Department of Economics Working Paper Series. Prices and Quantities in Health Care Antitrust Damages

Transcription

1 Department of Economics Working Paper Series Prices and Quantities in Health Care Antitrust Damages by R. Forrest McCluer Greylock McKinnon Associates Martha A. Starr American University No January Copyright 2014 by R. Forrest McCluer and Martha A. Starr. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

2 2 Prices and Quantities in Health Care Antitrust Damages R. Forrest McCluer* Greylock Mckinnon Associates Martha A. Starr American University January 2014 Abstract Antitrust analysis conventionally assumes that illegal agreements among competitors raise prices and lower quantities, relative to lawful competition. However, markets for healthcare services have tendencies towards overprovision, which may increase when competition declines. This paper examines this possibility using data from a well-known antitrust case in Wisconsin. We find that, in parts of the state where physician groups illegally divided up markets, costs of physician services rose by about 10% more than they did elsewhere, with about half of this increase due to increased services. This suggests that higher quantities can contribute to healthcare antitrust damages, along with higher prices. Keywords: Antitrust, healthcare, physician services, anticompetitive conduct, damages. JEL Codes: I11, L4, K21, C43 * Please address correspondence to: R. Forrest McCluer, Greylock McKinnon Associates, One Memorial Drive, Suite 1410, Cambridge, MA 02142, fmccluer@gma-us.com, tel: (703) This paper was presented at the 3 rd Biennial Conference of the American Society of Health Economists, at Cornell University, June 20-23, We are grateful to Ana Aizcorbe, Cory Capps, Michael Chernew, Randy Ellis, Ted Frech, Thomas McGuire, Jim Neiberding, Eileen Reed, George Schink, Robert Tollison, and seminar participants at the U.S. Bureau of Economic Analysis for valuable comments on earlier versions of this work. McCluer worked in support of plaintiffs in the Marshfield Clinic cases. The views expressed in this paper are those of the authors and cannot be attributed to Greylock McKinnon Associates or any other employee or affiliate thereof. Remaining errors are the responsibility of the authors.

3 I. Introduction A core concern in antitrust analysis is whether an alleged reduction in competition in a given market has increased prices and reduced quantities supplied, causing allocative inefficiencies and a decline in consumer welfare. In their seminal framework linking economic reasoning and antitrust law, Landes and Posner (1981) focused attention on loss of output as the primary cost of the exercise of monopoly power. Easterbrook (1984) expresses a similar view that, if arrangements are anticompetitive, the output of those engaging in them must fall. Going beyond this view, Bork (1978) argued that, because consumer welfare is the proper focus of antitrust analysis, one needs to examine the specifics of a case to determine whether consumers have been harmed by a given set of anti-competitive practices, without assuming that a reduction in quantity has to be involved. 1 By this reasoning, practices that do not reduce or possibly even increase output could be the subject of antitrust scrutiny. For example, vertical price restraints that increase output above the levels that would have been demanded in the absence of restraints can be welfare reducing (Comanor 1985, Blair and Fesmire 1994, Rey and Tiore 2003). 2 Similarly, agreements among producers to restrict or divide markets are treated as illegal per se, regardless of how they affect quantity or price, because they inherently reduce the range of choices that consumers face (ABA 1996:181). Nonetheless, the idea that quantity reductions will be found in cases where firms are engaged in illegal competitive practices remains an expectation, as indicated in the frequently-cited ruling: Unless a contract reduces output in some market, to the detriment of consumers, there is no antitrust problem. 3 Issues of quantity can be especially problematic in antitrust cases related to markets for healthcare services. Consumers receive utility not from healthcare services per se, but rather from the benefits these services provide in terms of maintaining or improving their health (Grossman 2000). As they lack specialized medical knowledge, they need to rely on physicians and other trained professionals to determine the levels and mixes of services that best help to advance their health goals. Yet incentives that physicians face may often cause them to recommend more services or different mixes of services than would be consistent with optimization of social welfare. For one, because patients with health insurance pay only a fraction of the costs of services they receive, physicians acting as their agents may provide more referrals or recommend more tests or treatments that would be warranted if patients faced the full costs of the services they received (Feldstein 1973, Pauly 1986, McGuire 2000). For another, traditional fee-for-service medicine has tended to reinforce tendencies towards overprovision, 1 As Bork (1978: 34) has stated, [t]he goal of the law was clearly defined as the maximization of consumer welfare, and a dynamic principle was built into the rule of reason so that the interpretation of the law could change and adjust in its pursuit of that goal as economic understanding advanced. Others have been more explicit in identifying pure consumer welfare as the basis for antitrust analysis (e.g. Salop 2005). 2 See Gaynor and Vogt (2000) for discussion of vertical restraints in health care. 3 Chicago Professional Sports Limited Partnership v. National Basketball Association (Chicago Prof. Sports Ltd. II), 95 F.3d 593, 597 (7th Cir.1996)).

4 4 as it rewards physicians for providing higher levels of services, rather than for efficiently using resources to maintain and improve patients health (Robinson 2001; Emanuel and Fuchs 2008). From the point of view of antitrust policy, the issue of interest is whether the extent of overprovision in a given healthcare market is exacerbated or attenuated by changes in competition. Economic models allowing for possibilities of overprovision provide ambiguous predictions in this respect. In a model in which patients prefer to receive referrals to specialists, Iverson and Ma (2011) find that reductions in competition in markets for physician services will tend to lower levels of service provision because, if physicians do not have to compete as rigorously to attract and retain patients, they will not cater as much to patients requests for referrals. In contrast, in Allard, Léger and Rochaix s (2009) model in which patients face costs of switching from one physician to another, a decline in competition tends to increase overprovision because under less competitive conditions, physicians have to worry less about losing patients if they provide more services than patients would like. More generally, Ma and McGuire (2002) call attention to the role of provider networks in controlling utilization of healthcare services. In preferred-provider arrangements, health plans build networks of providers who agree to accept discounted payments and controls over utilization (e.g. utilization reviews, pre-admission authorizations, prior approvals, second opinions for costly procedures), in exchange for higher patient volumes from the plan s enrollees. When health plans have strong bargaining power relative to provider groups, they have good ability to insist on adherence to utilization targets, as they can credibly threaten to exclude high-utilization providers from their networks. But when plans have weak bargaining power relative to provider groups (as when the pools of patients they control are small, or there are few good-quality provider groups), they may not be able to credibly threaten to exclude higher-utilization providers from their networks. 4 This model too predicts that overprovision will tend to rise when competition among providers declines, ceteris paribus, by eroding health plans bargaining power relative to that of providers. But overall, the ambiguous predictions from theoretical models as to how changes in competition should be expected to affect levels of services provided in given healthcare markets suggest a need for further theoretical work in this area, as well as empirical investigations of specific cases. In view of this gap in understanding, this paper uses data from a well-known antitrust case from central Wisconsin in which physician groups were known to have illegally divided up markets -- and asks what happened to prices and quantities of physician services in that part of the state as the illegal agreements phased in. The reduction in competition would be expected to increase prices of physician services in the affected areas, relative to comparable areas that were unaffected by them; clearly this represents a central part of the damages experienced by healthcare consumers and third-party payers. But additionally, the plaintiffs in the case alleged that physicians in the area responded to the decline in 4 This parallels the bargaining framework used by the Federal Trade Commission to analyze hospital mergers; see Town and Vistnes (2001) and Capps, Dranove and Satterthwaite (2003). See also Ho (2009) on insurer-provider networks.

5 5 competition in part by increasing the quantities of services they provided to their patients, so that higher output as well as higher prices contributed to damages. This paper reexamines the question of whether output for physician services increased in central Wisconsin when these illegal market-allocation agreements phased in, using data from 40 million health insurance claims in the state in A problem that arises in trying to quantify output of physicians services concerns its heterogeneity, as services range from annual well-child visits to openheart surgery. The paper develops a method for disaggregating charges for physician services into quantity and price components, using a measure of quantity that values services at their average prices during the years covered by the analysis. This method bears some similarities to other work on indexes for prices and quantities in health care (e.g. Berndt et al., 2001; Aizcorbe and Nestoriak 2011), but whereas they are open-ended measures intended to monitor price changes on an ongoing basis, the method here focuses on quantifying effects of illegal conduct on prices and quantities in a given time and place. The next section of the paper describes the case analyzed in the paper, in which Marshfield Clinic, a large multi-specialty group practice known for being a high-quality provider in Wisconsin, played a central role. The third section presents the data to be used and explains the method for disaggregating charge data into price and quantity components. It also describes the difference-in-difference regression models used to compare changes in output and prices in the areas affected by the illegal marketallocation agreements, to those in otherwise similar areas that were not. The fourth section presents empirical results, and a final section concludes. In brief, we find that an important share of the extra increases in costs faced by patients in the part of the state where illegal arrangements were in effect, relative to increases faced by patients elsewhere, were associated with increased provision of services. Estimates from our preferred specification suggest that average annual patient costs rose by about 10% more in the part of the state in which anticompetitive agreements were in effect than they did elsewhere in the state, ceteris paribus, with an increase in the quantity of services provided accounting for about one-half of the extra increase. This underlines the need to consider whether higher quantities, as well as higher prices, may contribute to healthcare antitrust damages. II. The Marshfield Clinic class action suit In the 1990s, a series of lawsuits were brought against the Marshfield Clinic of central Wisconsin, one of the largest private, multi-specialty group practices in the United States. At that time, the clinic employed physicians who practiced at the main clinic in Marshfield and satellite clinics in the surrounding areas. Most of Marshfield Clinics physicians served on the medical staff at St. Joseph's Hospital, a 524-bed tertiary-care teaching institution adjacent to the main clinic. In addition, the Clinic owned and operated its own managed-care organization, Security Health Plan. It also had a

6 6 varied array of business arrangements with other physicians and healthcare providers in the area, aiming to create a regionally integrated healthcare system known for its quality of care. 5 Table 1 provides a brief chronology of the cases against Marshfield Clinic, as background to the issues discussed in the current paper. The initial suit was brought by Blue Cross-Blue Shield of Wisconsin and its HMO, Compcare, charging that Marshfield Clinic and its HMO, Security Health Plan of Wisconsin, were monopolizing physician services, fixing prices, and illegally dividing markets with competitors. At that time, the case raised thorny new legal and conceptual issues related to correct definitions of healthcare markets and valid methods of distinguishing between legal vs. illegal factors contributing to higher healthcare costs. 6 While the jury was persuaded by the charges against Marshfield, the appeals-court judge, distinguished legal thinker Richard Posner, took issue with much of the analysis presented in the first case, overturning most of the initial findings and remanding the case to district court, with instructions to plaintiffs to limit estimates of damages to those associated with illegal division of markets only. Damage estimates presented in the remand phase were in turn rejected by the district-court judge, among other things for failing to distinguish between legal and illegal factors contributing to Marshfield Clinic s higher patient costs. Summary judgment was issued in Marshfield s favor; BCBS appealed unsuccessfully. Subsequent class-action litigation (referred to as Rozema after the named plaintiffs) charged Marshfield Clinic and other healthcare providers with entering into business arrangements that illegally restricted trade and increased costs of physician services for people who lived in an eight-county area of influence (AOI). 7 Defendants in the case in addition to Marshfield Clinic were Security Health Plan, the Marshfield-owned HMO; North Central Health Protection Plan, an HMO based in Wassau; and Rhinelander Medical Center, based in Rhinelander. 8 Figure 1 shows the AOI and the defendants main locations. As evidence in the case established, agreements among these providers aimed to reduce competition between them via such routes as not opening offices in each others territories, not actively marketing in each others areas, and not building specialty practices in places where other providers had already established them. 9 Some of these agreements had started as early as 1978, but evidence presented in the case established that they had expanded in breadth and scope after The Clinic had has continued to grow since then, presently employing some 776 physicians and 6,600 support personnel in 54 locations (Marshfield Clinic 2012). 6 For further discussion of the Marshfield case and the issues it raised, see Troupis (1995), Sage (1997), Haas-Wilson and Gaynor (1998), Greenberg (1998), Coombs (2005), and McCluer and Starr (2013). 7 Counties in the AOI included Clark, Price, Lincoln, Oneida, Marathon, Taylor, Portage, and Wood. Formally, the class constituted people living in the AOI who had acquired physician services in the AOI after July 24, 1992 (as entered in a ruling after expert reports had been submitted). 8 The case originally included another defendant, Rice Clinic located in Stevens Point (Portage County), but the judge ruled there was insufficient evidence of its involvement in illegal agreements. 9 As a Marshfield official wrote of discussions held with Rhinelander in 1991, We do not want to see ourselves knocking heads for the same services for the same patient population (quoted in Judge Crabb, Order and Opinion, Oct. 2, 1997).

7 7 Experts for the plaintiffs argued that these agreements not only enabled physicians to charge relatively high prices for their services, but also to provide higher quantities of services than they would have provided under legal competitive conditions. This possibility had come up in the earlier Marshfield case, but was dismissed by Judge Posner as a strange inversion of the usual logic of cartelization, which is that cartelists drive up the market price by (or with the effect of) restricting their output. 10 In the class-action case, however, the plaintiffs experts developed the argument more fully, both by referring to scholarly research in health economics and by presenting estimates of damages that distinguished between those associated with higher prices and those associated with higher levels of services. 11 While the judge called the inclusion of increased quantity in damages a drastic departure from antitrust economics and unsupported by case law, she did not think it could be dismissed on this grounds alone, and on the contrary viewed the evidence as sufficient for a reasonable jury to conclude that plaintiffs paid more for physician services because of defendants' unlawful conduct (Judge Crabb, Order and Opinion, Oct. 2, 1997). Thus, she declined to grant summary judgment in the case, and it was subsequently settled. 12 IV. Data and methodology The data used for the analysis come from 40 million insurance claims for professional services submitted to Blue Cross-Blue Shield (BCBS) by all subscribers residing in the state of Wisconsin in 1988 to This is the same data as was used for the Rozema class action suit. During these years, BCBS was the largest provider of group health insurance in the state of Wisconsin, having some 10-13% of the statewide market. 14 The claims data report basic information on the patient (age, gender, relationship to the subscriber), the provider, and codes indicating the specific services the patient received. Some 400,000 unique medical procedures associated with physician services are found in the data, where procedures are F.3d 588 (7 th Cir. 1998). Posner conceded that McGuire s argument was a possibility, but thought there [was] no evidence of it. See McCluer and Starr (2013) for discussion. 11 A first expert, H. E. Frech III, argued that moral-hazard problems in healthcare can lead to overprovision of services, with anti-competitive conduct tending to increase utilization. As Judge Crabb summarized his opinion, Dr. Frech states that health care markets are subject to economic forces different from other industries. It is his opinion that most Americans consume a higher-than-optimal level of health care because of the moral hazard created by health insurance. When market competition and managed care are operating properly, they reduce the utilization of physician services. According to Frech, anticompetitive conduct leads to higher utilization (977 F. Supp (W.D. Wis. 1997)). 12 See Associated Press (1997). In the Rozema case, Judge Crabb ordered that Rice Clinic be dropped as a co-conspirator and that patients residing in Portage County be dropped from the class (Judge Crabb, Order and Opinion, October 2, 1997); this ruling came after the econometric analysis was submitted. Damages were computed as 6.9% times the total health care expenditures of residents of the AOI. The case settled for a portion of the estimated damages (Coombs 2005: 188). 13 Cases in which out-of-state residents travelled to Wisconsin to receive medical care are excluded from the analysis to avoid issues of sample-selection bias. 14 Figures from Table E, Office of the Commissioner of Insurance, State of Wisconsin ( ).

8 8 defined as unique combinations of the characteristics of the service: the Current Procedural Terminology (CPT), the standard 5-digit code used to classify physician services for billing purposes; three modifiers of the CPT (type of service, place of service, and number of units of service); the type of provider (medical doctor or osteopath); and the provider s specialty (e.g. general practitioner, radiologist, or thoracic surgeon). The prices in the analysis are the charges submitted to BCBS, not payments made by BCBS; this ensures that measured differences in prices across areas reflect differences in providers charges, rather than features of insurance coverage (e.g. deductibles, copayments, negotiated prices). We use the claims data to compute total real annual costs per patient, then use an index approach to decompose variations in total costs into price and quantity components. The first step is to compute total annual costs of physician services for individual i in year t, : (3) where is the price charged to individual i for service j obtained from provider k in year t, and is the related quantity. Aggregating all charges over each year for individual patients yields 2.26 million individual-year observations for individuals under age (We omit 41,504 records for individuals aged 65 and over from the analysis, given that the vast majority of people in this age group have their primary health insurance coverage through Medicare). While some individuals in the data have records for multiple years, complexities of matching the records imply it is difficult to structure the individual-year records into a complete (imbalanced) panel data set. 16 As a result, we opt to analyze the data as a pooled cross-section sample instead. To take into account general inflation in healthcare costs over the period, all prices are deflated by the Bureau of Labor Statistics consumer price index (CPI) for all medical services (1995=100). Next we take averages of prices for each service j across all individuals i, all providers k, and all years: 15 Note that some individuals in the data were covered under a BCBS plan for only part of the year. We have not tried to convert partial-year records to full-year equivalents, as it is difficult to identify such cases with certainty. 16 For example, some individuals submit claims in early years of the period analyzed and again in later years, but we cannot tell whether they incurred no costs in the intervening years or whether they were not covered by a BCBS plan.

9 9 (4) where we refer to as the index price for service j. Then specific prices paid for a service can be reexpressed as deviations from its index price, permitting us to re-write (3) as: (5) where the term is the deviation from the index price associated with individual i s purchase of service j from provider k in year t. Re-arranging and letting be the total quantity of service j received by individual i in year t (i.e. summing over providers), the individual s total costs of physician services can be decomposed as follows: (6) = (7) The first term, is a constant-price index of the quantity of services received by individual i in year t, where outputs are valued at their index prices. This represents the primary measure of quantity used in our analysis. In effect, holds prices constant at average levels prevailing in the period under investigation, so that increases in over time necessarily come from changes in quantities of services received and/or shifts in the composition of services towards those with higher average prices. 17 The second term,, is a measure of price deviations faced by individual i; it is a weighted average of the differences between the price the individual was charged for a given service at time t and the index price for that service, with weights given by the quantity of the service 17 In this sense, the quantity measure differs from a standard constant-price measure of output, which values quantities at prices from a given base year.

10 10 received by the individual in year t. Thus, can rise over time either because price deviations of the services acquired by individual i have increased and/or because the composition of services has shifted to services with higher deviations. For damage estimation, the key question is whether differential increases in quantities of services contributed to differential increases in costs among patients with given characteristics residing in the AOI, compared to counterparts who lived elsewhere. Table 2 and Figure 2 show averages for these variables for individuals residing in and out of the eightcounty AOI. In , average total annual costs were no higher in the AOI than they were in the rest of the state; thereafter, however, they exceeded them by $ (roughly 6.5 to 11.5%). This is consistent with information presented in the class-action case that the market-allocation agreements in which Marshfield Clinic was involved increased in breadth and scope after In the years when average total annual costs were higher in the AOI than in the rest of the state, the higher quantity of services tended to be more important in contributing to the higher costs than the price deviation, although with some variation in the relative importance of the two factors from year to year. Taking the period as a whole, average annual costs were $84 higher in the AOI than they were elsewhere, where $68 of the difference (or 80.5%) came from the higher quantity index. Of course, there may be socio-demographic differences between the AOI and the rest of the state that contribute to the relatively high quantities of physician services received in the AOI. Notably, because the areas served by Marshfield Clinic tend to be relatively rural, the baseline health of the population may tend to be lower than it is elsewhere due to lower income levels, lower levels of education, different occupational profiles, etc. Similarly, levels of competition among providers tend to be lower in rural areas, so it is possible that the higher price deviations registered in the AOI reflect the naturally higher pricing power of rural healthcare providers, rather than effects of illegal divisions of markets. Thus, we use a regression framework to account for legitimate factors explaining variations in total annual costs, the quantity index, and price deviations across areas. The standard benchmark method of estimating damages estimates a regression of the form: = + + α + (8) where is a vector of factors expected to influence the individual s healthcare costs (e.g. age, gender, type of health insurance, etc.), is a vector of year dummies intended to capture broadbased changes in real healthcare spending, and is a dummy variable equal to 1 if the individual

11 11 lives in the AOI. Then the estimated coefficient α indicates whether total annual costs per patient are higher in the AOI than they are elsewhere, ceteris paribus. To gauge the relative contributions of higher quantities and higher prices to higher charges in the AOI, we can run regressions of the form: = (9) = (10) However, if, and contain unmeasured but lawful sources of variation in costs, quantities and prices across areas and these correlate with, then regression estimates of, and will give biased representations of effects of illegal conduct in the AOI. A common concern in this respect is that the quality of care may vary systematically between physicians in one area and the other, and/or that patients with given observed characteristics may differ in unobserved ways that make their health conditions more complex than those of patients elsewhere, so that differences in costs, quantities and prices between the AOI and ROS reflect hedonic differences in the services received. 18 This problem can be addressed by making use of the fact that, while the data cover the period, the scope of the agreements behind the illegal market allocation broadened after Thus, we can estimate difference-in-difference regressions that in effect take initial differences in levels of the dependent variable between the AOI and ROS to be reflective of persistent unobserved heterogeneities between the two areas; then controlling for other observable factors that may have caused the dependent variable to change between 1988 and 1995, an increase in that variable over that period that is greater in the AOI than it is the ROS is suggestive of an effect of the market allocation. Specifically, we run regressions of the following form: = (16) 18 See McCluer and Starr (2013) for discussion. This issue was more important in the first phase of the Marshfield-related litigation, which compared patient costs between patients who received a majority of their care from Marshfield clinic to otherwise similar patients who received their care from other providers.

12 12 for the three dependent variables; this amounts to including year dummies that differ between the AOI and ROS. Then the test for an effect of the illegal market allocation for a given dependent variable is: ΔΔ = - } - { - } (17) See Abadie (2005) for methodological discussion and McCluer and Starr (2013) for an application using data from the remand phase of the Marshfield case. 19 In principle, some part of the differential increase in quantity in the AOI could have been associated with increases in the quality of care than would have been warranted under an economic calculus of costs, benefits and risks; in this case, the DID estimate of the effect of the illegal market agreement would overstate the anticompetitive increase in quantity. However, in the present case direct review of evidence on the quality of care did not suggest any basis for concern about a possible bias from this source. Notably, at the outset of the period Marshfield Clinic was widely perceived to have a substantial quality advantage over other providers in the state; while it continued to work on improving its procedures and facilities in the period, its efforts were well in line with those of other providers in the state. 20 So while we do not expect there to be bias from differential changes in quality in the present case, the possibility should be investigated in others, given that correct estimation of damages requires all legal sources of differential outcomes across providers or across areas to be accounted for. 21 Table 3 gives definitions of the variables used in the regression analysis along with sample means. Our information on individual characteristics is limited to what is reported on insurance claims, but these cover many of the factors that would be expected to affect use of healthcare services, especially age, gender, marital status, and type of insurance plan (general indemnity, Federal government workers, 19 This differs somewhat from the standard DID specification, which uses a stark division between a before-and-after period to identify the effect of interest (Rubinfeld 2010). In the present case, we favor the more flexible specification implied by (16), given that the illegal market-allocation agreements were known to have phased in at different times in different places within the AOI. Still, for purposes of comparison, we also estimated standard before-and-after DID regressions, using the formal definition of the class period (from 1992 on) as the market allocation period. Results in this case show an increase in total patient costs that is, not surprisingly, somewhat smaller than is the case when 1988 and 1995 are compared (i.e. an increase of $64 from to , vs. $83 from 1988 to 1995); however, the standard method traces a much larger share of this increase to increased quantity (90% vs. 53%). 20 For example, in the remand phase of the trial, BCBS expert Thomas McGuire noted that the share of procedures performed by specialists (rather than primary care physicians) did not rise faster at the Clinic than it did elsewhere, as might be expected if its quality of care was improving at an extraordinary rate. See also McCluer and Starr (2013). 21 Hammer and Sage (2002) and Vogt and Town (2006) suggest that claims of increased quality accompanying increases in market power should be looked at skeptically, as they are not often substantiated by review of clinical outcomes, Moreover Gaynor (2006) finds that quality of care is better favored by competition than concentration.

13 13 etc.). 22 We incorporate effects of age and gender by interacting age ranges with gender; this allows age profiles of health costs, quantities and price deviations to differ between men and women. To account for other differences across areas that may contribute to the higher costs found in the AOI, we include county-level measures of the socio-demographic characteristics of the population, including: the share of the population with a high school education or more, the population density, the unemployment rate, real per capita income, real medical-assistance payments per capita, and birth and death rates. The basic specification closely parallels that which was used in the Rozema class action case. We also estimate a number of alternative specifications, adding explanatory variables and varying the estimation method used. A possibility that came up repeatedly in the Marshfield-related litigation cases was that healthcare prices in given parts of Wisconsin were relatively high because of legal competitive factors, instead of or in addition to illegal market division. First, because ratios of physicians to population are known to be relatively low in areas of low population density, and the AOI is largely rural, this may confer some natural ability to price above marginal costs and/or provide extra services to their patients unrelated to illegal behavior. 23 Because the counties in the AOI all have relatively low population density yet vary in the degree of concentration in the market for physician services, we add the Herfindal-Hirshman index (HHI) to the variables included in the regression analysis. The HHI measures market concentration as the sum of the squared market shares of all providers in the market; specifically, we measure it as the share of all billing events for all providers used by residents of the county. 24 Another issue related to legal competitive factors concerns effects that health-maintenance organizations (HMOs) may have on prices and quantities (Baker 1994, Baker and Corts 1995, Haas- Wilson 2003). On one hand, if a given local market has a well-developed HMO sector, it may tend to act as a check on price increases, as insurance companies may negotiate harder for lower prices to limit patients out of pocket costs and avoid loss of business to managed care. Baker (1994) and Baker and Corts (1995) refer to this as a market discipline effect. In this case, total costs and price deviations may tend to be lower in counties where HMO penetration is higher, ceteris paribus. On the other hand, the more people have substituted into managed care in a given market, the more likely it 22 Identifying information such as name and address was either masked or not made available. 23 As Posner wrote in his opinion on BCBS s appeal (65 F.3d 1406 (7 th Cir.) 1995), If the Marshfield Clinic is a monopolist in any of these areas it is what is called a natural monopolist, which is to say a firm that has no competitors simply because the market is too small to support more than a single firm. If an entire county has only 12 physicians, one can hardly expect or want them to set up in competition with each other. Twelve physicians competing in a county would be competing to provide horse-and-buggy medicine. 24 The HHI is commonly used in analyses of the competitiveness of health care markets (e.g. Lynk 1995, Schneider et al 2008). As discussed in Frech, Langenfeld and McCluer (2004), computing the HHI in terms of the location of the patient rather than the provider avoids issues of sample selection and case mix. We also ran the regressions using HHIs computed from providers shares of the value of total billings rather than the total number; there is no qualitative difference in results, which is not surprising given the high correlation between the two measures (0.95).

14 14 will be that those remaining in traditional indemnity insurance plans are likely to have worse underlying health. Baker (1994) and Baker and Corts (1995) call this as a market segmentation effect. In this case, total costs and quantities may be higher in counties where HMO penetration is high, as the pool of patients remaining outside of the HMO sector will have relatively poor health. To account for these possibilities, the regressions include a measure of HMO penetration, namely, the share of the county s population enrolled in an HMO. A complication is that some of the defendants in the class-action suit were HMOs, including one wholly owned by Marshfield Clinic; consequently, if people enrolled in defendant HMOs were included in the measure of HMO penetration, the degree of competition coming from managed care in the AOI would be overstated. 25 So in computing HMO enrollment for counties in the AOI, we exclude people who were enrolled in Security Health Plan or North Central Health Protection Plan from the penetration rate. V. Empirical results Table 4 presents results from the basic difference-in-difference specification, which controls for sources of persistent unobserved heterogeneities between the AOI and ROS and uses differential changes between the areas to gauge the effects of illegal competitive behavior. Before looking at the comparison of changes in the AOI vs. ROS, it is first valuable to examine the estimated effects of the other explanatory variables, which suggest that the specification captures a number of interesting and/or expected variations across individuals in costs, quantity and price deviations. The results indicate that, for both men and women, total annual spending on physician services rose significantly with age; for example, total costs were $417.6 (=$ ) higher for women in the age age range than for otherwise similar women in the age range, while for men the analogous difference was $ (=$ (-3.2)). This is consistent with a large literature documenting the strong dependence of healthcare costs on age (e.g. Alemayehu and Warner 2004). The increase occurs earlier for women than it does for men, although for men spending rises relatively steeply from age 45 on. 26 In both cases, increases in spending with age are largely due to increases in the quantity of services; estimates for the price-deviation equation show some shift towards relatively more costly services after childhood, but it is modest compared to the increase in quantity. Married subscribers had somewhat lower total spending on physician services than single subscribers, ceteris paribus, which has mostly to do with the lower quantity of services acquired; for example, of the $50.90 difference in spending between married and single subscribers, $48.40 was due to the lower quantity index. This is broadly consistent with clinical evidence that people who have good marriages and good networks of social relations tend to have better health (Cohen 2004, Parker-Pope 2010). Not surprisingly, total spending is substantially and significantly higher for people in high insurance risk-sharing plans, where this is almost entirely due to very high quantity of services. Total 25 As McCrary and Rubinfeld (2009) caution, including covariates that are causally related to the conspiracy on the right-hand side of a regression biases estimates of damages due to the conspiracy. 26 Note that results are for physician services only.

15 15 spending is also relatively high for subscribers enrolled in BCBS s Federal Employee Program, and relatively low for subscribers enrolled in national programs, where in both cases differences in the quantity index are much more important than price deviations. Among the county-level variables, total spending on physician services was relatively low for individuals in counties with relatively high shares of the population with a high school degree or more; the effect is due almost equally to lower quantity and a lower price deviation, although only the latter effect is significant. Ceteris paribus, total spending was relatively high for people living in counties with high population densities, due to both higher quantities and higher prices. Individuals in counties with relatively high unemployment had relatively high total spending, due almost entirely to relatively high quantities of services. This seems to run counter to Ruhm s (2000) finding that recessions are good for your health, although the types of cyclicalities he identifies (e.g. motor vehicle accidents) may affect spending on hospital services more than spending on physicians. Individuals in counties with relatively high per capita income had relatively high spending due to both quantities and prices; in contrast, those in counties with relatively high medical assistance per capita had relatively high spending, due entirely to relatively high price deviations. Relatively high birth and death rates had no significant effect on total spending or the quantity index, but they were associated with relatively low price deviations. Turning to the comparison of changes in the AOI vs. the ROS, the results indicate that, controlling for other factors, total annual spending on physician services in the AOI rose by $303.6 between 1988 and 1995, while it rose by $231.0 in the ROS; the difference between the two of $82.6 is statistically significant. 27 This is consistent with the expectation that the illegal market-allocation agreements boosted spending on physician services. 28 With respect to the source of the increase, the estimates show that both quantities and price deviations rose significantly in both the AOI and the ROS between 1988 and 1995, where the increases in quantities and price deviations were both larger in the AOI than they were in the ROS. Looking at the decomposition of the $82.6 differential increase in total costs in the AOI, the estimates suggest that $49 (59.3%) of this increase came from the greater increase in quantity in the AOI relative to the ROS. 29 This supports the expectation that some part of the illegal market power acquired by physicians in the AOI due to the market-allocation agreements resulted in intensification of service provision, in addition to higher prices. Table 5 shows results incorporating the HHI and HMO penetration variables using alternative specifications. As shown in Panel (B), adding these two variables to the basic DID specification has only modest effects on the estimated effects of being in the AOI. The estimated effect on total 27 Interestingly, this estimate of the effect of the illegal market allocation is higher than in the analogous estimate of $62 from a simple dummy-variable specification [8], suggesting the latter is biased downward rather than up. 28 See also McCluer and Starr (2013) for related results and discussion. 29 In the dummy-variable specification [8], the higher quantity of services contributes a somewhat smaller share (43.2%) of the higher level of total spending on physician services in the AOI, ceteris paribus.

16 16 spending on physician services is virtually unchanged at $82.7 (vs. $82.6 in the basic DID). The quantity index declines from $49.0 to $44.2, while that on the price deviation moves up from $33.5 to $38.5, so that the relative importance of the change in quantity index in the differential change in spending in the AOI is a bit smaller (53.4% vs. 59.3%). Interestingly, controlling for whether a person lived in the AOI, an increase in the HHI was associated with a significant increase in total costs, which was due to a significant increase in the price deviation with no significant change in quantity. This indicates that, at least in Wisconsin during this period, concentration per se was not associated with increased quantity, but rather the business practices and agreements in use in the AOI. 30 The results also show that, ceteris paribus, costs were higher in counties with relatively high HMO enrollments, largely reflecting effects on the price deviation. This finding is more consistent with market segmentation than with market discipline, although in the former case we expected higher costs to reflect higher quantity rather than higher price. Broadly, these results suggest that basic results concerning the differential increases in prices and quantities of physicians services in the AOI are not due to omitted competition-related variables. Conceivably, effects of increases in the HHI on output and prices may be nonlinear: For example, it may only be in highly concentrated markets that further concentration permits anticompetitive increases in output. To test for this possibility, we re-estimate the previous model including a spline for markets with HHIs above 2500, the cut-off between moderately and highly concentrated markets in the current merger guidelines of the U.S. Department of Justice and Federal Trade Commission (2010). As shown in Panel (C), the results suggest that increases in the HHI in highly concentrated markets have no extra effect on total annual spending on physician services, although this masks some contrasting effects on quantities and prices. For the quantity index, increases in the HHI below 2500 are estimated to lower the quantity of services acquired by a given individual, but above 2500 further increases in concentration tend to raise it; this is consistent with the idea that overprovision of services is not a simple function of the degree of market power, but that situations of highly concentrated market power may encourage it. In contrast, increases in the HHI boost the pricedeviation in markets that are unconcentrated to moderately concentrated, with no further effect in highly concentrated markets. Finally, it has been suggested that comparing measures of spending, quantities and prices for physician services in the AOI to the whole rest of the state of Wisconsin may be inappropriate, given that the AOI mostly covers areas of low population density. Thus, we re-run the analysis confining the sample to individuals in the 60 counties with a population density below 200 residents per square mile; this reduces the sample size to 619,932 individual-year observations. As shown in Panel (D), this has some notable effects on magnitudes of estimated coefficients. It increases the magnitude of the differential increase in total costs in the AOI (from $82.7 to $105.7), although it leaves the 30 In fact, if the regressions are run using observations from the ROS only, the estimated effect of the HHI is positive and significant in the regressions for total costs and the price deviation, but negative in the regression for quantity, although the latter effect is significant at a 10% level only.

17 17 contribution of the quantity index to this increase in the same range (56.4%). This indicates that, if anything, failing to confine the sample to counties with relatively low population densities causes underestimation of the damages associated with acquiring healthcare services in the AOI, not overstatement. In this part of the state, increases in the HHI increase both the quantity index and the price deviation. Here too increases in HMO enrollment tend to boost total costs due a greater price deviation, although the estimated magnitude of the effect is smaller. Altogether, the results in Table 5 confirm that results of the basic specification are robust to incorporation of additional competitionrelated variables and to other plausible changes in specification. VI. Conclusions In sum, our econometric analysis indicates that, in Wisconsin in the period, quantities of physician services rose more quickly in the AOI than they did elsewhere, consistent with the idea that the reduction in competition amongst Marshfield Clinic and its co-conspirators created an environment in which they could increase both prices and quantities of services by more than would have been possible without the anticompetitive agreements. In our preferred difference-in-difference specification (Panel (B) of Table B), which controls for persistent unobserved heterogeneities across areas and for other competition-related factors, real patient costs rose by an extra $82.7 (9.9%) in the AOI between 1988 and 1995, with 53.4% of this increase associated with an extra increase in quantity. With all of our estimates of differential increases in total costs being between $62 and $115, and estimates of the contribution of increased quantity mostly in the 35-60% range, the magnitudes of our preferred estimates are in the middle of these ranges. Again we underline that, although concerns that difference-in-difference estimates of in principle may pick up differential changes in quality of care as well as effects of illegal competitive conduct, in the present case direct review of evidence on changes in the quality of care did not suggest any basis for concern about this potential source of bias in the damage estimates. Taken broadly, our findings illustrate that estimates of the damages due to the illegal market allocation that focused solely on differential increases in prices would have understated the amount of damages by an appreciable amount. The main implication of our findings is that, especially in cases where a service-intensive practice style is a part of the competitive strategy of a provider accused of illegal anticompetitive conduct, methods of damage estimation should not be based on price only, but rather should also consider whether increased quantities of services contributed to the monetary losses experienced by consumers and health insurers. Losses from this source are not entirely missing from the standard damage-estimation method: Because this method multiplies the price differential due to anticompetitive behavior by the actual quantity, and some part of the latter reflects the extra overprovision associated with this behavior, some part of the extra quantity factors into the estimated loss. But to account for the fact that these units would not have been bought at all under legal competitive conditions, the extra quantity of services valued at the competitive benchmark price should also be added in. In the end, it

18 18 is an empirical question whether consumers and/or insurers in a given case are entitled to damages from overprovision of services relative to what they would have bought in the absence of anticompetitive behavior. The method of disaggregating damages from prices and quantities presented in this paper provides a way to check.

19 Figure 1. Area of Influence (AOI) 19

20 20 Figure 2. Trends in average annual costs and contributions from quantity and price (a) costs of health care services Average annual (b) the difference between the AOI and ROS due to quantity and price Decomposition of Note: Values are converted to 1995 dollars using the medical care CPI (1995=100).