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https://doi.org/10.1177/1077558718781606
Medical Care Research and Review
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DOI: 10.1177/1077558718781606
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Empirical Research
The Privatization of Public
Hospitals: Its Impact on
Financial Performance
Zo Ramamonjiarivelo1,
Robert Weech-Maldonado2, Larry Hearld2,
Rohit Pradhan3, and Ganisher K. Davlyatov2
Abstract
This study examined the effects of public hospitals’ privatization on financial
performance. We used a sample of nonfederal acute care public hospitals from 1997
to 2013, averaging 434 hospitals per year. Privatization was defined as conversion
from public status to either private not-for-profit (NFP) or private for-profit (FP)
status. Financial performance was measured by operating margin (OM) and total
margin (TM). We used hospital level and year fixed effects linear panel regressions
with nonlagged independent and control variables (Model 1), lagged by 1 year (Model
2), and lagged by 2 years (Model 3). Privatization to FP was associated with 17%
higher OM (Model 2) and 9% higher OM (Model 3), compared with 3%, 4%, and 6%
higher OM for privatization to NFP for all three Models, respectively. Privatization
to FP was associated with 7% higher TM (Model 2) and privatization to NFP was
associated with 2% higher TM (Model 3).
Keywords
public hospitals, privatization, financial performance
This article, submitted to Medical Care Research and Review on October 8, 2015, was revised and
accepted for publication on May 12, 2018.
1Texas State University, San Marcos, TX, USA
2University of Alabama at Birmingham, Birmingham, AL, USA
3University of Arkansas for Medical Sciences, Little Rock, AR, USA
Corresponding Author:
Zo Ramamonjiarivelo, School of Health Administration, Texas State University, Encino Hall-Suite 250,
601 University Drive, San Marcos, TX 78666-4684, USA.
Email: zhr3@txstate.edu
781606MCRXXX10.1177/1077558718781606Medical Care Research and ReviewRamamonjiarivelo et al.
research-article2018
2 Medical Care Research and Review 00(0)
Introduction
Being financially sound is essential to ensuring the continuous delivery of health care
services among hospitals. However, as “providers of last resort,” government-owned,
public hospitals are especially challenged at maintaining sound financial health. As
safety-net providers, they are supposed to offer health care services to every person,
regardless of the person’s ability to pay or health insurance coverage (Anderson,
Boumbulian, & Pickens, 2004; Villa & Kane, 2013). During the past few decades,
several factors, such as decreased Medicare and Medicaid reimbursements, and
increased competition have contributed to the financial difficulties experienced by
some public hospitals (Anderson et al., 2004; Ko, Derose, Needleman, & Ponce, 2014;
Legnini et al., 1999; McCaughey, 2015; Needleman & Ko, 2012; Ramamonjiarivelo,
Weech-Maldonado, Hearld, & Pradhan, 2014). Financially distressed public hospitals
can represent a fiscal burden to state and local governments. For instance, the New
York City’s public hospital system had a deficit of $1.2 billion in 2017. That deficit is
projected to reach $1.8 billion in 2020. To fill these deficits, the New York City gov-
ernment plans to provide $1.8 billion in 2018 and $1.9 billion in 2020 to the New York
City’s public hospital system (Ford, 2017).
Privatization, defined as ownership conversion from public status to either pri-
vate for-profit (FP) or private not-for-profit (NFP) status (Legnini et al., 1999;
Ramamonjiarivelo et al., 2015; Tradewell, 1998), has been found to be one of the
strategies chosen by the city, county, or state governments that own financially
distressed public hospitals (Burns, Shah, Frank, & Powell, 2009; Legnini et al.,
1999; Ramamonjiarivelo et al., 2015; Sloan, Ostermann, & Conover, 2003; Weil,
2011). There are several possible advantages of public hospital privatization. First,
privatization may enhance hospital efficiency (Bovbjerg, Marsteller, & Ullman,
2000; Ramamonjiarivelo, Epané, Hearld, McRoy, & Weech-Maldonado, 2016;
Tiemann & Schreyögg, 2010, 2012; Villa & Kane, 2013) and reduce operating
costs. For instance, Ramamonjiarivelo et al. (2016) found that privatization resulted
in improved efficiency; current assets turnover, and fixed assets turnover increased,
whereas the number of full-time equivalent employees decreased, after privatiza-
tion. They also found that privatization improved productivity in terms of case-mix
adjusted admissions per full-time equivalent employees (Ramamonjiarivelo et al.,
2016).
Second, privatization offers hospitals a new infusion of capital funding that may
result in the acquisition of updated technology, recruitment of capable managers and
clinical staff, restoration of infrastructure (Bovbjerg et al., 2000; Burgess & Wilson,
1996; Desai, Lukas, & Young, 2000; Siegel, 1996; Wessel, 1995), and improved qual-
ity (Bovbjerg et al., 2000; Tiemann & Schreyögg, 2010). Finally, privatization releases
public hospitals from the grip of politics and bureaucracy, offering hospitals more
freedom and flexibility in decision making (Bovbjerg et al., 2000; Siegel, 1996).
On the other hand, given the major role of public hospitals as safety net providers,
there are concerns that privatization may reduce access to care for the indigent as
Ramamonjiarivelo et al. 3
converted hospitals might not be as committed to serving the poor (Desai et al., 2000;
Thorpe, Florence, & Sieber, 2000; Villa & Kane, 2013). In addition, privatized hospi-
tals tend to close services that are valuable to the community but unprofitable, such as
HIV/AIDS services and trauma centers (Villa & Kane, 2013). However, to the extent
that financially distressed public hospitals are privatizing, this may ensure continued
operations for a hospital that may otherwise close.
New Contributions
Since research suggests that poor financial performance (Sloan et al., 2003) and
financial distress (Ramamonjiarivelo et al., 2015) are among the reasons for govern-
ment entities to privatize public hospitals, it is important to know whether privatiza-
tion actually improves financial performance. Previous research has investigated this
question. For example, Thorpe et al. (2000) investigated the impact of several types
of ownership conversions on financial performance; they found no significant change
in total margin (TM) after privatization to NFP status and a marginally significant 2.6
percentage point increase in TM after privatization to FP status. Likewise, Villa and
Kane (2013) studied the impact of privatization to NFP status and found no signifi-
cant change in TM, but a significant 6.1 percentage point increase (p < .05) in operat-
ing margin (OM), after privatization. These studies provide important baseline
information about the potential impact of privatization; however, they are less infor-
mative regarding the challenges and opportunities facing public hospitals throughout
the United States today. For example, the Thorpe et al. (2000) study examined priva-
tization activities from 1990 to 1997, and thus, do not reflect more recent changes in
the political and resource environments of public hospitals. Similarly, the study by
Villa and Kane (2013) focused on privatization activities among 22 hospitals in three
states and may not generalize to the broader population of public hospitals throughout
the United States.
The purpose of this study is to update and extend this work by exploring whether
privatization has a positive impact on financial performance of privatized public hos-
pitals and further explore whether privatization to FP status results in higher financial
performance compared with privatization to NFP status.
We do so in several ways. First, our study uses more recent (1997-2013) and com-
prehensive (a sample of public hospitals in the United States) data. Second, our study
incorporates both OM and TM as measures of financial performance, providing a
more comprehensive view of financial performance. Third, we differentiate privatiza-
tion into two types and examine their respective impact on financial performance.
Fourth, we use models with nonlagged and lagged independent variables to assess
changes in financial performance in the year of privatization and one and two years
after privatization. Specifically, our analysis explores whether privatization to FP sta-
tus results in higher financial performance compared with privatization to NFP status.
Finally, our study further explores the operational strategies used by privatized hospi-
tals to improve financial performance.
4 Medical Care Research and Review 00(0)
Conceptual Framework and Hypotheses
This study uses agency theory and property rights theory (PRT) to explore the associa-
tion between privatization and financial performance. Both theories have been used in
public hospitals’ privatization studies (Ramamonjiarivelo et al., 2015; Tiemann &
Schreyögg, 2010, 2012). Agency theory seeks to explain the issues pertaining to the
relationship between the principal (the owner of the firm) and the agent (the manager
or management team). While the principal expects the agent to serve the principal’s
interest, agency theory posits that the principal and the agent may have conflicting
agendas and motives and it is difficult and costly to the principal to monitor the actions
of the agent (Clarkson, 1972; Eisenhardt, 1989). The agent’s main objective is to pur-
sue his or her own interests, even at the expense of the interests and objectives of the
principal (Tiemann & Schreyögg, 2010). As a result, the misalignment of the princi-
pal’s and agent’s agendas and objectives may negatively affect the organization’s per-
formance. One mechanism that the principal can use to align the interests of the agent
with those of the principal is by using financial incentives based on the agent’s perfor-
mance. While the rift between the principal’s and agent’s motives exists in all three
ownership types (public, private NFP, and private FP), public ownership has the most
difficulty aligning the agent’s motives with the principal’s due to the dual principal–
agent relationship. The first relationship pertains to the relationship between the pub-
lic, as owners, and politicians, as agents. The second relationship consists of the
relationship between politicians, representing the interests of owners (public), and
managers, as agents (Cuervo & Villalonga, 2000, p. 582). This may result in potential
interference in public hospitals’ affairs by politicians seeking to please the public for
reelection, which may affect a public hospital’s performance (Cuervo & Villalonga,
2000; Tiemann & Schreyögg, 2010). There are also constraints with respect to the use
of financial incentives in public entities. Decisions pertaining to the managerial com-
pensation of public hospitals are held at public meetings (Eldenburg & Krishnan,
2003). The public, who is the primary source of funding for public hospitals, may put
pressure on cost containment and be against generously compensating managers
(Eldenburg & Krishnan, 2003). Based on these conditions, public hospitals may not be
able to provide enough incentives to managers to align the goals of principal and
agent. This misalignment may result in lower financial performance among public
hospitals compared with private hospitals (Eldenburg & Krishnan, 2003).
On the other hand, private hospitals typically face one principal–agent relationship,
that between shareholders (the principal—who are the owners of the organization) and
managers (agent) for FP hospitals, and between the board of directors (the principal
who represents philanthropists and community members) and managers (agent) for
NFP hospitals (Brickley & Van Horn, 2002). In addition, politicians are not directly
involved in private hospitals’ fiscal affairs. Consequently, private hospitals may find it
easier to align the principal’s and agent’s agendas in ways that the agent can deliver
better financial performance. Such alignments may consist of managerial incentives
decided by the board of directors or “Compensation Committee” (Internal Revenue
Service, 2006), in terms of bonuses or increases in annual compensation based on
Ramamonjiarivelo et al. 5
performance, for private NFP hospitals; and managerial incentives from shareholders,
in terms of hospital co-ownership through share-based compensations, for private FP
hospitals (Brickley & Van Horn, 2002). Based on these premises, we expect privatiza-
tion of public hospitals to enhance financial performance.
Hypothesis 1: Public hospitals experience better financial performance after
privatization.
Private NFP hospitals are charitable entities under 501 (c) (3) classification of the
Internal Revenue Code, which grants NFPs their tax-exempt status. NFP hospitals
cannot raise capital by selling shares to investors. They raise capital from excess rev-
enue over cost reinvested in the organization, tax-exempt bonds, and philanthropists’
tax-deductible donations (Chang & Jacobson, 2010; Pauly, 1987). However, donations
do not grant philanthropists the right to share the residual income of the hospital
among themselves. Like other NFP organizations, NFP hospitals face a “nondistribu-
tion constraint” that legally prohibits the distribution of profits to any individuals
including the philanthropists and the managers (Brickley & Van Horn, 2002; Pauly,
1987). Since the trustees of NFP hospitals do not have the right to own residual income
or the right to distribute it to managers, NFP hospitals do not have the same incentives
as FP hospitals to effectively align their interests and with those of the managers.
Furthermore, the proceeds from the sale of the hospital are not distributed among phi-
lanthropists, but rather invested in a foundation to benefit the community (Gray, 1986,
1997; Marsteller, Bovbjerg, & Nichols, 1998). Given the tax-exempt status of NFP
hospitals, they are expected to provide community benefits, such as providing care
regardless of ability to pay as long as it does not threaten the financial viability of the
hospital. As such, they are supposed to accept the loss from serving patients with pub-
lic insurance, execute community health needs assessments and health promotions,
and conduct basic research (Chang & Jacobson, 2010; Claxton, Feder, Shactman, &
Altman, 1997).
On the other hand, FP hospitals have easier access to capital by issuing stock, which
is less expensive than debt financing (Claxton et al., 1997); FP hospitals can have
access to both debt and equity financing. FP hospitals are owned by shareholders, and
their primary purpose is to maximize shareholders’ wealth. Given their profit seeking
behavior, FP hospitals must pay property, sales, and income tax. However, unlike pub-
lic and NFP hospitals, FP hospitals’ shareholders have the right to share residual
income and the proceeds from the sale of the hospital among themselves (Gapenski,
2004; Gray, 1986, 1997; Marsteller et al., 1998).
Property rights refers to an individual’s or organization’s right of ownership to a
resource; the right to have control over the resource; the right to consume the resource;
the right to make a decision pertaining to the use of the resource; the right to exploit
the resource to achieve one’s goals and objectives; the right to earn residual income
from the resource; and the right to sell the resource to other individuals or entities
(Eggertsson, 1990; Furubotn & Pejovich, 1972; Hart, 1995; Mahoney, 2004; Tiemann
& Schreyögg, 2009). PRT claims that if a person has the right to make decisions
6 Medical Care Research and Review 00(0)
regarding the use of a resource and the right to receive residual income from the use of
the resource, that person will make the best decision that will maximize the organiza-
tion’s profit and ultimately maximize that person’s residual income (Harding &
Precker, 2000). Thus, Tiemann and Schreyögg (2009, 2010) and Clarkson (1972) sug-
gest that FP status provides the most effective tools to resolve conflicting agendas
between the agent and the principal. FP status has more flexibility with respect to
executive and medical staff compensation decisions than NFP status (Claxton et al.,
1997). Since shareholders have the right to own and distribute residual income, they
can influence managers’ behavior to support their agendas by positively correlating
managers’ and physicians’ compensations with the hospital’s financial performance.
Furthermore, giving managers and physicians the right of ownership to the assets of a
FP hospital, for instance through employee stock options and profit sharing, may serve
as a strong incentive for the managers and physicians to meet the expectations of the
FP hospital (Claxton et al., 1997). In addition, given shareholders’ right to sell stocks
to others, and the fear that shareholders might sell their stocks and reinvest them in
other companies, managers and physicians of FP hospitals have a strong interest in
maximizing shareholders’ wealth. Therefore, we would expect FP hospitals to empha-
size efficiency and grow market share to achieve better financial performance than
NFP hospitals (Tiemann & Schreyögg, 2010; Weech-Maldonado et al., 2012). In addi-
tion, FP hospitals have no legal obligation to provide care for the needy since they are
not the recipients of public funding or philanthropist donations, and they must pay
income tax. Therefore, they can pursue more profitable ventures that may boost their
financial performance.
Since the trustees of NFP hospitals do not have the right to own residual income or
the right to distribute it to managers, NFP hospitals do not have the same incentives to
effectively align their interests with those of the managers. Therefore, we expect FP
hospitals to perform better financially than NFP hospitals. Indeed, some empirical
studies have provided evidence that FP hospitals and nursing homes exhibit higher
financial performance compared with their NFP counterparts (Tennyson & Fottler,
2000; Weech-Maldonado et al., 2012; Younis & Forgione, 2005). Therefore, based on
PRT and evidence from prior studies, it is hypothesized as follows:
Hypothesis 2: Public hospitals that convert to private FP status exhibit better finan-
cial performance compared with public hospitals that convert to private NFP
status.
Method
Data
This study used four data sources: (a) the American Hospital Association (AHA)
Annual Survey, (b) the Area Health Resources File (AHRF), (c) the Medicare Cost
Reports (MCR), and (d) the Local Area Unemployment Statistics (LAUS). The AHA
data file consists of hospital profile variables such as ownership status, number of
Ramamonjiarivelo et al. 7
hospital beds, teaching status, and multihospital system affiliation. The AHRF data
file contains demographic and economic information on counties. The MCR data file
contains financial data; it is the most validated and widely accepted data for hospital
financial analysis (Pink et al., 2005). The LAUS data file contains estimates of monthly
and annual averages of employment rates measured at various geographical levels
including metropolitan areas, cities, and counties.
Our sample consisted of all government-owned, nonfederal, acute care, general,
and surgical hospitals in the United States in 1997. These hospitals were followed
from year to year until 2013. Our original sample comprised 1,225 public hospitals.
We applied several exclusion criteria to construct our analytic sample. First, hospitals
that diversified to a skilled nursing facility (n = 6) or an ambulatory care facility (n =
2) were excluded. Second, we eliminated critical access hospitals (CAHs; n = 557)
because they have a different reimbursement environment (Centers for Medicare and
Medicaid Services, 2017). Third, we excluded hospitals that were acquired or merged
(n = 16) during the study period. Based on the data from the AHA, when hospitals are
merged, they become a new entity and use a different Medicare provider number.
When hospitals are acquired, they become part of the acquirer’s organization and stop
using their preacquisition Medicare Provider Number. Fourth, hospitals without com-
plete data throughout the study period were excluded (n = 139); hospitals that had
missing data in the earlier years and later years were kept in the sample. Fifth, hospi-
tals that underwent multiple ownership conversions (n = 34) during the study period
were excluded. Sixth, we removed 35 hospitals that closed, during the study period,
from the data file. Ultimately, our analytic sample comprised a cohort of 436 public
hospitals (7,386 hospital-year observations). Of the 436 public hospitals in 1997, 195
(45%) were public hospital districts or authorities, 149 (34%) were owned by counties,
42 (10%) were owned by cities, 33 (8%) were owned by states, and 17 (4%) were co-
owned by both cities and counties (total percentage is 101% due to rounding).
To ensure that the normality assumption was met for OM and TM, observations
with values 5 standard deviations above or below the mean were removed until skew-
ness was close to 0 and kurtosis close to 3 (Weech-Maldonado et al., 2012). A total of
102 hospital-year observations was removed from this process. As a result, the data
file for OM had an average of 418 hospitals per year and a total of 7,104 hospital-year
observations. Of these hospitals, 74 were converted to private NFP status and 28 were
converted to private FP status. The data file for TM had an average of 424 hospitals per
year and a total of 7,212 hospital-year observations. Of these hospitals, 70 were con-
verted to private NFP status and 26 were converted to private FP status.
Variables
The dependent variable for both hypotheses was financial performance, which was mea-
sured using OM and TM. These two measures reflect the overall financial performance
and profitability of the hospital. OM measures the percentage of income a hospital
exclusively generates from providing health care services relative to the expenses
incurred from providing such services. OM was calculated as Total Operating Income
8 Medical Care Research and Review 00(0)
divided by Total Operating Revenue. TM measures the percentage of income a hospital
generates from all its activities, including income from health care services and other
income, relative to the hospital’s total expenses. TM was calculated as Net Income
divided by Total Revenue (Gapenski, 2004). TM indicates the hospital’s ability to con-
trol overall expenses. The lower the overall expenses the higher the net income and TM.
Our independent variable was privatization. To test Hypothesis 1, privatization was
a dichotomous variable coded as “1” if the hospital privatized (the year of privatiza-
tion and subsequent years were coded as 1) and “0” if the hospital stayed public
(Pradhan, Weech-Maldonado, Harman, & Hyer, 2014). To test Hypothesis 2, two
dummy variables were created: one dummy variable for conversion to NFP status, and
another dummy variable for conversion to FP status.
This study controlled for organizational and market characteristics that may influence
hospitals’ financial performance. Organizational characteristics included nine variables:
number of beds in the hospital, teaching status, outpatient mix, occupancy rate, percent-
age of Medicare inpatient days, percentage of Medicaid inpatient days, multihospital
system membership, health network, and contract management. Market characteristics
included seven variables measured at county level and two variables measured at the
Health Service Area (HSA) level. County-level variables consisted of per capita income,
unemployment rate, percentage of population who were 65 years of age and older, num-
ber of physicians per 1,000 population, yearly change in unemployment rate, Medicare
Advantage penetration, and excess capacity. HSA-level variables consisted of
Herfindahl–Hirschman index (HHI) and percentage of FP hospitals in the HSA.
Analysis
We used analysis of variance to examine the difference in mean OM and mean TM
between hospitals that remained public, before and after NFP privatization, and before
and after FP privatization. Our aim was to assess whether hospitals that were priva-
tized had different financial performance, before and after privatization, compared
with hospitals that remained public. We also ran linear regression models with hospital
level and year fixed effects (FE). FE is the appropriate model to examine the impact of
privatization on financial performance, since it controls for time-invariant unobserv-
able variables that may explain between-hospital differences. Therefore, FE focuses
on within-hospital variations in financial performance as a result of privatization
(Allison, 2005; Wooldridge, 2006). The impact of privatization on financial perfor-
mance may not happen immediately after privatization but rather may be delayed in
time. Therefore, we ran additional FE linear regression models with the independent
and control variables lagged by 1 year and 2 years. Twelve FE linear regressions were
used to test our hypotheses:
Models for Hypothesis 1:
Yit = α + β1 × Prit + β2×Cit + β3 × Year + µit
Yit = α + β1 × Prit − 1 + β2×Cit−1 + β3 × Year + µit − 1
Yit = α + β1 × Prit − 2 + β2×Cit−2 + β3 × Year + µit − 2
Ramamonjiarivelo et al. 9
Models for Hypothesis 2:
Yit = α + β1 × Prnpit + β2 × Prfpit + β3 × Cit + β4 × Year + µit
Yit = α + β1 × Prnpit − 1 + β2 × Prfpit − 1 + β3 × Cit−1 + β4 × Year+ µit − 1
Yit = α + β1 × Prnpit − 2 + β2 × Prfpit − 2 + β3 × Cit − 2 + β4 × Year+ µit − 2
Where Y = dependent variables (OM and TM), Pr = privatization from public to private
status, Prnp = privatization from public to private NFP status, Prfp = privatization from
public to private FP status, C = control variables (organizational and market character-
istics), Year = year dummy variables, i = individual hospital, t = year, µ = error term.
Joint tests were used, following the regression models for Hypothesis 2, to test the
null hypothesis that the beta coefficients of OM and TM for hospitals that privatized
to FP and NFP are the same. Data cleaning was performed using Microsoft Excel 2016
and SAS version 9.2; data analyses were conducted using STATA Version 14.
Results
Pearson’s correlations of the independent variables did not reveal any correlation
above 0.80, a typical threshold to assess potential multicollinearity. Table 1 presents
the results of the analyses of variance. There was no significant difference in financial
performance (OM or TM) between hospitals that remained public and hospitals before
NFP privatization. However, hospitals that remained public had lower OM (−0.07)
compared with OM before FP privatization (−0.03) at (p ≤ .001). On the other hand,
hospitals that remained public had higher TM (0.03) compared with TM before FP
privatization (0.01) at (p ≤ .001).
Tables 2 and 3 present the results of the FE linear regression models for Hypothesis
1: nonlagged independent variables (Model 1), independent variables lagged by 1 year
(Model 2) and independent variables lagged by 2 years (Model 3). Hypothesis 1 was
supported. Compared with performance before privatization, on average, privatization
was associated with an increasing OM across the three Models (β = .03, .05, .06; p ≤
.001) and an increasing TM across Models 2 and 3 (β = .01; p ≤ .05) and (β = .02; p ≤
.001), respectively.
Tables 4 and 5 present the results of the FE linear regression models for
Hypothesis 2 followed by joint tests. Hypothesis 2 was supported. On average,
conversion from public to private FP status was associated with an increasing OM
for 1 year and 2 years after privatization. However, the increased profitability was
lower for 2 years after privatization (β = .09; p ≤ .05) than for 1 year after privatiza-
tion (β = .17; p ≤ .001). Conversion from public to private NFP status was associ-
ated with an increasing OM across the three models (β = .03; .04; .06; p ≤ .001).
The joint tests showed that FP conversions experienced greater OM than NFP con-
versions for Models 2 and 3.
A similar pattern was observed for TM. Privatization to FP was associated with an
increase in TM for 1 year and 2 years after privatization. However, the increased prof-
itability was lower for 2 years after privatization (β = .05; p ≤ .10) than for 1 year after
privatization (β = .07; p ≤ .001). Privatization to NFP was associated with an
10 Medical Care Research and Review 00(0)
increasing TM but only significant for Model 3 (β = .02; p ≤ .001). Again, regardless
of the direction of the increase in TM, FP privatization effect sizes were larger than
NFP privatization, across the three models. The joint tests showed that FP conversion
experienced greater TM than NFP conversion.
With respect to the associations between organizational and market factors with
OM and TM, we found similar results for OM (Tables 2 and 4) and TM (Tables 3 and
5), respectively. Overall, Model 1 showed several control variables significantly asso-
ciated with OM and TM compared with Models 2 and 3. Control variables hospital
beds, occupancy rate, multihospital system membership, health network, per capita
income, unemployment rate, excess capacity, and HHI were positively associated with
OM (Table 4, Model 1). Outpatient mix, contract management, and yearly change in
unemployment rate were negatively associated with OM (Table 4, Model 1). Control
variables occupancy rate, percentage of Medicare inpatient days, health network, per
capita income, and HHI were positively associated with TM (Table 5, Model 1). The
number of physicians per 1,000 population and Medicare Advantage penetration were
negatively associated with TM (Table 5, Model 1).
Table 1. ANOVA: Comparisons of the Means of Operating Margin and Total Margin
Between Hospitals That Remained Public and Privatized Hospitals.
Operating marginaTotal marginb
M SD M SD
Hospitals that remained public
(n = 5,459)c
−0.07 0.18 Hospitals that remained public
(n = 5,557)c
0.03 0.07
Hospitals that privatized to NFP (n = 1,193)cHospitals that privatized to NFP (n = 1,198)c
Before privatization to NP −0.06 0.17 Before privatization to NP 0.03 0.07
After privatization to NP −0.03 0.15 After privatization to NP 0.03 0.08
Hospitals that privatized to FP (n = 451)cHospitals that privatized to FP (n = 456)c
Before privatization to FP −0.03 0.15 Before privatization to FP 0.01 0.08
After privatization to FP 0.03 0.13 After privatization to FP 0.06 0.10
Note. ANOVA = analysis of variance; NFP = not-for-profit; FP = for-profit; OM = operating margin;
TM = total margin.
aThe differences in OM are significant at p ≤ .05 for (a) hospitals that remained public versus before
privatization to FP, (b) hospitals that remained public versus after privatization to NFP, (c) hospitals that
remained public versus after privatization to FP, (d) before privatization to NFP versus after privatization
to NFP, (e) before privatization to FP versus after privatization to FP, and (f) after privatization to NFP
versus after privatization to FP. The differences in OM are not significant for (a) hospitals that remained
public versus before privatization to NFP and (b) before privatization to NFP versus before privatization
to FP. bThe differences in TM are significant at p ≤ .05 for (a) hospitals that remained public versus before
privatization to FP, (b) hospitals that remained public versus after privatization to FP, (c) after privatization
to NFP versus after privatization to FP, and (d) before privatization to FP and after privatization to FP. The
differences in TM are not significant for (a) hospitals that remained public versus before privatization to
NFP, (b) hospitals that remained public versus after privatization to NFP, and (c) before privatization to
NFP versus and after privatization to NFP. cn Represents hospital-year observations.
11
Table 2. Fixed Effects Linear Regressions Predicting the Effect of Privatization on Operating Margina (n = 7,104 Hospital-Year
Observations).
Model 1: Nonlagged
IVs
Model 2: IVs lagged
by 1 year
Model 3: IVs lagged
by 2 years
β95% CI β95% CI β95% CI
Independent variable
Privatization .03**** [0.01, 0.05] .05**** [0.03, 0.07] .06**** [0.04, 0.08]
Control variables: Organizational factors
Hospital beds .0001** [0.0001, 0.0002] .0001** [0.00002, 0.0002] .0001*** [0.0000, 0.0002]
Teaching status −.01 [−0.02, 0.01] −.005 [−0.02, 0.01] −.01 [−0.02, 0.01]
Outpatient mix −.01**** [−0.01, −0.004] −.02**** [−0.02, −0.01] −.02**** [−0.02, −0.01]
Occupancy rate .13**** [0.10, 0.16] .07**** [0.04, 0.10] .03* [−0.001, 0.07]
Percentage of Medicare inpatient days .02 [−0.01, 0.05] .004 [−0.03, 0.04] −.01 [−0.04, 0.03]
Percentage of Medicaid inpatient days .001 [−0.03, 0.03] .02 [−0.01, 0.06] .02 [−0.02, 0.05]
System membership .02*** [0.005, 0.03] .03**** [0.01, 0.04] .01 [−0.003, 0.03]
Health network .02**** [0.01, 0.03] .01*** [0.004, 0.02] −.001 [−0.01, 0.01]
Contract management −.01** [−0.03, −0.002] −.003 [−0.02, 0.01] .02*** [0.007, 0.03]
Control variables: Market factors
Per capita income .0002**** [0.0001, [0.0003] .0002**** [0.0002, 0.0003] .0003**** [0.0002, 0.0004]
Unemployment rate .005**** [0.003, 0.007] .01**** [0.003, 0.01] .004**** [0.002, 0.006]
Percentage of population ≥65 .14 [−0.13, 0.40] .18 [−.10, 0.45] 0.16 [−0.12, 0.45]
Physicians/1,000 population −.005 [−0.01, 0.003] −.01* [−0.02, 0.00003] −.001 [−0.01, 0.01]
Yearly change in unemployment rate −.02** [−0.04, −0.002] −.01* −0.03, 0.01 −.01 [−0.03, 0.01]
Medicare Advantage penetration −.01 [−0.06, 0.04] .04 [−0.02, 0.10] .08*** [0.02, 0.15]
Excess capacity .0002** [0.0001, 0.0004] .0002 [−0.00005, 0.0004] .0001 [−0.0002, 0.0003]
Herfindahl–Hirschman index .03**** [0.02, 0.05] .03*** [0.01, 0.04] .02** [0.004, 0.04]
Percentage of FP hospitals .05**** [0.02. 0.08] .01 [−0.02, 0.04] .02 [−0.01, 0.06]
Overall F test 18.91**** 16.36**** 15.78 ****
Note. IVs = independent variables; CI = confidence interval.
aYear dummy variables were included in the analysis.
*p ≤ .10. **p ≤ .05. ***p ≤ .01. ****p ≤ .001.
12
Table 3. Fixed Effects Linear Regressions Predicting the Effect of Privatization on Total Margina (n = 7,212 Hospital-Year Observations).
Model 1: Nonlagged IVs Model 2: IVs lagged by 1 year Model 3: IVs lagged by 2 years
β95% CI β95% CI β95% CI
Independent variable
Privatization .005 [−0.01, 0.02] .01** [0.001, 0.03] .02*** [0.01, 0.03]
Control variables: Organizational factors
Hospital beds .00002 −[0.00001, 0.0001] −.0000 −[0.00005, 0.0001] .0000 −[0.00005, 0.0001]
Teaching status −.002 [−0.01, 0.01] .001 [−0.01, 0.01] .01 [−0.002, 0.02]
Outpatient mix −.002 [−0.01, 0.002] −.002 [−0.01, 0.002] −.005** [−0.01, −0.0003]
Occupancy rate .04**** [0.02, 0.06] .01 [−0.01, 0.03] .001 [−0.02, 0.02]
Percentage of Medicare inpatient days .03*** [0.007, 0.05] .001 [−0.02, 0.02] .004 [−0.02, 0.03]
Percentage of Medicaid inpatient days −.002 [−0.03, 0.02] −.01 [−0.03, 0.02] −.001 [−0.02, 0.02]
System membership .002 [−0.01, 0.01] .01 [−0.002, 0.02] .002 [−0.01, 0.01]
Health network .01*** [0.002, 0.01] .01* [−0.0002, 0.01] −.002 [−0.01, 0.004]
Contract management −.005 [−0.01, 0.003] −.0003 [−0.01, 0.01] .01 [−0.002, 0.02]
Control variables: Market factors
Per capita income .0001**** [0.0001, 0.0001] .0001*** [0.00002, 0.0001] .0001*** [0.00004, 0.0002]
Unemployment rate −.0001 [−0.001, 0.001 −.0003 [−0.01, 0.01] −.001 [−0.002, 0.001]
Percentage of population ≥65 −.12 [−0.29, 0.05] −.06 [−0.24, 0.11] .001 [−0.17, 0.19]
Physicians/1,000 population −.01*** [−0.01, −0.002] −.005* [−0.01, 0.001] −.002 [−0.01, 0.003]
Yearly change in unemployment rate −.01* [−0.02, 0.001] .001 [−0.01, 0.01] .002 [−0.01, 0.01]
Medicare Advantage penetration −.08**** [−0.12, −0.05] −.07**** [−0.11, −0.04] −.03 [−0.07, 0.01]
Excess capacity −.00005 [−0.0005, 0.0001] .00000 [−0.0001, 0.0001] .0001 [−0.0001, 0.0002]
Herfindahl–Hirschman index .02**** [0.01, 0.03] .02*** [0.004, 0.03] .01 [−0.001, 0.02]
Percentage of FP hospitals .02** [0.002, 0.04] .02** [0.003, 0.04] .02** [0.001, 0.04]
Overall F test 7.69**** 4.44**** 4.48****
Note. IVs = independent variables; CI = confidence interval.
aYear dummy variables were included in the analysis.
*p ≤ .10. **p ≤ .05. ***p ≤ .01. ****p ≤ .001.
13
Table 4. Fixed Effects Linear Regressions Predicting the Effect of Privatization to For-Profit or Not-For-Profit on Operating Margina (n =
7,104 Hospital-Year Observations).
Nonlagged IVs IVs lagged by 1 year IVs lagged by 2 years
β95% CI β95% CI β95% CI
Independent variable
Privatization to for-profit .02 [−0.06, 0.10] .17**** [0.09, 0.25] .09** [0.003, 0.17]
Privatization to not-for-profit .03**** [0.01, 0.05] .04**** [0.02, 0.06] .06**** [0.04, 0.08]
Control variables: Organizational factors
Hospital beds .0001**** [0.0001, 0.0002] .0001** [0.00001, 0.0002] .0001** [0.0000, 0.0002]
Teaching status −.01 [−0.02, 0.01] −.01 [−0.02, 0.01] −.01 [−0.02, 0.01]
Outpatient mix −.01**** [−0.01, −0.004] −.02**** [−0.02, −0.01] −.02**** [−0.02, −0.01]
Occupancy rate .13**** [0.10, 0.16] .07**** [0.04, 0.11] .04** [0.003, 0.07]
Percentage of Medicare inpatient days .02 [−0.02, 0.05] .003 [−0.03, 0.04] −.02 [−0.05, 0.02]
Percentage of Medicaid inpatient days −.001 [−0.03, 0.03] .02 [−0.01, 0.06] .02 [−0.02, 0.05]
System membership .02*** [0.005, 0.03 .03**** [0.01, 0.04] .01 [−0.003, 0.03]
Health network .02**** [0.01, 0.03] .01** [0.003, 0.02] −.001 [−0.01, 0.01]
Contract management −.01** [−0.03, −0.001] −.003 [−0.02, 0.01] .02*** [0.007, 0.03]
Control variables: Market factors
Per capita income .0002**** [0.0001, 0.0003] .0002**** [0.0002, 0.0003] .0003**** [0.0002, 0.0004]
Unemployment rate .005**** [0.003, 0.01] .01**** [0.003, 0.01] .004**** [0.002, 0.006]
Percentage of population ≥65 .13 [−0.13, 0.40] .19 [−0.08, 0.46] .19 [−0.10, 0.48]
Physicians/1,000 population −.01 [−0.01, 0.003] −.01* [−0.02, 0.0004] −.001 [−0.01, 0.01]
Yearly change in unemployment rate −.02** [−0.04, −0.002] −.01 [−0.03, 0.01] −.01 [−0.03, 0.007]
Medicare Advantage penetration −.01 [−0.06, 0.04] .05 [−0.01, 0.10] .08*** [0.02, 0.15]
Excess capacity .0002** [0.00003, 0.0004] .0001 [−0.0001, 0.0004] .0001 [−0.0001, 0.0003]
Herfindahl–Hirschman index .03**** [0.02, 0.05] .03*** [0.01, 0.04] .02** [0.004, 0.04]
Percentage of FP hospitals .06 [−0.02, 0.13] −.10** [−0.18, −0.02] −.002 [−0.09, 0.08]
Overall F test 18.38**** 16.16**** 15.25****
Note. IVs = independent variables; CI = confidence interval.
aYear dummy variables were included in the analysis.
*p ≤ .10. **p ≤ .05. ***p ≤ .01. ****p ≤ .001.
14
Table 5. Fixed Effects Linear Regressions Predicting the Effect of Privatization to For-Profit or Not-for-Profit on Total Margina (n = 7,212
Hospitals-Year Observations).
Nonlagged IVs IVs lagged by 1 year IVs lagged by 2 years
β95% CI β95% CI β95% CI
Independent variable
Privatization to for-profit .04 [−0.01, 0.09] .07*** [0.02, 0.12] .05* [−0.005, 0.10]
Privatization to not-for-profit .003 [−0.01, 0.01] .01 [−0.003, 0.02] .02*** [0.01, 0.03]
Control variables: Organizational factors
Hospital beds .00003 [−0.00002, 0.0001] −.0000 [−0.00005, 0.00005] .00000 [−0.00005, 0.0001]
Teaching status −.003 [−0.01, 0.01] −.0002 [−0.01, 0.01] .01 [−0.003, 0.02]
Outpatient mix −.002 [−0.01, 0.002] −.002 [−0.01, 0.002] −.005** [−0.01, −0.00002]
Occupancy rate .04**** [0.02, 0.06] .01 [−0.01, 0.03] −.002 [−0.02, 0.02]
Percentage of Medicare inpatient days .03*** [0.01, 0.05] .001 [−0.02, 0.02] .002 [−0.02, 0.02]
Percentage of Medicaid inpatient days −.002 [−0.02, 0.02] −.01 [−0.03, 0.02] .0005 [−0.02, 0.02]
System membership .002 [−0.01, 0.01] .01 [−0.002, 0.02] .001 [−0.01, 0.01]
Health network .01*** [0.002, 0.01] .01* [−0.0005, 0.01] −.002 [−0.01, 0.004]
Contract management −.004 [−0.01, 0.004] −.001 [−0.01, 0.01] .01 [−0.002, 0.02]
Control variables: Market factors
Per capita income .0001**** [0.0001, 0.0001] .0001*** [0.00002, 0.0001] .0001*** [0.00004, 0.0002]
Unemployment rate −.0001 [−0.001, 0.001] −.0001 [−0.001, 0.01] −.001 [−0.002, 0.001]
Percentage of population ≥65 −.12 [−0.29, 0.05] −.06 [−0.23, 0.12] .03 [−0.15, 0.21]
Physicians/1,000 population −.01*** [−0.01, −0.002] −.005* [−0.01, 0.001] −.002 [−0.01, 0.004]
Yearly change in unemployment rate −.01* [−0.02, 0.0004] .0002 [−0.01, 0.01] .002 [−0.01, 0.01]
Medicare Advantage penetration −.08**** [−0.12, −0.05] −.07**** [−0.11, −0.04] −.03 [−0.07, 0.005]
Excess capacity −.00005 [−0.0002, 0.0001] .00000 [−0.0001, 0.0001] .0000 [−0.0001, 0.0001]
Herfindahl–Hirschman index .02*** [0.01, 0.03] .01*** [0.004, 0.03] .01 [−0.001, 0.02]
Percentage of FP hospitals −.01 [−0.06, 0.04] −.03 [−0.08, 0.02] −.001 [−0.05, 0.05]
Overall F test 7.54**** 4.46**** 4.31****
Note. IVs = independent variables; CI = confidence interval.
aYear dummy variables were included in the analysis.
*p ≤ .10. **p ≤ .05. ***p ≤ .01. ****p ≤ .001.
Ramamonjiarivelo et al. 15
Discussion
The purpose of this study was to explore whether privatization enhances hospital
financial performance. Our findings suggest that privatization results in better finan-
cial performance, in terms of both OM and TM. Our findings are consistent with those
of Villa and Kane (2013). Based on the agency theory, the change in governance of the
newly privatized hospitals that allows a more direct and closer monitoring of the man-
agers by the board of directors, compared with the more complex and indirect agent–
principal relationship in public hospitals may lead to better financial performance after
privatization (Rodríguez, Espejo, & Cabrera, 2007). In addition, private status may
allow greater access to financial resources than public status (Picone, Shin-Yi, &
Sloan, 2002; Shen, 2003). This, in turn, may enable privatized hospitals to engage in
capital investment for facility renovation and technology adoption that could attract
more affluent patients and consequently increase revenue. Attracting more affluent
patients may enable privatized hospitals to provide more profitable services and
increase their prices (Villa & Kane, 2013).
In addition, our findings suggest that privatization to FP status results in better
financial performance compared with privatization to NFP status. FP privatization
results in both higher OM and TM 1 year and 2 years after privatization compared with
NFP privatization. According to PRT, the right to receive residual income may act as
an incentive for managers of FP hospitals, who can be part-owners of FP hospitals, to
aggressively improve the financial performance, compared with the managers of hos-
pitals that privatized to NFP status (Furubotn & Pejovich, 1972). On the other hand,
our results show that NFP privatization showed an immediate improvement in OM,
while FP privatization did not result in OM improvement until the first year after
privatization. Perhaps hospitals that privatize to NFP are able to experience greater
short-term improvements in operations given that public hospitals may share a more
similar culture with NFP than with FP hospitals.
We conducted post hoc analyses to assess the impact of privatization on payroll
expenses and employee benefits; privatized hospitals may cut human resources
expenses as a strategy to improve financial performance. We used the same variables
and models used in the previous regressions. We used payroll expenses per adjusted
patient day and employee benefits per adjusted patient day as the dependent variables.
We did not find significant relationships between privatization, FP versus NFP priva-
tizations, and payroll expenses across the three models (Tables not shown). However,
we found a decrease in employee benefits after privatization; Model 1 had the greatest
decrease compared with Models 2 and 3. The magnitude of the decrease gradually
diminished across the three models, but Model 3 was not significant (Tables not
shown). With respect to change in employee benefits after NFP and FP privatizations,
Model 1 showed that FP privatization had a greater decrease in employee benefits,
compared with NFP privatization. However, employee benefits after FP privatization
had a gradual increase across Models 2 and 3; it was significant for Model 3. Employee
benefits after NFP privatization continued to decrease for Model 2 and increased for
Model 3; it was not significant for Model 3 (Tables not shown).
16 Medical Care Research and Review 00(0)
We also estimated the predicted values of OM and TM, operating and total reve-
nues, operating and total expenses, payroll expenses, and employee benefits by own-
ership status (Table 6). We explored whether the enhanced financial performance,
after privatization, was due to increased revenues or reduced expenses. While hospi-
tals that remained public had the highest predicted operating revenue per adjusted
patient day ($1,357) compared with predicted operating revenues before NFP and FP
privatizations, we found that after FP privatization exhibited the highest predicted
value for operating revenue per adjusted patient day ($2,173) followed by after NFP
privatization ($1,600).
We also found that predicted operating expenses per adjusted patient day were
the highest after FP privatization ($2,097) followed by after NFP privatization
($1,598). The same pattern applies for predicted total expenses per adjusted patient
day after FP privatization ($2,125), and after NFP privatization ($1,633), as well as
predicted total payroll expenses per adjusted patient day and predicted total
employee benefits per adjusted patient day. While after FP privatization consis-
tently had the highest value in all types of expenses, it still exhibited the highest
predicted OM and TM. This can be explained by the fact that FP privatization
exhibited the highest operating and total revenues and the highest net operating
income (operating revenue − operating expense; $76 per patient day), compared
with ($2) for after NFP privatization and ($59) for hospitals that remained public.
After FP privatization also exhibits the highest net income (total revenue − total
expenses; $245 per patient day), compared with ($31) for after NFP privatization
and ($125) for hospitals that remained public. These findings suggest that FP hos-
pitals’ superior financial performance is driven by the revenue side, which could be
a combination of attracting more lucrative patients or offering more profitable ser-
vices. These findings also suggest that privatized hospitals do not tend to empha-
size cost containment as a strategy to enhance financial performance.
This study also found several significant relationships between organizational and
environmental factors and financial performance. Our results suggest that greater bar-
gaining power and access to resources from joining a health network or being affili-
ated with a multihospital system, and economies of scale achieved with greater
occupancy rate and greater proportion of Medicare enrollees are associated with better
financial performance.
With respect to market factors, our study suggests that a higher per capita income
is associated with higher OM and TM. Hospitals that serve a population with a higher
per capita income may improve their financial performance through higher pricing or
by offering more lucrative services. Less competitive markets are associated with
higher OM and TM. Hospitals in less competitive markets may be able to charge
higher prices and/or reduce marketing and other expenses associated with markets that
are more competitive. Finally, our results suggest that higher Medicare Advantage
penetration is associated with a decrease in TM. Prior studies have shown that increased
Ramamonjiarivelo et al. 17
Table 6. Predicted Values.
Stay public
From public to NFP From public to FP
Before
privatization
After
privatization
Before
privatization
After
privatization
Operating margina−0.07 −0.07 −0.04 −0.06 −0.01
Total marginb0.03 0.03 0.03 0.03 0.06
Operating revenue per adjusted
patient day ($)c
1,357 1,303 1,600 1,339 2,173
Total revenue per adjusted
patient day ($)d
1,559 1,529 1,664 1,591 2,370
Operating expense per adjusted
patient day ($)e
1,416 1,361 1,598 1,407 2,097
Total expense per adjusted
patient day ($)f
1,434 1,380 1,633 1,432 2,125
Total payroll expenses per
adjusted patient day ($)g
576 552 611 563 761
Total employee benefits per
adjusted patient day ($)h
150 130 151 140 164
Note. NFP = not-for-profit; FP = for-profit; OM = operating margin; TM = total margin. Predicted values were derived
from fixed effects regression models where OM, TM, revenues, expenses, total payroll expenses and total employee
benefits = f (privatization into FP or NFP), controlling for hospital beds, system membership, contract management,
health network, teaching status, occupancy rate, outpatient mix, percentage of Medicare inpatient days, percentage
of Medicaid inpatient days, per capita income, unemployment rate, percentage of population ≥65, physicians/1,000
population, Herfindahl–Hirschman index, excess capacity, Medicare Advantage penetration, yearly change in
unemployment rate and percentage of FP hospitals in the HSAs.
aAll pairwise comparisons significant at p ≤ .001 except for OM of hospitals that stayed public versus OM before NFP
and FP privatizations, which were are not significant. bAll pairwise comparisons significant at p ≤ .001 except for TM of
hospitals that stayed public versus TM before NFP privatization (significant at p ≤ .01); the difference between TM for
hospitals that stayed public and TM before FP privatization and difference between TM before NFP privatization and
TM before FP privatizations were not significant. cAll pairwise comparisons significant at p ≤ .001, except for operating
revenue of hospitals that stayed public versus before NFP privatization, hospitals that stayed public versus before FP
privatization, and before NFP privatization versus and FP privatizations, which were not statistically significant. dPairwise
comparisons of total revenue after FP privatization versus hospitals that remained public, before NFP privatization
versus after NFP privatization, and before NFP privatization versus after FP privatization significant at p ≤ .001.
Comparisons of total revenue between hospitals that remained public and after NP privatization, and total revenue
before and after NP privatizations were significant and p ≤ .05. eAll pairwise comparisons significant at p ≤ .001, except
for operating expense of hospitals that remained public versus before NFP privatization, and operating expense before
NFP privatization versus before FP privatizations, which were not significant. fAll pairwise comparisons significant at p ≤
.001, except for total expenses between hospitals that remained public and before NFP privatization, between hospitals
that remained public and before FP privatization, and before NFP privatization versus before FP privatizations, which
were not significant. gAll pairwise comparisons significant at p ≤ .001, except for payroll expenses of hospitals that
remained public versus after NFP privatization, which was significant at p ≤ .01. Comparisons of hospitals that remained
public versus before FP privatization, before NFP privatization versus before FP privatization, and after NFP privatization
versus before NFP privatization were not statistically significant. fPairwise comparisons of hospitals that remained
public versus before NFP privatization, and before NFP privatization versus after FP privatization significant at p ≤ .001.
Pairwise comparison before FP privatization versus after FP privatization significant at p ≤ .01. Pairwise comparisons
between hospitals that remained public versus after FP privatization, and before NFP privatization versus after NFP
privatization significant at p ≤ .05.
Medicare Advantage penetration is associated with a decline in profitability due to
various factors such as decrease in reimbursement (Large & Sear, 2005).
18 Medical Care Research and Review 00(0)
Limitations of the Study and Directions for Future Research
This study presents several limitations. First, due to data limitations, we were not able
to control for the proportions of privately insured and uninsured patients. Including
these proportions in our study could have given additional insights on whether serving
patients other than those enrolled in Medicare and Medicaid has an impact on financial
performance. Second, CAHs were excluded from our sample due to their different
reimbursement policy. Therefore, the results from this study are not generalizable to
CAHs; they should be studied as a separate group from non-CAHs. Finally, the deci-
sion to privatize may be endogenous with financial performance. For example, the
observed relationship between privatization and financial performance may be a result
of selection bias as hospitals that privatized may be located in markets that are more
affluent and with a higher proportion of FP hospitals, which may elicit profit-seeking
behavior from privatized hospitals. We mitigated these issues by (a) using a FE model,
whereby we controlled for between-hospital differences and focused on within-hospi-
tal changes in financial performance and (b) including time-varying market-level con-
trol variables, such as market competition, percentage of FP hospitals in the market,
Medicare Advantage penetration, and socioeconomic factors, in our regression mod-
els. Even so, there may be other time-varying omitted variables, at both the hospital-
and market-level, that may have affected our estimates.
Despite these limitations, this study provides important insights into the impact of
public hospitals’ privatization on financial performance. Additional empirical studies
on public hospitals are needed in terms of the impact of privatization on patient satis-
faction, employee satisfaction, physician satisfaction, competitive landscape, pricing
of health care services, access to health care services, quality of care, and community
orientation. Furthermore, the study of CAHs’ privatization could be the focus of future
studies.
Managerial and Policy Implications
The findings from this study provide several insights for health care management.
Public hospitals have played an important role in U.S. health care delivery system, yet
some of them have been privatized to stay competitive. Consistent with agency theory
and PRT, the findings from this study indicate that privatization enhances financial
performance, and privatization to FP status results in better financial performance rela-
tive to privatization to NFP status. Therefore, government entities that own public
hospitals could consider privatization as an alternative if financial crisis and fiscal
pressures occur. However, there are additional factors that should be considered when
weighing these options.
Though this study and prior studies have suggested that privatization has a posi-
tive impact on financial performance, it can have negative effects on other dimen-
sions of health care delivery such as access to and quality of care. For instance, this
study suggests that higher financial performance of privatized hospital is achieved
through increased operating revenue per patient day. Aggressive pricing of privatized
Ramamonjiarivelo et al. 19
hospitals might reduce access to care for patients not covered by Medicare and
Medicaid. In addition, research suggests that privatization results in decreased
uncompensated care (Desai et al., 2000; Needleman, Lamphere, & Chollet, 1999;
Thorpe et al., 2000), increased mortality rate for patients with acute myocardial
infarction (Shen, 2002), increased crude mortality rate (Picone et al., 2002), increased
pneumonia complication (Sloan, 2002), and increased probability of closures of non-
profitable but important services (Villa & Kane, 2013). Close monitoring of public
hospitals’ privatization is needed to ensure that privatization does not negatively
affect access to and quality of care.
Our findings also suggest that economies of scale achieved with higher occupancy
rate, and higher mix of Medicare enrollees can result in better financial performance.
The positive relationship between occupancy rate and financial performance might
explain the merger and acquisition trend among hospitals. Bigger may be better if it
does not significantly reduce market competition. Finally, the finding that joining a
health network is associated with increased OM and TM could be an incentive for
providers to engage in various interorganizational relationships offered by the
Affordable Care Act, such as the Medicare Shared Savings Program for Accountable
Care Organization, and the Partnership for Patients. Both programs serve as a means
to collaborate with other health care providers to better coordinate care, enhance effi-
ciency, and improve the quality of health care delivery across the continuum of care as
well as means to enhance efficiency and cut costs (Centers for Medicare and Medicaid
Services, 2013).
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of
this article.
ORCID iD
Zo Ramamonjiarivelo https://orcid.org/0000-0001-5756-3582
References
Allison, P. D. (2005). Fixed effects regression methods for longitudinal data using SAS. Cary,
NC: SAS Institute.
Anderson, R. J., Boumbulian, P. J., & Pickens, S. S. (2004). The role of public hospitals in urban
health. Academic Medicine, 79, 1162-1168.
Bovbjerg, R. R., Marsteller, J. A., & Ullman, F. C. (2000). Health care for the poor and unin-
sured after a public hospital’s closure or conversion. Washington, DC: Urban Institute.
Brickley, J. A., & Van Horn, R. L. (2002). Managerial incentives in nonprofit organizations:
Evidence from hospitals. Journal of Law and Economics, 45, 227-249.
20 Medical Care Research and Review 00(0)
Burgess, J. F., Jr., & Wilson, P. W. (1996). Hospital ownership and technical inefficiency.
Management Science, 42, 110-123.
Burns, L., Shah, R. J., Frank, A. S., & Powell, A. (2009). The impact of hospital ownership
conversions: Review of the literature and results from a comparative field study. In G. T.
Savage & M. D. Fottler (Eds.), Biennial review of health care management: Meso perspec-
tive (Vol. 8, pp. 171-229). Bingley, England: Emerald.
Centers for Medicare and Medicaid Services. (2013). Affordable Care Act: Helping providers
help patients. Retrieved from http://www.cms.gov/Medicare/Medicare-Fee-for-Service-
Payment/ACO/Downloads/ACO-Menu-Of-Options.pdf
Centers for Medicare and Medicaid Services. (2017). Critical access hospitals (Medicare learn-
ing network booklet). Retrieved from https://www.cms.gov/Outreach-and-Education/
Medicare-Learning-Network-MLN/MLNProducts/downloads/CritAccessHospfctsht.pdf
Chang, T., & Jacobson, M. (2010). What is the mission of a not-for-profit hospital? Evidence
from California’s seismic retrofit mandate. Retrieved from http://users.nber.org/~changt/
ChangJacobson0c19.pdf
Clarkson, K. W. (1972). Some implications of property rights in hospital management. Journal
of Law and Economics, 15, 363-384.
Claxton, G., Feder, J., Shactman, D., & Altman, S. (1997). Public policy issues in nonprofit
conversions: An overview. Health Affairs, 16(2), 9-28.
Cuervo, A., & Villalonga, B. (2000). Explaining the variance in the performance effects of
privatization. Academy of Management Review, 25, 581-590.
Desai, K. R., Lukas, C. V., & Young, D. W. (2000). Public hospitals: Privatization and uncom-
pensated care. Health Affairs, 19(2), 167-172.
Eggertsson, T. (1990). Economic behavior and institutions. Cambridge, England: Cambridge
University Press.
Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of Management
Review, 14, 57-74.
Eldenburg, L., & Krishnan, R. (2003). Public versus private governance: A study of incentives
and operational performance. Journal of Accounting and Economics, 35, 377-404.
Ford, R. (2017). Understanding New York’s public hospitals crisis. Retrieved from https://city-
limits.org/2017/10/18/understanding-new-yorks-public-hospital-crisis/
Furubotn, E. G., & Pejovich, S. (1972). Property rights and economic theory: A survey of recent
literature. Journal of Economic Literature, 10, 1137-1162.
Gapenski, L. C. (2004). Healthcare finance: An introduction to accounting and financial man-
agement (3rd ed.). Chicago, IL: Health Administration Press.
Gray, B. (1986). Financial capital and health care growth trends. In B. Gray (Ed.), For-profit
enterprise in health care (pp. 47-65). Washington, DC: National Academies Press.
Gray, B. (1997). Conversion of HMOs and hospitals: What’s at stake? Health Affairs, 16(2),
29-47.
Harding, A., & Precker, A. S. (2000, September). Understanding organizational reform: The
corporatization of public hospitals. Paper presented at the Health, Nutrition, and Population
Family of the World Bank’s Human Development Network, Washington, DC.
Hart, O. (1995). Firms, contracts, and financial structure. Oxford, England: Clarendon Press.
Internal Revenue Service. (2006). IRS exempt organizations (TE/GE): Hospital compliance
project final report. Retrieved from https://www.irs.gov/pub/irs-tege/frepthospproj.pdf
Ko, M., Derose, K. P., Needleman, J., & Ponce, N. A. (2014). Whose social capital matters? The
case of U.S. urban public hospital closures and conversions to private ownership. Social
Science & Medicine, 114, 188-196.
Ramamonjiarivelo et al. 21
Large, J. T., & Sear, A. M. (2005). The effects of Medicare Health Management Organizations
on hospital operating profit in Florida. Health Services Management Research, 18, 63-74.
Legnini, M. W., Anthony, S. E., Wicks, E. K., Meyer, J. A., Rybowski, L. S., & Stepnick, L. S.
(1999). Privatization of public hospitals. Retrieved from https://kaiserfamilyfoundation.
files.wordpress.com/1998/08/1450-privatization-of-public-hospitals-report.pdf
Mahoney, J. T. (2004). Economic foundations of strategy. Thousand Oaks, CA: Sage.
Marsteller, J. A., Bovbjerg, R. R., & Nichols, L. M. (1998). Nonprofit conversion: Theory, evi-
dence, and state policy options. Health Services Research, 33, 1495-1535.
McCaughey, B. (2015, April 28). Why New York City’s hospitals are headed for disaster. The
New York Post. Retrieved from http://nypost.com/2015/04/28/why-new-york-citys-hospi-
tals-are-headed-for-disaster/
Needleman, J., & Ko, M. (2012). The declining public hospital sector. In S. Rosenbaum & M.
A. Hall (Eds.), The health care “safety net” in a post-reform world (1st ed., pp. 200-216).
New Brunswick, NJ: Rutgers University Press.
Needleman, J., Lamphere, J., & Chollet, D. (1999). Uncompensated care and hospital conver-
sions in Florida. Health Affairs, 18(4), 125-133.
Pauly, M. V. (1987). Nonprofit firms in medical markets. American Economic Review, 77,
257-262.
Picone, G., Shin-Yi, C., & Sloan, F. (2002). Are for-profit hospital conversions harmful to
patients and to Medicare? Journal of Economics, 33, 507-523.
Pink, G. H., Holmes, G.M., D’Alpe, C., Strunk, L. A., McGee, P., Slifkin, R. (2005).
Financial indicators for critical access hospitals. Flex Monitoring Team Briefing. Paper
No. 7. Retrieved from: http://www.flexmonitoring.org/wp-content/uploads/2013/07/
BriefingPaper7_FinancialIndicators.pdf
Pradhan, R., Weech-Maldonado, R., Harman, J. S., & Hyer, K. (2014). Private equity ownership
of nursing homes: Implications for quality. Journal of Health Care Finance, 42(2), 1-14.
Ramamonjiarivelo, Z., Epané, J. P., Hearld, L., McRoy, L., & Weech-Maldonado, R. (2016).
The impact of privatization on efficiency and productivity: The case of US public hospi-
tals. Journal of Health Care Finance, 43(2), 104-123.
Ramamonjiarivelo, Z., Weech-Maldonado, R., Hearld, L., Menachemi, N., Epané, J. P., &
O’Connor, S. (2015). Public hospitals in financial distress: Is privatization a strategic
choice? Health Care Management Review, 40, 337-347.
Ramamonjiarivelo, Z., Weech-Maldonado, R., Hearld, L., & Pradhan, R. (2014). Public hos-
pitals in peril: Factors associated with financial distress. Journal of Health Care Finance,
40(3), 14-30.
Rodríguez, G. C., Espejo, C. A. D., & Cabrera, R. V. (2007). Incentives management during
privatization: An agency perspective. Journal of Management Studies, 44, 536-560.
Shen, Y. C. (2002). The effect of hospital ownership choice on patient outcomes after treatment
for acute myocardial infarction. Journal of Health Economics, 21, 901-922.
Shen, Y. C. (2003). Changes in hospital performance after ownership conversions. Inquiry, 40,
217-234.
Siegel, B. (1996). Public hospitals: A prescription for survival. Retrieved from http://www.com-
monwealthfund.org/~/media/Files/Publications/Fund%20Report/1996/Oct/Public%20
Hospitals%20%20A%20Prescription%20for%20Survival/siegel%20pdf.pdf
Sloan, F. A. (2002). Hospital ownership conversions: Defining the appropriate public oversight
role. In A. M. Garber (Ed.), Frontiers in health policy research (Vol. 5, pp. 123-166).
Cambridge: MIT Press.
22 Medical Care Research and Review 00(0)
Sloan, F. A., Ostermann, J., & Conover, C. J. (2003). Antecedents of hospital ownership conver-
sions, mergers, and closures. Inquiry, 40, 39-56.
Tennyson, D. H., & Fottler, M. D. (2000). Does system membership enhance financial perfor-
mance in hospitals? Medical Care Research and Review, 57, 29-50.
Thorpe, K., Florence, C., & Sieber, E. (2000). Hospital conversions, margins, and the provision
of uncompensated care. Health Affairs, 19(6), 187-194.
Tiemann, O., & Schreyögg, J. (2009). Effects of ownership on hospital efficiency in Germany.
Business Research, 2, 115-145.
Tiemann, O., & Schreyögg, J. (2010). Investigating the effects of hospital privatization on per-
formance and quality of care. Kiel, Germany: ZBW.
Tiemann, O., & Schreyögg, J. (2012). Changes in hospital efficiency after privatization. Health
Care Management Science, 15, 310-326.
Tradewell, R. L. (1998). Privatizing public hospitals: Strategic options in an era of industry-
wide consolidation (Policy Study No. 142). Retrieved from https://reason.org/wp-content/
uploads/files/f5ae49d45881eb1b195e591a1b425c51.pdf
Villa, S., & Kane, N. (2013). Assessing the impact of privatizing public hospitals in three
American states: Implications for universal health coverage. Value in Health, 16(1), S24-
S33.
Weech-Maldonado, R., Laberge, A., Pradhan, R., Johnson, C. E., Yang, Z., & Hyer, K. (2012).
Nursing home financial performance: The role of ownership and chain affiliation. Health
Care Management Review, 37, 235-245.
Weil, T. P. (2011). Privatization of hospitals: Meeting divergent interests. Journal of Health
Care Finance, 38(2), 1-11.
Wessel, R. H. (1995). Privatization in the United States. Business Economics, 30(4), 45-50.
Wooldridge, J. (2006). Introductory econometrics: A modern approach (3rd ed.). New Delhi,
India: Akash Press.
Younis, M. Z., & Forgione, D. A. (2005). Using return on equity and total profit margin to
evaluate hospital performance in the U.S.: A piecewise regression analysis. Journal of
Health Care Finance, 31(3), 82-88.
