THE ANTECEDENTS AND CONSEQUENCES

OF PUBLIC HOSPITAL PRIVATIZATION

by

ZO-HARIVOLOLONA RAMAMONJIARIVELO

ROBERT WEECH-MALDONADO, COMMITTEE CHAIR

LARRY HEARLD

NIR MENACHEMI

MICHAEL MORRISEY

STEPHEN O’CONNOR

A DISSERTATION

Submitted to the graduate faculty of The University of Alabama at Birmingham,

in partial fulfillment of the requirements of the degree of

Doctor of Philosophy

BIRMINGHAM, ALABAMA

2012

Copyright by

Zo-Harivololona Ramamonjiarivelo

2012

iii

THE ANTECEDENTS AND CONSEQUENCES

OF PUBLIC HOSPITAL PRIVATIZATION

ZO-HARIVOLOLONA RAMAMONJIARIVELO

PH.D. PROGRAM IN ADMINISTRATION-HEALTH SERVICES

ABSTRACT

The purpose of this study was to explore the antecedents and consequences of

public hospital privatization with special attention to financial distress and financial

performance. A national sample of public hospitals using secondary longitudinal data

from 1997 to 2009 was used in this study. Data set from the American Hospital

Association, the Area Resource File, the Medicare Cost Report and the Local Area

Unemployment Statistics were merged to test the hypotheses pertaining to each research

question.

Based on the resource dependence theory, both environmental variables and

organizational variables were included in the analyses. This study adopted the Altman Z-

score method to assess public hospital financial distress. Fixed-effects logistic regression,

random-effects logistic regression with state fixed-effects, and fixed-effects linear

regression were used in this study.

Key findings indicated that environmental variable HMO penetration was

positively associated with the odds of public hospital financial distress. Organizational

variables hospital size, participation in a health network, and outpatient mix were

significantly and negatively associated with the odds of experiencing financial distress.

Membership of a multihospital system was significantly and positively associated with

the odds of experiencing financial distress. Additional findings suggested financial

iv

distress increased the odds of privatization and privatization improved financial

performance in terms of operating margin and total margin. In addition, findings

suggested privatization to public for-profit status resulted in better financial performance

compared to privatization to public-not-for-profit status.

Keywords: public hospital, privatization, financial distress, financial performance,

resource

v

DEDICATION

This dissertation is dedicated to God Almighty, Father, Son, and Holy Spirit, who

has been the source of all the strength, knowledge, ability, and resources that I needed to

pursue my education and complete this study. This dissertation is also dedicated to my

beloved and wonderful parents, my mother Honorine Ramahefarivelo and my late father

Paul Rakotomamonjy, who both sacrificed to raise and educate their children.

vi

ACKNOWLEDGMENTS

I could not have completed this dissertation alone. It has taken the support and

contribution of many scholars, namely my dissertation chair, Dr. Robert Weech-

Maldonado, and the members of my dissertation committee, Dr. Larry Hearld, Dr. Nir

Menachemi, Dr. Michael Morrisey, and Dr. Stephen O‘Connor. I could never thank Dr.

Weech-Maldonado enough for his contributions to this study. Dr. Weech-Maldonado

freely gave his time, expertise, support, and never-ending patience. Dr. Weech-

Maldonado is a wonderful role model with respect to scholarship, mentorship, kindness,

and humility.

I also would like to express my most sincere appreciation to the members of my

dissertation committee whose areas of expertise I came to depend on. I convey my

heartfelt thanks to Dr. Larry Hearld for spending invaluable time and effort reading and

editing my dissertation and showing me how to clearly and logically express my ideas. In

addition, Dr. Hearld provided advice and guidance with respect to the choice of

appropriate methods that matched the structure of the data used in this study. I also

extend my sincere gratitude to Dr. Nir Menachemi who has been an exceptional mentor

and role model. He not only helped me to find a dissertation topic, but also has been

gracious enough to assist me whenever I need help. I also appreciate Dr. Menachemi‘s

relentless encouragement to complete this dissertation.

vii

I would like to express my deep appreciation to Dr. Michael Morrisey whose

expertise in economics and experience with the American Hospital Association were vital

assets for this study. Dr. Morrisey‘s enthusiasm with respect to the dissertation topic was

contagious and his contribution to the careful choice of the variables needed for this study

was monumental. I also would like to state my profound gratitude to Dr. Stephen

O‘Connor for all his contributions. Dr. O‘Connor spent time reading and editing my

dissertation and his knowledge and expertise on health care organizations were priceless.

The contributions of other scholars deserve my deepest gratitude. I am deeply

indebted to Dr. Rohit Pradhan for his expertise on Medicare Cost Report and Area

Resource File. Dr. Pradhan guided me through the data collection, data cleaning, and data

analysis processes. I am also thankful to Mr. Josué Patien Epané for his help in data

collection and data cleaning processes, and for teaching me Statistical Analysis System

(SAS).

The help and support of my colleagues, friends and family merit my sincere

appreciation. I express my heartfelt thanks to Dr. DeLawnia Comer-Hagans, Dr.

Anantachai Panjamapirom, Dr. Gouri Gupte, Dr. Shamly Austin, Mr. Bob and Becka

Montgomery, Mr. Bill and Martha Allen, Mr. Corky and Greta Clark, Ms. Susan Elmore,

and Ms. Carolyn King for being my faithful and unconditional cheer leaders. In addition,

I acknowledge Dr. Comer-Hagan‘s help in editing this dissertation. Last but not least, I

express my sincere gratitude to my mother, brothers and sister for their love and support.

viii

TABLE OF CONTENTS

Page

ABSTRACT………………………………………………………………………...……iii

DEDICTATION………………………………………..……………………………...….v

ACKNOWLEDGMENTS………………………………………………………...…...…vi

TABLE OF CONTENTS……………………………………………………………… viii

LIST OF TABLES……………………………...……………………………………..…xii

LIST OF FIGURE……………………………………….………………………...……xiv

CHAPTER

1 INTRODUCTION……………………………………………………………….….......1

The Ownership Structure of U.S. Hospitals………………………………………….……3

Historical Perspective on Public Hospitals……………………………………….……….6

Public Hospitals Before and During the 19th

Century .............................................6

Public Hospitals During the 20th

Century and Beyond ............................................8

Rationale for the Study…………………….…………………………………………….14

2 REVIEW OF LITERATURE ........................................................................................19

Literature Review……...…………………………………………………………………19

Privatization of public hospitals and other healthcare organizations ....................20

The antecedents of healthcare organizations conversion

from public to for-profit status ..................................................................23

The antecedents of healthcare organizations conversion

from public to not-for-profit status ............................................................25

The consequences of ownership conversion

from public to for-profit status on financial performance. ........................26

The consequences of ownership conversion

from public to not-for-profit status on financial performance. ..................27

Conceptual Framework and Hypotheses……………………………………………...…28

ix

Theoretical Background ........................................................................................ 28

Resource dependence theory. ....................................................................29

The Environment ...................................................................................................35

Environmental munificence .......................................................................36

Environmental dynamism ..........................................................................36

Environmental complexity. ........................................................................37

Financial Performance .......................................................................................... 39

The association between environmental munificence

and financial performance........................................................................ 40

The association between environmental dynamism

and financial performance. ....................................................................…42

The association between environmental complexity

and financial performance. ........................................................................43

The association between organizational size

and financial performance. ........................................................................45

The association between teaching status

and financial performance. ........................................................................46

The association between financial performance

and privatization. .......................................................................................48

The impact of privatization on hospital

financial performance. ...............................................................................49

3 RESEARCH METHODOLOGY…….……………...…………………………………….53

Research Design ....................................................................................................54

Data Sources................................................................................................................................55

Population and Sample ..........................................................................................56

Variables and their Operationalization ..................................................................58

Question 1: What are the organizational and environmental

factors associated with financial distress of public hospitals? ...................58

Question 2. Is financial distress associated with public

hospitals privatization? ............................................................................. 69

Question 3. Does privatization lead to a better financial

performance? ............................................................................................74

Analysis .................................................................................................................77

x

Potentially endogenous variables. .............................................................80

Models ...................................................................................................................80

Models for operating margin. ....................................................................81

Models for total margin. ............................................................................81

4 RESULTS……………...………………………………………………………………86

Results from Research Question 1 .........................................................................86

Results of fixed-effects logistic regressions. .............................................96

Results from Research Question 2 .........................................................................98

Results from Research Question 3 .......................................................................108

Results of hypothesis 7. ...........................................................................118

Results of Hypothesis 8 ...........................................................................123

Conclusion ...............................................................................................130

5 DISCUSSION……………………….………….……………………………………...……………134

Discussion of Findings from Research Question 1 ..............................................134

Findings from Hypothesis 1 .....................................................................135

Findings from Hypothesis 2 .....................................................................136

Findings from Hypothesis 3 .....................................................................136

Findings from Hypothesis 4 .....................................................................137

Findings from Hypothesis 5 .....................................................................137

Discussion of Findings from Research Question 2 ............................................. 140

Discussion of Findings from Research Question 3 ............................................. 142

Managerial and Policy Implications ....................................................................144

Limitations of the Study ......................................................................................147

Directions for Future Research ......................................................................…..148

xi

Conclusion .......................................................................................................... 159

LIST OF REFERENCES .................................................................................................150

APPENDIX A……………………………………………………………………………………….…164

IRB APPROVAL…………………………………………………………………………………...…172

xii

LIST OF TABLES

Table Page

1.1 Trends in Number of Hospitals and Hospital Beds by Ownership Type…………….12

3.1 Number of Hospitals per Year in the Master Data File………………………...........57

3.2 Normalization of Operating Margin………………………….……………………...79

3.3 Normalization of Total Margin………………………………………………………79

3.4 Summary of Variables Conceptual Definitions,

Operational Definitions, References and Data Sources …........................................82

4.1 Descriptive Analysis of all Variables………………………………………………..87

4.2 Independent Samples t-test on Dependent Variable

―Financial Distress‖…………...……………………………………………….……..90

4.3 Cross-tabulation and Pearson Chi-Square Test on

Dependent variable ―Financial Distress‖………………………...…………………..92

4.4 Pearson Correlation Matrix of Independent Variables………………………………93

4.5 Results of Fixed-effects Logistic Regression………………………………………..97

4.6 Descriptive Analysis of all Variables……..……………..……………………….….99

4.7 Independent Samples t-Tests on Dependent Variable Privatization…………….….102

4.8 Chi-square Test on Dependent Variable ―Privatization‖…………………………...103

4.9 Pearson Correlation Matrix Research Question 2…………………………………..104

4.10 Results of Random-effects Logistic Regression with State Fixed-Effects………..107

4.11 Descriptive Statistics on the Operating Margin Data Set…………………………109

4.12 Independent Samples t-Tests on Operating Margin……………………………….111

xiii

4.13 Correlation Matrix of Data Set for Operating Margin…………………………….112

4.14 Descriptive Statistics of total Margin Data………………………………………..114

4.15 Independent Samples t-Tests on Total Margin……………………………………116

4.16 Correlation Matrix Variables for Total Margin Data Set…………………….……117

4.17 Results of Fixed-effects Linear Regression on Operating Margin First

Hypothesis…………………………………………………………………………119

4.18 Results of Fixed-effects Linear Regression on

Total Margin First Hypothesis ………………….……………….…………………120

4.19 Operating Margin by Ownership Type………………...……...…….…………….124

4.20 Total Margin by Ownership Type…………...…………...……………...………..125

4.21 Results of Fixed-Effects Linear Regressions on Operating Margin-

Second Hypothesis…………………………………………………...….………...126

4.22 Results of Fixed-Effects Linear Regression on Total Margin -

Second Hypothesis…………………………………………………………………127

4.23 Summary of Findings from Hypothesis Testing…...……………………………...132

4.24 Summary of Findings for Each Research Question………...……………………..133

xiv

LIST OF FIGURE

Figure Page

1.1 Conceptual Framework………………………………………………………………34

1

CHAPTER 1

INTRODUCTION

In the past few decades there have been significant numbers of hospital ownership

status conversions. Ownership status conversion refers to any transactions that result in

the change of an organization‘s ownership status (Anderson, Allred, & Sloan, 2003;

Goodstein & Boecker, 1991; Hall & Conover, 2006; Needleman, 1999). Such

transactions can be performed either at the interorganizational or organizational level.

Interorganizational transactions resulting in ownership status change include merger,

sale, lease, or joint venture, among others; such transactions involve the transfer of

ownership from one organization to another (Anderson, et al., 2003; Goodstein &

Boecker, 1991; Legnini et al., 1999; Needleman, 1999).

Change in ownership status can also involve a single organization; the

management team or the board of directors purchases the organization‘s assets through a

leveraged buyout (Gray, Smelser, & Baltes, 2001) or the organization directly applies for

ownership conversion with legal authorities, but such transaction is not very common in

healthcare settings (Cutler & Horwitz, 1997; Needleman, 1999). An ownership

conversion is called privatization when it involves the conversion of an organization from

public to either private for-profit or private not-for-profit status (Wessel, 1995).

While hospital ownership conversion has occurred in various ways, from private

not-for-profit (NFP) to private for-profit (FP) or government (Gov), from FP to NFP or

Gov, and from Gov to FP or NFP, conversions from NFP to FP have been the focus of

2

most prior research (Burns, Shah, Frank, & Powell, 2009). Additionally, previous studies

have found that financial distress is the major factor leading to conversion but these

studies did not explore the reasons for financial distress (Amirkhanyan, 2007; Bovbjerg,

Held, & Pauly, 1987; Bovbjerg, Marsteller, & Ullman, 2000; Burns et al., 2009; Legnini

et al., 1999; Sloan, Ostermann, & Conover, 2003; Sloan, Taylor Jr, & Conover, 2000). A

more comprehensive study that investigates both the determinants and outcomes of

public hospitals ownership conversion will help stakeholders to pay close attention to

these factors so as to prevent financial distress and give empirical evidence whether

ownership conversion is the best way to turn the financial situation around. The purpose

of this study is to investigate the antecedents and consequences of the privatization of

non-federal general public hospitals. This study will attempt to answer the following

research questions:

1. What are the organizational and environmental factors associated with

financial distress of public hospitals?

2. Does financial distress precede public hospital privatization?

3. Does privatization lead to better financial performance?

The following three sections will discuss: (1) the ownership structure of U.S. hospitals,

(2) the historical perspective on U.S. public hospitals, (3) the rationale for the study.

3

The Ownership Structure of U.S. Hospitals

Hospitals are categorized by ownership status: government hospitals (Gov),

private not-for-profit hospitals (NFP), and private for-profit or investor-owned hospitals

(FP). Government hospitals are classified into two categories: federal hospitals that are

totally under the control and administration of the federal government such as the

Veterans‘ Affairs; and public hospitals that are under the administration of a city, county,

tax district, or state (Gapenski, 2004). The hospital‘s ownership status determines its

primary mission and sources of funding.

The principal mission of public hospitals is to provide healthcare for the indigent,

the needy and the uninsured and serve the community; they act as the ―provider of last

resort‖ or ―safety net‖ for the community. In addition, public hospitals are expected to

provide expensive and specialized healthcare services that private hospitals would not

deliver. Such services include neonatal intensive care, psychiatric treatments, burn care,

and trauma care. In addition, public hospitals play a major role in providing medical

education and clinical research (Andrulis, 1997; Bovbjerg et al., 1987; Brown, 1983;

Fishman, 1997).

Public hospitals obtain their capital in the form of governmental grants given by

the city, the county, or the state and excess revenue over costs from previous years. These

grants are business, property and personal income taxes collected from residents and

local businesses. While public hospitals are not allowed to raise capital through the sale

of stocks, they are allowed to sell tax-exempt bonds. In addition, public hospitals do not

pay property or corporate income tax and they are not allowed to share excess revenue

over costs among individuals (Gapenski, 2004).

4

Private not-for-profit hospitals are charitable entities under 501 (c) (3)

classification of the Internal Revenue Code. The main purpose of private not-for-profit

hospitals is to serve the community through the provision of quality healthcare, medical

education and research (Gray, 1986). Not-for-profit hospitals raise their capital from tax-

deductible contributions from philanthropists, tax-exempt bonds, and excess revenue over

costs, and governmental grants. As charities, not-for-profit hospitals do not pay property,

sales and corporate income taxes. In return, they are expected to provide charity care,

uncompensated care, and other community benefits such as the provision of preventive

care and healthcare education to the community. Even if not-for-profit hospitals do not

seek profit, they need to maintain good financial status to fulfill their mission (Gapenski,

2004).

Like public hospitals, not-for-profit hospitals are not authorized to raise capital

from the sale of stock and they are not allowed to share excess revenue over costs among

contributors. Excess revenue over costs should be reinvested in the hospital.

Furthermore, the proceeds from the sale of the hospital should not be distributed among

donors; they should be invested in a foundation that will serve the community. Not-for-

profit hospitals are allowed to pursue growth strategies (Clark, 1980; Gray, 1997; Gray,

1986; Gray, 1986; Marsteller, Bovbjerg, & Nichols, 1998).

For-profit hospitals are owned by shareholders. Their primary purpose is to

maximize shareholders wealth. For-profit hospitals raise capital by selling stocks to

investors, issuing bonds, and reinvesting retained earnings in the hospital‘s account. In

return, they pay property, sales, and income tax. For-profit hospitals are allowed to share

dividends among shareholders and shareholders have the right to divide the proceeds

5

from the sale of the hospitals (Gray, 1997; Gray, 1986; Marsteller, et al., 1998; Gapenski,

2004).

Regardless of ownership type, financial capital is one of the key resources of

every hospital. Hospitals require financial capital to support their operations, growth, and

survival. In addition to capital, hospitals also require other resources such as human

resources, medical equipments and technology, knowledge and reliable information to

adequately deliver quality healthcare. Lack of financial and other resources might induce

hospitals to engage in some strategic moves such as merger, sale, joint venture,

diversification, or ownership conversion. Such strategic change might help hospitals find

an alternative supply of resources and better access to knowledge and technology.

Comparing the three types of ownership, public hospitals carry the heaviest

burden in serving the community (Walker, 2005) and yet they have the least flexibility

regarding the ability to raise capital; they totally rely on the availability of funds from

government entities. However, the availability of funds depends on many factors such as

the capability of the residents and businesses to pay property and income taxes which is

contingent on the economic situation. During economic prosperity, government entities

might have more funds to give to public hospitals, but during an economic crisis, such

funds may deplete very quickly. Besides, the decision of the government to distribute tax

income among other public entities such as schools, public transportation, the post office,

and the armed forces might reduce the amount of capital granted to public hospitals

(Andrulis, 1997; Bovbjerg et al., 1987; Brown, 1983; Sataline, 2010).

Furthermore, unlike private hospitals, public hospital managers do not have the

freedom and the flexibility to decide and implement strategic change that would improve

6

the hospital‘s operation and enhance their financial situation. Public hospitals generally

operate under heavy bureaucracy and burdensome political pressure; politicians have the

power to decide on the strategic direction public hospitals should pursue (Siegel, 1996).

Regardless, all three types of hospitals have contributed to health care delivery in the

U.S; they all have their weaknesses and strengths. The next section presents a historical

perspective on public hospitals.

Historical Perspective on Public Hospitals

This section is divided into two sections: (1) public hospitals before and during

the 19th

century and (2) public hospitals during the 20th

century and beyond.

Public Hospitals Before and During the 19th

Century

The majority of public hospitals emerged from almshouses, but some were built

from scratch (Dowling, 1982). Almshouses were shelters for those in need of social

support such as the homeless, the drunkard, the mentally impaired, the outlaws, as well as

those with chronic illnesses. The provision of social, mental and physical support for the

needy was a practice from Great Britain that the immigrants wanted to carry on in the US

(Friedman, 1987). Almshouses were charitable organizations that were financially

supported by the local government; they were not-for-profit (Shi & Singh, 2004).

Since almshouses opened their doors to everyone in need without distinction, they

were not effective in providing the specific needs of their residents. Therefore,

specialized institutions such as orphanages, mental asylums and jails were established to

take care of people with specific social needs. Those with medical conditions remained in

7

the almshouse. Due to the stigma associated with the almshouse as the shelter for the

lowest class of the population, only the poor-sick stayed in the almshouses (Friedman,

1987). Those who could afford to pay for physicians preferred to be treated at home

(Starr, 1982). Thus, almshouses implicitly became the hospitals for the destitute.

Philadelphia General Hospital, founded in 1732, was the first public hospital that

emerged from almshouse status. It expanded, became successful, and acquired a

worldwide reputation in treating the sick as well as providing medical education and

research (Rosenberg, 1982). In 1736, New York City Almshouse morphed into Bellevue

Hospital and Charity Hospital in New Orleans was founded the same year (Rosenberg,

1982; NAPH, 2011). Due to the rapid growth of cities, most of the first public hospitals

were built in cities.

The growth of the population, the improvement in the quality of care, the progress

in medical education, and the professionalization of nursing served as catalysts to the

growth of public hospitals. Hospital use intensified due to the discovery of anesthesia by

William Thomas Green Morton in 1846, the discovery of antisepsis by Sir Joseph Lister

in 1867, and the discovery of x-rays by Wilhelm Rontgen in 1896 (McEachern, 1936;

Starr, 1982). The number of beds of all public hospitals increased from 12,000 in 1873 to

25,000 in 1889 (Dowling, 1982). Public hospitals became the largest hospitals relative to

the private ones and they totally relied on public funding. While public hospitals were

involved in training physicians and nurses they kept their primary mission which was

treating the very poor-sick (Starr, 1982). That mission stained the image of public

hospitals as they became undesirable for those who could afford better and more

expensive care (Dowling, 1982).

8

Public Hospitals During the 20th

Century and Beyond

The 20th

century experienced the fastest growth in hospital industry. This was

primarily due to population growth, increased confidence in hospital services, the

expansion of nursing and medical education and the professionalization of health

administration.

Despite this rapid expansion, the government felt the need to build more hospitals

firstly to care for the wounded soldiers of World War II and to provide additional care for

the communities. The Hospital Survey and Construction Act called the ―Hill-Burton Act‖

was signed in 1946 to authorize the issue of Federal grants, loans and loan guarantees for

hospital construction and health centers at states and community levels. Only public and

not-for-profit hospitals qualified for the Hill-Burton funds and the recipients of the funds

were expected to provide a certain amount of uncompensated care for 20 years

(Department of Health and Human Services, 1992). Since the Hill- Burton Act required

the provision of a certain amount of uncompensated care, for-profit hospitals did not

qualify for the program because the provision of uncompensated care was not the primary

purpose of for-profit hospitals.

Thus, the funding from the Hill-Burton Act enhanced the growth of public

hospitals even more. The recipients of the Hill-Burton funds were more competitive

relative to for-profit hospitals; they attracted more patients because they could offer

services at lower prices or free of charge for those who could not pay (Starr, 1982). The

number of public hospitals grew from 785 in 1946 to 1453 in 1965 (Friedman, 1987).

9

During the 20th century, the federal government did not only build more hospitals

and invest in healthcare education and research, but also provided access to healthcare to

the elderly and those who could not afford to pay. To provide better access to healthcare

for the retired and low income individuals, the government adopted Medicare and

Medicaid programs in 1965. The funding of these two programs, based on fee-for-

service, increased health care utilization and triggered escalating healthcare expenditures

(Starr, 1982). Under a fee-for-service plan, health care providers were reimbursed based

on the amount of services provided. Thus the plan encouraged providers to give as much

clinical care possible as to enhance revenue. In addition to the concern over raising

healthcare expenditures, there were growing concerns over the quality of care,

inefficiency, lack of access to care in rural areas, overcapacity, oversupply of physicians

and overspecialization (Starr, 1982). Consequently, in 1970, the government drew the

country‘s attention to the ―crisis in healthcare‖.

Among all hospitals, public hospitals were the most entrenched in crisis relative

to private hospitals as they were burdened with politics, depleted revenues and shrinking

government funding. They could not afford to renovate their buildings and were not able

to purchase updated medical equipment (Dowling, 1982).

Both the federal and state governments adopted some measures to reverse the

situation. Such measures included the Certificate of Needs (CON) laws and the

implementation of Medicare Prospective Payment System (PPS) in 1983. CON laws

provide legislation that individual states can adopt to restrict the building of new

constructions and curb overcapacity. States that adopted CON laws required health care

10

organizations to apply for an authorization to build or expand a facility. New York State

adopted the first CON law in 1964 (Stevens, 1989).

Medicare PPS was enacted by the Tax Equity and Fiscal Responsibility Act

(TEFRA) of 1982. With this law, the federal government took the initiative to impose

rates on services rendered to Medicare enrollees based on the Diagnosis-Related Groups

(DRGs) system. This system of payment combined all the services needed to treat a

certain disease; Medicare set the amount it paid for that particular bundle of services.

Medicare enrollees that belong to the same diagnosis group are charged the same rate.

Thus, a hospital benefits if its costs are below the PPS rate; it operates at a loss if its costs

are above the PPS rate (Shi & Singh, 2004).

In addition, the healthcare crisis has opened the opportunity to the emergence of

managed care organizations such as health maintenance organizations (HMO) as major

players in the healthcare industry. In order to control cost and utilization, HMOs provide

health insurance and contract with hospitals for the health care delivery of their enrollees.

Through tight control of consumption of specialized services by the systematic use of

primary care providers, health care utilization review, and strong negotiation on health

services costs, HMOs were successful in controlling healthcare expenditures during the

1980s.

Public hospitals have faced fierce competition with private hospitals. Private

hospitals are more attractive to the higher income population; as a result, the majority of

patients that public hospitals serve are low income people, the uninsured and the

underinsured. While private hospitals are more desirable than public hospitals, they

11

cannot provide all the services that public hospitals offer such as specialized tertiary

services including trauma care, HIV/AIDS treatments, and substance abuse rehabilitation

(Dowling, 1982). Furthermore, private hospitals prefer ―dumping‖ their uninsured

patients on public hospitals rather than treating them (Friedman, 1987). Thus, public

hospitals have coined themselves as ―safety net hospitals‖ as they serve the most

vulnerable population and provide critical services for critical needs (NAHP, 2011).

Since public hospitals have operated in an environment with fiscal pressure and

fierce competition coupled with an increasing number of uninsured and underinsured,

most of them have been in financial distress. Some of them could not survive and were

closed and some converted into private status. For example, the first public hospital,

Philadelphia General Hospital closed in 1976. A decreasing trend in the number of public

hospitals was noticed in the mid-1980s. The number of public hospitals decreased by

14 % between 1985 and 1995 (Legnini et al., 1999). Between 1980 and 2007 a total of

686 public hospitals converted into private status (Burns et al., 2009; Needleman,

Chollet, & Lamphere, 1997; Thorpe, Florence, & Sieber, 2000). Table 1.1 illustrates the

trends in the numbers of hospitals and hospital beds by ownership status from 1975 to

2008 (Department of Health and Human Services, 2011).

12

Table 1.1

Trends in Number of Hospitals and Hospital Beds by Ownership Type

Number of hospitals

and number of beds

by ownership type

1975 1980 1990 1995 2000 2006 2007 2008

Number of Hospitals

All hospitals 7,156 6,965 6,649 6,291 5,810 5,747 5,708 5,815

Nonfederal 1 6,774 6,606 6,312 5,992 5,565 5,526 5,495 5,602

Federal 382 359 337 299 245 221 213 213

Community2 5,875 5,830 5,384 5,194 4,915 4,927 4,897 5,010

Not-for-profit 3,339 3,322 3,191 3,092 3,003 2,919 2,913 2,923

For profit 775 730 749 752 749 889 873 982

State-Local

government 1,761 1,778 1,444 1,350 1,163 1,119 1,111 1,105

Number of Beds

All hospitals 1,465,828 1,364,516 1,213,327 1,080,061 983,628 947,412 945,199 9,5045

Nonfederal1 1,333,882 1,247,188 1,115,072 1,003,522 930,561 900,721 899,455 905053

Federal2 131,946 117,328 98,255 77,079 53,067 46,691 45,744 45,992

Community 941,844 988,387 927,360 872,736 823,560 802,658 800,892 808,069

Not-for-profit 658,195 692,459 656,755 609,729 582,988 559,216 553,748 556,651

For-Profit 73,495 87,033 101,377 105,737 109,883 115,337 115,742 120,887

State-Local

government 210,154 208,895 169,228 157,270 130,689 128,105 131,402 130,531

Source: Health, United States, 2010, page 372 (DHHS, 2011) 1 The category of nonfederal hospitals comprises psychiatric, tuberculosis and other respiratory diseases hospitals, and

long-term and short-term general and other special hospitals 2 Community hospitals are nonfederal short-term general and special hospitals whose facilities and services are

available to the public.

The information in Table 1.1 does not explicitly show how many hospitals

merged, got acquired, changed ownership or closed, but the numbers suggest one of these

strategies (Weil, 2011). For example, the decrease in the number of all hospitals from

7,156 in 1975 to 5,815 in 2008 suggests either hospital closures or mergers. The number

of all federal, nonfederal and community hospitals decreased between 1975 and 2008

except for the number of for-profit hospitals which increased from 775 (13% of all

community hospitals) to 982 (20%) between 1975 and 2008. This increase suggests the

growth of for-profit hospitals either through ownership change or entry of start-up

13

hospitals. The number of state-local government hospitals declined from 1,761 (30% of

all community hospitals) to 1,105 (22%) between the same period of time (DHHS, 2011).

Additionally, the capacity of hospitals measured as the number of beds also

demonstrates a decreasing trend for all federal, nonfederal and community hospitals

except for for-profit hospitals, which increased from 73,495 beds (8% of all community

hospital beds) to 120,887 beds (15%) between 1975 and 2008. The number of beds

owned by state-local government community hospitals decreased from 210,145 (22% of

all community hospital beds) to 130,531 (16%).

The history of public hospitals, has demonstrated that environmental factors such

as technology, war, population growth, government regulations and funding, economy,

advance in science, and competition have played a major role in the evolution of public

hospitals.

Government regulations can impose rules on but also allow flexibility for

healthcare organizations. Freedom of ownership status conversion is one of the

flexibilities granted to health care organizations. This freedom has permitted

organizations to solve organizational problems, pursue new opportunities, access

resources, or survive in a dynamic and competitive healthcare environment. The next

section discusses the rationale for the study.

14

Rationale for the Study

The purpose of this study is to investigate the impact of organizational and

environmental factors on public hospitals‘ financial distress, the impact of distress on

public hospitals‘ privatization, and the effect of public hospitals‘ privatization on

financial performance based on the resource dependence theory. This study focuses on

community public hospitals and does not include federal public hospitals like Veterans

Affairs. Since federal public hospitals operate in a different environment with special

rules and regulations and serve special populations such as the military, including them in

the study might bias the results.

The findings from the review of hospital ownership conversion literature revealed

that despite the different types of ownership conversion, the majority of the literature

studied hospital conversion from not-for-profit to for profit status or vice-versa. More

precisely, the number of hospital conversions from public ownership to private-not-for-

profit status slightly exceeded the number of hospital conversions from not-for-profit to

for-profit status between 1991 and 1997 and yet empirical studies on privatization of

public hospitals have been scarce (Burns et al., 2009). Nevertheless, the privatization of

public hospitals deserves greater attention given their major role in the life of the

community and the whole society.

Studying public hospitals is timely given their major role as safety net providers

to the community. The responsibility of public hospitals in providing care to the needy is

mostly needed during economic downturn when many people lose their jobs and

consequently their health insurance coverage and yet, public hospital funding decreases

the most during economic crisis. As of 2009, there were 51 million uninsured individuals

15

in U.S., representing a 10% increase from 2008 (U.S. Census Bureau, 2009). The

uninsured have imposed an additional strain in the operating environment of public

hospitals. Public hospitals members of the National Association of Public Hospitals and

Health Systems (NAPH), which represent 2% of all acute care hospitals, reported that

31% of their outpatient visits and 18% of their inpatient services were delivered to the

uninsured (NPHHI, 2009). More precisely, NAPH members provided 27% and 19% of

their total inpatient and outpatient services ($115 billion) to Medicaid patients and the

uninsured, respectively (NPHHI, 2009). Furthermore, 16% of their operating costs were

uncompensated relative to 6% of costs for all hospitals in the U.S.; in 2009, NAPH

members delivered 20% of all uncompensated care nationwide (NPHHI, 2009).

Given their public status, public hospitals are also expected to deliver expensive

and unprofitable tertiary services that private hospitals do not provide (Anderson,

Boumbulian & Pickens, 2004). The NAPH reported that specialty care provided by its

members represented 56% of all visits (NPHHI, 2009). Besides, public hospitals offer

substantial medical education and clinical research (Andrulis, 1997; Bovbjerg et al.,

1987; Brown, 1983; Fishman, 1997). While NAPH members represent only 2% of all

acute care hospitals, they provide medical training to more than 19,000 full-time

equivalent (FTE) medical and dental residents, representing 23% of all residents in acute

facilities (NPHHI, 2009).They also offer emergency relief in case of man-made or natural

disasters .

Despite the fact that public hospitals hold an important role in healthcare delivery,

they have faced the most challenging environment. Public hospitals have been struggling

to stay competitive while trying to overcome financial difficulties due to reduced

16

government funding and fiscal constraints, an increasing number of uninsured patients,

escalating healthcare costs, and pressure for efficiency. Moreover, public hospitals have

had a hard time competing for Medicare managed care contracts (Brown, 1983; Sataline,

2010; Siegel, 1996).

In summary, public hospitals have taken on the responsibilities that private

hospitals deem too costly and unprofitable to undertake (Andrulis, 1997). Therefore, lack

of access to adequate capital and expertise, intensifying competition, decreasing

operating income, and increasing expenditures and social burden have deeply affected the

financial situation of many public hospitals and as a result that they have experienced

financial distress (Bazzoli & Andes, 1995; Falik, 1983). Conceptually, a hospital is in

financial distress when it experiences a severe financial crisis that leads to radical

changes such as a merger, a reduction in the number of services, a diversification into

services unrelated to previous ones, an ownership conversion, a bankruptcy declaration

(Langabeer, 2006; Trussel & Patrick, 2010). Public hospitals have adopted these kinds of

survival strategies or exited the market (Bazzoli & Andes, 1995; Brown, 1983; Sataline,

2010).

Statistics have shown the declining trend in the number of public hospitals due to

either closure or privatization as demonstrated in Table 1. Privatization has been

considered the alternative solution to closure. Keeping the hospital open, under private

ownership, might preserve some access to healthcare for the community, ensure some

continuation of medical education and research, safeguard the access to emergency care

in case of national disaster and preserve some jobs. Privatization might turn the hospital

around and boost its financial health. Closure of a public hospital might result in a loss of

17

community benefits. Given the importance of public hospitals in healthcare delivery and

education, it is worth providing empirical evidence on why public hospitals privatize and

whether privatization has improved hospitals‘ outcomes (Sebelius, Frieden, & Sondik,

2010).

While previous studies on public hospitals privatization suggested poor financial

performance as the major factor leading to privatization, they did not investigate the

factors leading to poor financial performance. This study will investigate the

determinants of financial distress, then explore whether financial distress precedes

privatization and finally, examine whether privatization results in enhanced financial

condition.

This study fills the gap in the hospital ownership conversion literature by

empirically investigating both the antecedents and outcomes of public hospitals‘

privatization. Moreover, this study contributes to research based on the following points.

First, this study is more comprehensive compared to previous ones as it is based on a

national sample and covers thirteen years of data from 1997 to 2009. Second, it

contributes to knowledge by providing empirical evidence on three associations: the

impact of organizational and environmental factors on public hospitals financial distress,

the consequences of financial distress on public ownership status and the effects of

privatization on financial performance. A longitudinal study that follows the same

hospital over a certain number of years permits causal inference with respect to the

research questions that this study attempts to answer.

18

Third, studying public hospitals as a separate entity helps find more reliable and

helpful results for policy making. Prior studies on ownership conversion combined both

public hospitals and not-for profit hospitals into one category (Picone, Chou & Sloan,

2002; Shen, 2002, 2003; Sloan, 2002); the results from such studies might be biased since

public hospitals have a different operating environment than private not-for-profit

hospitals such as lack of access to private funding, heavy political constraints and

bureaucracy, lack of managerial flexibility and a heavier social burden.

Fourth, providing evidence on the outcomes of privatization and knowing whether

conversion from public to private for-profit status or from public to private not-for-profit

status results in a better outcome can help stakeholders make sound decisions based on

scientific evidence. Sixth, this is the first empirical study on public hospitals‘

privatization that applies a theoretical framework. Applying the resource dependence

theory will add value to extant literature as the majority of prior studies on ownership

conversion did not use theories; they did not examine the logical associations between the

main variables of interest. Theoretical frameworks are important because they serve as a

guide. They help researchers include other concepts and variables that might have been

overlooked. Moreover, theories are valuable tools for scientific research; they assist

researchers in logically and systematically explaining and predicting a phenomenon.

Furthermore, a study that includes a theoretical framework contributes to science as the

study tests the predicting strength of the theory (Hunt, 2002).

19

CHAPTER 2

REVIEW OF LITERATURE

This chapter contains two major sections: Section 1 includes the literature review

and section 2 includes the conceptual framework and hypotheses.

Literature Review

This section presents a review of the empirical studies on the antecedents and

consequences of ownership conversion of public hospitals. The search of the extant

literature was conducted by entering the phrases: ―ownership conversion‖, ―determinants

of ownership conversion‖, ―privatization of public hospitals‖, ―conversion of public

hospitals into private hospitals‖, ―consequences of privatization‖, and ―privatization‖ in

the following data bases: ABI Inform, JSTOR, Pubmed, Science Direct, Springer Link,

Web of Knowledge, Wiley Interscience, Business Source Premier, and Google Scholar,

respectively. Additionally, the reference list at the back of each article was checked

whether previously published relevant articles were cited.

In most cases, an organization changes ownership status through a transaction

with another organization of different ownership type. Such transactions include merger,

sale, lease, or joint venture (Anderson, Allred, & Sloan, 2003; Goodstein & Boecker,

1991; Hall & Conover, 2006; Needleman, 1999). An organization can directly change its

20

ownership status by filling out legal documents; a conversion that involves a single

organization rarely occurs in the healthcare setting (Cutler & Horwitz, 1997; Needleman,

1999). Public hospitals conversion into private not-for-profit or private for-profit status is

also called privatization of public hospitals (Legnini et al., 1999).

Ownership conversion is a dynamic phenomenon; a hospital might change

ownership status more than once in its lifetime (Burns et al., 2009; Cutler & Horwitz,

2000). Some healthcare organizations other than public hospitals such as public nursing

homes, have converted into private for-profit or private not-for-profit status

(Amirkhanyan, 2008; Amirkhanyan, 2007).

This literature review has four major sections: (1) the antecedents of public

healthcare organizations conversion into private for-profit status, (2) the antecedents of

public healthcare organizations conversion into private not-for-profit status, (3) the

consequences of public healthcare organizations conversions into private for-profit status

on financial performance, (4) the consequences of public healthcare organization

conversions into private not-for-profit status on financial performance.

Privatization of public hospitals and other healthcare organizations

As discussed in the previous chapter, public hospitals have been coined as the

―providers of last resort‖ or ―safety net hospitals‖ (Andrulis, 1997; Bovbjerg et al., 1987).

Public hospitals are mandated to provide healthcare services to everyone regardless of

health insurance status or ability to pay (Anderson, Boumbulian & Pickens, 2004) and yet

the expectations on public hospitals have been higher than that of private hospitals.

21

Public hospitals have been required to provide more charity care than private hospitals

and yet the funding from local or state government has diminished; they have lost

Medicaid patients to private hospitals and have had an increasing number of uninsured

and underinsured patients (Legnini et al., 1999). Public hospitals have been the providers

of expensive and tertiary services that private hospitals would not deliver. In addition,

public hospitals have played a major role in the provision of medical education. Medical

education enables hospitals to provide highly specialized care. While private hospitals

have also provided medical education, public teaching hospitals have been the major

providers of such highly specialized care freely to those unable to pay (Andrulis, 1997;

Bovbjerg, et al., 1987; Brown, 1983, Legnini et al., 1999). Moreover, public hospitals

have not been able to successfully compete for managed care contracts and they have no

freedom to raise capital, make major employee recruitment decision, and initiate

important strategic decisions to turn the financial performance of the hospital around.

All these factors have contributed to the financial crisis that most public hospitals

have been facing (Brown, 1983; Sataline, 2010). Consequently, public hospitals have

been motivated to change ownership status for financial stability and access to capital,

better patient mix, higher efficiency, and freedom from politics and public constraints.

Additionally, a hospital with budget deficit may change ownership status because the

local or state government cannot afford to financially support it anymore; therefore, it

becomes a financial burden to the community. Becoming private will set a public hospital

free from relying on tax income, which dramatically depletes during an economic crisis.

Becoming private also removes the public expectation of serving a large percentage of

low income population and providing costly tertiary services. Accordingly, handing the

22

operation of the hospital over to private entities through sale or joint ventures would

financially and fiscally relieve the community from social and fiscal burden (Burns et al.,

2009; Legnini et al., 1999; Sloan, Taylor Jr, & Conover, 2000; Needleman et al., 1997).

Furthermore, privatization has reflected the trend of public entities handing the

task of serving the public over to private entities (Sloan, et al., 2000). On the other hand,

privatization has been one of the long-term strategies for survival of public hospitals.

Likewise, privatization has also occurred in the nursing home industry (Amirkhanyan,

2007; Amirkhanyan, 2008).

The proponents of privatization of public hospitals argued that non-federal public

hospitals are the least efficient compared to private hospitals (Burgess & Wilson, 1996;

Coyne, Richards, Short, Shultz & Singh, 2009). Therefore, privatization helps hospitals

to be more efficient (Desai, Lukas, & Young, 2000) and save money (Bovbjerg et al.,

2000). Besides, privatization relieves the government from fiscal pressure and it enhances

tax revenue, specifically for privatization into for-profit status since for-profit hospitals

pay property and corporate income taxes (Gapenski, 2004). Moreover, privatization

releases public hospitals from the grip of politics and bureaucracy; and it offers hospitals

more freedom and flexibility in decision making (Bovbjerg, et al., 2000; Siegel, 1996).

Additionally, privatization offers hospitals better access to capital which may

result in the acquisition of updated technology, recruitment of capable managers and

clinical staff and restoration of infrastructure (Burgess & Wilson, 1996; Desai et al.,

2000; Siegel, 1996; Wessel, 1995), and improved healthcare quality (Bovbjerg, et al.,

2000). Some people argued that, in addition to public hospitals, private not-for-profit

hospitals have also acted as safety net providers for the community. Therefore,

23

privatizing public hospitals into not-for-profit hospitals will not alter the provision of

uncompensated care (Bovbjerg et al., 2000).

The opponents of privatization are concerned about the possible loss of access to

care for the indigent as the converted hospitals might not be committed to serving the

poor anymore; research has demonstrated that privatization decreased the level of

uncompensated care delivered to the needy (Thorpe et al., 2000; Desai et al., 2000).They

argued that only public hospitals are committed to the genuine ―open door‖ policy that

unconditionally ensures access to good care for everyone (Bovbjerg, et al., 2000; Desai,

et al., 2000). Conversion of public hospitals into for-profit status has raised concern that

the converted hospital will shut down expensive services and might eventually close the

hospital if it does not exhibit sound financial performance (Sataline, 2010).

The antecedents of healthcare organizations conversion from public to for-

profit status. To my knowledge, one quantitative study (Sloan et. al, 2003) and four case

studies investigated the antecedents of hospital ownership conversion from public to for-

profits status ( Bovbjerg et al., 2000; Burns et al., 2009; Legnini et al., 1999; Sloan, et al.,

2000) and another quantitative study examined the antecedents of nursing homes

privatization (Amirkhanyan, 2007). Sloan et al. (2003) combined public hospitals and

not-for-profit hospitals in one category and explored the antecedents of conversions as

well as mergers and closures; Amirkhanyan (2007) did not distinguish between

conversion from public to for-profit status and conversion from public to not-for-profit

status. Both the studies on hospitals and nursing homes demonstrated that some

environmental factors and organizational factors influenced this type of conversion.

24

The environmental factors that were associated with hospital conversion from not-

for-profit or public status into for-profit status included: low percentage of elderly people

in the population, high competition among hospitals, lower unemployment rate, high

percentage of HMO enrollment, and low per capita income (Sloan, et al., 2003). Higher

unemployment rate was associated with higher probability of closure (Sloan, et al., 2003).

The environmental factors that significantly affected public nursing homes conversion

into for-profit status included: low percentage of elderly people in the population, high

competition among nursing homes, high number of privatized nursing home facilities in

the county and fewer number of nursing homes owned by counties within the state

(Amirkhanyan, 2007).

The organizational factors that significantly affected ownership status were

financial condition, capacity and utilization. Low operating margin and high debt-to-

asset ratio increased the probability of not-for-profit and public hospital conversion into

for-profit status (Sloan, et al., 2003). Furthermore, the case studies on conversion of

public hospitals into for-profit status suggested current financial distress or anticipated

financial crisis, operating inefficiency, changing healthcare environment that has resulted

in higher competition for managed care contracts, decrease in Medicare and Medicaid

reimbursement, lack of access to capital needed for infrastructure renovation,

unwillingness of political leaders to increase tax rates for higher tax revenue that would

financially support public hospitals, inadequate reimbursement system, cumbersome

purchasing and recruitment processes, lack of flexibility in adopting competitive

strategies, and prevention of hospital closure, as the major reasons for hospital

privatization (Bovbjerg et al., 2000; Burns et al., 2009; Legnini et al., 1999; Sloan et al.,

25

2000). Besides, the study on nursing homes found that nursing homes with lower

occupancy rate, fewer beds, higher number of nursing staff hours per resident days, and

older infrastructure, were more likely to privatize and facilities affiliated with hospitals

were less likely to privatize (Amirkhanyan, 2007).

While Sloan et al. (2003) included financial variables such as operating margin

and debt-to-capitalization ratio as factors leading to ownership conversion; they did not

use the Altman z-score method to measure hospitals‘ financial conditions. A hospital

might have high operating margin and high debt-to-capitalization ratio at the same time;

it is difficult to determine whether the hospital is in good or bad financial condition. The

Altman Z-score that combines four financial ratios into one discriminant equation is a

better measure of financial condition than operating margin and debt-to-asset ratio as it

has a cut-off score that determines whether the organization is in financial distress or not.

The antecedents of healthcare organizations conversion from public to not-

for-profit status. The choice of not-for-profit instead of for-profit status reflects the

willingness of the public hospital to pursue its current mission which is to provide quality

care and serve the community without a profit motive. This choice might also suggest

that public hospitals avoid the takeover of a for-profit hospital. A for-profit buyer might

be tempted to close the facility to reduce competition among its own hospitals located in

the public hospital‘s area (Burns et al., 2009). Quantitative empirical studies of the

antecedents of ownership conversion from public to not-for-profit status have been

scarce.

26

Most of the reasons for converting from public to not-for-profit status, found in

case studies, were quite similar to the reasons for converting from public to for-profit

status including financial hardships and operating losses, increased competition and

inability to compete for managed care and other third-party payers contracts,

apprehension of funding reduction from Medicaid reimbursement and disproportionate

share hospital funding, delayed reimbursement, need for access to capital, freedom for

public governance constraints , and strong pressure for tax relief ( Bovbjerg, et al., 2000;

Burns et al., 2009; Legnini et al., 1999; Sloan et al., 2003; Sloan et al., 2000).

The consequences of ownership conversion from public to for-profit status on

financial performance. Four studies investigated the consequences of conversion from

public to for-profit status on hospitals financial performance (Thorpe et al., 2000; Shen

2003; Picone et al., 2002; Sloan, Taylor & Conover, 2000). The first three studies used

panel data of national samples of acute care hospitals and investigated more than one type

of ownership conversions (Thorpe et al., 2000; Shen 2003; Picone et al., 2002) and the

last study of Sloan, Taylor and Conover (2000) was a case study of 10 hospitals located

in Tennessee, North Carolina and South Carolina. Thorpe, Florence and Sieber (2000)

isolated public hospitals as a single entity; (Shen 2003 & Picone et al., 2002) combined

public hospitals and private not-for-profit hospitals into one category. The effects of

conversion from public to for-profit status on total margin and profit margin were mixed;

there was no significant change in total margin after conversion (Thorpe et al., 2000), but

Shen (2003) and Picone et al., (2002) found that total margin and operating margin

increased after conversion, respectively.

27

The findings regarding the effect of conversion on operating costs were

consistent; cost per admission decreased (Thorpe et al., 2000) and operating costs per

discharge decreased (Shen, 2003). Sloan, Taylor and Conover (2000) explored the

impact of conversion on internal rate of return relative and cost of capital and found

mixed results. One hospital‘s internal rate of return exceeded the cost of capital by 3.14

percentage points over 30 years and another hospital‘s rate of return was lower than the

costs of capital after conversion.

The consequences of ownership conversion from public to not-for-profit

status on financial performance. Three studies investigated the impact of conversion

from public to not-for-profit status on financial performance in terms of operating cost,

efficiency and internal rate of return (Shen, 2003; Sloan, Taylor Jr, & Conover, 2000;

Anonymous). Similar to the conversion from public to for-profit hospitals, operating

costs declined after conversion from public to not-for-profit status (Shen, 2003). The

study on the impact of conversion on operating efficiency found that efficiency was

enhanced after conversion (Anonymous). The case study that examined the impact of

conversion on internal rate of return found that one public hospital that converted into

not-for-profit status posted 253% internal rate of return relative to 4.71% cost of capital

after conversion(Sloan, Taylor Jr, & Conover, 2000).

The paucity of empirical studies on privatization of healthcare organizations and

mixed results has left a gap in the literature. This gap is evidenced by the lack of

empirical studies on the antecedents of public hospitals conversion into not-for-profit

28

status. Additionally, only one empirical study investigated the antecedents of public

hospitals‘ conversion into for-profit status and one single study explored the antecedents

of privatization of nursing homes. Overall, poor financial performance and increased

competition were found to be the major factors leading to privatization and privatization

resulted in improved financial performance and enhanced efficiency.

As stated earlier, these studies have some limitations as they did not apply some

theoretical frameworks; as a result, they omitted key environmental variables such as

excess capacity, number of active physicians in the county, and variables that measure

the level of environmental dynamism; these variables can affects organizational financial

performance. This study addresses these limitations. Table 2 in Appendix A presents the

summary of the methods and variables of the studies reviewed in this chapter.

Conceptual Framework and Hypotheses

Theoretical Background

As previously mentioned, the healthcare environment has shaped the healthcare

system in the U.S. Since the purpose of this study is to investigate the impact of

organizational and environmental factors on public hospitals‘ financial distress, the

impact of financial distress on public hospitals‘ privatization, and the effect of

privatization on financial performance, applying an organizational theory that takes the

environment into consideration is deemed appropriate for this study. The resource

dependence theory is one of the organizational theories that consider the environment as

a major determinant of an organization‘s strategic behavior. The following section

29

discusses the resource dependence theory and how it has been applied in previous

empirical studies.

Resource dependence theory. Every organization needs resources to fulfill its

vision, complete its mission and reach its goals. An organization has great challenges to

stay competitive if it does not possess adequate and sufficient resources. The resource

dependence theory (RDT) posits that ―the key to organizational survival is the ability to

acquire and maintain resources‖ (Pfeffer & Salancik, 1978, p.2). Therefore, resources are

crucial for the organization‘s life and yet they are scarce and not equally distributed

across organizations. Resource scarcity imposes constraints to the organizations (Aldrich

& Pfeffer, 1976). Constraints prevent organizations from reaching their objectives.

Consequently, organizations compete to acquire these resources.

Resources are viewed as the inputs that organizations need to produce outputs

and the environment refers to the ―organization‘s source of inputs and sink of outputs‖

(Pennings & Tripathi, 1978, p.172). In other words, the focal organization‘s environment

includes other entities from which it acquires resources and to which it sells products and

services. This definition implies that organizations are neither self-sufficient nor self-

reliant; they cannot acquire all the resources they need from within and they cannot

consume their own products and services (Stearns, Hoffman, & Heide, 1987). However,

the continuous availability of resources is uncertain. Uncertainty is one of the

characteristics of the environment and it refers to the fluctuation of resource availability

and the magnitude of challenges the organization has to face to acquire key resources

30

(Ulrich & Barney, 1984). Thus, scarcity of resources, combined with their uncertain

supply, makes resource acquisition a critical element to organizational survival.

An organization depends on other organizations to ensure continuous exchange of

resources. However, dependence on other organizations for resources implies lack of

independence and lack of power; those who possess resources have power over those

who do not have them (Galaskiewicz, 1985; Pfeffer, 2005; Pfeffer & Salancik, 1978).

The level of dependence on resources depends on how widely available and how

important the resource is to the organization (Jacobs, 1974). Accordingly, dependence is

defined as ―the product of the importance of a given input or output of the organization

and the extent to which it is controlled by a relatively few organizations‖ (Pfeffer &

Salancik, 1978, p.51). More specifically, an organization‘s dependence on a resource is

not critical to its survival unless it is a key resource and only a few suppliers possess it.

For example, even if physicians are important to a hospital, the hospital‘s dependence on

physicians is not that crucial if the hospital can hire physicians from many medical

schools and other hospitals. And if only one organization supplies magnetic resonance

imaging devices (MRI) but the hospital prefers to use computer tomography scans (CT

scan), then the hospital‘s dependence on MRI equipment is not imperative to the

hospital‘s survival.

Since an organization‘s possession and control of resources implies power,

organizations have to adopt various strategic moves to acquire and control resources

(Ulrich & Barney, 1984). Mergers and acquisitions, vertical or horizontal integration,

establishment of interorganizational coalitions, differentiations, and recruitment of board

members who can facilitate access to resources, are among the various strategic moves

31

that organizations adopt to reduce dependence on and increase control over resources

(Aldrich & Pfeffer, 1976; Pfeffer, 2005; Pfeffer & Salancik, 1978; Sofaer & Myrtle,

1991). Thus, resource dependence theory seeks to explain and predict the impact of the

environment and resource availability on an organization‘s behavior; consequently this

behavior can change the structure of the environment. For example, mergers,

acquisitions, and vertical and horizontal integrations may result in higher market

concentration.

The major assumptions of the RDT can be summarized as follows: (a)

organizations seek to maximize their power and independence from other organizations

by acquiring critical resources, (b) the environment imposes constraints on the

organizations as it does not possess all the required resources to sustain all organizations

and the supply of these scarce resources is uncertain, (c) organizations can survive in

their environment as long as they obtain continuous access to resources, (d) organizations

are capable of changing the environment; the various strategic moves that organizations

undertake to ensure resource acquisition will change environmental condition (Aldrich &

Pfeffer, 1976; Pfeffer, 2005; Ulrich & Barney, 1984).

RDT is a suitable theory that can explain why hospitals change ownership status

because ownership status is based on fiscal and legal requirements. Since previous

studies revealed that the major cause of ownership conversion is poor financial

performance, RDT might help explain why some public hospitals have poor financial

performance and consequently convert into private status. Public hospitals might seek

conversion because the government does not have enough financial resources to support a

hospital operating at a deficit.

32

Previous studies suggested that the unwillingness of the community and

politicians to financially support a public hospital operating at a loss, which has become a

heavy fiscal burden to the community, is one of the reasons for a public hospital‘s

conversion (Bovbjerg et al., 2000). Some other studies suggested the inability of public

hospitals to hire human resources with adequate capabilities and competencies as one of

the reasons for conversion (Burns et al., 2009). In other words, public hospitals change

ownership because the alternative type of ownership has more resources or offers easier

access to resources. As a result of the improved access to resources through conversion,

converting hospitals might be able to regain their financial health and stay competitive.

Moreover, converting hospitals might get resources other than financial capital if they

change ownership. For example, a public hospital will have more autonomy to acquire

advanced technology and hire capable managers and medical staff if it privatizes.

RDT has been applied in several empirical studies of various industries of

healthcare organizations. Given the importance of the environment in RDT, these studies

included both environmental and organizational factors as determinants of various

strategic moves such as adoption of innovation in clinical practices (Zinn, Weech, &

Brannon, 1998), delivery of specialized care (Banaszak-Holl, Zinn, & Mor, 1996;

Campbell & Alexander, 2005; Goldberg & Mick, 2010; Weech-Maldonado, Qaseem, &

Mkanta, 2009; Zinn, et al., 1998), improvement of clinical practice (Alexander & Wells,

2008; Starkey, Weech-Maldonado & Mor, 2005; Zinn, Weimer, Spector, & Mukanel.,

2010), increased participation in health care delivery (Zakus, 1998), engaging in strategic

partnership ( Alexander & Morrisey, 1989; McKinney, Morrissey & Kaluzny, 1993;

Zinn, Mor, Castle, Intrator, & Brannon, 1999), community orientation (Proenca, Rosko,

33

& Zinn, 2000), and diversification into new services unrelated to previous ones

(Alexander, D'Aunno, & Succi, 1996a, 1996b). In addition, RDT has been applied to

explain the composition and function of hospital board members (Pfeffer, 1973); the

board of directors functions as boundary spanner that helps organizations to acquire

resources.

The studies mentioned above demonstrated that RDT has strong capabilities in

explaining an organization‘s strategic behavior. The reviews of the literature on resource

dependence theory confirmed this finding (Davis & Cobb, 2009; Nienhuser, 2008).

Interestingly, since the major reason for public hospital privatization is to obtain easier

access to resources such as financial resources, human resources as well as updated

technology and since better access to resources is expected to enhance financial

performance, the resource dependence theory has never been applied to the study of the

antecedents of public hospitals financial performance, their privatizations and the

consequences of privatization on financial performance. Based on the resource

dependence theory, this study is conducted using the conceptual framework illustrated in

Figure 1.1.

34

Figure 1.1 Conceptual Framework

The conceptual framework in Figure 1.1 illustrates the associations between

environmental factors and public hospital financial distress, the associations between

organizational factors and public hospital financial distress, the effect financial distress

has on public hospital privatization, and the impact of privatization on subsequent

financial performance. For the scope of this study, the major environmental factors of

interest include environmental munificence, environmental dynamism and environmental

complexity. The main organizational factors of interest include hospital size and hospital

teaching status.

The bold arrows in Figure 1.1 represent the associations between the independent

variables of interest and the dependent variable as stated in the hypotheses presented in

the following section. The dotted arrows represent the associations between the control

variables and the dependent variables. In other words, the main independent variables of

interest such as environmental factors and organizational factors that are associated with

35

financial distress become control variables in the study of the impact of financial distress

on privatization. Likewise, the environmental factors and organizational factors act as

control variables in the study of the impact of privatization on financial performance.

The following section discusses the components of the environment, the

associations between the concepts as depicted in Figure 1, and the hypotheses.

The Environment

The components of the environment that directly affect an organization‘s

operations and performance are called the task environment (Dill, 1958). The task

environment is comprised of customers including retailers and wholesalers; suppliers of

inputs such as human, natural and financial resources, and infrastructure; competitors for

customers and inputs; and regulatory bodies (Dill, 1958). Prior studies have attempted to

conceptualize the environment into several dimensions (Duncan, 1972; Emery & Trist,

1965; Aldrich, 1979; Mintzberg, 1979; Child, 1972; Thompson, 1976). Dess and Beard

(1984) combined the six dimensions of the task environment from Aldrich‗s study and

reduced them into three dimensions so as to represent all the different dimensions

conceptualized from previous studies (Aldrich, 1979; Dess & Beard, 1984). These

dimensions are environmental munificence, environmental dynamism and environmental

complexity (Dess & Beard, 1984). Sharfman and Dean (1991) performed additional

studies to elaborate the measurement of these three dimensions (Sharfman & Dean Jr,

1991).

36

These three environmental dimensions have become the most widely used

dimensions in empirical studies. Besides, they are the most appropriate for this study as

they were conceptualized based on the resource dependence theory (Boyd, 1990; Dess &

Beard, 1984; Dess & Rasheed, 1991) and they have helped the literature understand the

impact of the environment on organizational performance.

Environmental munificence. Environmental munificence refers to the

availability of resources in the task environment to support the needs and growth of all

the organizations operating in a specific industry; resources can be scarce or abundant.

Scarcity of resources makes it more difficult for organizations to survive and it affects

their performance. Munificence has been operationalized as per-capita income, overall

population growth, growth rate of the elderly population, growth in total sales growth in

total employment, and number of physicians in the county (Alexander, et al., 1996a;

1996b; Dess & Beard, 1984; Margarethe & Bantel, 1993; Trinh & Begun, 1999).

Environmental dynamism. Environmental dynamism refers to the degree of

stability or instability of the environment; it is the extent to which the events in the

environment are unpredictable and change occurs rapidly (Begun & Kaissi, 2004; Child,

1972; Dess & Beard, 1985; Keats & Hitt, 1988). Moreover, an environment becomes

dynamic when there is high speed of technological change and fast growth in the size and

number of all the organizations operating in the same industry (Kotha & Nair, 1995;

Simerly & Li, 2000).

37

Environmental dynamism has been measured as the fluctuation of total sales and

instability of total unemployment (Boyd, 1990; Dess & Beard, 1984) or the volatility in

industry sales and operating income (Keats & Hitt, 1988). In studies of healthcare

organizations, environmental dynamism has been measured subjectively through the

survey of management teams in terms of how they perceive the changes in the

environment (Kumar, Subramanian, & Strandholm, 2002). Other studies used objective

measures such as HMO penetration, changes in employment rate, changes in poverty

level and changes in population size (Menachemi, Mazurenko, Kazley, Diana, & Ford,

2012; Menachemi, Shin, Ford, & Yu, 2011), and the coefficient of variation of total

admissions to the county‘s community hospitals over a moving three-year period (Lee

and Alexander, 1999).

Environmental complexity. Environmental complexity refers to the

heterogeneity and homogeneity of the environment (Begun & Kaissi, 2004; Dess &

Beard, 1984; Dill, 1958); it is defined as the product of the number of components in the

task environment and the number of factors in each task environment component

(Duncan, 1972). For example, healthcare organizations interact with different

components of the task environment such as suppliers, third party payers, patients,

regulatory agencies, teaching universities and nursing schools, and the community. Each

of these components in the task environment comprises various factors. Suppliers include

suppliers of medical equipment, suppliers of prescription drugs and suppliers of

information technology. Third party payers include Medicare, Medicaid, private health

insurance, and health maintenance organizations. Therefore, when we multiply the

38

number of components with the number of factors, we know the degree of complexity of

the environment. A higher number indicates a complex environment; a lower number

indicates a simple environment.

In addition, the interaction between these different components of the

environment increases complexity. For example, the implementation of Medicare part D

that provides prescription coverage for Medicare enrollees makes the environment more

complex because it involves the hospital, the patient, Medicare and pharmaceutical

companies (Begun & Kaissi, 2004). Begun and Kaissi (2004) assumed the healthcare

environment has been highly complex.

Environmental complexity has been measured as the extent of wage and wealth

distribution within a Metropolitan statistical area (Stearns et al., 1987), or the diversity of

products (Dess & Beard, 1984). Other studies used market concentration to

operationalize complexity. These studies argued that highly concentrated markets are less

complex because they have fewer competitors and highly competitive markets are more

complex because they have a larger number of competitors (Boyd, 1990; Keats & Hitt,

1988).

Studies in healthcare settings have measured environmental complexity

subjectively and objectively. Subjective measures include the perceived intensity of

competition ( in terms of level of health maintenance organizations (HMO) penetration in

the market, Herfindahl Hirschman Index (HHI) (Kim, 2010; Weech-Maldonado et al.,

2009), excess capacity (Weech-Maldonado, et al., 2009), number of hospitals in the

county (Trinh & Begun, 1999), the geographic distance between two hospitals

(Alexander, et al., 1996a, 1996b), and the state of malpractice crisis (Menachemi, et al.,

39

2012). Other studies also have suggested that urban areas are more complex than rural

areas since organizations operating in urban areas interact with a higher number of

entities in their task environment than organizations operating in rural areas (Dansky,

Milliron, & Gamm, 1996). In addition, hospitals operating in rural areas are less complex

since they have fewer competitors relative to hospitals operating in urban areas

(Alexander, et al., 1996a, 1996b).

Financial Performance

Financial performance is one of the dimensions of organizational performance.

Performance refers to the extent to which an organization achieves certain objectives that

are measurable (Lenz, 1980). Thus, financial performance can be measured in terms of

economic growth and profitability, solvency or availability of cash, efficiency, revenue,

capital structure and asset utilization (Coyne et al., 2009; Das, 2009; Molinari,

Alexander, Morlock, & Lyles, 1995; Pink, et al., 2005). As previously mentioned, the

worst financial performance that leads an organization to the brink of bankruptcy or

closure refers to financial distress (Langabeer, 2006).

The following section will discuss the hypotheses based on the empirical

literature that investigated the associations between environmental munificence,

environmental dynamism and environmental complexity and financial performance,

respectively; the associations between organizational factors, namely: hospital size,

hospital affiliation with a medical school, hospital participation in hospital network, and

hospital operation under contract management and financial performance, respectively;

40

the association between financial performance and privatization; and the effect of

privatization on financial performance; as depicted in Figure 1.

The association between environmental munificence and financial

performance. Several empirical studies have demonstrated that environmental

munificence is positively associated with performance. These studies found that firms

operating in more munificence environments exhibited superior performance in terms of

growth and profitability relative to those operating in an environment with scarce

resources (Baum & Wally, 2003; Capon, Farley, & Hoenig, 1990; Davies & Walters,

2004; Fuentes-Fuentes, Albacete-Saez, Llorens-Montes, 2004; Goll & Rasheed, 2004;

Goll & Rasheed, 1997; Jogaratnam, Tse, & Olsen, 1999; Kotha & Nair, 1995; Pelham,

1999).

The positive association between environmental munificence and financial

performance has exhibited strong empirical evidence as demonstrated in the results from

the meta-analysis of 88 studies published between 1929 and 1987 on the determinants of

financial performance. The results indicated that environmental munificence, in terms of

industry growth, had a positive impact on firms‘ financial performance (Capon, et al.,

1990).

Some empirical studies in the healthcare environment have examined the

association between environmental munificence and financial performance. Kim (2010)

investigated the factors affecting financial distress among not-for-profit hospitals. The

findings suggested that environmental scarcity, in terms of high unemployment rate,

41

increased the probability of financial distress among hospitals located in a Metropolitan

Statistical Area (MSA) (Kim, 2010).

Likewise, Friedman and Shortell (1988) suggested that environmental

munificence, measured as deflated family income, was positively associated with hospital

operating margin and net income margin (Friedman & Shortell, 1988). Additionally,

munificence, in terms of higher percentage of people aged 65 or older, was found to have

a positive association with financial performance (Brecher & Nesbitt, 1985; Friedman &

Shortell, 1988).

Organizations operating in a munificent environment tend to exhibit high

financial performance because the abundance of resources makes them cheaper to

acquire and easy access to resources results in less organizational strain; organizations

can spend most of their efforts increasing productivity instead of spending additional

resources to acquire other resources. For example, it is cheaper to hire physicians and

nurses during high unemployment rate but they become expensive when few of them are

unemployed. Cheaper inputs results in less expenses and accordingly higher income.

Furthermore, resources such as a population with a higher per capita income and

a higher number of insured positively impacts provider financial performance because

this type of population can afford to pay for healthcare expenses. As a result, providers

have less uncompensated care, can charge higher prices for the services, and receive

timely reimbursement. Therefore, it is hypothesized that environmental munificence is

associated with better financial performance.

42

Hypothesis 1: Public hospitals operating in a more munificent environment are

less likely to experience financial distress than public hospitals

operating in a less munificent environment.

The association between environmental dynamism and financial

performance. Several studies have investigated the association between environmental

dynamism on organizational performance. These studies have demonstrated that firms

operating in a more dynamic environment exhibited poor performance compared to those

operating in a more stable environment (Anand & Ward, 2004; Baum & Wally, 2003;

Goll & Rasheed, 2004; Goll & Rasheed, 1997; Keats & Hitt, 1988; Pelham, 1999; de

Hoogh et al., 2004).

Hospitals have been operating in a dynamic environment (Begun & Kaissi, 2004).

The dynamism of the environment causes difficulty in planning and acquiring resources

because of high incidence of change; organizations do not know what might occur on a

daily basis and yet they will be surprised if they do not plan in advance. If they plan in

advance, the resources spent in planning might be wasted if their plans do not fit the

emerging environment. Furthermore, the fluctuation of resource supply due to

environmental change makes it difficult to acquire resources. As discussed in the

previous section, when resources are scarce they are expensive and expensive resources

deplete income. For example, economic recession results in unpredicted and dramatic job

loss; and job loss leads to loss of income and health insurance. People who are

unemployed and uninsured are not able to pay for health services. Consequently,

providers experience inflated uncompensated care, shrinking revenue and increased

43

deficit. Thus, operating in a dynamic environment is more difficult and it affects

organizational outcome. Therefore, it is hypothesized that a dynamic environment has a

negative impact on financial performance.

Hypothesis 2: Public hospitals operating in a more dynamic environment are

more likely to experience financial distress than public hospitals

operating in a more stable environment.

The association between environmental complexity and financial

performance. The negative association between environmental complexity, in terms of

industry, and financial performance has been widely confirmed according to the findings

from the meta-analysis on the determinants of financial performance. This study found

that high industry concentration was positively associated with financial performance. In

other words, an environment with low industry concentration or a more competitive and

complex environment was associated with poor financial performance (Capon, et al.,

1990).

Some empirical studies that investigated the association between environmental

complexity and financial performance indicated that high complexity was associated with

poor financial performance (Kim, 2010; Friedman & Shortell, 1998; Brecher & Nesbitt,

1985). Kim (2010) found that high environmental complexity, in terms of industry

concentration and HMO penetration, was associated with financial distress among

hospitals in Metropolitan Statistical Areas (Kim, 2010). Similarly, Friedman and Shortell

(1988) suggested that a complex environment with higher competition from nursing

homes and other hospitals was negatively associated with hospital profitability.

44

Additionally, the findings from the study of not-for-profit hospitals in New York State

and New York City revealed that higher competition was associated with poor financial

performance (Brecher & Nesbitt, 1985).

The studies mentioned above have associated environmental complexity with

high competition. When there are many organizations, in the same industry, that compete

for the same key resources, it is more difficult to acquire these resources as they become

scarce. As discussed previously, scarce resources are expensive; they lead to higher

expenses and consequently negative financial outcome. Additionally, competing for

scarce key resources means that organizations have to spend other resources such as time

and human resources to obtain these key resources. Such resources could have been used

to increase the organization‘s efficiency and productivity if the environment was less

competitive; fewer organizations would need the same key resources.

Furthermore, operating in a complex environment is more challenging as it

requires complex production, complex information processing, and complex decision

making that increases the intensive use of organizational resources and reduces

efficiency. Besides, operating in a complex environment implies that the focal

organization has to interact with a large number of other organizations, which requires

additional consumption of resources. Therefore, it is hypothesized that a complex

environment is negatively associated with financial performance.

Hypothesis 3: Public hospitals operating in a more complex environment are

more likely to experience financial distress than public hospitals

operating in a less complex environment.

45

The association between organizational size and financial performance. The

size of the organization has been determined to have a positive association with

organizational performance. Larger organizations are more competitive since they can

reduce escalating costs through their capability of realizing economies of scale (Hall &

Weiss, 1967). Moreover, large firms have substantial financial capital that enables them

to invest in more profitable business and increase their total profits as well as their return

on investment (Baumol, 1967; Hall & Weiss, 1967). Additionally, large organizations

are able to accumulate slack resources (Sharfman, Wolf, Chase, & Tansik, 1988). Slack

resources refer to extra resources, such as financial reserves and inventory that firms can

put aside, to initiate changes in order to meet future environmental challenges (Sharfman

et al., 1988; Wan & Yiu, 2009; Dawley, Hoffman, & Brockman, 2003). Therefore, large

organizations are better equipped to face the environment than smaller organizations.

Larger organizations are more competitive as they can have access to

resources more cheaply and easily than smaller organizations; they have bargaining

power over suppliers as they purchase large quantities of supplies (Porter, 1985).

Bargaining power results in cheaper supplies and consequently lower costs and higher

financial performance. Several studies have demonstrated that firm size is positively

associated with firm performance (Dawley et al., 2003; Hall & Weiss, 1967; Goll &

Rasheed, 2004; Kotha & Nair, 1995; McNamara, Luce, & Tompson, 2002; Simerly & Li,

2000; Wan & Yiu, 2009).

The size of the hospital, usually measured as the number of hospital beds is an

important characteristic of a hospital as it reflects its competitive strengths and market

share. Similar to larger corporations, larger hospitals are more competitive relative to

46

smaller ones since they can achieve economies of scale (Hall & Weiss, 1967). Some

empirical studies showed evidence that hospital size was associated with superior

financial performance. Brecher and Nesbitt (1985) investigated the major factors

associated with financial situations of not-for-profit hospitals in New York State and New

York City. Their findings suggested that hospital size was associated with higher

operating margin and return on assets. Similarly, Kim (2010) explored the factors

associated with financial distress of not-for-profit hospitals; the findings suggested that

larger hospitals located in non-Metropolitan Statistical Areas were less likely to be in

financial distress in term of cash flow (Kim, 2010). Likewise, larger hospitals were less

likely to file bankruptcy in a study that examined the factors associated with hospitals

filing bankruptcy (Landry & Landry III, 2009). Therefore, it is hypothesized that smaller

public hospitals have higher risk of being in financial distress than larger public hospitals.

Hypothesis 4: Larger public hospitals are less likely to experience financial

distress than smaller public hospitals.

The association between teaching status and financial performance. Some

studies have demonstrated that teaching status is associated with higher financial

performance. Tennyson and Fottler (2000) examined the financial performance of

hospitals that are members of hospital systems in Florida in 1986 and 1992. They found

that teaching status was positively associated with operating margin in 1992. Likewise,

another study on the determinants of financial performance of U.S. hospitals, based on a

national sample, suggested that teaching hospitals had higher total profit margin

compared to non-teaching hospitals (Younis & Forgione, 2005).

47

Teaching hospitals have been operating in the same competitive, complex and

challenging environment as non-teaching hospitals. They have been facing pressure to cut

costs, they have also competed with other hospitals, they have provided more

uncompensated care relative to non-teaching hospitals and they have experienced cuts in

Medicare and Medicaid funding (Guterman, 2003; Needleman, Lamphere, & Chollet,

1999). Medicare has been the major source of funds for graduate medical education

(Rosko, 2004).

However, being affiliated to a medical school is prestigious. Hospitals affiliated to

medical schools have had the reputation of providing high quality care for complex

procedures and they have been well known and highly respected in the healthcare

environment (Ayanian & Weissman, 2002; Kupersmith, 2005; Reuter & Gaskin, 1997).

In most cases, teaching hospitals have a monopoly of ground breaking technology to treat

specific conditions and deliver highly specialized services (Moses III, Thier, &

Matheson, 2005; Reuter & Gaskin, 1997). Good reputation and a monopoly of

sophisticated procedures have permitted teaching hospitals to charge fees up to 10%

higher than non-teaching hospitals (Reuter & Gaskin, 1997).

In addition to the ability to provide higher quality care and acquire advanced

technology, teaching hospitals have a competitive advantage relative to non-teaching.

Teaching hospitals are usually larger than non-teaching hospitals (Reuter & Gaskin,

1997). As discussed in the previous section, large organizational size facilitates the

achievement of economies of scale that smaller organizations cannot obtain. In addition,

larger organizations have bargaining power over suppliers as they purchase larger

quantities of supplies; bargaining power facilitates the acquisition of resources at a lower

48

price. Thus, teaching hospitals with a larger number of residents were found to have

positive financial performance (Langabeer II, 1998). Besides, teaching hospitals have

larger endowment funds than non-teaching hospitals (Rosko, 2004); endowments serve as

financial protection against adversities. Therefore, it is hypothesized that teaching public

hospitals are shielded from financial crisis compared to non-teaching public hospitals.

Hypothesis 5: Public hospitals affiliated with a medical school are less likely to

experience financial distress than non-affiliated public hospitals.

The Association between financial performance and privatization.

Some empirical studies on the association between financial performance and

privatization showed evidence that firm with poor financial performance that face fiscal

pressure were more likely to privatize than firms with higher financial performance. Poor

financial performance such as low operating margin and high debt-to-asset ratio were

found to increase the probability of public hospitals‘ privatization (Sloan, et al., 2003). In

addition, current financial distress and apprehension that the financial crisis will worsen

were reported to be the major reasons for privatization (Bovbjerg et al., 1987; Bovbjerg et

al., 2000; Burns et al., 2009; Legnini et al., 1999; Sloan et al., 2000).

The resource dependence theory argues that organizations need key resources to

successfully fulfill their missions and survive and the availability of resources enhances

operating and financial performance. Therefore, when organizations exhibit poor

financial performance, they engage in different strategic repositioning that will enhance

their access to key resources. Publicly owned organizations operate under the most

stringent and the least flexible environment that restrict the access to key resources and

49

yet they are the ones in the poorest financial condition. Converting into private status will

give public organizations the freedom and independence to adequately access key

resources such as financial capital, updated technology, and knowledgeable human

resources that can efficiently manage the organizations and boost their competitive

position.

Privatization has been one of the solutions that publicly owned firms adopt when

they face financial distress. Privatization is an attractive strategic move because it offers

solutions to multiple issues. First, privatization will give firms easy access to financial

resources offered by private investors. Second, easy access to financial resources enables

the organization to acquire other resources such as qualified human resources and

updated technology, for example. Third, privatization provides the government a relief

from fiscal burden. Fourth, privatization provides the government additional income from

the sale of the firm‘s assets and enhanced tax revenue. Fifth, privatization enables the

organization to continue its operations and preserve jobs. Therefore, it is hypothesized

that public hospitals in financial distress will privatize.

Hypothesis 6: Public hospitals in financial distress are more likely to privatize

than public hospitals exhibiting higher financial performance.

The impact of privatization on hospital financial performance. As discussed

in the literature review section, empirical studies have demonstrated that hospital

financial performance and efficiency improved after privatization (Thorpe, et al., 2000;

Shen, 2003; Bovbjerg et al., 2000; Anonymous). Since public hospitals provide care

based on public funding, the principal purpose of public hospitals is to provide care to

50

everyone regardless of ability to pay and deliver non-profitable tertiary services. Public

hospitals do not focus on making profit and they do not expect to make profit. And since

the provision of public funding is not based on the hospital‘s positive financial

performance but on its financial needs and availability of funds, public hospitals do not

have the incentive to improve their efficiency and consequently financial performance.

However, private hospitals have less social and bureaucratic burden and more

freedom in acquiring key resources relative to public hospitals and they do not have to

deal with politics. Less burden improves efficiency and as a result, financial performance.

Based on the resource dependence theory, the ability to access and obtain key resources

is the means for organizational survival (Pfeffer & Salancik, 1978) and survival is

determined by good financial condition. Consequently, privatization improves hospital

financial performance. Therefore, it is hypothesized that privatization results in better

financial outcomes.

Hypothesis 7: Privatized public hospitals have higher financial performance

after privatization than before privatization.

Some empirical studies provided evidence that for-profit hospitals exhibit

superior financial performance compared to not-for-profit hospitals. Friedman and

Shortell (1988) demonstrated that for-profit hospitals had higher operating margin and

higher profit margin relative to not-for-profit hospitals (Friedman & Shortell, 1988).

Tennyson and Fottler (2000) suggested that private for-profit ownership was positively

associated with superior operating margin, total margin and return on assets of hospitals

affiliated with multihospital systems(Tennyson & Fottler, 2000). Younis and Forgione

51

(2005) also found that compared to not-for-profit hospitals, for-profit hospitals had higher

total profit margin (Younis & Forgione, 2005).

Furthermore, the evidence from three studies on the impact of public hospitals

privatization demonstrated that hospitals that converted into for-profit status significantly

reduced the level of uncompensated care provided to the patients (Desai et al., 2000;

Needleman et al., 1999; Thorpe et al., 2000), whereas the level of uncompensated care

did not change among public hospitals that converted into not-for-profit status (Desai et

al., 2000). A decrease in the level of uncompensated care may result in increased patient

revenue, which consequently enhances a hospital‘s financial condition.

Since the ultimate goal of for-profit hospitals is to maximize profit and

consequently shareholder return, it emphasizes efficiency to achieve high financial

performance. In addition, for-profit hospitals have no social obligation to provide care for

the needy since they are not the recipients of public funding or philanthropist donations

and they have to pay income tax. Therefore, they are able to pursue profitable ventures

that boost their financial performance. Furthermore, for-profit hospitals are the most

flexible regarding access to financial resource as they are able to raise capital through the

sale of stocks. Greater access to financial resources implies easier acquisition of other

resources. As discussed previously, easier access to resources leads to higher

performance. Therefore, it is hypothesized that conversion into private for-profit status

generates a higher financial performance than conversion into private not-for-profit

status.

52

Hypothesis 8: Public hospitals that converted into for-profit status exhibit

higher financial performance compared to public hospitals that converted into not-for-

profit status.

53

CHAPTER 3

RESEARCH METHODOLOGY

This chapter will discuss the methodology applied in this study: the research

design, the data sources, the population and sample, the dependent and independent

variables and their respective operationalization, and the analytical methods used to test

the hypotheses. The purpose of this study was to answer three research questions:

1. What are the organizational and environmental factors associated with

financial distress of public hospitals?

2. Does financial distress precede public hospital privatization?

3. Does privatization lead to better financial performance?

These questions were answered based on the conceptual framework depicted in

Figure 1. Research question 1 was answered by testing hypotheses 1, 2, 3, 4, 5.

Hypothesis 1: Public hospitals operating in a more munificent environment are

less likely to experience financial distress than public hospitals

operating in a less munificent environment.

Hypothesis 2: Public hospitals operating in a more dynamic environment are

more likely to experience financial distress than public hospitals

operating in a more stable environment.

Hypothesis 3: Public hospitals operating in a more complex environment are

more likely to experience financial distress than public hospitals

operating in a less complex environment.

54

Hypothesis 4: Larger public hospitals are less likely to experience financial

distress than smaller public hospitals.

Hypothesis 5: Public hospitals affiliated with a medical school are less likely to

experience financial distress than non-affiliated public hospitals.

Research question 2 was answered by testing hypothesis 6.

Hypothesis 6: Public hospitals in financial distress are more likely to privatize

than public hospitals exhibiting higher financial performance.

Research question 3 was answered by testing hypotheses 7 and 8.

Hypothesis 7: Privatized public hospitals have higher financial performance after

privatization than before privatization.

Hypothesis 8: Public hospitals that converted into for-profit status exhibit higher

financial performance compared to public hospitals that converted

into not-for-profit status.

Research Design

This study used a non-experimental longitudinal design. Longitudinal panel data

sets are the most appropriate data structure to clearly determine the effects of the

independent variables on the dependent variables and infer causality.

Data Sources

This study used longitudinal panel data sets from 1997 to 2009. Four data sets

from: (1) the American Hospital Association (AHA) Annual Survey of Hospitals, (2) the

Bureau of Health Profession‘s Area Resource File (ARF), (3) the Medicare Cost Report

55

(MCR) from the Centers for Medicare and Medicaid Services, and (4) the Local Area

Unemployment Statistics (LAUS) from the Bureau of Labor Statistics were merged and

analyzed. AHA, ARF and MCR data files have been used in prior empirical studies on

the impact of environmental and organizational factors on hospital strategic adaptation to

the environment such as mergers, closures, conversions into non-acute care services, and

ownership conversions (Alexander, D'Aunno, & Succi, 1996a; Alexander, et al., 1996b;

Mark, 1999; Sloan, et al., 2003).

The AHA data file contains the results of an annual nationwide survey of all

hospitals in the US. It has organizational information such as ownership status, type of

services the hospital provides (e.g. general medical and surgical services, or specialty

services), census division, and hospital size. It also contains information regarding

hospital affiliation with other organizations such as affiliation with a hospital system, a

medical school, or a healthcare organization network. In addition, the AHA data includes

information on healthcare utilization, clinical and non-clinical staff, and financial data.

The ARF data file contains demographic and economic information on counties

such as age, race, sex, unemployment rate, income level, and population density. It also

includes information on hospital services utilization, regional classification, rural versus

urban classification, the number of healthcare workforce, and the level of Medicare HMO

penetration.

The MCR data file contains the variables to be included in the hospital financial

analysis. The financial data in the MCR is the most validated and widely accepted data

for hospital financial analysis (Pink, et al., 2005; Schumann, 2008).

56

The LAUS data file contains the estimates of monthly and annual averages of

total employment, total unemployment, and unemployment rates at various geographical

levels including metropolitan areas, cities, census regions and divisions, as well as

counties.

Population and Sample

The population for this study was all publicly owned, non-federal, acute care,

general and surgical hospitals in the U.S. as of 1997, which is first year of the study

period. These hospitals were followed year after year up to 2009. Federal hospitals,

outpatient hospitals, specialty hospitals, critical access hospitals, and long-term care

facilities were not included in the study. Hospital-year was the unit of analysis.

To derive the analytic sample, several exclusion criteria were applied. First,

hospitals that converted to a skilled nursing facility (N= 6), an ambulatory care facility

(N=1), or a critical access hospital (N=520), during the study period were excluded from

the analytic sample. The Critical Access Hospital (CAH) designation was established by

the Balanced Budget Act of 1997. Critical Access Hospitals are small rural community

hospitals that have 15 or fewer acute care beds, with additional 10 swing beds, and

receive cost-based Medicare reimbursement (AHA, 2012). Since CAHs operate in a

different reimbursement environment, it was deemed appropriate to exclude them for this

study. Second, we excluded hospitals that were acquired or merged (N= 9) during the

study period. When a hospital is acquired by another hospital, the submission of its

financial report is under the responsibility of the parent organization (Bartrum, 2009).

Thus, the former hospital ceases submitting financial report after a merger. Third,

57

hospitals without complete finanical reports during certain years of the study period were

excluded from the analytic sample (N= 43). Finally, hospitals that experienced multiple

ownership conversions (N= 26) during the study period were excluded from the analytic

sample. Therefore, the final analytic sample consisted of 608 hospitals. Table 3.1

presents the number of hospitals per year that were included in the study.

Table 3.1

Number of Hospitals per Year in the Master Data File

Three copies of the master data file were constructed and customized to meet the

requirement for each research question, respectively. For research question 1, no change

was needed, thus the exact master data was used in the analysis (N=608). For research

question 2, data for hospitals that closed (N=20) as well as data for hospitals after

privatization were deleted from the master data file. Research question 2 investigated

whether financial distress precedes privatization, thus, data from hospitals that closed and

data after privatization were irrelevant.

Year Number of

Hospitals

1997 608

1998 605

1999 604

2000 599

2001 595

2002 590

2003 543

2004 537

2005 530

2006 527

2007 526

2008 526

2009 467

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Research question 3 required each hospital to have complete yearly financial

reports from 1997 until at least 2008. Therefore, 67 hospitals that converted but did not

submit their financial reports through 2008, and the 20 hospitals that closed during the

study period were deleted from the master data file.

Variables and their Operationalization

Table 3.4 summarizes the variables conceptual definitions, operational

definitions, references and data sources. The variables pertaining to each research

question are discussed in the following sections.

Question 1: What are the organizational and environmental factors

associated with financial distress of public hospitals?

Dependent variable.

Financial distress. The dependent variable for hypotheses 1-5 was a dichotomous

variable (1 = yes; 0= no) that indicates whether a hospital is in financial distress in a

given year. Financial distress is an indicator of the financial health of the hospital, and it

can be used to predict the likelihood of a hospital in meeting its debt obligations. The

Altman Z-score model was used to determine whether a public hospital was in financial

distress (Altman, 1968; Altman, 2000; Langabeer, 2006; Almwajeh, 2004). The Altman

Z-score model was especially designed to detect financial distress. It was developed by

Edward Altman as a response to the complaints from scholars regarding the ambiguous

conclusions drawn from simple ratio analyses in determining whether a firm is in

financial distress or not. Simply using individual financial ratios to interpret the financial

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situation of an organization can be misleading as individual financial ratios do not really

reflect the whole situation. For example, a firm might have a negative profit margin but

still may have an adequate liquidity ratio; hence, it is difficult to decide whether the firm

is in risk of being in financial distress or not (Altman, 1968). Conversely, lack of liquidity

is not reflected in the margins (Bazzoli & Andes, 1995; Kim, 2010; Langabeer, 2006).

Therefore, combining all the most relevant financial ratios into one discriminant function

enhances financial situation estimation and helps draw a more definitive conclusion as to

whether a firm is in financial distress.

The first Altman z-score model was developed based on the study of financial

statements, over twenty years, of 66 manufacturing corporations equally divided into two

groups: the group with bankrupt firms and the group without bankrupt firms. Altman

included twenty two financial ratios, mostly used in empirical studies and corporate

finance. The final discriminant function comprised of five financial ratios that measured:

net liquid assets, cumulative profitability, true productivity of the firm assets, market

value of the firm‘s assets, and the use of assets to generate sales. The discriminant

analysis assigned a specific weight for each of these measures and the Z-score is the sum

of the weighted financial ratios. The analysis provided cut-off scores that separate the

bankrupt group from the group with average financial performance and the group with

superior financial performance. The lower the Z-score, the higher the firm‘s potential of

being bankrupt (Altman, 1968; Altman, 2000). The final model was able to predict

financial distress within two years with 82% to 94% accuracy.

Altman designed other Z-score models for non-manufacturing firms. Altman Z-

score models have been applied by scholars to predict the probability of firm‘s

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bankruptcy and has been recognized as having a high predictive capability (Altman,

2000) and it was used to predict the financial distress of teaching hospitals (Langabeer,

2006) and rural hospitals in Western Pennsylvania (Almwajeh, 2004). While the main

purpose of the Altman Z-score is to predict financial distress, it has been found to be a

useful managerial tool to assess the financial situation of the organization. Therefore,

using the Altman Z-score to assess whether a hospital is in financial distress or not is

justified in this study (Almwajeh, 2004; Calandro, 2007).

For this study, the Altman Z-score model designed for service and retail firms

applied in Langabeer (2006) was used to estimate public hospitals financial condition.

The discriminant function is formulated as follows:

Z= 6.56 X1 + 3.26X2 + 6.72X3 + 1.05X4 where: 6.56, 3.26, 6.72 and 1.05 are the

respective weights of the financial ratios X1, X2, X3 and X4. The sum of the weighted

independent variables constitutes the Z-score.

X1 = Net Working Capital (Current Assets - Current Liabilities)/ Total Assets

X2 = Net Assets (Total Assets-Total Liabilities) /Total Assets

X3 = Excess Revenue over Expenses (Total Revenue-Total Expenses) /Total

Assets

X4 = Fund balance/ Total liabilities

X1 is defined as the ratio of net working capital to total assets. Net working capital

is measured as the difference between current assets and current liabilities. Thus, this

ratio represents the value of the hospital‘s liquid assets as a percentage of total assets.

Altman (1968, 2000) suggested that the ratio of net working capital to total assets proved

to be the most valuable liquidity ratio relative to current ratio and quick ratio.

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X2 was originally defined as the ratio of retained earnings to total assets. This

study used net assets instead of retained earnings given that public hospitals do not

accumulate earnings from prior years. Thus, in this study X2 is defined as the ratio of net

assets to total assets. It represents the value of the hospital‘s net worth as percentage of

total assets. It measures the extent to which one dollar worth of total asset is financed by

liability and net assets, respectively. High net assets to total assets ratio means that the

hospital has more assets than debts.

X3 was originally defined as the ratio of earnings before income taxes to total

assets; however, public hospitals do not pay taxes. Therefore, excess revenue over

expenses was used in place of earnings before income taxes. In this study, X3 is defined

as the ratio of excess revenue over expenses to total assets. This is a profitability ratio

that measures how much profit a hospital can make out of one dollar worth of total assets.

X4 was originally defined as the ratio of book value of equity to the book value of

debt. This ratio represents the capital structure of the hospital; it is the extent to which the

assets are financed by debt and equity, respectively. Public hospitals do not have equity,

but the book value of equity can be derived by subtracting total liabilities from total

assets, which is equal to fund balance. In this study, X4 is defined as the ratio of total fund

balance to total liabilities.

The Altman Z-score for each individual hospital was derived based on the

discriminant function described above. Langabeer (2006) divided the Altman Z-score

into three categories: the hospital is in finanical distress if its Z-score is less than 1.1, the

hospital is in neither in finanical distress nor in good financial condition if its Z-score is

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greater than 1.1 but less than 2.6, and the hospital is in good finanical condition its Z-

score is greater than 2.6. For this study, the Altman Z-score was divided into two

categories. The hospital is in financial distress if its Z-score is less than 1.1. The hospital

is not in financial distress if its Z-score is greater than 1.1. Since the purpose of research

question 1 was to determine whether the hospital was in financial distress or not, it was

deemed appropriate to divide the hospitals in two groups: those in financial distress and

those not in financial distress.

Independent variables. Similar to prior studies on the environment, county-based

data were used for the environmental level variables: environmental munificence,

environmental dynamism, and environmental complexity.

Environmental munificence. Environmental munificence (hypothesis 1) refers to

the amount of resources available in the environment that sustains hospital‘s operations

and growth. In this study, the environmental munificence construct was operationalized

with four variables: county per capita income, county unemployment rate, percentage of

people who are 65 years old or older in the county, and number of active physicians per

1000 persons in the county.

County per capita income reflects the availability of resources that enables the

population to consume hospital services. When the county has higher per capita income,

its population can better afford paying for healthcare services; therefore, the demand for

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healthcare services is higher and reimbursement is greater (Mark, 1999; Sloan, 2003). In

other words, a county with high per capita income is a wealthy county. Consequently, it

has more abundant resources and capable of supporting the mission of the public hospital,

such as the provision of indigent care (Alexander, D‘Aunno & Succi, 1996b; Ginn &

Young, 1992; Lee & Alexander, 1999).

Likewise, the county unemployment rate has an effect on whether the majority of

the population in the county is able to pay for healthcare services. A higher

unemployment rate means that more people do not have health insurance; therefore they

cannot pay for healthcare services. Thus, a county with a high unemployment rate has

lower demand for healthcare services, and it is not as munificent as a county with a lower

unemployment rate (Sloan, 2003; Gregory & Young, 1992). From a public hospital

perspective, the hospital may have to serve a larger patient population which can translate

into higher healthcare costs. Therefore, the county with a higher unemployment rate is

less munificent relative to a county with a lower unemployment rate.

Similarly, the percentage of elderly people (65 years of age and older) in the

county shows whether the county is a munificent environment or not. If the county has a

higher percentage of elderly people, it needs more healthcare services as elderly people

are in general sicker than younger people. Higher demand for healthcare services from

the elderly generally means more resources for public hospitals, in terms of patient

revenue, since the elderly are generally covered by Medicare (Sloan, 2003).

Number of active physicians per 1000 population also reflects the availability of

resources that hospitals can use. Physicians are key resources for hospitals; hospitals

cannot deliver healthcare services if they do not have an adequate supply of physicians

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(Alexander, D‘Aunno & Succi, 1996b). If the county has a larger supply of physicians, it

means that the county is a more munificent environment. In this case, public hospitals

have greater bargaining power over physicians, so they can negotiate lower compensation

for employed physicians (Ginn & Young, 1992). Number of active physicians per 1000

population was measured as the number of nonfederal active physicians per 1,000 county

population.

Environmental dynamism. Environmental dynamism (hypothesis 2) refers to the

degree of instability of the environment due to the frequency and unpredictability of

changes. An unstable environment results in uncertain availability of resources; making it

more difficult for hospitals to predict whether there will be enough resources to support

their operations. As stated previously, the unemployment rate reflects the availability of

resources and its fluctuation reflects the fluctuation of resources in the environment.

Environmental dynamism was measured as the yearly change in unemployment rate at

the county level (Menachemi, Mazurenko, Kazley, Diana, & Ford, 2012; Menachemi,

Shin, Ford, & Yu, 2011).

Environmental complexity. Environmental complexity (hypothesis 3) is the extent

to which the environment is homogenous or heterogeneous. In this study, environmental

complexity is operationalized with three variables: market concentration, excess capacity,

and Medicare HMO penetration.

Prior studies have argued that a more competitive environment is more complex

than an environment with high industry concentration. An environment with a higher

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level of competition comprises more organizations competing for the same resources.

Therefore, operating in a more competitive environment is more complex than operating

in an environment with fewer competitors.

Competition refers to the extent to which the market is concentrated; it is whether

a few hospitals have the major market share in the county, or all the hospitals have

relatively the same market share. If only a few hospitals own the largest market share in

the county, the market is highly concentrated. High market concentration means less

competition, and less concentration means more competition. The Hirschman-Herfindahl

Index (HHI) has been used to measure market concentration across different healthcare

organizations (Bowblis, 2010; Lee & Alexander, 1999; Mark, 1999; Sloan, et al., 2003;

Starkey, et al., 2005; Weech-Maldonado, et al., 2009, 2012; Kim, 2010). A higher HHI

implies greater market concentration. Market share was measured in terms of acute-care

patient days. HHI was defined as the sum of squared market shares (acute-care patient

days for individual hospital/total acute-care patient days of all the hospitals in the county)

(Kim, 2010). Given its negatively skewed distribution, HHI was dichotomized as 1 if

equal to or below the mean (greater competition) and 0 if greater than the mean (less

competition).

Excess capacity has also been used to measure the degree of competition.

Competition for patients is stronger when there is excess capacity; hospitals compete for

patients to fill the extra empty beds (Weech-Maldonado, et al., 2009, 2012). Excess

capacity was measured was the average number of unoccupied beds per hospital in the

county.

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Medicare HMO penetration can increase the level of environmental complexity

because the higher the number of HMO enrollees, the higher the number of HMO

contracts that the hospital must deal with. As stated previously, the existence of large

number of entities that the organization needs to interact with makes the environment

more complex. In addition, the greater the HMO penetration, the greater the leverage of

managed care organizations in negotiating hospital prices, which can result in lower

Medicare reimbursement compared to fee-for-service Medicare (Shen, Wu, & Melnick

2010). Medicare HMO penetration was measured as the percentage of Medicare HMO

enrollees relative to the total number of Medicare eligibles in the county. This study

included two organizational independent variables: hospital size and teaching status.

Hospital size. Hospital size (hypothesis 4), measured as the total number of beds

in the hospital, has been the most widely used measure of hospital size (Anderson,

Allred, & Sloan, 2003; Sloan, Ostermann, & Conover, 2003). While the number of

licensed beds has been used in prior studies, the number of licensed beds was not

available from the AHA data set prior to 2003. Therefore, the number of licensed beds

could not be used in this study.

Teaching status. Teaching status (hypothesis 5) was coded ―1‖ if the hospital did

not have a teaching status and ―0‖ if it had a teaching status. Hospitals can engage in

medical education in three different, but not mutually exclusive, ways: 1) membership of

the Council of Teaching Hospitals and Health Systems (COTH); 2) affiliation to a

medical school; and 3) provision of residency programs. The hospital was defined as

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having teaching status if it met one or more of the above criteria (Alexander & Morrisey,

1989; Bazzoli & Andes, 1995; Bazzoli, Chan, Shortell, & D'Aunno, 2000).

Control variables. The following variables were used as control variables:

Outpatient mix, occupancy rate, and payer mix. Since hospitals differ in the number of

outpatient visits, occupancy rate, payer mix, and geographical location, and since these

variables may affect hospital financial performance, it was essential to include them as

control variables.

Outpatient mix. Hospitals offer both inpatient and outpatient services. Donnelly

(2011) suggested that since reimbursement of inpatient services is highly controlled by

payers relative to those of outpatient services, inpatient services are less profitable than

outpatient services (Donnelly, 2011). Therefore, we expect a hospital with higher

outpatient service mix to be less likely in finanical distress.

To determine the outpatient mix, it was necessary to convert outpatient visits to

its inpatient days equivalent. Research has suggested that outpatient services are less

resource intensive relative to inpatient services; resources consumed to provide one

outpatient service are equivalent to one third of the resources consumed to provide a

service for one inpatient day (Detsky, O'Rourke, Naylor, Stacey, & Kitchens, 1990;

Insight, 2002; Vujicic, Addai, & Bosomprah, 2009). Thus, to convert outpatient visits

into inpatient days equivalent, total outpatient visits was divided by three and total

equivalent inpatient days equaled the sum of (total outpatient visits /3) and total inpatient

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days. Therefore, outpatient mix was operationalized as Outpatient mix = (total outpatient

visits/3)/total equivalent inpatient days.

Occupancy rate. Occupancy rate reflects the level of inpatient services utilization

and the efficiency of the hospital. Occupancy rate affects financial performance because

empty beds do not generate revenue. Trussel and Patrick (2010) suggested that lower

occupancy rate was associated with financial distress among rural hospitals in

Pennsylvania. Occupancy rate was measured at the hospital level as the ratio of total

inpatient days to the total number of beds *365 (Trussel & Patrick, 2010).

Payer mix. Payer mix reflects the proportions of Medicare and Medicaid patients.

For public hospitals, payments from Medicare and Medicaid can represent an important

source of revenue. For example, public hospitals members of the National Association of

Public Hospitals generate 21% of their revenues from Medicare and 35% from Medicaid

(NAPH, 2011). Therefore, having a higher proportion of Medicare and Medicaid

payments can contribute to public hospital survival, especially if the hospital serves a

larger proportion of uninsured patients. Medicare mix was measured as total Medicare

inpatient days divided by total inpatient days, and Medicaid mix as total Medicaid

inpatient days divided by total inpatient days. The reference group represents those

patients that were neither covered by Medicare nor Medicaid. Year and hospital

identification number are needed for longitudinal panel data sets. Year estimates the

effect of time in term of the annual overall trend of the dependent variable (Pradhan,

69

2010; Weech-Maldonado, Qaseem & Mkanta, 2009; Wooldridge, 2006). Year dummy

variables were created using year 2009 as the reference group.

Hospital ID was needed to track each hospital over time. The hospital‘s Medicare

Provider Number was used as the hospital identification number (Pradhan, 2010; Weech-

Maldonado, Qaseem & Mkanta, 2009; Wooldridge, 2006).

Question 2. Is financial distress associated with public hospitals

privatization?

Dependent variable. Ownership conversion is the dependent variable for

hypothesis 6. According to previous studies, ownership conversion is one of the

consequences of hospital financial distress. Other consequences include hospital closure,

diversification into services totally unrelated to previous ones, or bankruptcy filing,

among others (Alexander, D'Aunno, & Succi, 1996a, 1996b; Bazzoli & Andes, 1995;

Landry & Landry III, 2009; Sloan, Ostermann, & Conover, 2003). For the scope of this

study, the dependent variable ―ownership conversion‖ was a dichotomous variable coded

as ―1‖ if the hospital privatized and ―0‖ if the hospital did not privatize.

Independent variable. Financial distress measured by the Altman Z-score

(Langabeer, 2006) described as the dependent variable in question 1 became the

independent variable for research question 2. This variable was lagged one year and two

years before privatization because privatization does not occur immediately after the

hospital recognizes its financial distress.

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Control variables. Control variables were also lagged by one year and two years.

The environmental and organizational variables used to determine the factors associated

with financial distress in question 1 were used as control variables for question 2. The

dotted arrows depicted in Figure 1 represented the associations between the control

variables and the dependent variable. Thus, environmental factors (munificence,

dynamism, complexity) and organizational factors (hospital size, teaching status) were

used as control variables in predicting public hospital privatization.

From the resource dependence perspective, lack of resources, fluctuation of

resources and difficulty in acquiring resources can serve as incentives for a public

hospital to privatize since privatization offers more flexibility in resources acquisition

such as better payer mix and stronger capital structure. Therefore, it is expected that a

public hospital located in a more munificent environment is less likely to privatize; a

public hospital located in a more dynamic environment is more likely to privatize; and a

public hospital located in a more competitive environment is more likely to privatize.

Larger organizations are slower to initiate change than smaller organizations.

Hannan and Freeman (1984) argued that the larger the size of the organization the

stronger the structural inertia that prevents it from initiating change. For large public

hospitals, factors that generate structural inertia include larger infrastructure and large

number of stakeholders. A large number of stakeholders indicate that it is more difficult

to convince the stakeholders to initiate a radical change such as privatization as they may

have divergent interests. In addition, a larger public hospital serves a larger community,

with a strong voice, that might oppose privatization for fear of loss of community

benefits (Legnini et al., 1999). Therefore, it is expected that larger public hospital are less

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likely to privatize than smaller public hospitals. The same argument applies to teaching

status. Teaching hospitals are usually larger than non-teaching hospitals; therefore, they

are less likely to privatize compared to non-teaching hospitals. In addition, outpatient

mix, occupancy rate, payer mix, and states were used as control variables. Since a higher

outpatient mix and a higher occupancy rate are expected to enhance public hospital

finanical performance, therefore, a public hospital with high outpatient mix and high

occupancy rate is less likely to privatize. As discussed in the previous section, Medicare

and Medicaid account for almost 56 % of public hospitals revenue (NAPH, 2011),

therefore, a hospital with higher Medicare and Medicaid enrollees is less likely to

privatize. The operationalization of outpatient mix, occupancy rate and payer mix was

described under research question 1.

Variables ―states‖ and ―metropolitan vs. non-metropolitan location‖ were also

used as control variables. States refer to the geographical state where the hospital is

located. States have different regulations. For example, Alabama, Massachusetts and

Ohio have CON laws; other states such as California, Colorado and Indiana do not have

CON laws. In some states CON laws oversee ownership conversion, for example,

Connecticut and Washington State (National Conference of State Legislatures, 2012).

The non-existence of CON law in Texas from 1985 might explain the higher number of

conversions in that state compared to other states (Needleman, Chollet & Lamphere,

1997). Therefore, it was necessary to control for states for research question 2. States

were measured as a set of dummy variables representing the 50 states of the United States

using Fips State Codes.

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The location of the hospital in a metropolitan county or in a non-metropolitan

county can have an impact on privatization. Operating in a metropolitan county requires

more resources because of the need to interact with a greater number of organizations

compared to operating in a non-metropolitan county (Alexander, D'Aunno, & Succi,

1996a, 1996b; Dansky, Milliron, & Gamm, 1996). For example, a hospital located in a

metropolitan area interacts with a larger number of third party payers, especially private

payers given the larger number of businesses and employees in a metropolitan area

relative to a non-metropolitan area. Furthermore, a metropolitan hospital not only

interacts a with various types of healthcare organizations, but also faces a larger number

of competitors compared to a non-metropolitan hospital, which contributes to its

difficulty in making sound finanical performance and stay competitive (Alexander,

D‘Aunno, & Succi, 1996a, 1996b). Therefore, a metropolitan hospital is more likely to

privatize compared to a non-metropolitan hospital.

Variable ―metropolitan vs. non-metropolitan location‖ was determined according

to the Rural-Urban Continuum Codes of 2003 (RUCC) published by the Economic

Research Service of the United States Department of Agriculture (USDA & ERS, 2004).

The RUCC of 2003 are the most recent codes and they are based on the 2000 census. The

RUCC classify each county in the U.S. into metropolitan (metro) counties and

nonmetropolitan (nonmetro) counties. Metro counties are determined based on the

population size of their respective metropolitan areas and nonmetro counties are

determined based on their degree of urbanization and adjacency to a metropolitan area or

areas.

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Metro counties are counties located in metropolitan areas with a population size

ranging from fewer than 250,000 to more than 1 million. Nonmetro counties are those

with an urban population ranging from less than 2,500 to more than 20,000 and the

county is either adjacent or not adjacent to a metropolitan area. Counties that RUCC

coded as 1, 2, 3 are metro counties and counties coded as 4, 5, 6, 7, 8, 9 are non-metro

counties (USDA & ERS, 2004). Following RUCC, metropolitan vs. non-metropolitan

location was a dichotomous variable indicating whether the hospital is located in a

metropolitan county or not. Metropolitan location was coded as ―1‖, and non-

metropolitan location was coded as ―0‖.

Finally, three interorganizational variables (membership of a multihospital

system, operation under contract management, and participation in a healthcare network)

were used as control variables. Hospitals that are engaged in these types of

interorganizational relationship can have difficulty initiating ownership change because

doing so might lead to the loss of privileges they enjoy as a member of these associations.

For example, a hospital under contract management is less likely to privatize given that

the management of the hospital is under the responsibility of the contractor, making it

difficult for the hospital to initiate such change. In addition, from the resource

dependence theory, hospitals engage in these types of interorganizational relationships in

order to obtain easier access to resources. Therefore, we expect public hospitals members

of a multihospital system, under contract management or part of a healthcare network to

be less likely to privatize.

Membership of a multihospital system, operating under contract management, and

participation in a healthcare network were measured as dichotomous variables coded as

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1‖ if the hospital engaged in such relationship and ―0‖ if the hospital did not. Year and

hospital ID were also included in the analysis.

Question 3. Does privatization lead to a better financial performance?

Dependent variable. The dependent variable for hypotheses 7 and 8 is financial

performance, which is measured as the Operating Margin and Total Margin. These two

measures reflect the overall financial performance and profitability of the hospital.

Operating margin measures the percentage of income a hospital exclusively generates

from providing health care services relative to the expenses incurred from providing such

services. Operating Margin equals Total Operating Income divided by Total Operating

Revenue. Total Margin 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. Total margin indicates the hospital‘s ability to control overall

expenses. The lower the overall expenses the higher the net income and total margin.

Total margin equals Net Income divided by Total Revenue (Gapenski, 2004).

The financial variables from the MCR needed to be recalculated because several

hospitals had either multiple financial reports for a certain fiscal year, or the financial

report for a fiscal year covered less than 12 months. Each row in the data set was checked

to assess whether the condition of 12 months of reporting for one fiscal year was met.

When that condition was not met, the following criteria were used to adjust the reports to

12 months.

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When there was more than one report in one fiscal year (X1 and X2 ) and these

reports covered more than 12 months, the adjustment was computed as follows:

Report = (X1 +X2) / (total number of months) * 12

When there were two reports (X1 and X2) for one fiscal year and they added up

to 12 months, the adjustment was computed as follows: Report = X1 + X2

When there were two reports (X1 and X2) for one fiscal year and one of these

reports covered 12 months, then that report was kept and the other report was

deleted from the data set.

When there was one report for one fiscal year and it did not cover 12 months or

covered more than 12 months, then the report was adjusted as follows:

Report = (X / total number of months) * 12.

Independent variable. Ownership conversion became the independent variable.

To test hypothesis 7, whether privatization leads to a better financial performance, the

independent variable ―ownership conversion‖ was a dichotomous variable coded as ‗1‖ if

the hospital privatized (the year of privatization and subsequent years were coded as 1)

and ―0‖ if the hospital stayed public. In addition, for public hospitals that privatized, the

years before conversion were coded as ―0‘ (Pradhan, 2010). To test hypothesis 8, whether

the hospitals that converted into private for-profit status had a higher financial

performance compared to those that converted into private not-for-profit status, two

dummy variables were created: one dummy variable for conversion to private not-for-

profit status, and another one for conversion to private for-profit status. The hospitals that

did not convert were used as the reference group.

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Control variables. The environmental, organizational, and control variables used

in question 1 were used as control variables for question 3: environmental factors

(munificence, dynamism, complexity), organizational factors (hospital size and teaching

status), and outpatient mix, payer mix and occupancy rate. These variables were used in

research question 1 to predict public hospital financial distress. Since research question 3

predicts financial performance, it is deemed important to control for these variables given

their association with financial condition. A hospital located in a less munificent, more

dynamic and more complex environment is expected to have poorer financial

performance compared to a hospital located in a more munificent, less dynamic and less

complex environment. Larger hospitals have competitive advantage given their ability to

generate economies of scale and their bargaining power over suppliers and buyers.

Therefore, larger hospitals are expected to have higher financial performance compared

to smaller hospitals. Likewise, since teaching hospitals are usually larger than non-

teaching hospitals, they are expected to have higher financial performance than non-

teaching hospitals. In addition, their reputation of delivering higher health care quality

allows them to charge higher price than non-teaching hospitals. Teaching hospitals are

also expected to be in better financial condition that non-teaching hospitals due to their

large endowment funds.

Higher occupancy rate is expected to be positively associated with financial

performance as it generates patient revenue. Therefore, a hospital with a higher

occupancy rate is expected to have higher financial performance than a hospital with a

lower occupancy rate. The same expectation applies to a hospital with a higher outpatient

mix. As discussed previously, hospitals have more freedom in setting the price of

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outpatient service compared to the price of inpatient services which are highly controlled

by payers. Therefore, a higher proportion of outpatient services relative to inpatient

services is expected to generate higher revenue. The measurement of these variables was

defined in the previous sections.

Similar to questions 1 and 2, Year and Hospital ID needed for longitudinal panel

data were also included in the analysis.

Analysis

Fixed-effects (FE) and random effects (RE) are the most widely used models to

study panel data. FE model is used when there is an assumption that there are

unobservable characteristics that do not change over time but these characteristics are

thought to be correlated with the independent variables. Therefore, FE model is used to

control for all these time-invariant unobserved factors. Failing to do so leads to biased

results due to omitted variables (Allison, 2005; Wooldridge, 2006).

On the other hand, the RE model is used when we think that there are some

unobservable characteristics of each observation and we assume that these characteristics

are uncorrelated with the independent variables. Therefore, they needed to be included in

the analysis (Allison, 2005; Wooldridge, 2006). In this study, both fixed effects and

random effects regressions were used, and the type of regression depends on the nature of

the dependent variable.

Research question 1 had a dichotomous dependent variable: financial distress

coded as ―1‖ if the hospital as in financial distress and ―0‖ otherwise. Therefore, fixed-

effects logistic regression was used to analyze the models for question 1. Research

78

question 2 had a dichotomous dependent variable: ownership conversion coded as ―1‖ if

the hospital privatized and ―0‖ otherwise. The independent variables were lagged by one

year and two years given that hospitals may engage in privatization after they experience

financial distress during a certain number of years. Two models were used, the first

model with independent variables lagged by one year and the second model with

independent variables lagged by two years. Fixed-effects logistic regression models were

attempted but the models did not converge properly; the lack of within-group variation

did not allow the completion of the analysis with fixed-effects model. Therefore, random-

effects logistic regression models with state fixed-effects were used in the analysis.

For research question 3, the dependent variables: operating margin and total

margin were both continuous variables; linear fixed-effects regression models were the

appropriate method. Normal distribution assumption with respect to the dependent

variable should not be violated in linear regression. To ensure normal distribution of

operating margin and total margin, skewness needed to be equal or close to zero and

kurtosis equal or close to three (Weech-Maldonado et al., 2012). The deletion of

observations with five most extreme values for each dependent variable was firstly

conducted. Then, the deletions of extreme values that were five standard deviations above

and below the mean were performed until operating margin and total margin met the

conditions for skewness and kurtosis (Weech-Maldonado, et al., 2012). Table 3.2 and

table 3.3 summarize the processes used to make operating margin total margin normally

distributed.

79

Table 3.2

Normalization of Operating Margin

Univariate

Analysis

N Mean Standard

Deviation

Extreme values Skewness Kurtosis Kolmogorov

Smirnoff

P-value Lowest Highest

1* 6658 -0.107 0.387 < -5.662 > 0.629 -9.943 185.041 <0.01

2** 6649 -0.102 0.325 < -3.538 > 0.586 -5.424 44.384 <0.01

3 6587 -0.081 0.226 < -1.677 > 0.586 -3.060 13.637 <0.01

4 6539 -0.070 0.193 < -1.200 > 0.586 -2.374 8.943 <0.01

5 6507 -0.065 0.179 < -1.011 > 0.586 -2.060 7.378 <0.01

6 6486 -0.062 0.171 < -0.945 > 0.586 -1.898 6.769 <0.01

7 6472 -0.060 0.166 < -0.912 > 0.586 -1.789 6.375 <0.01

8 6458 -0.059 0.162 < -0.864 > 0.586 -1.673 5.954 <0.01

9 6454 -0.058 0.161 < -0.851 > 0.586 -1.640 5.838 <0.01

*After the first univariate analysis, the five highest and lowest values were deleted

**After the second univariate analysis, observations that were five standard deviation above and below the mean were

delete and the same process was performed until skewness close to zero and kurtosis close to three were attained

Table 3.3

Normalization of Total Margin

Univariate

Analysis

N Mean Standard

Deviation

Extreme values Skewness Kurtosis Kolmogorov

Smirnoff

P-value Lowest Highest

1* 6659 -0.026 0.196 < -1.480 > 0.638 -33.129 1647.300 <0.01

2** 6647 -0.031 0.096 < -0.798 > 0.636 -0.538 18.847 <0.01

3 6596 -0.031 0.078 < -0.411 > 0.443 -0.109 4.710 <0.01

4 6579 -0.031 0.075 < -0.306 > 0.380 -0.110 3.259 <0.01

*After the first univariate analysis, the five highest and lowest values were deleted

**After the second univariate analysis, observations that were five standard deviation above and below the mean were delete and the same process was performed until skewness close to zero and kurtosis close to three were attained

Statistical packages Statistical Analysis System (SAS) versions 9.2 and 9.3,

International Business Machines Statistical Package for Social Sciences (IBM SPSS)

80

version 19 and Microsoft Excel 2007 were used in the data cleaning phase and SAS and

STATA version 11 were used in the descriptive statistics and regression analyses.

Potentially endogenous variables. Special attention was given to three

interorganizational variables: membership of a multihospital system, operation under

contract management and participation in a healthcare network. They were considered

potentially endogenous to financial distress measured by the Altman Z-score and to

financial performance measured by total margin and operating margin. Hospitals might

initiate these types of interorganizational partnerships because they already face financial

hardship. These kinds of partnerships could be the strategic moves that hospitals initiate

to solve their financial problems. Therefore, these variables were included in the analysis

of research questions 1 and 3 following the methodology applied by Morrisey,

Menachemi, Cawley and Ginter (2010). To control for potentially endogenous variables,

three models were constructed. The first model included the independent variables. In the

second model, the potentially endogenous variables were added to the regression

equation and the third model contained all the independent, endogenous and control

variables. The next section summarizes the models used in this study.

Models

Research question 1 used three models: Fixed-effects logistic regressions

Model 1. Financial distress = f (environmental factors, organizational factors)

Model 2. Financial distress = f (environmental factors, organizational factors, potentially

endogenous variables)

Model 3. Financial distress = f (environmental factors, organizational factors, potentially

endogenous variables, control variables)

81

Research question 2 used two models of random-effects logistic regressions with state

fixed-effects.

Ownership conversion = f (financial distress, control variables lagged by one year)

Ownership conversion = f (financial distress, control variables lagged by two years)

Research question 3 used three models for each dependent variable Operating Margin and

Total Margin: linear fixed-effects regressions.

Models for operating margin.

Model 1. Operating Margin = f (conversion)

Model 2. Operating Margin = f (conversion, potentially endogenous variables)

Model 3. Operating Margin = f (conversion, potentially endogenous variables, control

variables)

Models for total margin.

Model 1. Total Margin = f (conversion)

Model 2. Total Margin = f (conversion, potentially endogenous variables)

Model 3. Total Margin = f (conversion, potentially endogenous variables, control

variables)

For research question 2, environmental and organizational factors and the three

interorganizational variables (multihospital system membership, contract management

and health network participation) were included as control variables.

For research questions 1 and 3, environmental and organizational factors were included

as control variables.

Table 3.4 summarizes the variables definition and their operationalization.

82

Table 3.4

Summary of Variables Conceptual Definitions, Operational Definitions, References and Data Sources.

Used as Dependent Variables and Independent Variables

Variable Conceptual Definition Operational definition

Reference Data

Source

Financial

distress/perform

ance

Hospital financial

condition Altman Z-score= 6.56 X1 + 3.26X2 + 6.72X3 + 1.05X4

X1 = Net Working Capital / Total Assets

X2 = Net Assets /Total Assets

X3 = Excess revenue over expenses/ Total Assets

X4 = Fund Balance/Total liabilities

Altman Z-score is dichotomous

Coded 1 if in financial distress < 1.1

Coded 0 if not in financial distress > 1.1

Langabeer, 2006

MCR*

Ownership

conversion

Change of ownership

status resulting from

interorganizational

transaction such as hospital

sale, merger, lease, or joint

venture

Dichotomous variable for research question 2 and question 3 (first

hypothesis)

coded 1 if the hospital privatized

coded 0 if the hospital did not privatize

Two dummy variables for research question 3 (second hypothesis)

Public to NP: coded 1 if the hospital privatized

coded 0 if the hospital did not privatize

Public to FP: coded 1 if the hospital privatized

coded 0 if the hospital did not privatize

Burns, Shah, Frank

& Powell, 2009

Sloan, Osterman &

Conover, 2003

Needleman&

Chollet, 1997

AHA **

Used as Dependent Variable

Financial

performance

Hospital financial situation Operating margin =( net patient revenue – total operating expense)

/net patient revenue

Total margin = net income/total revenue

Pink et al. 2005

Pink et al. 2005

MCR

83

Used as Independent Variables and Control Variables- Environmental Factors

Variable Definition Reference Data

Source

Munificence

Resources available in the

environment

County per capita income

County unemployment rate

Percent of population 65 years of age and older

Number of active physicians per 1,000 population

Lee & Alexander,

1999

Mark, 1999

Sloan et al., 2003

Weech-Maldonado,

Qaseem & Mkanta,

2009

Alexander,

D‘Aunno & Succi,

1996b

Ginn & Young,

1992

ARF †

LAUS‡

Complexity

Degree of competition and

market concentration in the

county

Market concentration =Herfindahl Index=∑squared market share of all

the hospitals in the county.

Market share for each hospital is measured in term of total acute-care

patient days for individual hospitals /the total acute-care patient days in

the county

Dichotomous = 1 if =< mean, mean =0.846

= 0 if > mean

Excess capacity=total number of unoccupied beds in the county/total

number of hospitals in the county

Medicare HMO penetration= percentage of Medicare HMO enrollees in

the county population relative to the total number of Medicare eligibles

Kim, 2010

Weech-Maldonado,

Qaseem & Mkanta,

2009

Bowblis, 2010

Weech-Maldonado,

Qaseem & Mkanta,

2009

AHA - ARF

ARF

ARF

Dynamism The extent to which there

are changes in the

healthcare environment

and changes are

unpredictable

Yearly change in county unemployment rate

Menachemi et al.,

2011, 2012

LAUS

84

Used as Independent Variables and Control Variables- Organizational Factors

Variable Definition Measure Reference Data

source

Hospital size Bed size Total number of beds in the hospital Anderson, Allred,

& Sloan, 2003

Sloan, Ostermann,

& Conover, 2003

AHA

Teaching status The extent to which the

hospital is COTH member,

affiliated to a medical

school or offers residency

program

Dichotomous

Coded 1 if no teaching activities

Coded 0 if with teaching activities

Bazzoli & Andes,

1995

AHA

Control Variables

Variable Definition Measure Reference Data

source

Occupancy rate Extent to which the

hospital beds are used by

patients

Occupancy rate =total inpatient days at the hospital level /( # beds *365) Trussel & Patrick,

2010

MCR

Outpatient mix Proportion of outpatients

relative to inpatients

Total outpatient visits (equivalent inpatient days)

= total outpatient visits/3

Total equivalent inpatient days = (total outpatient patient visits / 3 ) +

total inpatient days

Outpatient mix = (total outpatient visit /3) / Total equivalent inpatient

days

Detsky et al., 1990

Insight, 2000

Vujicic, Addai &

Bosomprah, 2009

AHA

Payer mix Proportion of Medicaid

and Medicare enrollees Proportion of Medicare and Medicaid patients

Continuous variables

Medicaid mix = Medicaid inpatient days/total inpatient days

Medicare mix = Medicare inpatient days / total inpatient days

and private payers mix

Kazley and Ozcan

(2007)

AHA

Metropolitan vs.

non-

metropolitan

location

Whether the hospital

operates in a metropolitan

or nonmetropolitan area

Rural-Urban Continuum Codes, 2003.

Dichotomous: metro = 1; nonmetro = 0

Metro counties: codes 1,2,3 = 1

Nonmetro counties: codes 4,5,6,7,8,9 = 0

Dansky, Milliron &

Gamm, 1996

Kazley & Ozcan,

2007

ARF

85

Potentially Endogenous Variables

Variable Definition Measure Reference Data

source

Multihospital

system

membership

Whether the hospital is a

member of a multihospital

system

Dichotomous variable

Yes=1

No = 0

Hsieh, Clement,

Bazzoli, 2010

Zinn, Proenca &

Rosko, 1997

AHA

Participation in a

healthcare

network

Whether the hospital is a

participant in a healthcare

network

Dichotomous variable

Yes=1

No = 0

Bazzoli et al.

(2000)

AHA

Operating under

contract

management

Whether the hospital is a

under contract

management or not

Dichotomous variable

Yes=1

No = 0

Alexander and

Morrisey (1989)

AHA

Other information needed for the analysis

Hospital ID Unique identifier for each

hospital Medicare Provider Number AHA

MCR

Year

The impact of time on the

hospitals‘ situation

AHA

MCR

LAUS

ARF

FIPS State Code ARF AHA

FIPS County

Code

ARF AHA

Combined FIPS

state and FIPS

county code

ARF

AHA

LAUS * MCR means Medicare Cost Report **AHA means American Hospital Association

† ARF means Area Resource File ‡LAUS means Local Area Unemployment Statistics

86

CHAPTER 4

RESULTS

This chapter discusses the results and findings from the analyses that tested the

hypotheses pertaining to the three research questions this study attempted to answer. The

following analyses were performed for each research question: univariate analyses

(means and standard deviation and frequency tables), bivariate analyses (independent

samples t-tests and chi-squared tests), and regression analyses. For all the analyses,

hospital-year was the unit of analysis and sample size (N) accounts for repeated

observations.

Results from Research Question 1

Research question 1 investigated whether environmental factors and

organizational factors were associated with public hospital financial distress.

Table 4.1 presents the descriptive analysis of the variables included in the regression

analyses. Continuous variables are listed with their respective number of observations,

mean, standard deviation, and the lowest and highest measure of each variable.

Categorical variables are listed with their respective number of observations and coding.

87

Table 4.1

Descriptive Analysis of all Variables a

N Mean

(%)

Std. Dev. Minimum Maximum

Dependent variable – financial distress

Not in financial distress 6,316 (87.03)

In financial distress 941 (12.97)

Independent variables

Per capita income 7,249 26,541.19 8,883.36 0 132,728.00

Unemployment rate 7,238 5.74 2.64 1.10 28.20

Percentage of population>= 65 7,239 0.14 0.04 0.05 0.45

Physicians per 1000 population 7,239 1.86 1.98 0 35.00

Excess capacity 7,239 55.87 36.70 1.00 291.00

HHI

HHI > mean 5,199 (71.64)

HHI < =mean 2,058 (28.36)

Medicare HMO penetration 7,249 9.41 13.59 0 55.00

Change in unemployment rate 7,232 0.06 0.24 -0.68 2.07

Hospital beds 7,256 177.00 197.00 0 2,186.00

Teaching status

Teaching 1,594 (21.97)

No teaching 5,660 (78.03)

Control variables

Occupancy rate 7,155 0.56 0.19 0.02 1.00

Medicare mix 7,256 0.45 0.20 0 0.98

Medicaid mix 7,256 0.23 0.18 0 0.99

Outpatient mix 7,256 0.43 0.25 0 0.99

Potential endogenous variables

System membership

Non member 4,953 (68.25)

Member 2,304 (31.75)

Contract Management

No contract 6,138 (84.58)

Contract 1,119 (15.42)

Health network

No network 5,465 (75.31)

Network 1,792 (24.69) a Hospital-year is the unit of analysis, N accounts for repeated observations

88

Table 4.2 presents the results from the independent samples t-test on the

dependent dichotomous variable ―financial distress‖ and the continuous independent

variables. The number of observations of independent variables from the hospitals not in

financial distress varied between 6,297 and 6,315. The number of observations for the

hospitals in financial distress varied between 910 and 925. The null hypothesis of

independent samples t-test states that two independent samples come from populations

with the same mean with respect to the independent variables such as per capita income,

percentage of population of 65 years of age and over and number of active physicians per

1000 population (Norusis, 2006).

Two independent samples t-test can be performed based on the assumption that

the two samples have the same variances with regards to the independent variables. The

Folded F-test is needed to test the assumption of equality of variance. The Satterthwaite t-

test is the appropriate test if the assumption of equality of variance is violated; the Pooled

t-test is used otherwise (UCLA, 2012). The Satterthwaite t-test was significant at p <

0.05 for per capita income, number of physicians per 1000 population, Medicare HMO

penetration, number of hospital beds, occupancy rate, Medicare mix, Medicaid mix and

private mix indicating these variables do not have the same mean for the hospitals not in

financial distress and those in financial distress. The Pooled t-test was significant at p <

0.05 for variables unemployment rate and outpatient mix; it was marginally significant at

p < 0.10 for variable change in unemployment rate. Hospitals in finanical distress tended

to be located in counties with higher unemployment rate, higher Medicare HMO

penetration, and higher change in unemployment rate, higher per capita income and

higher number of physicians per 1000 population compared to hospitals not in finanical

89

distress. In addition, hospitals in financial distress were smaller, had lower outpatient mix

and Medicare mix and higher Medicaid mix.

90

Table 4.2

Independent Samples t-test on Dependent Variable “Financial Distress a

Variable Not in Financial Distress

In Financial Distress t-Value

N Mean Std. Dev.

N Mean Std. Dev

Per capita income 6,308 26,311.10 8582.10

941 28,083.50 10,562.50 -4.91*

Unemployment rate 6,301 5.68 2.64

937 6.11 2.66 -4.60**

Percentage of population>= 65 6,300 0.14 0.04

939 0.14 0.04 1.33

Physicians per 1000 population 6,300 1.82 1.96

939 2.14 2.10 -4.32*

Excess capacity 6,300 55.89 37.09

939 55.76 33.91 0.11

Medicare HMO penetration 6,308 8.43 12.77

941 16.00 0.55 -13.29*

Change in unemployment rate 6,297 0.05 0.24

935 0.07 0.24 -1.77*

Hospital beds 6,315 179 194.90

941 164 213.20 1.97*

Occupancy rate 6,245 0.56 0.19

910 0.57 0.22 -2.35*

Outpatient mix 6,315 0.44 0.25

941 0.41 0.25 2.61**

Medicare mix 6,315 0.46 0.19

941 0.40 0.23 8.69*

Medicaid mix 6,315 0.23 0.17

941 0.28 0.22 -7.40*

T-test **p < 0.05 *p < 0.10 a Hospital-year is the unit of analysis. N accounts for repeated observations

91

A cross-tabulation followed by Chi-square tests was performed to test the null

hypothesis that the population has the same percentage of hospitals in financial distress

and those not in financial distress with respect to dichotomous variables Hirschman

Herfindahl index, system membership, health network and teaching status (Norusis,

2006). The Chi-square tests of these variables were statistically significant at p<0.05,

except for variable contract management which was significant at p < 0.1 indicating that

these variables did not have the same percentage of hospitals in financial distress and

hospitals not in financial distress.

A total of 941 observations (13%) of all observations were in financial distress

for each of the categorical variables except for teaching status with a total of 938

observations in financial distress. Of these 941 observations, 44% were located in areas

with higher competition, 52% were members of multihospital systems, 15% were under

contract management, and 18% participated in health network. Of the 938 observations

in financial distress with respect to teaching status, 626 (67%) did not have teaching

status. Table 4.3 summarizes the findings from the cross-tabulation and Pearson Chi-

square tests and table 4.4 presents the Pearson correlation matrix of the independent

variables.

92

Table 4.3

Cross-tabulation and Pearson Chi-Square Test on Dependent variable “Financial

Distress”a

Pearson Chi-square statistic **

p < 0.05 * p < 0.1

a Hospital-year is the unit of analysis

Variable Chi-square

statistic No financial

distress (%)

Financial distress

(%)

HHI

123.14 **

HHI>mean 4,668 (64.32) 531 (7.32)

HHI<=mean 1,648 ( 22.71) 410 (5.65)

System

Membership

208.26 ** Non member 4,503 (62.05) 450 (6.20)

Member 1,813 (24.98) 491 (6.77)

Contract Management

0.090 * No contract 5,339 (73.57) 799 (11.01)

Contract 977 (13.47) 142 (1.96)

Health network

25.73 ** No network 4,695 (64.70) 770 (10.61)

Network 1,621 (25.66) 171(2.36)

Teaching status

81.58 ** Teaching 1,281 (17.66) 313 (4.31)

No teaching 5,035 (69.41) 625 (8.62)

93

Table 4.4

Pearson Correlation Matrix of Independent Variables

1 2 3 4 5 6 7 8 9 10 11 12 13

1. Financial distress 1

2. Per capita income 0.07**

1

3. Percentage of

population >= 65 -0.02

-0.17** 1

4. Unemployment

Rate 0.05**

-0.19** -0.02 1

5. Physicians per 1000

population 0.05**

0.38** -0.19** -0.17** 1

6. Excess capacity

-0.001

0.34** -0.22** -0.15** 0.50** 1

7. Medicare HMO

penetration 0.19**

0.39** -0.19** 0.005 0.22** 0.031** 1

8. Change in

unemployment rate 0.02

0.21** 0.04* 0.32** 0.03* 0.04* 0.14** 1

9. Hospital beds

-0.02* 0.34** -0.18** -0.10** 0.50** 0.66** 0.31** 0.05** 1

10. Occupancy rate

0.03* 0.27** -0.21** -0.05** 0.37** 0.32** 0.30** 0.01 0.50 1

11. Outpatient mix

-0.03* 0.06** 0.04* -0.19** -0.01 -0.09** -0.12** -0.21** -0.15** 0.31** 1

12. Medicare mix

-0.11** -0.17** 0.26** -0.02 -0.25** -0.26** -0.27** 0.01 -0.36** -0.54** 0.25** 1

13. Medicaid mix

0.10** 0.3* -0.14** -0.13** 0.07** 0.11** 0.14** -0.01 0.22** 0.44** -.023** -0.72** 1

Pearson correlation coefficient **p < 0.001 *p < 0.05

94

The correlation matrix in table 4.4 depicts the Pearson correlation coefficients

among independent variables. The null hypothesis for Pearson correlation states that

there is no significant association between two variables. While pairs of several variables

had significant associations, their correlation coefficients did not indicate potential for

multicollinearity issue. Multicollinearity arises when the correlation coefficient is equal

to or above 0.80 (Field, 2009), it becomes problematic because it produces biased and

inflated standard errors (Wooldridge, 2006). The correlation between Medicare mix and

Medicaid mix had the highest correlation with an absolute value of 0.72. The negative

correlation coefficients between percentage of population 65 years of age and over and

Medicare HMO penetration, and occupancy rate seem counterintuitive as elderly people

are more likely to use healthcare services.

The next section discusses the inferential statistics that tested the five hypotheses

associated with research question 1. The first hypothesis tested whether environmental

munificence was negatively associated with financial distress. The second and third

hypotheses tested whether environmental dynamism and environmental complexity were

positively associated with financial distress, respectively. The fourth hypothesis tested

whether hospital size was negatively associated with financial distress and the fifth

hypothesis tested whether teaching status was negatively associated with financial

distress.

The test of these hypotheses consisted of three fixed-effects logistic regression

models. Logistic regression is appropriate for dichotomous dependent variables; it

addresses the violation of normality assumption, due to non-linear dependent variable,

required in linear regression. Fixed-effects model assumes that some unobserved

95

variables, which do not change over time, are associated with the independent variables.

Thus, the model controls for these time-invariant variables such as geographical location.

Three models were used to address some concern over the potential endogeneity of three

interorganizational variables membership of a multihospital system, operation under

contract management and participation in a health network to financial distress. Hospitals

might initiate such arrangements because they have already experienced financial distress

(McCue & Furst, 1986). These arrangements might be the solution to financial distress.

The first regression model used the environmental and organizational variables only, the

second model included the variables with potential endogeneity to financial distress, and

the third model was the full model comprising all the variables from the second model

and the control variables (Morrisey, Menachemi, Cawley & Ginter, 2010). The overall

likelihood ratio Chi-square tests of the three models were significant at p < 0.0001.

Results of fixed-effects logistic regressions.

Hypothesis 1. Hypothesis 1was not supported; environmental munificence was

not associated with lower probability of financial distress.

Hypothesis 2. Hypothesis 2 was not supported. High environmental dynamism in

terms of yearly change in unemployment rate was not associated with higher probability

of financial distress.

Hypothesis 3. Hypothesis 3 was partially supported by models 1 and 3. A higher

environmental complexity in terms of greater Medicare HMO penetration was

significantly associated with a greater odds of experiencing financial distress, OR = 1.03,

p < 0.05, 95% CI [1.00, 1.06]. High environmental complexity in terms of market

96

concentration and excess capacity was not associated with higher odds of experiencing

financial distress.

Hypothesis 4. Hypothesis 4 was supported by the three models. Larger hospitals

were significantly less likely to experience financial distress than smaller hospitals, OR =

0.996, p < 0.05, 95 % CI [0.99, 1.00].

Hypothesis 5. Hypothesis 5 was not supported. Teaching status was not

associated with financial distress.

The results also indicated that variable system membership was significantly

associated with financial distress. The odds of experiencing finanical distress for

hospitals members of multihospital systems was 2.80 times greater than the odds of

experiencing financial distress for stand-alone hospitals, OR = 2.80, p < 0.001, 95% CI

[1.67, 4.68]. Variable contract management was not associated with financial distress.

Variable health network was significantly and negatively associated with financial

distress. Hospitals participating in health networks were significantly less likely to

experience financial distress than non-participating hospitals, OR = 0.63, p < 0.05, 95%

CI [0.43, 0.93].

In addition, variable outpatient mix was negatively associated with financial

distress. The association was marginally significant. Higher proportion of outpatients was

associated with smaller odds of experiencing financial distress, OR = 0.14, p < 0.10, 95%

CI [0.14, 1.18]. Table 4.5 presents the results of the analyses for research question 1.

97

Table 4.5

Results of Fixed-effects Logistic Regressions e

Financial distress Model 1a (N = 2412) d Model 2b (N=2412) d Model 3c (N= 2344) d

Odds

Ratio

Standard

Error 95% CId Odds

Ratio

Standard

Error 95% CId Odds

Ratio

Standard

Error 95% CId

Munificence

Per capita income 1.003 0.03 0.95 1.06 0.99 0.03 0.94 1.05 0.99 0.03 0.94 1.04

Unemployment rate 1.04 0.05 0.95 1.14

1.04 0.05 0.95 1.14

1.05 0.50 0.66 1.15

Percent >=65 1.07 0.56 0.96 1.18 1.06 0.55 0.96 1.18 1.05 0.06 0.95 1.17

Physicians/1000pop 0.94 0.16 0.67 1.31 0.90 0.16 0.64 1.28 0.91 0.16 0.64 1.30

Complexity

HHI 0.79 0.17 0.52 1.21 0.77 0.17 0.50 1.19 0.73 0.16 0.47 1.14

Excess capacity 1.002 0.004 0.99 1.01 1.002 0.004 0.99 1.01 1.00 0.005 0.99 1.00

HMO penetration 1.03** 0.01 1.001 1.06 1.02 0.15 0.99 1.05 1.03 ** 0.02 1.00 1.06

Dynamism

Change in

unemployment rate 0.99 0.38 0.46 2.11

0.99 0.39 0.46 2.13

0.81 0.32 0.38 1.78

Organizational factors

Hospital beds 0.996** 0.001 0.99 1.00 0.996** 0.001 0.99 1.00 0.996 ** 0.001 0.99 1.00

Teaching status 1.31 0.44 0.68 2.54 1.22 0.41 0.63 2.37 1.25 0.43 0.63 2.45

Potential endogenous

variables

System

Membership

2.80*** 0.73 1.68 4.67

2.79*** 0.73 1.67 4.67

Contract

management

1.33 0.27 0.89 1.99

1.36 0.29 0.90 2.05

Health network 0.65** 0.12 0.45 0.94 0.63 ** 0.12 0.43 0.93

Control variables

Occupancy rate 0.59 0.34 0.19 1.83

Outpatient mix 0.41* 0.22 0.14 1.18

Medicare mix 0.99 0.53 0.35 2.85

Medicaid mix 1.17 0.58 0.44 3.08

Overall Chi-square test 103.41*** 129.87*** 139.76***

*p < 0.1 **p < 0.05 ***p < 0.001 a Model with environmental and organizational independent variables b Model with environmental and organizational independent variables and endogenous variables c Full model d CI means confidence interval d N represents hospital-years e year dummy variables are included in the analysis

98

Results from Research Question 2

Research question 2 asked whether financial distress precedes public hospital

privatization (hypothesis 6). Two models of random effects logistic regression with state

fixed-effects were constructed. The first model included independent variables lagged by

one year and the second model contained independent variables lagged by two years.

Lagging the independent variables was appropriate for research question 2 due to the fact

that hospitals do not immediately decide to privatize as soon as they experience financial

distress. For research question 2, all the environmental, organizational and potential

endogenous variables were used as control variables. Table 4.6 summarizes the number

of observations with respect to both continuous and categorical variables. The number of

observations ranged between 6,407 and 6,432.

99

Table 4.6

Descriptive Analysis of all Variables a

N Mean

(%)

Std. Dev. Minimum Maximum

Dependent variable – privatization

No privatization 6,285 (97.71)

Privatization 147 (2.29)

Independent variable

Not in financial distress 5,759 (89.84)

In financial distress 673 (10.46)

Control variables

Per capita income 6,424 26,390.64 8,814.20 0 132,728.00

Unemployment rate 6,413 5.69 2.63 1.10 28.20

Percentage of population>= 65 6,416 0.14 0.04 0.05 0.45

Physicians per 1000 population 6,416 1.86 2.01 0 35.00

Excess capacity 6,417 56.82 37.09 1.00 291.00

HHI

HHI > mean 4,616 (71.77)

HHI < =mean 1,816 (28.23)

Metro vs. non-metro location

Non-metro 3,210 (49.97)

Metro 3,214 (50.03)

Medicare HMO penetration 6,424 9.33 13.64 0 55.00

Change in unemployment rate 6,407 0.05 0.24 -0.68 2.07

Hospital beds 6,431 181.00 200.18 0 2,186.00

Teaching status

Teaching 1,454 (22.61)

No teaching 4,978 (77.39)

Occupancy rate 6,340 0.56 0.19 0.02 1.00

Medicare mix 6,431 0.45 0.20 0 0.96

Medicaid mix 6,431 0.23 0.18 0 0.99

Outpatient mix 6,431 0.43 0.25 0 0.99

System membership

Non member 4,554 (70.80)

Member 1,878 (29.20)

Contract Management

No contract 5,399 (83.94)

Contract 1,033 (16.06)

Health network

No network 4,847 (75.36)

Network 1,585 (24.64)

a Hospital-year is the unit of analysis

100

Independent samples t-tests were performed to test whether the population of

public hospitals that privatized had the same means as the population of public hospitals

that did not privatize with respect to the continuous independent variables. The t-tests

were significant at p <0.05 for all variables except for outpatient mix indicating that the

means of this variable was the same for population of public and private hospitals

respectively. Hospitals that privatized tended to be located in areas with lower per capita

income, lower unemployment rate, lower number of physicians per 1000 population,

lower excess capacity, higher Medicare HMO penetration, higher change in

unemployment rate and higher percentage of population 65 years of age and over,

compared to hospitals that remained public. They also tended be smaller and had lower

occupancy rate, lower outpatient mix, higher Medicare mix, lower Medicaid mix. Table

4.7 presents the summary of the independent samples t-tests.

Cross-tabulation followed by Chi-square tests was conducted to investigate the

association of categorical variables with privatization variable. All the independent

variables were significant at p < 0.05 except for variables HHI and health network that

were marginally significant at p < 0.10. One hundred and forty seven hospitals (2.29%)

privatized. Among hospitals that privatized, 46 (31%) were in financial distress, 32

(28%) were located in more competitive markets, 60 (41%) were located in metropolitan

areas, 76 (52%) were members of multihospital systems, 25 (17%) operated under

contract management, 46 (31%) participated in health care network, and 134 (91%) did

not provide medical education.

101

Among hospitals that remained public, (hospital-years N = 6,285), 10% were in

financial distress (hospital-years N = 627). Table 4.8 summarizes the cross-tabulation and

Chi-square tests. The correlation matrix in Table 4.9 shows the same patterns as in the

correlation matrix for research question 1. The correlation between Medicare mix and

Medicaid mix had the highest number in term of absolute value.

102

Table 4.7

Independent Samples t-Tests on Dependent Variable Privatization a

No Privatization Privatization

Variable N Mean Std. Dev. N Mean Std. Dev t-Value

Per capita income 6,278 26,434.50 8,837.80 146 24,502.80 7,516.1 3.06*

Unemployment rate 6,266 5.70 2.63 147 5.21 2.37 2.47*

Percentage of population>= 65 6,270 0.140 0.04 146 0.145 0.04 -2.67*

Physicians per 1000 population 6,270 1.87 2.02 146 1.62 1.43 1.99*

Excess capacity 6,272 57.07 37.15 145 46.11 32.44 4.01*

Medicare HMO penetration 6,278 9.39 13.67 146 6.71 11.90 2.68*

Change in unemployment rate 6,260 0.05 0.23 147 0.01 0.19 2.71 *

Hospital beds 6,285 182.00 201.00 146 119.00 148.4 5.04*

Occupancy rate 6,195 0.56 0.00 145 0.50 0.02 3.51*

Outpatient mix 6,285 0.43 0.25 146 0.41 0.29 0.33

Medicare mix 6,285 0.45 0.20 146 0.49 0.23 -2.19*

Medicaid mix 6,285 0.24 0.18 146 0.20 0.19 2.45*

t- Value *p < 0.05 a Hospital-year is the unit of analysis

103

Table 4.8

Chi-square Tests on Dependent Variable “Privatization” a

Pearson Chi-square statistic **

p < 0.05 *

p < 0.1 a Hospital-year is the unit of analysis

Variable Chi-square Statistic No privatization (%) Privatization (%)

Financial distress

69.67** No distress 5,658 (87.97) 101 (1.57)

Distress 627 (9.75) 46 (15.38)

HHI

3.10* HHI > mean 4,501 (69.98) 115 (7.32)

HHI<=mean 1,784 ( 27.74) 32 (5.65)

Metro vs. non-metro location

4.77** Non-metro 3,124 (48.63) 86 (1.34)

Metro 3,154 (49.10) 60 (0.93

System

Membership

36.85** Non member 4,483 (69.70) 71 (1.10)

Member 1,802 (28.67) 76 (51.70)

Contract Management

0.10 No contract 5,277 (82.04) 122 (1.90)

Contract 1,008 (15.67) 25 (2.42)

Health network

3.58* No network 4,746 (73.79) 101 (1.57)

Network 1,539 (23.93) 46 (0.72)

Teaching status

16.29** Teaching 1,441 (22.40) 13 (0.02)

No teaching 4,844 (97.31) 134 (2.69)

104

Table 4.9

Pearson Correlation Matrix Research Question 2

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Financial distress 1

2. Privatization

0.10*** 1

3. Per capita income

0.08*** -0.03** 1

4. Percentage of

population >= 65 -0.05*** 0.03** -0.18*** 1

5. Unemployment rate 0.04** -0.03** -0.18*** -0.02* 1

6. Physicians per 1000

population 0.07*** -0.02 0.37*** -0.18*** -0.17*** 1

7. Excess capacity

0.03 -0.04** 0.34*** -0.22*** -0.15*** 0.49*** 1

8. Medicare HMO penetration

0.23*** -0.03** 0.38*** -0.19*** -0.01 0.22*** 0.32*** 1

9. Change in unemployment rate

0.003 -0.03** 0.21*** 0.04** 0.31*** 0.04** 0.05** 0.13*** 1

10. Hospital beds

0.02 -0.05** 0.36*** -0.16*** -0.09*** 0.50*** 0.65*** 0.33*** 0.05*** 1

11. Occupancy rate

0.06*** -0.05*** 0.28*** -0.21** -0.06*** 0.36*** 0.32*** 0.32*** 0.02 0.50*** 1

12. Outpatient mix

-0.04** -0.01 0.07*** 0.04** -0.18*** -0.01 -0.08*** -0.12*** -0.19*** -0.14*** -0.29*** 1

13. Medicare mix

-0.15*** 0.03** -0.21*** 0.27*** -0.01 -0.27*** -0.27*** -0.29*** 0.002 -0.36*** -0.53*** 0.24*** 1

14. Medicaid mix 0.11*** -0.03** 0.06*** -0.15***

0.13***

0.08*** 0.12*** 0.16*** -0.01 0.23*** 0.42*** -0.22*** -0.70***

1

Pearson correlation coefficient ***

p < 0.001 **

p < 0.05 *

p < 0.1

105

The following section discusses the findings from the random-effects logistic

regression with state fixed effects for research question 2.

Hypothesis 6 was supported by models 1 and 2. Financial distress was

significantly associated with privatization. Model 1 found that the odds of privatization

for hospitals in financial distress was 3.48 times greater than the odds of privatization for

hospitals not in financial distress, OR = 3.48, p < 0.001, 95% CI [2.11, 5.75]. Model 2

found that the odds of privatization for hospitals in financial distress was 2.59 times

greater than the odds of privatization for hospitals not in financial distress OR = 2.59,

p < 0.001, 95% CI [1.39, 4.84]. The control environmental variables as well as hospital

size were not significantly associated with privatization. Model 1 found a marginally

significant positive association between teaching status and privatization. The odds of

privatization for hospitals without teaching status was 2.09 times greater than the odds for

public hospitals with teaching status, OR = 2.09, p < 0.10, 95% CI [0.89, 4.93]). That

association was strongly significant for model 2. The odds of privatization for hospitals

without teaching status was 3.07 times greater than the odds for public hospitals with

teaching status, OR = 3.07, p < 0.005, 95% CI [1.035, 9.12].

In addition, both models found that affiliation with multihospital systems was

positively and significantly associated with privatization. That association was strongly

significant for model 1, OR = 2.49, p<0.001, 95% CI [1.57, 3.96]. The odds of

privatization for hospitals affiliated to a multihospital system was 2.49 times greater than

the odds of privatization for stand-alone hospitals. The association between multihospital

system affiliation and privatization was marginally significant for model 2, OR = 1.64,

p<0.1, 95% CI [0.94, 2.88].

106

Model 1 found a marginally significant negative association between contract

management and privatization. Hospitals operating under contract management were less

likely to privatize than hospitals without contract management, OR = 0.62, p<0.1, 95%

CI [0.36, 1.07]. Besides, occupancy rate had a strongly significant negative association

with privatization for model 1. Hospitals with higher occupancy rate were significantly

less likely to privatize than hospitals with lower occupancy rate, OR = 0.20, p < 0.05,

95% CI [0.05, 0.80]. Table 4.10 summarizes the findings from the two models.

107

Table 4.10

Results of Random-effects Logistic Regressions with State Fixed-effects d

Model 1a (N = 5,705) Model 2b (N= 5,124)

Privatization Odds

Ratio

Standard

Error 95% CIc

Odds

Ratio

Standard

Error 95% CIc

Financial distress 3.48*** 0.89 2.11 5.75 2.59 ** 0.82 1.39 4.84

Munificence

Per capita income 1.02 0.26 0.97 1.08 1.03 0.32 0.97 1.10

Unemployment rate 0.95 0.47 0.86 1.05 1.00 0.51 0.915 1.11

Percent >=65 1.04 0.31 0.99 1.10 1.01 0.38 0.94 1.09

Physicians/1000pop 1.10 0.12 0.89 1.35 0.97 0.14 0.73 1.28

Complexity

HHI 0.93 0.27 0.53 1.64 1.21 0.37 0.66 2.20

Excess capacity 1.00 0.00 0.99 1.00 1.00 0.01 0.99 1.01

HMO penetration 0.99 0.12 0.97 1.01 0.99 0.01 0.97 1.02

Metro vs. non- metro location 1.40 0.35 0.86 2.29 1.43 0.40 0.83 2.46

Dynamism

Change in unemployment

rate 0.63 0.39 0.19 2.14 1.30 0.78 0.40 4.23

Organizational factors

Hospital beds 1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00

Teaching status 2.09* 0.91 0.89 4.93 3.07** 1.70 1.04 9.12

System membership 2.49*** 0.59 1.57 3.96 1.64* 0.47 0.94 2.88

Contract management 0.62* 0.17 0.36 1.07 1.04 0.32 0.57 1.93

Health network 0.87 0.20 0.57 1.36 0.89 0.22 0.54 1.44

Occupancy rate 0.20** 0.14 0.05 0.80 0.40 0.32 0.80 1.98

Outpatient mix 0.85 0.59 0.22 3.33 1.19 0.94 0.25 5.65

Medicare mix 0.34 0.25 0.84 1.40 0.73 0.62 0.14 3.90

Medicaid mix 0.57 0.43 0.13 2.52 0.96 0.86 0.17 5.51

Overall Wald-Chi-square test 200.74*** 146.45***

*p<0.1 **p < 0.05 ***p < 0.001 a Model with independent variables lagged by one year b Model with independent variables lagged by two years c CI means confidence interval d Hospital-year was the unit of analysis and year dummy and state codes dummy variables were included in the analysis

108

Results from Research Question 3

Research question 3 was answered by testing two hypotheses. Hypothesis 7 tested

whether privatization resulted in better financial performance and hypothesis 8 tested

whether hospitals that converted into private for-profit demonstrated a better financial

performance compared to hospitals that converted into private not-for-profit status.

Financial performance was measured in terms of operating margin and total margin. Two

separate data files for operating margin and total margin were constructed to test each

hypothesis resulting in four linear fixed-effects regressions. The same method that

addressed endogeneity of variables multihospital system membership, contract

management and health network was used for research question 3. Since financial

performance was the dependent variable, potential endogeneity might arise from these

variables. The next section presents the descriptive statistics of the two data sets followed

by the findings from the linear fixed-effects regressions.

Table 4.11 summarizes the descriptive statistics from the data set that considered

operating margin as the dependent variable. The number of hospital-years ranged

between 6,375 and 6,454. Operating margin had a mean of -16%, a standard deviation of

16%, a minimum value of -86%, and a maximum value of 63%. The summary of the

process used to ensure that operating margin was normally distributed before the analysis

is included in Chapter 2.

109

Table 4.11 Descriptive Statistics on the Operating Margin Data Set a

N Mean (%) Std. Dev. Minimum Maximum

Dependent variable

Operating Margin 6,454 -0.06 0.16 -0.86 0.63

Independent variable –privatization *

No privatization 5,832 (90.36)

Privatization 622 (9.64)

Independent variable –Public to For-Profit**

No privatization 6,298 (97.58)

Public to for-profit 156 (2.42)

Independent variable- Public to not-for-profit**

No privatization 5,988 (92.78)

Public to not-for-profit 466 (7.22)

Control variables

Per capita income 6,447 26,564.33 8,678.47 0 132,728.00

Unemployment rate 6,437 5.73 2.64 1.10 28.20

Percentage of population > = 65 6,439 0.14 0.04 0.05 0.45

Physicians per 1000 population 6,439 1.84 1.97 0 35.00

Excess capacity 6,440 56.50 36.45 1.00 291.00

HHI

HHI > mean 4,704 (72.89)

HHI < = mean 1,750 (27.11)

Medicare HMO penetration 6,447 9.24 13.45 0 55.00

Change in unemployment rate 6,433 0.06 0.25 -0.68 2.07

Hospital beds 6,454 181.00 198.40 0 2,186.00

Teaching status

Teaching 1,340 (20.76)

No teaching 5,114 (79.24)

Occupancy rate 6,375 0.56 0.19 0.02 1.00

Medicare mix 6,454 0.46 0.19 0 0.96

Medicaid mix 6,454 0.23 0.17 0 0.99

Outpatient mix 6,454 0.43 0.25 0 0.97

Potential endogenous variables

System membership

Non member 4,511 (69.89)

Member 1,943 (30.11)

Contract Management

No contract 5,447 (84.40)

Contract 1,007 (15.60)

Health network

No network 4,833 (74.88)

Network 1,621 (25.12) a Hospital-year was the unit of analysis

* Independent variable for first hypothesis ** Independent variable for second hypothesis

110

Independent samples t-tests on operating margin were performed with respect to

dichotomous independent variable ―privatization‘ as well as control variables HHI,

system membership, contract management, health network and teaching status. All the

variables met the equality of variance assumption and their respective Statterthwaite tests

were statistically significant at p < 0.05, suggesting that the groups had different means

with respect to operating margin.

Hospitals that privatized had a higher mean operating margin (-1%) compared to

hospitals that remained public (- 6%). Hospitals members of multihospital systems as

well as those under contract management and those involved in health network tended to

have higher operating margin. Non-teaching hospitals tended to have higher operating

margin relative to teaching hospitals. Table 4.12 summarizes the independent samples t-

test on operating margin and table 4.13 presents the correlation matrix of the variables.

The same correlation pattern as in the first two research questions was noticed in the data

set with normally distributed operating margin.

111

Table 4.12

Independent Samples t-Tests on Operating Margin

* All t-Values significant at p < 0.05

Operating Margin

Variable N Mean Std. Dev. t-Value*

Privatization

Public vs. Private

No privatization 5,832 -0.06 0.16

-9.87

Privatization 622 -0.01 0.12

HHI

> mean 4,707 -0.04 0.13 13.12

=<mean 1,750 -0.11 0.21

System

Membership

Non member 5,411 -0.07 0.16 -8.57

Member 1,943 -0.03 0.15

Contract

Management

No contract 5,447 -0.06 0.17 -4.27

Contract 1,007 -0.04 0.13

Health network

No network 4,833 -0.06 0.17 -6.55

Network 1,621 -0.04 0.14

Teaching status

Teaching 1,340 -0.13 0.22 -13.98

No teaching = 1 5,114 -0.04 0.13

112

Table 4.13

Correlation Matrix of Data Set for Operating Margin

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Operating margin

1

2. Per capita income

-0.08** 1

3. Unemployment rate

-0.03* -0.18** 1

4. Percentage of

population >= 65 -0.01 -0.15** -0.01 1

5. Physicians per 1000

population -0.04* 0.34** -0.18** -0.17** 1

6. Excess capacity

0.00 0.32** -0.15** -0.19** 0.48** 1

7. Medicare HMO

penetration -0.12** 0.38** -0.01** -0.16** 0.20** 0.30**

1

8. Change in

unemployment rate -0.00

0.21**

0.33** 0.04* 0.03* 0.04* 0.15** 1

9. Hospital beds

-0. 09** 0.33** -0.10** -0.16** 0.51** 0.65** 0.31** 0.04* 1

10. Occupancy rate

0.28** 0.25** -0.06** -0.19** 0.37** 0.32** 0.29** 0.01 0.50** 1

11. Outpatient mix

-0.06** 0.04* -0.20** 0.05* -0.02 -0.11** -0.13** -0.23** -0.17** -0.30** 1

12. Medicare mix

-0.15** -0.02** -0.01 0.25** -0.22** -0.24** -0.27** 0.01 -0.36** -0.51** 0.25** 1

13. Medicaid mix

-0.10** 0.01 0.13** -0.14** 0.06** 0.10** 0.11** -0.20 0.21** 0.41** -0.23** -0.72** 1

14. Private mix

-0.08** 0.21** -0.15** -0.17** 0.24** 0.22** 0.22** 0.01 0.23** 0.20** -0.05** -0.49** -0.26** 1

Pearson correlation coefficient **p < 0.001 *p < 0.05

113

The next section presents the descriptive statistics of the data set that was used for

dependent variable total margin. The number of hospital-years ranged between 6,497 and

6,579. Table 4.14 summarizes the descriptive statistics of the variables. Total margin had

a mean of 3% with a minimum value of -34% and a maximum value of 38%.

114

Table 4.14

Descriptive Statistics of Total Margin data a

N Mean (%) Std. Dev. Minimum Maximum

Dependent variable

Total Margin 6,579 0.03 0.08 -0.34 0.38

Independent variable –privatization *

No privatization 5,960 (90.59)

Privatization 619 (9.41)

Independent variable –Public to For-Profit**

No privatization 6,423 (97.63)

Public to for-profit 156 (2.37)

Independent variable- Public to not-for-profit**

No privatization 6,116 (92.26)

Public to not-for-profit 463 (7.04)

Control variables

Per capita income 6,572 26,830 8,960.10 0 132,728.00

Unemployment rate 6,562 5.72 2.63 1.10 28.20

Percentage of population > = 65 6,564 0.14 0.04 0.05 0.45

Physicians per 1000 population 6,564 1.88 1.99 0 35.00

Excess capacity 6,565 57.17 36.84 1.00 291.00

HHI

HHI > mean 4,715 (71.67)

HHI < = mean 1,864 (28.33)

Medicare HMO penetration 6,572 9.52 13.62 0 55.00

Change in unemployment rate 6,558 0.06 0.25 -0.68 2.07

Hospital beds 6,579 185.00 201.84 0 2,186.00

Teaching status

Teaching 1,469 (22.33)

No teaching 5,110 (77.67)

Occupancy rate 6,497 0.56 0.19 0.02 1.00

Medicare mix 6,579 0.46 0.19 0.02 1.00

Medicaid mix 6,579 0.23 0.17 0 0.99

Outpatient mix 6,579 0.43 0.25 0 0.97

Endogenous variables

System membership

Non member 4,607 (70.03)

Member 1,972 (29.97)

Contract Management

No contract 5,572 (84.69)

Contract 1,007 (15.31)

Health network

No network 4,939 (75.07)

Network 1,640 (24.93)

* Independent variable for first hypothesis ** Independent variables for second hypothesis a Hospital-year was the unit of analysis

115

The summary of the independent samples t-test on the association between total

margin and dichotomous variables privatization, HHI, system membership, contract

management, health network and teaching status in table 4.15 demonstrated that a total of

619 hospital-years (1.11%) had private status. Hospitals that privatized had a mean total

margin of 4% relative to 3% for those that remained public. While the Folded test was

significant at p < 0.05 for variable privatization with respect to total margin, the

Statterthwaite test was not significant, indicating that the mean of total margin between

the observations that privatized and those that did not privatize were the same.

Statterthwaite test was significant at p < 0.05 for HHI, system membership, health

network and teaching status.

116

Table 4.15

Independent Samples t-Tests on Total Margin a

t-Value *p < 0.05 a Hospital-year was the unit of analysis

Table 4.16 presents the correlation matrix of the variables from the data set used

to make total margin normally distributed. Similar to previous correlations matrices, the

correlation between Medicaid mix and Medicare mix had the highest correlation

coefficient in terms of absolute value.

Total Margin

Variable

N Mean Std. Dev. t-Value

Privatization

Public vs. Private

No privatization 5,960 0.03 0.07

-1.22

Privatization 619 0.04 0.09

HHI

> mean 4,715 0.03 0.07

3.28*

= < mean 1,864 0.03 0.09

System

Membership

Non member 4,607 0.03 0.07

-2.60*

Member 1,972 0.04 0.08

Contract

Management

No contract 5,572 0.03 0.08 1.19

Contract 1,007 0.03 0.07

Health network

No network 4,939 0.03 0.08

2.41*

Network 1,640 0.04 0.08

Teaching status

Teaching 1,469 0.02 0.08

-4.88* No teaching 5,110 0.03 0.07

117

Table 4.16

Correlation Matrix Variables for Total Margin Data Set

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Total margin

1

2. Per capita income

0.01 1

3. Unemployment rate

-0.05** -0.19** 1

4. Percentage of

population >= 65 -0.04* -0.16** -0.01 1

5. Physicians per 1000

population 0.01 0.36** -0.18** -0.18** 1

6. Excess capacity

0.05** 0.32** -0.15** -0.20** 0.49** 1

7. Medicare HMO

penetration -0.06** 0.39** -0.01 -0.17** 0.21** 0.30**

1

8. Change in

unemployment rate -0.03*

0.20**

0.33** 0.04* 0.03* 0.04* 0.15** 1

9. Hospital beds

0.03* 0.34** -0.10** -0.17** 0.50** 0.65** 0.32** 0.04* 1

10. Occupancy rate

0.01 0.26** -0.06** -0.20** 0.36** 0.32** 0.30** 0.01 0.50* 1

11. Outpatient mix

0.01 0.05** -0.20* 0.04* -0.01 -0.09** -0.13** -0.23** -0.16** -0.30** 1

12. Medicare mix

0.08** -0.18** -0.01 0.27** -0.25** -0.27** -0.28** 0.00 -0.37* -0.51** 0.24** 1

13. Medicaid mix

-0.09** 0.03* 0.13** -0.15** 0.07** 0.11** 0.14** -0.01 0.23** 0.41** -0.22** -0.72** 1

14. Private mix

-0.01 0.21** -0.15** -0.18** 0.26* 0.23** 0.23** 0.01 0.23** 0.20** -0.05* -0.50** -0.25** 1

Pearson correlation coefficient **p < 0.001 *p < 0.05

118

Results of hypothesis 7. The next section presents the results from the two fixed-

effects linear regression analyses used to test hypothesis 7, one for dependent variable

operating margin and one for dependent variable total margin. Table 4.17 presents the

results of the fixed-effects regression models predicting operating margin on

dichotomous variable privatization and table 4.18 presents the results of the fixed-effects

linear regression models predicting total margin on dichotomous variable privatization.

119

Table 4.17

Results of Fixed-effects Linear Regression on Operating Margin First Hypothesis e

Model 1 a (N= 6,454) Model 2 b (N= 6,454) Model 3c (N= 6,341)

Operating Margin Coefficient Standard

Error 95% CI d

Coefficient Standard

Error 95% CI d

Coefficient Standard

Error 95% CI d

Privatization 0.06*** 0.008 0.04 0.07 0.05*** 0.008 0.37 0.68 0.05*** 0.008 0.04 0.07

Potential endogenous

variables

System

membership

0.011 0.007 -0.002 0.02

0.01* 0.007 -0.001 0.03

Contract

management

-0.009 0.006 -0.02 0.003

-0.02** 0.006 -0.029 -0.004

Health network 0.02*** 0.004 0.008 0.03 0.02*** 0.004 0.007 0.03

Munificence

Per capita income 0.0004 0.001 -0.001 0.001

Unemployment

rate

0.0002 0.001 -0.002 0.002

Percent >=65 0.06 0.13 -0.19 0.31

Physicians/1000pop -0.001 0.003 -0.008 0.01

Complexity

HHI -0.009 0.006 -0.020 0.002

Excess capacity -0.00005 0.0001 -0.0002 0.0001

HMO penetration -0.0001 0.0003 -0.001 0.001

Dynamism

Change in

unemployment rate

-0.002 0.008 -0.02 0.014

Organizational factors

Hospital beds 0.0001** 0.00004 0.00002 0.0002

Teaching status -0.000 0.008 -0.02 0.02

Occupancy rate 0.08*** 0.02 0.05 0.11

Outpatient mix 0.05*** 0.01 0.02 0.07

Medicare mix 0.05** 0.02 0.02 0.08

Medicaid mix 0.01 0.02 -0.02 0.04

Overall F- test 10.69*** 9.74*** 7.05***

*p < 0.1 **p < 0.05 ***p < 0.001 a Model with privatization dummy variable as independent variable b Model with privatization dummy independent variable and endogenous variables c Full model d CI means confidence interval e Hospital-year is the unit of analysis, year dummy variables were included in the analysis

120

Table 4.18

Results of Fixed-effects Linear Regression on Total Margin First Hypothesis d

Total Margin Model 1 a (N = 6,579)

Model 2 b (N = 6,463)

Model 3 c (N = 6,463)

Coefficient Standard

Error 95% CI e

Coefficient Standard

Error 95% CI e

Coefficient Standard

Error 95%CI e

Privatization 0.02 *** 0.005 0.01 0.03 0.02 *** 0.01 0.01 0.03 0.02 *** 0.005 0.008 0.03

Potential endogenous

variables

System

membership

0.004 0.004 -0.004 0.013

0.004 0.004 -0.004 0.013

Contract

management

-0.01 * 0.004 -0.015 0.001

-0.01 ** 0.004 -0.02 -0.0003

Health network 0.01 *** 0.004 0.004 0.015 0.01 *** 0.003 0.004 0.02

Munificence

Per capita income 0.001 ** 0.0004 0.0001 0.002

Unemployment rate

-0.001 0.001 -0.002 0.000

Percent > = 65 -0.26** 0.08 -0.42 -0.09

Physicians/1000pop -0.001 0.002 -0.006 0.003

Complexity

HHI 0.001 0.004 -0.007 0.008

Excess capacity -0.0001 0.0001 -0.0002 0.00004

HMO penetration -0.005 ** 0.0002 -0.001 -0.0001

Dynamism

Change in

unemployment rate

-0.001 * 0.005 -0.021 0.001

Organizational factors

Hospital beds 0.00002 0.00002 -0.00002 0.00007

Teaching status 0.020 0.005 -0.008 0.012

Occupancy rate 0.02 * 0.010 -0.001 0.039

Outpatient mix 0.007 0.009 -0.010 0.025

Medicare mix 0.03 ** 0.010 0.006 0.045

Medicaid mix 0.005 0.010 -0.014 0.025

Overall F-tests 8.44*** 7.74*** 5.85***

*p < 0.1 **p < 0.05 ***p < 0.001 a Model with privatization as independent variable b Model with privatization variable and potential endogenous variables c Full model d Hospital-year is the unit of analysis, year dummy variables were included in the analysis e CI means confidence interval

121

Hypothesis 7 was supported; operating margin and total margin were statistically

significant at p < 0.001 for all the three models, respectively. Compared to hospitals that

remained public, privatized hospitals had 6% higher operating margin, p < 0.001, 95% CI

[0.04, 0.07] for the model 1. Privatized hospitals had 5.3% higher operating margin, p <

0.001, 95% CI [0.37, 0.68] for the model 2 and 5% higher operating margin p < 0.001,

95% CI [0.04-0.07] for the full model. In addition, all three models found that compared

to public hospitals that remained public, privatized hospitals had 2% higher total margin,

p < 0.001, 95% CIs [0.01, 0.03], [0.01,0.03], and [0.008, 0.03], respectively.

Operating margin was also positively associated with health network for the

second model and third model; compared to hospitals without health network, those

involved in health network had 2% higher operating margin, p < 0.001, 95% CI [0.008,

0.03]. Multihospital system membership was marginally associated with operating

margin. Compared to hospitals not affiliated with multihospital system, those with

affiliation had 1.2% higher operating margin, p < 0.10, 95% CI [-0.001, 0.03]. Contract

management was negatively associated with operating margin. Hospitals engaged in

contract management had 2% lower operating margin than those without contract

management; this association was marginally significant at p < 0.10, 95% CI [-0.029, -

0.004]. Control variables hospital beds, occupancy rate, outpatient mix and Medicare mix

were positively associated with operating margin. One hundred units increase in hospital

beds was associated with 1% increase in operating margin, p < 0.05, 95% CI [0.00002,

0.0002]; one unit increase in occupancy rate was associated with 8% increase in

operating margin, p < 0.001, 95% CI [0.05. 0.11]; one unit increase in outpatient mix

was associated with 5% increase in operating margin, p < 0.001 95% CI [0.02, 0.07]; and

122

one unit increase in Medicare mix was associated with 5% increase in operating margin,

p < 0.05, 95% CI [0.02, 0.08].

Similar to operating margin, contract management was also negatively associated

with total margin. Compared to hospitals without contract management, those with

contract had total margin 1% lower for model 2, the association was marginally

significant at p < 0.10, 95% CI [-0.004, 0.013]. For model 3, contract management was

significantly associated with 1% decrease in total margin, p < 0.05, 95% CI [-0.02, -

0.0003]. In addition, involvement in health network increased total margin by 1%, for

both models 2 and 3, p < 0.001, 95% CIs [-0.004, 0.015], and CIs [0.004, 0.02],

respectively. Variables per capita income, occupancy rate and Medicare mix were

positively associated with total margin. An increase of $1,000 in per capita income was

associated with 0.1% increase in total margin, p < 0.05, 95% CI [0.0001, 0.002]; one unit

increase in occupancy rate was marginally associated with 2% increase in total margin,

p < 0.10, 95% CI [-0.001, 0.039], and one unit increase in Medicare mix was associated

with 3% increase in total margin, p < 0.05, 95% CI [0.006, 0.045].

Variables percentage of population 65 years of age and older, HMO penetration

and change in unemployment rate were negatively associated with total margin. One unit

increase in percentage of population 65 years of age and older was marginally associated

with 26% decrease in total margin, p < 0.05, 95% CI [-0.42, -0.09], one unit increase in

HMO penetration was associated with 0.5% decrease in total margin, p < 0.05,

95% CI [-0.001, -0.0001]; and one unit increase in change in unemployment rate was

marginally associated with 0.1% decrease in total margin, p < 0.1, 95% CI [-0.021,

0.001].

123

Results of Hypothesis 8. This section discusses the results of hypothesis 8, which

tested whether hospitals that converted into private for-profit status exhibited better

financial performance than hospitals that converted into private not-for-profit status.

Two independent dummy variables were constructed: private for-profit and

private not-for-profit using public hospitals as control group. Similar to the structure of

the data sets used to test hypothesis 7, two data sets, one with normally distributed

operating margin and another with normally distributed total margin were used in the

analysis. Fixed-effects linear regression models with joint tests, were used to test

hypothesis 8. Joint tests were designed to test the null hypothesis that the Beta

coefficients of operating margin and total margin for hospitals with for-profit status and

those with not-for-profit status were equal. Potential endogeneity of variables

membership of a multihospital system, contract management and participation in health

network on financial performance was taken into account using the same method in

research question 1 and the first part of research question 3. The same data set for

operating margin and total margin used to test hypothesis 7 were used to test hypothesis

8. Tables 4.11 and 4.14 summarize the number of observations, means , standard

deviations and the minimum and maximum values of the variables based on the data sets

with normally distributed operating margin and total margin, respectively. The

independent samples t-tests for dependent variables operating margin and total margin

are presented in table 4.12 and table 4.15, respectively. Tables 4.13 and 4.13 contain the

correlation matrices of the data sets for operating margin and total margin, respectively.

Table 4.19 compares the sample sizes and operating margin by the three

ownership type: public, private not-for-profit and private for-profit. There was a total of

124

6,454 hospital-years, 5,832 (90.36%) were public, 156 (2.42%) for-profit and 466

(7.22%) for-profit. Public hospitals had the lowest mean operating margin (-6%) and for-

profit hospital had the highest mean of (4%). The one sample t-test for each ownership

type was statistically significant at p < 0.001, respectively suggesting that the mean

operating margin of each ownership type was different from the population mean.

Table 4.19

Operating Margin by Ownership Type a

N (%) Mean Std. Dev. T-statistic µ=0

Public 5,832 (90.36%) -0.06 0.16 -29.60*

For-profit 156 (2.42%) 0.04 0.12 3.92*

Not-for-profit 466 (7.22%) -0.03 0.12 -4.41*

Total 6,454 (100.00%)

* p < 0.001

a Hospital-year is the unit of analysis

Table 4.20 summarizes the sample sizes and one sample t-tests of total margin by

ownership type. Of the total of 6,454 hospital-years, 5,832 (90.36%) were public, 156

(2.42%) were for-profit and (7.22%) were not-for-profit. T-statistics were statistically

significant at p < 0.001 indicating that the mean of total margin for each sample was

different from the population mean. Similar to operating margin, for-profit hospitals had

the highest mean of total margin (5%), public and not for profit hospitals had the same

mean (3%).

125

Table 4.20

Total Margin by Ownership Type a

N Mean Std. Dev. T-statistic µ=0

Public 5960 (90.59%) 0.03 0.07 32.22*

For-profit 156 (2.37%) 0.05 0.10 6.40*

Not-for-profit 463 (7.04%) 0.03 0.08 7.86*

Total 6579 (100.00%)

*p < 0.001

a Hospital-year was the unit of analysis

Tables 4.21 and 4.22 present the fixed-effects linear regression models for the

operating margin and total margin data sets.

126

Table 4.21

Results of Fixed-Effects Linear Regressions on Operating Margin - Second Hypothesis d

Operating Margin Model 1a (N = 6,454) Model 2b (N = 6,454) Model 3c (N = 6,341)

Coefficient SE h 95% CIe Coefficient SE h 95% CIe Coefficient SE h 95% CIe

For-profit f 0.09*** 0.01 0.06 0.11 0.08*** 0.01 0.06 0.11 0.08*** 0.01 0.05 0.11

Not-for-profit f 0.04*** 0.01 0.02 0.06 0.04*** 0.01 0.02 0.06 0.04*** 0.01 0.02 0.05

Potential endogenous variables

System

membership

0.01 0.01 -0.003 0.02

0.01* 0.01 -0.002 0.03

Contract management -0.01 0.01 -0.20 0.004 -0.01*** 0.01 -0.26 -0.002

Health network 0.02*** 0.004 0.01 0.03 0.02*** 0.004 0.01 0.03

Munificence

Per capita income 0.0003 0.001 -0.001 0.001

Unemployment rate 0.0002 0.001 -0.002 0.002

Percent > = 65 0.05 0.13 0.19 0.30

Physicians/1000pop -0.001 0.003 -0.01 0.01

Complexity

HHI -0.01 0.01 -0.02 0.02

Excess capacity 0.00004 0.0001 -0.0002 0.0002

HMO penetration -0.00003 0.0003 -0.001 0.001

Dynamism

Change in unemployment

rate

-0.002 0.01 -0.02 0.01

Organizational factors

Hospital beds 0.0001*** 0.00004 0.00005 0.0002

Teaching status -0.0006 0.008 -0.016 0.01

Occupancy rate 0.08*** 0.02 0.050 0.11

Outpatient mix 0.05*** 0.01 0.022 0.05

Medicare mix 0.04** 0.02 0.015 0.07

Medicaid mix 0.007 0.02 -0.022 0.04

Overall F test 10.52* 9.66* 7.28* *p < 0.1 **p < 0.05 ***p < 0.001 a Model with for-profit and not for-profit dummies as independent variables b Model with for-profit and not for-profit dummies and potential endogenous variables

c Full model d Year dummy variables were included in the analysis, hospital-year is the unit of analysis

e CI means confidence interval f Public hospitals as control group g SE means standard error

127

Table 4.22

Results of Fixed-Effects Linear Regression on Total Margin Second Hypothesis d

Total Margin Model 1a (N = 6,579) Model 2b (N=6.579) Model 3c (N = 6,463)

Coefficient SE g 95% CIe Coefficient SE g 95% CIe Coefficient SE g 95% CIe

For-profit f 0.04 *** 0.01 0.02 0.05 0.03*** 0.009 0.02 0.05 0.03*** 0.01 0.02 0.05

Not-for-profit f 0.01 * 0.01 -0.0003 0.02 0.01* 0.006 -0.002 0.02 0.01* 0.006 -0.001 0.02

Potential endogenous

variables

System

membership

0.004 0.004 -0.005 0.01

0.004 0.004 -0.005 0.012

Contract

management

-0.006 0.004 -0.01 0.002

-0.01* 0.004 -0.02 0.0005

Health network 0.01*** 0.003 0.004 0.2 0.009** 0.003 0.004 0.12

Munificence

Per capita income 0.001** 0.001 0.0002 0.002

Unemployment rate 0.001 0.001 -0.002 0.0004

Percent > = 65 -0.26** 0.08 -0.42 -0.10

Physicians/1000pop -0.001 0.0021 -0.01 0.003

Complexity g

HHI 0.001 0.004 -0.01 0.01

Excess capacity -0.0001 0.0001 -0.0002 0.00003

HMO penetration -0.0004** 0.0002 -0.001 -0.0001

Dynamism

Change in

unemployment rate

-0.01* 0.01 -0.02 0.001

Organizational factors

Hospital beds 0.0003 0.00002 -0.00002 0.0001

Teaching status 0.002 0.005 -0.010 0.01

Occupancy rate 0.02* 0.01 -0.001 0.04

Outpatient mix 0.008 0.009 -0.01 0.03

Medicare mix 0.02** 0.01 0.005 0.04

Medicaid mix 0.006 0.01 -0.01 0.03

Overall F-test 8.22*** 7.58*** 5.79***

*p < 0.1 **p < 0.05 ***p < 0.001 a Model with for-profit and not for-profit dummy variable as independent variables b Model with for-profit and not for-profit dummy variable as independent

variables endogenous variables c Full model d Year dummy variables were included in the analysis, hospital-year is the unit of analysis e CI means confidence

interval f Public hospitals as control group g means standard error

128

Hypothesis 8 was supported. Model 1 found that hospitals that converted to for-profit

status had 9% higher operating margin than hospitals that remained public, p <0.001,

95% CI [0.06,0.11] relative to 4% higher for hospitals that converted to not-for-profit

status , p < 0.001, 95% CI [0.02, 0.06]. Models 2 and 3 found that compared to hospitals

that remained public, hospitals that converted to for-profit had 8% higher operating

margin, p < 0.001, 95% CIs [0.06, 0.11], [0.05,0. 11] relative to 4% higher for hospitals

that converted to not-for-profit status, p < 0.001, 95% CIs [0.02, 0.06], [0.02, 0.05] .

In addition, model 1 found that compared to hospitals that remained public,

hospitals that converted to for-profit status had 4% higher total margin, p < 0.001, 95%

CI [0.02, 0.05] relative to 1% higher, p < 0.1, 95% CI [-0.0003, 0.02]for hospitals that

converted to not-for-profit status. Model 2 and 3 found that compared to hospitals that

remained public, hospitals that converted to for-profit status had 3% higher total margin,

p < 0.001, 95% CI [0.02, 0.05], relative to 1% for hospitals that converted to not-for-

profit status, p < 0.1, 95% CIs [-0.002, 0.02], [-0.001, 0.02]. These results indicated that

1% increase in total margin for hospitals that converted into not-for-profit status was

marginally significant.

Furthermore, the joint tests confirmed that the difference in operating margin

between for-profit and not-for-profit hospitals were statistically significant at p < 0.001

across the three models, with for-profit hospitals having 5% higher operating margin for

model 1 and 4% higher operating margin for models 2 and 3. The difference in total

margin was also statistically significant at p < 0.001 for model 1 at p < 0.05 for models 2

and 3. For-profit hospitals had 3% higher total margin than not-for-profit hospitals for

model 1 and 2% higher total margin for models 2 and 3.

129

With respect to the potentially endogenous variables, participating in health

network was positively associated with both operating margin and total margin across

models 2 and 3. Compared to hospitals without network, those with network had 2%

higher operating margin, p < 0.001, 95% CI [0.01, 0.03], for both models 2 and 3. In

addition, hospitals with network has 1% higher total margin for model 2 and 0.9% higher

for model three at p < 0.001, p < 0.05, 95% CI [0.004, 0.2], [0.004, 0.12], respectively.

Membership of multihospital systems was marginally associated with 1% increase in

operating margin for model 3, p < 0.10; it was not significant for total margin. Contract

management was negatively associated with both operating margin and total margin.

Compared to hospitals not engaged in contract management, those with contract

management had 1% lower operating margin, p < 0.001, 95% CI [-0.26, -0.002] and 1%

lower total margin with marginal significance, p < 0.1, 95% CI [-0.02. 0.0005].

While no environmental variable was associated with operating margin, some

organizational variables hospital beds, occupancy rate, outpatient mix and Medicare mix,

were positively associated with operating margin. One hundred units increase in hospital

beds was associated with 1% increase in operating margin p < 0.001, 95% CI [0.00005,

0.0002]. One unit increase in occupancy rate was associated with 8% increase in

operating margin, p < 0.001, 95% CI [0.05, 0.11]. One unit increase in outpatient mix

was associated with 5% increase in operating margin, p < 0.001, 95% CI [0.022, 0.05]

and one unit increase in Medicare mix was associated with 4% increase in operating

margin, p < 0.05, 95% CI [0.015, 0.07].

130

Some environmental variables were associated with total margin. Per capita

income was positively association with total margin. An increase of $1,000 in per capita

income was associated with an increase of 0.1% in operating margin, p < 0.05, 95% CI

[0.0002, 0.002]. Variables percentage of population of 65 years of age and over, HMO

penetration and change in unemployment rate, were negatively associated with operating

margin. One unit increase in percentage of population of 65 years and older was

associated with 26% decrease in total margin p < 0.05, 95% CI [-0.42, -0.10] and one unit

increase in HMO penetration was associated with 0.04% decrease in total margin,

p < 0.05, 95% CI [-0.001, -0.0001]. One unit increase in change in unemployment rate

was marginally associated with 1% decrease in total margin, p < 0.10, 95% CI [-0.02,

0.001].

Occupancy rate and Medicare mix were the organizational variables associated

with total margin. One unit increase in occupancy rate was marginally associated with

2% increase in total margin p < 0.10, 95% CI [-0.001, 0.04] and one unit increase in

Medicare mix was associated with 2% increase in total margin, p < 0.05, 95% CI [-0.01,

0.03].

Conclusion

This study investigated the association of environmental factors, in terms of

munificence, complexity and dynamism with public hospital financial distress; the

association of organizational factors, in terms of hospital size and teaching status with

public hospital financial distress; the effect of financial distress on public hospital

privatization, and the impact of privatization on financial performance. This study found

that environmental munificence and environmental dynamism was not associated with

131

financial distress; environmental complexity, in terms of HMO penetration, was

positively associated with the odds of being in financial distress. In addition, this study

found that hospital size, in terms of number of beds was negatively associated with the

odds of being in financial distress. Financial distress was associated with privatization

and privatization had a positive association with financial performance. Additionally,

public hospitals that converted into for-profit status had better financial performance

relative to public hospitals that converted into not-for-profit status. Though no

environmental variable was positively associated with operating margin, variables

percentage of population 65years of age or older, Medicare HMO penetration, and yearly

change in unemployment rate had a negative impact on total margin. Table 4.23

summarizes the results of hypothesis testing and table 4.24 summarizes the statistically

significant associations between the dependent variable and independent variables

pertaining to each research question.

132

Table 4.23

Summary of Findings from Hypotheses Testing

Hypothesis

Statement Findings

Hypothesis 1 Public hospitals operating in a more munificent environment are less likely to experience financial

distress than public hospitals operating in a less munificent environment.

Not supported

Hypothesis 2

Public hospitals operating in a more dynamic environment are more likely to experience financial

distress than public hospitals operating in a more stable environment.

Not supported

Hypothesis 3 Public hospitals operating in a more complex environment are more likely to experience financial

distress than public hospitals operating in a less complex environment.

Partially

supported

Hypothesis 4 Larger public hospitals are less likely to experience financial distress than smaller public hospitals. Supported

Hypothesis 5 Public hospitals affiliated with a medical school are less likely to experience financial distress than

non-affiliated public hospitals.

Not supported

Hypothesis 6 Public hospitals in financial distress are more likely to privatize than public hospitals exhibiting

higher financial performance. Supported

Hypothesis 7 Privatized public hospitals have higher financial performance after privatization than before

privatization. Supported

Hypothesis 8 Public hospitals that converted into for-profit status exhibit higher financial performance compared

to public hospitals that converted into not-for-profit status. Supported

133

Table 4.24

Summary of Findings from Each Research Question a

Antecedents of

Financial

Distress b

Impact of Financial

Distress on

Privatization c

Impact of Privatization on Financial

Distress b

Operating Margin Total margin

Endogenous variables

System

membership + + + Contract

management - - - Health network - + + Munificence Per capita income +

Unemployment rate

Percent > = 65 - Physicians/1000pop Complexity HHI Excess capacity HMO penetration + - Dynamism

Change in unemployment

rate - Organizational factors Hospital beds - + Teaching status + Occupancy rate - + + Outpatient mix - + Medicare mix + + Medicaid mix a Negative sign indicates negative association with dependent variable and positive sign indicates positive association b Results taken from the full model c Results compiled from models 1 and 2

134

CHAPTER 5

DISCUSSION

The aim of this chapter is to further discuss the findings and results presented in

chapter 4, compare these findings with findings from prior studies, and discuss the

managerial and policy implications of these findings as well as the limitations of this

study.

The purpose of this study was to investigate the environmental and organizational

factors associated with financial distress, explore whether financial distress precedes

privatization and assess whether privatization leads to better financial performance.

Financial distress was measured using the Altman Z-score method that combined the

mostly used financial ratios in one discriminant function. The discriminant function

customized for service and retail firms was applied in this study. Based on these

questions, eight hypotheses were tested on a national sample of public hospitals using a

panel longitudinal data from 1997 until 2009. The following section discusses the

findings from each hypothesis pertaining to each research question.

Discussion of Findings from Research Question 1

Research question 1 explored whether environmental factors and organizational

factors were associated with financial distress. The first three hypotheses investigated the

association between environmental factors and financial distress. Langabeer, 2006 is the

only study on health care organizations that applied the Altman Z-score method using the

135

same discriminant function for service and retail firms used in this study, but he did not

include environmental variables. Langabeer (2006) predicted financial distress of

teaching hospitals with special focus on organizational factors. Kim (2010) studied

financial distress of nonprofit hospitals using a different approach of measuring financial

distress. He used financial strengths index, a composite measure of profitability, liquidity,

financial leverage and physical facilities. Kim (2010) used both environmental and

organizational variables. Though Langabeer (2006) and Kim (2010) did not exactly use

the same methods and variables as in this study, they are the mostly comparable with the

results from research question 1.

Findings from Hypothesis 1

Hypothesis 1 proposed that environmental munificence, in terms of per capita

income, county unemployment rate, percent of elderly people of 65 years of age and

older, and number of active physicians per 1000 population, was negatively associated

with financial distress. Hypothesis 1 was rejected, indicating that lack of resources in the

environment had no direct impact on financial distress. This finding is not consistent with

Kim (2010), which found a significant positive associated with higher unemployment rate

and financial distress. Other findings from Kim (2010) are contrary to the expected

direction of the association between environmental variables and financial distress;

higher percentage of physicians and higher percentage of population 65 years and older

were positively associated with financial distress.

136

Findings from Hypothesis 2

Hypothesis 2 proposed that environmental dynamism, in terms of yearly change

in county unemployment rate was positively associated with public hospital financial

distress. This hypothesis was not supported, suggesting that environmental dynamism had

no direct impact on financial distress. No other empirical study on hospital financial

distress used this measure of environmental dynamism, so it cannot be compared to prior

studies.

Findings from Hypothesis 3

Hypothesis 3 proposed environmental complexity in terms of market competition,

excess capacity, and HMO penetration was positively associated with financial distress.

This hypothesis was partially supported; higher Medicare HMO penetration was

associated with greater odds of being in financial distress; this finding corroborates the

finding from Kim (2010). This finding might indicate the consequence of aggressive

pricing HMOs exert on their contractors which squeezes margins and consequently affect

hospital financial performance.

While this study did not find a significant association between HHI and financial

distress, Kim (2010) found that high market concentration was negatively associated with

financial distress among not-for-profit hospitals indicating that higher competition (lower

HHI) was associated positively associated with financial distress. The next two

hypotheses, hypothesis 4 and hypothesis 5 tested whether organizational factors, hospital

size and teaching status, were negatively associated with financial distress.

137

Findings from Hypothesis 4

Hypothesis 4 proposed that large public hospitals, in terms of number of hospital

beds, were less likely to experience financial distress. Hypothesis 4 was supported; this

finding was stronger than the results from Langabeer (2006). Though Langabeer found a

strong and significant positive correlation between number of beds and Altman Z-score

(correlation = 0.832, p<0.001), the regression analysis from Langabeer did not find a

significant association between number of beds and financial distress (Langabeer, 2006).

Similar to Langabeer (2006), Kim (2010) did not find a significant association between

number of beds and financial distress. The highly significant finding from this study

corroborates the results from prior studies suggesting organizational size has a positive

impact on financial performance (Goll & Rasheed, 2004; Kotha & Nair, 1995;

McNamara et al., 2002; Simerly & Li, 2000; Wan & Yiu, 2009). Several factors such as

economies of scale (Hall & Weiss, 1967), accumulation of slack resources (Sharfman et

al., 1988; Wan & Yiu, 2009; Dawley et al., 2003), and bargaining power over suppliers

(Porter, 1985) were found to be among the reasons for positive association between

organizational size and financial performance.

Findings from Hypothesis 5

Hypothesis 5 proposed that compared to non-teaching hospitals, teaching

hospitals were less likely to experience financial distress. While the result shows the

expected direction of the association between teaching status and financial distress,

hypothesis 5 was not supported; this finding is not consistent with Kim (2010) who found

a positive association between teaching status and higher probability of financial distress.

138

The findings with respect to the association of potentially endogenous variables and

control variables with financial distress are discussed in the next section.

Multihospital system membership and health network had significant association

with financial distress. Hospitals members of multihospital systems were found to be

three times more likely to be in financial distress compared to stand-alone hospitals. This

finding is counterintuitive from the resource dependence theory which argues that

multihospital systems provide several resources to their members such as financial

support, technology and expertise, thus members are less likely to experience financial

distress. However, this association might support the argument that multihospital

membership is endogenous to financial distress. Hospitals in financial distress might seek

affiliation with multihospital systems to solve their financial problems. This finding

might also suggest that hospitals in financial distress are easy targets for acquisition as

they can be purchased at bargain. Thus, acquisition of public hospitals in financial

distress could be a strategy for multihospital systems to expand their market share and

competitive position.

Participation in health network was negatively associated with financial distress.

This finding is consistent with the findings from prior studies. Some studies that

investigated the association between hospitals participation in health network and

financial performance found that network membership was positively associated with

financial performance (Bazzoli, et al., 2000; Broyles, Brandt & Biard-Holmes, 1998;

Nauenberg, Brewer, Basu, Bliss, & Osborne, 1999). Broyles et al. (1998) investigated

the impact of network membership on the financial performance of rural hospitals in

Oklahoma; their findings indicated that participation in a network was associated with

139

higher net cash flow and lower services costs, lower labor costs, and lower non-labor

costs (Broyles, et al., 1998).

The negative association between health network and financial distress can be

explained from resource dependence perspective. Healthcare organization network is an

example of interorganizational relations that healthcare organizations initiate to have

better access to resources. The resource dependence theory argues that organizations

engage in various kinds of interorganizational relationship to secure key resources for

their survival (Pfeffer & Salancik, 1978). In the healthcare setting, such resources include

financial and human resources, legal support, knowledge, technologies, and the capability

to deliver high quality care, among others (Bazzoli, Shortell, Ciliberto, Kralovec, &

Dubbs, 2001; Federico, 2005; Nauenberg, et al., 1999; Bazzoli et al., 2000).

Additionally, hospitals participating in a network enhance their images and

competitive positions that they can leverage in negotiating contract with managed care

organizations. Participants also get access to the additional knowledge and insights on

best practices. Moreover, healthcare organization networks increase participants‘

bargaining power with suppliers and purchasers; and they enhance economies of scale

and scope leading to cost savings and hospitals‘ efficiency and effectiveness (Bazzoli et

al., 2000; Broyles, et al., 1998; Federico, 2005). The strongest motive that leads

healthcare organizations to participate in a network is the expectation of higher financial

performance and enhanced efficiency (Nauenberg, et al., 1999).

Control variable outpatient mix was marginally and negatively associated with

financial distress. This finding was not consistent with Langabeer (2006) which found no

140

significant association between outpatient services and financial distress. However, the

negative association between outpatient service and financial distress supports the

argument that the provision of outpatient services is not highly controlled by Medicare

and Medicaid; as a result, providers can achieve better profit in providing a higher

percentage of outpatient services. Additionally, outpatient services are less resource

intensive, which enhances hospital efficiency.

Discussion of Findings from Research Question 2

Hypothesis 6 explored whether financial distress precedes privatization.

Hypothesis 6 was supported across the two models. No prior study applied the Altman Z-

score to predict privatization; it is not possible to compare this finding with findings from

prior studies.

This study also found positive associations with organizational variables teaching

status and multihospital system membership with privatization. Teaching status was

marginally associated with privatization in model 1, but the association was highly

significant in model 2 . This finding is not consistent with Sloan, Ostermann and Conover

(2003) that found no significant association between teaching status and conversion into

for-profit status. Since teaching hospitals require more resources than non-teaching

hospitals; they might prefer privatization as a strategy to enhance their access to

resources (Blumenthal & Weissman, 2000; Feder & Hadley, 1987). In addition, if

privatization of teaching hospital was achieved through acquisition by a multihospital

system, it can be explained from the acquirer‘s perspective. Acquiring a teaching hospital

141

is an attractive investment since it enhances the acquirer‘s access to medical expertise

and improves its image as owner of a teaching institution, which consequently reinforces

the private hospital chain‘s strategic posture (Blumenthal & Weissman, 2000).

This study also found that hospitals members of multihospital systems are more

likely to privatize relative to stand-alone hospitals. This association might be explained

from a political perspective. Given their size, multihospital systems can be highly

influential in the community and in politics; thus it is easier for them to convince the

community to convert a hospital. In addition, the financial burden that a struggling

hospital member imposes on the whole system might encourage the system to put

pressure on the hospital to privatize. Since the finding from research question 1 suggests

that hospitals in financial distress might seek affiliation with multihospital systems to

enhance their financial performance, if affiliation does not lead to a better financial

condition, then privatization could be the second solution.

Occupancy rate had a negative association with financial distress. This finding

was consistent with Amirkhanyan (2007); high occupancy rate was found to be

negatively associated with nursing home privatization. High occupancy rate reflects the

organization‘s market share and high market share indicates greater revenue and

consequently higher financial performance. Therefore, hospitals with high occupancy rate

are less likely to privatization. Contract management was found to be negatively

associated with privatization, this finding is logical. Since contractors manage the

hospitals, it is in their interest to keep the managerial control of the hospital, therefore,

the hospital is less likely to privatize if it is under contract management.

142

The findings that per capita income, unemployment rate, and percent of

population 65 years of age and older, HHI, and HMO penetration were not associated

with privatization were not consistent with prior study (Sloan, Osterman & Conover,

2003) which found that higher per capita income, higher unemployment rate, higher

percentage of elderly and higher HHI decreased the likelihood of conversion from public

or not-for-profit status to private-for-profit status. However, the negative association

between unemployment rate and conversion is counterintuitive (Sloan, Osterman &

Conover, 2003.) The discrepancies of the results from these studies might be due to the

fact that Sloan, Osterman & Conover combined public hospitals and private not-for profit

hospitals in one category. The next section discusses the findings from research question

3.

Discussion of Findings from Research Question 3

Research question 3 assessed whether privatization improved financial

performance was addressed by testing hypotheses 7 and 8. Hypothesis 7 proposed that

privatization was positively associated with financial performance. Hypothesis 8

proposed that hospitals that privatized into for-profit status exhibited higher financial

performance compared to hospitals that privatized into private not-for-profit status.

Financial performance was measured in terms of operating margin and total margin. Both

hypotheses were supported.

These findings with respect to the positive impact of conversion to for-profit

status on total margin are consistent with Shen (2003). The findings on the positive

impact of conversion to for-profit status on operating margin are consistent with Picone

143

et al., (2002). This finding confirms that privatization does improve financial

performance, and could be a better alternative to closure. In addition, if the hospital

purpose has shifted from delivering affordable care to profit maximization and then

converting to for-profit status is the best choice.

This study also found participation in health network, hospital beds, occupancy

rate, outpatient mix and Medicare mix to be positively associated with operating margin.

In addition, the association between multihospital membership and operating margin was

positive and marginally significant; contract management was found to have a negative

impact on both operating margin and total margin. The finding that multihospital system

membership is positively associated with operating margin seems to be inconsistent with

the results from research question 1 which suggested that membership of multihospital

system significantly increased the odds of being in financial distress. However, the use of

the Altman Z-score as indicator of financial distress instead of financial ratios such as

operating margin might explain this discrepancy; lower operating margin does not

necessarily indicate finanical distress. Furthermore, the association between multihospital

system membership with operating margin is marginally significant at p < 0.10.

An interesting finding from this study, which is counterintuitive, is the positive

association between Medicare mix and total margin and operating margin and the

negative association between percentage of population 65 years of age and older and total

margin. If the percentage of the elderly is greater, it should lead to greater Medicare mix;

therefore greater percentage of the elderly should enhance financial performance.

However, this discrepancy might be explained due to the fact that not everyone who is

Medicare eligible is enrolled in Medicare. Therefore, higher percentage of population 65

144

years or older could be associated with poorer financial performance. Older people

without Medicare coverage might result in a finanical burden to the hospital because

older people are on average sicker than younger people. The following section will

discuss the managerial and policy implications of the findings from this study.

Managerial and Policy Implications

The findings from this study provide some insights for health care management.

Hospitals have played an important role in health care delivery, yet they are highly

resource intensive. Therefore, monitoring the financial situation of hospitals should be

among the major responsibilities of management team. The Altman Z-score, which is a

managerial tool to watch the financial condition of an organization, has not been widely

applied in health care management and health services research. The findings from this

study suggested that the Altman Z-score can help determine public hospital financial

condition; the combination of the most important financial ratios into one discriminant

function facilitates the evaluation of the organization‘s financial condition. Though the

primary purpose of using the Altman Z-score is to detect financial distress, it could be

used as a managerial tool to regularly check the financial condition of the organization

(Calandro, 2007) and consequently solve the problem before it is too late.

This study also found that privatization leads to better financial performance;

therefore, management team could consider privatization as an alternative if financial

crisis and fiscal pressure occur. However, the decision to privatize a hospital should be

taken seriously. Though, this study and prior studies suggested that privatization have a

positive impact on financial performance, it can have some negative effects on other

145

dimensions of healthcare delivery such as access to and quality of care. For instance,

extant literature suggested that privatization resulted in decreased uncompensated care

(Thorpe et al., 2000; Desai, Lucas & Young, 2000; Needleman, Lamphere & Chollet,

1999), increased probability of trauma center closures (Shen, 2003), increased mortality

rate on patients with acute myocardial infarction (Shen, 2002), increased crude mortality

rate (Picone, Shin, Sloan, 2002), and increased pneumonia complication (Sloan, 2002).

Table 1.1 demonstrated the shrinking market share of public hospitals and public

hospital beds at the expense of private for-profit hospitals and since privatization is

mostly conducted through the sale of public hospitals to multihospital systems, there is

growing consolidation trend in the health care industry resulting in fewer competitors that

have the monopoly of certain markets. Hospital anti-trust laws should take a closer look

at every instance of privatization that involves acquisition to avoid the growth of fewer

but very large multihospital systems, which might results in aggressive pricing for

patients not covered by Medicare and Medicaid.

In addition, CON laws could be used to monitor privatization in order to make

sure that the needed services are not closed after privatization. The close monitoring of

health care quality after privatization is also needed to ensure that privatization leads to

improved quality of care as well as better financial performance. Higher quality of care

was found to be positively associated with better financial performance in the nursing

home industry (Weech-Maldonado, Neff & Mor, 2003).

146

In addition, the finding that participation in health network is negatively

associated with financial distress and positively associated with financial performance in

terms of both operating margin and total margin gives insight to managers to consider

participation in health network to enhance their financial situation. The finding from this

study also suggests that contract management is negatively associated with financial

performance in terms of both operating and total margins. Therefore, management team

should impose more stringent contract that ensures that the financial situation of the

hospital is improved through contract management if it plans to engage in such contract.

Among the environmental factors, Medicare HMO penetration was the major

factor positively associated with financial distress and negatively associated with

operating total margin. There is no way to avoid Medicare HMO penetration, but one

alternative to prevent its negative effect could be efficiency enhancement. Medicare

HMO contractors require efficient delivery of care, if the organization is efficient, then it

might get Medicare HMO contracts and able to increase revenue and enhance financial

performance.

This study also found that hospital size was negatively associated with financial

distress and positively associated with operating margin. This finding might explain the

merger and acquisition trend among hospitals, however bigger is better as long as it does

not lead to excess capacity and increased market concentration. Moreover, the finding

that greater outpatient mix was negatively associated with financial distress and

positively associated with financial performance makes the provision of outpatient

services an attractive strategy to health care providers. Overall, this study found that

compared to environmental factors, organizational factors have greater and more direct

147

impact on public hospital financial condition. Therefore paying more attention to

organizational factors might be more helpful than spending time worrying about the

environment. The following section discusses the limitations of this study.

Limitations of the Study

This study has some limitations with respect to the variables included in the

analysis. Given that this study was conducted with data covering 13 years, from 1997 to

2003, some of the variables deemed appropriate for this study could not be captured. For

example, some studies have demonstrated that the measure of environmental munificence

in terms of number of active physicians in the county could be more appropriate if the

separate number of specialists per capita and generalists per capita were used; high

number of primary care physicians per capita might represent a ―negative munificence‖

for hospitals as they can prevent hospitalization (Menachemi et al., 2011). However, the

number of specialists per capita was not included in the ARF data before 2005. The same

situation applied to hospital size in terms of number for beds. Using the number of

licensed beds is more appropriate as it reflects the actual number of beds the hospital can

legally install; however, the number of licensed beds was not included in the AHA data

before 2003.

This study is also limited due to the fact that variables to measure political

environment and tax rate were not available. Since public hospitals are burdened with

politics, controlling for political environment might yield different results. The same

concern applies to the lack of tax rate variable. Since public hospitals rely on

148

government funding, which is contingent on tax income, controlling for tax rate might

provide different results.

The unavailability of data with respect to the proportion of privately insured,

underinsured and uninsured patients was another limitation to this study. Since the role of

public hospitals as safety net providers requires them to provide health care services

regardless of the patient‘s ability to pay, including the proportion of privately insured and

uninsured patients could have given additional insights on whether serving patients other

than those enrolled in Medicare and Medicaid has an impact on financial performance

and privatization. The next section discusses the direction for future research.

Directions for Future Research

This study demonstrated the dearth of empirical studies on public hospital

privatization as well as the lack of the application of the Altman Z-score model in studies

of financial performance of different health care organizations such as independent group

practice, HMOs, critical access hospitals, nursing homes, and ambulatory care hospitals.

While several studies combined public hospitals and private not-for-profit hospitals in

one category this approach might not yield reliable results since public hospitals and

private not-for profit hospitals do not have the same operating context (Sloan, Osterman

& Conover, 2003; Sloan, 2002; Picone et al., 2002; Shen, 2002, 2003; Anderson et al.,

2003). Therefore, future studies should consider studying public hospitals as a separate

entity.

149

In addition, studies on ownership conversion from private not-for-profit status to

for-profit status have drawn the attention of most research, leaving a gap in the literature

with respect to public hospital privatization. Further empirical studies on public hospitals

are needed in terms of the impact of privatization on patient satisfaction, employee

satisfaction, physician satisfaction, competitive landscape, pricing of health care services,

access to health care services, and quality of care.

Conclusion

This study investigated the antecedents and consequences of public hospital

privatization with focus on financial distress and financial performance and from the

resource dependence perspective. A national sample of public hospitals using data from

1997 to 2008 was used in this study, environmental variables and organizational variables

were used in the analyses. The Altman Z-score method was applied to detect financial

distress.

The findings from this study suggested that environmental variable HMO

penetration was positively associated with financial distress; organizational variables

health network, hospital beds and outpatient mix were negatively associated with

financial distress; and membership of a multihospital system was positively associated

with financial distress, indicating that membership of a multihospital system is

endogenous to financial distress. Additional findings indicated financial distress preceded

privatization and privatization resulted in enhanced financial performance in terms of

operating margin and total margin.

150

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164

APPENDIX A

Table A.1

Summary of Empirical Studies on the Consequences of Privatization Determinants and Antecedents of Privatization in

Healthcare Organizations

Authors Purpose Data and Methods Variables Results

Frank Sloan

Jan Osterman

Christopher Conover,

2003

Investigated the antecedents

of hospital conversions,

mergers, and closures.

Conversion from NP or

Gov to FP

Theory: Finance/economic

theory on Net Present Value

of future cash flow from

operations and from sale of

the facility

Data: 1985-1996

AHA 1985-1996

Medicare Cost Report

Area Resource File

Data from Mark(1999)

N= 5,089 (all short stay

general and surgical non-

federal community hospitals.

Methods

Multinomial logit model for

the three strategies

(conversion, merger, closure).

Each observation was entered

in the model 11 times.

2 logistic regressions for

ownership change from NP or

Gov to FP and from FP

Dependent variables

Multinomial model

Probability of conversion,

merger or closure

Logistic regression

Probability of ownership

conversion from NP or

Gov to FP

Probability of conversion

of from FP to NP or Gov

Independent variables

For multinomial logistic and

regular logistic regressions

Hospitals financial health

Operating margin

Operating margin squared

Operating margin cubed

Debt-to-capitalization

County demand

characteristics

Percent 65 years old or

older

Per capita income ($1995)

Unemployment rate

Population density

Percent HMO enrollees

County supply

Conversion from NP or

Gov to FP was preceded

by chronically low margins

and high debt-to-asset ratio

Conversion from FP to NP

or Gov occurred quickly

after a decline in profit

margin

Conversion occurred more

in the South

Higher percent of elderly

people, higher debt-to-asset

ratio, higher percentage of

HMO enrollees, higher

unemployment rate, and

higher percentage of

Medicaid patients were

associated with higher

likelihood of closure

Large hospital size and

higher occupancy rate were

associated with lower

probability of closure

Higher per capita income

and higher unemployment

rate were associated with

lower probability of

conversion

165

Authors Purpose Data and Methods Variables Results

characteristics

Short-stay hospital bed-to-

population ratio

Herfindahl index

Hospital structure

Bed size (licensed)

Occupancy rate

Major teaching hospital

Minor teaching hospital

Public ownership

Nonprofit ownership

Percent Medicaid (% of

hospital days)

Higher per capita income

was associated with higher

probability of merger

High operating margin was

associated with lower

probability of conversion,

merger or closure

Higher competition was

associated with higher

probability of merger and

closure

Public hospitals and not-

for-profit hospitals were

less likely to engage in

merger compared to for-

profit hospitals

Anna Amirkhanyan,

2007

Investigated the

determinants of public

nursing homes privatization

Gov to FP or NFP

Data:1998-2000

OSCAR

N= 622 county public

facilities

Logistic regression analysis

3 models (privatization vs. no

privatization)

Multinomial logistic

regression: privatization vs.

closure vs. no change

Dependent variable

From Gov to NP/NFP

Closure

No change

Independent variables

% county population in

poverty

Nursing staff hours per

resident per day

Facility occupancy

% county Medicaid

residents in the facility

% elderly (65+) population

in the county

Physical deficiencies

Facility size

# home health agencies in

the county

# facilities privatized in

the state

Variables associated with

privatization

Low occupancy rate

HHI

High % elderly

Smaller facility

Non-affiliation to a hospital

Higher staffing hours per

resident per day

Higher number of

privatized facilities in the

state

Fewer number of public

homes

166

Authors Purpose Data and Methods Variables Results

Anna Amirkhanyan,

2007 (continued)

# public facilities in the

state

# facilities in the county

that pay for Medicaid

Facility in a state with

CON regulation

Organized groups in the

facility

Facility is hospital

affiliated

Divested facility

HHI

Table A.2

Consequences of Privatization on Financial Performance in Healthcare Organizations

Authors Purpose Data and Methods Variables Results

Gabriel Picone

Shin Yi-Chou

Frank Sloan, 2002

Investigated the

consequences of hospital

conversions from NP or Gov

to FP and vice versa on

mortality rate and Medicare

payment per hospital stay

Data : 1982, 1984, 1989,

1994

National Long-Term Care

Survey

AHA

Medicare Cost Reports

AHA and MCR data were

used to validate ownership

conversion info

Data was computed over 5

years before conversion and

three years after conversion

N= 3,645 hospitals

N= 73,503 Medicare

enrollees

Dependent variables

Mortality rates

Medicare payment per

admission

Hospital level of analysis

Operating margin

Bedsize

Total employment

Total wage and salary

Total wage to annual

number of adjusted patient

days (output)

Total salary to annual

number of adjusted patient

days (output)

Patient level of analysis

For conversion from FP to

NP or Gov

Mortality rate decreased

after conversion from FP

to NP or Gov

Medicare payment

monotonically increased

from 5 years before

conversion up to 3 years

after conversion

For conversion from Gov

or NP to FP

Crude mortality rate (no

control variable) increased

after conversion from NP

or Gov to FP

167

Authors Purpose Data and Methods Variables Results

Hospital level analysis

Patient level analysis

Primary Sampling Unit

(PSU) fixed effects

regression

Hospital fixed effects

regression

(Difference-in-difference)

Diagnostic cost group

score (DxCG)

Activity of daily living

(ADL)

Predicted mortality at one

year

Medicare payment

monotonically increased

from 5 years before

conversion up to 3 years

after conversion

Increased operating margin

Decreased employment

Decreased salaried

Decreased output

Yu-Chu Shen, 2003

Yu-Chu Shen, 2003

(continued)

Investigated the effects of

conversion on financial

performance, staffing,

capacity and unprofitable

care

Conversions in all directions

Data; 1985-1998

1985-1999

AHA, ARF

Conversion between 1987

and 1998 was analyzed (2-

year pre-conversion

information)

N=5,200 hospitals

Used 2-year pre-conversion

data

Hospital fixed-effects model

for the entire sample and a

matched sample of converted

and non-converted hospitals

using propensity score

method (difference-in-

difference)

Dependent variables

Financial performance

Total profit margin

Operating cost per

discharge

Patient revenue per

discharge

Staffing ratios

FTE per hospital bed

FTE registered nurses per

hospital bed

FTE licensed practical and

vocational nurses per

hospital bed

Capacity and patient load

Number of hospital beds in

general services

Total number of patient

discharges

Total number of outpatient

visits

Unprofitable care

Admission to emergency

room

Share of total Medicaid

discharges

NP to Gov and FP

increased profit margin

Gov or NP to FP reduced

workforce

GOV or NP to FP

decreased operating cost

per discharge

Decreased staffing ratio

after Gov or No to FP and

Gov or FP to FP

Gov or NP to FP increased

the probability of trauma

center closures, increased

share of outpatient visits

in the emergency

department, increased

Medicaid discharge

No type of conversion

affected the amount of

unprofitable care

168

Authors Purpose Data and Methods Variables Results

Independent variables

All types of conversion

Control variables

Hospital fixed effects

Teaching status

Hospital market area

(within 15-mile radius

from zip-code location

HHI

Percentage of FP, NP, Gov

hospitals in the area

Urban/rural

Anonymous

Anonymous

Investigated the effects

conversion from Gov to FP

and Gov to NP on efficiency

and quality.

Hospitals in Germany

Data: 1996-2007

N= 1,015 hospitals that

received DRG payment from

social health insurance and

private health insurance

companies

Data envelopment analysis

(DEA)

Random effects linear

regression for longitudinal

model with DEA efficiency

scores

Propensity score

Input variables

Supplies expenses

including operational

expenses, capital and

depreciation expenses

FTE clinical staff

FTE nursing staff

FTE medical-technical

staff

FTE administrative staff

FTE other staff members

Output variables

# treated patients per case

per year

1-Average mortality rate

Control variables

Staffed Beds

HHI

Teaching status

Beds hired to self-

employed ambulatory

physicians

% patients over 65

Case mix

Positive impact of

privatization on hospital

efficiency

Gov to FP outperformed

Gov to NP

Less competitive markets

enhanced efficiency

Larger effect of

privatization on efficiency

after DRG implementation

in 2003

169

Table A.3

Healthcare Industry: Case Studies

Authors Purpose Methods Findings

Frank Sloan

Donald Taylor

Chris Conover, 2000

Investigated the causes

and effects of conversion

on financial performance

To see whether the

community received a fair

price from sale or lease of

hospitals

All conversion types

10 case studies of hospitals in

Tennessee, North and South Carolina

Mixed methods: case study and

quantitative

1 case on Gov to NP

2 cases on Gov to FP

Data:

AHA 1987-1995

MCR

Tennessee Joint Annual Reports of

Hospitals

Consultation with board

Motives for conversion: to enhance financial

viability, to preserve local jobs, efficiency,

Consequences of conversion

Maintained hospital viability

Increased profitability

Concern of loss of control to an organization

controlled by outsiders.

From Gov or NP to FP

Decreased availability of services for community

health and patient education

Decreased Medicare share

Higher real rate of return vs. cost of capital (1

case)

Lower real rate of return vs. cost of capital (1

case)

From Gov to NP

Higher real rate of return vs. cost of capital

Mark W. Legnini

Stephanie E. Anthony

Elliot K. Wicks

Jack A. Meyer

Lise S. Rybowski

Larry S. Stepnick

1999

Studied the determinants and

effects of privatization of

public hospitals from private

NP and quasi-public.

5 public hospitals in 5 states

Privatizations resulted from

lease, merger, sale,

management contract,

consolidation, joint venture,

partnership and system

affiliation

Site visits (two hospitals)

Intensive structured interviews of 10

to 20 individuals per site

Phone interviews

Other data

AHA data, Newspapers

Additional research to describe and

analyze 10 more conversions

Motivations for conversion

Financial difficulties due to increasingly

competitive environment

Attract broad range of patients who can afford to

pay healthcare services

Freedom from public governance constraints

Access to capital

Motivation of private sector to expand market

power

Strategy for long-term survival of private

hospitals

Process of conversion

Long political processes: need to embrace

―perceived opposition‖ and ―appease affected

170

Authors Purpose Methods Findings

Mark W. Legnini

Stephanie E. Anthony

Elliot K. Wicks

Jack A. Meyer

Lise S. Rybowski

Larry S. Stepnick

1999 (continued)

parties‖

Private organizations that succeeded in the

transactions had good track record in serving the

needy and the vulnerable and exhibited

acceptable concern for the hospital‘s mission.

Challenges of public hospitals

Public hospitals faced more constraints than

private hospitals: fewer patient care revenue,

financial burden of charity care, loss of Medicaid

patients to private hospitals

Increasing number of uninsured patients

Randall Bovbjerg

Jill Marsteller

Frank Ullman, 2000

Studied the effects of public

hospitals closures or

conversion on the healthcare

for the poor and uninsured

Reasons and effects of

conversion

Case study

Studied 5 public hospitals

Reasons of conversion

Inefficiency

Competition

Political preferences

Effects of conversion

Improved efficiency

Overall access for the uninsured remained

unchanged as before (3 converted hospitals)

Limited local funding

More importance of state and federal funding

171

Authors Purpose Methods Findings

Lawton Burns

Rajiv J. Shah

Frank A. Sloan

Adam Powell

2009

Lawton Burns

Rajiv J. Shah

Frank A. Sloan

Adam Powell

2009 (continued)

Studied the impact of

ownership conversion on

hospitals strategy content,

process and some hospital

operation and intended and

realized strategy on 16

hospitals

Different types of

conversions:

NP to FP

FP to NP

Gov to NP

Gov to FP

Applied the strategic management

perspective

On site and phone interviews

Site visits (5 hospitals)

Focus group of executives

Intentions to change ownership (intended

strategy) were driven by

Motivation for governance change

Financial consideration

Current and or anticipated financial crisis

Lack of funds for capital investment

Lack of access to bond markets for government

owned hospitals

Inability to compete for managed care contract

Financial and market forces drive ultimate

decision for NP converting to FP

Gov and NP preferred NP buyer but settle with

FP because of financial considerate

Failure of FP to make needed capital investment

(FP to NP)

Antipathy to large systems and FP ownership

(FP to NP)

Limitation of public ownership in implementing

competitive strategy and positioning (Gov to NP

and Gov to FP)

Avoidance of public bidding and potential sale to

FP which would ultimately close the facility

(Gov to NP)

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