Directory UMM :Data Elmu:jurnal:J-a:Journal of Accounting and Public Policy:Vol19.Issue1.Jan2000:
Journal of Accounting and Public Policy 19 (2000) 9±40
The impact of medicare capital prospective
payment regulation on hospital capital
expenditures
Ran Barniv a,*, Kreag Danvers b, Joanne Healy a
a
b
Department of Accounting, Graduate School of Management, Kent State University,
P.O. Box 5190, Kent, OH 44242-0001, USA
Department of Accounting, Eberly College of Business, Indiana University of Pennsylvania,
Pennsylvania, USA
Abstract
Our study examines the impact of the capital prospective payment system (CPPS),
implemented by Medicare in 1991, on capital expenditures and cost-eective behavior of
non-proprietary hospitals. As noted in the paper, we use audited ®nancial statement
data for a large national sample of hospitals. Univariate analyses demonstrate a statistically signi®cant decline in capital expenditures in the years following the CPPS
regulation without signi®cant changes in relative aggregate operating expenses. These
preliminary ®ndings suggest that CPPS induces some cost-eective behavior by hospital
managers. Ordinary least-squares (OLS) regressions indicate that capital expenditures
before and after CPPS are dierently aected by the changes in most explanatory
variables. Further OLS regressions indicate that high-cost (low-cost) hospitals decrease
(increase) capital expenditures following CPPS, once other factors are controlled for.
Managerial accounting implications for hospitals include the eect of the regulation on
capital budgeting decisions. Greater accounting disclosure may be necessary so that
alternative modes of coping with the regulation can be discerned. Policymakers and
regulators should also be aware that although reductions in capital expenditures may
have favorable short-term eects of reducing health care costs, a potentially negative
public health impact may result if capital expenditures continue to decrease. Ó 2000
Published by Elsevier Science Ltd. All rights reserved.
*
Corresponding author. Tel.: +1-330-672-2545-x379; fax: +1-330-672-2548.
E-mail address: Rbarniv@bsa3.kent.edu (R. Barniv).
0278-4254/00/$ - see front matter Ó 2000 Published by Elsevier Science Ltd. All rights reserved.
PII: S 0 2 7 8 - 4 2 5 4 ( 9 9 ) 0 0 0 2 6 - 5
10
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
1. Introduction
The 1983 amendments to the Social Security Act introduced the Medicare
prospective payment system (PPS) and modi®ed the mechanism through which
payments are made to hospitals for providing care to Medicare patients (see
e.g., Cotterill, 1991, p. 79; Soderstrom, 1993, pp. 155±156; Eldenburg and
Kallapur, 1997, p. 32). Although the 1983 amendments altered payments of
hospital inpatient operating costs, implementation of the capital cost prospective payment system (CPPS) was deferred and Medicare continued payments based on actual capital-related expenses (Wedig et al., 1989, p. 518). The
debate on when and how to implement the new regulation continued for almost eight years. 1 Finally, CPPS was implemented with a 10 year gradual and
proportional transition period on October, 1 1991, and became eective for
®scal years beginning on this date (Burke, 1991, p. 36; Grimaldi, 1991, p. 72).
Articles published during the early 1990s expressed concerns about unfavorable consequences of the capital regulation, including a potential reduction in
future capital expenditures by hospitals (Luggiero, 1990, p. 3; Burke, 1991,
pp. 34, 35; Anderson, 1992, p. 38).
The purpose of our study is to explicitly investigate the impact of the ®nal
CPPS regulation on capital expenditure decisions in the hospital industry, and
to assess whether it induces overall cost-eective behavior by managers. We
hypothesize that hospitals may reduce capital expenditures as a result of the
new regulation. Furthermore, we attempt to determine how hospitals have
adapted to the post-CPPS environment. Previous articles have not empirically
scrutinized these issues.
Using data from audited ®nancial statements provided by the Merritt Systemâ database, we examine capital expenditures for a national sample of nonproprietary general service hospitals prior and subsequent to the ®nal CPPS
regulation. 2 The database includes ®nancial accounting data for 2048 hospitals from 1988 to 1996 of which 1949 are useful for our study.
We found a statistically signi®cant decline in capital expenditures in the
years following the implementation of the ®nal Medicare capital regulation.
Another important issue examined in this study is whether CPPS had the desired eect in promoting overall cost-eective behavior. Although a reduction
in capital expenditures was observed, operating expenses such as salaries and
1
In 1986, the proposed regulation for incorporating capital payments into PPS was issued by the
United States Department of Health and Human Services (Cotterill, 1991, p. 79). However, the
®nal capital PPS regulation was delayed on several other occasions and actual cost-based payments
continued until 1991 (Grimaldi, 1991, p. 72).
2
We use the term non-proprietary hospitals throughout the manuscript. These hospitals are nonpro®t and include local and state controlled units, but exclude federally controlled hospitals (AHA,
1996, p. A7).
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
11
other expenses (i.e., the aggregate of drugs, supplies, operating leases, and
rental expenses) as percentages of revenues remained unchanged across the two
subperiods. Furthermore, pro®tability improved perhaps due to declining interest rates. The CPPS regulation seems to motivate managers to reduce capital
expenditures and to change other variables such as ®nancial leverage, but not
to change operating expenses.
We examined the impact of hospital characteristics on capital expenditures
across subperiods and found that the impacts of certain explanatory variables
were statistically dierent before and after CPPS. In response to CPPS, hospital
managers have changed several ®nancial and operational characteristics, which
could explain some aspects of the reduction of capital expenditures following
CPPS. For example, the other-expense variable, which includes drugs, supplies,
leases and rentals, was signi®cantly negative in the period subsequent to CPPS.
This result indicates that operating leases and rentals may potentially substitute
for capital expenditures. Other empirical results suggest that high-cost (lowcost) hospitals decrease (increase) capital expenditures following CPPS, once
other factors are controlled for. This ®nding supports Medicare's expectations
regarding the redistribution eect of the new regulation (Federal Register, 1991,
pp. 43427±43428). An important public policy issue is that although reductions
in capital expenditures may reduce health care cost, policymakers need to be
cognizant of the potentially negative long-term impact on public health.
2. Some regulatory background
Prior to the Social Security Amendments (United States Statutes at Large,
1983, Public Law 98-21, Section 601, pp. 149±163), Medicare reimbursed
hospitals on a retrospective cost basis. Under this cost-based reimbursement
scheme, Medicare-related hospital revenue was tied directly to actual operating
expenses incurred, providing no incentives for hospitals to control either risk or
cost. In response to in¯ationary concerns and the solvency of the Medicare
program, the United States Congress directed the Department of Health and
Human Services to develop a new reimbursement method that would incorporate incentives for hospital eciency (Guterman and Dobson, 1986, p. 97).
PPS was implemented in 1983, but certain hospital costs, including capitalrelated expenses, were speci®cally excluded and continued to be reimbursed on
a retrospective cost basis (Wedig et al., 1989, p. 518).
Prior to CPPS, Medicare reimbursed hospitals for actual capital-related
expenses, primarily depreciation and interest expenses. 3 In addition, some
3
Medicare reimburses for annual capital-related expenses, such as depreciation and interest, and
not for actual capital expenditures, such as construction of buildings or acquisition of equipment
(Federal Register, 1996, p. 46196).
12
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
other less substantial expenses such as insurance were reimbursed. Following
the implementation of CPPS, Medicare began to reimburse these expenses
based on predetermined national averages. Medicare promoted two objectives of CPPS which are relevant for our study. First, CPPS was expected to
stimulate eciency in capital spending and induce hospitals to become
more cost-eective, thereby reducing total Medicare cost. Second,
CPPS might provide redistribution of payments and encourage hospitals
with lower capital-related expenses, for example hospitals in rural areas, to
increase capital expenditures (Federal Register, 1991, 30 August, pp. 43428±
43517).
Through this regulation, Medicare fundamentally changed the way it pays
hospitals for capital expenses attributable to Medicare inpatients. Rather
than basing reimbursements on actual expenses, hospitals would receive a
payment per case based on factors similar to the existing PPS system (Burke,
1991, p. 36). The CPPS regulation aects all PPS hospitals and provides for a
10 year gradual and proportional transition period that precedes full implementation of the new system (Grimaldi, 1991, pp. 72±76). During this transition period, Medicare established two tiers of hospitals, based upon
hospital-speci®c capital-related expenses relative to an adjusted national average. Hospitals were classi®ed as low-(high-) cost if their capital-related
expenses were lower (higher) than the national average. Low-cost hospitals
are reimbursed based on a fully prospective method, while high-cost hospitals
are reimbursed based on a hold-harmless method (Burke, 1991, p. 36). 4 We
expect this new payment scheme to impact capital budgeting decisions by
hospital management.
3. Previous literature and incremental contribution
Prior accounting studies examined hospital behavior in response to federal
regulatory changes. Several of these studies analyzed issues associated with the
1983 Medicare PPS. For example, Soderstrom (1993) scrutinized hospital
management's tendency to increase income through changing admission or
reporting policies, whereas Eldenburg and Kallapur (1997) examined the tendency of hospitals to maximize cash ¯ows by changing patient mix and cost
4
In the initial year, the fully prospective method pays hospitals a blend of 90% hospital-speci®c
cost and 10% federal rate. An additional 10% federal rate replaces hospital-speci®c cost each year
until 2001. Those hospitals subject to the hold-harmless method receive the higher of (1) 85% of
previously obligated capital costs, plus a federal rate based payment for new capital items, or (2)
100% of the federal rate (Grimaldi, 1991, pp. 72±76). In 2001, payments to all hospitals will be fully
prospective (Grimaldi, 1991, pp. 72±76).
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
13
allocations. 5 In addition, studies that examined the impact of the 1983 PPS on
hospital ®nancial and operating characteristics found that hospitals coped
fairly well by undertaking cost-cutting behavior (Guterman et al., 1988, p. 68;
Feder et al., 1987, pp. 869±872; Friedman and Shortell, 1988, p. 264). 6
Other studies focused on state level regulatory eects. Blanchard et al. (1986,
pp. 11±14) studied hospitals in the State of Washington that were subject to
revenue regulation and suggested that those hospitals report biased budget
information to increase revenue. Eldenburg and Soderstrom (1996, pp. 23±24)
re-examined Blanchard et al.'s (1986, pp. 1±2, 14) hypotheses and concluded
that hospitals bias budget information and shift costs across payor types to
increase reimbursement. 7
Research that deals speci®cally with the issue of hospital capital costs within
the prospective payment system is very limited. For example, studies discuss
the potential impacts of a CPPS-type payment structure on the cost of capital
and capital structure decisions (Boles, 1986; Wedig et al., 1988, 1989). Prior to,
and concomitant with the ®nal capital regulation, some conjectures were made
regarding its impact on the hospital industry's ®nancial viability, future levels
of capital expenditures and access to capital (Burke, 1991; Anderson, 1992;
Health Industry Today, 1992). 8 Cotterill (1991) and Kauer (1995) examined
issues related to the ®nal capital regulation. Recently, Lynch (1998, pp. 1±3)
5
Eldenburg (1994) investigated management information sharing with physician groups as a
hospital cost control mechanism in response to the 1983 PPS. Lambert and Larcker (1995, pp. 1±3)
found that hospitals more vulnerable to the 1983 PPS regulation tend to implement more bonusbased compensation contracts as motivational devices. Carey (1994, p. 275) found that during the
years following PPS implementation small, rural hospitals tended to allocate more costs to
outpatient departments than did large, urban hospitals.
6
Studies also investigated the market response of for-pro®t hospitals related to PPS events
(Folland and Kleiman, 1990) and the eect of a diagnosis related group (DRG) payment system on
the eciency of New Jersey general acute-care hospitals (Borden, 1988). These studies found little
eect of a DRG payment system on either the market value of for-pro®t hospitals (Folland and
Kleiman, 1990, pp. 61±66) or the eciency of hospitals in New Jersey (Borden, 1988, pp. 87±93).
7
Hospital accounting studies also provide evidence that disclosing quasi-dividend payments to
physicians may enhance the informativeness of hospital operating statements (Mensah and Chiang,
1996), classi®cation of hospital ®nancial ratios (Zeller et al., 1996) and the relative eciency of
proprietary versus non-proprietary hospitals (Carter et al., 1997). Mensah and Li (1993) extended a
translog budget model within a not-for pro®t setting. Mensah et al. (1994a) examined accounting
conservation and earnings management by HMOs, whereas Mensah et al. (1994b) examined
earnings management by HMOs that may be subject to political cost.
8
Burke (1991, p. 34) indicates that hospitals may experience unfavorable consequences due to the
unpredictable characteristics of CPPS and suggests that analysts anticipated that hospitals will
reduce their capital expenditures to maintain pro®tability. Anderson (1992, p. 38) presents survey
®ndings of hospital CEOs that show 48% of hospitals expected CPPS to generate pressure for
reducing equipment expenditures over the following ®ve years. A survey of hospital CFOs found
that roughly half anticipated decreases in facilities investments during the phase-in of the capital
PPS regulation (Health Industry Today, 1992, p. 10).
14
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
found that hospitals in California marginally change capital investment behavior and reduce the use of long-term debt in response to changes in Medicare
regulation.
Our study diers from the prior literature in several aspects. First, we examine empirically the impact of CPPS on capital expenditures, while prior
articles do not provide empirical evidence on this issue. Second, we scrutinize
how hospitals have adapted to the post-CPPS environment by studying potential changes in their cost-eective behavior. Third, we examine the impact of
other hospital characteristics on capital expenditures before and after the implementation of CPPS. Fourth, we examine hospital response to regulation
utilizing audited ®nancial statement information for a large, national sample of
PPS hospitals, whereas many previous studies use a single-state sample. The
results of our study are relevant for policymakers, regulators, hospital management, creditors, accounting standards setters and scholars as well as other
users of ®nancial and managerial accounting information.
4. Research methods
4.1. Hypotheses
Non-pro®t hospitals use standard capital budgeting techniques to allocate
scarce capital resources (Cleverley and Felkner, 1984, p. 45). The decoupling of
Medicare payments from actual costs requires more rigorous project evaluation since hospitals are forced to assume a higher level of risk for investment
decisions. Also, increasing unpredictability in the update factors of capital
payments (Burke, 1991, p. 34) leads to a higher risk adjusted cost of capital.
Therefore, it may be expected that increasing uncertainty and lowering expected future cash ¯ow under CPPS decreases the number of acceptable investment projects. Our primary objective is to identify whether changes in the
level of hospital expenditures for property, plant and equipment (PPE) are
associated with the issuance of the ®nal CPPS regulation. This leads to the ®rst
hypothesis tested in our study:
H1: Non-proprietary hospitals subject to CPPS will exhibit no
change in capital expenditures across the pre-regulation and postregulation periods. The alternative hypothesis is that hospitals subject to CPPS will decrease capital expenditures in the post-CPPS
period.
While, the ®rst hypothesis examines changes in capital expenditures, it does
not consider other means available to hospital managers for adapting to the
post-CPPS environment. For example, to the extent that managers substitute
operating leases or rentals for capital expenditures, an increase in these oper-
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
15
ating expenses may be observed. The regulation may result in overall costeective behavior if capital expenditures decline by more than the increase in
these operating expenses. This is the logic behind the second alternative
hypothesis. Thus, the second hypothesis examines behavior by managers
for operational activities other than capital expenditures, across the two
subperiods.
H2: Non-proprietary hospitals subject to CPPS do not change certain operating expenses across the pre-regulation and post-regulation periods. The alternative hypothesis is that hospitals increase
certain operating expenses during the second subperiod.
The third hypothesis examines how hospitals subject to Medicare's two-tier
classi®cation may dierently change capital expenditures. While all PPS hospitals are expected to be aected by the capital regulation, particular hospitals
may be less adversely impacted than others. For example, Medicare expected
that payments to high-cost hospitals (primarily large, urban hospitals) would
decrease, and therefore, these hospitals might reduce capital expenditures.
However, payments to low-cost hospitals (primarily small, rural hospitals)
would increase subsequent to CPPS (Pallarito, 1991, p. 4) and the higher expected payments to these hospitals may increase capital expenditures (Federal
Register, 1991, pp. 43427±43428). This leads to the statement of our third
hypothesis:
H3: Non-proprietary hospitals de®ned by Medicare as low-cost and
those de®ned as high-cost will exhibit no change in capital expenditures across the pre-regulation and post-regulation periods. The
alternative hypothesis is that high-cost hospitals will decrease their
capital expenditures, while low-cost hospitals will increase their
capital expenditures.
Both univariate and multivariate analyses were used to test the three
hypotheses. In particular, we assessed the impacts of hospital characteristics
on capital expenditures through the use of ordinary least-squares (OLS)
multivariate regression models. Furthermore, we examined the eects of
changes in these characteristics on capital expenditures across the two subperiods.
4.2. Variables
The general de®nitions for ®nancial statement items used to generate the
following variables are based on AICPA (1997). The expected direction for
each estimated coecient in the regressions is provided below and in Tables 5
and 6.
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4.2.1. Dependent variable
The variable used to measure capital expenditures represents the change in
net PPE, adjusted for depreciation and in¯ation. This variable is used in both
univariate and multivariate analyses.
CEXGFA (capital expenditures/CIPI)t /gross ®xed assetstÿ1 . Capital expenditures are calculated as the change in net ®xed assets from year t ÿ 1 to
year t, plus depreciation expense in year t. Thus the numerator of CEXGFA
includes purchases of ®xed assets in period t minus the book value of the
disposed assets during the period. Construction-in-progress is included in the
numerator and the denominator, but it is not separately available in the database. Gross ®xed assets at time t ÿ 1 include all depreciable and non-depreciable ®xed assets at historical cost before accumulated depreciation. Except
for the CIPI adjustment, CEXGFA corresponds to the Zeller et al. (1996,
p. 170) capital expenditure growth rate variable. 9
4.2.2. Independent variables
We used the following eight independent variables in our ®rst regression
model and for the univariate analyses. 10 These variables include capital related
characteristics along with pro®tability, liquidity, eciency and ®nancial leverage measures. The ®rst two variables include depreciation in the numerators
since it is the primary capital-related expense reimbursed by Medicare. 11
Similar capital expense variables are presented by Deloitte and Touche (1996,
p. 235) and Zeller et al. (1996, p. 170). Although we hypothesize the directions
of various independent variables on capital expenditures, the complexity of
the analyses makes interpretations of several coecients rather dicult. All
independent variables, except the denominator of the ®rst, are measured at
period t.
DNFA depreciation expenset /net ®xed assetstÿ1 . Net ®xed assets are
computed as gross ®xed assets less accumulated depreciation. Examining only
the numerator suggests that, as purchases increase depreciation potentially
increases, and as equipment is sold depreciation may decrease, depending on
9
To adjust capital expenditures for in¯ationary eects we use Medicare's capital input price index
(CIPI), which re¯ects changes in the price levels of a market basket of capital resources used by
hospitals for inpatient Medicare services. The CIPI measures the input price change in capitalrelated expenses and is used by Medicare in determining increases in capital prospective payment
rates (Federal Register, 1996, pp. 46196±46203).
10
Several factors have been promoted to in¯uence hospital capital expenditure decisions,
including size, patient mix and capital structure (Pallarito, 1990, p. 33). Cotterill (1991, p. 84) found
variables that are associated with hospital capital costs include location, bed size, age of plant and
®nancing structure. Several of these variables are used in our study.
11
The Pearson correlation coecient between the ®rst two independent variables is 0.065 which is
statistically signi®cant at the 0.01 level, two-tailed, given the large sample of more than 10 000
observations.
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
17
the age of equipment. These interactions would lead to a positive coecient,
not considering the eect of the denominator. Therefore, capital expenditures
are expected to be positively associated with DNFA and this relationship may
continue following CPPS. 12
DTA depreciation expense/total assets. The denominator of this variable
includes nondepreciable assets, which are not represented in the denominator
of DNFA. Similar to DNFA, we may expect a positive relation between DTA
and CEXGFA and the positive relation may continue during the second period. However, a negative relation may be anticipated if hospitals reduce the
growth rate in capital expenditures along with decreasing other non-®xed assets. This negative relation may continue following CPPS. Given the complexity of the relation and the potentially dierent impacts of the numerator
and the denominator, the ®nal impact is not clear and interpretation of the
estimated coecient should be done with caution.
ROA excess of revenues over expenses/total assets. Hospitals that have a
higher numerator may have more funds available for capital expenditures.
Therefore, there may be a positive relationship between ROA and CEXGFA.
The relationship could weaken following CPPS since hospitals may not invest
immediately in capital expenditures due to uncertainty in the update factors
used in the DRG-based formula to calculate reimbursement. This and other
measures of ROA are used by Soderstrom (1993, p. 179) and Mensah and
Chiang (1996, p. 230).
FBTA fund balance/total assets. FBTA may be negatively related to
capital expenditures since hospitals with relatively higher equity in their capital
structure probably use more costly ®nancing. 13 However, because higher equity may enable hospitals to more easily ®nance capital expenditures we may
also expect a positive relation between FBTA and CEXGFA. Therefore, the
®nal impact of FBTA on CEXFGA and the change in the relationship are
complicated and may be positive or negative. Our capital structure variable is
identical to the equity ®nancing variable used in Zeller et al. (1996, p. 169).
Other measures of ®nancial leverage are used by Soderstrom (1993, p. 179) and
Mensah et al. (1994a, p. 80).
LNTA the natural log of total assets. This variable is used to control for
size eect. Since large hospitals tend to be more capital intensive they are
12
Note that prior to 1991, actual depreciation expense was reimbursed by Medicare (Cotterill,
1991, p. 80). Following CPPS depreciation is reimbursed with a declining proportion of 10% a year.
After 2001 depreciation will re¯ect only an accrual (Grimaldi, 1991, pp. 72±73).
13
Although prior studies argue that tax-exempt debt is a cheaper source of ®nancing than a
hospital's internal funds (e.g., Payne, 1995, p. 38), Boles (1986, p. 202) suggests that a future CPPStype of regulation may encourage hospitals to decrease the proportion of debt ®nancing in their
capital structure. In addition, the regulation may change the relative cost of debt versus equity
because of less reimbursement for interest expense (Boles, 1986, p. 202).
18
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expected to invest more in new PPE, but to be more adversely aected by
CPPS. Therefore, hospital's size is anticipated to be positively related to capital
expenditure levels, and this relationship may become negative subsequent to
CPPS.
PPEAGE accumulated depreciation/depreciation expense. This variable is
used as a proxy for the age of the facility. We anticipate a negative relationship
with CEXGFA because as facilities become older accumulated depreciated is
expected to increase relative to annual depreciation expense. This relationship
may be less negative following CPPS because as ®xed assets continue to age
some replacement of these assets is necessary. Similar variables are used by
Cotterill (1991, p. 82), Soderstrom (1993, p. 179), and Zeller et al. (1996,
p. 170).
TURNOVER net patient revenue/total assets. TURNOVER is used as
a measure of asset eciency. We anticipate a negative relationship between
TURNOVER and CEXGFA because ecient hospitals should be more
successful at controlling capital expenditures, and this negative relationship
may strengthen following CPPS. This variable is used by Deloitte and
Touche (1996, p. 238) and a similar variable is used by Zeller et al. (1996,
p. 170).
LBDF assets designated for capital acquisition/net ®xed assets (see
AICPA, 1997, p. 131). The liquid board-designated fund ratio could be
expected to increase over time because hospitals may require more internally
generated funds to guard against uncertain and declining reimbursement for
capital-related expenses. We anticipate a negative relationship between LBDF
and CEXGFA, which is expected to become more negative following CPPS
because hospitals may be inclined to accumulate these funds for future capital
expenditures.
Four additional variables are included to examine other means of adaptation to CPPS by managers. These variables are useful to assess the relative level
of operating expenses, which may help to determine adaptation of hospital
managers to the post-CPPS environment. Missing values for these variables
reduce the 10 227 available observations (hospital years) by more than 2300.
The following two variables represent certain expenses that potentially substitute for capital expenditures.
OTHREV (medical supplies and drugs + insurance + other expenses, including operating leases)/net patient revenue. There are several components in
the numerator, and therefore, it is not clear if OTHREV will change following
CPPS. 14 To the extent that operating leases and rentals are material components of other expenses, we may expect a negative relationship between
14
A separation of the numerator into components such as operating leases and rentals is
unavailable.
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
19
OTHREV and CEXGFA, which is anticipated to become more negative
following CPPS.
SALREV salary and bene®t expense/net patient revenue. To the extent
that a reduction in capital expenditures induces more utilization of labor, we
may expect this variable to increase and to have a negative impact on capital
expenditures.
The next two variables re¯ect the impact of interest expense on capital
expenditures.
INTREV interest expense/net patient revenue. Due to the decline in municipal bond interest rates across the subperiods examined, we may expect this
ratio to decline. Lower interest expense relative to net patient revenue combined with higher capital expenditures in the prior period could generate a
negative relationship between this variable and CEXGFA. Subsequent to
CPPS, the change in the impact is expected to be positive because low interest
rates may induce management to ®nance capital expenditures with debt. This
may result in increasing interest expense following CPPS and provide a positive
change in the relation between INTREV and CEXGFA. INTREV may also be
aected by reimbursement changes and revenue trends across the subperiods
which could increase the denominator and along with decreasing capital expenditures further result in a positive change in the relation between INTREV
and CEXGFA.
INTTD interest expense/total liabilities. Since this variable represents an
eective interest rate for hospitals, we expect a decline in INTTD across subperiods. Prior to CPPS, this variable may have a negative impact on capital
expenditures. For the reasons described for the previous variable, we expect a
positive change in the estimated coecient following CPPS.
In addition, we use four control variables that re¯ect inpatient utilization,
the proportion of revenue generated by Medicare, the competitive environment
and outpatient service mix. Data for these variables are available for less than
3000 hospital-years. These or similar variables are used by Cotterill (1991),
Eldenburg (1994), Eldenburg and Soderstrom (1996), Deloitte and Touche
(1996).
OCCPCY inpatient days/(beds in service 365). The inpatient occupancy
percentage measures the utilization of inpatient capacity (see Renn, 1991, p.
25). We expected this variable to continue to decline across subperiods due to
ongoing shifts toward outpatient activity and reduced average length of hospital stay. To the extent that this variable has an impact on capital expenditures, we anticipated a positive relationship between OCCPCY and CEXGFA,
which may increase with the implementation of CPPS because higher occupancy rates may begin to result in higher levels of reimbursement for capital
expenditures.
MEDICR gross medicare revenue/gross patient revenue. The percentage
of gross patient revenue derived from Medicare may increase across subperiods
20
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due to increasing population age. To the extent that this variable has an impact
on capital expenditures, we anticipated a positive relationship between
MEDICR and CEXGFA, which is expected to decrease following CPPS due to
the unpredictability of update factors utilized in the new DRG-based reimbursement formulas.
MATH total number of hospitals in market area. We expected this measure of market concentration to decrease due to merger and acquisition activity
within the industry. To the extent that this variable has an impact on capital
expenditures, we anticipated a positive relationship between MATH and
CEXGFA, which is expected to decrease following CPPS because consolidation may reduce duplication of capital expenditures.
OPPCT gross outpatient revenue/gross patient revenue. Due to the ongoing shift toward outpatient utilization, we expected this variable to increase
across subperiods. To the extent that outpatient services are less costly, we
anticipate a negative relationship between OPPCT and CEXGFA, which is
expected to become more negative following CPPS.
4.3. Testing the hypotheses
We tested the ®rst hypothesis using univariate analysis and more importantly using multivariate analyses. For the second hypothesis we concentrated
on four variables: OTHREV, SALREV, INTREV and INTTD. In addition
to capital expenditures, these are the most useful measures in the database to
examine other types of cost-eective behavior by hospital managers following
CPPS. For testing the third hypothesis, we partitioned the sample into highand low-cost hospitals, using the hospital-speci®c classi®cation provided to
the authors by Medicare and an urban and non-urban partition. We used
these partitions to examine changes in CEXGFA by hospital type and how
these changes are aected by the explanatory variables across the two
subperiods.
4.4. Dummy variables and estimating changes in slope coecients
For further analyses of the hypotheses, this section presents a methodology
for examining the impact of changes in the independent variables on capital
expenditures. We examined the changes in these relationships before and after
the implementation of CPPS using dummy variables to estimate changes in
slope coecients. Two equations are used: restricted (1) and unrestricted (2)
(see discussion by Johnston, 1984, pp. 207±228; Kennedy, 1992, pp. 220±225).
Henceforth, we refer to this method as a regression with slope-dummies.
Furthermore, we examined whether OLS regression assumptions were satis®ed.
Results for three models, which include 8, 12 or 16 variables are reported. Due
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
21
to space limitations, only the eight-variable restricted and unrestricted models
are presented in this section:
CEXGFAj b0 b1 DNFAj b2 DTAj b3 ROAj b4 FBTAj
b5 LNTAj b6 PPEAGEj b7 TURNOVERj
b8 LBDFj ej ;
1
CEXGFAj b0u b0u BFAF b1u DNFAj b2u DTAj b3u ROAj
b4u FBTAj b5u LNTAj b6u PPEAGEj
b7u TURNOVERj b8u LBDFj
b1 BFAF DNFAj b2 BFAF DTAj
b3 BFAF ROAj b4 BFAF FBTAj
b5 BFAF LNTAj B6 BFAF PPEAGEj
b7 BFAF TURNOVERj b8 BFAF LBDFj ej ;
2
where the dummy variable BFAF 0 prior to CPPS and BFAF 1 subsequent to CPPS. Each interaction element consists of the value of the respective independent variable for each observation subsequent to CPPS, and
zero otherwise. The coecient b0u re¯ects the dierence between the expected
CEXGFA before and after CPPS, given that the other variables are included in
the model.
Eq. (1) assumes that the restricted estimated coecients are the same prior
and subsequent to the implementation of CPPS. For Eq. (2) the expected
model prior to CPPS is
b0u b1u DNFAj b2u DTAj b3u ROAj b4u FBTAj b5u LNTAj
b6u PPEAGE b7u TURNOVER b8u LBDFj ;
and after CPPS the expected model is
b0u b0u b1u b1 DNFA b2u b2 DTA b3u b3 ROA
b4u b4 FBTA b5u b5 LNTA b6u b6 PPEAGE
b7u b7 TURNOVER b8u b8 LBDF:
A Chow-F test is used to test the joint hypothesis that b0u b1 b2 b3
b7 b8 0, and t-tests are used to examine hypotheses
0; b2 0; b3 0; b4 0; b5 0; b6 0; b7 0; b8 0 on individual estimated coecients.
b4 b5 b6
b0u 0; b1
22
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
5. Data and sample selection
This study empirically examines the impact of CPPS on hospital investment levels for a large sample of non-proprietary hospitals from 1988 to
1996. The data source is the Merritt Systemâ , a private credit and investment
analysis database system, which contains audited annual ®nancial statement
information, socio-economic information and supplementary operational
statistics for 2048 hospitals, representing all 50 states and the District of
Columbia. 15 Since about 4100 general acute care hospitals participate in
Medicare and ®le cost reports (Deloitte & Touche LLP, 1996, p. 1) our
sample comprises roughly 50% of this population. This relatively large sample
permits inferences to be comfortably made to the non-proprietary population.
With some exceptions, the Merritt Systemâ tends to exclude hospitals that are
either speciality service providers, federal government controlled, or proprietary.
Control and service codes from the AHA (1996) are identi®ed for all hospitals in the database and used to exclude any remaining proprietary and
special service hospitals from the sample. As shown in Table 1, 21 hospitals
represent a proprietary ownership type, while 78 hospitals have speciality
service codes. The remaining 1949 hospitals used in the study represent nonproprietary, non-federal, general service types.
A total of 17 541 hospital-years are obtained for the nine-year period, 1988±
96, of which 7796 belong to the subperiod 1988±91 (prior to CPPS) and 9745
pertain to the subperiod 1992±96 (subsequent to CPPS). Missing data elements
for CEXGFA and other variables reduce the number of hospital-years. To test
the third hypothesis, we use a high-cost/low-cost partition of hospitals obtained by the authors from Medicare, which results in 4356 and 4634 useful
hospital-years, respectively.
6. Results
6.1. Univariate tests for the ®rst hypothesis
Table 2 shows descriptive statistics, t-tests, and Wilcoxon-Zs for the dependent variable prior and subsequent to CPPS. Subsequent to CPPS,
CEXGFA shows a statistically signi®cant decrease. For example, the mean
(median) CEXGFA is reduced from 9.1% (7.7%) prior to CPPS to 7.8% (6.6%)
15
The Merritt Systemâ (now Merritt Millennium) is the product of Van Kampen Management
Inc. Oakbrook Terrace, Illinois. All rights reserved. The following is the database's web site:
www.merrittmillennium.net.
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
23
Table 1
Sample selection criteria and sample size
Panel A: Total number of hospitals and hospital-years
Hospitals
Hospital-years
Number of hospitals in databasea
Less proprietary hospitals
Less specialty service hospitals
2,048
(21)
78
18,432
(189)
702
Non-proprietary general service
hospitals used in study
1,949
17,541
Panel B: Number of hospital-years prior and subsequent to capital prospective payment system
(CPPS) with available data for the dependent variable, capital expenditures to gross ®xed assets
(CEXGFA)
Prior to CPPS Subsequent to CPPS Total
1988±1991
1992±1996
1988±1996
Hospital-years
Less 1988 (The base year)
7,796
(1,949)
9,745
17,541
(1,949)
Less hospital-years with missing data
5,847
1;614
9,745
3;561
15,592
5;175
4,233
6,184
10,417
Net hospital-years used
a
â
Source: The Merritt System , now Merritt Millennium (the following is that database's web site:
www.merrittmillennium.net), Van Kampen Management Inc., Oakbrook Terrace, Illinois.
subsequent to CPPS. Thus, the univariate analysis suggests a preliminary rejection of the ®rst hypothesis. 16
6.2. Univariate tests for the second hypothesis
Table 3 shows the descriptive statistics and statistical tests for the independent variables across subperiods, including the eight variables used in the
®rst regression (Panel A). Additional operating expense and cost of ®nancing
variables (Panel B) and control variables (Panel C) are used in 12-variable and
16-variable regression models, respectively. As shown in Panel A, both DNFA
and DTA signi®cantly increase across the subperiods. Also, it appears that
hospitals in the sample operate at signi®cantly higher levels of pro®tability in
the period subsequent to CPPS. For instance, the mean (median) of ROA
16
In addition, we examined capital expenditures for a small sample of speciality hospitals
subject to CPPS. A minor reduction in capital expenditures for this control sample is
signi®cant. CEXGFA declines from a mean of 6.5% prior to CPPS to a mean of 6.2%
insigni®cant t-test of 0.42) subsequent to CPPS. This results supports the conclusion that
reduction in capital expenditures is primarily a consequence of CPPS.
not
not
(an
the
24
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
Table 2
Summary statistics for the dependent variable, capital expendituresa to gross ®xed assets
(CEXGFA),b prior and subsequent to CPPS
Sample size
First quartile
Mean
Median
Third quartile
Standard deviation
t-test
Wilcoxon-Z
Prior to CPPS
Subsequent to CPPS
4,233
0.051
0.091
0.077
0.114
0.066
6,184
0.044
0.078
0.066
0.098
0.058
10.72
11.77
a
Capital expenditures are measured by the change in net ®xed assets adjusted for depreciation.
Construction in progress is included in capital expenditures and gross ®xed assets. See Table 1 for
source.
b
CEXGFA (Capital expenditures/CIPI)t /gross ®xed assetstÿ1 .
*
Signi®cant at a probability of less than 0.01 (one-tailed test).
signi®cantly increases from 3.6% (3.7%) during 1988±1991 to 4.4% (4.4%)
during 1992±1996. This improving pro®tability may stem from a statistically
signi®cant decline in the cost of ®nancing as discussed below. The proxy for
average plant age (PPEAGE) increases signi®cantly from 7.54 (7.36) to 8.30
(8.08) across the two subperiods. We also found a signi®cant increase in
TURNOVER, representing increased eciency in utilization of total assets.
Finally, LBDF signi®cantly increases, which may represent accumulation of
funds designated for future replacement of property and equipment to protect
against anticipated declines in Medicare reimbursement. Since it is dicult to
assign reasons for variable changes based on univariate tests, this discussion
should be interpreted with caution.
The ®rst operating expense variable, OTHREV, has not changed signi®cantly across the two subperiods, which may indicate that any potential increases in operating leases and rental expenses, relative to revenues, are oset
by decreases in drugs and supplies. Panel B of Table 3 also shows that SALREV remains unchanged across subperiods, which indicates that managers
may not substitute labor for reduced capital spending. This part of the univariate analyses provides an indication that hospitals have not signi®cantly
changed their operating expenses subsequent to CPPS. In addition INTREV
and INTTD signi®cantly decline subsequent to CPPS. One possible explanation for the declines in both variables may be the reduction in interest rates
during the period. For example, the average interest rate on municipal bonds
decreased from 7.3% in the ®rst subperiod to 6.0% in the second subperiod
(Standard and Poor's Bond Guide).
Panel C presents four control variables. OCCPCY signi®cantly decreases,
which may re¯ect ongoing incentives to substitute outpatient for inpatient care.
25
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
Table 3
Summary statistics for independent variablesa
Prior to CPPS
Subsequent to CPPS
Dierences
Mean
Median
S.D.
Meanb
0.124
0.051
0.044
0.456
11.367
8.300
0.904
0.358
0.122
0.050
0.044
0.470
11.331
8.084
0.869
0.253
0.028
0.013
0.046
0.201
1.146
2.412
0.277
0.385
Panel B: Expense and cost of ®nancing variables
OTHREV
0.396
0.392
0.476
SALREV
0.531
0.517
0.475
INTREV
0.036
0.033
0.024
INTTD
0.074
0.073
0.047
0.400
0.522
0.028
0.065
0.391
0.517
0.026
0.061
0.084
0.105
0.018
0.168
0.004
)0.009
)0.008
)0.009
)0.001
0.000
)0.007
)0.012
Panel C: Control variables
OCCPCY
0.669
MEDICR
0.436
MATH
6.500
OPPCT
0.262
0.624
0.449
3.734
0.340
0.631
0.451
2.000
0.332
0.162
0.098
5.869
0.129
)0.045
0.013
)2.766
0.078
)0.044
0.012
)1.000
0.082
Mean
Median
Panel A: Primary ®nancial statements
DNFAd
0.115
0.113
DTA
0.050
0.049
ROA
0.036
0.037
FBTA
0.450
0.454
LNTA
11.013 11.018
PPEAGE
7.539
7.363
TURNOVERd
0.888
0.853
LBDF
0.261
0.168
0.675
0.439
3.000
0.250
S.D.
variables
0.026
0.012
0.047
0.195
1.090
1.998
0.258
0.301
0.189
0.094
9.290
0.138
0.009
0.001
0.008
0.006
0.354
0.761
0.016
0.097
Medianc
0.009
0.001
0.007
0.016
0.313
0.721
0.016
0.085
a
Independent variables: DNFA, DTA, ROA, LNTA, PPEAGE, TURNOVER, LBDF,
OTHREV, SALREV, INTREV, INTTD, OCCPCY, MEDICR, MATH, OPPCT (see text for
explanations).
b
Signi®cance based on t-test.
c
Signi®cance based on Wilcoxon-Z.
d
The distributions of DNFA and TURNOVER have extreme observations that in¯uence descriptive and test statistics. The summary statistics presented re¯ect trimming of the two extreme
upper and lower observations.
*
Signi®cant at a probability of less than 0.01 (two-tailed test).
**
Signi®cant at a probability of less than 0.05 (two-tailed test).
MEDICR signi®cantly increases across subperiods, which seems to be consistent with demographic trends. MATH signi®cantly declines, which may
represent increased merger and acquisition activities during the second subperiod. Finally, OPPCT signi®cantly increases probably as a result of
increasing outpatient utilization.
In sum, beyond the reduction in capital expenditures, we do not ®nd major
changes in operating expenses representing cost-eective behavior. Improving
pro®tability seems to be due to declining interest rates and improvement in
eciency (TURNOVER). Some items relevant for further analysis, such as
26
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
operating leases and rentals, are unavailable. 17 One accounting implication of
these results is that increased accounting disclosure may be required so that
alternative modes of coping with changes in regulation can be discerned.
6.3. Multivariate regression analyses for the ®rst and second hypotheses
Table 4 presents a matrix of Pearson correlations among CEXGFA and the
16 independent variables used in the OLS regression models. Most bivariate
correlations between the independent variables are small, but at least nine paircorrelations are slightly higher (e.g., the correlation between LBDF and FBTA
is 0.297). Further analyses discussed below indicates that multicollinearity is
not a material problem in the following regression analyses.
Table 5 presents results for the OLS regressions with slope-dummies for the
eight-variable and 12-variable models using CEXGFA as the dependent variable. For the eight-variable model, the restricted OLS regression is highly
signi®cant (F-test of 243.5) and has moderate explanatory power (adjusted-R2
of 0.159). All estimated coecients for the independent variables, except
TURNOVER, are statistically signi®cant.
The unrestricted model includes the eight hospital-speci®c variables, the
dummy variable (BFAF) and the eight interaction variables. The adjusted R2
substantially increases to 0.266. The unrestricted regression tests the hypothesis
of no change in coecients across the two subperiods. The reported Chow-F is
statistically signi®cant, suggesting rejection of the joint hypothesis that at least
one coecient has changed.
Since no violation of OLS regression assumptions was observed for the
eight-variable model, we used standard t-tests for testing the estimated individual coecients. The Durbin±Watson and White-v2 tests indicated, respectively, that assumptions of uncorrelated residuals and homoscedasticity of
the residuals are not violated. 18 The insigni®cant estimated coecient for
17
Furthermore, hospital managers may reduce operating leverage in response to increased risk
associated with CPPS. Although managers have incentives to increase the proportion of variable
costs relative to ®xed cost, relevant data are not available.
18
We examined the assumptions that the expected value of the residuals is zero and the residuals
are normally distributed. The assumption of normality of the residuals could not be rejected using
the Kolmogorov±Smirnov statistics, and the expected value of the residuals does not dier
signi®cantly from zero for the eight-variable and 12-variable models presented in Table 5. The
variance in¯ation factors (VIFs) for the independent variables in the restricted models vary from
1.011 to 1.857 with tolerance levels between 0.984 and 0.728. The VIFs for the independent
variables in the unrestricted regressions vary from 1.027 to 8.919 with a tolerance level between
0.973 to 0.112. These ®ndings indicate that there are no signi®cant impacts of correlated variables
(Neter et al., 1986, p. 392). In addition, the collinearity diagnostic, eigenvalues
The impact of medicare capital prospective
payment regulation on hospital capital
expenditures
Ran Barniv a,*, Kreag Danvers b, Joanne Healy a
a
b
Department of Accounting, Graduate School of Management, Kent State University,
P.O. Box 5190, Kent, OH 44242-0001, USA
Department of Accounting, Eberly College of Business, Indiana University of Pennsylvania,
Pennsylvania, USA
Abstract
Our study examines the impact of the capital prospective payment system (CPPS),
implemented by Medicare in 1991, on capital expenditures and cost-eective behavior of
non-proprietary hospitals. As noted in the paper, we use audited ®nancial statement
data for a large national sample of hospitals. Univariate analyses demonstrate a statistically signi®cant decline in capital expenditures in the years following the CPPS
regulation without signi®cant changes in relative aggregate operating expenses. These
preliminary ®ndings suggest that CPPS induces some cost-eective behavior by hospital
managers. Ordinary least-squares (OLS) regressions indicate that capital expenditures
before and after CPPS are dierently aected by the changes in most explanatory
variables. Further OLS regressions indicate that high-cost (low-cost) hospitals decrease
(increase) capital expenditures following CPPS, once other factors are controlled for.
Managerial accounting implications for hospitals include the eect of the regulation on
capital budgeting decisions. Greater accounting disclosure may be necessary so that
alternative modes of coping with the regulation can be discerned. Policymakers and
regulators should also be aware that although reductions in capital expenditures may
have favorable short-term eects of reducing health care costs, a potentially negative
public health impact may result if capital expenditures continue to decrease. Ó 2000
Published by Elsevier Science Ltd. All rights reserved.
*
Corresponding author. Tel.: +1-330-672-2545-x379; fax: +1-330-672-2548.
E-mail address: Rbarniv@bsa3.kent.edu (R. Barniv).
0278-4254/00/$ - see front matter Ó 2000 Published by Elsevier Science Ltd. All rights reserved.
PII: S 0 2 7 8 - 4 2 5 4 ( 9 9 ) 0 0 0 2 6 - 5
10
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
1. Introduction
The 1983 amendments to the Social Security Act introduced the Medicare
prospective payment system (PPS) and modi®ed the mechanism through which
payments are made to hospitals for providing care to Medicare patients (see
e.g., Cotterill, 1991, p. 79; Soderstrom, 1993, pp. 155±156; Eldenburg and
Kallapur, 1997, p. 32). Although the 1983 amendments altered payments of
hospital inpatient operating costs, implementation of the capital cost prospective payment system (CPPS) was deferred and Medicare continued payments based on actual capital-related expenses (Wedig et al., 1989, p. 518). The
debate on when and how to implement the new regulation continued for almost eight years. 1 Finally, CPPS was implemented with a 10 year gradual and
proportional transition period on October, 1 1991, and became eective for
®scal years beginning on this date (Burke, 1991, p. 36; Grimaldi, 1991, p. 72).
Articles published during the early 1990s expressed concerns about unfavorable consequences of the capital regulation, including a potential reduction in
future capital expenditures by hospitals (Luggiero, 1990, p. 3; Burke, 1991,
pp. 34, 35; Anderson, 1992, p. 38).
The purpose of our study is to explicitly investigate the impact of the ®nal
CPPS regulation on capital expenditure decisions in the hospital industry, and
to assess whether it induces overall cost-eective behavior by managers. We
hypothesize that hospitals may reduce capital expenditures as a result of the
new regulation. Furthermore, we attempt to determine how hospitals have
adapted to the post-CPPS environment. Previous articles have not empirically
scrutinized these issues.
Using data from audited ®nancial statements provided by the Merritt Systemâ database, we examine capital expenditures for a national sample of nonproprietary general service hospitals prior and subsequent to the ®nal CPPS
regulation. 2 The database includes ®nancial accounting data for 2048 hospitals from 1988 to 1996 of which 1949 are useful for our study.
We found a statistically signi®cant decline in capital expenditures in the
years following the implementation of the ®nal Medicare capital regulation.
Another important issue examined in this study is whether CPPS had the desired eect in promoting overall cost-eective behavior. Although a reduction
in capital expenditures was observed, operating expenses such as salaries and
1
In 1986, the proposed regulation for incorporating capital payments into PPS was issued by the
United States Department of Health and Human Services (Cotterill, 1991, p. 79). However, the
®nal capital PPS regulation was delayed on several other occasions and actual cost-based payments
continued until 1991 (Grimaldi, 1991, p. 72).
2
We use the term non-proprietary hospitals throughout the manuscript. These hospitals are nonpro®t and include local and state controlled units, but exclude federally controlled hospitals (AHA,
1996, p. A7).
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
11
other expenses (i.e., the aggregate of drugs, supplies, operating leases, and
rental expenses) as percentages of revenues remained unchanged across the two
subperiods. Furthermore, pro®tability improved perhaps due to declining interest rates. The CPPS regulation seems to motivate managers to reduce capital
expenditures and to change other variables such as ®nancial leverage, but not
to change operating expenses.
We examined the impact of hospital characteristics on capital expenditures
across subperiods and found that the impacts of certain explanatory variables
were statistically dierent before and after CPPS. In response to CPPS, hospital
managers have changed several ®nancial and operational characteristics, which
could explain some aspects of the reduction of capital expenditures following
CPPS. For example, the other-expense variable, which includes drugs, supplies,
leases and rentals, was signi®cantly negative in the period subsequent to CPPS.
This result indicates that operating leases and rentals may potentially substitute
for capital expenditures. Other empirical results suggest that high-cost (lowcost) hospitals decrease (increase) capital expenditures following CPPS, once
other factors are controlled for. This ®nding supports Medicare's expectations
regarding the redistribution eect of the new regulation (Federal Register, 1991,
pp. 43427±43428). An important public policy issue is that although reductions
in capital expenditures may reduce health care cost, policymakers need to be
cognizant of the potentially negative long-term impact on public health.
2. Some regulatory background
Prior to the Social Security Amendments (United States Statutes at Large,
1983, Public Law 98-21, Section 601, pp. 149±163), Medicare reimbursed
hospitals on a retrospective cost basis. Under this cost-based reimbursement
scheme, Medicare-related hospital revenue was tied directly to actual operating
expenses incurred, providing no incentives for hospitals to control either risk or
cost. In response to in¯ationary concerns and the solvency of the Medicare
program, the United States Congress directed the Department of Health and
Human Services to develop a new reimbursement method that would incorporate incentives for hospital eciency (Guterman and Dobson, 1986, p. 97).
PPS was implemented in 1983, but certain hospital costs, including capitalrelated expenses, were speci®cally excluded and continued to be reimbursed on
a retrospective cost basis (Wedig et al., 1989, p. 518).
Prior to CPPS, Medicare reimbursed hospitals for actual capital-related
expenses, primarily depreciation and interest expenses. 3 In addition, some
3
Medicare reimburses for annual capital-related expenses, such as depreciation and interest, and
not for actual capital expenditures, such as construction of buildings or acquisition of equipment
(Federal Register, 1996, p. 46196).
12
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
other less substantial expenses such as insurance were reimbursed. Following
the implementation of CPPS, Medicare began to reimburse these expenses
based on predetermined national averages. Medicare promoted two objectives of CPPS which are relevant for our study. First, CPPS was expected to
stimulate eciency in capital spending and induce hospitals to become
more cost-eective, thereby reducing total Medicare cost. Second,
CPPS might provide redistribution of payments and encourage hospitals
with lower capital-related expenses, for example hospitals in rural areas, to
increase capital expenditures (Federal Register, 1991, 30 August, pp. 43428±
43517).
Through this regulation, Medicare fundamentally changed the way it pays
hospitals for capital expenses attributable to Medicare inpatients. Rather
than basing reimbursements on actual expenses, hospitals would receive a
payment per case based on factors similar to the existing PPS system (Burke,
1991, p. 36). The CPPS regulation aects all PPS hospitals and provides for a
10 year gradual and proportional transition period that precedes full implementation of the new system (Grimaldi, 1991, pp. 72±76). During this transition period, Medicare established two tiers of hospitals, based upon
hospital-speci®c capital-related expenses relative to an adjusted national average. Hospitals were classi®ed as low-(high-) cost if their capital-related
expenses were lower (higher) than the national average. Low-cost hospitals
are reimbursed based on a fully prospective method, while high-cost hospitals
are reimbursed based on a hold-harmless method (Burke, 1991, p. 36). 4 We
expect this new payment scheme to impact capital budgeting decisions by
hospital management.
3. Previous literature and incremental contribution
Prior accounting studies examined hospital behavior in response to federal
regulatory changes. Several of these studies analyzed issues associated with the
1983 Medicare PPS. For example, Soderstrom (1993) scrutinized hospital
management's tendency to increase income through changing admission or
reporting policies, whereas Eldenburg and Kallapur (1997) examined the tendency of hospitals to maximize cash ¯ows by changing patient mix and cost
4
In the initial year, the fully prospective method pays hospitals a blend of 90% hospital-speci®c
cost and 10% federal rate. An additional 10% federal rate replaces hospital-speci®c cost each year
until 2001. Those hospitals subject to the hold-harmless method receive the higher of (1) 85% of
previously obligated capital costs, plus a federal rate based payment for new capital items, or (2)
100% of the federal rate (Grimaldi, 1991, pp. 72±76). In 2001, payments to all hospitals will be fully
prospective (Grimaldi, 1991, pp. 72±76).
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
13
allocations. 5 In addition, studies that examined the impact of the 1983 PPS on
hospital ®nancial and operating characteristics found that hospitals coped
fairly well by undertaking cost-cutting behavior (Guterman et al., 1988, p. 68;
Feder et al., 1987, pp. 869±872; Friedman and Shortell, 1988, p. 264). 6
Other studies focused on state level regulatory eects. Blanchard et al. (1986,
pp. 11±14) studied hospitals in the State of Washington that were subject to
revenue regulation and suggested that those hospitals report biased budget
information to increase revenue. Eldenburg and Soderstrom (1996, pp. 23±24)
re-examined Blanchard et al.'s (1986, pp. 1±2, 14) hypotheses and concluded
that hospitals bias budget information and shift costs across payor types to
increase reimbursement. 7
Research that deals speci®cally with the issue of hospital capital costs within
the prospective payment system is very limited. For example, studies discuss
the potential impacts of a CPPS-type payment structure on the cost of capital
and capital structure decisions (Boles, 1986; Wedig et al., 1988, 1989). Prior to,
and concomitant with the ®nal capital regulation, some conjectures were made
regarding its impact on the hospital industry's ®nancial viability, future levels
of capital expenditures and access to capital (Burke, 1991; Anderson, 1992;
Health Industry Today, 1992). 8 Cotterill (1991) and Kauer (1995) examined
issues related to the ®nal capital regulation. Recently, Lynch (1998, pp. 1±3)
5
Eldenburg (1994) investigated management information sharing with physician groups as a
hospital cost control mechanism in response to the 1983 PPS. Lambert and Larcker (1995, pp. 1±3)
found that hospitals more vulnerable to the 1983 PPS regulation tend to implement more bonusbased compensation contracts as motivational devices. Carey (1994, p. 275) found that during the
years following PPS implementation small, rural hospitals tended to allocate more costs to
outpatient departments than did large, urban hospitals.
6
Studies also investigated the market response of for-pro®t hospitals related to PPS events
(Folland and Kleiman, 1990) and the eect of a diagnosis related group (DRG) payment system on
the eciency of New Jersey general acute-care hospitals (Borden, 1988). These studies found little
eect of a DRG payment system on either the market value of for-pro®t hospitals (Folland and
Kleiman, 1990, pp. 61±66) or the eciency of hospitals in New Jersey (Borden, 1988, pp. 87±93).
7
Hospital accounting studies also provide evidence that disclosing quasi-dividend payments to
physicians may enhance the informativeness of hospital operating statements (Mensah and Chiang,
1996), classi®cation of hospital ®nancial ratios (Zeller et al., 1996) and the relative eciency of
proprietary versus non-proprietary hospitals (Carter et al., 1997). Mensah and Li (1993) extended a
translog budget model within a not-for pro®t setting. Mensah et al. (1994a) examined accounting
conservation and earnings management by HMOs, whereas Mensah et al. (1994b) examined
earnings management by HMOs that may be subject to political cost.
8
Burke (1991, p. 34) indicates that hospitals may experience unfavorable consequences due to the
unpredictable characteristics of CPPS and suggests that analysts anticipated that hospitals will
reduce their capital expenditures to maintain pro®tability. Anderson (1992, p. 38) presents survey
®ndings of hospital CEOs that show 48% of hospitals expected CPPS to generate pressure for
reducing equipment expenditures over the following ®ve years. A survey of hospital CFOs found
that roughly half anticipated decreases in facilities investments during the phase-in of the capital
PPS regulation (Health Industry Today, 1992, p. 10).
14
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
found that hospitals in California marginally change capital investment behavior and reduce the use of long-term debt in response to changes in Medicare
regulation.
Our study diers from the prior literature in several aspects. First, we examine empirically the impact of CPPS on capital expenditures, while prior
articles do not provide empirical evidence on this issue. Second, we scrutinize
how hospitals have adapted to the post-CPPS environment by studying potential changes in their cost-eective behavior. Third, we examine the impact of
other hospital characteristics on capital expenditures before and after the implementation of CPPS. Fourth, we examine hospital response to regulation
utilizing audited ®nancial statement information for a large, national sample of
PPS hospitals, whereas many previous studies use a single-state sample. The
results of our study are relevant for policymakers, regulators, hospital management, creditors, accounting standards setters and scholars as well as other
users of ®nancial and managerial accounting information.
4. Research methods
4.1. Hypotheses
Non-pro®t hospitals use standard capital budgeting techniques to allocate
scarce capital resources (Cleverley and Felkner, 1984, p. 45). The decoupling of
Medicare payments from actual costs requires more rigorous project evaluation since hospitals are forced to assume a higher level of risk for investment
decisions. Also, increasing unpredictability in the update factors of capital
payments (Burke, 1991, p. 34) leads to a higher risk adjusted cost of capital.
Therefore, it may be expected that increasing uncertainty and lowering expected future cash ¯ow under CPPS decreases the number of acceptable investment projects. Our primary objective is to identify whether changes in the
level of hospital expenditures for property, plant and equipment (PPE) are
associated with the issuance of the ®nal CPPS regulation. This leads to the ®rst
hypothesis tested in our study:
H1: Non-proprietary hospitals subject to CPPS will exhibit no
change in capital expenditures across the pre-regulation and postregulation periods. The alternative hypothesis is that hospitals subject to CPPS will decrease capital expenditures in the post-CPPS
period.
While, the ®rst hypothesis examines changes in capital expenditures, it does
not consider other means available to hospital managers for adapting to the
post-CPPS environment. For example, to the extent that managers substitute
operating leases or rentals for capital expenditures, an increase in these oper-
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
15
ating expenses may be observed. The regulation may result in overall costeective behavior if capital expenditures decline by more than the increase in
these operating expenses. This is the logic behind the second alternative
hypothesis. Thus, the second hypothesis examines behavior by managers
for operational activities other than capital expenditures, across the two
subperiods.
H2: Non-proprietary hospitals subject to CPPS do not change certain operating expenses across the pre-regulation and post-regulation periods. The alternative hypothesis is that hospitals increase
certain operating expenses during the second subperiod.
The third hypothesis examines how hospitals subject to Medicare's two-tier
classi®cation may dierently change capital expenditures. While all PPS hospitals are expected to be aected by the capital regulation, particular hospitals
may be less adversely impacted than others. For example, Medicare expected
that payments to high-cost hospitals (primarily large, urban hospitals) would
decrease, and therefore, these hospitals might reduce capital expenditures.
However, payments to low-cost hospitals (primarily small, rural hospitals)
would increase subsequent to CPPS (Pallarito, 1991, p. 4) and the higher expected payments to these hospitals may increase capital expenditures (Federal
Register, 1991, pp. 43427±43428). This leads to the statement of our third
hypothesis:
H3: Non-proprietary hospitals de®ned by Medicare as low-cost and
those de®ned as high-cost will exhibit no change in capital expenditures across the pre-regulation and post-regulation periods. The
alternative hypothesis is that high-cost hospitals will decrease their
capital expenditures, while low-cost hospitals will increase their
capital expenditures.
Both univariate and multivariate analyses were used to test the three
hypotheses. In particular, we assessed the impacts of hospital characteristics
on capital expenditures through the use of ordinary least-squares (OLS)
multivariate regression models. Furthermore, we examined the eects of
changes in these characteristics on capital expenditures across the two subperiods.
4.2. Variables
The general de®nitions for ®nancial statement items used to generate the
following variables are based on AICPA (1997). The expected direction for
each estimated coecient in the regressions is provided below and in Tables 5
and 6.
16
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
4.2.1. Dependent variable
The variable used to measure capital expenditures represents the change in
net PPE, adjusted for depreciation and in¯ation. This variable is used in both
univariate and multivariate analyses.
CEXGFA (capital expenditures/CIPI)t /gross ®xed assetstÿ1 . Capital expenditures are calculated as the change in net ®xed assets from year t ÿ 1 to
year t, plus depreciation expense in year t. Thus the numerator of CEXGFA
includes purchases of ®xed assets in period t minus the book value of the
disposed assets during the period. Construction-in-progress is included in the
numerator and the denominator, but it is not separately available in the database. Gross ®xed assets at time t ÿ 1 include all depreciable and non-depreciable ®xed assets at historical cost before accumulated depreciation. Except
for the CIPI adjustment, CEXGFA corresponds to the Zeller et al. (1996,
p. 170) capital expenditure growth rate variable. 9
4.2.2. Independent variables
We used the following eight independent variables in our ®rst regression
model and for the univariate analyses. 10 These variables include capital related
characteristics along with pro®tability, liquidity, eciency and ®nancial leverage measures. The ®rst two variables include depreciation in the numerators
since it is the primary capital-related expense reimbursed by Medicare. 11
Similar capital expense variables are presented by Deloitte and Touche (1996,
p. 235) and Zeller et al. (1996, p. 170). Although we hypothesize the directions
of various independent variables on capital expenditures, the complexity of
the analyses makes interpretations of several coecients rather dicult. All
independent variables, except the denominator of the ®rst, are measured at
period t.
DNFA depreciation expenset /net ®xed assetstÿ1 . Net ®xed assets are
computed as gross ®xed assets less accumulated depreciation. Examining only
the numerator suggests that, as purchases increase depreciation potentially
increases, and as equipment is sold depreciation may decrease, depending on
9
To adjust capital expenditures for in¯ationary eects we use Medicare's capital input price index
(CIPI), which re¯ects changes in the price levels of a market basket of capital resources used by
hospitals for inpatient Medicare services. The CIPI measures the input price change in capitalrelated expenses and is used by Medicare in determining increases in capital prospective payment
rates (Federal Register, 1996, pp. 46196±46203).
10
Several factors have been promoted to in¯uence hospital capital expenditure decisions,
including size, patient mix and capital structure (Pallarito, 1990, p. 33). Cotterill (1991, p. 84) found
variables that are associated with hospital capital costs include location, bed size, age of plant and
®nancing structure. Several of these variables are used in our study.
11
The Pearson correlation coecient between the ®rst two independent variables is 0.065 which is
statistically signi®cant at the 0.01 level, two-tailed, given the large sample of more than 10 000
observations.
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
17
the age of equipment. These interactions would lead to a positive coecient,
not considering the eect of the denominator. Therefore, capital expenditures
are expected to be positively associated with DNFA and this relationship may
continue following CPPS. 12
DTA depreciation expense/total assets. The denominator of this variable
includes nondepreciable assets, which are not represented in the denominator
of DNFA. Similar to DNFA, we may expect a positive relation between DTA
and CEXGFA and the positive relation may continue during the second period. However, a negative relation may be anticipated if hospitals reduce the
growth rate in capital expenditures along with decreasing other non-®xed assets. This negative relation may continue following CPPS. Given the complexity of the relation and the potentially dierent impacts of the numerator
and the denominator, the ®nal impact is not clear and interpretation of the
estimated coecient should be done with caution.
ROA excess of revenues over expenses/total assets. Hospitals that have a
higher numerator may have more funds available for capital expenditures.
Therefore, there may be a positive relationship between ROA and CEXGFA.
The relationship could weaken following CPPS since hospitals may not invest
immediately in capital expenditures due to uncertainty in the update factors
used in the DRG-based formula to calculate reimbursement. This and other
measures of ROA are used by Soderstrom (1993, p. 179) and Mensah and
Chiang (1996, p. 230).
FBTA fund balance/total assets. FBTA may be negatively related to
capital expenditures since hospitals with relatively higher equity in their capital
structure probably use more costly ®nancing. 13 However, because higher equity may enable hospitals to more easily ®nance capital expenditures we may
also expect a positive relation between FBTA and CEXGFA. Therefore, the
®nal impact of FBTA on CEXFGA and the change in the relationship are
complicated and may be positive or negative. Our capital structure variable is
identical to the equity ®nancing variable used in Zeller et al. (1996, p. 169).
Other measures of ®nancial leverage are used by Soderstrom (1993, p. 179) and
Mensah et al. (1994a, p. 80).
LNTA the natural log of total assets. This variable is used to control for
size eect. Since large hospitals tend to be more capital intensive they are
12
Note that prior to 1991, actual depreciation expense was reimbursed by Medicare (Cotterill,
1991, p. 80). Following CPPS depreciation is reimbursed with a declining proportion of 10% a year.
After 2001 depreciation will re¯ect only an accrual (Grimaldi, 1991, pp. 72±73).
13
Although prior studies argue that tax-exempt debt is a cheaper source of ®nancing than a
hospital's internal funds (e.g., Payne, 1995, p. 38), Boles (1986, p. 202) suggests that a future CPPStype of regulation may encourage hospitals to decrease the proportion of debt ®nancing in their
capital structure. In addition, the regulation may change the relative cost of debt versus equity
because of less reimbursement for interest expense (Boles, 1986, p. 202).
18
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
expected to invest more in new PPE, but to be more adversely aected by
CPPS. Therefore, hospital's size is anticipated to be positively related to capital
expenditure levels, and this relationship may become negative subsequent to
CPPS.
PPEAGE accumulated depreciation/depreciation expense. This variable is
used as a proxy for the age of the facility. We anticipate a negative relationship
with CEXGFA because as facilities become older accumulated depreciated is
expected to increase relative to annual depreciation expense. This relationship
may be less negative following CPPS because as ®xed assets continue to age
some replacement of these assets is necessary. Similar variables are used by
Cotterill (1991, p. 82), Soderstrom (1993, p. 179), and Zeller et al. (1996,
p. 170).
TURNOVER net patient revenue/total assets. TURNOVER is used as
a measure of asset eciency. We anticipate a negative relationship between
TURNOVER and CEXGFA because ecient hospitals should be more
successful at controlling capital expenditures, and this negative relationship
may strengthen following CPPS. This variable is used by Deloitte and
Touche (1996, p. 238) and a similar variable is used by Zeller et al. (1996,
p. 170).
LBDF assets designated for capital acquisition/net ®xed assets (see
AICPA, 1997, p. 131). The liquid board-designated fund ratio could be
expected to increase over time because hospitals may require more internally
generated funds to guard against uncertain and declining reimbursement for
capital-related expenses. We anticipate a negative relationship between LBDF
and CEXGFA, which is expected to become more negative following CPPS
because hospitals may be inclined to accumulate these funds for future capital
expenditures.
Four additional variables are included to examine other means of adaptation to CPPS by managers. These variables are useful to assess the relative level
of operating expenses, which may help to determine adaptation of hospital
managers to the post-CPPS environment. Missing values for these variables
reduce the 10 227 available observations (hospital years) by more than 2300.
The following two variables represent certain expenses that potentially substitute for capital expenditures.
OTHREV (medical supplies and drugs + insurance + other expenses, including operating leases)/net patient revenue. There are several components in
the numerator, and therefore, it is not clear if OTHREV will change following
CPPS. 14 To the extent that operating leases and rentals are material components of other expenses, we may expect a negative relationship between
14
A separation of the numerator into components such as operating leases and rentals is
unavailable.
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
19
OTHREV and CEXGFA, which is anticipated to become more negative
following CPPS.
SALREV salary and bene®t expense/net patient revenue. To the extent
that a reduction in capital expenditures induces more utilization of labor, we
may expect this variable to increase and to have a negative impact on capital
expenditures.
The next two variables re¯ect the impact of interest expense on capital
expenditures.
INTREV interest expense/net patient revenue. Due to the decline in municipal bond interest rates across the subperiods examined, we may expect this
ratio to decline. Lower interest expense relative to net patient revenue combined with higher capital expenditures in the prior period could generate a
negative relationship between this variable and CEXGFA. Subsequent to
CPPS, the change in the impact is expected to be positive because low interest
rates may induce management to ®nance capital expenditures with debt. This
may result in increasing interest expense following CPPS and provide a positive
change in the relation between INTREV and CEXGFA. INTREV may also be
aected by reimbursement changes and revenue trends across the subperiods
which could increase the denominator and along with decreasing capital expenditures further result in a positive change in the relation between INTREV
and CEXGFA.
INTTD interest expense/total liabilities. Since this variable represents an
eective interest rate for hospitals, we expect a decline in INTTD across subperiods. Prior to CPPS, this variable may have a negative impact on capital
expenditures. For the reasons described for the previous variable, we expect a
positive change in the estimated coecient following CPPS.
In addition, we use four control variables that re¯ect inpatient utilization,
the proportion of revenue generated by Medicare, the competitive environment
and outpatient service mix. Data for these variables are available for less than
3000 hospital-years. These or similar variables are used by Cotterill (1991),
Eldenburg (1994), Eldenburg and Soderstrom (1996), Deloitte and Touche
(1996).
OCCPCY inpatient days/(beds in service 365). The inpatient occupancy
percentage measures the utilization of inpatient capacity (see Renn, 1991, p.
25). We expected this variable to continue to decline across subperiods due to
ongoing shifts toward outpatient activity and reduced average length of hospital stay. To the extent that this variable has an impact on capital expenditures, we anticipated a positive relationship between OCCPCY and CEXGFA,
which may increase with the implementation of CPPS because higher occupancy rates may begin to result in higher levels of reimbursement for capital
expenditures.
MEDICR gross medicare revenue/gross patient revenue. The percentage
of gross patient revenue derived from Medicare may increase across subperiods
20
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
due to increasing population age. To the extent that this variable has an impact
on capital expenditures, we anticipated a positive relationship between
MEDICR and CEXGFA, which is expected to decrease following CPPS due to
the unpredictability of update factors utilized in the new DRG-based reimbursement formulas.
MATH total number of hospitals in market area. We expected this measure of market concentration to decrease due to merger and acquisition activity
within the industry. To the extent that this variable has an impact on capital
expenditures, we anticipated a positive relationship between MATH and
CEXGFA, which is expected to decrease following CPPS because consolidation may reduce duplication of capital expenditures.
OPPCT gross outpatient revenue/gross patient revenue. Due to the ongoing shift toward outpatient utilization, we expected this variable to increase
across subperiods. To the extent that outpatient services are less costly, we
anticipate a negative relationship between OPPCT and CEXGFA, which is
expected to become more negative following CPPS.
4.3. Testing the hypotheses
We tested the ®rst hypothesis using univariate analysis and more importantly using multivariate analyses. For the second hypothesis we concentrated
on four variables: OTHREV, SALREV, INTREV and INTTD. In addition
to capital expenditures, these are the most useful measures in the database to
examine other types of cost-eective behavior by hospital managers following
CPPS. For testing the third hypothesis, we partitioned the sample into highand low-cost hospitals, using the hospital-speci®c classi®cation provided to
the authors by Medicare and an urban and non-urban partition. We used
these partitions to examine changes in CEXGFA by hospital type and how
these changes are aected by the explanatory variables across the two
subperiods.
4.4. Dummy variables and estimating changes in slope coecients
For further analyses of the hypotheses, this section presents a methodology
for examining the impact of changes in the independent variables on capital
expenditures. We examined the changes in these relationships before and after
the implementation of CPPS using dummy variables to estimate changes in
slope coecients. Two equations are used: restricted (1) and unrestricted (2)
(see discussion by Johnston, 1984, pp. 207±228; Kennedy, 1992, pp. 220±225).
Henceforth, we refer to this method as a regression with slope-dummies.
Furthermore, we examined whether OLS regression assumptions were satis®ed.
Results for three models, which include 8, 12 or 16 variables are reported. Due
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
21
to space limitations, only the eight-variable restricted and unrestricted models
are presented in this section:
CEXGFAj b0 b1 DNFAj b2 DTAj b3 ROAj b4 FBTAj
b5 LNTAj b6 PPEAGEj b7 TURNOVERj
b8 LBDFj ej ;
1
CEXGFAj b0u b0u BFAF b1u DNFAj b2u DTAj b3u ROAj
b4u FBTAj b5u LNTAj b6u PPEAGEj
b7u TURNOVERj b8u LBDFj
b1 BFAF DNFAj b2 BFAF DTAj
b3 BFAF ROAj b4 BFAF FBTAj
b5 BFAF LNTAj B6 BFAF PPEAGEj
b7 BFAF TURNOVERj b8 BFAF LBDFj ej ;
2
where the dummy variable BFAF 0 prior to CPPS and BFAF 1 subsequent to CPPS. Each interaction element consists of the value of the respective independent variable for each observation subsequent to CPPS, and
zero otherwise. The coecient b0u re¯ects the dierence between the expected
CEXGFA before and after CPPS, given that the other variables are included in
the model.
Eq. (1) assumes that the restricted estimated coecients are the same prior
and subsequent to the implementation of CPPS. For Eq. (2) the expected
model prior to CPPS is
b0u b1u DNFAj b2u DTAj b3u ROAj b4u FBTAj b5u LNTAj
b6u PPEAGE b7u TURNOVER b8u LBDFj ;
and after CPPS the expected model is
b0u b0u b1u b1 DNFA b2u b2 DTA b3u b3 ROA
b4u b4 FBTA b5u b5 LNTA b6u b6 PPEAGE
b7u b7 TURNOVER b8u b8 LBDF:
A Chow-F test is used to test the joint hypothesis that b0u b1 b2 b3
b7 b8 0, and t-tests are used to examine hypotheses
0; b2 0; b3 0; b4 0; b5 0; b6 0; b7 0; b8 0 on individual estimated coecients.
b4 b5 b6
b0u 0; b1
22
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
5. Data and sample selection
This study empirically examines the impact of CPPS on hospital investment levels for a large sample of non-proprietary hospitals from 1988 to
1996. The data source is the Merritt Systemâ , a private credit and investment
analysis database system, which contains audited annual ®nancial statement
information, socio-economic information and supplementary operational
statistics for 2048 hospitals, representing all 50 states and the District of
Columbia. 15 Since about 4100 general acute care hospitals participate in
Medicare and ®le cost reports (Deloitte & Touche LLP, 1996, p. 1) our
sample comprises roughly 50% of this population. This relatively large sample
permits inferences to be comfortably made to the non-proprietary population.
With some exceptions, the Merritt Systemâ tends to exclude hospitals that are
either speciality service providers, federal government controlled, or proprietary.
Control and service codes from the AHA (1996) are identi®ed for all hospitals in the database and used to exclude any remaining proprietary and
special service hospitals from the sample. As shown in Table 1, 21 hospitals
represent a proprietary ownership type, while 78 hospitals have speciality
service codes. The remaining 1949 hospitals used in the study represent nonproprietary, non-federal, general service types.
A total of 17 541 hospital-years are obtained for the nine-year period, 1988±
96, of which 7796 belong to the subperiod 1988±91 (prior to CPPS) and 9745
pertain to the subperiod 1992±96 (subsequent to CPPS). Missing data elements
for CEXGFA and other variables reduce the number of hospital-years. To test
the third hypothesis, we use a high-cost/low-cost partition of hospitals obtained by the authors from Medicare, which results in 4356 and 4634 useful
hospital-years, respectively.
6. Results
6.1. Univariate tests for the ®rst hypothesis
Table 2 shows descriptive statistics, t-tests, and Wilcoxon-Zs for the dependent variable prior and subsequent to CPPS. Subsequent to CPPS,
CEXGFA shows a statistically signi®cant decrease. For example, the mean
(median) CEXGFA is reduced from 9.1% (7.7%) prior to CPPS to 7.8% (6.6%)
15
The Merritt Systemâ (now Merritt Millennium) is the product of Van Kampen Management
Inc. Oakbrook Terrace, Illinois. All rights reserved. The following is the database's web site:
www.merrittmillennium.net.
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
23
Table 1
Sample selection criteria and sample size
Panel A: Total number of hospitals and hospital-years
Hospitals
Hospital-years
Number of hospitals in databasea
Less proprietary hospitals
Less specialty service hospitals
2,048
(21)
78
18,432
(189)
702
Non-proprietary general service
hospitals used in study
1,949
17,541
Panel B: Number of hospital-years prior and subsequent to capital prospective payment system
(CPPS) with available data for the dependent variable, capital expenditures to gross ®xed assets
(CEXGFA)
Prior to CPPS Subsequent to CPPS Total
1988±1991
1992±1996
1988±1996
Hospital-years
Less 1988 (The base year)
7,796
(1,949)
9,745
17,541
(1,949)
Less hospital-years with missing data
5,847
1;614
9,745
3;561
15,592
5;175
4,233
6,184
10,417
Net hospital-years used
a
â
Source: The Merritt System , now Merritt Millennium (the following is that database's web site:
www.merrittmillennium.net), Van Kampen Management Inc., Oakbrook Terrace, Illinois.
subsequent to CPPS. Thus, the univariate analysis suggests a preliminary rejection of the ®rst hypothesis. 16
6.2. Univariate tests for the second hypothesis
Table 3 shows the descriptive statistics and statistical tests for the independent variables across subperiods, including the eight variables used in the
®rst regression (Panel A). Additional operating expense and cost of ®nancing
variables (Panel B) and control variables (Panel C) are used in 12-variable and
16-variable regression models, respectively. As shown in Panel A, both DNFA
and DTA signi®cantly increase across the subperiods. Also, it appears that
hospitals in the sample operate at signi®cantly higher levels of pro®tability in
the period subsequent to CPPS. For instance, the mean (median) of ROA
16
In addition, we examined capital expenditures for a small sample of speciality hospitals
subject to CPPS. A minor reduction in capital expenditures for this control sample is
signi®cant. CEXGFA declines from a mean of 6.5% prior to CPPS to a mean of 6.2%
insigni®cant t-test of 0.42) subsequent to CPPS. This results supports the conclusion that
reduction in capital expenditures is primarily a consequence of CPPS.
not
not
(an
the
24
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
Table 2
Summary statistics for the dependent variable, capital expendituresa to gross ®xed assets
(CEXGFA),b prior and subsequent to CPPS
Sample size
First quartile
Mean
Median
Third quartile
Standard deviation
t-test
Wilcoxon-Z
Prior to CPPS
Subsequent to CPPS
4,233
0.051
0.091
0.077
0.114
0.066
6,184
0.044
0.078
0.066
0.098
0.058
10.72
11.77
a
Capital expenditures are measured by the change in net ®xed assets adjusted for depreciation.
Construction in progress is included in capital expenditures and gross ®xed assets. See Table 1 for
source.
b
CEXGFA (Capital expenditures/CIPI)t /gross ®xed assetstÿ1 .
*
Signi®cant at a probability of less than 0.01 (one-tailed test).
signi®cantly increases from 3.6% (3.7%) during 1988±1991 to 4.4% (4.4%)
during 1992±1996. This improving pro®tability may stem from a statistically
signi®cant decline in the cost of ®nancing as discussed below. The proxy for
average plant age (PPEAGE) increases signi®cantly from 7.54 (7.36) to 8.30
(8.08) across the two subperiods. We also found a signi®cant increase in
TURNOVER, representing increased eciency in utilization of total assets.
Finally, LBDF signi®cantly increases, which may represent accumulation of
funds designated for future replacement of property and equipment to protect
against anticipated declines in Medicare reimbursement. Since it is dicult to
assign reasons for variable changes based on univariate tests, this discussion
should be interpreted with caution.
The ®rst operating expense variable, OTHREV, has not changed signi®cantly across the two subperiods, which may indicate that any potential increases in operating leases and rental expenses, relative to revenues, are oset
by decreases in drugs and supplies. Panel B of Table 3 also shows that SALREV remains unchanged across subperiods, which indicates that managers
may not substitute labor for reduced capital spending. This part of the univariate analyses provides an indication that hospitals have not signi®cantly
changed their operating expenses subsequent to CPPS. In addition INTREV
and INTTD signi®cantly decline subsequent to CPPS. One possible explanation for the declines in both variables may be the reduction in interest rates
during the period. For example, the average interest rate on municipal bonds
decreased from 7.3% in the ®rst subperiod to 6.0% in the second subperiod
(Standard and Poor's Bond Guide).
Panel C presents four control variables. OCCPCY signi®cantly decreases,
which may re¯ect ongoing incentives to substitute outpatient for inpatient care.
25
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
Table 3
Summary statistics for independent variablesa
Prior to CPPS
Subsequent to CPPS
Dierences
Mean
Median
S.D.
Meanb
0.124
0.051
0.044
0.456
11.367
8.300
0.904
0.358
0.122
0.050
0.044
0.470
11.331
8.084
0.869
0.253
0.028
0.013
0.046
0.201
1.146
2.412
0.277
0.385
Panel B: Expense and cost of ®nancing variables
OTHREV
0.396
0.392
0.476
SALREV
0.531
0.517
0.475
INTREV
0.036
0.033
0.024
INTTD
0.074
0.073
0.047
0.400
0.522
0.028
0.065
0.391
0.517
0.026
0.061
0.084
0.105
0.018
0.168
0.004
)0.009
)0.008
)0.009
)0.001
0.000
)0.007
)0.012
Panel C: Control variables
OCCPCY
0.669
MEDICR
0.436
MATH
6.500
OPPCT
0.262
0.624
0.449
3.734
0.340
0.631
0.451
2.000
0.332
0.162
0.098
5.869
0.129
)0.045
0.013
)2.766
0.078
)0.044
0.012
)1.000
0.082
Mean
Median
Panel A: Primary ®nancial statements
DNFAd
0.115
0.113
DTA
0.050
0.049
ROA
0.036
0.037
FBTA
0.450
0.454
LNTA
11.013 11.018
PPEAGE
7.539
7.363
TURNOVERd
0.888
0.853
LBDF
0.261
0.168
0.675
0.439
3.000
0.250
S.D.
variables
0.026
0.012
0.047
0.195
1.090
1.998
0.258
0.301
0.189
0.094
9.290
0.138
0.009
0.001
0.008
0.006
0.354
0.761
0.016
0.097
Medianc
0.009
0.001
0.007
0.016
0.313
0.721
0.016
0.085
a
Independent variables: DNFA, DTA, ROA, LNTA, PPEAGE, TURNOVER, LBDF,
OTHREV, SALREV, INTREV, INTTD, OCCPCY, MEDICR, MATH, OPPCT (see text for
explanations).
b
Signi®cance based on t-test.
c
Signi®cance based on Wilcoxon-Z.
d
The distributions of DNFA and TURNOVER have extreme observations that in¯uence descriptive and test statistics. The summary statistics presented re¯ect trimming of the two extreme
upper and lower observations.
*
Signi®cant at a probability of less than 0.01 (two-tailed test).
**
Signi®cant at a probability of less than 0.05 (two-tailed test).
MEDICR signi®cantly increases across subperiods, which seems to be consistent with demographic trends. MATH signi®cantly declines, which may
represent increased merger and acquisition activities during the second subperiod. Finally, OPPCT signi®cantly increases probably as a result of
increasing outpatient utilization.
In sum, beyond the reduction in capital expenditures, we do not ®nd major
changes in operating expenses representing cost-eective behavior. Improving
pro®tability seems to be due to declining interest rates and improvement in
eciency (TURNOVER). Some items relevant for further analysis, such as
26
R. Barniv et al. / Journal of Accounting and Public Policy 19 (2000) 9±40
operating leases and rentals, are unavailable. 17 One accounting implication of
these results is that increased accounting disclosure may be required so that
alternative modes of coping with changes in regulation can be discerned.
6.3. Multivariate regression analyses for the ®rst and second hypotheses
Table 4 presents a matrix of Pearson correlations among CEXGFA and the
16 independent variables used in the OLS regression models. Most bivariate
correlations between the independent variables are small, but at least nine paircorrelations are slightly higher (e.g., the correlation between LBDF and FBTA
is 0.297). Further analyses discussed below indicates that multicollinearity is
not a material problem in the following regression analyses.
Table 5 presents results for the OLS regressions with slope-dummies for the
eight-variable and 12-variable models using CEXGFA as the dependent variable. For the eight-variable model, the restricted OLS regression is highly
signi®cant (F-test of 243.5) and has moderate explanatory power (adjusted-R2
of 0.159). All estimated coecients for the independent variables, except
TURNOVER, are statistically signi®cant.
The unrestricted model includes the eight hospital-speci®c variables, the
dummy variable (BFAF) and the eight interaction variables. The adjusted R2
substantially increases to 0.266. The unrestricted regression tests the hypothesis
of no change in coecients across the two subperiods. The reported Chow-F is
statistically signi®cant, suggesting rejection of the joint hypothesis that at least
one coecient has changed.
Since no violation of OLS regression assumptions was observed for the
eight-variable model, we used standard t-tests for testing the estimated individual coecients. The Durbin±Watson and White-v2 tests indicated, respectively, that assumptions of uncorrelated residuals and homoscedasticity of
the residuals are not violated. 18 The insigni®cant estimated coecient for
17
Furthermore, hospital managers may reduce operating leverage in response to increased risk
associated with CPPS. Although managers have incentives to increase the proportion of variable
costs relative to ®xed cost, relevant data are not available.
18
We examined the assumptions that the expected value of the residuals is zero and the residuals
are normally distributed. The assumption of normality of the residuals could not be rejected using
the Kolmogorov±Smirnov statistics, and the expected value of the residuals does not dier
signi®cantly from zero for the eight-variable and 12-variable models presented in Table 5. The
variance in¯ation factors (VIFs) for the independent variables in the restricted models vary from
1.011 to 1.857 with tolerance levels between 0.984 and 0.728. The VIFs for the independent
variables in the unrestricted regressions vary from 1.027 to 8.919 with a tolerance level between
0.973 to 0.112. These ®ndings indicate that there are no signi®cant impacts of correlated variables
(Neter et al., 1986, p. 392). In addition, the collinearity diagnostic, eigenvalues