During the 1990s, managed care displaced indemnity insurance to
become the dominant form of health insurance in the private sector
(Glied 2000). Over the same period, a wave of hospital mergers,
acquisitions, and hospital system expansions occurred. In 1990, the
mean, population weighted, hospital concentration, measured with the
Herfindahl-Hirschman index (HHI) (1) in Health Services Areas (HSA) was
0.1913. By 2000, it had risen to 0.2596. Over 90 percent of the increase
in concentration is a consequence of mergers, acquisitions, and hospital
system expansions. (2)
Understanding the determinants of hospital market power allows the
development of policies to manage hospital market power, which is
important because hospital market power increases, this has negative
effects on markets. First, as hospital market power improves
hospitals' bargaining position with payers and increases the cost
of hospital care for the privately insured population (Dranove and
Satterthwaite 2000; Gaynor and Vogt 2000, provide excellent reviews of
the literature studying this effect). Second, hospital market power is
also associated with decreased hospital quality (Kessler and McClellan
2000; Gowrisankaran and Town 2003). Finally, there is some evidence that
hospital market power decreases access to health services for
underserved populations (Aizer, Currie, and Morretti 2004).
It is conventional wisdom that the rise of managed care
precipitated the hospital consolidations and concentration in the 1990s.
(3) Graphical analysis is consistent with conventional wisdom. Figure 1
graphs the mean, population-weighted levels of hospital HHI and HMO
penetration across all HSAs. (4) Hospital concentration and HMO
penetration share a common, upward trend throughout most of the 1990s.
In the late 1990s there was a break in the relationship, as HMO
penetration declined while hospital concentration continued to increase.
While suggestive, the correlation does not prove that there is a causal
link between HMO penetration and hospital concentration.
[FIGURE 1 OMITTED]
This paper tests whether there is a causal relationship, examining
the proposition that the rise of managed care caused hospitals to
consolidate in the 1990s. Our models explicitly account for the
possibility that markets are systematically heterogeneous and this
heterogeneity may bias cross-sectional estimates of the parameters of
interest.
Our estimates indicate that the rise in managed care did not cause
the increase in hospital concentration. In every specification we
estimated, the coefficients on managed care penetration are not
different from zero at traditional levels of confidence. Furthermore,
our data analysis fails to suggest other possible cause(s) of the
hospital consolidation wave.
The next section discusses some of the reasons why managed care
might lead to hospital consolidation. The following sections present the
methods, data, results, and discussion of our findings.
HOSPITAL CONSOLIDATION AND MANAGED CARE
Why Managed Care May Cause Hospital Consolidation
There are at least three reasons why managed care might cause
hospital consolidation. First, managed care may reduce the demand for
hospital beds and create excess capacity in the market. Second, managed
care may change the bargaining power of hospitals relative to health
insurers. Third, the value of contracting with an integrated hospital
system may be greater for managed care organizations (MCOs) than
indemnity insurers.
One of the theories underlying managed care is that by monitoring
and controlling health care use, insurers can reduce health care
expenditures and perhaps increase enrollee health. In the RAND health
insurance experiment, enrollment in the prepaid Group Health Cooperative
of Puget Sound reduced the likelihood of a hospital admission by 35
percent compared with a fee-for-service population (Manning et al.
1987). (5) However, as an explanation of hospital consolidation this
finding needs embellishment because most economic models of mergers
predict that the incentive to merge increases with the demand for the
product (e.g., Deneckere and Davidson 1985). The intuition behind this
result is straightforward--the larger the market the larger the profit
gains from market power.
However, it is possible to conceive of circumstances in which a
decline in demand for inpatient services leads to hospital
consolidation. If demand falls far enough so the market can no longer
support the old number of hospitals under the old ownership structure,
then there may be an incentive to merge.
According to neo-classical economic theory, if the reduction in
demand leads to lower prices and if price falls below average variable
cost, the market will remove capacity in some way. Hospital closure is
one way to reduce capacity. But, because hospital assets have "high
specificity" and few alternative uses, hospitals may seek to raise
price above average cost by combining operations in order to achieve
efficiencies which reduce average costs and/or to use market power to
raise prices. This explanation requires that hospitals value autonomy as
well as profits. If autonomy were not valued, the hospitals would have
been better off by consolidating earlier. Given the hospital
industry's historical basis as a decentralized, community-based
system, the assumption of autonomy being valued is plausible.
The second reason that increases in managed care might trigger
hospital consolidation is based on the ability of MCOs to bargain
effectively with hospitals over prices. By selective contracting, MCOs
increase their bargaining leverage with hospitals vis-a-vis indemnity
plans, increasing the price elasticity of demand for hospital services.
This explanation applies to a broad shift in contracting practices that
may have been initiated by the rise of HMOs and were subsequently
adopted by other forms of managed care and non-managed care health
insurance plans. However, the effect of increasing the price elasticity
of demand on the benefits of consolidation is theoretically ambiguous.
The third possible reason why managed care may increase hospital
consolidation is that by forming an integrated delivery system the
hospitals are better able to manage patient care and better able to
engage in capitation arrangements with health plans. Better management
occurs through vertical (with physician groups) and horizontal (with
other hospitals) arrangements that can manage care for a geographically
dispersed population. MCOs then contract with the integrated delivery
systems, delegating care management to the integrated delivery system.
But, the development of integrated delivery systems is a weak
explanation for the effect of managed care on hospital consolidation.
Capitated hospital arrangements never became widespread and integrated
delivery systems ultimately "did not work" (Burns and Pauly
2002).
Empirical Evidence
There are three studies of the relationship between managed care
penetration and hospital market competition/hospital propensity to
merge. Dranove, Simon, and White (2002) (DSW) use data on
physicians' reports of managed care revenue and find that the
change in hospital concentration in 68 large metropolitan statistical
areas (MSAs) between 1981 and 1994 is positively correlated with the
level of managed care concentration in 1993/1994. This research has some
limitations. First, the time period of the analysis is terminated before
most of the hospital consolidations and increase in managed care in the
1990s. Second, DSW focus on large metropolitan areas in which hospital
mergers are less likely to lead to significant increases in market
power. The mean end-of-period HHI they report is 0.126--well below
concentration levels that would give the Federal Trade Commission or the
Department of Justice cause for concern. (6) Third, the measure of
managed care penetration is the mean percent of physician practice
revenues from managed care. This measure is not widely used and does not
directly capture the more commonly used measure of product market
penetration. (7) The measure is also a function of physician prices in a
location and may be more subject to endogeneity problems than simple
managed care penetration rates. However, an important advantage of
DSW's measure is that it includes all forms of managed care, not
just HMOs. Fourth, DSW regress the change in concentration on the
end-of-period level of managed care penetration. They argue that this is
a reasonable approach because managed care penetration in most cites was
close to zero at the beginning of their sample. However, in this type of
statistical analysis, the difference between being "close" to
zero and being exactly zero can matter. Failure to difference a variable
can still lead to bias due to measurement error. The magnitude of the
bias will be a function of the average size of the error and the
correlation between the error and the unobservable, market-specific
component of hospital concentration. We replicate and discuss the DSW
results in greater detail in "Results."
Sloan, Ostermann, and Conover (2003) use individual hospital data
to study the factors associated with merger, closure, and conversions.
They find no significant association between market-level HMO
penetration and the likelihood that a hospital will merge. Chernew
(1995) analyzes the relationship between the change in the number of
hospitals and managed care penetration in 175 large MSAs between 1982
and 1987. He finds a negative and significant relationship between
managed care penetration and the number of hospitals in an MSA. Like
DSW, Chernew's analysis occurs before the rise in managed care.
Also, Chernew's analysis does not explicitly focus on changes in
market structure due to consolidation. The number of hospital
competitors in a market can change due to consolidation (although a
consolidation does not necessarily change the physical number of
hospitals in a location) or because of entry or exit.
METHODS
We estimate the impact of managed care on hospital consolidation by
relating the change in HMO penetration to one of several possible
measures of hospital concentration change. Using a model similar to DSW,
we assume that hospital concentration is related to contemporaneous HMO
penetration:
[H.sub.it] = [[alpha].sub.i] + [HMO.sub.it][[beta].sub.t] +
[x'.sub.it][[gamma].sub.t] + [e.sub.it] (1)
where [H.sub.it] is the hospital concentration in market i at time
t, HMO penetration is denoted by [HMO.sub.it], [x.sub.it] is a vector of
market characteristics, [[alpha].sub.i] is a time-invariant unobserved
(to the researcher) market fixed effect, and [e.sub.it] is a market/time
shock. In some specifications we replace [HMO.sub.it] with the current
number of HMOs operating in the HSA. We let [[beta].sub.t] depend on
time as changes in technology or unmeasured market conditions may affect
the incentive for hospitals to consolidate in response to managed care.
(8) We also estimate a specification that uses 2-year lagged values of
HMO penetration allowing for the possibility that hospitals respond with
a lag to HMO penetration.
It is possible that the unobserved market fixed effect is
correlated with HMO penetration. There are many possible reasons for
this correlation. For example, locations in which hospitals have high
[alpha]'s may be unattractive for HMOs to enter because they have
few hospitals with which to contract. If [alpha] is correlated with HMO
penetration, ordinary least squares (OLS) estimation of (1) will lead to
biased estimates of [[beta].sub.t]. This bias can be eliminated by
taking differences of (1) across time.
Taking differences between period t and period t - 1 gives
[DELTA][H.sub.it] = [DELTA][HMO.sub.it][[beta].sub.t] +
[HMO.sub.it-1][[beta].sup.e] + [DELTA][x'.sub.it][[gamma].sub.t] +
[x'.sub.it-1][[gamma].sup.e] + [u.sub.t] (2)
where [DELTA] is the standard difference operator (i.e.,
[DELTA][H.sub.it] [equivalent to] [H.sub.it] - [H.sub.it - 1]),
[[beta].sup.e] = [[beta].sub.t] - [[beta].sub.t - 1], [[gamma].sup.e], =
[[gamma].sub.t] - [[gamma].sub.t - 1], and [u.sub.it] = [e.sub.it]
[e.sub.it - 1].
We use a long difference of 10 years to define our change variable,
with 1990 as the base year. (9) We chose 1990 as the starting year
because that is the approximate beginning of the horizontal (within
market) consolidation wave. The mean, population-weighted increase in
the HHI between 1985 and 1990 was 0.0007--a very small difference over
the 5-year period. However, the increase in average HHI between 1990 and
1991, the years of the smallest increase in our sample, was 0.0035, a
rate of increase that is approximately 25 times larger than the annual
rate of increase over the preceding 5 years.
Measures of the Change in Concentration
The most common measure of competition used by economists is the
HHI. The HHI is the sum of the squared market shares of all firms in a
market:
[HHI.sub.it] = [N.summation over (i=1)][s.sup.2.sub.it] (3)
where s is the market share of the hospital system within the
market, [s.sub.it] = [q.sub.it]/[[summation].sup.N.sub.j=1][g.sub.it],
and q is a measure of output/capacity while N is the number of market
participants. The HHI will change with entry, exit, and shifts in the
distribution of shares across hospitals. In order to control for changes
in the HHI that are unrelated to consolidation (which is the phenomena
of interest), we include counts of the number of entrants and exits in
the list of control variables.
We made two important decisions in calculating HHI. The first
concerns the boundaries (both geographic and product) of the market. As
we discuss in the data section, we use HSA as the geographic boundary
and short-term, nongovernmental inpatient care as the product market. We
tested the sensitivity of this decision by repeating our analysis using
HHI constructed from using the MSA as the geographic boundary. Our
findings are insensitive to this decision. (10)
The second concerns the appropriate measure of output. Our primary
measure of output is the total number of staffed beds. We tested the
sensitivity of this decision by estimating the parameters using three
alternative HHI measures. First, we use a HHI measure based on inpatient
days. Second, we formulated a "Strong HHI" measure by treating
only hospitals that are owned by a centralized system as a single firm.
These are identified as hospitals with the same American Hospital
Association (AHA) ID. Third, because HHI can change for reasons
unrelated to consolidation (e.g., the distribution of output/ capacity
changes or there is entry into or exit from the market), we form a HHI
measure (AMHHI) that changes only when the ownership/system membership
changes. AMHHI is defined as
[DELTA][MHHI.sub.it,t] = [[N.sub.r.summation over (i=1)]
s(O.sub.t]).sup.2.sub.ir] - [[N.sub.r.summation over (i=1)]
s.sup.2.sub.ir] (4)
where s([O.sub.t]).sup.2.sub.ir] is the share based on the
distribution of output/capacity from period r aggregated to the hospital
using the ownership/system structure in period t (r
Robustness Analysis
The long difference approach removes any time-invariant,
market-level unobserved factors that may affect concentration. However,
it is still possible that time varying factors (i.e., the error term in
(2)) are correlated with our measures of HMO penetration, and if so, our
estimates will be biased. We attempt to examine the sensitivity of our
conclusions to this possibility by using an instrumental variables
approach.
We use as instruments the number of nonspecialist physicians per
capita in 1990, the percent of one-employee firms, the percent of the
firms that have between one and five employees, and the percent of firms
with over 50 employees in 1990. These variables are plausibly unrelated
to changes in hospital market structure and they predict HMO
penetration.
We hypothesize that HMOs will have more bargaining power and
consequently lower costs of doing business in markets with higher
numbers of primary care physicians per capita. Baker and Brown (1999)
used the size distribution of employers to instrument for HMO
penetration in their analysis of the impact of managed care on
mammography providers.
It is well known that if the instrument set is a poor predictor of
the endogenous variables conditional on all of the control variables,
then small-sample bias can be very large (Stock and Staiger 1997).
Following the suggestion of Stock and Staiger (1997) and Bound, Jaeger,
and Baker (1995), we performed F-tests of the joint significance of the
instrument set for the endogenous variables, [DELTA]HMO Penetration and
HMO Penetration. The F-statistics are 2.63 and 4.50 for the regressions
with corresponding p-values of .03 and .001, respectively. Both of these
F-statistics are significantly greater than 1--a value below which that
Bound, Jaeger, and Baker (1995) suggest should be "a cause for
concern." (11)
Data
An observation in our analysis is an HSA, defined by the National
Center for Health Statistics (Makuc et al. 1991) as one or more counties
that are relatively self-contained with respect to the provision of
routine hospital care. This definition is operationalized by using an
algorithm that groups counties by minimizing travel by patients within
the area to areas outside of the defined group. Thus, unlike MSAs, the
HSA relies on patient flows instead of geopolitical boundaries to
identify hospital "markets." (12)
This definition is not ideal. The ideal unit of observation for our
study would be an antitrust market. However, defining antitrust markets
is notoriously difficult. It requires detailed analysis of each hospital
under consideration, and there is still substantial disagreement within
the economic and legal communities on the appropriate methodology to use
(Werden 1990; Capps et al. 2002). While it is important to recognize
that HSAs are not antitrust markets, they are probably a closer
approximation to them than MSAs. Most economists believe that antitrust
markets typically are smaller than MSAs, and HSAs, on average, are
significantly smaller than MSAs.
The data in this study come from several sources. Data on hospitals
come from the AHA hospital survey which contains information on hospital
location, size (beds and inpatient days), and system membership. AHA
system membership information contains some inaccuracies. We use a
"corrected" AHA system ID constructed by Kristin Madison which
has been updated by researchers at Carnegie Mellon University. (13) See
Madison (2004) for more information on this data. The AHA survey also
tracks mergers and acquisitions of different hospitals in an appendix.
In most of the analysis, we use data from 1990 to 2000 but in some of
our analyses we used data from the 1985 hospital survey. Each hospital
is assigned to an HSA according to its address in the 1990 AHA Survey.
Our information on HMO penetration and the number of HMOs operating
in an HSA comes from InterStudy. InterStudy collects information on the
number of HMO enrollees by health plan. The HMO data come from
InterStudy census data (InterStudy 1985-1987, 1988-2001) and Group
Health Association HMO Directories (Group Health Association of America
1989-1992). The InterStudy data are used to form county-level measures
of HMO penetration and the number of HMOs following the methodology of
Wholey, Engberg, and Bryce (2006). County-level market measures come
from the Area Resource File (ARF, Bureau of Health Professions 1999).
State-level wage data come from the Bureau of Labor Statistics
Occupational Employment Statistics.
HSA measures were constructed by first measuring HMO enrollment,
ARF characteristics, and wage data at the county level. The ARF provided
county-level measures of the number of primary care physicians, median
per-capita income, unemployment rate, population of the HSA, and percent
of the population over 65 years of age. Information on the size
distribution of employers comes from the Census's County Business
Patterns. County data were then aggregated into HSAs using a crosswalk
between counties and HSAs. The HSA-level measures were constructed as
weighted averages of all counties in the HSA, with the weights being the
proportions of the county's population in the HSA. State-level,
hospital certificate of need (CON) regulations are from the American
Health Planning Association.
Our primary sample is HSAs with a population of more than 100,000
and more than one hospital in 1990. We dropped the monopoly HSAs as
those areas, by definition, cannot experience a horizontal consolidation
(unless there is entry and then merger/system formation--a very unlikely
possibility). (14) We also present results using a population threshold
of 500,000.
Table 1 lists the dependent and explanatory variables as well as
the instruments in the analysis and the unweighted means and standard
deviations of these variables. The right-hand side variables were
selected because they plausibly affect hospital profitability either
through the interactions of supply and demand or through regulatory
interventions. There is a large increase in hospital concentration and
HMO penetration during the 1990s. Approximately 40 percent of all HSAs
experienced a horizontal hospital merger. There are also large declines
in inpatient days per capita (20 percent) and the number of beds per
capita (17 percent). In ancillary analysis we performed, the decline in
inpatient days and beds per capita appears unrelated to HMO penetration.
(15)
RESULTS
Table 2 presents the parameter estimates from our primary
specifications. Column (1) presents the results from our base
specification. The coefficient on the change in HMO penetration is
negative and not significantly different from zero and the coefficient
on the 1990 HMO penetration is positive, small, and also insignificantly
different from zero. Column (2) presents the coefficients estimates with
lagged change in HMO penetration as the right-hand side variable of
interest. The coefficient on HMO penetration is small in magnitude and
insignificant.
Table 2, column (3) presents the results with inpatient days used
to measure HHI. The coefficient on the change in HMO penetration is
negative and significantly different from zero at the 5 percent level of
confidence, while the coefficient on the 1990 HMO penetration is
negative and insignificantly different from zero. In column (4) we
present the coefficient estimates when we increase the HSA population
threshold to 500,000. The coefficients on both HMO penetration variables
are negative and insignificant. The joint test of both the coefficient
on HMO penetration variables in all specifications fails to reject the
null hypothesis that they are different from zero. The last column of
Table 2 presents the results using the change in the logarithm of the
number of HMOs in the HSA as the independent variable. The coefficient
is negative and insignificant. The results in Table 2 do not suggest
that the rise of managed care caused hospital consolidation.
Table 3 presents the results using the change in Strong HHI and the
AMHHI as dependent variables for the two different population threshold
samples. Broadly consistent with the estimates in Table 2, none of the
HMO penetration variables is significantly different from zero at
traditional levels of confidence in any specification.
Table 4 presents the IV estimates (both the first and second
stages) of the HMO penetration coefficients on our four different HHI
measures. Again, none of the HMO penetration coefficients are
significant at traditional levels of confidence. However, some of the
coefficients are large, albeit imprecisely estimated.
The only other coefficient on our control variables that is
consistently significant across specifications is the 1990 population.
Larger initial populations are associated with smaller changes in
concentration. Given that population is correlated with the number of
hospitals in a location and that HHI is a nonlinear function of the
number of hospitals, this correlation is not surprising. However, it is
noteworthy that the obvious explanatory variables to not explain
cross-sectional differences in the change in hospital concentration. It
remains a puzzle why some locations became more concentrated while in
other areas the hospitals did not consolidate. (16)
Explaining the Difference between Our Estimates and DSW
There are several possible reasons why our results differ from DSW.
First, we difference both our left-hand and right-hand variables of
interest while DSW do not difference their measure of HMO penetration.
Second, DSW use a more inclusive measure of managed care but one that
may have more measurement error. Third, they examine a different time
frame, 1981-1994. Fourth, they use the MSA as the unit of analysis.
Fifth, they limit their analysis to the largest 62 MSAs with population
over 800,000 and no significant HMO penetration in the early 1980s.
In order to determine the source of our differing conclusions we
attempt to replicate their results with our data. We use the largest 62
MSAs and data from 1985 to 1994 and we exclude the MSAs DSW identify has
having significant HMO penetration in the early 1980s. We use a very
similar, but not identical, set of control variables and instruments.
(17)
Table 5 presents the results of our attempts to replicate DSW. In
column (1) we present the coefficient estimates of this effort. As in
DSW, the coefficient on managed care penetration is positive and
significant. (18) In column (2) we estimate the same equation but
replace the level of managed care penetration with the change in the
managed care penetration. The coefficient on the managed care variable
declines and becomes insignificant.
In columns (3) and (4) of Table 5 we estimate the parameters as in
columns (1) and (2) on the same sample, but using OLS. The parameter
estimates in column (3) is smaller than the estimates in column (1) but
it is still significant. However, the OLS estimates of the impact of the
differenced managed care variables are positive and significant. In
columns (5) and (6) we replicate the analysis in columns (1) and (2)
using a HHI that is based on bed size. The coefficients on both HMO
penetration measures are insignificant. In the last two columns of Table
5, we estimate the same specification as in columns (3) and (4) but use
a more inclusive sample selection rule. We include all MSAs with a
population greater than 500,000. Both coefficients on managed care
population are insignificant in this sample.
Our estimates suggest that the results in DSW are sensitive to the
decisions not to difference the managed care variable, the measure of
hospital size, the sample selection criteria, and the time period they
studied. While these findings are very suggestive, they are not
conclusive because the data we use are not identical to DSW.
CONCLUSIONS
It is widely believed that the rise of managed care caused the
hospital consolidation wave of the 1990s. In this study we test this
proposition using data on managed care penetration and hospital
consolidation from 1990 to 2000. Our results suggest that the common
wisdom is false--managed care penetration is not significantly related
to hospital consolidation. This finding is robust to different
specifications, time frames, and sample selection criteria. Furthermore,
our analysis does not find other correlates of hospital consolidation
leaving the question of what caused the hospital merger wave an open
one.
ACKNOWLEDGMENTS
This work was supported by the Robert Wood Johnson Foundation
through a grant from the Changes in Health Care Financing and
Organization initiative (Grant #50491). We received helpful comments
from two anonymous referees.
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Data in Market De-lineation for Hospital Merger Cases." Journal of
Health Economics 8 (4): 363-76.
Wholey, D. R., J. Engberg, and C. Bryce. 2006. "A Descriptive
Analysis of Average Productivity among Health Maintenance Organizations,
1985 to 2001." Health Care Management Science 9: 189-206.
NOTES
(1.) A HHI of 0 is perfect competition and 1 is pure monopoly.
(2.) The unweighted HHI is 0.41 in 1990 and 0.47 in 2000.
(3.) For example, Evans, Cuellar, and Gertler (2005) say,
"Hospital consolidation is likely a response to managed care"
(p. 214). Also, see Dranove, Simon, and White (2002), Employee Benefit
Research Institute (1999), Czajkowski (1999), Grembowski et al. (2002),
McCue, Clement, and Luke (1999) and Hollis (1997).
(4.) Our measure of hospital concentration includes direct
ownership of hospital assets as well as membership in a hospital system.
(5.) While managed care may reduce demand, the advancement of
medicine has surely had a larger effect on the demand for inpatient
days. According to our estimates, from 1990 to 2000 average inpatient
days per capita declined by 54 percent.
(6.) It is also likely that antitrust markets are much smaller than
the large MSAs and thus they likely have significant measurement error.
(7.) For example, Baker and Brown (1999) use HMO penetration data
from Group Health Association of America as a measure of managed care
penetration.
(8.) It is likely that expectations about future HMO penetration
affect contemporaneous merger decisions. Our specification is consistent
with this if expectations are based on contemporaneous HMO penetration.
In so far as other information is used to formulate expectations, this
information is embedded in [x.sub.i]. If contemporaneous HMO penetration
is not relevant in the formulation of expectations regarding future HMO
penetration, then our empirical approach will not capture this
phenomena.
(9.) We have performed our analysis on both a longer period,
1985-2000, and a shorter period, 1992-1998. The results from those
analyses are qualitatively identical to those we present here. These
results are available from the authors upon request.
(10.) Results are available from the authors upon request.
(11.) The F-test suggests that the small sample bias will be less
than 0.30. The presence of this bias will not overturn any of our
conclusions.
(12.) A literature in antitrust analysis suggests that using
patient flows to define markets can be misleading (Werden 1990; Capps et
al. 2002). However, it is probably the case that using patient flow data
to identify markets is more accurate than using existing geopolitical
boundaries.
(13.) We thank Marty Gaynor and colleagues for providing us with
this data.
(14.) In order to check the robustness of our findings, we have
also performed the analysis limiting the sample to those HSAs with less
than 11 hospitals. Again, the qualitative results using this sample are
identical to those we present here. These results are available from the
authors upon request.
(15.) These results are available from the authors upon request.
(16.) Burns and Pauly (2002) argue that hospital consolidation
during this period is driven by consultants and thus cross-sectional
variation in consolidation is the consequence of differences in the
influence of consultants across locations. In so far as we find no other
meaningful correlates of consolidation, our results are consistent with
this hypothesis.
(17.) DSW use the percent of workforce self employed and percentage
of workforce employed in large firms in 1992 as instruments. We use the
percentage of firms in each size category in 1992 and 1985 as
instruments.
(18.) The F-statistics on the first stage regression are smaller
than in DSW.
Address correspondence to Robert J. Town, Ph.D., Division of Health
Policy and Management, School of Public Health, University, of
Minnesota, Mayo Mail Code 729, 420 Delaware St. SE, Minneapolis, MN
55455. Douglas Wholey, Ph.D, and Roger Feldman, Ph.D., are with the
Division of Health Policy and Management, School of Public Health,
University of Minnesota, Minneapolis, MN. Lawton R. Burns, Ph.D., is
with the Health Care Systems Department, The Wharton School, University
of Pennsylvania, Philadelphia, PA.
Table 1: Summary Statistics (Unweighted)
(Standard Deviations in Parentheses)
Variable 1990 2000
Dependent variables
HHI (beds) 0.2821 (0.15) 0.3491 (0.17)
HHI (inpatient) 0.3173 (0.18) 0.3827 (0.19)
Merger HHI (base = 1990) 0.2821 (0.15) 0.3392 (0.17)
Strong HHI (beds) 0.2653 (0.14) 0.2893 (0.16)
Other explanatory variables
HMO penetration 6.2 (7.1) 19.3 (14.8)
Percent elderly 12.9 (2.8) 13.0 (2.7)
Population 521,575 (854,179) 603,632 (967,455)
Median per capita income $16,544 ($3,277) $25,123 ($5,408)
Percent population in poverty 14.2% (5.70%) 12.8% (4.86%)
Beds per 1,000 population 6.02 (2.11)
Percent FP hospital in 1990 12.4% (17.6%)
Hospital CON regulation in 55.5 (49.8)
1990
Instruments
Primary care physicians 0.28 (0.084)
per 1,000 in 1990
Number of establishments with 53.0 (3.3)
one employee
Number of establishments with 20.3 (1.3)
five to nine employees in
2000
Number of establishments with 2.8 (0.53)
50-99 employees in 2000
Number of establishments with 1.6 (0.40)
more than 99 employees in
2000
N 444
[DELTA] or
Variable %[DELTA]
Dependent variables
HHI (beds) 0.0623 (0.099)
HHI (inpatient) 0.0617 (0.11)
Merger HHI (base = 1990) 0.0533 (0.093)
Strong HHI (beds) 0.020 (0.074)
Other explanatory variables
HMO penetration 12.9 (11.0)
Percent elderly -0.08% (0.75)
Population 11.1% (10.0%)
Median per capita income 13.4% (6.3%)
Percent population in poverty -1.37% (1.66%)
Beds per 1,000 population
Percent FP hospital in 1990
Hospital CON regulation in
1990
Instruments
Primary care physicians
per 1,000 in 1990
Number of establishments with
one employee
Number of establishments with
five to nine employees in
2000
Number of establishments with
50-99 employees in 2000
Number of establishments with
more than 99 employees in
2000
N
HHI, Herfindahl-Hirschman index; HMO, health maintenance
organization; FP, for profit; CON, certificate of need.
Table 2: Long-Differenced, OLS Estimates of the Impact of HMO
Penetration on Change in Hospital HHI (Standard Errors in
Parentheses)
Dependent Variable and Sample
[DELTA]HHI (Beds) [DELTA]HHI (Beds)
Population > Population >
Variable 100,000 (1) 100,000 (2)
[DELTA]HMO penetration -0.054 (0.049) --
[DELTA]HMO 0.0030 (0.052)
penetration[t.sub.t-2]
HMO penetration in 1990 0.023 (0.12) --
[DELTA] Logarithm of number of -- --
HMO
Number of entrants -0.013 (0.013) -0.013 (0.013)
Number of exits 0.0068 (0.0029) 0.0068 * (0.0029)
Beds per 1,000 population in 0.0026 (0.0029) 0.0025 (0.0026)
1990
N 444 444
p-value of F-test of joint .53 --
significance of HMO variables
[R.sup.2] 0.045 0.042
Dependent Variable and Sample
[DELTA]HHI (Days) [DELTA]HHI (Beds)
Population > Population >
Variable 100,000 (3) 500,000 (4)
[DELTA]HMO penetration -0.090 (0.055) -0.014 (0.069)
[DELTA]HMO --
penetration[t.sub.t-2]
HMO penetration in 1990 -0.0081 (0.13) -0.16 (0.10)
[DELTA] Logarithm of number of -- --
HMO
Number of entrants -0.017 (0.012) -0.030 (0.021)
Number of exits 0.0055 (0.0028) 0.0048 (0.0026)
Beds per 1,000 population in 0.0028 (0.0031) -0.0026 (0.0049)
1990
N 444 115
p-value of F-test of joint .27 .28
significance of HMO variables
[R.sup.2] 0.050 0.17
Dependent Variable
and Sample
[DELTA]HHI (Beds)
Population >
Variable 100,000 (5)
[DELTA]HMO penetration --
[DELTA]HMO
penetration[t.sub.t-2]
HMO penetration in 1990 --
[DELTA] Logarithm of number of -0.0040 (0.0096)
HMO
Number of entrants -0.013 (0.013)
Number of exits 0.0070 * (0.0029)
Beds per 1,000 population in 0.0025 (0.0030)
1990
N 444
p-value of F-test of joint --
significance of HMO variables
[R.sup.2] 0.043
Note: The following control variables are included in the
regression but not reported: percent elderly in 1990, change
in the percent elderly, logarithm of per capita income in 1990,
logarithm of change in the per capita income, unemployment rate
in 1990, change in the unemployment rate, percent poverty in
1990, change in the percent poverty, logarithm of the change in
population, CON indicator in 1990, percent FP hospital in 1990,
and log of the HSA land area.
* Significant at .5% level of confidence.
OLS, ordinary least squares; HMO, health maintenance
organization; HHI, Herfindahl-Hirschman index; CON,
certificate of need; FP, for profit; HSA, Health Service Area.
Table 3: Estimates of the Impact of HMO Penetration on Change
in Hospital Merger HHI (Standard Errors in Parentheses)
[DELTA] Merger HHI
Population Population
> 100,000 > 500,000
Variable (1) (2)
[DELTA]HMO penetration 0.051 (0.042) 0.055 (0.051)
HMO penetration in 1990 0.15 (0.098) -0.12 (0.092)
Number of entrants -- --
Number of exits -- --
Beds per 1,000 0.0021 (0.0083) 0.0033 (0.0057)
population in 1990
N 444 115
p-value of F-test of joint .32 .16
significance of HMO
variables
[R.sup.2] 0.051 0.13
[DELTA]HHI (Beds) Strong
Ownership Definition
Population Population
> 100,000 > 500,000
Variable (3) (4)
[DELTA]HMO penetration -0.051 (0.042) -0.035 (0.052)
HMO penetration in 1990 -0.023 (0.080) -0.041 (0.067)
Number of entrants -0.037 ** (0.0084) -0.048 ** (0.013)
Number of exits 0.0070 ** (0.0026) 0.0024 (0.0014)
Beds per 1,000 -0.0011 (0.0026) -0.0029 (0.0030)
population in 1990
N 444 115
p-value of F-test of joint .48 .72
significance of HMO
variables
[R.sup.2] 0.085 0.30
Note: The following control variables are included in the regression
but not reported: percent elderly in 1990, change in the percent
elderly, logarithm of per capita income in 1990, logarithm of change
in the per capita income, unemployment rate in 1990, change in the
unemployment rate, percent poverty in 1990, change in the percent
poverty, logarithm of the change in population, CON indicator in 1990,
percent FP hospital in 1990, and log of the HAS land area.
** Significant at 1% level of confidence.
HMO, health maintenance organization; HHI, Herfindahl-Hirschman
index; CON, certificate of need; FP, for profit;
HSA, Health Service Area.
Table 4: IV Estimates of the Impact of HMO Penetration
(Standard Errors in Parentheses)
Dependent Variable
[DELTA]HHI [DELTA]HHI
(Beds) (Patients)
Population > Population >
Variable 100,000 (1) 100,000 (2)
[DELTA]HMO penetration -0.023 (.32) -0.16 (.31)
1990 HMO penetration 0.36 (.50) 0.21 (.41)
Over identification restrictions 3.24 (.20) 8.08 (.09)
test ([chi square].sup.2(4)])
(p-value)
First Stage Estimates
[DELTA] HMO
Penetration
Primary care physicians per 1,000 0.11 (0.068)
in 1990
Percent of establishments with one -2.03 (2.59)
employee in 1990
Percent of establishments with five -3.29 (2.61)
to nine employees in 1990
Percent of establishments with 50 -1.56 (3.79)
or more employees in 1990
Partial [R.sup.2] 0.025
F 2.63
(p-value) (.03)
Dependent Variable
[DELTA]Merger [DELTA]HHI
HHI (Beds) Strong
Population > Population >
Variable 100,000 (3) 100,000 (4)
[DELTA]HMO penetration -0.19 (.24) 0.22 (.22)
1990 HMO penetration -0.024 (.32) 0.0028 (.32)
Over identification restrictions 13.55 (.01) 13.26 (.01)
test ([chi square].sup.2(4)])
(p-value)
First Stage Estimates
1990 HMO
Penetration
Primary care physicians per 1,000 0.049 (0.041)
in 1990
Percent of establishments with one -0.69 ** (0.21)
employee in 1990
Percent of establishments with five -0.67 (0.41)
to nine employees in 1990
Percent of establishments with 50 -2.69 ** (0.79)
or more employees in 1990
Partial [R.sup.2] 0.021
F 4.50
(p-value) (.0001)
Note: The following control variables are included in the regression
but not reported: percent elderly in 1990, change in the percent
elderly, logarithm of per capita income in 1990, logarithm of change
in the per capita income, unemployment rate in 1990, change in the
unemployment rate, percent poverty in 1990, change in the percent
poverty, logarithm of the change in population, CON indicator in 1990,
percent FP hospital in 1990, and log of the HSA
land area.
** Significant at 1% level of confidence.
IV, instrumental variable; HMO, health maintenance organization;
HHI, Herfindahl-Hirschman index; CON, certificate of need; FP, for
profit; HSA, Health Service Area.
Table 5: Replication of Dranove, Simon, and White (2002)--Estimating
the impact of HMO Penetration on Hospital Concentration
Dependent Variable Is [DELTA]HHI
IV Estimation, MSA Population > 800,000
1985-1994
(1) (2)
HMO 0.21 * (0.11) --
penetration
[DELTA]HMO -- 0.085 (0.12)
penetration
1992 log 0.020 (0.013) 0.023 * (0.013)
population
[DELTA]Log per 0.13 (0.14) 0.081 (0.14)
capita income
[DELTA]Log population -0.14 (0.092) -0.15 (0.094)
[DELTA]Percent elderly -0.0032 (0.081) -0.048 (0.079)
[DELTA]Population 0.000011 (0.000022) 0.000043 (0.00022)
density
N 62 62
1st stage F (p value) 1.32 (.24) 2.24 (.021)
Over-identification .29 .23
restriction p-value
Dependent Variable Is [DELTA]HHI
OLS Estimation, MSA Population > 800,000
1985-1994
(3) (4)
HMO 0.13 ** (0.034) -
penetration
[DELTA]HMO -- 0.096 * (0.046)
penetration
1992 log 0.00085 (0.016) 0.022 * (0.011)
population
[DELTA]Log per -0.070 (0.12) 0.087 (0.14)
capita income
[DELTA]Log population -0.15 (0.14) -0.14 (0.069)
[DELTA]Percent elderly 0.012 (0.045) -0.046 (0.061)
[DELTA]Population 0.000047 (0.00014) 0.00043 (0.00014)
density
N 62 62
1st stage F (p value) [R.sup.2] = .19 [R.sup.2] = .15
Over-identification -- --
restriction p-value
Dependent Variable Is [DELTA]HHI
IV Estimation, MSA Population > 800,000
1985-1994 Dependent Variable Is
[DELTA]HHI measured using bed size
(5) (6)
HMO 0.077 (0.048) --
penetration
[DELTA]HMO -- -0.010 (0.059)
penetration
1992 log 0.0082 (0.0056) 0.0096 (0.0059)
population
[DELTA]Log per 0.13 (0.068) 0.097 (0.066)
capita income
[DELTA]Log population -0.019 (0.030) -0.027 (0.033)
[DELTA]Percent elderly -0.043 (0.036) -0.065 (0.028)
[DELTA]Population 0.000023 (0.000090) -O.000016 (0.00011)
density
N 62 62
1st stage F (p value) 1.18 (.32) 2.52 (.0086)
Over-identification .42 .35
restriction p-value
Dependent Variable Is [DELTA]HHI
OLS Estimation, MSA Population > 500,000
1985-1994
(7) (8)
HMO 0.13 ** (0.037) --
penetration
[DELTA]HMO -- 0.076 (0.044)
penetration
1992 log 0.033 (0.011) 0.035 (0.013)
population
[DELTA]Log per 0.049 (0.12) 0.090 (0.13)
capita income
[DELTA]Log population -0.17 * (0.074) -0.17 * (0.075)
[DELTA]Percent elderly -0.085 (0.054) -0.095 (0.055)
[DELTA]Population -0.0000066 (0.00014) 0.0000054 (0.00014)
density
N 92 92
1st stage F (p value) [R.sup.2] = .19 [R.sup.2] = .15
Over-identification -- --
restriction p-value
* Significant at 5% level of confidence.
** Significant at 1% level of confidence.
HMO, health maintenance organization; HHI, Herfindahl-Hirschman
index; IV, instrumental variable; MSA, metropolitan statistical
area; OLS, ordinary least squares.