘the number of intensive-care patient bed days occupied to the total number of patient bed days occupied’’. A hospital is classified as ‘‘None’’ if it does not have any intensive-care patient bed days, it is classified as ‘‘High’’ if its severity of illness is greater than the median severity of illness (cross-sectional median ¼ 0.048 for 1996 and 0.052 for 1997), and a hospital is classified as ‘‘Low’’ if its severity of illness is less than or equal to the median severity of illness.

US. These arguments therefore provide support for our use of the Herfindahl index. A second possible explanation for the observed differences in efficiency between ownership types may be attributed to differences in patient populations with different severity of illness. Specifically, if public hospitals attract a different type of patient (more or less severe) than the patients who use private hospitals, then hospital production functions may be different simply due to differences in the patient populations. This would imply that neither type of hospital is more or less efficient and that any observed difference can be attributed to their different production functions. We therefore examine whether severity of illness can explain the observed differences between publicly and privately owned hospitals. For this, we partition district hospitals into three groups based on their severity of illness: None, high, and low. 13 We measure the severity of illness as the ratio of ‘‘the

13

We note that tests of this and the preceding alternative explanations focus only on district hospitals. Tests could not be done for the regional hospitals due to their small sample size, which would likely reduce the power of our statistical tests (see Tables 5 and 6).

number of intensive-care patient bed days occupied to the total number of patient bed days occupied’’. A hospital is classified as ‘‘None’’ if it does not have any intensive-care patient bed days, a hospital is classified as ‘‘High’’ if its severity of illness is greater than the median (cross-sectional median ¼ 0.048 for 1996 and 0.052 for 1997), and a hospital is classified as ‘‘Low’’ if its severity of illness is less than or equal to the median severity of illness. These results are reported in Table 6. As can be seen from Table 6, private hospitals without intensive-care units do outperform their public counterparts. However, the results also suggest that for hospitals with either ÔLowÕ or ÔHighÕ levels of severely ill patients, there are no differences in efficiency between public and private hospitals. This latter result is thus consistent with the notion that hospitals treating severely ill patients have different production functions and such differences may be contributing to the observed differences between public and private hospitals. In addition, any disproportionate presence of teaching hospitals in any one group could also make our results vulnerable to the alternative explanation that differences in ownership are arising from differences due to teaching versus nonteaching activities. To examine the sensitivity of

H. Chang et al. / European Journal of Operational Research 159 (2004) 513–527

teaching mission to our conclusions, we re-estimated our model after deleting hospitals associated with medical schools. 14 The results not reported here indicate that our conclusions are invariant to the inclusion or exclusion of these teaching hospitals. Hence, teaching mission does not appear to confound our results on differences in efficiency between public and private hospitals. Finally, in the US, publicly supported hospitals treat indigent and uninsured patients to a much greater extent than private hospitals. These patients typically do not have health insurance and so wait until they are very ill to get treatment. In addition, a number of these patients suffer trauma (car accidents, gun shot wounds, and the like) and need the services of a trauma center (often located in publicly supported hospitals since they are not profitable). Such a scenario gives rise to incentive differences and consequently leads to dissimilarities in patient populations between public and private hospitals. Unlike the US however, the NHI program in Taiwan insures everybody and all patients have the choice to visit the hospital of their choice. Hence, we would expect patients to always choose hospitals where they believe they will receive the most appropriate treatment regardless of the type of ownership. Therefore it is unlikely that there are any incentive differences for patients to use public versus private hospitals or vice versa. Hence, there is no a priori reason to believe that differences in patient populations would contribute to the observed differences between public and private hospitals. 4.6. Influence of differences in quality of service One of the key limitations of our study is the lack of control for measures of quality differences amongst sample hospitals. However, we note that our study is not unique among studies of hospital efficiency in its conspicuous absence of a quality measure. We explored several alternative avenues for obtaining quality measures, which seems to be

14 Our sample has only 3 of the regional and 3 of the district hospitals that are associated with medical schools. Of these, there is 1 public district hospital and the remaining are private hospitals.

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fraught with measurement error. For example, interviews with the manager of operations of one of the sample public hospitals indicated that people tend to perceive degree of specialization as an indicator of quality. More specialized hospitals are perceived to be of high quality compared to less specialized hospitals and the number of departments is an appropriate measure for specialization. In contrast, an interview with the assistant director of quality control at a private hospital suggested that quality matters but that there are many quality measures. Different hospitals may employ different measures of quality based on their differences in missions. For instance, the assistant director suggested that it would not be reasonable and fair to compare his hospitalÕs infection rate with other hospitals in town and then conclude that the quality of service in his hospital is poor if it turned out that they have a relatively high infection rate. He added that the high infection rate is generally associated with the severity of illness. Hence, in this example, quality as measured by infection rate has to be understood in the context of the hospitalsÕ mission and why more severe patients visit them. It could be, for example, that the physicians associated with this hospital enjoy a better reputation in town. Burgess and Wilson (1996) provide an extensive discussion of the limitations associated with direct measures of quality. For example, the frequency of return visits of a patient cannot be an appropriate measure of quality for one who is being treated for a chronic ailment. Finally, we also note that failure to account for high quality, irrespective of how it is defined, will only lead to such ÔqualityÕ hospitals being inefficient rather than efficient, and thus lead to a downward bias in our tests of differences.

5. Conclusion In this paper, we use the non-parametric DEA to empirically examine the relation between ownership and operating performance of a sample of Taiwan regional and district hospitals. Our results indicate that for both regional and district hospitals, public hospitals are less efficient than private hospitals. These results are consistent with the

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predictions of standard property rights theory. However, this does not imply that private hospitals have higher operating efficiency because they are better managed. It is possible that (a) private hospitals may achieve their efficiency by rejecting more complex cases, and (b) they concentrate in some profitable services because of their ability to limit the scope of their services. Indeed, our results show that private hospitals without intensive-care units do outperform their public counterparts. However, the results also suggest that for hospitals with either ÔLowÕ or ÔHighÕ levels of severely ill patients, there are no differences in efficiency between public and private hospitals. This latter result is thus consistent with the notion that hospitals with severely ill patients have a different production function. Thus, for hospitals that treat severely ill patients, the observed differences between public and private hospitals perhaps represent those arising from differences in their production functions. Like other empirical studies examining hospital efficiency, our results are subject to some caveats. A principal limitation is our inability to get a proxy metric to measure the ÔqualityÕ of service in each hospital. To the extent that there are differences in the quality of service, both within each level of hospital, as well as across the two levels of hospitals, our results may be driven by such differences in quality, as it remains uncontrolled in this study. However, it should also be noted that prior studies examining differences in quality across for-profit and not-for-profit hospitals have not been able to consistently document any significant differences. Notwithstanding these caveats, our results suggest that from a public policy perspective, the Taiwan government may opt to privatize public hospitals for the efficient utilization of health care resources. On the other hand, management of public hospitals can emulate the mix of inputs and outputs of efficient hospitals to improve their production efficiency.

Acknowledgements We are grateful to Mark Anderson, Rajiv Banker, Peter Chalos, Raj Mashruwala, Anne Wu,

two anonymous reviewers and Jyrki Wallenius (the editor) for their comments and suggestions; and to Chia-Chi Hsiao and Sheng-Cheng Yang from the Directorate-General of Budget, Accounting and Statistics, the Executive Yuan-Taiwan, and ChingHo Lin from the Legislative Yuan-Taiwan for assistance in obtaining the data. Appendix A. DEA-based statistical tests of differences in inefficiency The following DEA-based tests are based on test statistics described in Banker (1993). Let N 1 and N 2 be the number of sample public and private hospitals, respectively. Assuming that the inefficiencies hj are distributed exponentially for public and private hospitals, with means 1 þ r1 and 1 þ r2 , respectively, then we can test the null hypothesis H0 : r1 ¼ r2 (indicating that public and private hospitals have the same inefficiency distribution) against the alternative hypothesis H1 : r1 < r2 (indicating that public hospitals are on average less efficient than private hospitals) by employing the following test statistic: , X X ^ Texp ¼ ðhj  1Þ ðh^j  1Þ; ðA:1Þ j2N 1

j2N 2

which asymptotically follows the F -distribution with (2N 1; 2N 2) degrees of freedom. If the inefficiencies hj are assumed to be half-normally distributed for public and private hospitals, with means 1 þ r1 and 1 þ r2 respectively, then we can test the null hypothesis against the alternative hypothesis described above by employing the following test statistic: , X X 2 2 ^ Thn ¼ ðhj  1Þ ðh^j  1Þ ; ðA:2Þ j2N 1

j2N 2

which asymptotically follows the F -distribution with (N 1; N 2) degrees of freedom. References Ahuja, G., Majumdar, S.K., 1998. On the sequencing of privatization in transition economies. Industrial and Corporate Change 7 (1), 109–152.

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