Community Benefit Activities of Private, Nonprofit Hospitals

Community Benefit Activities of Private,

Nonprofit Hospitals

Gloria J. Bazzoli Jan P. Clement

Virginia Commonwealth University

Hui- Min HsiehHealthCore, Inc.

Abstract The definition of hospital community benefits has been intensely debated for many years. Recently, consensus has developed about one group of activities being central to community benefits because of its focus on care for the poor and on needed community services for which any payments received are low relative to costs. Dis-agreements continue, however, about the treatment of bad debt expense and Medicare shortfalls. A recent revision of the Internal Revenue Service’s Form 990 Schedule H, which is required of all nonprofit hospitals, highlights the agreed- on set of activities but does not dismiss the disputed items. Our study is the first to apply definitions used in the new IRS form to assess how conclusions about the adequacy of nonprofit hospi-tal community benefits could be affected if bad debt expenses and Medicare shortfalls are included or excluded. Specifically, we examine 2005 financial data for California and Florida hospitals. Overall, we find that conclusions about community benefit adequacy are very different depending on which definition of community benefits is used. We provide thoughts on new directions for the current policy debate about the treatment of bad debts and Medicare shortfalls in light of these findings.

Introduction

There has been much debate over the adequacy of private, nonprofit hospi-tal community benefit activities given their tax- exempt status. Two major issues have been central to this debate: (1) what activities should qualify as “community benefits” and (2) whether private, nonprofit hospitals pro-vide enough of these community benefits, however defined, to warrant their favorable tax status. Answering the second question has been dif-

Journal of Health Politics, Policy and Law, Vol. 35, No. 6, December 2010 DOI 10.1215/03616878-2010-036 © 2011 by Duke University Press

1000 Journal of Health Politics, Policy and Law

ficult because the definitional issues inherent in the first question have not been completely resolved. In the last few years, the hospital industry, trade associations, and federal policy makers have increasingly agreed on one set of activities that all believe do constitute community benefit in that they relate to servicing the needs of indigent and underserved indi-viduals and to providing needed services to the community for which monies received are inadequate to cover costs. Efforts that began with the Catholic Health Association (CHA) and VHA, Inc., followed by hearings held by Senator Chuck Grassley’s (R- IA) U.S. Senate Finance Committee, and culminating in actions by the Internal Revenue Service, have focused much attention on this set of activities and led to specific approaches for quantifying and reporting their unreimbursed costs in federally filed IRS forms.

Lack of agreement continues, however, about two items that some argue should be counted as community benefits while others contend should not. Specifically, these are bad debt expense, namely, uncollectible payments that patients were expected to pay, and unreimbursed costs of the Medi-care program, frequently called Medicare shortfalls.

The objective of our article is to examine the sensitivity of conclusions about the adequacy of private nonprofit hospital community benefits to alternative definitions of community benefits, using methods developed by health services researchers to make such assessments. In particular, we examine what conclusion would be reached if one focused strictly on the set of activities for which agreement has been reached about commu-nity benefit status and how this conclusion is affected if the definition is expanded to include the currently disputed items of bad debt and Medicare shortfalls. We also examine a third definition used frequently by health services researchers to provide an additional point of reference.

We are limited to studying only California and Florida hospitals because they have detailed hospital financial reporting that allows us to identify and quantify many of the community benefit components speci-fied in the new IRS form. This assessment, although limited to two states and based on data collected before the new form was developed, is impor-tant because existing studies use community benefit definitions that differ markedly from those that are now the focus of policy debate. Our study also gives a benchmark for where the industry stood that can be used to contrast changes in community benefit activities. Based on our findings, we suggest directions for future policy development that may help resolve remaining areas of dispute about hospital community benefit definition and measurement.

Bazzoli, Clement, and Hsieh ■ Community Benefits 1001

The next two sections provide background on community benefit defi-nition and measurement and also on the methods and findings of existing research. This is followed by a description of our study methods, includ-ing our conceptual framework, data resources, model specification, and analytic approaches. Results are then presented, and a concluding section discusses policy implications and areas for future research.

Background on Defining and Measuring Community Benefits

A U.S. Government Accountability Office (GAO) report in 2008 provided a thorough history of the issues and policy context relevant to defining and measuring hospital community benefits. This report noted that the origins of the current debate date back to a 1956 IRS revenue ruling (Rev. Rul. 56 – 185, 1956 – 1 C.B. 202), which stated that hospitals qualifying as 501(c)(3) charitable organizations were expected to provide charity care, namely, free health services to the poor who were unable to afford their care, to the best of the hospital’s financial ability. A later 1969 ruling (Rev. Rul. 690 – 545, 1969 – 2 C.B. 117) replaced the original standard with a broader community benefit definition that required nonprofit hospitals to demonstrate how they reduced governmental burden and promoted gen-eral welfare. This later ruling not only expanded the range of activities that could be deemed community benefits but also provided substantial latitude to nonprofit hospitals in defining what among their many activities counted as community benefits.

In the years after the 1969 IRS ruling, questions about the adequacy of nonprofit hospital community benefit provision rose to national attention and policy debate. Trocchio (2006) noted that several U.S. congressional committees in the 1980s and 1990s questioned whether there were any real differences between for- profit and nonprofit hospitals, whether non-profit hospitals continued to deserve their tax- exempt status, and whether there should be a level of effort test that nonprofit hospitals had to meet to continue their tax exemption. CHA and VHA, Inc., became involved with this issue during this period, given its relevancy for their nonprofit hospital members. Together, the organizations identified member approaches for providing and documenting their community benefit activities.

In 2006 CHA and VHA, Inc., established specific definitions and guide-lines for measuring community benefit activities, publishing A Guide for Planning and Reporting Community Benefit in that year (Catholic Health Association [CHA] 2006a). These guidelines identified the unreimbursed


costs of the following activities as community benefits: charity care, Medicaid, other means- tested government programs, health professions education, research, community health improvement services, financial and in- kind contributions to charitable organizations, community benefit operations, and community building activities.

Missing from the list were bad debt expense and unreimbursed Medi-care costs, which many nonprofit hospitals were counting as community benefits. CHA and VHA, Inc., took the position that these should not be considered because bad debt could be generated by patients who were not necessarily needy and might represent ineffective hospital efforts to col-lect on their bills. Further, they believed that hospitals needed to have clear guidelines to identify patients eligible for charity care a priori. In terms of Medicare shortfalls, CHA and VHA, Inc., noted that these may be due to inefficiency rather than underpayment and that serving Medicare benefi-ciaries was not a differentiating feature of nonprofit hospitals (U.S. GAO 2008). The American Hospital Association (AHA), however, took the position that both these items should be considered community benefits because a large share of bad debt was likely attributable to poor patients, some of whom did not follow through in applying for coverage through public programs. Also, the AHA felt that underpayments by Medicare did relieve the government of substantial financial burden and that many Medicare patients were needy given their fixed incomes. These differing views on the treatment of bad debt expense and Medicare shortfalls were also reflected in state community benefit reporting. Of the nineteen states in 2006 that required these reports, CHA (2006b) found that six included bad debt expense and nine included Medicare shortfalls.

The IRS faced growing pressure in 2007 from policy makers to develop more precise tools and guidance for measuring hospital community bene-fits, especially as the result of hearings held by Senator Grassley. In these hearings, Grassley emphasized the CHA and VHA, Inc., standards and sided with their arguments about bad debt expense and Medicare short-falls, indicating that the former merely “clouds the picture” (Grassley 2006). He also exhorted the IRS to provide more specificity and direc-tion to nonprofit hospitals in their federal reporting of community benefit activities.

In December 2007 the IRS introduced a revision to its Form 990 Schedule H, which is filed annually by all private, nonprofit hospitals with 501(c)(3) status. Prior to this revision, Schedule H allowed for open- ended, general descriptions of hospital community benefit activities. The new version, however, identifies particular activities under a section labeled “charity


care and other community benefits” and asks hospitals to report for each activity (1) the numbers of persons served, (2) total expenses involved in these activities, (3) direct offsetting revenues, and (4) resulting net com-munity benefit expense (i.e., total expense less direct revenues).1 The com-munity benefit activities identified in this section are charity care at cost, unreimbursed Medicaid, unreimbursed costs from other means- tested government programs, community health improvement services and community benefit operations, health professions education, subsidized health services, research, and cash and in- kind contributions to commu-nity groups. In addition, the schedule asks hospitals to report unreim-bursed costs for community building activities. The above activities are consistent with the items identified in the CHA and VHA, Inc., guidelines. In a separate section, Schedule H requests data on bad debt expense and unreimbursed Medicare costs. Hospitals are also asked to provide written rationale for why some or all of these latter costs should be considered community benefits. Thus, although the new Schedule H certainly high-lights and emphasizes the set of activities for which most stakeholders agreed constitute community benefits, it did not dismiss entirely the two items in dispute.

Background on Prior Community Benefit Research

Given the lack of clarity and diversity in prior community benefit defini-tions, it is not surprising that prior research studies of hospital community benefit provision have used very different measures. For example, Norton and Staiger (1994) and Sutton, Milet, and Blanchfield (2002) both exam-ined hospital care for uninsured individuals and thus were most consistent with the original 1956 IRS standards. They contrasted charity care provi-sion among nonprofit and for- profit hospitals, and found that both types of hospitals provided similar amounts. Based on this finding, they concluded that nonprofit hospitals were not providing adequate community benefits because the monies they would otherwise pay in taxes were not resulting in higher levels of charity care.

Several studies examined hospital uncompensated care, namely, unre-imbursed costs resulting from charity care and bad debt. A number of studies found that nonprofit hospitals provide more uncompensated care

1. A copy of the form can be accessed at www.irs.gov/pub/irs- pdf/f990sh.pdf.


than do for- profit hospitals (Bazzoli et al. 2006; Davidoff et al. 2000; Thorpe, Seiber, and Florence 2001; Congressional Budget Office 2006). However, the critical question for policy debate is whether this additional provision by nonprofit hospitals is sufficient to warrant their tax exemp-tions. To examine this, Schneider (2007) used the sum of for- profit hospi-tal uncompensated care and the federal and state income taxes they paid as a benchmark to assess the adequacy of nonprofit community benefit provision. Her empirical analysis found that nonprofit hospitals were defi-cient in providing sufficient uncompensated care relative to this for- profit hospital benchmark, ceteris paribus. Morrisey, Wedig, and Hassan (1996) and Kane and Wubbenhorst (2000) used similar approaches and obtained similar results. Morrisey, Wedig, and Hassan (1996) concluded that 20 to 40 percent of nonprofit hospitals provided insufficient uncompensated care relative to a for- profit benchmark, and Kane and Wubbenhorst (2000) concluded that about 50 percent of nonprofit hospitals were deficient given this type of benchmark.

Some researchers have examined more expansive community benefit definitions. For example, Herzlinger and Krasker (1987) and Arrington and Haddock (1990) examined the scope of hospital services, the num-ber of emergency room visits (as an access measure), and involvement in health professions education. Herzlinger and Krasker (1987) focused only on hospitals in multihospital systems and concluded that nonprofit hospitals were not substantially different on many study measures relative to for- profit hospitals. However, Arrington and Haddock (1990) examined both system and nonsystem hospitals and found that nonprofit hospitals provided more community benefits than did for- profit hospitals.

Two additional studies also looked at a broader set of activities and quantified their associated unreimbursed costs. Nicholson et al. (2000) assessed uncompensated care costs, losses on medical research, and price discounts for private patients provided by nonprofit hospitals. They con-trasted this to a benchmark based on the sum of these plus profits and esti-mated taxes paid by for- profit hospitals in three large multihospital sys-tems. They found that there was about a 20 percent deficit in community benefits provided by nonprofit hospitals overall relative to the for- profit benchmark. Schneider (2007) examined a community benefit measure that included uncompensated care costs, Medicare and Medicaid short-falls, price discounts for privately insured patients, and policy discounts given to hospital employees. With this broader measure, her analysis found that nonprofit hospitals in California met or exceeded the for- profit bench-mark, holding hospital, market, and community characteristics constant.

https://www.researchgate.net/publication/12139461_Measuring_Community_Benefits_Provided_by_For-Profit_and_Nonprofit_Hospitals?el=1_x_8&enrichId=rgreq-62629d1e-2cdc-43a8-a0aa-8195c48e9081&enrichSource=Y292ZXJQYWdlOzUwOTM2NTk3O0FTOjE1NzY0MDY4ODY3Njg3MkAxNDE0NTk1ODc3MjY4

https://www.researchgate.net/publication/12385672_Alternative_Funding_Policies_for_the_Uninsured_Exploring_the_Value_of_Hospital_Tax_Exemption?el=1_x_8&enrichId=rgreq-62629d1e-2cdc-43a8-a0aa-8195c48e9081&enrichSource=Y292ZXJQYWdlOzUwOTM2NTk3O0FTOjE1NzY0MDY4ODY3Njg3MkAxNDE0NTk1ODc3MjY4

https://www.researchgate.net/publication/5990587_Paying_their_way_Do_nonprofit_hospitals_justify_their_favorable_tax_treatment?el=1_x_8&enrichId=rgreq-62629d1e-2cdc-43a8-a0aa-8195c48e9081&enrichSource=Y292ZXJQYWdlOzUwOTM2NTk3O0FTOjE1NzY0MDY4ODY3Njg3MkAxNDE0NTk1ODc3MjY4



This finding differs from those reached with her narrower community benefit definition that measured only uncompensated care costs.

Overall, there has been great variability in the definitions of community benefits used in prior research, and these definitions do not link well to the focal activities identified in the new IRS form. Nor do prior definitions used in research allow an assessment of how community benefit adequacy could be affected when the disputed items are included or excluded. How-ever, the findings do illustrate the sensitivity of conclusions to the specific definition of community benefits used. Thus it is important to examine how the definitions used in the new IRS form might affect assessments of community benefit provision.

Study Framework and Methods

In this section, we present the conceptual underpinnings of our study and then discuss our data, measures, and analytic methods.

Conceptual Framework

Similar to Nicholson et al. (2000), we conceptualize hospital commu-nity benefits as public goods in that individuals receive benefits either directly or indirectly from consuming these goods, and firms producing them typically collect insufficient revenues to cover production costs. Weisbrod’s (1980) economic theory envisioned that nonprofit hospitals filled an important role in providing public goods given that for- profit firms will avoid producing them and government officials will provide low quantities as viewed by some community stakeholders because of diverse voter preferences. Thus government officials grant tax- exempt status to private nonprofit hospitals because these hospitals can serve a role in meeting community needs that are unmet by for- profit firms and government agencies.

However, hospital provision of community benefits depends on several other factors, not just a hospital’s ownership status. In particular, these factors relate to the supply and demand for community benefits. Supply factors include an individual hospital’s capacity to provide community benefits and the ability of other institutions in the market to produce these benefits. Demand relates to community characteristics that influence the amount and types of community benefits needed.

Schneider (2007) categorized these demand and supply factors into three groups: hospital characteristics, community characteristics, and mar-




ket characteristics. The most important hospital characteristic of interest to this study is hospital ownership status. We specifically examine private hospitals within three ownership groups: church- affiliated nonprofit hos-pitals, non- church- affiliated nonprofit hospitals, and for- profit hospitals. We separate church- affiliated institutions because their religious ties may mean greater commitment to serving community needs. Other hospital characteristics may influence hospital involvement in community benefit provision, such as hospital size, teaching status, system affiliation, dispro-portionate share hospital (DSH) status, and status as a trauma center. Hos-pitals with more staffed beds and those that are system affiliated may have more capacity or resources to draw on to provide community benefits. Those with residency training programs are involved with health profes-sions education and also may provide more care to low- income and unin-sured individuals to provide learning experiences for health professionals they are training. Hospitals with DSH status, either through Medicaid or Medicare, serve greater numbers of low- income individuals and receive additional payments that may offset their unreimbursed costs. Hospitals with trauma centers may have large numbers of uninsured trauma victims whom they are obligated to treat.

Community characteristics can affect the level of need in a hospital’s service area and thus its level of community benefit provision. Communi-ties with larger shares of uninsured people will likely have greater need for charity care. Ironically, greater prevalence of uninsured individuals in the population might lead to lower levels of unreimbursed Medicaid costs because fewer individuals are covered by these programs and thus gen-erating unreimbursed program costs. Hospitals located in more prosper-ous areas, as measured by per capita income, may have lower community benefit needs because of less need for charity care and more prevalent private insurance coverage. Schneider (2007) also suggested that higher birth rates per one thousand women or rural location may influence com-munity benefit provision.

Hospital market characteristics can also play an important role. Hospitals located in areas with high health maintenance organization (HMO) mar-ket share or high levels of hospital competition may have fewer resources to support community benefit activities. Hospitals located in markets with a greater supply of hospital beds relative to the population may experience less need for community benefit provision in that there are more institu-tions involved with these activities. Similarly, Schneider (2007) argued that if other hospitals are providing greater amounts of charity care, there may be less unmet need in the community, and individual hospitals could




2. Additional information on the state financial reporting systems is available for Florida at ahca.myflorida.com/MCHQ/CON_FA/fa_data/index.shtml and for California at www.oshpd .ca.gov/HID/Products/Hospitals/AnnFinanData/CmplteDataSet/index.html.

3. Generally, we defined extreme values as those greater (or less) than the mean plus (or minus) three standard deviations. We defined extreme values by standardizing variables so

reduce their involvement in this particular community benefit activity. Finally, work by Duggan (2002) suggested that the behavior of nonprofit hospitals in a market can be influenced by the presence of large numbers of for- profit hospitals. Specifically, nonprofit hospitals operating in mar-kets with many for- profit institutions may behave more like their for- profit rivals, which could mean less community benefit provision.

Data Sources and Sample

Several data sources were brought together for this analysis. The pri-mary ones were the California Hospital Financial Disclosure Report and the Florida Hospital Uniform Reporting System, both of which require hospitals to report financial data and utilization statistics annually using a uniform chart of accounts.2 These data were merged with the AHA annual survey to obtain information on hospital characteristics and other variables used to construct hospital market variables. Area Resource File sociodemographic data and U.S. Census Bureau estimates for the number of uninsured individuals were also used to measure community charac-teristics that may affect demand for community benefits. Data from the Centers for Medicare and Medicaid Services on hospital Medicare DSH status and on hospital Medicare payments for graduate medical education (GME) were also obtained. Finally, InterStudy data were used to measure county HMO market share.

Our study examined 2005 data for all the sources noted above. The selected year represents a baseline period before the CHA/VHA published its 2006 guidelines and before the revision of Form 990 Schedule H. Thus 2005 represents a period that can be used in future research to compare changing hospital behavior after these community benefit definitions, and reporting standards became more widely accepted and implemented.

Overall, we identified 169 private, nonprofit hospitals and 73 for- profit hospitals in the 2005 California hospital files that were general medical/surgical hospitals and 70 nonprofit and 84 for- profit general medical/surgi-cal hospitals in the 2005 Florida data. After elimination of hospitals that had extensive missing data, converted ownership in 2005, or had values of variables considered extreme,3 our final sample included 143 nonprofit


that they were measured relative to hospital bed size. This was done separately by hospital ownership type.

4. Although we were able to measure most items identified in the revised IRS Form 990 Schedule H, there were a few we could not: community health improvement services and com-munity benefit operations, subsidized health services, cash and in- kind contributions to commu-nity groups, and community building activities. Data reported in Gray and Schlesinger 2009 for Maryland hospitals suggest that these items represent a small portion of the primary community benefit activities emphasized in the new IRS form. In particular, they represented about 12 per-cent of Gray and Schlesinger’s community benefit measure in 2005. However, their measure did not include unreimbursed Medicaid shortfalls, which for our California and Florida hospitals was about 60 to 70 percent of our primary community benefit measure (see table 2). Factoring our data into their study, we expect that the items we are able to measure represent about 90 to 95 percent of primary community benefit activities emphasized in the IRS 990 form.

and 62 for- profit California hospitals and 63 nonprofit and 74 for- profit hospitals in Florida. This represents about 85 to 90 percent of hospitals in the various state and ownership categories. A comparison of included and excluded hospitals in relation to bed size, ownership type, teaching status, presence of a trauma center, and rural location indicated no significant differences for these characteristics in Florida. In California there were also no significant differences with the exception that a disproportionate number of excluded hospitals in this state were major teaching hospitals that were members of the Association of American Medical College’s Council of Teaching Hospitals.

Variable Construction

We constructed individual community benefit items using Form 990 Schedule H worksheets and the California and Florida financial data. Table 1 provides a brief description of the variables that we were able to measure.4 Some modifications to variable construction were essential given the lack of certain items in the California or Florida financial report-ing systems. In particular, the instructions for converting charge- based measures (e.g., reported charity care, bad debt expense) to costs allowed hospitals to use either an institutional cost- to- charge ratio or their own cost- accounting methodology. Because we lack information on the latter, an institutional cost- to- charge ratio was used. We followed IRS worksheet instructions and subtracted Medicaid provider taxes, research costs, and health professionals training costs from hospital expenses when calculat-ing this ratio. For unreimbursed Medicaid costs, hospitals are instructed to use net Medicaid patient service revenues, excluding any Medicaid GME received. Unfortunately, neither the California nor the Florida system reports Medicaid GME separately. To correct for this, we allocated a por-


Table 1 Community Benefit Activity Definitions Based on IRS Form 990 Schedule H Items and Taxes Paid by Hospitals

Charity Care and Other Community Benefits (Part I, Section 7 of Form 990 Section H)

Charity care costs Measured as hospital-reported deductions from revenue, translated to costs by institutional cost-to-charge ratio.

Unreimbursed Measured as the sum of (1) Medicaid gross patient revenues Medicaid costs translated to costs with institutional cost-to-charge ratio,

(2) Medicaid provider taxes, and (3) an allocation of health professions education cost based on a hospital’s Medicaid patient share of discharges. Net Medicaid revenues are then deducted from this sum to obtain unreimbursed Medicaid costs.

Unreimbursed Measured as county indigent care gross patient revenues costs—other translated to costs with institutional cost-to-charge ratio means-tested less net revenues received from county to offset these costs government (measured only in Calif.). programs from county indigent programs Health professions Measured as reported costs of training all health professionals education cost that were unallocated to Medicaid (see above) less the sum (net of revenues) of revenues received for training purposes (such as student

tuition received) and Medicare GME payments.Research costs Measured as direct costs of research activities less revenues (net of revenues) received to offset research costs.

Bad Debt and Medicare Shortfalls (Part III, Form 990 Section H)

Bad debt expenses Measured as bad debt expense, which hospitals report on (measured as a charge basis, translated to costs by institutional costs) cost-to-charge ratio.Unreimbursed Measured as Medicare gross patient revenues translated to Medicare costs costs with institutional cost-to-charge ratio less net

Medicare revenues received by the hospital (excluding any Medicare GME payments).

Taxes Paid by Hospitals

Taxes Represents the sum of estimated state and federal income taxes, sales tax, and property tax for for-profit hospitals.

Additionally, any income taxes reported by nonprofit hospi-tals they may report for unrelated businesses are included.

Sources: Florida data come from Florida Agency for Health Care Administration Database; California data come from California Office of Statewide Health Planning and Development Database.

Note: GME = graduate medical education


tion of health professions education cost to Medicaid based on a hospital’s Medicaid patient share of hospital discharges. This approach means that unreimbursed Medicaid and health professions education costs both are not measured precisely. However, there should be no overall effect of this on our primary community benefits measures, which sums across items, because any under- (or over- )estimate of unreimbursed Medicaid costs should be offset by an equal over- (or under- )estimate of unreimbursed health professions educational costs. Another essential deviation was in relation to research costs. The IRS worksheets ask hospitals to compare the direct and indirect costs of research to total research revenues received. However, we had data only on direct costs of research and payments to offset direct costs, not information related to indirect costs.

We also constructed measures for taxes paid by hospitals. The Cali-fornia and Florida financial reports had items related to provisions for federal and state income taxes, but we observed problems with these vari-ables. Many for- profit system hospitals had zero values despite having positive net incomes and despite hospital financial reporting instructions that income taxes were to be distributed to individual system hospitals based on their net incomes. Given this, we estimated for- profit hospital income taxes based on the net income they reported and state and federal tax laws in 2005. We also estimated property taxes. We obtained data on industrial property tax rates from the Minnesota Taxpayers Association, which annually collects data on property tax rates for individuals and companies in rural areas and large cities for all U.S. states (Minnesota Taxpayers Association 2007). Following Nicholson et al. (2000), we esti-mated a for- profit hospital’s property taxes by taking the value of net fixed assets (less accumulated deprecation) and multiplying this by the respec-tive urban and rural tax rates in the documentation noted above. Also, fol-lowing this earlier study, we estimated sales tax by multiplying a for- profit hospital’s supply expenses by the sales tax rates applicable in each state. The latter was obtained from the Federation of Tax Administrators and reflected state sales tax rates as of January 1, 2007, because historical data were not available (Federation of Tax Administrators 2007). Finally, a few nonprofit hospitals reported paying income taxes in California, and we included these because they represented a contribution to the community just as taxes paid by for- profit hospitals. Our tax measure is thus the sum of estimated federal and state income tax, property tax, and sales tax for for- profit hospitals and any reported income taxes of nonprofit hospitals.

Construction for many of the other study variables was straightforward given the variables identified above in the conceptual framework discus-



5. There is no general consensus for defining markets in health services research. Geopoliti-cal boundaries such as counties or metropolitan statistical areas are commonly used given that analysis of patient travel patterns is sometimes difficult to conduct and may be endogenous given the phenomena under study. We selected counties for our study given that our analysis encompasses both urban and rural hospitals, which could not be accommodated if metropolitan areas were instead chosen.

sion. Additional discussion of a few variables is warranted. In relation to ownership status, we obtained this information from the California and Florida financial reports rather than the AHA. Hospital teaching status was measured through two variables: membership in the Council of Teaching Hospitals (COTH) of the Association of American Medical Colleges, which is indicative of a major commitment to teaching; and a measure for non- COTH members indicating the presence of residency training programs.

In relation to community and hospital market characteristics, all mea-sures were developed using the county as the relevant market area.5 The Herfindahl- Hirschman Index used in the analysis was based on total hos-pital admissions as reported in the American Hospital Association data and used the sum of admissions for system hospitals in a county when they belonged to the same hospital system. Our models include a variable indicating high for- profit hospital presence in a market, which we defined separately for California and Florida based on the percentage of hospitals in a county that were for- profit. This variable took the value of 1 for coun-ties that had greater than the median value on this variable in their state and was 0 otherwise, and thus will indicate whether hospital provision of community benefits differs in markets with substantial versus little for- profit penetration. Finally, our measure for charity care provided by other hospitals includes care delivered not only by the for- profit and nonprofit hospitals under study but also by public and district hospitals because their efforts in this regard will reduce demands on private hospitals to provide these services.

Analytic Methods

The model estimated in our analysis takes the following form:

log (CB)n = β1 + β20wnn + β3Hospn + β4Commn + β5Markn + εn (1)

where CB represents community benefits, namely, the sum of unreim-bursed costs for community benefit activities, measured in logs given the skewness of this variable. In the equation, n references individual hospi-


tals. Ownn represents ownership status, which in our case will be nonprofit, church- affiliated and nonprofit, non- church- affiliated (for- profit ownership is the omitted category). Hospn represents other hospital characteristics. Commn is community characteristics that could affect demand. Markn is hospital market characteristics that could affect the supply of community benefit activities in the market.

We estimated equation (1) separately for California and Florida hospi-tals because Chow tests indicated that the models had significantly dif-ferent structures in the two states. It was also worthwhile to do separate analysis because California has mandated community benefit reporting, but Florida does not. We estimated the models first using ordinary least squares, applying robust estimation techniques given the clustering of community and hospital market variables. Also, two- stage least squares models were estimated, again with robust techniques, recognizing the point raised by Schneider (2007) that charity care offered by other hos-pitals in a market could be endogenous. This would arise if, for example, a hospital under study was a major provider of indigent care in its com-munity. If this were so, there would be less need for other hospitals in the market to provide charity care. Two instruments were used in this analysis: (1) following Schneider (2007), the sum of subsidies, transfers from restricted funds, and tax appropriations that other hospitals receive to support their indigent care activities; and (2) the percentage of all hos-pital beds in the county owned by public or district hospitals. Our results section reports the results of our specification tests for the instruments, which suggested that they were valid choices.

Empirical Results

Table 2 reports average unreimbursed costs for each individual primary community benefit activity plus bad debts, Medicare shortfalls, and taxes paid by ownership type. For simplicity, we combined church- affiliated and non- church- affiliated nonprofit hospitals in this table. Also, to improve comparability across ownership types, we constructed the variables on a per- bed basis because nonprofit hospitals tend to be larger than for- profit hospitals. In both California and Florida, nonprofit hospitals have more charity care costs per bed than for- profit hospitals. Florida nonprofit hos-pitals have greater unreimbursed Medicaid costs per bed than for- profit hospitals, but this was not true in California. The other unreimbursed cost items for primary community benefit activities are much smaller in magnitude than the first two, and when significant differences are present,


Tab

le 2

M

ean

Un

reim

bu

rsed

Co

sts

for

Co

mm

un

ity

Ben

efit

Act

ivit

ies

per

Ho

spit

al B

ed: N

on

pro

fit

and

Fo

r-Pr

ofi

t H

osp

ital

s in

Cal

ifo

rnia

an

d F

lori

da,

200

5

C

alif

orni

a H

ospi

tals

Fl

orid

a H

ospi

tals

O

vera

ll

O

vera

ll

Non

profi

t Fo

r-Pr

ofit

NFP

and

N

onpr

ofit

For-

Profi

t N

FP a

nd

H

ospi

tals

H

ospi

tals

FP

Mea

n H

ospi

tals

H

ospi

tals

FP

Mea

n

Prim

ary

com

mun

ity b

enefi

t act

iviti

es

Cha

rity

car

e co

sts

10

,149

* 7,

363*

9,

303

18,7

22**

8,

673*

* 13

,381

U

nrei

mbu

rsed

Med

icai

d co

sts

36,6

12

38,8

22

37,2

78

23,1

52*

16,9

66*

19,8

88

Unr

eim

burs

ed c

osts

for

oth

er m

eans

-tes

ted

gove

rnm

ent p

rogr

ams

4,39

7*

2,55

2*

3,83

5 n.

a.

n.a.

n.

a.

Hea

lth p

rofe

ssio

ns e

duca

tion

cost

(ne

t of

reve

nues

) 1,

389

581

1,14

2 3,

985*

* 80

5**

1,92

8

Res

earc

h co

sts

(net

of

reve

nues

) 29

1**

7**

203

126*

6*

61

To

tal o

f pr

imar

y co

mm

unity

ben

efit a

ctiv

ities

52

,992

47

,983

51

,477

45

,107

**

26,4

68**

33

,945

Bad

deb

t and

Med

icar

e sh

ortf

all

B

ad d

ebts

at c

ost

14,5

34

11,6

15

13,6

52

24,5

27**

14

,950

**

19,4

06

Unr

eim

burs

ed M

edic

are

cost

s 60

,744

**

38,9

29**

54

,118

23

,342

20

,037

21

,549

To

tal o

f ba

d de

bt a

nd M

edic

are

7

4,58

3**

50,6

50**

67

,358

46

,268

**

34,8

20**

39

,978

Taxe

s pa

id

6**

23,2

98**

6,

754

0**

25,8

65**

13

,735

Num

ber

of h

ospi

tals

14

3 62

20

5 63

74

13

7

Sour

ces:

Aut

hor a

naly

sis

of in

form

atio

n fr

om C

alif

orni

a O

ffice

of S

tate

wid

e H

ealt

h Pl

anni

ng a

nd D

evel

opm

ent D

atab

ase

and

Flo

rida

Age

ncy

for H

ealt

h C

are

Adm

inis

trat

ion

Dat

abas

e as

wel

l as

AH

A a

nnua

l sur

vey

data

, Are

a R

esou

rce

Fil

e, U

.S. C

ensu

s, C

ente

rs f

or M

edic

are

and

Med

icai

d S

ervi

ces,

and

Int

erSt

udy

data

. See

text

for

spe

cific

var

iabl

e co

nstr

ucti

ons.

Not

es: N

FP

= n

ot f

or p

rofit

; FP

= f

or p

rofit

; n.a

. = n

ot a

ppli

cabl

e*F

or-p

rofit

and

non

profi

t hos

pita

ls s

igni

fican

tly

diff

eren

t wit

hin

stat

e at

p ≤

0.0

5 le

vel

**Fo

r-pr

ofit a

nd n

onpr

ofit h

ospi

tals

sig

nific

antl

y di

ffer

ent w

ithi

n st

ate

at p

≤ 0

.01

leve

l


nonprofit organizations provided more. Summing over all primary com-munity benefit activities, we see significant differences across ownership types in Florida but not in California. This finding appears to be largely due to the unreimbursed Medicaid costs variable, which is the major item contributing to the total.

Examining bad debts at cost and also unreimbursed Medicare costs, we see that bad debt costs are higher in Florida nonprofit hospitals relative to for- profits, but the same is not true in California. Finally, unreimbursed Medicare costs were substantial for all hospitals and were especially high for California nonprofit hospitals.

Table 3 reports descriptive statistics on all dependent and explanatory variables used in the multivariate analysis. Multivariate analysis is impor-tant because factors other than hospital bed size might affect provision of community benefits, and it will be important to control for these factors when comparing for- profit and nonprofit hospitals. The community benefit measures here represent sums of individual items, with community bene-fits 1, equaling the sum of unreimbursed costs for charity care, Medicaid, county indigent programs, research, and health professions education and also taxes paid. Unlike table 2, these are total amounts, not per- bed amounts. Community benefits 2 equals community benefits 1 plus bad debt costs and Medicare shortfalls. The final variable labeled as tradi-tional uncompensated care measure is the sum of charity care costs, bad debts costs, and taxes paid. We examined the latter because it was consis-tent with measures used in several earlier studies and thus represented a point of comparison.

It is interesting that across the two states, average community benefits 1 and 2 are not substantially different, yet the traditional uncompensated care measure is nearly twice as large in Florida than in California. This most likely reflects the larger values of charity care and bad debts reported for both nonprofit and for- profit hospitals in Florida relative to California as reported in table 2.

Table 4 reports the results from the multivariate analysis using ordinary least squares and examining the three different definitions of community benefit activities. Looking at the ownership variables and the primary community benefit definition, the results suggest that nonprofit, non- church- affiliated hospitals in both states and church- affiliated hospitals in California are deficient in providing community benefits. Namely, the coefficients on these variables are negative and significant, implying that their community benefits are significantly less than the sum of for- profit community benefits and taxes paid, holding other factors constant. The


findings in the final set of columns, which focus strictly on uncompensated care and taxes, are also consistent with these conclusions for the primary community benefit definition. However, one finds different results once one accounts for both bad debt costs and Medicare shortfalls. Looking at community benefits definition 2 in the middle columns of table 4, we observe that there is no significant difference between the two nonprofit hospital groups and the omitted for- profit group. This suggests that the nonprofit groups provide enough of these community benefits to match those provided by for- profits, including the taxes the latter pay, all else equal. This finding is consistent with Schneider (2007) when she used an expanded community benefit definition that included Medicare shortfalls, as does our second community benefits definition.

Table 5 provides parameter estimates for our two- stage least squares models. The specification tests at the bottom of the table indicate that our instruments are valid. Namely, the first specification test, which assesses the joint significance of the instruments in a reduced form model with charity care provided by other hospitals as the dependent variable, indi-cates that these variables are significantly related to this variable. The overidentification test, for which the null hypothesis is that the instru-ments are correlated with the error term of the community benefit models, is rejected in all cases, although marginally so with a p = 0.11 for the tra-ditional uncompensated care measure in California. The pattern of results for the ownership variables overall are very similar to those in table 4. Generally, we find underprovision of community benefits relative to for- profit hospitals when the primary definition of community benefits is used and for the traditional uncompensated care measure. For the most part, these results become insignificant when the expanded definition of com-munity benefits is used, except for the negative coefficient for nonprofit, non- church- affiliated hospitals in Florida, which is marginally significant at the p = 0.10 level.

Tables 4 and 5 also provide similar findings for many other variables in the model. For hospital characteristics, larger hospitals (as measured by bed size) consistently provide more community benefits than smaller hospitals. This is true regardless of how community benefits are mea-sured. Teaching hospitals in California, whether COTH affiliated or not, typically provided more community benefits, but this was not true in Flor-ida. Similarly, hospitals with trauma centers in California provided more community benefits regardless of how it was measured, but this also was not found in Florida. For community characteristics, the ordinary least squares models suggested the paradoxical finding that greater propor-


Tab

le 3

M

ean

s an

d S

tan

dar

d D

evia

tio

ns

of

Dep

end

ent

and

Exp

lan

ato

ry V

aria

ble

s

Cal

ifor

nia

Hos

pita

ls

Flo

rida

Hos

pita

ls

Stan

dard

Stan

dard

Mea

n D

evia

tion

Mea

n D

evia

tion

Hos

pita

l ave

rage

: Unr

eim

burs

ed c

osts

for

com

mun

ity b

enefi

t act

iviti

es

C

omm

unity

ben

efits

defi

nitio

n 1

($ m

illio

ns)a

12

.746

15

.970

13

.822

18

.240

C

omm

unity

ben

efits

defi

nitio

n 2

($ m

illio

ns)b

27

.043

28

.837

24

.447

29

.590

T

radi

tiona

l unc

ompe

nsat

ed c

are

mea

sure

($

mill

ions

)c

6.07

4 6

.096

12

.154

12

.920

Hos

pita

l cha

ract

eris

tics

Prop

ortio

n ch

urch

-affi

liate

d .1

92

.395

.0

61

.241

Pr

opor

tion

nonp

rofit

, non

-chu

rch-

affil

iate

d .5

02

.501

.4

01

.492

St

affe

d an

d se

t-up

bed

s (0

0s)

2.17

7 1.

572

2.69

1 2.

361

Pr

opor

tion

syst

em-a

ffilia

ted

.758

.4

29

.816

.3

89

Prop

ortio

n w

ith r

esid

ency

teac

hing

pro

gram

s .0

91

.289

.0

68

.253

Pr

opor

tion

CO

TH

mem

bers

.0

55

.228

.0

41

.199

Pr

opor

tion

DSH

rec

ipie

nts

.831

.3

76

.605

.4

90

Prop

ortio

n w

ith tr

aum

a ce

nter

.1

83

.387

.0

95

.295

Com

mun

ity c

hara

cter

istic

s (c

ount

y)

Prop

ortio

n of

pop

ulat

ion

unin

sure

d .1

89

.037

.1

55

.032

Pe

r ca

pita

inco

me

(000

s)

37.0

03

10.1

95

34.2

11

8.33

5

Bir

ths

/ 1,0

00 w

omen

age

s 15

to 4

4 69

.090

8.

540

63.5

58

7.22

4

Prop

ortio

n lo

cate

d in

rur

al c

omm

unity

.0

59

.237

.1

43

.351

(con

tinu

ed)

Tab

le 3

(C

on

tin

ued

)

Cal

ifor

nia

Hos

pita

ls

Flo

rida

Hos

pita

ls

Stan

dard

Stan

dard

Mea

n D

evia

tion

Mea

n D

evia

tion

Mar

ket c

hara

cter

istic

s (c

ount

y)

HM

O m

arke

t sha

re

.438

.1

69

.213

.1

33

Her

finda

hl-H

irsc

hman

Ind

ex

.273

.2

55

.444

.2

63

Hos

pita

l bed

s / 1

,000

pop

ulat

ion

1.89

8 .5

34

2.78

5 .7

57

Prop

ortio

n in

mar

kets

with

hig

h %

for

-pro

fit h

ospi

tals

.5

30

.500

.6

46

.480

C

hari

ty c

are

prov

ided

by

othe

rs (

000,

000s

) 54

.526

61

.972

29

.947

33

.552

Inst

rum

enta

l var

iabl

es u

sed

in 2

SLS

(cou

nty)

Su

m o

f re

stri

cted

fun

d tr

ansf

ers,

indi

gent

sub

sidi

es, a

nd p

ublic

sup

port

fo

r in

dige

nt c

are

prov

isio

n fo

r ot

her

hosp

itals

in a

cou

nty

($ m

illio

ns)

2.32

0 3.

983

38.3

54

86.1

39

Prop

ortio

n of

hos

pita

l bed

s in

cou

nty

that

are

pub

licly

ow

ned

.126

.1

18

.095

.1

67N

umbe

r of

hos

pita

ls

205

137

Sour

ces:

Aut

hor a

naly

sis

of in

form

atio

n fr

om C

alif

orni

a O

ffice

of S

tate

wid

e H

ealt

h Pl

anni

ng a

nd D

evel

opm

ent D

atab

ase

and

Flo

rida

Age

ncy

for H

ealt

h C

are

Adm

inis

trat

ion

Dat

abas

e as

wel

l as

AH

A a

nnua

l sur

vey

data

, Are

a R

esou

rce

Fil

e, U

.S. C

ensu

s, C

ente

rs f

or M

edic

are

and

Med

icai

d S

ervi

ces,

and

Int

erSt

udy

data

. See

text

for

spe

cific

var

iabl

e co

nstr

ucti

ons.

Not

es: C

OT

H =

Cou

ncil

of

Teac

hing

Hos

pita

ls; D

SH =

dis

prop

orti

onat

e sh

are

hosp

ital

; HM

O =

hea

lth

mai

nten

ance

org

aniz

atio

n; 2

SLS

= t

wo-

stag

e le

ast

squa

res

a Equ

als

the

sum

of

unre

imbu

rsed

cos

ts fo

r ch

arit

y ca

re, M

edic

aid,

cou

nty

indi

gent

pro

gram

s (C

A o

nly)

, res

earc

h, a

nd h

ealt

h pr

ofes

sion

s ed

ucat

ion

and

also

ta

xes

paid

by

hosp

ital

sb E

qual

s co

mm

unit

y be

nefit

s 1

defi

niti

on a

s ab

ove

plus

cos

ts f

or b

ad d

ebt a

nd M

edic

are

shor

tfal

lsc E

qual

s su

m o

f ch

arit

y ca

re c

osts

, bad

deb

t cos

ts, a

nd ta

xes

paid

by

hosp

ital

s

Tab

le 4

M

ult

ivar

iate

An

alys

is o

f C

om

mu

nit

y B

enefi

t A

ctiv

itie

s: R

ob

ust

OLS

Reg

ress

ion

Mo

del

s

C

omm

unity

Ben

efits

C

omm

unity

Ben

efits

T

radi

tiona

l Unc

ompe

nsat

ed

D

efini

tion

1 D

efini

tion

2 C

are

Mea

sure

C

alif

orni

a Fl

orid

a C

alif

orni

a Fl

orid

a C

alif

orni

a Fl

orid

a

Hos

pita

l cha

ract

eris

tics

Non

chur

ch, n

onpr

ofit

–.38

1***

-.

519*

* –.

074

–.22

8 –.

419*

**

-.28

8*

Chu

rch-

affil

iate

d, n

onpr

ofit

–.51

3***

.0

79

–.03

2 .0

98

–.41

9***

–.

133

St

affe

d an

d se

t-up

bed

s (0

0s)

.388

***

.273

***

.379

***

.248

***

.338

***

.234

***

Sy

stem

-affi

liate

d .4

14**

–.

169

.193

–.

058

.122

.1

20

Res

iden

cy te

achi

ng p

rogr

ams

.421

***

.030

.1

92

–.08

1 .2

17**

.0

97

CO

TH

mem

ber

.672

***

.281

.5

04**

* .1

69

.285

* .1

18

DSH

rec

ipie

nt

.255

.1

44

–.19

7 –.

099

.095

.0

81

Tra

uma

cent

er

.378

***

–.04

9 .2

85**

* .1

81

.433

***

.280

*C

omm

unity

cha

ract

eris

tics

Prop

ortio

n of

pop

ulat

ion

unin

sure

d –7

.622

**

.810

–1

3.04

5***

–1

.212

–6

.708

**

1.59

6

Per

capi

ta in

com

e (0

00s)

–.

016*

–.

001

–.01

8***

–.

0002

–.

020*

**

.001

B

irth

s / 1

,000

wom

en a

ges

15 to

44

.006

–.

004

.006

.0

01

–.00

02

–.00

5

Rur

al c

omm

unity

–.

105

–.77

3***

–.

336

–.72

3***

–.

196

–.77

3***

Mar

ket c

hara

cter

istic

s

H

MO

mar

ket s

hare

.9

41

–.05

6 –.

104

.012

.7

26

–.65

6

Her

finda

hl-H

irsc

hman

Ind

ex

1.14

0***

.1

74

.498

.2

73

–.03

1 .2

23

Hos

pita

l bed

s / 1

,000

pop

ulat

ion

.096

.2

13

.026

.1

63

–.09

4 .0

59

(con

tinu

ed)

Tab

le 4

(C

on

tin

ued

)

C

omm

unity

Ben

efits

C

omm

unity

Ben

efits

T

radi

tiona

l Unc

ompe

nsat

ed

D

efini

tion

1 D

efini

tion

2 C

are

Mea

sure

C

alif

orni

a Fl

orid

a C

alif

orni

a Fl

orid

a C

alif

orni

a Fl

orid

a

H

igh

% f

or-p

rofit

hos

pita

ls

.402

**

–.05

7 .0

01

–.18

0 .0

52

.001

C

hari

ty c

are

prov

ided

by

othe

rs (

mill

ions

) .0

02

–.00

4*

.003

* –.

002

–.00

04

–.00

4*In

terc

ept

14.9

34**

* 15

.286

***

18.2

06**

* 15

.802

***

16.3

70**

* 15

.350

***

R2

.521

.6

29

.585

.6

67

.544

.6

34

Sour

ces:

Aut

hor a

naly

sis

of in

form

atio

n fr

om C

alif

orni

a O

ffice

of S

tate

wid

e H

ealt

h Pl

anni

ng a

nd D

evel

opm

ent D

atab

ase

and

Flo

rida

Age

ncy

for H

ealt

h C

are

Adm

inis

trat

ion

Dat

abas

e as

wel

l as

AH

A a

nnua

l sur

vey

data

, Are

a R

esou

rce

Fil

e, U

.S. C

ensu

s, C

ente

rs f

or M

edic

are

and

Med

icai

d S

ervi

ces,

and

Int

erSt

udy

data

. See

text

for

spe

cific

var

iabl

e co

nstr

ucti

ons.

Not

es: O

LS

= o

rdin

ary

leas

t squ

ares

; CO

TH

= C

ounc

il o

f Te

achi

ng H

ospi

tals

; DSH

= d

ispr

opor

tion

ate

shar

e ho

spit

al; H

MO

= h

ealt

h m

aint

enan

ce o

rga-

niza

tion

*p ≤

0.1

0; *

*p ≤

0.0

5; *

**p ≤

0.01

Tab

le 5

M

ult

ivar

iate

An

alys

is o

f C

om

mu

nit

y B

enefi

t A

ctiv

itie

s: R

ob

ust

Tw

o-S

tag

e Le

ast

Squ

ares

Reg

ress

ion

Mo

del

s

C

omm

unity

Ben

efits

C

omm

unity

Ben

efits

T

radi

tiona

l Unc

ompe

nsat

ed

D

efini

tion

1 D

efini

tion

2 C

are

Mea

sure

C

alif

orni

a Fl

orid

a C

alif

orni

a Fl

orid

a C

alif

orni

a Fl

orid

a

Hos

pita

l cha

ract

eris

tics

Non

profi

t, no

nchu

rch

–.32

4**

–.64

4***

–.

052

–.34

5*

–.42

8***

–.

406*

*

Chu

rch-

affil

iate

d –.

412*

* .0

63

.003

.0

76

–.43

6***

–.

175

St

affe

d an

d se

t-up

bed

s (0

0s)

.367

***

.293

***

.372

***

.270

***

.340

***

.254

***

Sy

stem

-affi

liate

d .4

00*

–.09

4 .1

87

.040

.1

28*

.198

R

esid

ency

teac

hing

pro

gram

s .6

47**

–.

117

.279

* –.

253

.211

* –.

101

C

OT

H m

embe

r .6

27**

* .3

30

.486

***

.329

.1

93*

.152

D

SH r

ecip

ient

.0

80

–.00

2 –.

262

–.12

0 .2

87**

.0

48

Tra

uma

cent

er

.444

***

–.19

5 .3

12**

* –.

005

.426

***

.147

Com

mun

ity c

hara

cter

istic

s

Pr

opor

tion

of p

opul

atio

n un

insu

red

14.8

47

3.21

1 –4

.389

.9

20

–9.5

33

4.50

8

Per

capi

ta in

com

e (0

00s)

.0

09

.008

–.

008

.009

–.

024*

* .0

12

Bir

ths

/ 1,0

00 w

omen

age

s 15

to 4

4 –.

033

–.00

2 –.

009

.005

.0

05

–.00

4

Rur

al c

omm

unity

1.

014

–.67

4***

.0

91

–.60

5***

–.

335

–.66

7***

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ket c

hara

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s

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ket s

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290

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1.

171

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Sour

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of in

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om C

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and

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wel

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AH

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vey

data

, Are

a R

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e, U

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s, C

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rs f

or M

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are

and

Med

icai

d S

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and

Int

erSt

udy

data

. See

text

for

spe

cific

var

iabl

e co

nstr

ucti

ons.

Not

es: C

OT

H =

Cou

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of

Teac

hing

Hos

pita

ls; D

SH =

dis

prop

orti

onat

e sh

are

hosp

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; HM

O =

hea

lth

mai

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ance

org

aniz

atio

n*p

≤ 0

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**p

≤ 0

.05;

***

p ≤

0.01


tions of uninsured people led to fewer community benefits in California. However, this relationship was insignificant in the two- stage models. In Florida, fewer community benefits were provided among hospitals in rural areas, but this was not true in California. Few significant relationships were present for the market factors.

Discussion

Although a consensus has developed that one set of activities is relevant as hospital community benefit activities, agreement has not been reached about the treatment of bad debt expense and Medicare shortfalls. The recent revision of IRS Form 990 Schedule H, although providing tools for more precise measurement of many community benefit activities, left issues about hospital bad debts and Medicare shortfalls open and unre-solved. Our analysis tested whether an inclusive versus exclusive definition of hospital community benefits mattered when using common approaches developed by health services researchers to assess the adequacy of non-profit hospital community benefit provision relative to a for- profit hospital benchmark that accounts for their community benefits and taxes paid. Overall, we found that for California and Florida nonprofit hospitals, conclusions about the adequacy of their community benefits were highly dependent on the definition of community benefits used.

Specifically, our major finding was that private, nonprofit hospitals in California and Florida were not, as a group, providing adequate com-munity benefits when the primary IRS definition of community benefits was used. This was the case for non- church- affiliated nonprofit hospitals in California and Florida and for church- affiliated hospitals in California. These findings were robust across different estimation methods. When both bad debts and Medicare shortfalls were included, we found that the nonprofit hospital community benefits generally met the for- profit bench-mark. Overall, our results suggest that inclusion of Medicare shortfalls may be especially important to concluding that nonprofit hospitals provide sufficient community benefits. Namely, when we examined the traditional uncompensated care measure, we still observed deficiencies for private, nonprofit hospitals, and only when Medicare shortfalls and bad debts are captured together do these deficiencies disappear. This conclusion is con-sistent with the results of Schneider (2007), in that she found deficits in community benefit provision when only uncompensated care was exam-ined, but these deficits were eliminated when other items, including Medi-care shortfalls, were included in the analysis.


There are important limitations to our analysis. We were not able to measure all items identified in Form 990 Schedule H that are considered primary community benefit activities. As noted above, though, data com-piled by Gray and Schlesinger (2009) suggest that these omitted activities are likely to be only a small portion of community benefit activity. Also, we focus on a small sample of hospitals, but we are capturing around 85 to 90 percent of the for- profit and private, nonprofit hospitals in the states studied. Finally, the data we examined came from a source other than the eventual IRS forms. This has the disadvantage that we might be missing or undercounting important activities, but there is also the countervailing advantage in that the data are not influenced by incentives to creatively report community benefit activities given the items on which the IRS form focuses. In addition, our 2005 study data reflect a period before the publi-cation of the CHA and VHA, Inc., guidelines in 2006 and the subsequent revision of Form 990 Schedule H and thus do not reflect hospital behavior changes that resulted as a consensus was building for community benefit definitions. However, our analysis provides a baseline on hospital involve-ment in relevant activities that is not available from earlier studies.

Despite these limitations, our findings provide insights into why the debate over the inclusion or exclusion of bad debt expense and Medicare shortfalls has remained intense and difficult to resolve. Both sides argu-ing either for or against inclusion of these items have made reasonable arguments to support their positions. Those arguing against inclusion note that bad debts may include some patients who are not necessarily needy and might represent ineffective hospital collection efforts. They also note that Medicare shortfalls may not represent underpayment of hospitals but inefficiency. Those arguing for inclusion note that some patients generat-ing bad debt are economically disadvantaged and that Medicare shortfalls do reduce financial burden on government agencies, which was the intent expressed in the 1969 IRS rulings. Our findings suggest that bad debts and Medicare shortfalls represent big dollar items, as evident in table 2, and thus are likely important to many nonprofit hospitals in making the case that their tax exemptions are warranted.

In light of these positions and our findings, perhaps the best strategy for policy makers and the hospital industry is to refocus the current debate on identifying the circumstances under which some or all of hospital bad debts and Medicare shortfalls should be counted toward community bene-fit. In terms of bad debt, nonprofit hospitals are already moving in this direction by redefining their charity care policies so that financially needy individuals are captured as charity cases a priori rather than being counted


toward bad debt (Stati, Hurley, and Cunningham 2005). Hospitals have also revised their charity care policies so that they can rely on information such as a person’s enrollment in state- subsidized food or housing pro-grams or in prescription aid programs rather than rely on more sensitive and difficult to collect income and asset information (Evans 2009). Also, Evans (2010) noted that in response to the new IRS Form 990, nonprofit hospitals are developing statistical approaches that segment that portion of their bad debt expenses that might be considered charity care because patients generating these bills live in low- income zip codes. These hospital responses, though, have occurred in an ad hoc way without clear guidance from trade associations or policy makers.

Guidelines and standards for nonprofit hospitals could focus on the types of patients or services for which resulting bad debts or Medicare shortfalls may qualify as community benefits. Studying approaches used by hospitals currently to deal with bad debt expense would be a good starting point for developing such standards. For Medicare shortfalls, it may be best to identify certain types of Medicare patients (e.g., those with SSI or without supplemental insurance) or certain services (e.g., those less frequently provided by for- profit hospitals or that are generally known to be unprofitable given existing PPS formula) and use these as a basis to segment Medicare shortfalls into the portion relevant to community bene-fit measurement. Also, useful data may be gleaned from the first round of Form 990 Schedule H submissions because hospitals are asked to report their rationale for why some or all of their bad debts and Medicare short-falls should be counted as community benefits. Ultimately, a refocused discussion on these issues might provide a way to break existing dead-locks over whether bad debt expense and Medicare shortfalls count as community benefits. Given the results of our analysis, resolution of these issues is important because the inclusion or exclusion of these disputed items is likely to be relevant for judging the adequacy of nonprofit hospital community benefit activities.

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Community Benefit Activities of Private, Nonprofit Hospitals

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