Competitive spillovers across non-profit and for-profit nursing homes

Journal of Health Economics 22 (2003) 1–22

Competitive spillovers across non-profit andfor-profit nursing homes

David C. Grabowskia,∗, Richard A. Hirthb

a Department of Health Care Organization and Policy, University of Alabama at Birmingham,330 RPHB 1655 University Boulevard, Birmingham, AL 35294, USA

b Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA

Received 1 February 2002; received in revised form 1 July 2002; accepted 21 August 2002

Abstract

The importance of non-profit institutions in the health care sector has generated a vast empiricalliterature examining quality differences between non-profit and for-profit nursing homes. Recenttheoretical work has emphasized that much of this empirical literature is flawed in that previousstudies rely solely on dummy variables to capture the effects of ownership rather than accountingfor the share of non-profit nursing homes in the market. This analysis considers whether competitivespillovers from non-profits lead to higher quality in for-profit nursing homes. Using instrumentalvariables to account for the potential endogeneity of non-profit market share, this study finds thatan increase in non-profit market share improves for-profit and overall nursing home quality. Thesefindings are consistent with the hypothesis that non-profits serve as a quality signal for uninformednursing home consumers.© 2002 Elsevier Science B.V. All rights reserved.

JEL classification: I11; L15; L31

Keywords: Consumer information; Non-profit nursing homes; Quality; Instrumental variables

1. Introduction

KennethArrow (1963)was the first to hypothesize that non-profit organizations existin health care markets to provide quality assurance to poorly informed consumers. Thequality of care provided by the US nursing home industry has received a great deal ofattention over the last three decades (e.g.US Senate, 1974; Institute of Medicine, 1986; USGeneral Accounting Office, 1998; Institute of Medicine, 2001). A common theme within

∗ Corresponding author. Tel.:+1-205-975-8967; fax:+1-205-934-3347.E-mail address: [email protected] (D.C. Grabowski).

0167-6296/03/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved.PII: S0167-6296(02)00093-0

2 D.C. Grabowski, R.A. Hirth / Journal of Health Economics 22 (2003) 1–22

these reports is that low quality may result from opportunistic behavior by for-profit nursinghomes, which account for over two-thirds of the nearly 17,000 nursing homes in the US. Ifnon-profit homes, often affiliated with religious or charitable organizations are less willingto compromise care for the sake of profit then ownership status may provide a low costsignal that the promised quality (i.e. quality commensurate with the price being charged)will be delivered.

Given the favorable tax treatment that non-profit nursing homes receive, there has beena great deal of interest in evaluating whether the non-profit sector serves a socially usefulfunction. Unfortunately, most of the existing empirical work is flawed, because it treats eachownership type as if it existed in isolation without controlling for the ownership of compet-ing firms in the marketplace. Recent theoretical work has emphasized that finding (or notfinding) quality differences between the two ownership types does not constitute verificationof the benefits (or lack thereof) associated with the non-profit sector (Hirth, 1999). The exis-tence of differences across for-profit and non-profit homes is neither necessary nor sufficientto conclude that non-profit enterprise is socially desirable. Omitting a measure of non-profitmarket share, which could capture a quality spillover effect, may bias the coefficient on theownership variables and yield misleading policy implications. Using 1995–1996 data fornearly all US nursing homes, this study offers empirical analyses that include a measure ofnon-profit market share to analyze the role of the non-profit sector in the provision of quality.

2. Conceptual framework

Economic theory implies that for-profit firms will produce the socially efficient productarray when consumers can easily evaluate products before purchase, contract over deliveryterms, monitor contractual compliance, and obtain redress for violations. When these con-ditions are not met, however, producers may have some discretion to misrepresent quality(Hansmann, 1980). There are reasons to believe that this may in fact be the case in the nurs-ing home industry. Although care is fairly non-technical in nature, monitoring of care canoften be difficult, and the learning period may be non-trivial relative to the length-of-stay insome instances.1 The patient is often neither the decision-maker nor able to easily evaluatequality or communicate concerns to family members and staff. Furthermore, the elderly whoseek nursing home care are disproportionately the ones with no informal family support tohelp them with the decision process (Norton, 2000). Finally, there are relatively few nursinghome-to-nursing home transfers. Using 1994–1996 minimum data set assessments,Hirthet al. (2000)document a transfer rate of 4.4% in New York and 8.0% in Maine during the first

1 Some forms of nursing home quality may be easily observable by prospective patients (or their agents), suchas size of the room, cleanliness of the facility, and the number of staff. Other dimensions of quality are frequentlymore difficult for patients to ascertain, for example, the quality of nursing home staff. This type of information maytake considerable time to learn. For example, problems such as bedsores or infections may take weeks (or evenmonths) to develop. Even after problems develop, residents or their proxies must still draw difficult inferencesabout whether the problems were truly attributable to inadequate care and whether alternative care arrangementswould have prevented them.Garber and MaCurdy (1992)document an average length-of-stay for chronic care (i.e.non-Medicare) nursing home patients of approximately 125 days with substantial variation around this mean. Thus,the learning period may be relatively long compared to the typical length-of-stay for many nursing home patients.

D.C. Grabowski, R.A. Hirth / Journal of Health Economics 22 (2003) 1–22 3

6 months post-admission, with substantially lower rates later in a stay. Movement amonghomes may be impeded by tight markets due to supply constraints such as certificate-of-need(CON) and construction moratorium laws and health concerns regarding relocation (termed“transfer trauma” or “transplantation shock”).

The following framework considers both a model in which some nursing home consumersare poorly informed regarding quality and a model in which all consumers are well informed.The broad implications of this theoretical framework are reviewed here, and the reader isreferred elsewhere for a formal mathematical treatment of these issues (seeHirth, 1999).

2.1. Asymmetric information model

We first consider a model of the nursing home market in which some consumers lackinformation on quality (i.e. the asymmetric information case). In this model, for-profithomes are assumed to maximize profit and will provide less than the promised level ofquality if the presence of uninformed consumers makes that strategy profitable. We assumea strictly enforced non-distribution constraint, which prohibits payment of profits to ownersor employees, on the non-profit sector.2 The non-distribution constraint motivates honestbehavior by non-profits, ensuring that they deliver the promised level of quality and donot simply act as ‘for-profits in disguise’. Because the for-profit sector does not deliverthe first best outcome unless the fraction of poorly informed consumers is low, there ex-ists an opportunity for non-profit status to serve as a signal of quality by attracting thosepoorly informed consumers into non-profit homes (Arrow, 1963; Hansmann, 1980). Becausenon-profits disproportionately attract those uninformed consumers, consumers remainingin the for-profit sector are better informed than a random draw from the patient population.Thus, the larger the non-profit market share, the higher the likelihood that for-profits deliverthe promised quality. This can be thought of as an ‘Inverse Gresham’s Law’ under whichthe good (non-profits delivering the promised quality) drive out the bad (those for-profitsattempting to exploit a poorly informed clientele by delivering less than the promised levelof quality). Effectively, the non-profit sector exerts a beneficial, competitive spillover effecton the performance of the for-profit sector. As a result, even if non-profit and for-profithomes are observed to have similar quality, eliminating the non-profit sector could havedeleterious welfare consequences by changing the prevailing market equilibrium.

Thus, this model generates two predictions. First, an increase in non-profit market sharewill improve quality in for-profit facilities. And second, by improving quality in the for-profitsector, an increase in non-profit market share will also improve overall market quality.

2.2. Full Information model

We next consider an alternative model with well-informed consumers (i.e. the full infor-mation case) where non-profit status is no longer a necessary signal of quality. In this case,non-profit homes are still thought to stake out the high quality end of the market due to man-agerial preferences (for example, managers of non-profits may value their community image

2 As the non-distribution constraint becomes less binding, non-profit status becomes an increasingly imperfectsignal of quality (Hirth, 1999).


as high quality caregivers).3 Effectively, non-profits crowd high quality for-profits out of themarket, ceding the low price/low quality portion of the market to their for-profit competitors.In this environment, quality differences between for-profit and non-profit homes may besubstantial, but prices reflect these differences and the distribution of ownership types at themarket level is irrelevant to consumer well being. As a result, the quality of care in for-profithomes will decrease as the share of non-profit homes increase. However, the level of marketquality depends only on the willingness to pay of consumers and public payers and will beunaffected by the share of non-profit homes. An empirical model, which only accounts forthe effect of ownership type and does not include price measures (which are typically unob-served in secondary data sets used for nursing home research), would mistakenly attributethis observed quality difference across sectors to the desirability of the non-profit sector.

Thus, this model generates two predictions. First, an increase in non-profit market sharewill decrease quality in for-profit facilities. And second, non-profit market share will haveno effect on overall market quality.

3. Previous literature

The majority of previous empirical studies of the relationship between ownership andquality measure the effect of profit status by using a dummy variable for type of ownershipbut omit a measure of the relative prevalence of for-profit and non-profit firms. This omis-sion can bias inferences about the effects of ownership. If beneficial spillovers occur (due topoorly informed consumers), the coefficient on an ownership variable will be biased towardszero, because the performance of for-profits and non-profits will tend to converge in areaswith high non-profit shares. Conversely, if provision of high quality care by non-profitscrowds out the provision by for-profits (as would occur in the case of full consumer in-formation), the coefficient will likely be biased away from zero. Not surprisingly, previousstudies that measure the effect of ownership type on quality have yielded little in the wayof consistent findings. Three different reviews of this literature have drawn three differentconclusions regarding the relationship between ownership type and quality.O’Brien et al.(1983)suggests that quality is identical across non-profit and for-profit facilities.Hawesand Phillips (1986)conclude that studies generally find quality superior in non-profit facil-ities. Finally,Davis (1991)argues that the evidence is inconclusive in regards to whethernon-profit facilities provide higher quality. More recent work in the literature has continuedto find mixed evidence on this issue (Holtmann and Ullman, 1991; Gertler, 1992; Cohenand Spector, 1996; Harrington et al., 2001).

There has been only limited empirical work incorporating a measure of non-profit marketshare into the analysis. Using data from the 1985 National Nursing Home Survey (NNHS),

3 There is some evidence that non-profit nursing homes are more likely to stake out this high price/high qualityend of the market. First, data from the 1985 National Nursing Home Survey (NNHS) show that, on average, theprivate-pay price at a non-profit nursing home is greater than the private-pay price at a for-profit facility (Hirth,1993). This trend persists across the distribution with non-profits dominating the highest price categories. Second,the data analyzed within this study indicate that non-profits have a higher proportion of private-pay residentsrelative to their for-profit counterparts. On average, the typical non-profit facility has 31% private-pay residentswhile a for-profit home has 21% private-pay residents.


Hirth (1993)estimated the effect of ownership on measures of staffing skill mix (i.e. regis-tered nurse hours divided by total hours) and found that non-profit ownership was associatedwith higher quality. He also found evidence favoring the asymmetric information model inthat increased non-profit market share improved both overall and for-profit quality. Usingthe 1987 National Medical Expenditure Survey (NMES),Spector et al. (1998)estimatedthe effect of non-profit status on various measures of quality including mortality, infections,bedsores, hospitalizations and functional disabilities and found that non-profit homes pro-vide a higher quality of care than for-profit homes. However, these authors did not find thatincreased non-profit market share would raise either overall or for-profit quality.4 Thus, theresults of this study favor the full information model outlined above.

Although both these studies have found higher quality in the non-profit sector, it is un-clear whether this higher quality reflects beneficial spillovers (the asymmetric informationcase) or the crowd-out by non-profits of high quality for-profits (the full information case).This paper offers a series of innovations towards resolving this issue. First, previous em-pirical analyses that have incorporated non-profit market share have relied on nationallyrepresentative samples of nursing facilities (i.e. the NNHS and the NMES). Although wediscuss our estimation strategy in detail below, this study proposes to use data for all of theapproximately 17,000 Medicaid and Medicare certified nursing homes in the US, whichrepresent over 96% of all facilities nationwide. Furthermore, we will use recent nursinghome data (1995–1996) and have access to a range of structural, procedural, and outcomemeasures of quality.

Finally and most importantly, those previous studies that have included a non-profitmarket share variable have been based on limited econometric specifications. The directevidence regarding this issue has been based on single equation models that are identifiedsolely by the inclusion of a non-profit market share variable. Unfortunately, these singleequation models are observationally equivalent with two very different interpretations. Con-sider the asymmetric information case. If an increase in non-profit market share improvesquality in the for-profit sector, one interpretation is that this result was due to the competitivespillover effect. However, an equally plausible alternative interpretation is that the relation-ship between non-profit market share and quality reflects unobserved demand for qualityand non-profit share at the market-level. For example, those areas with a strong preferencefor nursing home quality (e.g. increased consumer oversight; stricter regulatory enforce-ment) may be exactly those areas that adopt tax laws or regulatory procedures favoringnon-profits. In order to address this potential issue, we employ an instrumental variables(IVs) estimation method to check the robustness of our single equation results.

4. Empirical specification

The standard empirical approach to examining the effect of the non-profit sector on theprovision of quality within the nursing home industry has been to estimate a reduced form

4 Spector et al. (1998)only show the overall market results, but from other unreported work, the authors concludethat there is little evidence to support “Hirth’s conjecture that increased non-profit market share would raise qualityin the for-profit segment of the market” (p. 649).


equation that includes dummy variables measuring ownership type. This approach fails toaccount for non-profit market share, which would capture the spillover effects associatedwith the non-profit sector. This omission may bias the coefficient on the categorical owner-ship variable and lead to misleading policy implications. However, in order to benchmarkour work to the current literature, we present an initial set of estimates using this “dummyvariable” approach. The basic specification for this approach is:

Y = Nβ + Xδ + ε (1)

whereY refers to the quality measure,N a vector of ownership dummy variables,X includesan intercept and a set of exogenous controls, andε the residual.

The variableN represents the three nursing home ownership types: non-profit (28%of all facilities), for-profit (66%) and government owned (6.6%). Of the 4688 non-profithomes found in the 1995–1996 Online Survey, Certification, and Reporting (OSCAR) file,1114 facilities were church related, 3299 were corporate owned, and 275 were classified as“other non-profit.” Of the 11,174 for-profit homes, 9603 facilities were corporate owned,1228 were owned by a partnership and 343 were individually owned. The 1116 governmentfacilities were owned and operated by the state (113 facilities), county (558 facilities),city (144 facilities), city/county (105 facilities), hospital district (192 facilities) and federalgovernment (4 facilities).

In this study, qualityY was represented by several outcome, process and resource-basedmeasures.5 The outcome-oriented measure of quality was the number of residents withpressure sores (or decubitis ulcers), commonly associated with immobility in the elderly.Pressure sores are areas of the skin and underlying tissues that erode as a result of pressureor friction and/or lack of blood supply. Pressure sores can often be prevented or resolved byfrequently repositioning the immobile resident. The proportion of residents with catheters,feeding tubes and physical restraints were also used as procedural measures of quality. Be-cause labor constitutes 60–70% of nursing home costs, these procedures may be employedas labor-saving practices on the part of nursing homes with potential negative consequencesfor resident health (Zinn, 1993). Finally, two resource-based measures were used as prox-ies for quality. First, the total number of registered nurses (RNs), licensed practical nurses(LPNs) and nurses’ aides (NAs) per resident days was used to represent quality. In orderto account for staffing skill mix, the proportion of registered nurses (RNs) per total nursingstaff was used as a second resource-based measure.

A series of exogenous variablesX were included as controls in this study. The exoge-nous demand variables were the median income of people living in the nursing home’scounty; the population of individuals over age 65 in the county; two measures of the healthstatus (case mix) of the home’s residents; and a Herfindahl index. The first measure ofcase mix is based on an activities of daily living (ADL) index, which includes bathing,dressing, eating, toileting and walking. A home’s ADL score was calculated by summing

5 Given the fact that nursing homes bundle the board and care functions, overall nursing home quality wouldlikely encompass both aspects of technical quality (e.g. staffing, pressure sore rates) and resident amenities (e.g.home-like atmosphere, organized resident activities). Unfortunately, amenities are not typically included withinadministrative data files such as the OSCAR system. Thus, we rely solely on technical measures of quality withinthis study.


the number of ADLs that residents needed assistance with at the time of the survey anddividing by the total number of residents in the home.6 The result is an index of the averageneed for assistance of the residents in each facility. ADLs form the cornerstone of nurs-ing home resident classification and play a major role in all state-level Medicaid case-mixadjustment payment systems.Fries (1990)argues that ADLs explain case mix for the vastmajority of nursing home residents except for those heaviest care individuals. Thus, weinclude a second case mix variable measuring the proportion of residents requiring skillednursing care. This variable should capture those heaviest case-mix residents within thefacility.

A Herfindahl index is a measure that is negatively related to the competitiveness of amarket. This index was constructed by summing the squared market shares of all facilities inthe county. The index ranges from 0 to 1, with higher values signifying a higher concentrationof facilities. As a note,Kessler and McClellan (2000)have argued that the inclusion of aHerfindahl index within hospital quality regressions may be endogenous because hospitalswith higher quality will obtain higher market shares. Historically, this issue has not beenrelevant for the nursing home industry where certificate-of-need laws and constructionmoratoria prevented expansion, but recent work has argued that these regulations may beless important towards constraining nursing home quality competition (Grabowski, 2001).As a sensitivity check, all of the results presented within this study are robust to replacingthe Herfindahl index with a measure of the number of homes within the marketplace.

Importantly, the county was used to approximate the market for nursing home care withinthis study. Most economic studies have used the county as a proxy for the nursing homemarket (e.g.Nyman, 1985; Cohen and Spector, 1996). As noted byBanaszak-Holl et al.(1996), the county may be a reasonable approximation of the market for nursing home caregiven patterns of funding and resident origin. For example, federal block grant funds forlong-term care services are distributed at the county level. Furthermore,Gertler (1989)foundthat 75% of residents in New York facilities had previously lived in the county where thehome was located. Similarly,Nyman (1994)found 80% of residents in Wisconsin facilitieschose a nursing home located in the county in which they resided before entering the home.Importantly, all of the analyses presented in this study are robust to excluding those 720counties with only one nursing home.

An exogenous supply variable included withinX was the CMS area hospital wage index.Binary indicators were also included for whether the home was part of a multiple-facilitychain and for whether the nursing home was a hospital-based facility. A proxy measure ofthe facility’s age was also included and will be described in more detail below. Finally, a

6 A potential limitation of the ADL index is that lower quality within a facility may lead to greater ADLdependency. However, the nursing home case-mix literature has generally argued that need-based factors suchas ADLs are sufficiently invariant to provider influence (e.g.Fries et al., 1994). The ADL most likely to beinfluenced by quality of care is toileting. That is, homes providing more extensive bladder and bowel retrainingprograms are likely to have fewer residents dependent in toileting. Thus, we re-estimated the model with a modifiedADL measure that did not include toileting. The results were not sensitive to this change. There also been recentexperimental evidence suggesting that nursing home patients exposed to aggressive rehabilitative programs such asweight bearing exercises have achieved significant improvements in the ADLs that have historically been thoughtof as exogenous like walking and transferring. However, very few facilities have instituted these experimentalprograms.


number of variables were included to measure the generosity and method of state Medi-caid reimbursement. Medicaid is the dominant payer of chronic nursing home care in theUS accounting for approximately 50% of expenditures and 70% of bed-days. Rather thanincluding a facility-level reimbursement rate, which would be endogenous to a facility’squality level, the analysis utilized the average rate for the state (Grabowski, 2001). If thestate deals in aggregates, no individual home can affect the state’s reimbursement rate. Thus,to the individual home, the average state Medicaid rate was exogenous. In addition to theMedicaid reimbursement rate, state-level indicators were also included for payment systemtype (i.e. retrospective, prospective, flat-rate, and combination), whether the state used acase mix adjusted reimbursement methodology, whether a state reimbursed hospital-basedfacilities differently and whether the state allowed an upward adjustment during the rateyear due to additional cost information.

Although the dummy variable approach measures differences in quality across sectors, itdoes not account for potential quality spillovers. Thus, we next estimate a series of modelsthat include a measure of non-profit market share to account for the role of spillovers acrosssectors. The basic specification for this revised approach can be expressed as

Y = Sγ + Xδ + ε (2)

whereS refers to the non-profit market share. Although previous empirical work has con-sidered a specification that includes nursing home ownership measures at both the homeand market levels, this approach may be flawed due to multicollinearity (i.e. home andmarket level ownership are associated with a statistically significant correlation coefficientof 0.43) and the potential endogeneity of ownership at the home level. Thus, this currentstudy will follow the approach ofKessler and McClellan (2001)in the hospital literatureby examining only spillovers at the market-level. With this specification, we test for theeffect of non-profit market share on for-profit quality by limiting the analysis to only thosefor-profit facilities. Then, we test the effect of non-profit market share on overall quality byincluding all nursing homes in the model.

Although this revised specification now accounts for the potential spillovers across sec-tors, it may suffer from bias due to the suspected endogeneity of non-profit-market shareand quality. Assume that non-profit market shareS has the following reduced form

S = Zλ + Xγ + µ (3)

whereX is the same set of variables that appeared in the quality equation,Z a set of variablescorrelated with non-profit market share but not the error term in the quality equation, andµ the residual.

A key econometric issue is that non-profit shareS may be correlated with the error termin the quality equation. Although there is limited within-home and within-market variationover time in ownership, there may be unobservable factors that influence both the demandfor quality and non-profits at the market-level. If this is the case, the error termsε andµ

will be correlated, which violates the assumptions underlying the linear regression model.However, we can still generate a consistent estimate of the effect of non-profit market shareon quality if we can identify a set of variablesZ that are correlated with non-profit sharebut notε, the error term in the quality equation. GivenZ, we can calculate an IV estimateof the effect of non-profit share on quality.


In this study, we identify a set of variablesZ. A first plausible instrument is the growth inthe demand for nursing home care. Those areas in which nursing home demand is growingrapidly are likely to have a higher for-profit share because the capital market constraints facedby non-profit firms make rapid expansion difficult. Thus, there may be greater incentive forentry and expansion by for-profit owners than by their counterparts in the non-profit sector.There is evidence from both the nursing home and hospital industries that a percentagechange in the aging population influences the proportion of non-profit firms (Hansmann,1987; Steinwald and Neuhauser, 1970). Furthermore,Sloan et al. (2001)use this instrumentin a recent analysis of the effects of ownership type on hospital quality. In this study, we usedthe percentage change in the population aged 65 and above for the 5-year period 1991–1996as an instrument.

A potential criticism of this instrument is that growth in demand—to the extent that itis correlated with the entry of new nursing homes—may also be correlated with qualityif homes improve their quality through learning over time. Some of the growth within thenursing home industry has occurred through the expansion of existing homes rather thanthe entry of new facilities. Total beds in the US grew from 1.31 to 1.81 million (an increaseof 38%) between 1978 and 1998 while the number of facilities grew from 14,264 to 17,458(an increase of 22%) over this same 20-year period (Harrington et al., 1999). However, inan effort to control for the provision of quality by new firms, we include a dummy variablewithin the model measuring whether the facility has had fewer than three previous OSCARsurveys under its current provider number.7

Second, the non-profit market share in other health care industries may also serve as aplausible instrument by identifying those areas that are favorable towards non-profit healthcare production. For example, those areas with a higher hospital non-profit market shareare expected to also have a larger non-profit nursing home share. The relative share ofnon-profits in different parts of the country is rooted in historical factors such as the age ofthe city and different patterns of voluntarism and charitable provision that have little to dowith the advanced technology and prevalence of third party payment that characterize thecurrent health care environment; e.g. seeStevens (1989)for a history of the organizationalstructure of the US hospital industry. However, hospital non-profit market share may berelated to the provision of nursing home quality because certain nursing homes may facecompetition from hospitals for certain services. As a result, we used a lagged measure, thenon-profit share of hospital beds in 1986, as an instrument.

A potential criticism of this second instrument is that patients choose non-profits dueto an underlying demand for high quality across all types of health providers. If this isthe case, then the hospital share instrument may be correlated with the error term in thenursing home quality equation. In support of Stevens’ argument above, the difference inthe cross-sectional non-profit hospital share is longstanding. Using multiple editions of theAmerican Hospital Association annual survey of hospitals, we find that hospital non-profitmarket share is remarkably static over time, which provides evidence of a weak correlationbetween current consumer tastes for non-profit hospital care and unobserved nursing homequality.

7 This proxy measure will capture newly certified facilities, but it will also detect facilities that changed ownershipand thus received a new provider number.


Thus, the identifying assumption is that high demand growth and lagged hospital non-profitmarket share are correlated with S, the non-profit nursing home market share, but are notcorrelated withε, the error term in the quality equation. In the first stage of IV estimation(Eq. (3)), non-profit market share is regressed on these instruments plus the other regres-sorsX from the second stage of IV estimation. In the second stage, quality is regressedon the instrumented non-profit market share and the other regressorsX. The quality of theinstruments used within the analysis is discussed below.

Efficient estimates of the parameters are given by the weighted least squares (WLS)estimator where the weights are the number of residents per home. Importantly, for thebedsore, catheter, tube feeding and physical restraint measures,Y refers to the proportion ofresidents in a facility that satisfy a particular definition. Although the WLS approach doesnot recognize the binary nature of these dependent variables, it does facilitate the tractableestimation of the IV models. As an important sensitivity check, the WLS estimations provedrobust to other specifications. Because these measures were reported at the facility-level (e.g.the proportion of residents with a bedsore), it was straightforward to convert the data intobinary choices grouped at the facility-level. An alternate set of analyses using these groupedfacility-level data for maximum-likelihood estimates of a probit or logistic model generatedmarginal effects that were similar in magnitude and precision to the single equation WLSresults presented here.

A final methodological point concerns the “grouped” nature of certain explanatory vari-ables (e.g. non-profit market share), which may have introduced heteroskedasticity andbiased the estimates of the parameter standard errors. When the true specification of theresidual variance-covariance matrix follows a grouped structure,Moulton (1990)has shownthat estimates of the standard errors will be biased downwards. A straightforward and un-restrictive approach to addressing this issue was to adjust the standard errors with theHuber–White robust estimator accounting for intra-county correlation.

5. Data

The analyses contained within this study used merged data from five distinct sources(seeTable 1 for summary statistics). The primary data source was the Online Survey,Certification, and Reporting system. The OSCAR system contains information from statesurveys of all federally certified Medicaid and Medicare homes in the US. Certified homesrepresent almost 96% of all facilities nationwide (Strahan, 1997). Collected and maintainedby the Centers for Medicaid and Medicaid Services (CMS), the OSCAR data are used todetermine whether homes are in compliance with federal regulatory requirements. Everyfacility is required to have an initial survey to verify compliance. Thereafter, states arerequired to survey each facility no less often than every 15 months, and the average isabout 12 months (Harrington et al., 1998a). The data for this analysis were collected withinthe 15-month interval of October 1995 through December 1996 and contain 16,978 uniquenursing home surveys. If a home was surveyed multiple times during this 15-month interval,the most recent survey was included in the dataset.

Four other data sources were utilized within this study to supplement the OSCARdata. First, the OSCAR data were merged with aggregate county level demographic,


Table 1Descriptive statisticsa

Variables Number ofhomes

Mean S.D.

Proportion with bedsores 16,978 0.071 0.075Proportion with catheters 16,978 0.078 0.092Proportion with feeding tubes 16,978 0.065 0.086Proportion with physical restraints 16,978 0.17 0.17Total nursing staff per resident day 14,638 3.73 2.38Registered nurses per total nursing staff 14,604 0.114 0.088Non-profit market share 16,978 0.24 0.22Percentage change in elderly (>65) population (1991–1996)

(ARF)16,978 0.057 0.076

Lagged (1986) hospital non-profit share (AHA) 16,303 0.55 0.35For-profit facility 16,978 0.66 0.47Not-for-profit facility 16,978 0.28 0.45Government owned and operated facility 16,978 0.066 0.25Hospital-based 16,978 0.13 0.34Chain facility 16,978 0.52 0.50Facility has fewer than three previous surveys 16,978 0.15 0.36Average number of activities of daily living with which the

residents needed help16,978 3.66 0.62

Proportion of skilled nursing residents in the home 16,978 0.36 0.32Median per capita county income (ARF) 16,978 23,017.24 5,594.54Population >65 (ARF) 16,978 82,578.85 170,176.80Herfindahl index 16,978 0.20 0.23CMS area wage index (CMS) 16,978 9,374.01 1,769.94The average Medicaid rate (Harrington et al., 1998b) 16,978 85.28 20.64Hospital facilities reimbursed differently (Harrington et al.,

1998b)16,978 0.18 0.39

Prospective reimbursement system (Harrington et al., 1998b) 16,978 0.76 0.43Retrospective reimbursement system (Harrington et al., 1998b) 16,978 0.014 0.12Flat-rate reimbursement system (Harrington et al., 1998b) 16,978 0.17 0.37Combines prospective and retrospective systems (Harrington

et al., 1998b)16,978 0.059 0.24

Allows rate adjustment upward during or after a rate period(Harrington et al., 1998b)

16,978 0.41 0.49

Employs case-mix reimbursement (Harrington et al., 1998b) 16,978 0.55 0.50

a The data are from the 1995–1996 Online Survey, Certification and Reporting (OSCAR) system unless oth-erwise noted. The other data sources are the Area Resource File (ARF), the Centers for Medicare and MedicaidServices (CMS), the American Hospital Association (AHA), and the1996 State Data Book on Long-Term CareProgram and Market Characteristics (Harrington et al., 1998b).

socio-economic and health status data from the Bureau of Health Professions’ Area Re-source File (ARF). Second, state-level Medicaid reimbursement methods and levels wereobtained from the1996 State Data Book on Long-Term Care Program and Market Char-acteristics published byHarrington et al. (1998b). Third, the CMS hospital area wageindexes were linked with the data. And finally, we obtained a measure of the share ofnon-profit hospitals from the 1986 American Hospital Association annual survey ofhospitals.


6. Results

6.1. Specification tests

As a model sensitivity check, we employed a Hausman test for endogeneity. Under thistest, we generally rejected the null hypothesis of the exogeneity of non-profit market sharefor the quality equations across both the for-profit and overall market specifications. Wealso constructed an augmented regression test developed byDavidson and MacKinnon(1993)that also generally rejected the null hypothesis of exogeneity of non-profit marketshare. Our test of the overidentifying restrictions (Wooldridge, 2002) failed to reject the nullhypothesis of appropriate specification in every case presented below except the staffingintensity model. Thus, although we present both the WLS and IV results below, we willprimarily focus the discussion on the IV estimates.

6.2. Quality of the instruments

The plausibility of the IV estimates presented here hinge on the strength of the instrumentsused within the analysis.Bound et al. (1995)have argued that the use of instruments thatjointly explain little of the variation in the endogenous variables can do more harm thangood. If a set of instruments is weakly correlated with the endogenous explanatory variable,then the authors have shown that even a small correlation between the instruments and theerror can seriously bias estimates. Their results suggest that the partialR2 andF-statisticson the excluded instruments in the first-stage regression are useful as rough guides to thequality of the IV estimates.Staiger and Stock (1997)argue that 10 is an acceptable valueof theF-statistic associated with the hypothesis that the coefficients on the instruments inthe first-stage regression are jointly equal to zero.

The set of instruments used in this study meets the standard of Staiger and Stock. In thefirst stage IV estimation, the hypothesis that the coefficients on the instruments are jointlyequal to zero is rejected. The first-stage coefficients,F-statistic, and partialR2 associatedwith the excluded instruments are presented inTable 2for both the for-profit and overallfirst-stage results. For the for-profit only model, the instruments have anF-statistic equal to27.77 and R2 = 0.02. For the overall model including all ownership types, the instrumentshave anF-statistic equal to 33.79 and R2 = 0.05. All of the first-stage coefficients on theinstruments were of the expected sign. An increased growth in the aging population overthe 1991–1996 period was associated with fewer non-profits, and an increase in the laggedhospital non-profit share was associated with a higher non-profit nursing home share. Bothof these instruments were statistically significant at the 1% level in both specifications.

In addition to the assumption regarding the instruments being strongly associated with theendogenous variable, there is also the requirement that the instrument must not be correlatedwith the error term in the second stage of IV estimation. If it is still correlated, then theinstrumented variable will still be endogenous. Although it is impossible to confirm the nullhypothesis that these instruments are uncorrelated with the error term in the quality equation,a standard practice within the health economics literature is to report whether the instrumentsare correlated with those observable factors believed to be correlated with the unobservablefactors that affect the second-stage error term. Thus,Table 3takes each instrument and


Table 2First stage of IV estimation (dependent variable= non-profit market share)

Regressors For-profit homes All homes

Percentage change in elderly (1991–1996) −0.22 (−3.49) −0.31 (−3.61)Lagged hospital non-profit share 0.069 (6.12) 0.10 (7.29)Hospital-based 0.0055 (0.51) 0.065 (7.15)Chain 0.0040 (0.96) −0.015 (−3.23)New facility −0.0098 (−1.69) −0.016 (−2.31)Average ADLs −0.0041 (−0.93) −0.0061 (−1.16)Percent skilled care 0.0078 (0.99) −0.0080 (−0.94)Herfindahl index −0.23 (−14.63) −0.038 (−1.61)Per capita income (US$ 1000s) 0.0036 (2.48) 0.0037 (2.24)Population age >65 (10,000s) −0.00035 (−1.54) −0.00055 (−1.36)Wage index (1000s) −0.011 (−1.94) −0.014 (−1.96)Medicaid rate 0.0013 (4.34) 0.0017 (5.09)Hospital-based reimbursed differently 0.038 (2.84) 0.039 (2.38)Retrospective system 0.051 (1.41) 0.076 (1.98)Flat rate system −0.033 (−2.57) −0.034 (−2.22)Combination system 0.018 (1.27) −0.015 (−0.83)Rate adjustment allowed 0.0080 (0.80) −0.0063 (−0.52)Case-mix adjusted system 0.027 (2.39) 0.042 (3.09)Constant 0.10 (2.63) 0.13 (2.61)R2 0.26 0.19 R2 0.02 0.05F-statistic of instruments 27.77 33.79Number of observations 10,697 16,303

Notes: The estimations are weighted by the total number of residents in each facility and the Huber–White adjustedt-statistics (corrected for intra-county correlation) are presented in parentheses.

divides the variables used within this study by those observations that are above the meanof the instrument and those that are below the mean.Table 3presents the means for thenon-profit market share, explanatory and quality measures across these two groups.

Means of the groups for the non-profit market share indicate that those markets with lowdemand growth and lagged hospital non-profit share have higher non-profit market share.These results are consistent with the assumption that the instruments are correlated withthe endogenous variable. In comparing the means of the explanatory variables, the twogroups are statistically different across most of the variables. However, the large number ofnursing homes (N = 16,978) within our study provides a high degree of precision. Despitebeing statistically significant, the case-mix measures, hospital-based measure, Herfindahlindex, Medicaid index and wage rate variables are all fairly similar across the two groups.For example, the two case-mix measures do not differ by more than 4% for either of thetwo instrument groupings. Not surprisingly, ownership status at the facility-level, which iscorrelated with non-profit market share, appears to be related to the instruments. Becausemost chain owned homes are for-profit, chain ownership also appears to be related to theinstruments. Furthermore, the elderly population appears to be quite dissimilar across thetwo groups. This dissimilarity may be due to a heavily skewed distribution of these measuresdue to their grouped nature.


Table 3Comparison of variables by IV groups

Variable Age change (%) (1991–1996) Lagged hospital non-profit share

Below-average Above-average Below-average Above-average

Non-profit share 0.29 0.21∗∗ 0.20 0.29∗∗

Explanatory variablesNon-profit owned 0.29 0.21∗∗ 0.20 0.29∗∗Government owned 0.084 0.065∗∗ 0.077 0.075Investor owned 0.63 0.72∗∗ 0.72 0.63∗∗Chain owned 0.47 0.58∗∗ 0.55 0.49∗∗New facility 0.11 0.09∗∗ 0.12 0.09∗∗Hospital-based 0.064 0.056∗ 0.070 0.054∗∗Average ADLs 3.61 3.76∗∗ 3.69 3.67∗∗Percent skilled 0.33 0.33 0.32 0.33∗∗Medicaid rate 91.33 86.24∗∗ 86.10 91.11∗∗Wage index 9,299 10,005∗∗ 9,568 9,637∗Income 23,305 24,504∗∗ 22,222 24,918∗∗Herfindahl index 0.19 0.17∗∗ 0.21 0.17∗∗Population >65 79,048 108,507∗∗ 106,963 81,923∗∗

Quality measuresBedsores 0.062 0.070∗∗ 0.068 0.065∗∗Physical restraints 0.17 0.19∗∗ 0.18 0.18Feeding tubes 0.063 0.073∗∗ 0.073 0.063∗∗Catheters 0.066 0.070∗∗ 0.070 0.066∗∗Total nurse staff 2.82 2.87 2.95 2.78∗∗RNs per total staff 0.11 0.12∗∗ 0.10 0.13∗∗

Number of homes 9,437 7,541 7,266 9,712

Notes: The means are weighted by the total number of residents in each facility.∗ Statistically different at 5% level.∗∗ Statistically different at 1% level.

As expected, a higher percent age growth was associated with lower quality and a higherlagged hospital non-profit market share was associated with higher quality. These compar-isons represent crude (or unconditional) IV estimates of the effect of non-profit market shareon quality. For example, a 1 percentage point increase in the non-profit market share was as-sociated with 0.1 percentage point decrease in bedsores (the 1991–1996 percentage changebetween-group difference in bedsores of 0.8 percentage points divided by between-groupdifference in non-profit market share of 8 percentage points).

6.3. Effect of ownership on nursing home quality

The conceptual framework provided two tests of the asymmetric and full informa-tion models. In the asymmetric case, an increase in non-profit market share will improveboth for-profit and overall market quality, and in the full information case, an increase innon-profit market share will decrease for-profit quality and have no effect on overall quality.Before turning to the analyses that incorporate non-profit market share, we examine results

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Table 4Weighted least squares estimates of the effect of ownership type on nursing home quality

Regressors Dependent variables

Pressure ulcers Physical restraints Catheters Feeding tubes Total nurse staff RNs per total staff

Non-profit −0.0084 (6.95) 0.017 (3.68) −0.011 (8.17) −0.016 (7.95) 0.14 (2.92) 0.0045 (1.82)Government −0.0093 (4.48) 0.041 (4.30) −0.0056 (1.99) 0.0016 (0.57) −0.66 (8.02) 0.0056 (1.56)R2 0.11 0.10 0.16 0.20 0.09 0.16N 16,978 16,978 16,978 16,978 14,638 14,604

Notes: All estimations are weighted by the total number of residents in each facility with the absolute value of the Huber–White adjustedt-statistics (corrected forintra-county correlation) presented in parentheses. All models include variables measuring the hospital area wage index, a Herfindahl index, the median per capita income,the number of elderly individuals in the county, the average Medicaid reimbursement rate, Medicaid reimbursement system, an activities of daily score, the proportion ofresidents requiring skilled care and binary indicators for chain owned, hospital-based and newly surveyed facilities.


from the standard dummy variable approach used within the literature (seeTable 4). Next,we incorporate the role of competition across sectors by examining the effect of non-profitmarket share on for-profit quality (seeTable 5) and overall quality (seeTable 6). Importantly,bedsores, physical restraints, catheters and tube feedings are negative measures of quality(e.g. more bedsores entails lower quality). Only information associated with non-profitstatus (for-profit status is the reference category) and non-profit market share are reportedin these tables. The full results are available upon request from the authors.

The standard dummy variable approach from the literature indicates that non-profit own-ership was associated with an increase in nursing home quality based on five of the sixquality measures. Non-profit status was associated with 0.84 percentage points (or 11.8%of the mean) fewer bedsores, 1.1 percentage points (or 14.1%) fewer catheters and 1.6percentage points (or 24.6%) fewer tube feedings. Non-profit homes were associated with0.14 (or 3.8%) more nursing staff and a 0.005 (or 3.9%) higher staffing skill mix. However,non-profit quality was found to be 1.7 percentage points (or 10%) lower when representedby the physical restraint measure. The coefficient on the non-profit dummy variable wasstatistically significant at the 10% level for all six of the quality measures. The evidence wasinconclusive as to whether quality was higher in government sector relative to the for-profitsector.

Although this model captured the effect of non-profit ownership, it may have providedbiased estimates of the overall effect because it did not account for the spillover effect ofthe non-profit sector on nursing home quality. Thus, the results presented inTables 5 and 6examine the effect of non-profit market share on nursing home quality.Table 5provides thefirst test of the asymmetric and full information models by isolating the effect of non-profitmarket share on quality in the for-profit sector. For each of the six measures of nursing homequality,Table 5contains two columns of results. The first column for each quality measurereports the WLS estimation results and the second column reports the IV estimation results.

Across both the WLS and IV models, an increase in non-profit market share was asso-ciated with higher for-profit quality for all six measures except for the WLS total nursingstaff case. These results were statistically significant at the 10% level for the tube feed-ing, catheter, staffing skill mix, total nurse staff (IV only), bedsore (IV only), and thephysical restraint (WLS only) measures. The magnitude of the quality spillovers fromthe non-profit sector to the for-profit was fairly sizable. Specifically, the non-profit mar-ket share elasticity of quality implied by the IV estimate from the bedsores model was−0.19[−0.056(0.24/0.071)],−0.44 for the feeding tube model,−0.12 for the restraintmodel,−0.13 for the catheter model, 0.09 for the total staff model and 0.32 for the staffingskill mix model. These results imply that a 10% increase in non-profit market share wasassociated with between 0.9 and 4.4% increase in for-profit nursing home quality. Thus,these first results are consistent with the asymmetric information case where an increase innon-profit market share provides a positive quality spillover within the for-profit sector.

Table 6reports the second test of the asymmetric and full information models by exam-ining the effect of non-profit market share on overall quality. Once again, the first columnfor each quality measure reports the WLS results and the second column reports the IVestimation results. Similar to the for-profit quality results, an increase in non-profit marketshare was associated with higher overall quality for all six measures except for the WLStotal nursing staff case. The non-profit market share coefficient was statistically significant

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Table 5Weighted least squares (WLS) and instrumental variables (IVs) estimates of the effect of non-profit market share on for-profit nursing home quality


Bedsores Physical restraints Catheters Tube feedings Total nurse staff RNs per total staff

WLS IV WLS IV WLS IV WLS IV WLS IV WLS IV

Non-profitshare

−0.0031 (0.72) −0.056 (2.40) −0.036 (2.02) −0.085 (1.05) −0.0076 (1.84) −0.041 (1.68) −0.015 (2.32) −0.12 (3.10) −0.016 (0.08) 1.46 (1.79) 0.041 (4.19) 0.23 (3.57)

R2 0.11 0.09 0.11 0.11 0.16 0.15 0.18 0.12 0.06 0.04 0.15 0.05N 11,174 10,697 11,174 10,697 11,174 10,697 11,174 10,697 10,191 9743 10,171 9723

Notes: All estimations are weighted by the total number of residents in each facility with the absolute value of the Huber–White adjustedt-statistics (corrected for intra-county correlation) presented in parentheses. All modelsinclude variables measuring the hospital area wage index, a Herfindahl index, the median per capita income, the number of elderly individuals in the county, the average Medicaid reimbursement rate, Medicaid reimbursementsystem, an activities of daily score, the proportion of residents requiring skilled care and binary indicators for chain owned, hospital-based and newly surveyed facilities.

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Table 6Weighted least squares (WLS) and instrumental variables (IVs) estimates of the effect of non-profit market share on overall nursing home quality


Bedsores Physical restraints Catheters Tube feedings Total nurse staff RNs per total staff

WLS IV WLS IV WLS IV WLS IV WLS IV WLS IV

Non-profitshare

−0.013 (4.78) −0.031 (2.05) −0.0069 (0.62) −0.0035 (0.07) −0.017 (5.78) −0.016 (1.03) −0.025 (5.87) −0.089 (3.47) −0.12 (1.09) 0.29 (0.60) 0.018 (3.13) 0.16 (4.13)

R2 0.11 0.11 0.10 0.10 0.16 0.16 0.20 0.17 0.08 0.08 0.16 0.05N 16,978 16,303 16,978 16,303 16,978 16,303 16,978 16,303 14,638 14,017 14,604 13,984

Notes: All estimations are weighted by the total number of residents in each facility with the absolute value of the Huber–White adjustedt-statistics (corrected for intra-county correlation) are presented in parentheses. Allmodels include variables measuring the hospital area wage index, a Herfindahl index, the median per capita income, the number of elderly individuals per square mile, the average Medicaid reimbursement rate, Medicaidreimbursement system, an activities of daily score, the proportion of residents requiring skilled care and binary indicators for chain owned, hospital-based and newly surveyed facilities.


at the 10% level for the bedsore, tube feeding, staffing skill mix and the catheter (WLS only)measures. The results were relatively large in that the non-profit market share elasticity ofquality implied by the IV estimate from the bedsores model was−0.10. Similarly, theelasticity implied by the IV estimate from the feeding tube model was−0.33,−0.05 for thecatheter model and 0.22 for the staffing skill mix model. Thus, based on these estimates, a10% increase in non-profit market share was associated with between 0.5 and 2.2% increasein overall nursing home quality. Although these results are not as conclusive in terms ofstatistical significance as the for-profit estimates, they once again are generally consistentwith the asymmetric information case where an increase in non-profit market share improvesoverall quality. Further, the smaller magnitudes of the overall effects in the pooled models(relative to the spillover effects estimated in the for-profit only models) are reassuring interms of model specification. Had the overall effects been as large as the spillover effects, itwould have implied that the quality of non-profits rises as rapidly as the quality of for-profitswhen non-profit market share grows. Such a finding would have raised the concern thatthe non-profit market share coefficient was only capturing a correlation with unobservedvariables that simultaneously cause higher quality and higher non-profit market shares.

7. Discussion

The existing empirical literature on ownership and quality has generally focused single-mindedly on whether or not quality differences exist across sectors. The literature basedon this focus has yielded highly inconsistent findings on whether non-profit nursing homesare “better” than for-profit facilities. We argue that inferring quality differences from thecoefficient of a non-profit ownership dummy variable is vulnerable to bias that may beresponsible for at least part of the inconsistent findings on quality. The key implications ofthe approach offered in this analysis are that the existence of differences is neither necessarynor sufficient to conclude that non-profit enterprise is socially desirable. This paper offers anovel instrumental variables approach to incorporating the ownership of a firm’s competitorsinto the analysis to test the asymmetric and full information models of the nursing homemarket. The empirical results presented within this paper favor an asymmetric informationmodel. Thus, non-profit ownership may help alleviate inefficiencies associated with poorlyinformed consumers.

Weisbrod (1988)has outlined two goals for public policy towards the non-profit sector:(1) public policy should help encourage non-profits to achieve their social goals, and (2)public policy should help achieve a better balance of institutional responsibilities betweennon-profits, for-profits and governments. With these two goals in mind, this model hasseveral policy implications. If non-profits have a competitive advantage in “trustworthiness”while for-profits have greater incentives for efficiency, intersectoral competition can yieldbetter outcomes than a market consisting exclusively of one type of firm. If non-profitsattract the most poorly informed consumers, the likelihood that for-profits behave honestly(i.e. deliver the promised level of quality) rises with the non-profit market share. Likewise,competition from for-profit firms can limit inefficiency or the exercise of market power bynon-profits. Recent work byKessler and McClellan (2001)found that areas with a strongerpresence of for-profit hospitals have 2.4% lower overall hospital expenditures.


The limited literature on mixed industries has focused on the negative side of compe-tition: for-profits might erode non-profits’ ability to simultaneously achieve social goalsand break even. Such an outcome is certainly possible, particularly if the non-distributionconstraint is non-binding, which would provide little protection from entry by “for-profitsin disguise” that debase non-profit status as a signal of trustworthy behavior. However, thispaper demonstrates the beneficial aspects of intersectoral competition by showing that con-vergence in behavior of for-profit and non-profits may not be bad. Failure to observe largedifferences is not prima facie evidence that the non-profit sector is not socially beneficial.

The recent appearance of more vigorous competition and cost containment pressureswithin the health care sector have drawn into question whether the cross-subsidization of“good works” that non-profits are presumed to practice should in fact be continued (Salkeverand Frank, 1992). For example, published quality rankings and quality monitoring throughmanaged care networks may have already lessened quality spillovers within the hospitalsector. Both the federal government and several national services have recently begun topublish quality rankings of nursing facilities. However, quality rankings are likely to be moreuseful in sorting out the poorest facilities in absolute terms than in verifying that quality ofcare is commensurate with the prices charged. Additionally, the growth of managed carewithin the long-term care sector has lagged behind the acute care market. As a result, manyof the broader trends in the health care sector may be less relevant for the nursing homemarket. Thus, the non-profit sector may continue to serve a socially beneficial role withinthe nursing home industry.

Although we find evidence consistent with the type of spillovers predicted by an asym-metric information model, we recognize that this study does not directly observe the sort-ing of poorly informed consumers into the non-profit sector. Thus, we cannot rule outthe possibility that the observed spillover effect (though inconsistent with the alternative,full information model) arises through some other mechanism (e.g. a greater presence ofnon-profits generates more vigorous non-price competition in quality). If an exclusivelyfor-profit market fails to deliver optimal quality, however, it is worth noting that the ulti-mate welfare implications of the spillover effect would be similar even if the mechanismthrough which it operates is not the sorting of poorly informed consumers into the non-profitsector.

Finally, there have been a number of explanations put forth for the low level of qual-ity often observed in the nursing home market. Health economists have generally focusedon the presence of supply constraints such as CON and construction moratoria that haverestricted entry and thereby impeded competition within the nursing home industry; seeGrabowski (2001)for a review of this literature. The nursing home industry has argued thatlow nursing home quality is attributable to inadequate payment levels by state Medicaidprograms (e.g.American Health Care Association, 2001). Less attention has been paid tothe lack of quality information available to many patients and the implications that asymme-tries of information between nursing homes and patients may have for consumer welfare.Due to physical, cognitive and emotional disabilities, many nursing home consumers mayfall far short of thehomo economicus assumed in most economic models of behavior. Thispaper has provided evidence consistent with the hypothesis that non-profit status may re-main a useful and relatively inexpensive signal that the promised level of quality will bedelivered.


Acknowledgements

We would like to thank Jim Burgess, Vivian Ho, Joseph Newhouse, and two anonymousreferees, and seminar participants at the AEA annual meetings, the APHA annual meetingsand the University of Pennsylvania for helpful comments on an earlier draft of this paper.We are also grateful to Robert Edwards for his excellent research assistance.

References

American Health Care Association, 2001. A briefing chartbook on shortfalls in Medicaid funding for nursinghome care. Prepared by BDO Seidman, LLP, Accessed 6 October 2001 at:http://www.ahca.org/brief/seidman/seidmanstudy.pdf.

Arrow, K.J., 1963. Uncertainty and the welfare economics of medical care. American Economic Review 53,941–973.

Banaszak-Holl, J., Zinn, J.S., Mor, V., 1996. The impact of market and organizational characteristics on nursingcare facility service innovation: a resource dependency perspective. Health Services Research 31 (1), 97–117.

Bound, J., Jaeger, D.A., Baker, R.M., 1995. Problems with instrumental variables estimation when the correlationbetween the instruments and the endogenous explanatory variable is weak. Journal of the American StatisticalAssociation 90 (430), 443–450.

Cohen, J.W., Spector, W.D., 1996. The effect of Medicaid reimbursement on quality of care in nursing homes.Journal of Health Economics 15, 23–48.

Davidson, R., MacKinnon, J.G., 1993. Estimation and Inference in Econometrics, 4th ed. McGraw-Hill, NewYork.

Davis, M.A., 1991. On nursing home quality: a review and analysis. Medical Care Review 48, 129–166.Fries, B.E., 1990. Comparing case-mix systems for nursing home payment. Health Care Financing Review 11 (4),

103–119.Fries, B.E., Schneider, D.P., Foley, W.J., Gavazzi, M., Burke, R., Cornelius, E., 1994. Refining a case-mix measure

for nursing homes: resource utilization groups (RUG-III). Medical Care 32 (7), 668–685.Garber, A.M., MaCurdy, T.E., 1992. Payment source and episodes of institutionalization. In: Wise, D.A. (Ed.),

Topics in the Economics of Aging. University of Chicago Press, Chicago, pp. 249–271.Gertler, P.J., 1989. Subsidies, quality and the regulation of nursing homes. Journal of Public Economics 38 (1),

33–52.Gertler, P.J., 1992. Medicaid and the cost of improving access to nursing home care. The Review of Economics

and Statistics 74, 338–345.Grabowski, D.C., 2001. Medicaid reimbursement and the quality of nursing home care. Journal of Health

Economics 20 (4), 549–569.Hansmann, H.A., 1980. The role of non-profit enterprise. Yale Law Journal 89, 835–901.Hansmann, H., 1987. The effect of tax exemption and other factors on the market share of non-profit versus

for-profit firms. National Tax Journal 40, 71–82.Harrington, C., Carillo, H., Thollaug, S.C., Summers, P.R., 1998a. Nursing Facilities, Staffing, Residents, and

Facility Deficiencies, 1991 Through 1996. Department of Social and Behavioral Sciences, University ofCalifornia, San Francisco, CA.

Harrington, C., Swan, J.H., Griffin, C. Clemena, W., Bedney, B., Carillo, H., Shosak, S., 1998b. 1996 State DataBook on Long-Term Care Program and Market Characteristics. Department of Social and Behavioral Sciences,University of California, San Francisco, CA.

Harrington, C., Swan, J.H., Wellin, V., Clemena, W., Carrillo, H.M., 1999. 1998 State Data Book on Long Term CareProgram and Market Characteristics. Department of Social and Behavioral Sciences, University of California,San Francisco, CA.

Harrington, C., Woolhandler, S., Mullan, J., Carillo, H., Himmelstein, D.U., 2001. Does investor ownership ofnursing homes compromise the quality of care? American Journal of Public Health 91 (9), 1452–1455.

http://www.ahca.org/brief/seidman/seidmanstudy.pdf

http://www.ahca.org/brief/seidman/seidmanstudy.pdf


Hawes, C., Phillips, C.D., 1986. The changing structure of the nursing home industry and the impact of ownershipon quality, cost and access. In: Gray, B.H. (Ed.), For-profit Enterprise in Health Care. National Academy Press,Washington, DC, pp. 492–541.

Hirth, R.A., 1993. Consumer Information and Ownership in the Nursing Home Industry. Ph.D. dissertation.University of Pennsylvania, Philadelphia, PA.

Hirth, R.A., 1999. Consumer information and competition between non-profit and for-profit nursing homes. Journalof Health Economics 18 (2), 219–240.

Hirth, R.A., Banaszak-Holl, J.C., McCarthy, J.F., 2000. Nursing home-to-nursing home transfers: prevalence, timepattern and resident correlates. Medical Care 38 (6), 660–669.

Holtmann, A.G., Ullman, S.G., 1991. Transactions costs, uncertainty, and not-for-profit organizations. Annals ofPublic and Cooperative Economy 62, 641–653.

Institute of Medicine, 1986. Improving the Quality of Care in Nursing Homes. Committee on Nursing HomeRegulation, National Academy Press, Washington, DC.

Institute of Medicine, 2001. Improving the Quality of Long-term Care. Committee on Improving Quality inLong-Term Care, National Academy Press, Washington, DC.

Kessler, D.P., McClellan, M.B., 2000. Is hospital competition socially wasteful? Quarterly Journal of Economics115 (2), 577–615.

Kessler, D., McClellan, M., 2001. The effects of hospital ownership on medical productivity, NBER WorkingPaper No. 8537.

Moulton, B.R., 1990. An illustration of a pitfall in estimating the effects of aggregate variables on micro units.The Review of Economics and Statistics 72 (2), 334–338.

Norton, E.C., 2000, Long-term care. In: Cuyler, A.J., Newhouse, J.P. (Eds.), Handbook of Health Economics, vol.1. Elsevier, Amsterdam, pp. 955–994.

Nyman, J.A., 1985. Prospective and ‘cost-plus’ Medicaid reimbursement, excess Medicaid demand, and the qualityof nursing home care. Journal of Health Economics 4, 237–259.

Nyman, J.A., 1994. The effects of market concentration and excess demand on the price of nursing home care.The Journal of Industrial Economics 42 (2), 193–204.

O’Brien, J., Saxberg, B.O., Smith, H.L., 1983. For-profit or not-for-profit nursing homes: does it matter?Gerontologist 23, 341–348.

Salkever, D.S., Frank, R.G., 1992. Health services. In: Clotfelter, C.T. (Ed.), Who Benefits from the Non-profitSector? The University of Chicago Press, Chicago, pp. 24–54.

Sloan, F.A., Picone, G.A., Tayler, D.H., Chou, S.-Y., 2001. Hospital ownership and cost and quality of care: isthere a dime’s worth of difference? Journal of Health Economics 20 (1), 1–21.

Spector, W.D., Selden, T.M., Cohen, J.W., 1998. The impact of ownership type on nursing home outcomes. HealthEconomics 7, 639–653.

Staiger, D., Stock, J.H., 1997. Instrumental variables regression with weak instruments. Econometrica 65 (3),557–586.

Steinwald, B., Neuhauser, D., 1970. The role of the proprietary hospital. Law and Contemporary Problems 35 (4),817–838.

Stevens, R., 1989. In Sickness and in Wealth. Basic Books, New York.Strahan, G.W., 1997. An overview of nursing homes and their current residents: data from the 1995 national

nursing home survey. Advance Data Number 280, National Center for Health Statistics, Rockville, MD.US General Accounting Office, July 1998, California Nursing Homes: Care Problems Persist Despite Federal and

State Oversight. Report to the Special Committee on Aging, US Senate Pub. No. HEHS-98-202. US GAO,Washington DC.

US Senate, Subcommittee on Long-term Care, Senate Special Committee on Aging, 1974. Nursing Home Carein the United States: Failure in Public Policy. US Government Printing Office, Washington, DC.

Weisbrod, B.A., 1988. The Non-profit Economy. Harvard University Press, Cambridge, MA.Wooldridge, J.M., 2002. Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge, MA.Zinn, J.S., 1993. The influence of nurse wage differentials on nursing home staffing and resident care decisions.

The Gerontologist 33, 721–729.

Competitive spillovers across non-profit and for-profit nursing homes

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