who self-insures in workers - american risk and insurance

Asymmetric Information: Evidence from Self-Insured Hospitals July 6, 2007

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Asymmetric Information: Evidence from Self-Insured Hospitals in PA for Workers’ Compensation Liability

Mu-Sheng Chang*

Temple University

Abstract

The objective of this paper is to dissect the determinants of why hospitals self-insure to assume

their workers’ compensation (WC) liability. Using cross-sectional hospital data in Pennsylvania

(PA), this empirical study examines firm-specific characteristics of hospitals, such as their

financial condition, organizational structure, facilities, accreditation status, and market factors. The

principle finding of logistic regressions suggests a significant link between the control type of

hospital and self-insurance for worker’s compensation coverage. Nonprofit hospitals are more

likely to self-insure, while for-profit hospitals, mainly managed by multi-state health care systems,

prefer market insurance to avoid burden in compliance with self-insurance laws in various states.

Aside from large firm size, self-insured hospitals are associated with higher severity of treated

patients, membership in a health care system, and concentration of businesses within a state. This

analysis also verifies the competition hypothesis that a higher percentage of self-insured

competitors in the market is related to an increased likelihood of self-insurance among other

hospitals. The prevalence of self-insurance among hospitals provides vivid evidence that

employers in high-risk industries are quite often driven to choose alternative risk transfer because

of asymmetric information in the commercial market of workers’ compensation.

________________ * The author is currently a Ph.D. student at Temple University. I am indebted to my advisors, Thomas Getzen, Mary A. Weiss (Chair), and Jacqueline Zinn for their supervision and numerous discussions throughout my dissertation. I am grateful to George Knehr, Gloria J. Bazzoli, and Tim Wisecarver for providing data and valuable insights. I have benefited from comments made by J. David Cummins, Guy David, R. B. Drennan Jr., Barbara B. Manaka, and David B. Smith. I appreciate the shared insight regarding self-insurance offered by many professionals, such as Kenneth M. Hoffman at the University of Pennsylvania Health System, Thomas F. Johnston at Temple University, Danielle McNichol and Charles Sanbe, Jr. at Temple University Health System, Ted Schlert at Catholic Health East System, Dianne Salter at Jefferson Health System, Frank Cummings and Ron Fabrizio at Abington Memorial Hospital, Richard Graham at Crozer-Keyston Health System, Valerie Cupo at Universal Health Services, Inc., Cheryl Lutz at Geisinger Health System, and John Kristel and Bruce Jones at Community Health Services. All errors remain my responsibility. Contact author: Mu-Sheng Chang, [email protected], Tel: (215)253-5889, Fax: (215)204-4712

mailto:[email protected]


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I. Introduction

Akerlof (1970) put forth the “lemons” principle that “bad” products drive “good” products from the

marketplace if information is asymmetrically distributed between buyers and sellers. He pioneered the

research on asymmetric information through his concept of classifying consumers using the

“good/bad” dichotomy. A similar argument is made by Harrington and Danzon (2000, 2001) that low-

risk firms are driven out of the market and self-insure for workers’ compensation (WC) coverage,

whereas high-risk firms are less likely to self-insure because of cross subsidies from low- to high-risk

firms in the commercial market. Yet, firm-level empirical evidence has not been published to lend

support to their inference. Due to asymmetric information between insurer and insured, employers with

good risk may not be able to obtain traditional insurance coverage at rates reflecting their individual

risk level but at the higher (average) market rates instead.1 Thus, they may resort to an alternative risk-

financing vehicle of self-insurance to assume their liability for WC coverage.

This article examines the determinants of self-insurance for WC among hospitals in

Pennsylvania. WC is mandatory for employers, and employers can choose to cover their liability

through either market insurance or self-insurance.2 Self-insurers retain risk on their own, and self-

insurance is usually regarded as one of risk transfer alternatives. Chang and Weiss (2007) indicate that

the main growth in self-insurance for WC comes from health services, and hospitals are the primary

establishments in this sector.3 Moreover, Butler and Worrall (1993, p.130) claim that understanding

why firms choose to self-insure is important because it offers an example of how firms make choices

under uncertainty. Investigation into the factors related to self-insured hospitals can offer a good

window into why employers choose alternative risk transfer rather than purchase commercial insurance.

Fundamentally, hospitals should choose to self-insure if the benefits from self-insurance exceed the

1 The picture of Alternative Risk Transfer (ART), authored by Holzheu, Karl, and Raturi, Swiss Re, Sigma No. 1/2003, p.12. 2 According to State Workers’ Compensation Laws, published by DOL in January 2006, only two states have had an elective type of WC law: New Jersey and Texas. In New Jersey WC coverage is technically elective, but in practice is virtually compulsory. 3 Chang and Weiss (2007) suggest that the health sector is significantly associated with self-insurance in their state-level research on self-insurance and market insurance. Source: Bureau of Labor Statistics.


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costs involved with traditional insurance. Because WC is a major risk exposure for firms,

understanding the benefits of self-insurance is critical to employers who must make decisions about

risk management.

Regulatory permission for self-insurance has been in existence in most states for decades.4 In

2004, the share of benefits paid by self-insured employers accounted for 24 percent of the total benefits

in WC, up from around 15 percent in the late 1970s.5 The emergence of self-insurance in the WC

market reflects an increased interest in risk retention and an enhanced capability to predict losses with

accuracy. Furthermore, self-insurance represents about three quarters of the total alternative risk

transfer (ART) market, while WC accounts for the largest amount of self-insurance in the property-

liability insurance market.6 Certain characteristics of self-insurers are likely to be influential in the

choice of this alternative risk transfer mechanism.

This empirical analysis of self-insured hospitals in Pennsylvania (PA) uses data from the

American Hospital Directory (AHD) and the American Hospital Association (AHA). The self-

insurance status of each hospital is verified by the Self-Insurance Division of the Bureau of Workers’

Compensation in Harrisburg, PA. This type of firm-level data is unprecedented and unique in the

research on self-insurance for WC. The regression model used for the analysis has a binary dependent

variable: one if the hospital self-insures, zero otherwise. A logistic regression on cross-sectional

hospital data in 2005 is used to empirically examine the determinants of self-insurance.

The results of the logistic regressions suggest that the control type of hospital is significantly

linked to the choice of self-insurance. Nonprofit hospitals are, on average, larger than their for-profit

counterparts and more likely to self-insure their WC liability. Large hospitals may be able to meet the

financial requirements of self-insurance, and this would explain the positive association with self- 4 For details see State Workers’ Compensation Laws, published by the U.S. Department of Labor, or Chang and Weiss (2007). Only North Dakota and Wyoming do not allow self-insurance. Except for Texas which first allowed self-insurance in 1993, other states approved self-insurance before 1982. 5 Workers’ Compensation: Benefits, Coverage, and Costs 2004, published by National Academy of Social Insurance (NASI) in July 2006, Washington, DC, by Ishita Sengupta, Virginia P. Reno, and John F. Burton, Jr., Table 4, p.12. 6 The picture of Alternative Risk Transfer (ART), authored by Holzheu, Karl, and Raturi, Swiss Re, Sigma No. 1/2003, p.17.


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insurance. Large nonprofit hospitals that treat severe patient populations and operate solely in PA tend

to self-insure, while for-profit hospitals, mainly managed by multi-state health care systems, prefer

market insurance due to the burden of complying with regulatory requirements of self-insurance in

various states. Heterogeneous regulatory requirements state by state impede multi-state health care

systems to choose self-insurance. The analysis also verifies the competition hypothesis that the

presence of self-insured competitors in the market (i.e., a county) encourages more self-insurance

among other hospitals.

Most previous studies on WC are usually based on state-level analysis, suggesting that factors

leading to self-insurance consist of high WC cost, industry affiliation, large firm size, higher severity

of injury, and a cross-subsidy effect caused by the residual market (e.g., Baranoff 2000; Butler and

Worrall 1993; Carroll 1994; Harrington and Danzon 2000, 2001; Kwon and Grace 1996; Thomason,

Schmidle, and Burton 2001; Chang and Weiss 2007). However, firm-specific factors may be related to

a firm’s decision to self-insure (e.g., financial strength, organizational structure, services provided,

accreditation, and market concentration). After all, WC is purchased by individual employers, and they

make the decision whether to self-insure for WC liability. Besides, a residual market does not exist in

PA because of the presence of the State Workers' Insurance Fund (SWIF). In reality, residual markets

are only present in 25 jurisdictions.7 Implications merely based on residual market arguments fail to

account for self-insurance in those states without existence of the residual market.

In addition, the previous studies on asymmetric information primarily draw on auto insurance

for evidence. D’Arcy and Doherty (1990) suggest that adverse selection persists in the automobile

insurance market. Dionne and Doherty (1994) put forward an alternative model to address adverse

selection and commitment, and their evidence from California suggests some automobile insurers

attract portfolios of predominantly low-risk drivers. Unlike auto insurance bought by individual

7 According to Residual Market Management Summary 2005, published by NCCI, 25 jurisdictions have residual markets, including District of Columbia. Residual market is created for the employers that can’t buy insurance in the voluntary market. For a detailed discussion see Chang and Weiss (2007).


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customers, WC insurance is purchased by employers in various industries. In general, an individual

customer can be classified easily as high or low risk. Nonetheless, it is more complicated to categorize

employers than auto drivers because there are good and bad risks in the high- or low-risk industries.

Thus far, existing research on information asymmetry or adverse selection has not obtained evidence

from WC.

This research expands the domain of the literature in several ways. This is the first study to

provide firm-level analysis on self-insurance in WC. Micro analysis at the firm level complements the

state-level studies and allows consideration of factors associated with individual self-insurers. Second,

the popularity of self-insurance in hospitals demonstrates an empirical case that high-risk hospitals are

very likely to choose self-insurance. This case provides answer to the inference suggested by Butler

and Worrall (1993) that some firms are more efficient in providing self-insurance. Moreover, this

evidence is in stark contrast to the argument presented in the state-level studies that low-risk employers

are driven to self-insurance. Third, the evidence from WC offers additional insight to studies on

asymmetric information. Decisions of whether to choose self-insurance or market insurance made by

employers are different from those by auto-insurance buyers. Fourth, the data set from PA offers

supplementary evidence to existing studies because the decision to self-insure can be isolated from

residual market arguments. As self-insurance becomes more acceptable as a tool of risk retention, these

results can serve as a valuable resource for risk managers who consider this alternative financing

mechanism for WC coverage. Regulators can benefit from understanding the determinants of self-

insurance in effectively regulating self-insurers and look into ways of reducing regulatory burdens

brought on by different state-level requirements on self-insurance.

The remainder of this paper is structured as follows. Section II briefly describes the hospital

sector. Section III presents the self-insurance market in Pennsylvania and its regulatory environment.

Section IV develops hypotheses arising from the theoretical literature. Section V contains the details of


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the methodology, data, and results. Section VI contains discussions of the health care system and the

modification factor. Conclusions are summed up in Section VII.

II. Hospitals and Incidence Rates

The first hospital in the United States, the Pennsylvania Hospital, was founded by Benjamin Franklin

in 1757.8 Since then, nonprofit ownership has been the dominant organizational form in the hospital

industry. Two crucial regulations, which had a huge impact on the hospital landscape, are (1) the Hill-

Burton Act of 1948 and (2) the creation of Medicare and Medicaid in 1965.9 The first regulation

provided funds for communities across the country to construct non-profit hospitals to care for the poor.

The second regulation puts into social security coverage those older than 65 years of age and the

economically challenged, attracting more for-profit hospitals into the market. According to the 2007

AHA Hospital Statistics, 70 percent of total beds in the U.S. are associated with nonprofit hospitals,

while nonprofit hospitals just account for 60 percent of the total number of hospitals in 2005.

Nonprofit hospitals, on average, accommodate more beds than for-profit counterparts. Table 1 displays

general statistics about the number of hospitals and beds by hospital control type. In addition, nonprofit

hospitals provide community services in exchange for tax-exempt status, while for-profit hospitals are

subject to the business tax code. The former can issue tax-exempt bonds through local and state

governments, while the latter has access to the public capital markets in order to raise capital.10

Government hospitals are referred to as those controlled by state and local government.

8 Source: Encyclopedia of Public Health, edited by Lester Breslow, Macmillan Reference USA, Gale Group, 2002, Volume 2, New York, NY 10019. 9 See related information in Hospitals: What They Are and How They Work, 3rd edition, authored by Don Griffin, Sudbury, MA, Jones and Bartlett Publishers, 2006. 10 See David (2005) and Gray (1991) for a detailed discussion of for-profit and nonprofit hospitals.


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Table 1 Number of Nonfederal Hospitals and Beds in 2005

Control type Hospitals Beds Beds /Hospital Nonprofit 2,958 60% 561,106 70% 190 For-profit 868 18% 113,510 14% 131 Government 1110 22% 127,695 16% 115

Total 4,936 100% 802,311 100% Source: AHA Hospital Statistics 2007.

As far as nonfatal incidence rates are concerned, hospitals are as risky as firms in the

manufacturing sector. Nonfatal incidence rates represent the number of injuries and illness per 100

full-time workers, and these are indicative of the magnitude of occupational risk that workers face in

their workplaces. Chang and Weiss (2007) indicate that health services are characterized by high

nonfatal incidence rates but very low fatal rates in their industry-level analysis. 11 As the main

establishments in health services, hospitals have even higher nonfatal incidence rates than the average

for this sector.12 This high rate of nonfatal injury for employees in hospital settings exceeded the

average for all workplaces nationwide in the private sector. This is demonstrated in Figure 1 which

contains the nonfatal incidence rates over the period 1992-2001.

As evidenced by increasing numbers of occupational injuries and illnesses, rates of

occupational injury to health care workers have risen over the past decade. In contrast, two of the most

hazardous industries, agriculture and construction, are safer today than they were a decade ago.13

Health care workers are exposed to a wide range of hazards on the job, including needlestick injuries,

back injuries, latex allergies, violence in the workplace, and stress. Workers incur approximately 30

needlestick injuries per 100 beds per year at an average hospital (EPINet 1999).14 Some of these

11 Fatal rates in health services are even lower than those in the finance and business services sectors. 12 Source: Bureau of Labor Statistics. Hospitals account for about 40 percent of employment in health services each year from 1994 to 2000. 13 Source: Center for Disease Control, Health Care Workers: National Institute for Occupational Safety and Health, 2007, http://www.cdc.gov/niosh/topics/health care/. 14 Detailed discussion on needlestick injuries is available at Center for Disease Control: Preventing Needlestick Injuries in Health Care Settings. http://www.cdc.gov/niosh/2000-108.html#3. Precise national data are not available on the annual number of needlestick and other percutaneous injuries among health care workers; however, estimates indicate that 600,000

http://www.cdc.gov/niosh/topics/healthcare/


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injuries put health care professionals at risk of infection for bloodborne pathogens such as the hepatitis

B virus (HBV), hepatitis C virus (HCV), and human immunodeficiency virus (HIV). An infection with

any of these pathogens can be potentially life threatening. In addition, Nelson et al. (2003) indicate that

the prevalence of work-related back injuries in the nursing profession is among the highest of any

profession, and annual prevalence rates of back injury among nurses were 47% in the United States in

2000. The reason might be in large part due to the female workforce in health care. Women account for

nearly 80 percent of the health care work force, 15 and nurses represent the majority of the working

population in health care services (Peled 2005).

Figure 1 Nonfatal Incidence Rates for Hospitals: 1992-2001

02468

101214

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Non

fata

l rat

e

Private industry Health services Hospitals Manufacturing

Note: Nonfatal incidence rates represent the number of injuries and/or illnesses per 100 full-time workers, including cases with lost work & without lost work, and were calculated as: (N/EH) X 20,000,000 where: N = number of injuries and illnesses; EH = total hours worked by all employees during the calendar year; 200,000 = base for 100 equivalent full-time workers (Working 40 hours per week, 50 weeks per year). BLS Data after 1991 exclude fatal injuries & illness. Data for 2002 and beyond are not strictly comparable to prior year data due to changes in recordkeeping of Occupational Safety and Health Administration (OSHA). Source: Bureau of Labor Statistics (BLS).

to 800,000 such injuries occur annually (Henry and Campbell 1995; EPINet 1999). About half of these injuries go unreported (Roy and Robillard 1995; EPINet 1999; CDC 1997a; Osborn et al. 1999). 15 Same as footnote 14.


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III. Workers’ Compensation and Self-Insurance in Pennsylvania

Self-insurance is a risk transfer alternative to market insurance. With self-insurance status approved by

the state, the self-insured employer provides protection against loss by setting aside money for claims

and implements all of the services an insurer typically performs, such as claims management, actuarial

services, legal counsel, loss control, and administration of the program. Services can be carried out in

house or outsourced to the third party service providers. The permit to be a self-insurer is issued

annually by the state authorities, and security requirements are adjusted correspondently.16 Self-insured

employers maintain direct responsibility for the payment of the claims filed by the workers who

become ill or are injured in the course of employment. Self-insurers pay the losses incurred by their

own employees, have control over the claims, and increase their cash flow rather than pay premiums

for commercial insurance up front.17

Pennsylvania Workers’ Compensation Act went into effect in 1915. Since then, employers with

proper financial strength have had the option of covering their liability through self-insurance.

According to the report from the Self-Insurance Division of the Bureau of WC in PA, there are 788

individual self-insurers and 18 group self-insurance funds as of March 15, 2006.18 In 2005, self-

insured employers paid $22 of every $100 WC benefits in PA.19 Moreover, the share of WC benefits

provided by self-insured employers in PA is pretty close to the national average in the United States.

Self-insurers in health services (SIC 80) represent the largest portion of self-insurance in PA

with respect to the number of self-insured employers, the number of covered workers, or the amount of

covered payroll. Share of workers covered by self-insured hospitals account for more than 17 percent

16 Security are surety bonds, letters of credit or cash or negotiable government securities held in trust to be used for the payment of a self-insurer’s workers’ compensation liability upon order of the Bureau of Workers’ Compensation if the self-insurer fails to pay its liability due to its financial inability or due to the self-insurer filing for bankruptcy or being declared bankrupt or insolvent. Source: Pennsylvania Workers’ Compensation Act, chapter 125.2. 17 Some self-insurers still have to buy excess insurance for risk beyond their retention. 18 The ad hoc data offer was in response to the request made by Mary A. Weiss and the author to the Self-Insurance Division, Harrisburg, PA, and is unpublished data. We comply with the confidentiality agreement set by the Division. 19 Pennsylvania Worker Compensation and Workplace Safety: Annual Report Fiscal Year 2005/06, Department of Labor and Industry, Commonwealth of Pennsylvania.


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of total workers covered by self-insurance programs in PA. Table 2 highlights the prevalence of self-

insurance in health services in the Commonwealth of Pennsylvania. An interesting finding is that the

low-risk industries of Finance, Insurance, and Real Estate (SIC 60), and Business Services (SIC 73) do

not show much interest in choosing self-insurance as a risk transfer vehicle for WC.

Table 2 PA Individual Self-Insured Employers by Industry

SIC Industry \ Percent \ Criteria Share of total self-insured employers

Share of total covered workers

Share of total covered Payroll

01 Agriculture, Forestry, & Fisheries 0.9 0.4 0.4 10 Mining 2.4 0.8 1.0 15 Construction 1.1 0.0 0.0 20 Manufacturing 18.3 9.6 13.9 40 Transportation, Communications, & Utilities 8.9 6.0 7.8 50 Trade (Wholesale & Retail) 6.3 10.9 5.2 60 Finance, Insurance, & Real Estate 2.0 4.4 4.9 Service (subtotal SIC 70-89) 52.5 49.1 45.8

70 Hotels & Lodging 1.8 0.3 0.2 72 Personal Services - - - 73 Business Services 1.9 2.3 0.1 75 Automotive Repair & Parking 0.1 0.0 0.1 79 Amusement & Recreation - - - 80 Health Services 42.5 30.1 31.5

(806) (Hospitals) (11.2) (17.4) (15.4) 81 Legal Services 0.1 0.0 0.0 82 Educational Services 1.9 12.0 11.7 83 Social Services 1.5 0.8 0.5 86 Membership Organizations 0.9 3.2 1.5 90 State & Local Government 7.5 18.9 20.9 Self-insured Employers in all Sectors (%) 100% 100% 100%

Note: Percent statistics are calculated by the amount of specific SIC sector relative to the total of all sectors. Industries with less than 0.5% employment ratio are not included. “0%” stands for less than 0.05%, while “-“represents no self-insurers in that industry. Source: PA Department of Labor & Industry, Bureau of Workers’ Compensation, Self-Insurance Division, an ad hoc data offer as of March 15, 2006.


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Special Note about PA Self-insurance

The number of covered workers for each self-insurer ranges from zero to more than 100,000.20 The

mean and median are 1,301 and 294, respectively. It is surprising at first glance to see zero number of

employees for some self-insurers in PA. A discussion with George Knehr, the chief of the Self-

Insurance Division in Harrisburg, offered a hands-on explanation about a consolidated self-insurance

program in the self-insurance market.21 This program allows ownership-affiliated employers to apply

for self-insurance as a group, while each of them remains as an individual self-insurer. In the meantime,

the applicant of a consolidated self-insurance program guarantees the payment of all claims incurred

by the affiliates or subsidiaries within this program. 22 For instance, a national holding company in

self-insuring its operating affiliates or subsidiaries in Pennsylvania may also include itself or

other subsidiaries that do not currently have PA employees just in case down the road it would

have PA employees due to restructuring or another business reason. Thus, it is possible to see a self-

insurer with zero or just a dozen of workers.

An additional example can further clarify the firm size of the self-insurers. A large health care

system now consists of 49 individual self-insured employers, many with only a handful of employees.

Self-insured employers with few employees, say of 200 employees or less, are usually participating in

a consolidated self-insurance program. In fact, of the 788 self-insurers, only about 210 are single entity

self-insurers; the other 578 are part of 138 consolidated programs. Therefore, the firm size of a self-

insurer cannot just be measured by a single business unit with respect to financial strength. Just

examining a single business unit in analysis, researchers who ignore the consolidated self-insurance

program may come up with misleading interpretation. However, we cannot firmly conclude that firm

20 A list of self-insured employers in PA is available in the website of the Bureau of Workers’ Compensation, http://www.dli.state.pa.us/landi/cwp/view.asp?a=138&Q=58481. 21 This discussion took place on March 20, 2006. 22 Source: Pennsylvania Workers’ Compensation Act, chapter 125.4. According to another discussion with George Knehr, the chief of the Self-Insurance Division, Harrisburg, on October 16, 2006, a consolidated self-insurance program is quite common across jurisdictions although some authorities are more concerned about accounting for the various subsidiaries in a consolidated program.

http://www.dli.state.pa.us/landi/cwp/view.asp?a=138&Q=58481


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size does not play a key role in self-insurance. Of about 200,000 employers in PA, only 788 of them

are individual self-insurers. This is less than one percent in terms of the number of employers in PA.

When it comes to benefits paid by self-insurance, the individual self-insurers consistently pay out

about 20 percent of the annual compensation benefits paid each year. Accordingly, it is more accurate

to infer that the firm size of a single entity self-insurer is large, while that of a self-insurer under a

consolidated self-insurance program may not necessarily be as sizable.

IV. Theoretical Background and Hypotheses

One major issue is whether high-risk employers are more likely to self-insure than their low-risk

counterparts.23 Harrington and Danzon (2000, 2001) contend that low-risk employers are more likely

to self-insure, while Kwon and Grace (1996, p.262 and p. 274) point out that most employers with high

risks were driven to choose self-insurance, and self-insured employers tend to possess better loss

experience. However, their studies do not explain the prevalence of self-insurance among hospitals in

the health sector characterized by above-average nonfatal incidence rates.24 In light of high nonfatal

incidence rates that may lead to high premiums of WC insurance, hospitals have more economic

incentives to look for a risk transfer alternative in the hope of dealing with WC liability more

effectively. From the perspective of Harrington and Danzon (2000, 2001), high-risk hospitals are

supposed to be less likely to self-insure because of cross subsidies from low risks to high risks in the

commercial insurance market. If attention is focused on hospitals only, whether self-insured hospitals

have better loss experience than their counterparts deserves further empirical study on individual

employers.

23 Scores of researchers model adverse selection and asymmetric information by using the Akerloff’s (1970) framework of classifying customers as high and low-risks (e.g., Rothschild and Stiglitz 1976; Smith and Stutzer 1990, 1995; Dionne and Doherty 1994; Ligon and Thistle 2005). 24 See Chang and Weiss (2007) for a detailed discussion of industry affiliation in self-insurance.


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This study takes into account determinants of self-insurance for hospitals from six aspects: (1)

financial condition, (2) organizational structure, (3) facility or service, (4) accreditation, (5) market

factors, and (6) loss experience. Butler and Worrall (1993, p.132) suggest that choosing to self-insure

instead of purchasing market insurance depends on which is cheaper for the firm, and some firms are

more efficient in providing self-insurance in the sense that they give up relatively less income when

protecting themselves against future losses. The property-liability insurance market is characterized by

underwriting cycles, and thus pricing and availability of WC insurance are changing every year.25

Nevertheless, the employers that choose a self-insurance program tend to have a long-term mindset

and don’t change their choice year by year. Thus, an examination of firm-specific factors is of great

value to realize why employers choose self-insurance instead of market insurance.

Employers should demonstrate sufficient financial ability to gain self-insurance status from the

state authorities. Thus, financial condition is a great concern of whether an employer can be permitted

as a self-insurer. Self-insurers tend to be large employers (Baranoff 2000; Carroll 1994; Harrington

and Niehaus 2000; Kwon and Grace 1996; Thomason et al. 2001; Trieschmann and Gustavson 1995).

Revenues are indicative of the hospital size. Large hospitals that house a huge pool of employees are

more capable of predicting expected losses and paying incurred losses out of their funded reserves. As

a result, coefficient of this variable is expected to be positive. [Note: asset/debt ratio, a proxy of

financial strength, could be added in analysis of solvency factor when data are obtained]

Organizational structure of control is usually categorized into nonprofit, for-profit, and

government hospitals. Nonprofit hospitals came into existence long before their for-profit counterparts

and offer the majority of employment in health services.26 In general, the former is larger than the

latter in terms of average number of beds per hospital. Malani and David (2005) document four major

theories of why firms take nonprofit status: quasi-altruistic motives, government-funded provisions,

25 See Lamm-Tennant and Weiss (1997) for a complete discussion of underwriting cycles. 26 See Philipson (2000, p.325): 85 percent of employment in health services is for non-profit with concentration in hospitals.


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tax subsidies, and the signal of quality. Besides, as compared to for-profit hospitals, nonprofit hospitals

are not constrained to distribute profits and also denied access to equity funding. Thus, nonprofit

hospitals keep much more excess cash than their for-profit counterparts. Equipped with large cash

reserves, nonprofit hospitals can readily take on WC risk by self-insurance. Indeed, excess cash could

be used to self-insure for nonprofit hospitals, since it cannot be used for dividends or stock buy-

backs. For-profits take that cash and distribute to shareholders. Hence, a nonprofit variable is expected

to be positively related to self-insurance. In addition, even small or medium firms can self-insure under

a consolidated self-insurance program. Therefore, in self-insurance, it is crucial whether a hospital,

especially of small or medium size, is a member of a health care system.27 Due to the economy of scale,

the presence of a health care system offers an incentive for a hospital to self-insure. However, another

concern is the system cluster that makes a difference in management of a health care system. A

centralized health system that has overall authority for operating decisions and policy formulation is

more inclined to self-insure than an independent one composed of autonomous members. Therefore, it

is expected to see a positive sign for the coefficient of a centralized variable.

Academic medical centers are hospitals intimately affiliated with medical schools, most of

which are credited with rich histories of more than a century.28 Those institutions typically have

teaching and research missions in addition to providing clinical inpatient care. Such centers usually

offer a complete array of medical treatments and are able to provide care to individuals who have

various physical problems, including employees injured in their hospital settings. Besides, those

centers are usually owned by the associated academic institution. As such, workers in the hospitals and

27 Health care system is defined by American Hospital Association (AHA) as a corporate body that owns, leases, religiously sponsors, and/or manages health provider facilities. 28 In PA, medical schools comprise Drexel University (2002), Thomas Jefferson University (1824), Pennsylvania State University (1963), Temple University (1901), University of Pennsylvania (1765), and University of Pittsburgh (1886). The year in the quote represents the beginning year of the medical school in that university. Source: Association of American Medical Colleges, http://www.aamc.org/medicalschools.htm.

http://www.aamc.org/medicalschools.htm


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schools may become a large pool of self-insurance for WC.29 The variable of academic medical center

is expected to be positively related to self-insurance.

Facilities are generally classified into short term acute care, psychiatric, rehab, long term,

critical access, and children hospitals. The most common type is short term acute care. In order to

differentiate hospitals of this facility type from others, the case mix index (CMI)30 is closely watched

because of its implication of how sick the patients treated in a hospital are. This index is very useful in

analysis since it indicates the relative severity of a patient population. If a hospital deals with more

serious patients, it is expected to see more complicated medical treatments offered and possibly more

risks involved. At their state-level analysis, Butler and Worrall (1991) and Carroll (1994) find that high

injuries are linked to self-insurance. The CMI is not perfect but an approximate proxy of risk for the

individual hospitals. Moreover, workers who became ill or were injured in the workplace are more

likely to be treated in the hospitals of short term acute care because in-house treatment is efficient and

less costly. Thus, the sign is expected to be positive for the short-term-acute-care variable. Specialty

hospitals (e.g., psychiatric, rehab, long term, critical access, and child) are less likely to treat injured

employees through internal expertise. Besides, some of them are within a health care system or kind of

limited by small samples. Thus, no specific signs are predicted for the coefficients of variables

pertinent to specialty hospitals. CMI is anticipated to be positively associated with self-insurance.

Accreditation is a symbol that a hospital has met specific requirements for medical treatment.

On the one hand, it is expected that a hospital with better standard management is more likely to

reduce injuries in the workplace and obtain market insurance easily. On the other hand, if the insurers

cannot properly price the insurance, accredited hospitals may choose self-insurance to avoid overly

high premiums. Thus, the signs are not expected to be positive or negative for accreditation variables

29 According to a discussion with Thomas F. Johnston, director of workers’ compensation at Temple University on April 12, 2007, most of claims in the university come from staff in housekeeping, maintenance, and security police departments. 30 This index information is taken from the Medicare Provider Analysis and Review (MedPAR) file which is updated annually by Centers for Medicare and Medicaid Services (CMS) based on the federal fiscal year. The Medicare case mix index (CMI) is based on the Medicare Hospital Inpatient Prospective Payment System for the corresponding federal fiscal year.


16

of the teaching and Joint Commission on Accreditation of Health Care Organizations (JCAHO),

respectively.

Market factors, e.g., medical costs in area and market concentration, are expected to have an

impact on the choice of self-insurance. The Medicare Adjusted Average Per Capita Cost (AAPCC)

provides a good proxy of relative cost by county. If a hospital is located in an area where medical costs

are relatively higher, an alternative risk transfer will be more likely to be chosen for cost efficiency.

Besides, two indexes, Herfindahl-Hirschman Index (HHI) and relative self-insurance HHI, are used to

measure market concentration by the market share of revenues in the public district of each county.31

As the market becomes more competitive and HHI is lower, the competition in the market puts

pressure on high-risk hospitals to search for an alternative of handling WC. The coefficient of the

former is expected to be negative. If more competitors in the market self-insure and self-insurance HHI

becomes higher, risk management adopted by competitors sets an example for other hospitals to learn

and imitate. In consequence, the coefficient of the latter is expected to be positively linked to choice of

self-insurance.

The experience rating modification factor (MOD) determines whether the risk of an

employer is better or worse than the hazard of the average risk in the classification to which it has been

assigned. The WC rates are based on covered payroll, covered employees’ risk classifications, and an

experience modifier. Therefore, this proxy is indicative of the actual loss experience of an individual

employer compared to similarly classified counterparts. An employer with MOD of 1.0 has the

industry average loss experience. A higher-than-one MOD represents a higher-than-average loss

experience and would result in an increased premium cost, while a less-than-one MOD would lead to a

reduction in the employer’s premiums. Based on prior 3-year period of loss experience, the factor is

31 HHI is the sum of squared market share of each firm competing in a market. Self-insurance HHI is equal to the total market share of self-insured competitors divided by the total market share of competitors in the market.


17

usually established and valid for one year.32 Thus, this factor is not always constant for an employer.

Unfortunately, MOD is not available for self-insured hospitals. Thus, this variable is not included in

the model but analyzed separately in Section VI to see if hospitals that purchase market insurance are

of better or worse loss experience.

V. Methodology, Data, and Results

Methodology

To examine the determinants of self-insured hospitals, this study employs a bivariate logistic

regression that models self-insurance status as the dependent variable: one if the hospital self-insures,

zero otherwise. Independent variables consist of five categories expected to influence hospitals to self-

insure. They are financial condition, organizational structure, facility, accreditation status, and market

factors. As Carroll (1994, p.173) notes, the ideal model for the research on self-insurance would use

firm-level data and would have a binary dependent variable. This paper, based on individual hospital

data, accomplishes Carroll’s dream to some degree 13 years after her proposal.

Data

Hospital data were primarily obtained from the American Hospital Directory (AHD) and the American

Hospital Association (AHA). Initially, 291 hospitals were listed in the AHD as of July 2, 2006,

including insolvent and acquired hospitals before 2005.33 The accounting data for hospitals are in the

fiscal year 2005. This list of hospitals was then confirmed with the American Hospital Association

(AHA) 2006 guide. Self-insurance status for each hospital is confirmed by the Self-Insurance Division

of the Bureau of Workers’ Compensation in Harrisburg PA.34 Membership of a health care system is in

32 See details of experience rating in Pennsylvania Workers’ Compensation Manual of Rules, Classification and Rating Values for Workers’ Compensation and For Employers Liability Insurance, effective October 1, 2006. 33 See American Hospital Directory (AHD) website www.ahd.com for details. This list does not include hospitals managed by the Department of Veterans Affairs, Washington, DC. 34 This task of confirming which hospital self-insures is very difficult because the same hospital might have different names in the American Hospital Directory and in the Bureau of WC. Special thanks must be forwarded to George Knehr, the Chief of the Self-Insurance Division in Harrisburg, PA, and his coworkers. The confirmation was completed in September 8, 2006.

http://www.ahd.com/


18

the 2006 AHA guide, while case mix index (CMI) and accreditation data are available in the AHD

web.35 The data of medical cost by county are based on the Medicare ratebook from Centers for

Medicare & Medicaid Services (CMS). Modification factor data are obtained from Pennsylvania

Compensation Rating Bureau (PCRB), but only available for employers who do not self-insure.36

Summary statistics and correlation for all variables are displayed in Table 3 and Table 4,

respectively. Although only forty-seven percent of hospitals in PA self-insure to assume their WC

liability, those self-insured hospitals account for 64 percent of total hospital revenues. When it comes

to control type, 70 percent of hospitals in PA are nonprofit. In terms of facility type, short-term-acute-

care hospitals account for 66 percent of total hospitals in PA and create 95 percent of total hospital

revenues. The other facility types represent a small fraction, respectively. In addition, fifty-five percent

of nonprofit hospitals self-insure for their WC liability, while only twenty-one percent of for-profit

hospitals choose self-insurance. Hospitals with nonprofit, short term acute care, and teaching

characteristics tend to have higher revenues, while for-profit, psychiatric, and rehab hospitals are

correlated with lower revenues. More serious patients, signaled by the proxy of CMI, are treated more

in hospitals with higher revenues, nonprofit, short term acute care, and teaching characteristics. Rehab

and psychiatric hospitals treat less severe patients. Revenue, nonprofit, and rehab variables are

positively correlated with self-insurance. By contrast, a negative correlation exits between the for-

profit variable and self-insurance. Overall, if located in a county where many of their competitors self-

insure, hospitals are more likely to choose self-insurance.

Empirical results

The logistic regression indicates that there exits a positive and significant relationship between

self-insurance and revenues (i.e., the proxy of hospital size). As expected, larger hospitals are more

35 A hospital's teaching status is obtained originally from its most recent Medicare Cost Report (W/S S2, line 25). 36In PA, the class code of hospital is 961. Besides, self-insured employers, whether hospitals or otherwise, are not required to report their experience data to the Pennsylvania Compensation Rating Bureau (PCRB), and none do so as a matter of course. Accordingly, there are no published experience modifications for self-insured risks. This comment was made by Tim Wisecarver, the president of PCRB, as of March 9, 2007.


19

capable of demonstrating financial ability to meet self-insurance requirements. Meanwhile, smaller

hospitals within a health care system can self-insure under a consolidated self-insurance program.

Table 5-6 shows results of coefficients for determinants of self-insured hospitals for one entire sample

and many subsamples in different scenarios. In addition to the full sample, the bivariate models of

determinants of self-insurance were further tested by separating the entire sample into hospitals with

revenues of more than 100 million and those with revenues of less than 100 million in Table 5. In the

subsample of smaller hospitals, membership in a health care system plays a significant role because

smaller hospitals can also self-insure under a consolidated program. In general, the huge magnitude of

revenues is a critical determinant of self-insurance among hospitals, especially among nonprofit

hospitals. The revenue variable isn’t significantly related to self-insurance in the subsamples of large

and for-profit hospitals. Nevertheless, large hospitals may not choose self-insurance because of factors

other than the income concern. For-profit hospitals do not merely take large revenues into account in

managing WC coverage.

The control type of hospital is consistently and significantly linked to self-insurance. Nonprofit

hospitals are more likely to self-insure their WC coverage, while for-profit hospitals prefer market

insurance. By and large, this condition is in part due to the smaller size of for-profit hospitals. The

preference of market insurance among for-profit hospitals may also attribute to risk aversion in

corporate managers who would rather concentrate on profit pursuit than being distracted by managing

self-insurance programs. Gray (1991, p.50) put forward two drawbacks of for-profit hospitals: (1)

frequent changes of ownership as a result of acquisitions, divestitures, mergers, and reorganization,

and (2) propensity to go out of business. In conjunction with the size factor, these two shortcomings

hinder investor-owned hospitals to adopt a self-insurance program for their WC coverage that requires

a long-run commitment to some degree. Besides, 86 percent of for-profit hospitals are managed by


20

health care systems that have businesses across many states.37 Self-insurance regulation that varies

state by state may mitigate the incentives for the health care systems with multi-state presence to self-

insure their WC liability. The next section will explore this issue in depth.

Furthermore, hospitals treating more severe patient populations are more likely to manage their

worker’s compensation liability by self-insurance. Consistent with Carroll’s (1994) finding that higher

injury rates are associated with greater usage of self-insurance at the state level, this discovery at the

firm-level analysis is instrumental in that hospitals under more severe working environments have a

higher likelihood of resorting to alternative risk transfers. Those self-insured employers retain risk on

their own, gain control over the claims, and avoid being overcharged by insurers because of

information asymmetry between buyers and sellers. Besides, rehab hospitals are significantly related to

self-insurance. Because of lower CMI, rehab hospitals treat less severe patients and choose to self-

insure for different reasons. Nevertheless, 65 percent of rehab hospitals are a part of numerous health

care systems, and some of them self-insure under a consolidated program. Hence, it is far from

conclusive to draw concrete inference from 17 rehab hospitals that represent just one percent of total

revenues of hospitals in PA.

When it comes to market factors, self-insurance HHI is significantly relevant to self-insurance,

indicating that an increase in self-insured competitors in the market is associated with increased choice

of self-insurance among other hospitals. This may imply that hospitals located in the same county and

treating similar patient populations are more inclined to choose the same risk transfer tool for WC. A

spectrum of attributes results in the difference among geographic counties such as crime rates,

demographic factors, and so on. Even though medical cost variable is not significantly connected with

self-insurance, geographic location seems to play a part in the choice of self-insurance among hospitals.

37 56 out of 65 for-profit hospitals are managed by heath care systems as compared to 82 out of 175 nonprofit hospitals.


21

Table 3 Summary Statistics of 251 Hospitals

Variables N Min. 1st Q Mean Median 3rd Q Max. Std. Dev.Revenues (000) 233 2,278 46,632 393,612 129,830 406,589 6,398,553 746,188 Beds 244 10 74 188 144 235 1,452 172 CMI 229 0.531 1.037 1.216 1.207 1.377 2.515 0.327 County Rate 251 1.043 1.043 1.066 1.043 1.089 1.337 0.034 HHI 251 1,003 2,067 4,064 2,898 5,504 10,000 2,564 Self-HHI 251 0 0.026 0.523 0.673 0.906 1 0.383

Note: The revenue data are in the fiscal year of 2005. CMI represents case mix index. The variable County Rate represents the relative medical cost by county where the hospital is located. HHI is the sum of squared market share of each firm competing in a county, and market share is calculated in terms of revenues. The Self-HHI variable is the self-insurance Herfindahl-Hirschman Index, equal to the total market share of self-insured competitors divided by the total market share of competitors in the market. Statistics of total 251 hospitals with $91.7 billion revenues

Variables Frequency Share of hospitals

Revenues (billion)

Share of total revenues

Self-insured Hospitals 117 47% 58.6 64% Control type:

Nonprofit 175 70% 80.1 87% For-profit 65 26% 11.2 12% Governmental 11 4% 0.4 1%

Facility type: Short term acute care 165 66% 87.4 95% Psychiatric 26 10% 1.0 1% Rehab 17 7% 0.9 1% Long term 20 8% 0.8 0.8% Critical access 9 4% 0.3 0.3% Children 9 4% 1.3 1.5%

Within a health care system 138 55% 66.0 72% Teaching status 84 33% 71.2 77% JCAHO 192 76% 86.1 94%

Self-insurance percentage given control type Nonprofit For-profit Government Self-insurance percentage 55% 21% 67% Note: Government hospitals are referred to as nonfederal, consisting of those controlled by state and local government. Self-insurance percentage given facility type Short term

acute care Psychiatric Rehab Long term

Critical access Children

Self-insurance percentage 51% 42% 76% 15% 33% 20% Self-insurance percentage given other variables Teaching status Within a health care system JCAHO Self-insurance percent age 55% 41% 48%


22


23

Table 5 Logistic Regressions of Self-Insured Hospitals on Determinants

Variable Full sample Large Hospitals (Revenues >100m) Small Hospitals

(Revenues < 100m) Intercept -17.086*** -.705 -9.291 -.038 -22.293 ** -4.765 (4.238) (2.577) (6.850) (4.191) (9.929) (5.017) Financial factor

Ln(Revenues) .643*** .252 1.091 ** (.157) (.255) (.477)

Organizational factors Nonprofit Dummy 1.263*** 2.528*** .310 (.451) (.805) (.716) For-profit Dummy -2.423*** -3.114*** -2.224 **

(.537) (.885) (.978)

System Dummy -.454 -.053 -.718 1.233 .437 1.944 **

(.339) (.346) (.468) (.481) (.637) (.815)

Facility factors

ShortTerm Dummy .187 1.233 -.162

(.493) (1.449) (.818)

Psychiatric Dummy 1.614** -22.102 2.575 ***

(.633) (2.748) (.884)

Rehab Dummy 3.422*** 3.194*** 20.680 22.982 3.912 *** 3.515 ***

(.783) (.793) (2.730) (2.646) (.965) (1.003)

Ln(Case Mix Index) .994 3.095** -3.828 *

(.806) (1.522) (1.729)

Accreditation factors

Teaching Dummy -.207 -1.479** 2.588 **

(.409) (.583) (1.295)

JCAHO Dummy -.392 .262 -.279 -.103 -1.784 ** -1.057

(.436) (.400) (.658) (.722) (.808) (.766)

Market factors

Ln(County Rate) -.127 -.042 6.962 3.000 -19.640 * -25.825 *

(5.235) (5.426) (8.215) (8.719) (10.945) (13.232)

Ln(HHI) .421 -.020 .253 -.120 .255 .425

(.276) (.279) (.406) (.420) (.472) (.553)

Self-HHI 1.415*** 1.363*** 1.193* 1.317* 2.128 *** 2.579 ***

(.435) (.431) (.659) (.677) (.717) (.837)

Predicted Percentage Correct 74.2 71.5 70.8 76.6 81.6 81.8 Number of Observations 233 228 130 128 103 99 Note: The Dependent variable takes the value 1 when the hospital self-insures and 0 otherwise. The Nonprofit (or for-profit) dummy is equal to one if a hospital is nonprofit (for-profit). The System dummy is set to one if a hospital is within a health care system. ShortTerm (or Psych and Rehab) dummy is equal to one if the facility of a hospital is short term acute care (or psychiatric and rehabilitation, respectively). The Teaching (or JACHO) dummy is equal to one if a hospital has a teaching (or JACHO) status. JACHO is the Joint Commission on Accreditation of Health Care Organizations. The variable County Rate represents the relative medical cost by county where the hospital is located. HHI is the sum of squared market share of each firm competing in a county, and market share is calculated in terms of revenues. The Self-HHI variable is the self-insurance Herfindahl-Hirschman Index, equal to the total market share of self-insured competitors divided by the total market share of competitors in the market. The variables, R, CMI, Rate, and HHI, are in natural-logarithm. NA represents unavailable results because of singularity. Standard errors are in parentheses below each coefficient. *** Significant at 1 percent; ** Significant at 5 percent; *Significant at 10 percent.


24

Table 6 Logistic Regressions of Self-Insured Hospitals on Determinants - subsamples

Variable Nonprofit hospitals For-profit hospitals ShortTerm Hospitals

Intercept -14.51*** .601 -24.032 -14.721 -15.946 *** 1.925 (4.933) (2.967) (2.180) (2.162) (5.434) (3.053) Financial factor

Ln(Revenues) .631*** .396 .620 *** (.181) (.366) (.201)

Organizational factors Nonprofit Dummy 2.657 *** (.794) For-profit Dummy -3.091 ***

(.831)

System Dummy -.288 -.199 1.160 1.855 -.568 -.408

(.378) (.375) (1.551) (1.645) (.403) (.403)

Facility factors

ShortTerm Dummy .546 .325

(.648) (1.225)

Psychiatric Dummy -.990 .935

(1.254) (1.430)

Rehab Dummy 2.287* 2.522** 4.229*** 3.645**

(1.224) (1.270) (1.198) (1.210)

Ln(Case Mix Index) 2.245** -.922 2.668 **

(1.102) (1.615) (1.165)

Accreditation factors

Teaching Dummy -.333 -.647 -.756

(.458) (1.510) (.468)

JCAHO Dummy -.445 -.106 18.144 18.559 -.322 .265

(.466) (.453) (.218) (.216) (.493) (.474)

Market factors

Ln(County Rate) .153 .414 -1.214 .406 1.476 1.325

(5.976) (6.065) (25.676) (25.304) (5.994) (6.129)

Ln(HHI) .286 -.215 -.536 -.915 .167 -.320

(.329) (.325) (.946) (.937) (.339) (.333)

Self-HHI 1.511*** 1.434*** -.986 -.891 1.273 *** 1.273 **

(.487) (.483) (1.473) (1.371) (.509) (.502)

Predicted Percentage Correct 68.7 68.3 88.3 88.3 72.0 70.8 Number of Observations 163 161 60 60 164 161 Note: The Dependent variable takes the value 1 when the hospital self-insures and 0 otherwise. The Nonprofit (or for-profit) dummy is equal to one if a hospital is nonprofit (for-profit). The System dummy is set to one if a hospital is within a health care system. ShortTerm (or Psych and Rehab) dummy is equal to one if the facility of a hospital is short term acute care (or psychiatric and rehabilitation, respectively). The Teaching (or JACHO) dummy is equal to one if a hospital has a teaching (or JACHO) status. JACHO is the Joint Commission on Accreditation of Health Care Organizations. The variable County Rate represents the relative medical cost by county where the hospital is located. HHI is the sum of squared market share of each firm competing in a county, and market share is calculated in terms of revenues. The Self-HHI variable is the self-insurance Herfindahl-Hirschman Index, equal to the total market share of self-insured competitors divided by the total market share of competitors in the market. The variables, R, CMI, Rate, and HHI, are in natural-logarithm. NA represents unavailable results because of singularity. Standard errors are in parentheses below each coefficient. *** Significant at 1 percent; ** Significant at 5 percent; *Significant at 10 percent.


25

VI. Health Care System and Modification Factor

The revenues generated by hospitals within health care systems represent around 72 percent of total

revenues in PA in 2005. Out of 39 health care systems that have businesses in PA, 16 of them have

operations in PA only, while the rest have businesses in multiple states that range from 2 to 51 in

reference to the number of jurisdictions. Table 7 demonstrates correlation among determinants of self-

insurance for health care systems in PA. When it comes to revenues, the mean and median for 39

health care systems are more than three times larger than those for the entire sample of 251 hospitals.

Given that the sample of health care systems comprises the large hospital entities, the revenue factor

isn’t significantly correlated with self-insurance. This implies that a huge amount of revenues is not the

only critical concern of self-insurance for health care systems. Moreover, nonprofit health care systems

tend to have businesses in PA only, while for-profit systems are more likely to have multi-state

presence. Summary statistics of health care systems are displayed in Table 8, indicating that 50 percent

of nonprofit health care systems self-insure, and 63 percent of health care systems that operate only in

PA choose self-insurance for WC coverage. Consistent with finding in Section V, for-profit health care

systems are less prone to self-insurance.

Health care systems with presence of businesses only in PA are prone to self-insurance, while

multi-state health care systems are negatively associated with choice of self-insurance programs. Due

to the self-insurance regulation dominated by state authorities, multi-state health care systems are less

motivated to self-insure. One of underlying hurdles is the burden and difficulty in coping with the

complexity of regulatory requirements for self-insurance programs under various states. To the

contrary, the single-state systems have the advantage of complying with one regulatory requirement

and are more likely to retain risk through self-insurance mechanisms. Logistic regressions of health

care systems in Table 9 also imply that nonprofit health care systems are more inclined to self-insure.

In addition, the revenue variable is not significant in any equation. Even the large health care systems


26

may not choose self-insurance programs because of other concerns (e.g., multi-state operations and

capital allocation).38 These findings complement the analysis of 251 hospitals in Section V.

With respect to the modification factors (MODs) of the employers that do not self-insure and

have available data in PCRB, the distribution of MODs is not biased to the right-hand side in Figure 2,

indicating that the commercial market is not overwhelmingly dominated by hospitals with worse loss

experience. In the aggregate, there are 57 MODs with mean 1.023 and median 0.96. The right-hand

side represents hospitals that have more-than-one MODs and worse loss experience than the average in

the hospital sector. If a cross subsidy had driven low-risk employers out of the market and yielded a

high proportion of high-risk employers in the market, the distribution of MODs should have biased

toward the right side. To the contrary, more than 50 percent of MODs are less than one. Some

hospitals with better loss experience are still in the market and purchase commercial insurance for WC

coverage. However, the further analysis is impaired by the unavailable confidential data behind each

MOD. A MOD doesn’t necessarily represent one employer. Sometimes a host of hospitals within a

health care system have one MOD. Hence, a MOD may represent an aggregate loss experience of an

employer or a health care system that comprise many hospitals. For example, only one MOD is issued

for eight hospitals in PA within the Catholic Health East System. Therefore, the results should be

interpreted cautiously.

38 One chief risk office in a large multi-state health care system shared with me its risk retention program on April 16, 2007. The system has operations in 11 states and retains risk of workers’ compensation by a high deductible plan rather than by a self-insurance program mainly because of different state regulations. The revenues generated by its PA hospitals surpassed those of many self-insured health care systems. Another risk manager in a large health care system with a very low MOD cited a concern of capital allocation for buying high deductible plan. In view of growth strategy for hospitals, this system with businesses only in PA does not have to put aside a large amount of capital for self-insurance program but for expansion of services for customers.


27

Table 7 Correlations among Determinants of Health Care Systems in PA

Variable Self Ln(R) Nonprofit For-profit Central Academic PA System Multi Self 1 Ln(R) .254 1 Nonprofit .357* .511** 1 For-profit -.357* -.511** -1.000** 1 Central .397* .604** .721** -.721** 1 Academic .449** .532** .253 -.253 .424* 1 PA System .454** .545** .624** -.624** .760** .405* 1 Multi -.444** -.547** -.651** .651** -.809** -.402* -1.000** 1 Note: The variable Self is a dummy that takes the value 1 when the health care system self-insures and 0 otherwise. The Nonprofit (or For-profit) dummy is equal to one if a hospital is nonprofit (or For-profit). The Central dummy is set to 1 if a health care system has a centralized or moderately centralized system cluster code and 0 otherwise. The Academic dummy is set to 1 if a health care system is classified as an academic medical center. The variable PA System is a dummy that takes the value 1 when the health care system has businesses in PA only and 0 otherwise. The variable Multi is a dummy that takes the value 1 when the health care system has businesses in multi states and 0 otherwise. The variable State represents the number of states where the health care system has presence of businesses. The variable Revenues (R) is in the natural logarithm. **and * are significant at the 0.01 and 0.05 level (2-tailed), respectively.

Table 8 Summary Statistics of 39 Health Care Systems

Variables N Min. 1st Q Mean Median 3rd Q Max. Std. Dev.Revenues (000) 36 21,209 226,791 1,833,795 471,896 2,642,359 9,934,875 2,523,165 State 37 1 1 10 4 16 51 12.6

Note: The variable State represents the number of states where the health care system has presence of business. Frequency Variables Frequency Share of 39 Systems Self-insured health care system 14 a 36% Control type:

Nonprofit 25 64% For-profit 14 36%

System cluster: b Centralized 14 36% Decentralized 8 21% Independent 9 23% Academic medical center 4 10% Presence in PA only 16 41% Presence in multi states 22 c 59%

Self-insurance percentage given control type Nonprofit For-profit Self-insurance Percentage 50% 14%


28

Self-insurance percentage given three variables, respectively Academic Medical Center d Presence in PA only Presence in multi states Self-insurance Percentage 100% 63% 19% Note: a. Self-insured health care systems are counted when their self-insured member hospitals account for more than 90 percent of revenues. HealthSouth Corporation and University of Pittsburgh Medical Center have one member, respectively, that do not self-insure and represents less than 10 percent of entire system revenues. As such, both systems are treated as self-insured. This frequency excludes Community Health System, Excela heath, and QHR health care system with one or two members covered by self-insurance. One of its members in the Community Health System self-insures and represents 11 percent of total system revenues. 2 out of its 6 hospitals in QHR health care system self-insure and account for 50 percent of total revenues in 2005. One of its members in Excela heath self-insures and represents 38 percent of entire system revenues. b. 8 systems do not have cluster codes due to unavailable data. c. Data on Excela Health are unavailable. d. These academic medical centers are Jefferson Health System, Temple University Health System, University of Pennsylvania Health System, and University of Pittsburgh Medical Center.

Table 9 Logistic Regressions of Health Care Systems on Determinants

Variable (1) (2) (3) (4) (5) (6) (7)

Intercept -7.106 -3.849 -2.151 -1.466 ** -2.345 -.762 -3.611 (4.601) (5.279) (5.664) (.641) (5.421) (5.421) (5.834) Ln(Revenues) .329 .112 .112 .053 .060 .185

(.224) (.270) (.270) (.277) (.274) (.242) Nonprofit Dummy 1.699*

(.979) For-profit Dummy -1.699* (.979) Central Dummy 1.754 ** (.838) PA System Dummy 1.739**

(.903) Multi Dummy -1.655*

(.906) Ln(State) -.423

(.296)

Predicted Percentage Correct 60 65.7 65.7 70 71.4 70.6 73.5 Number of Observations 35 35 35 30 35 35 34 Note: The dependent variable takes the value 1 when the health care system self-insures and 0 otherwise. The Nonprofit dummy is equal to one if a hospital is nonprofit. The Central dummy is set to 1 if a health care system has a centralized or moderately centralized system cluster code and 0 otherwise. The variable PA System is a dummy that takes the value 1 when the health care system consists of hospitals located in PA only and 0 otherwise. The variable State represents the number of states where the health care system has presence of businesses. The variable Revenues and State are in the natural logarithm. **and * are significant at the 0.01 and 0.05 level (2-tailed), respectively.


29

Figure 2 Modification Factors of Non-Self-Insured Hospitals in PA

0.60 0.90 1.20 1.50 1.80 2.10 2.40

MOD

0

5

10

15

20

Freq

uenc

y

Mean = 1.0232Std. Dev. = 0.31075N = 57

Note: Fifty seven modification factors (MODs) exist in fiscal year 2005-06. PCRB calculated the MODs based on the loss data submitted by employers. The coefficients of skewness and kurtosis are 1.35 and 3.514, respectively. Source: Pennsylvania Compensation Rating Bureau (PCRB).

VII. Conclusion

This article using hospital-level data for determinants of self-insurance in WC provides evidence that

some firm-specific characteristics are of critical concern to the choice of a self-insurance program. The

control type of hospital is significantly linked to self-insurance. Larger nonprofit hospitals are more

interested in self-insurance to retain the risk exposure of WC, while smaller for-profit hospitals usually

depend on market insurance in part because of their multi-state presence. The higher the severity of the

patient population that a hospital treats is associated with the higher likelihood of self-insurance. The

finding of a significant relationship between self-insurance HHI and self-insurance is consistent with

the market competition hypothesis that self-insured contenders set good examples for other hospitals to

follow.

Hospitals are very risky in terms of nonfatal incident rates, and they are motivated to choose

risk transfer alternatives to manage their WC liability. This is in vivid contrast to the argument that


30

high-risk employers are less likely to self-insure because of the cross-subsidy theory. Employers within

high-risk industries are more economically stimulated to search for alternative methods to cover their

WC liability. They may have good or bad risks in their loss experience over the years. Self-insurance is

more treated as a long-term commitment. Once they decide to self-insure, employers usually stick to

the program for years.

The results of this study highlight the prevalence of self-insurance in the hospital sector and

address the factors associated with self-insured hospitals. Further research based on evidence from

other states or industries can complement this study and contributes to more discoveries about self-

insurance for workers’ compensation. By conducting an analysis on other high-risk industries or other

states, valuable comparison between the various industries and regulatory regions facilitates an

understanding of a more comprehensive picture of self-insurance in the U.S. Besides, time series

analysis on individual firms can help to explain the impact of the underwriting cycle on the decision to

self-insure. Unfortunately, it is very difficult to obtain this type of data.

References

Akerlof, Geroge A., 1970, The market for ‘lemons’: quality uncertainty and the market mechanism, Quarterly Journal of Economics 84 (August): 488-500.

Ballou, Jeffrey P. and Burton A. Weisbrod, 2003, Managerial rewards and the behavior of for-profit, governmental, and nonprofit organizations: evidence from the hospital Industry, Journal of Public Economics 87 (September):1895-1920.

Baranoff, Etti G., 2000, Determinants in risk-financing choices: the case of workers’ compensation for public school districts, Journal of Risk and Insurance 67: 265-280.

Butler, Richard J. and John D. Worrall, 1993, Self-insurance in workers’ compensation, Workers’ Compensation Insurance: Claim Costs, Prices, and Regulation, edited by David Durbin and Philip S. Borba, Kluwer Academic Publishers.

———, 1991, Claims reporting and risk bearing moral hazard in workers’ compensation, Journal of Risk and Insurance: 191-204.

Carroll, Anne M., 1994, The role of regulation in the demand for workers’ compensation self-insurance, Journal of Insurance Regulation (winter): 256-289.

Chang, Mu-Sheng and Mary A. Weiss, 2007, Self-insurance and market insurance: evidence form workers’ compensation insurance, working paper, The Fox School of Business, Temple University.

D’Arcy, Stephen P. and Neil A. Doherty, 1990, Adverse selection, private information, and lowballing insurance markets, Journal of Business 63: 145-164.


31

David, Guy, 2005, The Convergence between for-Profit and nonprofit hospitals in the United States, working paper, the Wharton School of Business.

Danzon, Patricia M. and Scott E. Harrington, 2001, Workers’ compensation rate regulation: how price controls insurance costs, Journal of Law & Economics 44: 1-36.

———, 2000, Rate regulation, safety Incentives, and loss growth in workers' compensation insurance, Journal of Business 73, Issue 4.

Dionne, G., and N. Doherty, 1994, Adverse selection, commitment, and renegotiation: Extension to and evidence from insurance markets, Journal of Political Economy 102 (April): 209–35.

EPINet, 1999, Exposure prevention information network data reports, University of Virginia: International Health Care Worker Safety Center.

Gray, Bradford H., 1991, The profit motive and patient care: the changing accountability of doctors and hospitals, Cambridge, MA: Harvard University Press.

Griffin, Don, 2006, Hospitals: what they are and how they work, 3rd edition, Sudbury, MA, Jones and Bartlett Publishers.

Holzheu, Thomas, Kurt Karl, and Mayank Raturi, 2003, The picture of alternative risk transfer (ART), Swiss Re, Sigma No. 1/2003.

Kwon, W. J. and Martin F. Grace, 1996, Examination of cross subsidies in the workers’ compensation market, Journal of Insurance Regulation 15 (winter): 256-89.

Lamm-Tennant, Joan, and Mary A. Weiss, 1997, International insurance cycles: rational expectations/institutional intervention, Journal of Risk and Insurance 64 (3): 415–39.

Ligon, James A. and Paul D. Thistle, 2005, The formation of mutual insurers in markets with adverse selection, Journal of Business 78 (March): 529-555.

Malani, Anup and Guy David, 2005, Forget quality: do non-profits even signal their status?, working paper, University of Virginia Law School and the Wharton School.

Nakamura, Peggy L.B., 2004, Workers’ compensation programs in health care organizations, Roberta Carroll, Editor, Risk Management Handbook, 4th edition, Chicago: AHA Press.

Nelson, A., G. Fragala, and N. Menzel, 2003, Myths and facts about back injuries in nursing, American Journal of Nursing 103: 32–40.

Peled, Keren, 2005, Workplace safety assessment and injury prevention in hospital settings, Work 25: 273–277, IOS Press.

Philipson, Tomas, 2000, Asymmetric information and the not-for-profit sector: does its output sell at a premium? P.325-345, in David M. Cutler, ed., the Changing Hospital Industry: Comparing Not-for-Profit and For-Profit Institutions, Chicago: University of Chicago Press.

Puelz, R. and A. Snow, 1994, Evidence on adverse selection: equilibrium signaling and cross-subsidization in the insurance market, Journal of Political Economy 102: 236-257.

Rothschild, M., and J. Stiglitz, 1976, Equilibrium in competitive insurance markets: an essay in the economics of imperfect information, Quarterly Journal of Economics 90 (November): 629–50.

Smith, B. D. and M. J. Stutzer, 1990a, Adverse Selection, aggregate uncertainty and the role for mutual insurance contracts, Journal of Business 63 (October): 493–510.

———, 1995, A theory of mutual formation and moral hazard with evidence from the history of the insurance industry, Review of Financial Studies 8 (summer): 545–77.

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