Transaction costs, quality, and economies of scale: examining contracting choices in the hospital industry

Journal of Corporate Finance 4 Ž1998. 321–345

Transaction costs, quality, and economies of scale: examining contracting choices in the hospital industry Jerilyn W. Coles

a,)

, William S. Hesterly

b

a

School of Management, Arizona State UniÕersity-West, 4701 W. Thunderbird Road, P.O. Box 37100, Phoenix, AZ 85069-7100, USA b Department of Management, DaÕid Eccles School of Business, UniÕersity of Utah, Salt Lake City, UT 84112, USA Received 1 January 1997; accepted 1 February 1998

Abstract This study examines make or buy decisions for 196 hospitals in the United States using transaction costs as the basis for analysis. We examine the potential effects of quality and economies of scale on these decisions. We find evidence to support the view that transaction costs, quality and economies of scale play an important role in the integration decision and that this role depends on whether the transaction is industry-specific or generic in nature. This study examines the contracting choices of many firms across multiple transactions with a significantly larger data set than previous work in the area. q 1998 Elsevier Science B.V. All rights reserved. JEL classification: I1; L2 Keywords: Organization; Contracting; Hospitals

1. Introduction The assertion of Williamson Ž1985, p. 92. that ‘‘all cost differences between internal and market procurement ultimately rest on transaction cost considerations’’ )

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constitutes the central premise of empirical research that examines transaction costs and vertical integration. This research focuses on asset specificity and uncertainty as the basis for the integration decision. Empirical evidence examining vertical integration consistently supports transaction cost explanations involving asset specificity and uncertainty Žsee Shelanski and Klein, 1995, for review.. As Caves and Bradburd Ž1988, p. 268. more strongly assert, only those models that focus on transaction cost explanations, specifically small numbers bargaining and asset specificity, show consistent explanatory power in case studies and statistical evidence. Asset specificity and uncertainty have clearly been the focus of transaction cost empirical research. Somewhat overlooked in transaction cost research, however, has been how the quality of goods or services impacts the firm’s contracting decisions. This is a particularly important issue in service industries where the subsequent effects of poor quality are an important competitive distinction for the firm. When the potential harm from poor supplier performance to the quality of a firm’s product or service is high, and performance is either costly or impossible to measure with the accuracy needed to fully assign costs to the supplier, then firms will resort to vertical integration to insure that an acceptable level of quality is maintained. Transaction costs – whether they stem from asset specificity, uncertainty, or measurement difficulties – are central to understanding vertical integration, but the impact of these factors should not be examined in isolation. As Williamson Ž1985. points out, ‘‘Larger firms will be more integrated into components than will smaller firms, cet. par.’’ Žp. 94., where larger firms are able to realize economies of scale at least equal to those available to contractors who would be servicing a number of smaller firms. The ability of outside contractors to achieve significant economies of scale may depend on the type of transaction being examined. The empirical evidence on the relation between economies of scale and transaction costs has been relatively minimal. One recent study to explicitly examine the interaction of these factors in the context of the make or buy choice ŽLyons, 1995. suggests that economies of scale moderate the influence of specific assets in determining integration. This paper seeks to extend our understanding and build on the existing literature of how transaction costs influence the make or buy choice in at least three major areas. First, we examine the impact not only of asset specificity and uncertainty on vertical integration but of quality as well. Second, this study extends the empirical literature in transaction cost theory by examining vertical integration in a service industry. Empirical work in the area of transaction costs has given little attention to service industries. Yet, scholars who focus on service industries imply that the dynamics of integration may be different in services. The main focus of this difference is that human assets tend to play a more central role in service firms relative to manufacturing ŽZeithaml et al., 1990.. Our sample is drawn from the hospital industry. This is an ideal industry for studying the impact

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of quality on the contracting choice because it brings together both significant quality concerns and measurement difficulty. Quality is particularly critical in its consequences in this industry and a hospital’s reputation for providing high quality services is an important dimension of competition in this market. At the same time, performance measurement is particularly difficult in an industry such as this where the behavior of individual parties and the relationship of these behaviors to outcomes is nearly impossible to observe. Consistent with the standard moral hazard problem, the ability of the firm to monitor an individual contractor in the provision of a hospital service that interacts with many other services that may be provided either by the firm or other contractors can be quite limited and certainly costly. It would be difficult in many cases to know how well a supplier has performed or to even approximate the costs of flawed performance, given the interdependence of hospital services. Thus, hospitals engage in transactions where inferior performance can potentially harm the quality of the care given to patients, and impose other significant costs on the hospital. In addition, measurement solutions and the ability of the hospital to assign the appropriate responsibility to the contractor are likely to be either too costly or grossly inadequate. Finally, we analyze the impact of asset specificity and economies of scale on the decision to integrate using a data set a great deal larger and with significantly more breath than those previously examined. We examine the make or buy decision using data on 13 transactions in 196 hospitals, giving us a final sample of over 2500 observations to begin our analysis. Our analysis of the data provides one of the first comprehensive tests of human asset specificity across many firms and many transactions. The empirical evidence on transaction costs to date has focused either on many transactions within one or two firms Že.g., Masten, 1984; Monteverde and Teece, 1982. or one transaction across many firms Že.g., Anderson, 1985, 1988; Eccles, 1981; Globerman and Schwindt, 1986; Joskow, 1985.. Our sample of transactions is also large relative to other transaction cost studies where the total number of observations has generally been fewer than 200. 1 More recently, Lyons Ž1995. examined the first international sample, surveying 102 UK engineering firms and providing data on 178 components for those firms. We have data on over ten times as many transactions as the largest studies to date. Our larger and more comprehensive sample gives us an opportunity to take a broader view of the contracting choice. We are able to disaggregate the sample, in this case by whether the service is industry-specific or generic in nature, and still examine over 900 transactions for each group. This disaggregation allows us to examine similar transactions, yet still have enough breadth within the groups to provide for variation in the transaction cost dimensions. Examining each individ-

1 For example, Anderson Ž1988. is based on 169 observations of salespersons in the electronic components industry. Masten Ž1984. used 34 observations from an aerospace firm, and Monteverde and Teece Ž1982. analyzed 133 observations from Ford and GM.

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ual transaction separately, provides much less variation in the levels of asset specificity and uncertainty than we have looking across groups of similar transactions. This breadth of transactions is important because it allows us to examine differences in potential impact on quality of care and harm to the patient posed by different types of transactions. Our sample includes six service transactions that are relatively generic in nature Že.g., housekeeping, food services.. Seven of the transactions are specific to the hospital industry Že.g., radiology, pharmacy. and have a direct impact on diagnosis and subsequent treatment of patients. 2 An important context for our study is the prominent role that the health care industry plays in the US economy. In 1992, 14% of GDP was devoted to health care ŽDranove and White, 1994.. The hospital industry accounted for 38% of health care costs in 1990 or 4.6% of GDP ŽDranove and White, 1994.. The recent focus of federal policy makers on reconfiguring the health care system points to the importance of understanding efficiency in health care delivery. The paper proceeds as follows. Section 2 discusses the transaction cost framework as applied to the hospital industry as the theoretical basis for our empirical work. The data collection methods, a discussion of the survey design, and a description of the sample are presented in Section 3. Section 4 presents the results of the econometric analysis of the data and Section 5 discusses those results in detail. The paper concludes with Section 6. 2. Transaction costs and vertical integration The transaction cost approach to vertical integration takes the transaction as its level of analysis. This approach addresses the comparative costs necessary to plan, coordinate, adapt, and monitor a transaction through alternative governance mechanisms. As the framework has been applied in empirical research, three major variables typically determine the relative costs of governing transactions through markets versus hierarchies: asset specificity, uncertainty, and the frequency of transactions 3 between two parties ŽWilliamson, 1975, 1985.. 2.1. Asset specificity A number of scholars have asserted that asset specificity is the most critical issue in assessing whether to bear the costs of internalizing a transaction rather 2 Since we focus on the transaction level, we constrain our choice of service components to include those services that are provided by all general hospitals as designated by the American Hospital Association. This gives us a set of transactions that are common across all of the organizations in the study in order to make transaction level comparisons. 3 Frequency refers to the regularity of the transaction. For the purposes of this particular study, all transactions that were examined occurred with a high level of frequency, and so we did not measure the effects of frequency on the decision to integrate.

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than contracting with an outside supplier ŽJoskow, 1988; Williamson, 1985.. Asset specificity can take several forms: physical asset specificity, human asset specificity, site specificity and dedicated assets. 4 For the purposes of this study, we focus on physical asset specificity and human asset specificity. Physical asset specificity describes the situation where assets are specially designed for use by an individual firm. These assets would have far less value to other firms, even in the same industry context. Human asset specificity refers to the same situation, but in the case of individuals whose firm-specific human capital makes them less valuable to other firms, even in the same industrial setting. The link between physical asset specificity and the contracting decision has received a great deal of attention in the empirical literature. These studies have used various measures to investigate a wide array of physical assets to determine if higher levels of physical asset specificity are related to a greater likelihood of integration. These studies report that measures as diverse as the amount of specialization in a component ŽMasten, 1984., capital intensity ŽMacDonald, 1985; MacMillan et al., 1986., small numbers of suppliers and buyers ŽLevy, 1985; MacDonald, 1985. and research and development expenditures ŽCaves and Bradburd, 1988. are all associated with a greater probability of integration. Following this evidence, we hypothesize that higher levels of physical asset specificity will be positively associated with the decision to integrate a particular hospital transaction. Human asset specificity affects the probability of internalizing a transaction in ways similar in logic to physical asset specificity. As individuals develop firmspecific human capital both the firm and the individual will find the employment relationship more attractive. Thus, human asset specificity leads to greater probability of vertical integration. The empirical evidence is scant, but a few studies have detected a relationship between human asset specificity and vertical integration ŽAnderson and Schmittlein, 1984; John and Weitz, 1988; Masten et al., 1991; Monteverde and Teece, 1982.. We therefore hypothesize that greater human asset specificity will lead to a higher probability that a service will be vertically integrated. 2.2. Uncertainty The transaction cost framework also posits uncertainty as a critical determinant of the costs of transacting in either a market or a firm. Uncertainty refers to the degree that decision makers in firms are able to accurately predict situations that affect the planning and adaptation of a transaction. This can include a variety of factors, such as demand for the product, changes in technology, and ability to effectively monitor performance of employees. Several studies report results that 4

For a more complete description of each of these four types of asset specificity, see Williamson Ž1985..

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support the notion that increases in uncertainty will result in a greater likelihood of vertical integration ŽAnderson, 1985; Anderson and Schmittlein, 1984; Levy, 1985; MacMillan et al., 1986; Masten, 1984; Walker and Weber, 1984, 1987.. Two studies, both of which specifically examined technological uncertainty, yielded contradictory results ŽBalakrishnan and Wernerfelt, 1986; Harrigan, 1985.. We examine technological uncertainty in the hospital industry as our measure of uncertainty. The use of the most current technology is particularly important in the delivery of many hospital services. The availability of state of the art technology can have an important impact on the clinical treatment of patients. Specifically, if a patient’s diagnosis and treatment are adversely affected by the use of out-of-date technologies, then the hospital could be held liable for potential damages. In addition, a hospital’s ability to attract high quality physicians Žalong with their patients. to the hospital is directly related to the technological resources that the hospital supplies. Contracting for services that involve continued and unpredictable investments to maintain the technology can lead to significant contracting difficulties for the hospital. For example, suppose a hospital contracts out a service that has a significant technological element. New developments in technology could render the methods used by the contractor obsolete before the terms of the contract expire. The contractor, however, may be unwilling to incur the additional costs of the new technology before the existing contract expires. If there are adverse consequences to this decision that result in a lawsuit, the hospital may have a difficult time measuring the extent of the contractor’s responsibility and consequently may have to bear, at least partially, the costs of the contractor’s behavior. This is the case because of the fact that the hospital is ultimately responsible for patient outcomes and service contractors bear only a portion of the burden for their behavior if that behavior results in an unfavorable outcome. Thus, if the hospital determines that an additional investment must be made in the service in order to maintain the quality of care and decrease potential liabilities, then at the least the hospital will incur additional bargaining costs to induce the contractor to invest in the technology. 5 Based on this reasoning, we examine the hypothesis that greater uncertainty will lead to a higher probability that a service will be vertically integrated. 6

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In contrast, Balakrishnan and Wernerfelt Ž1986. argue that, faced with high technological uncertainty, firms will avoid the risk of making large investments in soon-to-be obsolete technologies by relying on market provision of those technologies. Though we note this exception, we focus on this measure and its impact on both physical and human assets. 6 Specific assets in conjunction with environmental uncertainty can lead to situations where bounded rationality and opportunism will have an important impact on the contracting choice. Because of bounded rationality firms cannot specify all contingencies in contracts. Thus, incomplete contracts become inevitable in the face of uncertainty. The unspecified contingencies expose firms to the hazards of opportunism by others. Firms respond to these hazards by vertically integrating those transactions characterized by specificity and uncertainty.

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2.3. Economies of scale The influence of the transaction cost variables, asset specificity and uncertainty, is less clear when economies of scale are considered simultaneously. Larger firms Žhospitals. will be better able to realize economies of scale in service production than smaller firms Žassuming that neither sell services to outside customers.. This is because larger firms are more able than smaller firms to realize the economies of scale that are potentially available to outside contractors who serve an entire market. One previous empirical paper has examined the interaction of transaction cost economics and economies of scale, and found that in the case of specialized assets, the effects of economies of scale on the decision to integrate are significantly reduced ŽLyons, 1995.. In his study of 178 inputs of 102 engineering firms in the UK, Lyons Ž1995, p. 438. found that, ‘‘ . . . in the absence of economies of scale, specific assets do not significantly affect organization, and in the presence of specific assets, economies of scale have no influence’’. 2.4. Quality effects Another factor that may affect the make or buy choice is whether quality is an important feature of the delivered product or service. Service firms in general, and hospitals in particular, are extremely sensitive to quality effects. In the hospital industry, quality is a critical issue, and one that is important in distinguishing competitors. If a hospital has a history of providing technologically advanced and high quality medical services, given the relatively low price sensitivity of consumers, that hospital will have a distinct competitive advantage over other hospitals that do not have the same history of providing high quality treatment. Hospitals with a reputation for high quality are able to attract higher quality physicians to their staffs and in doing so, attract more patients and decrease the probability of facing costly litigation. When the importance of quality is combined with measurement problems, then contracting poses special hazards, consistent with the standard moral hazard problem. For example, if an outside supplier of emergency room services ŽER. makes a medical mistake that results in physical damage or death, the cost will not be born solely by the ER supplier. It is often difficult to assess the relative contribution of various parties to the negative outcome. The ER supplier may claim that others Že.g., emergency medical technicians, laboratory support, radiology, etc.. did not perform adequately. Even if the supplier’s blame could be unambiguously established, calculating and making the supplier bear the full costs for the harm suffered by the patient, and subsequently the firm, would likely be impossible. The hospital’s reputation for providing high quality services will also likely suffer even though it may have had no direct role in the mistake. In contrast, poor performance on the part of the laundry contractor is unlikely to have such serious consequences. The importance

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of quality in medical services, for example, makes it more likely that problems in service delivery – whether by in-house providers or outside suppliers – will result in damage to the firm that would be insignificant in less important areas. In addition, costs associated with malpractice claims and subsequent litigation can be quite substantial. Based on this reasoning, we hypothesize that hospitals will be more likely to integrate those services when there is a significant potential to impact quality and cause harm to a patient.

3. Methods 3.1. Sample selection and description The relevant population from which we draw the sample is all general hospitals. The American Hospital Association ŽAHA. Guide defines a general hospital as follows: ‘‘The primary function of the institution is to provide patient services, diagnostic and therapeutic, for a variety of medical conditions. A general hospital shall also provide: diagnostic X-ray services with facilities and staff for a variety of procedures, clinical laboratory service with facilities and staff for a variety of procedures and with anatomical pathology services regularly and conveniently available, and operating room service with facilities and staff ’’ ŽAmerican Hospital Association, 1989, p. A5.. We obtained a complete list of the relevant population from the 1989 AHA Guide. From a random start of this comprehensive list of the population, every seventh hospital was chosen as a sample hospital to obtain a systematic sample of 764 hospitals. Using the Kolmogorov–Smirnov test as a check of proportionality for the sample indicated that it approximated the population closely in regard to organizational control Žproprietary, state and local government or private nonprofit. and size Žmeasured by number of beds.. The survey was sent directly to the hospital administrator listed in the AHA guide. The hospital administrator is in a position analogous to the general manager of an autonomous plant. Through pre-survey interviews with hospital administrators, it was apparent that those occupying this position were most capable of answering the questions we posed. The administrator was generally the only individual in the hospital that had access to the information required to complete the survey. Follow-up post cards were sent to all administrators, and a follow-up survey was sent to non-respondents three weeks after the original mailing. Of the original sample, 204 participants returned the survey, or almost 27% of the sample population. This response rate compares favorably with Lyons Ž1994.

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response rate of 10% for UK engineering firms and also with response rates of 10 to 12% that are generally reported for surveys sent to CEO’s. Of the 204 questionnaires received, 196 Ž25% of the original sample. were useable for data analysis. Again using the Kolmogorov–Smirnov test as a comparison of responding hospitals with the population of all general hospitals showed no significant differences between the sample and the population in the dimensions of organizational control and size. A majority Ž69.9%. of the 196 hospitals are non-profit while 24.9% are public and 5.2% operated for-profit. The largest number Ž41.6%. of hospitals supports between 50 and 199 beds while 29.7% support less than 50 beds and 28.7% support greater than 200 beds. 3.2. Data collection Because it takes the transaction as the unit of analysis, studying transaction costs poses significant challenges, particularly in the collection of data. The measurement of transaction cost dimensions requires specific information about individual transactions within an organization. A survey instrument developed for a particular industry is generally not appropriate for other industries because of language and transaction characteristics specific to that industry. For this study, we develop a new survey instrument by modifying items used in previous studies Že.g., Anderson, 1988; Jacobsen, 1988.. The design of the questions combines information from previous studies of transaction costs, key transaction cost variables Anderson and Weitz Ž1986. suggest, and interviews with hospital administrators. To develop an instrument that is well-adapted to the hospital industry, we first conducted formal interviews with six hospital administrators in addition to more informal discussions with several other administrators. Administrators generally have responsibility for making decisions at the operational level as well as working with Boards of Trustees Žsimilar to Boards of Directors. and other constituencies to plan the strategic direction of the firm. In the interviews with the administrators, it was evident that these individuals had a clear understanding of the nature of individual service delivery throughout the hospital. Based on these interviews, we developed an instrument that requests information for a list of services that all general hospitals provide. The services we examined were: respiratory therapy, laboratory, housekeeping, laundry, computer services, food services, maintenance, physical therapy, landscaping, outpatient services, emergency room services, pharmacy services and radiology. We select these services because they apply to all general hospitals and represent both medical and operational services. The survey solicits information about both the transaction dimensions for the individual services as well as general organizational attributes. Through our interviews, we are able to design a survey to measure the transaction dimensions that utilized language and concepts that would be familiar to those in the industry.

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3.3. Measures Development of the survey questions relied on previous survey-based transaction cost studies, and the key variables identified by Anderson and Weitz Ž1986.. Of the seven key variables that they identify, we select three as important in this industry: company-specific capabilities, economies of scale, and environmental uncertainty. Anderson and Weitz Ž1986. suggest survey questions to measure the key transaction cost variables they propose. A combination of their suggestions and examples of question wording used by Jacobsen Ž1988. in her study of transaction cost relationships between hospitals and physicians provided the basis for our survey questions. 7 3.3.1. Vertical integration The dependent variable of interest is the decision to contract versus integrate a particular hospital service. We code contracted services as zero and integrated services as one. The survey asked the respondent to indicate which type of arrangement they use to provide each service at their hospital. Given the nature of the question, any type of contractual relationship would be viewed as a contracted service, regardless of the terms or duration of that arrangement. 3.3.2. Physical asset specificity Based on the survey by Jacobsen Ž1988., we included one question designed to measure physical asset specificity. Our interest is in the firm-specific nature of investments in equipment and facilities. To measure this, the question asked respondents to describe how much of the equipment in that department is not routinely used in the provision of the service at other hospitals. For example, there may be certain kinds of equipment used in specialized procedures such as surgical equipment for heart transplants. This may be used by a specialized group of surgeons that operate at that particular hospital, but it may be the only hospital in the area that provides this service, and therefore would require this specialized equipment. This question assesses how transferable these assets would be to another hospital. In this case, little specialized equipment would indicate a non-specific asset; while a high proportion of specialized equipment would indicate a specific asset. 3.3.3. Human asset specificity Anderson and Weitz Ž1986. suggest that the length of time it takes to train an individual with previous industry experience in another firm is a major contributor to firm-specific employee investment or specific, as opposed to general, human 7

A copy of the survey questions for the transaction cost variables is attached as Appendix A.

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capital. For example, service personnel in different hospital services become accustomed to the practice patterns of the physicians with whom they work. Physicians tend to have somewhat unique patterns of treatment and methods of diagnosis. The knowledge that service personnel gain from working with one set of physicians may not be transferable to another hospital where physicians potentially have significantly different practice patterns. If training is more firmspecific than industry-specific, then human asset specificity is high. Based on this reasoning, we asked respondents how long it takes an individual with previous experience in the service at another hospital to achieve a satisfactory performance level in the respondents’ hospital ŽFirm-Specific Training Time.. Another aspect of human asset specificity is the coordination required by employees in one service with other hospital employees. If the service requires a great deal of coordination with other services, there is a benefit to the organization of employing an individual who has developed relationships with other staff members. This employee will be more effective than an individual that does not have these same relationships. In addition, an individual that has developed these relationships at one hospital, cannot transfer these relationships to the same services at another hospital, since these relationships are firm-specific. To address this component, the second question asks about the interaction and coordination required by an individual employed in the service with other hospital service personnel ŽCoordination.. 3.3.4. Uncertainty In measuring uncertainty, we focus here on one type of uncertainty, the rate of technological change for each service. Anderson and Weitz Ž1986. suggest that the faster technology changes, then the more uncertain the environment, and thus, the more difficult contracting becomes. 8 In addition, the study of Lyons Ž1994. of the UK engineering industry uses a measure of uncertainty that assesses how frequently modifications need to be made to the specifications of an input. To address the issues of technological uncertainty, the next question on the survey asked how frequently new methods or technologies are introduced for each service. We code all the survey questions, except for the question on technological change, on a five-point likert scale with one being low and five being high. We

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Balakrishnan and Wernerfelt Ž1986. assert that investments in physical assets that change often may be better made by contractors that have the ability to provide economies of scale in the secondary market for these kinds of equipment. We find these arguments much less applicable in the hospital industry for two primary reasons. First, based on our discussion of uncertainty issues, it seems critical for the hospital to have access to current technologies, lowering to a great extent the variation of needs among market participants. Second, although it might be possible to upgrade capital equipment leased from a contractor, the service personnel would often work for the hospital. These individuals require continual updating of their skills in the service operation.

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code the technological change question on a four-point likert scale with one indicating the least change and four indicating the most change. 3.3.5. Economies of scale Generally, industrial organization scholars have hypothesized a positive relationship between firm size and vertical integration, based on economies of scale arguments ŽAnderson and Schmittlein, 1984.. In the laundry area, for example, a large hospital may find it feasible to hire an individual or group of individuals and own equipment to deal with the large volume of laundry in a large hospital. Whereas a small hospital may find it very costly to own all the necessary equipment and personnel to keep up with smaller amounts of laundry, and may choose to hire a contractor that provides linen and laundry services to the hospital as well as for other local businesses. In radiology, we may find a similar situation where large hospitals could own lots of specialized equipment and amortize the costs over many users. For this reason, we include size as a proxy for firm level economies of scale. In the hospital industry, a common measure of size is the number of beds the hospital supports. The natural logarithm of the number of beds is the measure of size that we use in the data analysis. 3.3.6. Harm In the hospital industry, the potential for harm is present in the medical services that the hospital provides that directly impact the clinical quality of the patient’s diagnosis and treatment. In addition, some services are involved in the critical care of patients on a regular basis, while other services participate primarily in the maintenance of patients during their recovery phase. In order to examine the impact of quality on the integration decision, we divide the services into three groups. The first group includes services where decisions are made on a regular basis in situations where the care of the patient is at a critical stage. Service personnel, often doctors, nurses and other skilled technicians, make regular judgments about patient care in situations that are a matter of life and death. These services include: respiratory therapy, radiology and emergency room. These services were determined to have the highest probability of presenting situations where there could be a significant impact on the quality of patient care. For this reason we use a dummy variable, Harm 2, coded 1 for these services and zero otherwise. The next group of services are involved with the patient’s clinical care and treatment at a time when the patient is not in the critical stage, or are services where personnel perform standard procedures directed by individuals in other services, generally doctors. Although these are medical services, the individuals in these services would not ordinarily be making decisions in a situation where the patient’s life would be at stake. These services include: laboratory, physical therapy, outpatient services, and pharmacy services. These services were considered important in terms of impact on quality, but not as potentially harmful as the first group of services. We construct a dummy variable, Harm 1, coded 1 for these

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services, and zero otherwise. The final group of services is not involved in the clinical treatment of patients and in our view would have little potential impact on the quality of patient care. These services are: housekeeping, laundry, food services, computer services, maintenance, and landscaping. 3.3.7. Industry-specific serÕices Based on our earlier arguments for economies of scale, it seems to be more likely that contractors would be an option for services that were more generic in nature. For those services that are industry-specific, there may not be enough clients in a market area to provide the economies of scale necessary for a contractor to be profitable. In this case, the firm may be constrained in their ability to contract out services. For example, while a landscaping contractor can service many types of businesses in an area, a contractor that provides respiratory therapy services would have far fewer firms to service. For this reason, we examined those services that were specific to the medical industry with a dummy variable coded one for medical industry-specific services Žrespiratory therapy, laboratory, computer services, radiology, physical therapy, outpatient services, emergency room services, and pharmacy services. 9 and zero for non-industry-specific services Žhousekeeping, laundry, food services, maintenance, and landscaping.. 3.3.8. Market size Larger markets offer greater opportunities for specialization ŽStigler, 1951.. Where markets have grown and matured to a point where there are economies in firms serving a large number of related customers, then we would expect that, all other things equal, we should observe less vertical integration in larger markets ŽStigler, 1968; Williamson, 1985.. For this reason, we control for market size using a question from the survey that asks the respondent how many other hospitals are also currently servicing their hospital’s market area. 10 While this

9 The medical services we selected for this study were based on two factors. First, the definition of a general hospital and second, discussions with hospital administrators as to the services they provide and the contractual forms generally used in the industry. Medical specialties, such as cardiology and orthopedics, if available, are almost always provided on a contractual basis by physicians operating their own businesses that see their patients both outside as well as within the hospital. In contrast, radiologists and emergency room physicians are much more likely to be employees of the hospital and to interact only with patients that are currently in the hospital for treatment. Services like physical therapy and respiratory therapy are provided by technicians that may work for the hospital or a contractor. 10 We also use a market share variable that asks respondents to identify what market share their hospital currently holds. The use of this control variable did not materially alter the results presented here, and because there was a large number of missing values and the data supplied by the respondents was very subjective, we do not include this variable in our analysis.

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question relies on the judgment of the hospital administrator in the area, providing more specific guidelines may obscure some of the important competitive dimensions for a particular organization. For example, a hospital may provide sophisticated tertiary services that are not provided by any other hospital in the hospital’s immediate geographic vicinity, whereas other services may be provided by many other hospitals within close proximity to the hospital. Our sense from interviewing hospital administrators, is that they have a keen sense of which other hospitals are their major competitors, both from a service delivery standpoint, as well as from the perspective of contracting arrangements with insurance companies in the local area. 3.3.9. Control Õariables There were a number of variables that we determined to have an impact on the contracting behavior of hospitals that we include as control variables. The first we examine is chain membership. These organizations may be very formal relationships, where hospital management is centralized at the chain headquarters and decisions are made over the entire chain. Other chain organizations are relatively informal from a management standpoint, and serve to provide members with support services including management assistance and purchasing services. It was our view that membership in a chain might have an impact on the integration decision based on the use of similar management decision techniques by hospitals in the chain. We control for this by using a dummy variable coded one if the hospital is a member of a chain. The second control variable we examine is the proprietary status of the hospital. The majority of hospitals are either private non-profit or proprietary hospitals. Given the additional incentive of proprietary hospitals to provide a return for shareholders, we control for for-profit hospitals by using a dummy variable coded one if the hospital is proprietary. All of the organizational characteristics discussed above are used for two purposes. One purpose is to control for the impact these individual firm level characteristics may have on the integration decision. The second purpose is to proxy for individual firm-level effects. Given the nature of the data, where we have a number of observations from each hospital, an important consideration is the firm level effects for each organization in the data set. In our subsequent analysis, we examine these effects both by the proxies identified above, and also by estimating random effects models for a panel data set. 11 Both sets of results will be reported and discussed.

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We also examined fixed effects models using individual firm-level dummy variables. Because of the sparseness of the data, and the propensity of a number of firms in our sample to integrate all services, the estimates of the fixed effects models were at times somewhat unstable, but generally provided much the same results as the random effects models reported here.

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4. Results Descriptive statistics for the sample are provided in Tables 1, 2 and 3. Table 1 reports means and standard deviations for the entire sample for the transaction dimensions, and the control variables. For comparison, descriptive statistics are also reported for both groups of services, generic services and industry-specific services. Pairwise t-tests for the difference in means are also reported for each of the transaction dimensions for each group. All of the variables are significantly different between the industry-specific and generic service groups. Table 2 reports the means, standard deviations, and ranges for the dependent and transaction cost variables for each service category. The level of integration of the services ranges from 0.99 for outpatient services to 0.51 for laundry services. As can also be seen from Table 2, the ranges of the responses for the transaction variables are limited. 12 For example, although the scale for training time is from 1 to 5, the range of responses for laboratory services on this dimension is only 1 to 4. Table 3 reports correlations for the variables. It is notable that the correlations between the dependent variable and training time, coordination and technological uncertainty are all positive and significant, while none of the other variables have a significant correlation with the dependent variable. With the exception of the relation between physical asset specificity and coordination, all of the correlations for the transaction cost variables are positive and significant. As could be expected, the correlation between the size of the hospital and the size of the market is positive and significant, as is the correlation between market size and for-profit status. In order to test the hypothesized relations, we analyze the data using a probit model. First, we test the transaction cost economics variables, while controlling for economies of scale, for-profit hospitals, market size, chain affiliation, and the level of harm associated with the service. The first column in Table 4 presents the results of this estimation for the full sample. In contrast to the univariate results reported in Table 3, the results reported in Table 4 indicate that coordination is the only positive and significant transaction cost variable in this analysis. The dummy variables for both harm variables are also significant and positive, indicating that services with a high potential to impact the quality of patient care are more likely to be integrated than services where there is less of a moral hazard problem. A Wald test fails to confirm that these coefficients are significantly different from each other. We include both dummy variables in our specification to show that both types of services, those

12 As noted in Section 3, physical asset specificity, training time and coordination are all coded on a scale of 1 to 5, five being the highest level of specificity. Technological uncertainty is coded on a scale of 1 to 4, four being the highest level of uncertainty.

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Table 1 Means, medians and standard deviations Žin parentheses. of variables Variable

All services

Generic services

Industry-specific services

Vertical integration

0.85 1.00 Ž0.36. 1.89 1.00 Ž1.50. 2.37 2.00 Ž0.79. 3.04 3.00 Ž1.03. 2.13 2.00 Ž0.88. 173.90 83.00 Ž193.63. 0.04 0.00 Ž0.21. 4.48 3.00 Ž6.17. 0.74 1.00 Ž0.44.

0.79 1.00 Ž0.42. 1.80 1.00 Ž1.48. 2.03 2.00 Ž0.74. 2.68 3.00 Ž1.04. 1.47 1.00 Ž0.63.

0.89 1.00 Ž0.32. 1.95 1.00 Ž1.52. 2.58 3.00 Ž0.74. 3.25 3.00 Ž0.96. 2.52 3.00 Ž0.76.

Physical asset specificity

Firm-specific training time

Coordination

Technological uncertainty

Size Žnumber of beds.

For-profit

Market size Žno. of hospitals.

Member of hospital chain

Pairwise t-tests for difference in means Significant p- 0.0001

Significant p- 0.001

Significant p- 0.0001

Significant p- 0.0001

Significant p- 0.0001

involved with critical care as well as with patient management, are more likely to be integrated than non-medical services. 13 Of the control variables, only the market size variable provides any explanatory power and it is marginally significant and negative. This would seem to suggest that the fewer hospitals in an area, the more likely firms are to integrate services. The model itself has significant Ž0.0001. predictive power, with a chi-square of 55.39. The significance of the harm dummy variables suggests that the potential for harm and subsequent impact on quality of care, is an important concern for specific service groups, particularly medical services. Our view is that since medical services have important quality and, potentially, legal ramifications, hospitals may view these transactions differently than transactions where these 13

We also ran this analysis combining both Harm 1 and Harm 2 into one category and the results are materially unchanged from what we currently report.

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Table 2 Means, standard deviations Žin parentheses. and ranges of variables by service categories Services

Vertical integration

Physical asset specificity

Firm-specific training time

Coordination

Technological uncertainty

Respiratory therapy

0.88 Ž0.33. 0–1 0.95 Ž0.21. 0–1 0.86 0.35 0–1 0.51 Ž0.50. 0–1 0.89 Ž0.32. 0–1 0.83 Ž0.37. 0–1 0.97 Ž0.17. 0–1 0.94 Ž0.23. 0–1 0.76 Ž0.43. 0–1 0.69 Ž0.47. 0–1 0.99 Ž0.07. 0–1 0.83

1.87 Ž1.50. 1–5 1.95 Ž1.54. 1–5 1.84 Ž1.54. 1–5 1.68 Ž1.39. 1–5 1.89 Ž1.54. 1–5 2.14 Ž1.47. 1–5 1.98 Ž1.53. 1–5 1.90 Ž1.49. 1–5 1.93 Ž1.51. 1–5 1.70 Ž1.44. 1–5 1.93 Ž1.53. 1–5 1.87

2.35 Ž0.74. 1–5 2.49 Ž0.74. 1–4 1.90 Ž0.66. 1–4 1.844 Ž0.73. 1–5 2.11 Ž0.67. 1–4 2.84 Ž0.76. 1–5 2.59 Ž0.73. 1–4 2.42 Ž0.72. 1–4 2.37 Ž0.68. 1–4 1.82 Ž0.75. 1–5 2.59 Ž0.70. 1–4 2.68

2.99 Ž1.02. 1–5 3.29 Ž1.01. 1–5 2.75 Ž0.95. 1–5 2.47 Ž0.95. 1–5 2.96 Ž0.93. 1–5 3.43 Ž0.99. 1–5 3.35 Ž0.92. 1–5 3.18 Ž0.93. 1–5 3.02 Ž0.90. 1–5 1.92 Ž1.01. 1–5 3.25 Ž0.88. 1–5 3.33

2.17 Ž0.70. 1–4 2.77 Ž0.67. 1–4 1.37 Ž0.54. 1–3 1.22 Ž0.46. 1–3 1.66 Ž0.67. 1–4 2.92 Ž0.81. 1–4 2.77 Ž0.73. 1–4 1.85 Ž0.67. 1–4 2.22 Ž0.70. 1–4 1.17 Ž0.43. 1–3 2.40 Ž0.70. 1–4 2.41

Ž0.38. 0–1 0.88 Ž0.33. 0–1

Ž1.52. 1–5 1.93 Ž1.53. 1–5

Ž0.74. 1–5 2.70 Ž0.75. 1–4

Ž0.92. 1–5 3.38 Ž0.99. 1–5

Ž0.69. 1–4 2.51 Ž0.72. 1–4

Laboratory

Housekeeping

Laundry

Food services

Computer services

Radiology

Maintenance

Physical therapy

Landscaping

Outpatient services

Emergency room services

Pharmacy services

ramifications were less significant. In addition, based on the previous work of Lyons Ž1995., we conjectured that the lack of significance we found for the economies of scale variable, number of beds in this case, is related to the specific

338

Variable

1

2

3

4

5

6

7

8

9

Ž1. Vertical integration

1.00

0.023 Ž0.281. 1.00

0.074 Ž0.000. 0.056 Ž0.008. 1.00

0.108 Ž0.000. 0.014 Ž0.518. 0.293 Ž0.000. 1.00

0.127 Ž0.000. 0.080 Ž0.000. 0.359 Ž0.000. 0.411 Ž0.000.

0.037 Ž0.68. y0.110 Ž0.000. 0.024 Ž0.255. 0.164 Ž0.000.

y0.030 Ž0.134. 0.030 Ž0.158. y0.033 Ž0.111. y0.026 Ž0.203.

y0.011 Ž0.579. y0.056 Ž0.008. 0.098 Ž0.000. 0.164 Ž0.000.

0.000 Ž0.999. y0.120 Ž0.000. y0.088 Ž0.000. y0.043 Ž0.038.

1.00

y0.007 Ž0.719. 1.00

y0.044 Ž0.034. y0.030 Ž0.138. 1.00

y0.041 Ž0.053. 0.473 Ž0.000. 0.057 Ž0.005.

0.040 Ž0.052. y0.126 Ž0.000. y0.156 Ž0.000.

1.00

y0.106 Ž0.000. 1.00

Ž2. Physical asset specificity Ž3. Firm-specific training time Ž4. Coordination

Ž5. Technological uncertainty Ž6. Size Žnumber of beds. Ž7. For-profit

Ž8. Market size Žno. of hospitals. Ž9. Member of hospital chain

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Table 3 Correlations of variables and significance levels Žin parentheses.

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Table 4 Probit estimates of vertical integration of hospital services Variable Physical asset specificity Firm-specific training time Coordination Technological uncertainty Size Žlog of beds. For-profit hospital Market size Žno. of hospitals. Member of hospital chain Harm 1: non-critical medical dummy Harm 2: critical medical dummy Constant

N Chi-square Log-likelihood

Full sample 0.010 Ž0.023. 0.068 Ž0.050. 0.088 ) Ž0.038. 0.079 Ž0.050. 0.004 Ž0.039. y0.051 Ž0.164. y0.012q Ž0.007. y0.068 Ž0.086. 0.244 ) ) Ž0.089. 0.282 ) ) Ž0.098. 0.455 ) Ž0.228. 2079 a 55.39 ) ) ) y804.99

Generic services 0.054 Ž0.039. y0.032 Ž0.080. 0.150 ) ) Ž0.057. 0.011 Ž0.099. y0.261) ) ) Ž0.059. 0.001 Ž0.255. y0.008 Ž0.010. y0.157 Ž0.133.

1.825 ) ) ) Ž0.358. 772 39.36 ) ) ) y356.15

Industry-specific services y0.030 Ž0.031. 0.175 ) ) Ž0.068. 0.017 Ž0.054. 0.124q Ž0.069. 0.240 ) ) ) Ž0.057. y0.148 Ž0.220. y0.016q Ž0.010. y0.011 Ž0.117. 0.398 ) ) Ž0.143. 0.433 ) ) Ž0.149. y0.083 ) Ž0.376. 1307 40.77 ) ) ) y422.45

Standard errors in parentheses: q p- 0.10; ) p- 0.05; ) ) p- 0.01; ) ) ) p- 0.001. a The size of the sample here decreases from the original 2561 for missing values in a number of both the explanatory as well as organizational level variables.

assets involved in certain types of transactions. In order to test this proposition and more closely examine the two different types of services, we divide the transactions on the basis of industry specificity. As Lyons Ž1995, p. 434. points out, ‘‘ . . . the market benefit of demand aggregation may be greatest when there is industry specificity, but no firm-specificity in the assets in question’’. Recall that we classify the transactions according to whether they are specific to the hospital industry. Industry-specific services are: respiratory therapy, laboratory, physical therapy, emergency room services, outpatient services, computer services, pharmacy services, and radiology. The remaining services are not specific to the hospital industry. These transactions could potentially be firm-specific, but would not require the same type of industry-specific assets as the medical services. These transactions are: housekeeping, laundry, food services, maintenance, and landscaping.

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We estimate probit models for each of these service groups separately, using the same set of variables as the previous specification. The second and third columns in Table 4 report the results of these analyses, and provide a very different picture from what the first model presents. For the generic services, indicated in the second column, we find significant, positive associations between the propensity to integrate and coordination. However, we find a significant negative relation between integration and size. For the industry-specific services, we find significantly positive relations for training time and for technological uncertainty. The size relation here is also positive and significant. Both models provide significant Ž0.0001. predictive power. We then analyze the data using a random effects probit model to correct for the fact that we have panel level data. These models are presented in Table 5. Overall, the results of the analysis are very similar to what was reported in Table 4. Column 1 presents the results for the full sample, where coordination is a

Table 5 Probit estimates of vertical integration of hospital services: Random effects models Variable

Full sample

0.142 ) Ž0.064. 0.029 Ž0.093. 0.235 ) ) Ž0.079. 0.101 Ž0.133. y0.333 ) ) Ž0.116. y0.054 Ž0.636. y0.008 Ž0.015. y0.157 Ž0.238.

2079 44.08 ) ) ) y782.94

772 39.48 ) ) ) y356.40

Firm-specific training time Coordination Technological uncertainty Size Žlog of beds. For-profit hospital Market size Žno. of hospitals. Member of hospital chain Harm 1: non-critical medical dummy Harm 2: critical medical dummy Constant

Standard errors in parentheses:

q

Industry-specific services

0.042 Ž0.035. 0.073 Ž0.053. 0.133 ) ) Ž0.050. 0.104 Ž0.066. 0.001 Ž0.071. y0.046 Ž0.248. y0.012 Ž0.011. y0.055 Ž0.144. 0.215 ) Ž0.087. 0.260 ) Ž0.111. 0.331 Ž0.380.

Physical asset specificity

N Chi-square Log-likelihood

Generic services

p- 0.10;

)

p- 0.05;

))

1.797 ) ) Ž0.579.

p- 0.01;

)))

y0.030 Ž0.037. 0.178 ) Ž0.083. 0.016 Ž0.067. 0.130q Ž0.080. 0.254 ) ) Ž0.078. y0.116 Ž0.217. y0.017 Ž0.017. y0.008 Ž0.150. 0.413 ) ) Ž0.150. 0.447 ) ) Ž0.159. y0.856 Ž0.479. 1307 4.94 ) y422.45

p- 0.001.

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significant predictor of the integration decision, as are the two moral hazard dummies. In column 2, for generic services, physical asset specificity is positive and significant, as is coordination. For the industry-specific services in column three, training time and technological uncertainty continue to have a positive and significant impact on the integration decision. We also find the same relations between the control variables, in particular size and the moral hazard dummy variables. 14 All models continue to have significant predictive power. The predictive power of both the transaction cost variables and the size variable raise some interesting empirical issues. First, clearly there are significant differences in the importance of the different transaction cost measures based on the type of service, industry-specific or generic. It is also clear that the impact of size is completely masked when all the transactions are grouped together. It is only at this disaggregated level that we find that size has implications for the decision to integrate or contract these different types of services.

5. Discussion Our results provide evidence that there are important differences between industry-specific services and generic services for the transaction dimensions proposed in this study. For industry-specific services, technological uncertainty and firm-specific training time are the transaction dimensions that have a significant impact on the contracting decision. Here the role of quality provides a potential explanation. Given that the potential to impact the quality of patient care is especially high with industry-specific services, hospitals may not be particularly sensitive to differences in physical asset specificity between transactions. Even for those services that are low in physical asset specificity, hospitals are strongly inclined to provide them in-house to avoid adverse quality effects that might occur if a contractor were to make an error. Human assets and uncertainty, as pointed out previously, pose potential quality problems if a hospital uses a contractor that locks a hospital into an obsolete technology, thus leaving it at a competitive disadvantage. The importance of physical asset specificity and coordination for generic services seems to fit with the conventional notions of transaction costs. This is particularly true for the coordination variable. Housekeeping personnel may interact on a regular basis with individuals from many of the hospital’s other services, while the laboratory personnel may interact primarily with individuals in their own service operation. For training time, as can be seen from Tables 1 and 2, 14

As in the models reported in Table 4, a Wald test fails to confirm that the coefficients on Harm 1 and Harm 2 are significantly different from each other.

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the generic services have a significantly lower mean. The same is true for technological uncertainty where services like landscaping, laundry and housekeeping have a range of 1 to 3 rather than 1 to 4. The relation of the transaction cost measures and size to the contracting choice for the hospitals in this study provides an interesting look at the potential trade-offs between asset specificity, uncertainty and economies of scale. The relation between hospital size, as a proxy for firm level economies of scale, and the decision to integrate clearly shows that the type of transaction plays a very important role in the trade-off between firm level economies of scale, industry level economies of scale, and the decision to integrate. The positive effect of firm size on the decision to integrate for medical services is consistent with the view that larger firms will find it economically efficient to integrate. Small firms, on the other hand, will chose to contract in order to take advantage of the contractors’ economies of scale that arise from servicing a number of firms similarly. It is also possible that larger hospitals have a larger stake in quality than smaller hospitals, because of large investments in technology or increased competition from other hospitals in their markets, and thus are more sensitive to quality issues. This would also explain why larger hospitals are more likely to integrate industry-specific services but not generic services where the impact of poor contractor performance is generally much lower. Clearly, hospitals consider a number of factors when making a contracting decision – not just asset specificity and uncertainty. The evidence in this study is consistent with the view that one of these factors is quality of the service provided. The finding in this study that large firms internalize medical services is likely to be more than just an economies of scale issue. Given the large quality implications for a hospital of poor performance of a medical service, the hospital has a strong incentive to closely monitor service providers. They could also have a significant absolute advantage and expertise in providing these services. The question this analysis raises is, are there economies of scale in monitoring these services as well as in the actual service ‘production’? This would reinforce the scale economies for the firm, by making it even more efficient for large hospitals to bring these services in house. In contrast, smaller hospitals may be more likely to achieve an advantage by contracting out these services to a provider who can exploit economies of scale. A potential explanation for why we see large firms contracting out non-medical services and smaller firms integrating, may have to do with the economies of scale in the market, rather than for the firm. Small hospitals are generally located in more rural areas, where there may be only one hospital for hundreds of miles. Large hospitals, however, are located in urban areas where there may be a number of other large hospitals located in close proximity. In the case of small hospitals, it may be that the geographical isolation means that a contractor would not be in a position to take advantage of the economies of servicing a number of similar firms. The converse is true for large hospitals, where contractors may enjoy

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significant economies by servicing a number of large hospitals in close proximity to each other. 6. Conclusion The role of quality and the interaction of transaction costs and economies of scale have received very little empirical attention in relation to the make or buy decision. The results reported here provide an important first test of these relationships in the context of a number of transactions critical to the firm’s operations. The evidence presented in this paper points to the fact that it is important to examine not only transaction costs and economies of scale, but to examine these factors in a context of similar types of transactions. We have also shown the value of being able to examine a broader set of transactions across a number of firms. Our comprehensive data set for the hospital industry allows us to group transactions by similar dimensions Žin this case industry-specific and generic services.; provides enough observations to examine each group separately Ž2500 in this case.; and measures physical asset specificity, human asset specificity, uncertainty and economies of scale effects. The effect of quality of the service provider on the make or buy decision is an important distinction in our data. Quality of the product or service is an important competitive advantage in many industries, but particularly for service firms. Hospitals must be in a position to closely monitor the service quality of the medical services within the hospital. While quality may also be important in non-medical services, the ability to monitor the quality of those services may not be a source of competitive advantage for the hospital, but the economies of monitoring may lie instead with a service contractor. There are a number of important limitations to this particular study that suggest the need for further empirical work. Generalizability is always an issue when using a one-industry design. This is an industry where both for-profit and non-profit firms operate in a similar competitive environment, with government being a major player in both delivery and payment for services. The impact of regulation on this industry may affect the behavior of these firms differently than firms in a more traditional competitive environment. This is also an industry in transition, where it may be difficult to provide broad generalizations through a long time period. Clearly, empirical work that employs more direct measures of quality and examines other industries is warranted. Acknowledgements The authors would like to thank Jeffrey Coles, Ken Lehn, Abagail McWilliams, Donald Siegel, Janet Smith, Mike Sykuta, an anonymous referee, and seminar participants at the Katz School of Business, University of Pittsburgh, for helpful comments on this work.

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Appendix A. Selected questionnaire items Ž1. For each of the following services, please circle the category that best describes whether the service is contracted, performed in-house, or not performed at all. If some combination is used, please choose the category that indicates how the major portion of the service is delivered. ŽDependent variable.. CONTRACTED

IN-HOUSE

NOT PERFORMED

Ž2. For each of the following services, please circle the category that best describes how much of the equipment in that department is specially designed or unique so that it is not routinely used in the provision of this service at another hospital. ŽPhysical asset specificity.. NONE

SOME

HALF

MOST

ALL

Ž3. Please circle the category that best describes how long it would take a new person with previous outside experience to achieve a satisfactory performance level delivering the service in your hospital. ŽFirm-specific training time.. VERY SHORT

SHORT

MODERATE

LONG

VERY LONG

Ž4. Please circle the category that best describes how much coordination is needed between individuals performing this service and hospital administrators andror personnel in other departments in this hospital. ŽCoordination.. VERY LITTLE

LITTLE

MODERATE

HIGH

VERY HIGH

Ž5. For each of the following services, please circle the category that best describes how frequently new methods or technologies are introduced. ŽTechnological uncertainty.. INFREQUENTLY MODERATE FREQUENTLY VERY FREQUENTLY

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