*

 The article reflects the views of the authors, not those of the Federal Trade

Commission or any individual Commissioner. We thank Denis Breen, Paul Pautler,

Lou Silvia, John Simpson, and participants in the FTC seminar series, for helpful

comments. Remaining errors are ours.

 The Competitive Effects of Not-for-Profit Hospital Mergers: 

A Case Study

 Michael G. Vita 

U.S. Federal Trade Commission*

and

Seth Sacher

Charles River Associates

Applying conventional horizontal merger enforcement rules to mergers of

nonprofit hospitals is controversial. Critics contend that the different

objective function of not-for-profits entities should mitigate, and possibly

eliminate, competitive concerns about mergers involving nonprofit

hospitals. We provide evidence relevant to this debate by analyzing ex

post a horizontal merger in a concentrated hospital market. Here, the

transaction reduced the number of competitors (both nonprofit) in the

alleged relevant market from three to two. We find that the transaction

resulted in significant price increases; we reject the hypothesis that these

price increases reflect higher post-merger quality. This study should help

policymakers assess the validity of current merger enforcement rules,

especially as they apply to not-for-profit enterprises.

JEL Nos. I11; K21; L31; L41; L44
1

 See Complaint In the Matter of Santa Cruz Hospital, et al. 188 F.T.C. 382 (1994).

I. Introduction

Although researchers have made innumerable attempts to analyze the

relationship between competition (as proxied by concentration) and performance (e.g.,

price), empirical evidence on the actual competitive effects of horizontal mergers is

scarce. Perhaps this is not surprising. When assessed by contemporary antitrust

standards, most mergers (even most horizontal mergers) do not present a serious risk of

competitive harm. The handful that do typically either will be blocked in their entirety,

or approved conditional on the completion of some remedial action (e.g., the divestiture

of a critical competitive asset to a third party) designed to ameliorate the risk of

competitive harm. Hence, candidates for the study of (plausibly) anticompetitive

mergers will arise only infrequently; when, for example, the enforcement agencies lose

a merger challenge in court, obtaining no competitive relief, or when the enforcement

agencies do not challenge a transaction for reasons unrelated to the transaction’s

perceived competitive effects.

This paper takes advantage of one of these rare opportunities. We provide an

econometric analysis of a horizontal merger in a concentrated hospital market,

Dominican Santa Cruz Hospital’s acquisition of its sole rival in the city of Santa Cruz,

California, AMI-Community Hospital. According to the FTC, only two competitors --

both not-for-profit -- remained in the relevant market after the transaction.1 Absent

substantial efficiencies, or a credible threat of entry, standard antitrust analysis would

predict that an increase in concentration of this magnitude likely would lead to higher
2

 See Statement of Chairman Janet D. Steiger in Support of Final Issuance of

Consent Order In the Matter of Dominican Santa Cruz Hospital, et al. 188 F.T.C. 382 (1994).

2

equilibrium prices. Consequently, this transaction would have been challenged by the

FTC, had the Commission been able to intervene before the transaction was completed.2

However, because the transaction was too small (in absolute size) to trigger the HartScott-Rodino filing thresholds, the FTC did not receive prior notification of the

transaction, and the parties were able to consummate the acquisition before the FTC

could seek a preliminary injunction. Ultimately, the FTC entered into a consent order

with Dominican Hospital, but the decree required only that Dominican notify the

Commission prior to any further acquisitions in the relevant geographic market – it did

not restore the premerger market structure. For this reason, this acquisition provides

an excellent opportunity to assess, ex post, the actual, as opposed to the predicted,

competitive consequences of a horizontal merger.

This study should be of interest for at least two reasons. First, as noted,

empirical studies of the price effects of horizontal mergers are comparatively rare,

notwithstanding their apparent importance to appraising the efficacy of federal merger

enforcement policy. Studies such as this should help policymakers assess whether the

enforcement decision rules embodied in the Merger Guidelines predict with an

acceptable degree of accuracy the competitive consequences of actual horizontal

mergers.
3

 See Lynk (1994, 1995) for a more detailed review of the relevant theory and

evidence. 

3

Second, and more specifically, the applicability to hospital markets of the

antitrust agencies’ approach to horizontal merger analysis (i.e., the Merger Guidelines)

recently has been called into question. A substantial share of hospital output

(approximately 90 percent) is produced by private and public nonprofit hospitals. 

Critics have contended that the antitrust agencies and courts have assumed that these

not-for-profit providers seek maximum profits, notwithstanding the substantial body of

theoretical and empirical analyses suggesting that nonprofit entities -- or more

specifically, certain types of nonprofit entities -- will eschew opportunities to profitably

exercise market power.3 For example (see Lynk (1995), pp. 440-41), it is conceivable that

a private nonprofit hospital sponsored and administered by the local community, might

function something like a consumer cooperative. If so, the incentives of producers and

consumers would be aligned, and any incentives the hospital might otherwise have to

raise prices anticompetitively would be attenuated. Alternatively, the behavior of a

nonprofit hospital whose profits are used to fund some particular set of activities

valued the firm’s managers – e.g., providing charity care to the poor – might be

indistinguishable from that of an identically situated for-profit entity.

Whether the (potentially) different incentive structure of not-for-profit hospitals

could attenuate the exercise of market power is of more than just academic interest. 

The courts that must adjudicate horizontal merger challenges also have found such
4

 See F.T.C. v. Butterworth Health Corporation and Blodgett Memorial Medical Center,

(U.S. District Court, Western District of Michigan, Southern Division), September 26,

1996, slip. op. at 27.

4

arguments compelling. In at least one case,4 a U.S. Federal District Court found that

the nonprofit, community-sponsored status of the merging parties was an important

factor in rebutting an otherwise convincing prima facie case against the merger of two

rival hospitals.

The transaction analyzed here provides an excellent opportunity to explore these

possibilities. The acquiring entity (Dominican Santa Cruz Hospital) is part of a chain of

Catholic hospitals operating in the western United States. Its sole remaining rival in

Santa Cruz county, Watsonville Community, is a locally-sponsored community

hospital. According to the arguments set forth above, Watsonville Community would

appear to be the type of nonprofit hospital least prone to exercise market power; any

such propensity to charge competitive prices would, moreover, place a powerful postmerger competitive constraint on Dominican’s ability to raise prices. Consequently, an

analysis of both entities’ (but especially Watsonville’s) post-merger pricing behavior

should provide a valuable insight into the behavior of nonprofit producers.

The next section reviews briefly the empirical literature on hospital competition. 

We review first those studies that have explored the empirical relationship between

concentration and hospital prices. These studies for the most part are cross-sectional in

nature, and do not specifically investigate the equilibrium effects of actual horizontal
5

 The year 1983 is significant in most analyses of hospital markets because this is

the year Medicare instituted the prospective payment system (PPS). It is also the year

in which California enacted legislation permitting selective contracting between health

plans and individual hospitals. 

5

mergers. The effects of mergers (including hospital mergers) are the focus of the

second, much smaller (but much more recent) body of research that we review. 

Section III describes briefly the details of the Dominican-Santa Cruz transaction. 

Section IV outlines the empirical strategy for studying the price effects of that merger. 

Section V presents the empirical results. 

II. Studies of Hospital Competition

A. Cross Sectional Studies

Most early (i.e.,pre-19835) studies of hospital competition were carried out using

a variant of the well-known “Structure-Conduct-Performance” (S-C-P) paradigm. As

noted by Bresnahan (1989, pp. 1012-13), the distinguishing features of this empirical

paradigm are reflected in the following assumptions: first, that price-cost margins can

be accurately measured with accounting data; and second, that cross-sectional variation

in market structure can be measured with a small number of observable variables

(including market concentration). Early studies of hospital competition varied the

standard SCP approach slightly by assuming that hospitals engaged mainly in quality,

rather than price, competition. Accordingly, the typical study from this period

attempted to discern the relationship between some measure of hospital costs (e.g., cost
6

 A notable exception was Noether (1988), who found that increased competition

reduced mark-ups over cost.

7

 For a comprehensive review of this literature see Pautler and Vita (1994). For

seminal works see Joskow (1980) and Robinson and Luft (1985).

8

 For example, Dranove et al. (1993) found that an increase in the HerfindahlHirschman Index (HHI) from 2500 to 5000 results in a price increase of approximately 3

percent for a basket of hospital services. Melnick et al. (1992) found that where a merger

reduces the number of competitors from three to two (assuming that the competitors

had equal shares), the per diem price for medical/surgical services increases by 9

percent. Other examples are Keeler, Melnick, and Zwanziger (1999), and Simpson and

Shin (1998). 

6

per admission) to some measure of competition.6 Usually, a negative relationship

between hospital concentration and costs was found (e.g., higher costs per admission

were observed in the less concentrated markets).7 Generally, this finding was

interpreted as reflecting the consequences of insurance-induced moral hazard, and

other principal-agent problems.

Studies using data from the mid-1980s and after, and which focus on the

California experience, present a different picture. Typically, these studies addressed

the relationship between market concentration and price, as opposed to market

structure and cost, and generally, they obtained results consistent with the traditional SC-P paradigm -- i.e., a positive relationship between concentration and price.8

While suggestive, these price-concentration studies do not provide direct

evidence of the effects of hospital mergers. One problem with drawing inferences about

the competitive effects of mergers from this literature is that the results are almost

surely sensitive to the way the geographic markets are defined, since this definition will
9

 For example, see Kessler and McClellan (1999) and Werden (1989). Only the

former have offered an alternative method for defining antitrust markets. Although

there seldom may be good practical alternatives to patient flow data, it is nonetheless

true that antitrust markets defined on this basis may lead to incorrect conclusions about

the competitive constraints faced by a particular pair of merging hospitals. 

7

determine the value of the concentration index. Most of these studies delineated

markets with political boundaries (e.g., counties or MSAs), although some have

attempted to use patient origin data to define markets (e.g., Melnick et al. (1992)). Even

the latter approach, while seemingly less arbitrary than using political boundaries, has

been criticized as presenting biased view of actual antitrust markets.9 The second

problem is more fundamental, and derives from the disfavor into which the S-C-P

approach has fallen. Although studies continue to be carried out in this paradigm,

many economists have grown skeptical about the validity of its maintained hypotheses

-- in particular, the notion that cross-sectional variation in prices can be explained with

a small number of observable variables (see Bresnahan (1989, pp. 1012-13). 

Consequently, alternative approaches to identifying and measuring market power have

been derived. In the following section, we discuss one such approach that has been

adopted as a means for assessing the competitive consequences of horizontal mergers.
10 Another form of “event study” sometimes used by economists are stock

market event studies, which examine the effect of an event (e.g., the announcement of a

merger) on the stock market value of some set of affected firms (e.g., rivals of the

merging entities). See MacKinlay (1997) for a general description of this approach. The

stock market event study method on a number of occasions has been used to assess the

consequences of horizontal mergers (see, e.g., Eckbo (1983)), and we are aware of at least

one attempt to use this method to evaluate the competitive consequences of hospital

mergers (Woolley (1989); see Vita and Schumann (1991) for a critique of this study). 

While the stock market event study method applied to horizontal merger analysis does

not require the researcher to identify the precise boundaries of the antitrust market, it

does require the researcher to identify at least some of the firms whose profits likely

would be affected by the transaction. 

11 Of course, one can do more. In the empirical section below, we also estimate

the effects of the merger on the price of (what appears to be) the merged entity’s closest

rival. Failure to find a positive price effect for this producer might mean either that this

firm (1) did not produce a close substitute; or (2) the merger did not create market

power. Finding a positive price effect would suggest (ceteris paribus) that the producer

(continued...)

8

B. Empirical Literature on Actual Mergers

An alternative strategy to the S-C-P paradigm for assessing the equilibrium

consequences of merger-induced changes in market structure is to examine directly,

through a comparison of the pre- and post-merger prices charged by the merged entity

(and, perhaps, its plausible rivals), the equilibrium effects of the transaction. This

“event study” approach obviates the necessity of defining the “relevant market.”10 If

the merger creates market power, then (after suitably controlling for other possible

shifts in the exogenous determinants of price) one should observe the merged entity

raising its price post-merger. It is unnecessary to identify the relevant market to carry

out this test -- at minimum, one requires only data for the merged entity.11
11 (...continued)

was in the relevant market, and that the merger was anticompetitive.

12 Connor, Feldman, and Dowd (1998), and Wicks, Meyer, and Carlyn (1998)

also attempted to assess the price effects of horizontal hospital mergers, but both

studies exhibit considerable methodological problems. Connor et al. estimate an

equation of the form: %? PRICE it = f(%? Xit, Mi), where the dependent variable is the

percentage change price of the ith hospital from period t-1 to period t, Xit are exogenous

variables, and Mi is a dummy variable set equal to 1 if hospital i merged during the

sample period. It is difficult to reconcile this specification with economic theory, which

predicts a relationship between the price level (not its rate-of-change) and market

structure. Additionally, it appears that the merger dummy variable is either 0 or 1 for

the entire sample period, instead of taking on different values for the pre- and postmerger periods. 

Wicks et al. (1998) compare pre- and post-merger price levels, but do not include

any control variables. Hence, it is difficult to know if any changes in prices reflect the

exercise of market power, or are the result of changes in exogenous price determinants.

9

Among the first researchers to employ this strategy were Barton and Sherman

(1984), who found that two mergers of microfilm producers increased both prices and

profits. A very similar empirical strategy was used by Kim and Singal (1993) in their

study of the price effects of airline mergers. Later, Schumann et al. (1992, 1997)

examined the price effects of mergers that took place in three different industries: 

cement, corrugated paperboard, and titanium dioxide.12

Barton and Sherman, and Kim and Singal used a very simple -- and restrictive --

empirical strategy for measuring the competitive impact of a horizontal merger. 

Essentially, they analyzed movements in the price of the product affected by the

merger, relative to the price of a substitute product hypothesized to face similar

demand and cost conditions, but unaffected (or at least less affected) by the merger. 

The competitive effects of the transaction were assessed through a simple t-test of the
13 Kim and Singal (1993, p. 554) rationalize this as follows: “Industry-wide

changes, like fluctuations in fuel prices, changes in labor cost, and seasonal or cyclical

variations in demand are likely to have an equivalent effect on routes of a similar

distance.” They go on to observe (footnote 20): “Local economic conditions may affect

some routes differently. We hope the large number of observations in our sample

averages out the noise.”

14 By “reduced form,” we mean that they regressed price on its exogenous

determinants (e.g., demand- and cost-shifters).

10

hypothesis that the pre-merger price ratio equaled the post-merger ratio, against the

alternative that the ratio increased. No other control variables were employed in the

analysis – the authors assumed, implicitly, that the reduced form price equations for

both products were identical, and therefore that the differences in the ratios of the

prices could be attributed solely to changes in market structure.13

Schumann et al. (1992, 1997) adopted a different approach – they estimated a

reduced form price equation with data spanning the pre- and post-merger periods.14

The competitive effect of the transaction was captured with a dummy variable set equal

to one for the post-merger period. By using this specification, Schumann et al. assume

that their control variables (consisting mainly of indices of demand and factor prices)

adequately control for the exogenous determinants of price, so that the coefficient on

the merger dummy variable can be interpreted as reflecting the competitive effects of

the transaction. If, however, there are unobserved exogenous determinants of price that

are correlated with the merger dummy, then the coefficient on this variable will reflect

the competitive effects of the transaction, as well as movements in these unobserved
11

price determinants. As a consequence, one will incorrectly estimate the price effects of

the transaction.

In the empirical section below, we propose an empirical framework that

combines elements of the Barton and Sherman, and Schumann et al. approaches. We

believe that this strategy will provide the best method for identifying accurately the

competitive effects of the acquisition. Before setting forth this empirical strategy, we

first describe in greater detail the events of the Dominican-Santa Cruz transaction. 

III. History of the Transaction

On March 8, 1990, Dominican Santa Cruz Hospital (“Dominican”), a 259-bed,

not-for-profit hospital, affiliated with the Catholic Healthcare West system, purchased

the only other hospital in the city of Santa Cruz, AMI-Community Hospital

(“Community”). Community, which was affiliated with American Medical

International, was licensed for 180 beds and was a for-profit entity. Dominican and

Community were located about two miles apart. The only other hospital in Santa Cruz

county was Watsonville Community Hospital, located about 14 miles south of the city. 

The city of Santa Cruz is located about 40 miles south of San Jose, and 80 miles south of

San Francisco. Santa Cruz county is bordered on the south and west by the Pacific

ocean, and on the north and east by the Santa Cruz mountains. 

The FTC’s analysis of patient flows suggested that the overwhelming majority of

the three Santa Cruz county hospitals’ patients resided in Santa Cruz county, and that
15 According to the 1992 Merger Guidelines (§1.51(c)), “the [FTC] regards markets

[with HHIs above 1800] to be highly concentrated . . . [when] the post-merger HHI

exceeds 1800, it will be presumed that mergers producing an increase in the HHI of

more than 100 points are likely to create or enhance market power or facilitate its

exercise.”

16 As then-FTC Chairman Steiger observed at the time, “[t]he facts of this case

provide sufficient reason to believe that this acquisition violates Section 7 of the Clayton

Act. Ordinarily, such facts would lead the Commission to seek a preliminary injunction

in federal district court.” See Statement of Chairman Janet D. Steiger in Support of Final

(continued...)

12

most Santa Cruz residents receiving inpatient hospital care received it from hospitals in

the county. Accordingly, the FTC’s complaint alleged that the relevant geographic

market was “Santa Cruz County and/or portions of Santa Cruz County.” According to

the Complaint, the merger increased the market share (of patient-days) of Dominican

from 62 percent to approximately 73 percent, and increased the market share (measured

by available beds) from 50 percent to 73 percent. The Herfindahl-Hirschman Index for

the relevant antitrust market increased by over 1,700 points, from approximately 4,620

to approximately 6,350 (measured by patient-days); and by over 2,300 points (from

approximately 3,770 to approximately 6,090) when measured by available beds. Under

the Merger Guidelines enforcement criteria, a transaction generating concentration

figures of this magnitude would be presumed anticompetitive. Absent compelling

evidence that such a merger would create substantial efficiencies, or that the exercise of

market power would be constrained by the threat of entry, normally the FTC would

seek to preliminarily enjoin such a transaction.15 Had the FTC had the opportunity to

seek a preliminary injunction in this case, it would have done so.16 However, as noted
16 (...continued)

Issuance of Consent Order In the Matter of Dominican Santa Cruz Hospital, et al. 188 F.T.C.

382 (1994).

17

 See Statements of Chairman Steiger, Commissioner Azcuenaga, and

Commissioner Yao In the Matter of Dominican Santa Cruz Hospital, et al. 118 F.T.C. 382

(1994).

13

earlier, the small absolute size of the transaction failed to trigger the Hart-Scott-Rodino

filing thresholds, and the FTC was not able to seek an enforcement action until after the

transaction was completed.

In March, 1993, approximately three years after the merger was consummated,

the FTC accepted a consent agreement with Dominican Santa Cruz Hospital and

Catholic Healthcare West. The consent order did nothing to restore the pre-merger

competitive environment; it required only that the respondents obtain the

Commission’s prior approval before acquiring any other hospitals in Santa Cruz

County. Although all of the FTC Commissioners concluded that the merger probably

had created significant market power, a majority of the FTC Commissioners concluded

that the agency had few good remedies available to it.17 The acquired hospital,

Community, already had been converted to a skilled nursing/rehabilitative care

facility. Thus, the effects of the merger could have been reversed only at considerable

cost. Further, Sutter Health, a major Northern California hospital chain, had announced

plans to construct an acute care hospital in Santa Cruz, and had already purchased a 3.8
18

 Sacramento Business Journal, March 16, 1992.

19 “Sutter Health, a major Northern California hospital chain, announced plans

to construct an acute care hospital in Santa Cruz, which would restore a third hospital

competitor in the market. The very real prospect that Sutter will enter this market,

before a divestiture decree could be obtained through litigation and a willing buyer

found, is an additional factor weighing against pursuit of a divestiture order.” See

Statement of Chairman Janet D. Steiger in Support of Final Issuance of Consent Order In

the Matter of Santa Cruz Hospital, et al. 188 F.T.C. 382 (1994).

14

Pit ' P(Wit, Zit, Mt)

acre site toward that end.18 The FTC reasoned that entry by this entity likely would

already have occurred by the time divestiture could be completed, thereby moving the

market closer to the pre-merger status quo more rapidly than could be accomplished

through the FTC’s administrative process.19 As it turned out, some time in the second

quarter of 1996, Sutter Health opened the Sutter Maternity and Surgery Center with 30

licensed and 21 staffed beds. 

IV. Methods and Data

A. The Empirical Model

To identify the equilibrium price effects of the Dominican/Santa Cruz

acquisition, we employ an empirical specification that borrows elements from

Schumann et al. (1992) and Barton and Sherman (1984). Like Schumann et al., we

estimate the reduced form equation for price:
20

 See note 13, above.

15

[1]

where Pit is the price of hospital i at time t; Zit is a vector of demand shifters (e.g.,

income); Wit is a vector of input prices; and Mt is a dummy variable set equal to one for

all time periods subsequent to the transaction. In section V, below, we estimate

equation [1] separately for Dominican Hospital and its closest remaining rival,

Watsonville Hospital.

Because we cannot observe all of the exogenous factors that might affect the

equilibrium prices of the merged entity and its competitors, we incorporate elements of

the approach used by Barton and Sherman (1984) and Kim and Singal (1993) in their

merger studies. Essentially, these authors analyzed movements in the price of the

product affected by the merger, conditional on the price of a substitute product that

faces similar demand and cost conditions, but which is unaffected by the merger. In

other words, this other product serves as a control group; it is assumed that the

exogenous determinants of price (e.g., input prices) affect the “control group” and the

“treatment group” equally.20 If true, and if the merger had no impact on equilibrium

price, then the relationship of the price of the merged entity to the price of the “control”

would be unchanged post-merger. Post-merger changes in this ratio can then be

imputed entirely to the transaction.

As noted earlier, the assumption that exogenous factors affect the merged entity

and the control group equally is restrictive – it is unlikely that all of the determinants of
21 As we discuss below, a positive coefficient potentially is also consistent with

post-merger quality improvements.

22 Knowing the dimensions of the market would be important if the merger was

anticompetitive, and if we wished to estimate the welfare loss associated with the

transaction. Then, we would also wish to know which other hospitals also raised their

prices (and by how much). This paper attempts to address a much simpler question: 

did the merged entity (and its closest rival) raise price post-merger?

16

price will be matched across the two groups. Accordingly, we include as regressors, in

addition to the price of the control group, the observable determinants of that price (e.g.,

factor prices faced by the peer group hospitals). This specification avoids the

unnecessarily restrictive assumption that all intertemporal differences in the covariance

of the two prices is attributable to the change in market structure. Below, the criteria

used to construct this control group (which we refer to as the “peer group”) are

discussed in greater detail.

Carrying out the analysis via the estimation of equation [1] eliminates the

necessity of specifying a geographic market for the outputs produced by the merging

hospitals. If the Dominican-Community merger was anticompetitive, then the

coefficient on the merger dummy variable should be positive,21 irrespective of the

dimensions of the market in which Dominican and Community competed prior to

merger.22 Conversely, if the merger was not anticompetitive, for whatever reason (e.g.,

low concentration in the relevant market, subsequent entry, offsetting efficiencies, or

not-for-profit status), then the coefficient on this variable should be zero (or negative, if
23 In the OSHPD data, there are various categories for both gross and net patient

revenue. Net revenue is equal to a hospital’s gross revenue minus any discounts that it

offers. In the data, the gross revenue figures distinguish between inpatient and

outpatient revenue, however, the net revenue figures do not. As noted by Dranove et al.

(1993), failure to account for discounts seriously understates the effect of competition on

price. Thus, several adjustments must be done in order to obtain estimates of net

inpatient revenue from the gross inpatient data. While OSHPD has been collecting

quarterly data from hospitals since approximately 1980, data prior to 1986 did not in

any way distinguish revenue by payer group. As a result, observations from prior to

1986 were eliminated. For data from 1986 to 1992, net inpatient price was calculated by

multiplying total net revenues from non-Medicare, non-Medicaid patients by the ratio

of gross inpatient revenue to gross total revenue at the hospital. While this net revenue

figure eliminates Medicare and Medicaid patients it does include revenue from some

patients in various non-Medicaid indigent programs. This net revenue figure is then

divided by discharges to obtain the average price paid per non-Medicare, non-Medicaid

acute-care inpatient. We also adjusted the number of discharges by the ratio (total

revenue-bad debt)/total revenue in order to account for bad debt. 

For data after 1992, patient revenue for various indigent programs is reported in

a separate category. In order to keep the observations consistent over time, revenue

from this category was added to the revenue figures for commercially insured patients. 

(continued...)

17

merger-related efficiencies reduced costs and prices). In neither case need we specify a

geographic market.

B. Description of the Variables

1. Price

Each calendar quarter, California-licensed hospitals file a Financial Data Report

with the Office of Statewide Health Planning and Development (OSHPD). These data

allow us to calculate quarterly observations (for 1986 through 1996, inclusive) of the

average net revenue received per inpatient acute-care admission for privately insured

patients.23 Of course, hospitals provide numerous inpatient services, some of which
23 (...continued)

Net price was then calculated using the same methodology as outlined for the 1986 to

1992 data. 

24 For a critical overview of the “acute care inpatient” product market definition

used in hospital merger investigations, see Sacher and Silvia (1998). 

25 DRGs refer to a system of classifying patients based on medical diagnoses and

surgical procedures. Originating at Yale University during the 1970s, the DRG system

has been widely adopted by payers and providers as a way of classifying patients. 

18

may or may not be demand- or supply-side substitutes. Nevertheless, a single measure

of inpatient price is consistent with the so-called “cluster” approach to defining hospital

product markets used in virtually all hospital merger investigations.24 

2. Control Variables

As is obvious, the unit of output employed in this study – an inpatient discharge

– is nonhomogeneous. Patient stays can and do vary substantially in terms of their

resource intensity. Consequently, cross-sectional and intertemporal comparisons of the

“price” of this output are meaningless unless one controls somehow for this

heterogeneity. 

We employ several such controls. First, like other researchers (e.g., Simpson and

Shin (1998)), we construct an index of hospital “casemix.” The Healthcare Financing

Administration (HCFA) assigns a “caseweight” to each diagnostic related group

(DRG).25 This index measures the “resource intensity,” used, on average, for each DRG

relative to other DRGs and over time. The OSHPD discharge data set includes the date
26 All hospitals in our “peer group” were located in an MSA for which HCFA

creates a wage index. The exception is El Centro Regional Medical Center in Imperial

County. For this hospital we used a composite HCFA wage index for non-metropolitan

areas of California. 

19

of discharge and DRG for each patient. Using these data, we created a quarterly

casemix indicator for each hospital used in the empirical analysis. This was done as

follows. Each non-Medicare/non-Medicaid discharge at each hospital for each quarter

was weighted using the HCFA caseweight index for the relevant DRG. The weighted

discharges were then summed and divided by the total number of discharges for each

quarter at each hospital to obtain the casemix index. 

As a further control for discharge heterogeneity, we also include the average

length-of-stay for privately insured patients. The rationale for including this measure is

straightforward – each additional day of hospitalization requires the consumption of

additional labor and material resources. One cannot compare the price of a discharge

across different time periods, or across different hospitals, unless one controls for

variations in length-of-stay.

Equilibrium hospital prices also will be affected by exogenous changes in factor

prices. We include two variables to control for these shifts. First, HCFA computes a

wage index for all urban areas (a county or set of counties) based on the salaries and

wages of various health care workers in the relevant locale.26 This index is used to

adjust hospital payments under the Prospective Payment System (PPS) for Medicare. 
27 Though Watsonville Hospital suffered damage from the earthquake, the

OSHPAD data provide little support for the proposition that its productive capacity

was substantively impaired by this damage. Whether measured by total patient days or

total discharges, the OSHPAD data suggest that Watsonville’s output actually increased

after the quake. A regression of total patient days against a time trend and the quake

dummy (=1 for 3rd quarter 1989 and afterwards) yields a coefficient on quake of 1069.83

(t=3.26). A similar regression using total discharges yields a coefficient (t-statistic)

on quake equal to 22.03 (0.22).

20

As an additional control variable, we include the BLS Producer Price Index for surgical

and medical instruments and apparatus.

It is well-established empirically that the growth of managed care institutions

(e.g., HMOs, PPOs) has facilitated more intense price competition among hospitals(e.g.,

Dranove, Shanley, and White (1993); Kralewski et al. (1992)), as well as greater

productive efficiency. Consequently, other things equal, we would expect to observe

lower prices in markets where selective contracting by managed care organizations is

more prevalent. Ideally, we would like to utilize some measure of the market share of

managed care institutions in the relevant market. Unfortunately, such data are not

readily available. As a proxy, we calculate for each hospital the percentage of total

discharges for which the expected payment source is an HMO or other prepaid health

plan.

We also control for the effects of the October 1989 northern California

earthquake, which inflicted some damage on Watsonville Hospital, and may have

reduced (exogenously) Watsonville’s productive capacity, leading to higher prices for

reasons unrelated to the Dominican transaction.27 To capture the competitive effects of
21

this event, we create a dummy variable (quake) equal to 1 for the 3rd quarter of 1989

and all subsequent periods, and 0 otherwise.

Last, similar to other empirical studies of hospital mergers (e.g., Lynk (1995),

Simpson and Shin (1998)), we include a number of other variables to control for

exogenous demand- and cost-side variation. These consist of per capita income, the

county-level unemployment rate, county population density, share of admissions

Medicare, and share of admissions MediCal.

Table 1 contains descriptive statistics for the variables used in empirical analysis.

C. Creation of the Peer Group

As noted above, an important element of our study is the creation of a group of

hospitals comparable to Dominican Santa Cruz and other in-market hospitals, and the

inclusion of their prices (and the exogenous determinants of their prices) as an

explanatory variable in the price equation. By so doing, we hope to control for

otherwise unobserved demand and cost factors, unrelated to the merger, that might

influence intertemporal price behavior at the merging hospitals. The State of California

has undertaken two studies to categorize hospitals into peer groups for purposes of

setting Medi-Cal reimbursement levels, the most recent in 1991 (Department of Health

Services, 1991). 
22

These studies form the basis for the construction of the peer group used here. 

The peer grouping method used by the State of California first placed specialty,

teaching, and prepaid hospitals in their own separate peer groups. The study then used

“cluster” analysis to group rural hospitals and other “unusual” hospitals (see State of

California (1991), § 5). After the latter were classified into these peer groups, only urban

short term facilities remained. These facilities were then subdivided into peer groups

on the basis of licensed bed size. In the 1982 study, Dominican Santa Cruz and

Community Hospital of Santa Cruz were placed in the “moderately-sized”urban

category, while Watsonville was placed in the “small-urban” hospital peer group. In

the 1991 study, Dominican Santa Cruz was placed in the “medium-sized urban”

hospital peer group, which consisted of all hospitals not elsewhere classified with

between 170 and 270 licensed beds. Watsonville was placed in the “moderately smallsized” urban hospital peer group, which consisted of all hospitals not elsewhere

classified with between 95 and 170 licensed beds. 

We used the following procedure to establish a peer group for the current study. 

First, to ensure that peer hospitals were located in markets as similar as possible to the

Santa Cruz market, hospitals located in counties that were part of very large Primary

Metropolitan Statistical Areas (PMSAs) were eliminated. This eliminated hospitals

located in the following counties: Los Angeles, Orange, Ventura, Riverside, San

Bernardino, San Diego, San Francisco, Alameda, Contra Costa, Marin, San Mateo and
23

Santa Clara. The competitive environment in such large urbanized areas likely is very

different from that found in the less urbanized area of Santa Cruz.

Next, the peer group was restricted to those hospitals that were placed in any of

the short term urban hospital peer groups in the 1991 California study, and were

licensed with between 100 and 300 beds in that year. While somewhat arbitrary, these

licensed bed cut-offs would appear to limit the sample to hospitals reasonably

comparable to the hospitals in Santa Cruz. This left 41 potential peer group hospitals. 

We next eliminated those hospitals in this group that were not between 100 and 300

licensed beds, and/or did not fall under one of the urban hospital groupings in the 1982

California Peer Group survey. This left 33 potential peer group hospitals. We next

eliminated any hospitals that did not report between 100 and 300 beds in the 1996 AHA

Guide. This left 25 potential peer group hospitals. We then eliminated all hospitals that

had themselves been involved in a horizontal acquisition as reported in the OSHPD

Hospital History Listing database. This group of 17 remaining hospitals comprise our

peer group (see Appendix A).
28 We conducted a Chow test to determine if the Watsonville and Dominican

data should be pooled. The Chow test rejected this restriction at the 5 percent

significance level. Accordingly, we estimate the price equation separately for each

hospital.

29

 See Newey and West (1987) and Greene (1997, p. 506). The Newey-West

estimator is a refinement of the White (1980) heteroskedasticity-consistent estimator; it

produces standard errors that consistently estimate the true covariance estimator

without requiring the researcher to specify the precise structure of the covariance

matrix. The Newey-West estimator does require the researcher to specify the maximum

length of the autocorrelation relationship; we experimented with lags of 1, 2, and 3. For

the Dominican equation, the corresponding t-statistics on the merge dummy ranged in

value from 1.986 (lag=1) to 2.47 (lag=3).

We also estimated the parameters of these equations using OLS and feasible GLS

using the Hildreth-Lu correction for first-order autocorrelation. These results are

similar to those reported in Tables 2 and 3; the principal difference is found in the

Watsonville equation, where the statistical significance of the coefficient on merge fell. 

In the Dominican price equation, the estimated coefficients (t-statistics) on merge were:

1153.6 (2.44) [OLS]; 1300.6 (2.66) [Hildreth-Lu]. In the Watsonville equation, the

estimated coefficients (t-statistics) on merge were: 497.5 (0.73) [OLS]; 550.6 (0.81)

[Hildreth-Lu].

24

V. Empirical Results

Tables 2 and 3 present the estimated parameters of the reduced form price

equation. Table 2 contains the results for Dominican; Table 3 presents the results for

Watsonville.28 The parameters are estimated using ordinary least squares;

autocorrelation and heteroskedasticity consistent standard errors are computed using

the Newey-West (1987) estimator.29 These results are consistent with the possibility that

the merger was anticompetitive.
30 We also estimate this equation using the log of the ratio of the Dominican

(Watsonville) price to the average peer group price. The basic results are unchanged.

25

The results in Table 2 present the strongest evidence of an anticompetitive postmerger price increase. In column (a), which presents the fully specified version of the

equation, the coefficient on merge, the merger dummy variable, suggests a substantial

post-merger price increase – over $1,000 per admission, which represents a price

increase of approximately 30 percent (Dominican’s pre-merger average real revenue per

admission was approximately $3,700). Most of the other coefficients in this equation

are statistically insignificant – the only exceptions are Dominican’s average length-ofstay (length-of-stay_d), which has the expected positive coefficient, and the Producer

Price Index for surgical and medical instruments (ppi_med), which also has the

expected positive coefficient.30

The finding of a post-merger price increase at Dominican is weakened, though

not eliminated, by imposing restrictions on the reduced form equation. In column (b) of

Table 2, we drop the quake dummy variable from the equation. In column (c), we

further restrict the peer group variables to have zero coefficients. As can be seen, these

restrictions cause the magnitude of the coefficient on merge to fall. Even in the most

restricted version of the equation (i.e., column (c)), however, the results indicate that the

merger raised price per admission by about $700. In this specification, the hypothesis
31 We test the null hypothesis that the parameters on the peer group variables

jointly equal zero. For the Dominican equation, the test statistic (distributed F(10, 19))

equals 1.14, which means that the null hypothesis can be rejected only at p=.39. For the

Watsonville equation, the corresponding test statistic and significance level are 1.23

(p=.33).

32 We can reject the null hypothesis that the true parameter on merge equals zero

at the 31 percent level in the unrestricted version of the equation (see Table 3, column

(a)). Columns (b) and (c) of Table 3 present restricted versions of the Watsonville price

equation. Comparing the coefficients on merge across these different specifications

shows that the estimated price effect of the transaction on Watsonville’s price is fairly

robust – it remains at about $500 per admission. The statistical significance of this

coefficient increases as these restrictions are imposed -- from p = .31 in column (a) to p =

.16 in column (c).

33

 See, e.g., Werden and Froeb (1994, p. 413). Werden and Froeb analyze the

competitive consequences of horizontal mergers in differentiated products markets

where firms engage in static price competition.

26

that the true value of this parameter equals zero is rejected at the p = .18 significance

level.31

Table 3 presents the estimated coefficients for the Watsonville price equation. 

The pattern of results is similar to that for Dominican. Most importantly, the coefficient

on merge in the Watsonville equation suggests a post-merger price increase, albeit of a

smaller economic magnitude (approximately one-half as large) than the estimated

increase at Dominican, and of a lesser degree of statistical significance.32 This finding --

a smaller, but positive price effect -- is consistent with the predictions of many

differentiated products oligopoly models,33 and is particularly noteworthy, given that

Watsonville is the type of hospital – namely, locally sponsored and administered –

hypothesized to be least likely to exercise market power. 
34

 See Statement of Commissioner Yao. Dominican claimed that Community

Hospital was inefficiently small, and that efficiencies could therefore be realized by

converting it to a skilled nursing/rehabilitation facility, and channeling its patients to

Dominican.

35 For a large number of clinical procedures there is empirical evidence that

outcomes improve with patient volume. See, e.g., Begg et al. (1998) and Selby et al.

(1996).

27

While the empirical results presented in Tables 2 and 3 are consistent with an

anticompetitive post-merger price increase, our inability to observe and measure

quality perfectly means that we cannot rule out the possibility that the price increases

reflect improvements in quality, rather than increased price-cost markups with

unchanged (or even diminished) quality levels. We are, however, skeptical about the

validity of this interpretation. First and foremost, the parties to the acquisition made no

such claims in defense of the transaction. Rather, the parties claimed that the

efficiencies from the transaction would derive from the realization of scale-related

production efficiencies.34 To the extent that such scale economies were realized, we

would expect prices to fall, other things held constant.

It is perhaps conceivable that consolidation of particular services at Dominican

could lead to volume-related quality increases – for example, because clinical outcomes

for some procedures improve as the procedure is performed with higher frequency at a

particular location.35 Then, Dominican might be able to capture some or all of the value

of this quality increase in the form of higher prices. 
36 The other possibility is that marginal cost increases with output. This

possibility is difficult to reconcile with the efficiency claims actually put forth by the

parties; i.e., that the merger allowed the merged entity to enjoy scale-related reductions

in unit cost.

28

The problem with this explanation is that it fails to explain the post-merger

increase in price at Watsonville Hospital. If the elimination of Community Hospital as a

provider of the services in question leads to higher (quality-unadjusted) prices at

Dominican because of the efficiencies described in the preceding paragraph,

Watsonville would either have to (1) cut its price (assuming that its quality remained

unchanged); or (2) somehow try to match Dominican’s quality increase. If Watsonville

captured some of the patient flow that otherwise would have patronized Community,

then it too might be able to realize volume-related quality increases. But if this

occurred, then it is unclear why prices would rise unless the transaction also had

competitive effects.36 In a competitive market, prices are determined by cost, not

demand (demand determines the equilibrium quantity, but price will be determined by

marginal cost). If the quality of certain services increases at both hospitals (but costs

remain unchanged), then there will be a market-wide increase in demand for the

service, leading to an increase in the total quantity sold. But if marginal costs are

constant, and prices are determined competitively, the price at which this service is sold

would not change. If prices increase, it suggests that the transaction has increased

market power, even if it simultaneously yielded efficiencies.
37 Elzinga and Hogarty (1973). 

29

It perhaps is conceivable that the merger led to other types of quality increases at

both Dominican and Watsonville that are not related to volume, but which manifest

themselves in greater resource use per patient. If so, we might observe an increase in

expenses per admission post-merger -- hence prices -- at both hospitals, other things

held constant. We carry out two tests of this hypothesis. First, in Tables 4 and 5 we

regress real expenses per inpatient admission against the same explanatory variables

employed in the regressions contained in Tables 2 and 3. We find little evidence that

the merger resulted in greater average expenses. Compared to the coefficients on the

merge dummy in the price equations, the corresponding coefficients in Tables 4 and 5

are small in magnitude, and never differ from zero at conventional levels of statistical

significance.

We conduct a second test of this efficiency hypothesis by examining data on

patient flows. If the transaction improved the quality of hospital care provided in Santa

Cruz County, we would expect to observe (ceteris paribus) a reduction in the proportion

of Santa Cruz County residents who seek hospital care outside of the county. That is,

we would expect to observe an increase in the Elzinga-Hogarty “little in from outside”

(“LIFO”) statistic.37 For a given year, the LIFO statistic equals the total number of

patients admitted at Santa Cruz hospitals who were Santa Cruz County residents,

divided by the total number of Santa Cruz county residents admitted at any hospital

(i.e., whether at a Santa Cruz County hospital or elsewhere). To test the efficiency
38 The estimated regression is: LIFO =.6881 - .0097*merge + e.

 (77.42) (-0.87)

T-statistics are in parentheses.

39

 See note 19, above. 

40 As noted earlier (see note 34, above), Dominican had argued that Community

(180 beds) was inefficiently small. If true, then it surely follows that a 21 bed hospital is

(continued...)

30

hypothesis, we regress the LIFO statistic against a constant and the merge dummy. If

the efficiency hypothesis is correct, we would expect to obtain a positive coefficient on

merge. As it turns out, however, the coefficient is negative and statistically

insignificant.38 Thus, this result also fails to support the efficiency hypothesis.

Two other aspects of these empirical findings merit comment. First, we observe

that the coefficient on entry (the dummy variable set equal to one for those time periods

after the entry of the Sutter mini-hospital) was not consistently negative across

specifications (e.g., compare Table 2 to Table 3), as theory would predict, and was never

different from zero at conventional significance levels. As noted earlier, the FTC’s

rationale for not pursuing a divestiture remedy in this matter was predicated in part on

the decision of Sutter to enter this market.39 It now appears that the FTC overestimated

the competitive impact of this entry. This error probably was attributable to the limited

scale and scope at which entry actually occurred. The new hospital was not a full-scale

acute care institution, but rather a very small (21 staffed beds) maternity and surgery

center. It is implausible that this institution would impose the same competitive

constraints on incumbent producers as did Community hospital.40
40 (...continued)

too small to provide efficiently the full range of services necessary to compete with

Dominican.

31

VI. Conclusion

The combination of Dominican Hospital and Community in Santa Cruz,

California, affords researchers a rare opportunity to study the competitive effects of a

horizontal merger in a concentrated antitrust market dominated by not-for-profit

producers. We have attempted to assess these effects by estimating a reduced form

price equation for the merged entity and its closest rival, Watsonville Community

Hospital. Controlling for casemix, input prices, and other cost- and demand-side

characteristics, our results suggest that both hospitals raised prices in the aftermath of

the transaction. The small scale entry that occurred after the merger was consummated

did not mitigate this price increase. These price increases -- and in particular, the price

increase at Watsonville hospital, a locally-sponsored and administered community

hospital -- suggest strongly that mergers involving not-for-profit hospitals are a

legitimate focus of antitrust concern. While one cannot rule out unequivocally the

possibility that this price increase reflects some unmeasured increase in hospital quality,

a more plausible interpretation is that the merger created market power that allowed

Dominican and Watsonville to increase price-cost markups. 
32

Table 1

Descriptive Statistics

Variable

Name

Description Mean Minimum Maximum

rprice_d real net revenue

per admission, 

Dominican

Hospital

 4434.55 3212.89 5882

rprice_w real net revenue

per admission, 

Watsonville

Hospital

 3897.98 1794.32 6490.128 

rprice_p real net revenue 

per admission,

peer group

hospitals

 4955.76 3454.33 6091.85

length-of-stay_d average length-ofstay,

Dominican

Hospital

 4.01 2.71 5.63

length-of-stay_w average length-ofstay,

Watsonville

Hospital

 3.99 2.71 6.79

length-of-stay_p average length-ofstay,

peer group

hospitals

 4.38 3.70 4.93

medi-Cal share_d share of

admissions

MediCal, 

Dominican

Hospital

 0.14 0.051 0.17

medi-Cal

share_w

share of

admissions

MediCal,

Watsonville

Hospital

 0.29 0.10 0.48
Variable

Name

Description Mean Minimum Maximum

33

medi-Cal share_p share of

admissions

MediCal,

peer group

hospitals

 0.21 0.17 0.24

medicare share_d share of

admissions

Medicare,

Dominican

Hospital

 0.39 0.33 0.44

medicare

share_w

share of

admissions

Medicare,

Watsonville

Hospital

0.31 0.22 0.40

medicare share_p share of

admissions

Medicare,

peer group

hospitals

 0.38 0.34 0.40

casemix_d casemix index,

Dominican

 0.85 0.75 1.04

casemix_w casemix index,

Watsonville

 0.76 0.67 0.87

casemix_p casemix index,

peer group

hospitals

 0.94 0.84 1.05

density_p population

density, peer

group counties

 101.43 89.97 110.07

density_s population

density,

Santa Cruz

County

 516.96 486.10 539.57

hmo_d share of

admissions HMO

insured,

Dominican

 0.25 0 0.50
Variable

Name

Description Mean Minimum Maximum

34

hmo_w share of

admissions HMO

insured,

Watsonville

 0.06 0 0.25

hmo_p share of

admissions HMO

insured,

peer group

 0.15 0.06 0.23

income_s real per capita

income,

Santa Cruz

County

 16104.97 14464.69 17700.51 

income_p real per capita

income, 

peer group

counties

 13253.29 12968.84 13703.24 

ppi_med producer price

index,

medical &

surgical

instruments

 121.19 107.27 131.27

unemploy_p unemployment

rate,

peer group

counties

 10.53 8.16 14.40

unemploy_s unemployment

rate,

Santa Cruz

County

 8.35 5.37 13.83

merge = 1 for quarters

after merger

occurred

 0.6304348 0 1 

entry = 1 for quarters

after entry

occurred

 0.1086957 0 1

run_qtr time trend 23.54348 1 48
Variable

Name

Description Mean Minimum Maximum

35

run_qtr2 time trend

squared

 732.5435 1 2304

wage_d HCFA wage

index,

Dominican

 1.22 0.97 1.42

wage_w HCFA wage

index,

Watsonville

 1.20 0.97 1.39

wage_p HCFA wage

index,

peer group

 1.16 1.12 1.24

expadm_d real expense per

admission,

Dominican

4118.93 3212.4 4986.1

expadm_w real expense per

admission,

Watsonville

3343.72 2582.8 3969.6

expadm_p real expense per

admission, peer

hospitals

4038.26 3185.02 4628.92

quake = 1 for 3rd quarter

1989 and after, 0

otherwise

.70 0 1

merge = 1 for 2nd quarter

1990 and after, 0

otherwise

.63 0 1

entry = 1 for 2nd quarter

1996 and after, 0

otherwise

.11 0 1
36

Table 2

Dominican Hospital Price Regression

Quarterly Data, 1986-96

Dependent Variable = real net revenue per private admission 

Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

merge 1179.92

(1.93)

1153.63

(1.99)

737.46

(1.356)

income_p -1.25

(-0.74)

-1.26

(-0.78)

income_s -0.19

(-0.70)

-.20

(-0.75)

-.25

(-1.57)

density_p -236.8

(-0.95)

-230.8

(-0.96)

density_s 47.89

(0.72)

45.59

(0.70)

-9.73

(-0.41)

unemploy_p -302.19

(–0.81)

-369.82

(-1.04)

unemploy_s 125.43

(0.68)

156.43

(0.88)

-75.37

(-2.53)

length-of-stay_d 461.66

(2.98)

437.96

(2.96)

469.86

(2.89)

length-of-stay_p -153.91

(-0.27)

-13.67

(-0.03)

hmo_d -284.10

(-0.12)

-448.06

(-.20)

-679.83

(-0.55)

hmo_p 6780.54

(0.55)

6587.93

(0.54)

casemix_p -9376.14

(-1.26)

-10185.27

(-1.42)

casemix_d 1081.09

(0.33)

1125.12

(0.34)

-1524.21

(-0.70)
Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

37

wage_p 94.71

(0.01)

926.02

(0.15)

wage_d -756.33

(-0.61)

-713.21

(-0.58)

-82.71

(-0.13)

rprice_p .14

(0.28)

.18

(0.36)

medicare share_d 1909.25

(0.42)

2625.13

(0.55)

3464.04

(1.37)

medicare share_p -9161.47

(-0.58)

-11677.03

(-0.76)

medi-Cal share_d 2410.38

(0.45)

1956.57

(0.37)

1109.31

(0.39)

medi-Cal share_p -4574.66

(-0.19)

-3710.94

(-0.15)

ppi_med 253.50

(1.89)

294.69

(3.18)

108.49

(2.16)

run_qtr -96.75

(-0.60)

-117.87

(-0.78)

26.40

(0.27)

run_qtr2 0.78

(0.40)

1.03

(0.56)

-0.74

(-0.89)

entry 280.62

(0.39)

257.16

(0.36)

670.12

(2.35)

quake 278.89

(0.63)

intercept 4634.23

(0.18)

1066.04

(0.04)

-1408.28

(-0.09)

N = 44

R2 = .89

N = 44

R2 = .88

N= 44

R2 = .84 

Newey-West heteroskedasticity and autocorrelation 

consistent standard errors (lag length = 1)
38

Table 3

Watsonville Hospital Price Regression

Quarterly Data, 1986-96

Dependent Variable = real net revenue per private admission 

Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

merge 428.42

(1.48)

497.47

(1.44)

524.30

(1.52)

income_p -1.44

(-0.58)

-0.83

(-0.34)

income_s -0.59

(-1.57)

-0.55

(-1.44)

-.63

(-3.17)

density_p 1.64

(0.01)

91.82

(0.31)

density_s -65.57

(-0.62)

-77.70

(-0.77)

-96.15

(-3.35)

unemploy_p -192.81

(-0.58)

89.83

(0.26)

unemploy_s 75.96

(0.37)

-68.12

(-0.31)

3.82

(0.07)

length-of-stay_w 661.65

(3.13)

589.53

(2.81)

746.46

(4.24)

length-of-stay_p 1564.40

(1.27)

1082.68

(0.97)

hmo_w -3001.02

(-1.27)

-2034.23

(-0.87)

-2926.96

(-1.15)

hmo_p -2909.30

(-0.19)

-5026.07

(-0.32)

casemix_p -1333.34

(-0.14)

1593.00

(0.19)

casemix_w 6441.82

(1.66)

6738.19

(1.76)

7241.37

(3.38)
Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

39

wage_p 3268.53

(0.28)

-1031.73

(-0.10)

wage_w -245.38

(-0.11)

92.20

(0.04)

-676.05

(-0.50)

rprice_p 0.02

(0.03)

-0.09

(-0.11)

medicare share_w 1547.97

(0.32)

3444.37

(-.70)

1220.01

(0.25)

medicare share_p -22510.47

(-1.47)

-16165.86

(-0.99)

medi-Cal share_w -4439.67

(-1.84)

-3690.78

(-1.47)

-3817.34

(-1.91)

medi-Cal share_p 3832.57

(0.11)

-2018.22

(-0.06)

ppi_med 472.47

(2.48)

279.54

(1.52)

228.73

(2.21)

run_qtr -108.34

(-0.47)

-25.37

(-.11)

109.46

(0.94)

run_qtr2 0.89

(0.29)

-.22

(-0.07)

-2.03

(-1.43)

entry -525.84

(-0.93)

-416.75

(-0.66)

25.75

(0.05)

quake -1023.69

(-1.82)

intercept 4964.34

(0.14)

16107.88

(0.48)

27732.12

(1.42)

N = 44

R2 = .88

N = 44

R2 = .87 

N = 44

R2 = .84

Newey-West heteroskedasticity and autocorrelation 

consistent standard errors (lag length = 1)
40

Table 4

Dominican Hospital Expense Regression

Quarterly Data, 1986-96

Dependent Variable = real inpatient expenses per admission 

Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

merge 143.94

(1.02)

110.41

(0.83)

84.87

(0.66)

income_p .22

(0.21)

0.24

(0.21)

income_s -0.27

(-1.62)

-0.28

(-1.63)

-0.24

(-3.93)

density_p -9.94

(-0.07)

-6.02

(-0.04)

density_s 3.38

(0.09)

-0.54

(-0.01)

-14.30

(-1.84)

unemploy_p 19.05

(0.13)

-53.27

(-0.37)

unemploy_s -25.51

(-0.32)

6.61

(0.08)

-15.64

(-0.79)

length-of-stay_d 7.34

(0.13)

-16.65

(-0.25)

-58.95

(-0.71)

length-of-stay_p 197.67

(0.76)

369.77

(1.37)

hmo_d -828.36

(-0.70)

-1003.58

(-0.87)

-417.71

(-0.76)

hmo_p -1170.30

(-0.32)

-1122.31

(-0.32)

casemix_p -644.95

(-0.24)

-1527.72

(-0.49)

casemix_d 311.36

(0.30)

308.32

(0.28)

-238.12

(-0.27)
Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

41

wage_p 4986.65

(1.69)

5853.47

(1.84)

wage_d -491.62

(-1.30)

-466.61

(-1.23)

294.96

(1.17)

expense_p 0.27

(0.56)

0.29

(0.61)

medicare share_d 735.17

(0.44)

1438.33

(0.82)

780.20

(0.47)

medicare share_p 1082.31

(0.18)

-1341.07

(-0.23)

medi-Cal share_d 1609.24

(0.49)

1045.77

(0.33)

395.13

(0.26)

medi-Cal share_p -6820.35

(-1.17)

-1341.07

(-0.23)

ppi_med -46.42

(-0.67)

-0.13

(-0.002)

22.78

(0.81)

run_qtr 138.48

(1.54)

120.37

(1.36)

80.30

(2.94)

run_qtr2 -1.15

(-1.04)

-0.93

(-0.82)

-0.78

(-2.34)

entry 341.79

(1.14)

323.94

(1.11)

419.14

(2.68)

quake 308.11

(1.70)

intercept 1780.65

(0.15)

-1707.39

(-0.17)

11244.89

(2.76)

N = 44

R2 = .96

N = 44

R2 = .96

N = 44

R2 = .94

Newey-West heteroskedasticity and autocorrelation 

consistent standard errors (lag length = 1)
42

Table 4

Watsonville Hospital Expense Regression

Quarterly Data, 1986-96

Dependent Variable = real inpatient expenses per admission 

Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

merge 123.47

(0.60)

121.37

(0.62)

155.93

(0.72)

income_p -0.93

(-0.90)

-0.95

(-0.95)

income_s -0.15

(-0.81)

-0.15

(-0.82)

-0.32

(-3.76)

density_p 63.92

(0.43)

61.4

(0.44)

density_s -2.58

(-0.07)

-2.32

(-0.06)

-34.23

(-2.86)

unemploy_p 151.03

(0.59)

143.26

(0.62)

unemploy_s -66.13

(-.044)

-62.28

(-0.45)

14.56

(0.50)

length-of-stay_w 223.34

(2.12)

225.21

(2.16)

240.91

(5.54)

length-of-stay_p -573.39

(-1.30)

-559.09

(-1.41)

hmo_w 174.69

(0.09)

148.52

(0.08)

-0.67

(0.00)

hmo_p -8915.0

(-1.64)

-8837.91

(-1.72)

casemix_p 1865.20

(0.52)

1785.43

(0.55)

casemix_w -793.64

(-0.52)

-805.12

(-0.53)

-919.8

(-0.83)
Coefficient

(t-statistic)

Coefficient

(t-statistic)

Coefficient

(t-statistic)

43

wage_p -1061.92

(-0.28)

-939.29

(-0.27)

wage_w 1799.07

(1.76)

1787.93

(1.87)

-170.24

(-0.41)

expense_p -0.42

(-0.42)

-0.42

(-0.42)

medicare share_w 4648.03

(1.93)

4597.5

(1.99)

2809.34

(1.73)

medicare share_p -1914.61

(-0.29)

-2077.00

(-0.32)

medi-Cal share_w -329.37

(-0.21)

-348.62

(-0.23)

-1349.59

(-1.38)

medi-Cal share_p -48.87

(-0.01)

160.19

(0.01)

ppi_med -56.90

(-0.68)

-51.61

(-0.71)

-21.18

(-0.36)

run_qtr 83.57

(0.53)

81.47

(0.53)

100.09

(2.17)

run_qtr2 -0.84

(-0.42)

-0.80

(-0.43)

-0.35

(-0.48)

entry 226.26

(0.63)

223.12

(0.64)

-70.75

(-0.49)

quake 27.83

(0.09)

intercept 19206.04

(1.22)

18897.04

(1.23)

25973.83

(3.71)

N = 44

R2 = .84

N = 44

R2 = .84

N = 44

R2 = .74

Newey-West heteroskedasticity and autocorrelation 

consistent standard errors (lag length = 1)
44

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APPENDIX A

PEER GROUP HOSPITALS

HOSPITAL COUNTY BEDS-1996

Mercy Hospital* Kern 261

San Joaquin Community

Hospital*

Kern 178

Feather River* Butte 121

Chico Community

Hospital

Butte 105

Oroville* Butte 120

El Centro Imperial 107

Natividad Monterey 181

Salinas Valley Monterey 180

Queen of the Valley* Napa 176

Redding Medical Center Shasta 162

Mercy Medical CenterRedding*Shasta 220

North Bay Medical

Center*

Solano 108

Sutter Solano Medical

Center*

Solano 109

Woodland* Yolo 103

Rideout* Yuba 128

Marian Medical Center* Santa Babara 225

Dameron* San Joaquin 211

*Non-profit hospital