Medicaid reimbursement and the quality of nursing home care

Journal of Health Economics 20 (2001) 549–569

Medicaid reimbursement and the quality of nursing home care David C. Grabowski∗ Department of Health Care Organization and Policy, School of Public Health, University of Alabama at Birmingham, RPHB 330, 1530 3rd Avenue South, Birmingham, AL 35294-0022, USA Received 1 March 2000; received in revised form 1 February 2001; accepted 12 March 2001

Abstract An influential series of papers have found that an increase in Medicaid reimbursement decreases the level of nursing home quality in the presence of certificate-of-need (CON) and construction moratorium regulations. Using more recent national data, an outcome-oriented measure of quality, and an alternative methodology, this study finds a positive, albeit small, effect of reimbursement on quality. Although this paper does find some evidence of excess demand within the market for nursing home care, this new finding is attributed to a decline in nursing home utilization over the last two decades. © 2001 Elsevier Science B.V. All rights reserved. JEL classification: I11; I18 Keywords: Medicaid; Quality; Nursing home; Regulation

1. Introduction Although many nursing homes provide good care, even in the face of severe financial constraints, the quality of care in government-certified nursing homes has been a matter of concern to consumers, health care professionals and policymakers for over 25 years. Over this time period, a series of studies and reports have documented low quality in this marketplace (e.g. US Senate, 1974, 1986; IOM, 1986, 1996; GAO, 1987, 1998). One major reason for this intense scrutiny by these various agencies is the high proportion of care that is publicly funded. Medicaid, the dominant purchaser of nursing home services in the United States (accounting for roughly 50% of all nursing home expenditures and 70% of all bed days), gives financially indigent individuals access to nursing homes by directly reimbursing ∗ Tel.: +1-205-975-8967; fax: +1-205-934-3347. E-mail address: [email protected] (D.C. Grabowski).

0167-6296/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 6 2 9 6 ( 0 1 ) 0 0 0 8 3 - 2

550

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

homes for the care of Medicaid residents. A potential initiative available to policymakers towards improving nursing home quality is the level of Medicaid reimbursement. However, an influential series of papers have found an increase in Medicaid reimbursement actually decreases the level of nursing home quality in the presence of certificate-of-need (CON) and construction moratorium regulations. This study attempts to reproduce this counterintuitive finding with more recent national data, a direct measure of quality, and an alternative methodology to examine the generalizability of the earlier result. Most economic analyses of the market for nursing home care have employed Scanlon’s (1980) model of a monopolistically competitive home that provides a common level of quality to both Medicaid and private-pay residents. Despite the different rates charged Medicaid and private-pay residents, a home is required by law to provide the same level of quality to all residents within a home regardless of payer source. In addition to this legal restriction, certain nursing home services such as medical care and dietary services are produced jointly for both payer types and may exhibit economies of joint production (Gertler and Waldman, 1992). A majority of states have CON and construction moratorium legislation designed to constrain the growth of the nursing home market and thereby control nursing home expenditures. Scanlon hypothesized that these policies impose a binding bed constraint on the market for nursing home care under which certain individuals are unable to gain access to care. State Medicaid programs pay, on average, approximately 70% of the private-pay price. As a result, a home will first accept higher-paying private-pay residents and then fill the remaining beds with Medicaid residents. Thus, private-pay demand is still satisfied under a binding bed constraint, but there exists an “excess demand” for nursing home beds among Medicaid eligible individuals. Gertler (1989) and Nyman (1985) extend the Scanlon model to make an important theoretical observation regarding the relationship between Medicaid reimbursement and quality. Without a binding bed constraint policy, an increase in the Medicaid reimbursement rate should theoretically improve quality (e.g. Nyman, 1985). However, in those markets with a binding bed constraint in place, nursing homes do not view a Medicaid payment as a reward for quality, because Medicaid residents are available (due to the binding bed constraint) regardless of the level of quality. This lack of quality competition for Medicaid residents means that the reward to a home for attracting an additional private-pay resident is reduced to the difference between the private-pay price and the Medicaid per diem rate. If a nursing home chooses to increase quality to attract an additional private-pay resident, one of the costs will be the forgone payment for the displaced Medicaid resident. Because common quality is provided across payer types, a higher Medicaid reimbursement rate is therefore, associated with a lower return to raising quality to attract private-pay residents. As a result, an increase in the Medicaid reimbursement rate is hypothesized to have the counterintuitive effect of decreasing quality. In addition to this theoretical contribution, Gertler (1989, 1992), Nyman (1985, 1988a,b, 1989b), Cohen and Spector (1996) and Grabowski (1999, 2001) examine the empirical relationship between the Medicaid reimbursement rate and the quality of nursing home care under CON policies. Utilizing 1980 New York State data, the Gertler studies employ a reduced-form model and measure quality by the inputs a nursing home uses to produce the goods and services it provides to its patients. The Nyman studies employ ordinary least squares (OLS) and two stage least squares (2SLS) models and measure quality by

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

551

the number of violations for all Wisconsin nursing homes using 1979 or 1983 home-level data. Cohen and Spector use the 1987 National Medical Expenditure Survey to examine a nationally representative sample of 2663 residents from 658 nursing homes. This study uses a 2SLS model and measures quality by staffing, mortality and pressure sores. Using 1995–1996 facility-level data for all US nursing homes, Grabowski employs a multi-part model and measures quality by the number of home-level deficiencies and several input and procedural measures of quality. Both the Nyman and Gertler studies lend support to the assertion that a change in reimbursement may decrease quality in the context of a bed constraint. First, Nyman (1985, 1988a, 1989b) finds no evidence that quality is higher within homes receiving higher reimbursement rates. In fact, quality is actually found to decrease when reimbursement rates are increased. Second, Nyman (1988b) found that nursing homes located in counties with a tighter bed supply spend significantly less money on resident care. Third, Gertler (1989) finds an increase in the return on Medicaid residents induces homes to admit more Medicaid residents and to lower quality. Similarly, Gertler (1992) shows that an increase in the Medicaid reimbursement rate improves access for Medicaid residents, but at the expense of increasing overall Medicaid expenditures and lowering overall quality. Gertler estimates that a 10% increase in total Medicaid expenditures results in a 4.1% increase in Medicaid residents receiving care and a 3.4% decrease in quality. More recent work in this area has provided a different picture of the effect of reimbursement on quality. Cohen and Spector (1996) found a positive relationship between reimbursement and quality, but this result is only statistically significant for the staffing variable and not the mortality and pressure sore measures. Grabowski (1999, 2001) found a positive relationship between reimbursement and quality, but this result was only statistically significant in certain cases. This current study will extend this earlier literature in four direct ways to examine the generalizability of the excess demand result. First, the studies that support the excess demand paradigm use nursing home data from a single state. Both New York and Wisconsin employed cost-plus Medicaid reimbursement methods during the earlier period of study. The “cost” portion of the Medicaid payment was determined retrospectively by costs incurred by the facility and the “plus” factor was based on capital expenses. Clearly, the cost portion of the payment rate was endogenous to quality. Earlier studies, however, isolated the plus factor, which was argued to be exogenous, to examine how an increase in reimbursement affected quality. More recent national studies have used each state’s average Medicaid payment rate in examining the effect of reimbursement on quality. If the state deals in aggregates (policing for bad apples aside), no individual home has enough market share or political clout to affect the state’s reimbursement rate. Thus, to the individual home, the average state Medicaid rate is exogenous. In an effort to bridge these two sets of studies, this paper will examine both state-level data using facility-specific reimbursement rates and national data using aggregate state-level reimbursement rates. Second, the previous studies of reimbursement and quality finding evidence of excess demand used nursing home data from the late 1970s and early 1980s. Over the last two decades however, there has been an important shift in the tightness of the nursing home bed supply. Occupancy rates, an indirect measure of excess demand, have been declining over the last two decades. The national occupancy rate was 92.9% in 1977, 91.8% in 1985 and 87.4% in 1995 (Strahan, 1997). Furthermore, there has been an increase in the

552

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

number of empty beds per 1000 community-dwelling elderly (age 65+) individuals. Using 1969 and 1973 data from 43 states, Scanlon (1980) found five empty beds per 1000 elderly individuals. Using national 1995 and 1996 data, Grabowski (1999) found 10 empty beds per 1000 elderly individuals. More directly, a recent study by Nyman (1993) provides support for the hypothesis that excess demand is lessening. Using a three-part test, he found no evidence of excess demand in three different states (Wisconsin, Minnesota and Oregon) in 1988 despite having found evidence of excess demand 5 years earlier in Wisconsin (Nyman, 1989a). Thus, a second extension of this study is the use of more recent (1995–1996) data to examine whether CON and moratoria policies are still important for this marketplace. Third, previous studies in this literature have generally measured quality using either input-based measures or regulatory violations. Input-based measures of quality have been well utilized within the nursing home literature, but they have been criticized due to an inability to definitively determine whether more staffing implies improved quality or increased inefficiency. Regulatory violations have also been well utilized, but have been criticized on the grounds that they do not accurately measure quality but rather the preferences of some regulatory body. This study will employ an outcome-oriented measure of quality in order to more directly measure quality. And finally, previous studies have assumed that homes provide care to both Medicaid and private-pay residents in a single “integrated” facility. However, roughly one in four Medicaid-certified homes have less than 10%, or greater than 90%, Medicaid residents. Unlike the earlier literature that assumes an integrated facility, this framework extends the Scanlon model by separately measuring the effect of Medicaid reimbursement on the decision to operate as an all-Medicaid, integrated or all-private facility, and the effect of reimbursement on the decision to provide quality conditional on this payer mix decision. The primary testable implications of this model are summarized here and the reader is referred to Grabowski (1999) for a full exposition of the model. Similar to the Scanlon approach, the model assumes a profit maximizing, monopolistically competitive home. However, the theoretical model is broken into two stages. In the first stage, the model examines a home’s decision to operate as an integrated facility or opt for a corner solution where it specializes by caring for only Medicaid or private-pay residents. The optimal private-pay price and quality level are chosen so that the marginal private-pay profits equals the opportunity cost of foregone Medicaid profits. Regardless of the presence of a bed constraint, homes will ultimately operate within the payer mix regime that yields the highest profit level. An increase in the Medicaid reimbursement rate raises the opportunity cost of foregone Medicaid profits making marginal Medicaid residents more profitable relative to marginal private-paying residents. If the Medicaid rate is low relative to the private-pay price, then homes will choose to specialize in caring for only private-pay residents. In this case, the firm maximizes profits by choosing the all-private regime. Conversely, if the Medicaid rate is large (relative to the private-pay price), then homes will maximize profits by choosing to care for only Medicaid residents. In the second stage of the model, the analysis considers the effect of reimbursement on quality conditional on a facility’s decision to operate as an integrated, all-Medicaid or all-private facility. Conditional on a facility choosing this integrated regime, this model derives similar predictions as the Gertler and Nyman framework. In the case without a binding bed constraint, an increase in reimbursement is hypothesized to improve quality.

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

553

Alternatively, the model generates the counterintuitive negative relationship between reimbursement and quality under a binding bed constraint. In the case of an all-Medicaid facility, an increase in reimbursement improves quality in the absence of a binding bed constraint. However, if a binding bed constraint is imposed on the marketplace, then the optimal level of Medicaid quality is trivial because firms will provide the minimum acceptable level of quality, regardless of reimbursement level. Unlike the integrated facility where the demand of those private-pay residents still depends on the provision of quality, there is no quality competition within an all-Medicaid facility under a bed constraint, and thus, no incentive to provide quality above the minimum threshold. Not surprisingly, a change in the Medicaid rate also has no effect on the quality of care within an all-private facility, regardless of the presence of a bed constraint.

2. Empirical specification Gertler (1989) examines the effect of the Medicaid reimbursement rate on a nursing home’s choices of quality, private-pay price, and payer mix. Because these concern comparative static effects, these estimates can be obtained from reduced-form models of the determinants of quality, private-pay price and payer mix. In the reduced-form, each of these dependent variables can be expressed as a function of exogenous demand and supply variables. This study will replicate this reduced-form approach in examining the effect of Medicaid reimbursement on quality. In an effort to replicate the Gertler study, this study will initially employ an input-based measure of nursing home quality, the number of registered nurses. As discussed above, studies that have employed input-based measures of quality have been criticized due to the difficulty of identifying quality responses from efficiency. Due to the high proportion of firms that operate for profit facilities, Gertler (1992) assumed cost minimization on the part of homes in utilizing an input-based measure of quality. As an alternative approach, Gertler (1989) and Gertler and Waldman (1992) develop methods of identifying quality responses from efficiency. The authors argue that conditional on quantity and input prices, the variation in costs across firms that is correlated with exogenous determinants of product demand reflects quality variation. This current study will adopt the cost minimization assumption employed by the Gertler (1992) study. This input-based reduced-form model is estimated by least squares. This study estimates three different models using an input-based measure of quality to test the generalizability of the excess demand finding of the earlier literature. First, this study extends the reduced-form model, which Gertler (1989) limited to New York homes in 1980, to all certified nursing homes in the United States for 1981. This specification of the model tests whether the excess demand result was relevant for the entire United States in this earlier period. Second, the study also applies the reduced-form model to New York data in 1995–1996 to test whether the excess demand result holds in a limited (i.e. state-level) specification with more recent data. During 1995 and 1996, New York regulated both the growth of new nursing home beds and the conversion of hospital beds to nursing home beds through a CON. And third, the study applies the reduced-form model to the entire United States using 1995–1996 data. This specification of the model tests whether the excess demand finding holds on a national basis for today’s nursing home market.

554

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

Given the criticism of input-based measures of quality, this paper next estimates this same reduced-form equation using an outcome-based measure of quality, the proportion of residents with facility-acquired pressure sores. In separate models using New York State and national data, these analyses test whether the counterintuitive result found in the previous literature holds with a more direct measure of quality. Given the structure of this quality measure, generalized linear models (GLM) was used to fit a binomial regression model grouped at the home-level (McCullagh and Nelder, 1989). This study also introduces a multi-part model to incorporate the idea that a change in the Medicaid reimbursement rate may affect both the facility-specific payer mix and the provision of quality given this choice of payer mix. Using 1995–1996 data for the entire US, this model will allow the decomposition of the effects of reimbursement on a part due to the choice of payer mix regime and a part due to quality. As such, two reduced-form models will be estimated as a function of exogenous demand and supply variables. In the first stage, a change in reimbursement affects the home’s decision to become an integrated, all-Medicaid or all-private facility. This part of the model is estimated with a discrete choice model to determine the change in the probabilities of a facility operating as all-Medicaid, all-private or integrated given a change in reimbursement. A polytomous (or multinomial) logit model, a widely used functional form, is used to estimate these probabilities. In the empirical analysis, the dependent variable is classified as all-Medicaid for those homes with greater than 90% Medicaid residents, all-private for those homes with greater than 90% private-pay resident or integrated for those with at least 10% Medicaid and private-pay. Conditional on the home choosing a particular payer mix regime, the final stage examines the effect of reimbursement on quality conditional on a home choosing a particular regime. Facility-acquired pressure sores will once again measure quality and GLM is used to fit a binomial model grouped at the home-level. Earlier studies of Medicaid reimbursement and quality have focused on a particular state with a CON regulation, but this current analysis uses national nursing home data for all nursing home markets in the United States. During 1996, 45 states regulated the growth of new beds or facilities through a CON or moratorium (Harrington et al., 1998b). Clearly, the national framework strengthens the generalizability of the findings relative to the earlier literature, but also leaves open the possibility that an excess demand paradigm may only be relevant for a minority of nursing home markets where CON or moratorium policies are most binding. In order to address this concern, each of the 1995–1996 national models are estimated separately for both all nursing homes nationwide and for those “tightest” markets in particular. The measure of market tightness used within the analysis is the lagged number of open beds in the market divided by the number of non-institutionalized elderly individuals over the age of 65 living in the county. A lagged measure is employed because a non-lagged measure may be endogenous to quality in those counties with fewer nursing facilities. The tightest markets are identified as those markets in the top quartile of the lagged tightness measure (those markets with less than 2.893 open beds per 1000 elderly age 65 and above). As a final estimation issue, observations within states or markets may not be independent of one another because there are many regulations that are particular to a given state or market. With state or market-level variables being the primary variables of interest, inference statistics in both the reduced-form and multi-part models have been corrected for intra-state cluster correlations. This is a variant of the Huber–White, or sandwich estimator.

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

555

3. Data The nursing home data are from one of two sources: the Medicaid–Medicare Automated Certification System (MMACS) and the Online Survey, Certification, and Reporting (OSCAR) System, which replaced MMACS as part of the Omnibus Reconciliation Act of 1987 (OBRA 87). Collected and maintained by the Health Care Financing Administration (HCFA), these data systems are used to determine whether homes are in compliance with federal regulatory requirements. These systems contain information from state surveys of all federally certified Medicaid (nursing facilities) and Medicare (skilled nursing care) homes, which constitute approximately 96% of all US nursing homes (Strahan, 1997). Every facility is required to have a survey to verify compliance. Thereafter, states are required to survey each facility no less often than every 15 months, and the average is about 12 months (Harrington et al., 1998a). The MMACS data used in this analysis were collected in calendar year 1981. Unfortunately, this study did not have access to MMACS data from the last 3 months of 1980. The empirical analyses contain MMACS data for 13,346 nursing homes (see Table 1 for descriptive statistics). The OSCAR data for this analysis were collected within the 15-month interval of 1 October 1995 and 31 December 1996 (which hereafter will be referred to Table 1 The 1981 nursing home, county and state-level characteristics (N = 13,346 facilities)a Variable Quality measures Number of registered nurses Facility characteristics Number of beds Hospital-based For profit facility Not for profit facility Government-owned and operated facility Market (county)-based characteristicsb HCFA area wage index (HCFA) Herfindahl index Median per capita county income (ARF) Elderly individuals (age 65+) per square mile (ARF) State-level characteristics The average Medicaid rate (Swan et al., 1988) Retrospective reimbursement system (Swan et al., 1988) Prospective reimbursement system (Swan et al., 1988) Combines prospective and retrospective systems (Swan et al., 1988) Flat-rate reimbursement system (Swan et al., 1988) Employs case-mix reimbursement (Swan et al., 1988)

Mean

S.D.

4.84

6.53

102.16 0.06 0.70 0.20 0.08

80.02 0.23 0.46 0.40 0.27

8989 0.53 8916 25.85

1135 0.33 1997 201.94

33.07 0.20 0.42 0.17 0.21 0.10

10.06 0.40 0.49 0.38 0.41 0.30

a The data are from the 1981 Medicare–Medicaid Automated Reporting System (MMACs) unless otherwise noted. The other data sources are the Area Resource File (ARF), the Health Care Financing Administration (HCFA), and Swan et al. (1988). b The statistics for the market-level variables are reported at the county-level (N = 2706).

556

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

as simply “1996”). The lagged measure of market tightness was calculated using OSCAR facility data for a 15-month interval in 1994–1995. Following the procedures of Harrington et al. (1998a), detailed edit procedures were implemented to ensure that the OSCAR data were as accurate as possible. The approximately 1500 Medicare-only (rehabilitative) homes contained in OSCAR were eliminated from the data set because they are not applicable to this analysis. Furthermore, approximately 200 dually Medicaid–Medicare certified homes were also eliminated from the data set because they served at least 70% Medicare residents and were considered to be primarily rehabilitative care facilities. Nursing homes in the state of Alaska were eliminated because market-level data was not available for this state. The empirical analyses contain OSCAR data for 15,463 nursing homes (see Table 2 for descriptive statistics). Several other data sources were utilized within this study to supplement the nursing home data. First, the MMACS and OSCAR data were merged with aggregate county level demographic, socioeconomic and health status data from the Bureau of Health Professions’ Area Resource File (ARF). Secondly, state-level Medicaid reimbursement methods and levels were obtained from various published sources (Swan et al., 1988; Harrington et al., 1996; Harrington et al., 1998b). Facility specific reimbursement information for New York State was obtained directly from the New York State Department of Health. Finally, the HCFA hospital wage indexes were linked with the data. As discussed above, quality will be measured by either an input- or an outcome-based measure of quality. The input-based measure of quality utilized within the analysis is the number of registered nurses (RNs). An RN, with advanced medical training, is hypothesized to have an important effect on a resident’s health. The outcome-based measure is the proportion of residents with facility-acquired stage II through stage IV pressure sores. Unfortunately, the structure of the data did not allow a categorization of pressure sores by stage. When comparing the empirical results against the predictions from the theoretical model, it is important to note that pressure sores are a negative measure of quality (i.e. a higher proportion of pressure sores equals lower quality). Pressure sores (or decubitis ulcers), commonly associated with immobility in the elderly, are areas of the skin and underlying tissues that erode as a result of pressure or friction and/or lack of blood supply. The severity of a bedsore can range from persistent skin redness (without a break in the skin) to large open lesions that can expose skin tissue and bone. Pressure sores have been found to be associated with an increased rate of mortality, but not hospitalization (Brandeis et al., 1990). Pressure sores are a particularly good measure of quality because they are preventable and treatable conditions (Kane et al., 1989). Although it is hard to calculate the proportion of total patient care cost attributable to the actual treatment of pressure sores, estimates have ranged from US$ 4000 to 40,000 per pressure ulcer, depending on stage (Hibbs, 1988; Frantz, 1989). For the national analyses, the Medicaid nursing home reimbursement rate is the average rate for the state. In 1981, intermediate care facilities (ICFs) and skilled nursing facilities (SNFs) were assigned different state-level average rates. In 1996, the distinction between ICFs and SNFs was no longer utilized by HCFA and a single rate applies for all facilities in the state. Some states reimburse Medicaid residents in hospital-based nursing homes using a different rate as opposed to the standard rate used in free-standing facilities. For these states in 1996, a dummy variable is utilized to indicate this difference. States broadly employ one of

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

557

Table 2 The 1995–1996 nursing home, county and state-level characteristics (N = 15,463 facilities)a Variable Quality measures Proportion of residents with facility-acquired pressure sores Number of registered nurses Facility characteristics Proportion Medicaid High Medicaid home (>90% Medicaid) Integrated home (>10 and <90% Medicaid) High private-pay home (>90% private-pay) Number of residents Hospital-based Chain facility For profit facility Not for profit facility Government-owned and operated facility Average number of activities of daily living with which the residents needed help Proportion of skilled nursing residents in the home Market (county)-based characteristicsb Lagged number of empty beds per 1000 non-institutionalized elderly over age 65c HCFA area wage index (HCFA) Herfindahl index Median per capita county income (ARF) Density — number of individuals over age 65 per square mile (ARF) State-level characteristics The average Medicaid rate (Harrington and Swan) Retrospective reimbursement system (Harrington and Swan) Prospective reimbursement system (Harrington and Swan) Combines prospective and retrospective systems (Harrington and Swan) Flat-rate reimbursement system (Harrington and Swan) Allows rate adjustment upward during or after a rate period (Harrington and Swan) Employs case-mix reimbursement (Harrington and Swan) Hospital facilities reimbursed differently (Harrington and Swan)

Mean 0.036 6.56

S.D. 0.036 8.57

0.73 0.21 0.77 0.02 96.68 0.06 0.53 0.69 0.24 0.06 3.65 0.31

0.20 0.41 0.42 0.12 62.28 0.24 0.50 0.46 0.43 0.24 0.59 0.27

8.92 8263 0.50 19213 28.82

12.17 1211 0.33 4380 191.46

86.67 0.02 0.77 0.06 0.15 0.41 0.54 0.19

23.18 0.13 0.42 0.24 0.36 0.49 0.50 0.39

a The data are from the 1995–1996 Online Survey Certification and Reporting (OSCAR) System unless otherwise noted. The other data sources are the Area Resource File (ARF), the Health Care Financing Administration (HCFA), and the State Data Book on Long-Term Care Program and Market Characteristics (Harrington and Swan). b The statistics for the market-level variables are reported at the county-level (N = 2901). c This variable is constructed using the 1994–1995 OSCAR and ARF files.

four reimbursement methodologies — prospective, combination, flat-rate or a retrospective system of reimbursement. The empirical analysis treats the prospective reimbursement category as the reference group. Because the retrospective system is most generous system of reimbursement and flat-rate the least generous, these dummy variables provide an additional test of the effect of reimbursement on quality. For the 1996 New York State analyses, the state employed a prospective facility-specific method for determining Medicaid reimbursement rates. The New York analyses use each facility’s Medicaid per diem for capital expenses. The value of capital was determined by historic cost and appraisal/reappraisal and actual

558

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

interest expense (Harrington et al., 1998b). Following the argument of the earlier literature, this capital rate is treated as exogenous to the provision of quality by the facility. Although in practice the various methodologies employed by states can become quite complex, the state-level systems can differ in two other broad ways. First, a dummy variable is included in the national analyses for those states that employ case-mix reimbursement methods, which pay different rates based on a home’s mix of resident needs and the costs of caring for those needs. Second, a dummy variable is also included for those states that allow an upward adjustment in their prospective rates based upon cost information that becomes available during the rate period. This upward adjustment variable is only available for the 1996 analyses. A number of exogenous demand and supply variables are included in the analysis. The exogenous demand variables are the median income of the people living in the nursing home’s market area; the population of individuals over age 65 per square mile; measures of the health status (case-mix) of the home’s residents; and an index of market concentration. The exogenous supply variable is the HCFA area wage rate. For the 1981 estimates, the 1983 HCFA area wage rate was the earliest year available. Additionally, facility-specific variables include the facility’s ownership status, corporate chain affiliation, and whether the facility is hospital-based. Unfortunately, the case-mix and the corporate chain affiliation measures were not available within the 1981 analysis. Additionally, Gertler (1989) includes the total number of residents in the reduced-form model. This variable is included in the 1996 analyses, but the 1981 analysis instead uses the total number of beds in the facility. A Herfindahl index is a measure that is negatively related to the competitiveness of a market. This index is constructed by summing the squared market shares of all facilities in the county. The index ranges from 0 to 1, with higher values signifying a greater concentration of facilities. Importantly, the county is used to approximate the market for nursing home care within this study. Although this decomposition is artificial, most economic studies have used the county as a proxy for the nursing home market (e.g. Nyman, 1985; Gertler, 1992; Cohen and Spector, 1996). As noted by Banaszak-Holl et al. (1996), the county may be a reasonable approximation of the market for nursing home care given patterns of funding and resident origin. For example, federal block grant funds for long-term care services are distributed at the county level. Furthermore, Gertler (1989) found that 75% of residents in New York State facilities had previously lived in the county where the home was located. Similarly, Nyman (1994) found 80% of residents in Wisconsin facilities chose a nursing home located in the county in which they resided before entering the home. The first measure of case-mix is based on the Katz Activities of Daily Living (ADL) index, which includes bathing, dressing, eating, toileting and walking (Katz, 1963). A home’s ADL score was calculated by summing the number of ADLs that residents needed assistance with and dividing by the total number of patients in the home. The result is an index of the average ill-health for the residents in each facility. The second case-mix measure is the proportion of residents requiring skilled nursing care. A potential estimation issue is whether these two variables accurately capture a facility’s case-mix. If they do not, there may be an issue of omitted variable bias. As a test, these two case-mix measures were regressed on the other right-hand side variables in the reduced-form model. These regressions indicate that Medicaid reimbursement does not affect observed case-mix, which provides support for the argument that reimbursement is unlikely to affect unobserved case-mix.

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

559

4. Results 4.1. Reduced-form model with an input-based measure of quality In an effort to replicate the counterintuitive finding from the earlier literature, the first set of analyses employ a reduced-form model with an input-based measure of quality (see Table 3). This model is estimated for the all US homes in 1981 and 1996, New York State homes in 1996, and those homes in the tightest quartile of nursing home markets in 1996. The 1981 model tests whether the earlier findings for specific states in the late 1970s and early 1980s generalize to the entire nation. This model indicates that an increase in the Medicaid rate has a positive, and statistically significant (P < 0.001), effect on quality. An increase in the Medicaid rate of US$ 1 increases the number of RNs by 0.14. The other Medicaid system variables also indicate positive returns to a more generous reimbursement methodology. Quality is highest under a retrospective-based system and lowest under a flat-rate system. For example, the shift from a prospective system to a flat-rate system is associated with a decrease in nearly two RNs per home. The 1996 New York State model replicates Gertler’s (1989) earlier work, which used 1980 New York data. This model is particularly important because it provides a test of whether the earlier finding was the result of a limited specification (i.e. the use of state-level data with facility-level reimbursement), or the result of an excess demand for nursing home beds. This state-level analysis also found a positive, and statistically significant (P < 0.01), effect reimbursement on an input-based measure of quality. An increase in reimbursement of US$ 1 increases the number of RNs by 0.13 in this model. This finding provides evidence that the earlier result was not a product of a limited specification, and that changes over time in the New York nursing home market may explain the difference in results. The 1996 national model further examines whether the excess demand result applies to today’s nursing home market. In examining the coefficient on the Medicaid reimbursement rate, there is once again a positive effect of Medicaid reimbursement on quality. The result is not statistically significant, but an increase in reimbursement of US$ 1 increases the number of RNs by 0.024. Once again, the Medicaid system variables indicate a positive effect of reimbursement on quality. Quality is significantly (P < 0.01) higher under a retrospective-based system, which is associated with 2.26 more RNs than a prospective-based system. The reduced-form model was next estimated for only the “tightest” quartile of nursing home markets in the United States, which were identified by a lagged measure of empty beds per elderly individuals (age 65+) living in the community. Even after isolating the analysis to those tightest markets, the reduced form analysis was unable to replicate the counterintuitive finding from the earlier literature. Although the result is not statistically significant, an increase in Medicaid reimbursement of US$ 1 is associated with an increase in 0.019 RNs. Similarly, a retrospective Medicaid system is associated with an increase in quality relative to a prospective system although, this result is also not statistically significant. There is some support for the excess demand paradigm in that the Medicaid rate coefficients are smaller for those tightest markets when compared with the results for all nursing home markets.

560

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

561

In summary however, these analyses were unable to reproduce the counterintuitive finding from the earlier literature across four specifications of a reduced-form model using an input-based measure of quality. 4.2. Reduced-form model with an outcome-based measure of quality The second set of analyses use this same reduced-form model, but employ an outcomeoriented measure of quality to examine 1996 data for New York State, the entire nation and the tightest quartile of markets nationally (see Table 4). This outcome-based measure of quality is the proportion of residents with facility-acquired pressure sores. Unlike the staffing measures used above, pressure sores are a negative measure of quality. As such, the interpretation of the coefficients will be the direct opposite of the staffing results (i.e. Table 4 Reduced-form outcome-based quality (pressure sores) model: estimated coefficients and standard errors in parenthesesa New York State all markets (1995–1996) Medicaid rate Retrospective system Flat-rate system Combination system Rate adjustment allowed Case-mix adjustment Herfindahl index Wage rate Hospital-based reimbursed differently Government-owned Not for profit Hospital-based Chain ownership Median per capita income Elderly (per square mile) Average ADL score Percent skilled care Constant N

Entire US tightest markets (1995–1996)

−0.0017 (0.0042) n/a n/a n/a n/a

−0.0031∗∗ (0.0012) −0.31∗ (0.14) −0.058 (0.058) −0.009 (0.085) −0.110∗ (0.048)

−0.0026∗ (0.0013) −0.03 (0.29) −0.158∗ (0.079) 0.092 (0.073) −0.028 (0.064)

n/a 0.60∗ (0.27) −0.000016 (0.000020) n/a

−0.015 (0.051) −0.083 (0.058) −0.000019 (0.000013) 0.108 (0.061)

0.0001 (0.059) −0.007 (0.082) 0.0000003 (0.000016) 0.159 (0.091)

−0.15 (0.13) 0.10 (0.10) −0.079 (0.93) −0.191∗ (0.090) 0.0000092∗∗ (0.0000035)

−0.033 (0.061) −0.072∗ (0.029) −0.058 (0.052) 0.017 (0.022) 0.0000039 (0.0000027)

0.071 (0.089) −0.006 (0.045) −0.101 (0.064) 0.033 (0.050) 0.0000049 (0.0000029)

−0.000060∗∗ (0.000023)

−0.000011 (0.000031)

−0.000051∗∗∗ (0.000014)

0.262∗∗∗ (0.066) −0.22 (0.14) −4.30∗∗∗ (0.37)

0.240∗∗∗ (0.031) 0.030 (0.043) −3.73∗∗∗ (0.16)

0.198∗∗∗ (0.045) −0.004 (0.067) −3.92∗∗∗ (0.23)

579 a

Entire US all markets (1995–1996)

15463

3902

Huber–White standard errors are in parentheses and are corrected for intra-state correlation. ∗ P < 0.05. ∗∗ P < 0.01. ∗∗∗ P < 0.001. n/a = not available/applicable

562

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

a negative Medicaid rate coefficient in the pressure sore model entails a positive effect of reimbursement on quality). The first specification of the outcome-based model examines New York State homes. Although the result is not statistically significant, an increase in reimbursement improves nursing home quality. In the 1996 national model for all markets, the coefficient on the Medicaid rate is negative and statistically significant at the 1% level. Thus, an increase in reimbursement will decrease the likelihood of pressure sores. Similarly, quality is found to be highest under a retrospective system of reimbursement. If the analysis is isolated to those tightest nursing home markets, the effect of the Medicaid rate on quality is still positive and statistically significant at the 5% level. In this case however, a flat-rate system of reimbursement is associated with a significantly higher quality relative to a prospective-based system. Although the coefficients from the binomial model presented in Table 4 are less intuitive to interpret than the least squares results, the coefficients can be expressed as odds ratios to illustrate the small size of the reimbursement effects. Depending on the model specification, an increase in reimbursement of US$ 1 is associated with between a 0.9969 (for all US markets) and a 0.9983 (for New York State) lower likelihood of a resident acquiring a pressure sore. The small magnitude of the pressure sore results illustrates a potential weakness regarding this quality measure. The majority (roughly 96%) of residents do not have facility-acquired pressure sores. As this proportion approaches 100%, it is impossible to show a sizeable effect in absolute terms. As a result, it is unclear whether the small effects are due to a limited effect of reimbursement on quality, or the fact that the pressure sores measure is approaching this upper bound. Future research with alternative outcome-based quality measures will be necessary to assist policymakers in determining the exact gains to increased Medicaid reimbursement. In summary, three specifications of a reduced-form model using an outcome-based measure of quality did not replicate the counterintuitive finding from the earlier literature. In fact, with a more direct measure of quality, the reduced-form model obtains a statistically significant result in the opposite direction of the previous literature for the national and tightest quartile models. 4.3. Multi-part model A final empirical approach decomposes the quality response into a part due to the effect of Medicaid reimbursement on the facility-specific payer mix and a part due to the effect of reimbursement on quality conditional on payer mix. Using 1996 national data, we first examine the effect of reimbursement on quality conditional on payer mix before turning to the effect of reimbursement on payer mix. Regardless of whether the analysis isolates those all-Medicaid or integrated facilities, there is once again a positive effect of Medicaid reimbursement on quality (see Table 5). However, this result is only statistically significant (at the 1% level) for those integrated facilities. Additionally, the magnitude of the quality effect on the Medicaid rate coefficient is much smaller in those all-Medicaid homes relative to the integrated homes. The fact that the quality response is larger in those integrated facilities, relative to those all-Medicaid facilities is rather paradoxical. In a competitive market, one would expect

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

563

564

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

a stronger effect of Medicaid reimbursement on quality in those all-Medicaid facilities. Although CON and bed construction moratoria may have lessened in importance over time within the nursing home market, this result provides some evidence that homes have limited incentive to compete for Medicaid residents on the basis of quality. In a model with excess Medicaid demand, Norton (2000) argues that policymakers may wish to consider a payment system that encourages the integration of Medicaid and private-pay residents. Norton presents a theoretical model that makes a facility’s payer mix an instrument of public policy and shows that quality of care can be increased by limiting the number of Medicaid residents in a home. The basic idea is to expose Medicaid residents to as many private-pay residents as possible. The multi-part estimates presented in this paper support the excess demand model of nursing home care. The signs of the Medicaid system variables are also less straightforward in this multi-part model. In examining the results for all nursing home markets, quality is highest under retrospective reimbursement (relative to a prospective system) for both the all-Medicaid and integrated specifications. However, the tightest market specifications show some support for an excess demand model in that a retrospective system is associated with lower quality for all-Medicaid facilities and the flat-rate system is associated with higher quality for those integrated homes. In turning to the effect of reimbursement on payer mix regime, a multinomial logit is used to estimate the change in probability of operating an all-Medicaid, integrated or all-private facility (see Table 6). In terms of model specification, a Hausman test indicates no evidence that the independence of irrelevant alternatives (IIA) assumption has been violated. For the purposes of interpretation, the integrated regime is used as the comparison group. The results support the predictions that an increase in Medicaid reimbursement will shift facilities away from the private-pay regime and towards the integrated regime and away from the integrated regime towards the all-Medicaid regime. However, this result is not statistically significant in either case. This finding holds if the analysis is isolated in those tightest nursing home markets and the effect of Medicaid reimbursement on the shift from the integrated towards the private-pay regime is statistically significant at the 5% level. Although this paper presents results decomposed across stages (or conditional on payer type), an additional step would be to combine the individual parts of the model to examine the full unconditional effect of Medicaid reimbursement on nursing home quality. Due to space constraints, this current paper cannot work through the combined multi-part model results. The reader is referred to Grabowski (1999), which employs a three-part model to examine the effect of Medicaid reimbursement on quality. The first stage of this model analyzes the choice of payer mix regime (public-only, integrated, private-only), the second stage models the choice of payer mix conditional on the home choosing to be an integrated facility, and finally, the third stage examines the quality decision conditional on payer type. A bootstrap method is used to construct the standard errors for this full multi-part model. Similar to the analyses presented here, the full multi-part model shows that an increase in reimbursement leads to significantly higher quality (i.e. lower pressure sores) regardless of the tightness of the market. Other sensitivity analyses not included in this paper also found a positive relationship between Medicaid reimbursement and quality. These sensitivity analyses included both reduced-form and multi-part models using other input-based quality measures (i.e. licensed

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

565

566

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

practical nurses and nurses’ assistants), models using alternative output-based quality measures (i.e. medication errors and facility-level deficiencies), models isolating only those states with CON and moratoria policies and models isolating only those for profit or not for profit facilities. In summary, despite employing various data, methodologies and quality measures, this analysis was not able to replicate the counterintuitive negative relationship between reimbursement and quality from earlier studies. In fact, this analysis found a statistically significant positive relationship between reimbursement and quality, especially in those integrated facilities that care for both Medicaid and private-pay residents. There was some evidence of the excess demand paradigm in that the quality effect was smaller in the tightest nursing home markets, but the positive effect of reimbursement on quality held even in those tightest markets.

5. Discussion and policy implications This paper has demonstrated that the conventional view within the health economics literature, that Medicaid reimbursement has a negative effect on nursing home quality in the context of CON and moratoria regulations, does not hold when the standard analysis is extended to alternative data, quality measures and methods. This finding gives rise to two important questions. First, what is the explanation for the difference in results relative to this earlier literature? And second, what do these new results imply for nursing home policymakers? Towards the first question, these new findings do not diminish the validity of the existing literature, but rather underscore the difficulty in generalizing the earlier finding in light of recent changes in the market for nursing home care. There has been a decline in nursing home utilization over the last two decades in the market for nursing home care (Bishop, 1999). In placing this decline into an historical context, nursing home markets were once almost universally characterized by high occupancy rates, long queues, and extended waiting times for entry by Medicaid recipients. Although these problems still exist in today’s marketplace, they are definitely less pervasive. As an example of this change, a recent report documented the emergence of “bed brokers” in the Chicago area to assist nursing homes in locating mentally ill individuals to fill beds once occupied by elderly individuals (Berens, 1998). This decline in nursing home utilization is also illustrated in a review of the findings from the economics literature examining the market for nursing home care over the last three decades. In early work, Scanlon (1980) found evidence of excess demand for the entire nation using data from the late 1960s and early 1970s. Gertler (1989) and Nyman (1985) later found evidence within particular states known to have tighter bed supplies with data from the late 1970s and early 1980s. With nationally representative data from 1987 however, Cohen and Spector (1996) found positive and significant effects of reimbursement on staffing. This current study using both state and national data for 1996 found a positive and statistically significant effect of reimbursement on input- and outcome-based measures of quality. Taken together, these various economic studies support the hypothesis that there has been a shift in the nursing home market over the past three decades.

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

567

Future research will be necessary to identify what policy, economic and demographic factors underlie the decline in nursing home utilization over time, but the most likely explanation is the market-based emergence of many new nursing home substitutes such as home health care, assisted living facilities, board and care homes, Continuing Care Retirement Communities (CCRCs) and Social Health Maintenance Organizations (SHMOs). The choice of one of these alternative arrangements over nursing home care depends on a range of factors including the person’s physical and mental health, finances and family situation, but the presence of these new long-term care options has most likely competed away some individuals who previously would have chosen to enter a nursing home. Other demographic oriented explanations for the downward trend in nursing home utilization include the recent compression in elderly morbidity (Fries, 1980; Manton et al., 1993) and the increasing longevity of men relative to women over the last three decades (Lakdawalla and Philipson, 1999). In turning to the implications for policymakers, an obvious issue for state Medicaid programs is whether an increase in Medicaid payments is warranted in the context of these new findings. Although this analysis found a positive relationship between reimbursement and quality, the magnitude of the findings presented here, especially the outcome-based results, would make it difficult to recommend large increases in Medicaid payment rates. Future research with alternative outcome-oriented measures of quality will be necessary to further analyze this issue. A second implication for policy involves the importance of CON and moratoria policies in the market for nursing home care. Although, this paper has argued that CON and moratoria policies have become less important for today’s marketplace, they are clearly not unimportant. The importance of CON and moratoria policies is supported by both the small magnitude of the outcome-based results mentioned above, and the smaller quality effects in those tightest nursing home markets. In 1998, 44 states still had CON or construction moratoria laws in place to regulate the growth of new nursing beds or facilities (Harrington et al., 1999). Similar to the earlier literature, the results from this study imply that a further repeal of these programs would encourage greater quality competition. A final implication for policy involves the different quality responses across those high Medicaid and integrated facilities. An increase in Medicaid reimbursement did not have a large effect on quality in high Medicaid facilities relative to integrated facilities. This result may be attributable to a lack of quality competition for Medicaid residents. To the extent that quality is a public good uniformly enjoyed by all residents in a facility, state Medicaid programs may wish to consider policies, such as waitlist laws and census requirements, that directly influence each facility’s payer mix (Norton, 2000). However, although, the nursing home literature has always assumed quality is a public good, an important priority for further research is to examine whether different payer types do in fact enjoy this uniform level of quality.

Acknowledgements I am thankful to Willard Manning, David Meltzer, Edward Lawlor, and the editor, Jonathan Gruber, for their helpful comments regarding this paper. Any errors are my own. I gratefully acknowledge financial support from the NIH Specialized Training Program in

568

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

Demography and Economics of Aging and a Health Services Research Training Grant T32 HS00084-01 provided by the Agency for Health Care Policy Research. References Banaszak-Holl, J., Zinn, J.S., Mor, V., 1996. The impact of market and organizational characteristics on nursing care facility service innovation: a resource dependency perspective. Health Services Research 31 (1), 97–117. Berens, M.J., 1998. The Bed Brokers: Warehousing the Mentally Ill in Nursing Home. Chicago Tribune, Section 1, pp. 1, 14. Bishop, C.E., 1999. Where are the missing elders? The decline in nursing home use, 1985 and 1995. Health Affairs 18 (4), 146–155. Brandeis, G.H., Morris, J.N., Nash, D.J., Lipsitz, L.A., 1990. The epidemiology and natural history of pressure ulcers in elderly nursing home residents. Journal of the American Medical Association 264, 2905–2909. Cohen, J.W., Spector, W.D., 1996. The effect of Medicaid reimbursement on quality of care in nursing homes. Journal of Health Economics 15 (1), 23–48. Frantz, R.A., 1989. Pressure ulcers costs in long-term care. Decubitus 2 (3), 59–60. Fries, J.F., 1980. Aging, natural death, and the compression of morbidity. New England Journal of Medicine 303 (3), 130–135. General Accounting Office, 1987, Medicare and Medicaid: stronger enforcement of nursing home requirements needed. Report to the Ranking Minority Member, Special Committee on Aging, US Senate Pub. No. HRD-87-113. US GAO, Washington, DC. General Accounting Office, 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. 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 (2), 338–345. Gertler, P.J., Waldman, D.M., 1992. Quality-adjusted cost functions and policy evaluation in the nursing home industry. Journal of the Political Economy 100 (6), 1232–1256. Grabowski, D.C., 1999. Medicaid reimbursement and the quality of nursing home care. Unpublished Ph.D. Dissertation, University of Chicago, Chicago, IL. Grabowski, D.C., 2001. Does an increase in the Medicaid reimbursement rate improve nursing home quality? Journal of Gerontology: Social Sciences 56B (2), S84–S93. Harrington, C., Swan, J.H., Bedney, B., Carillo, H., Studer, L., 1996. 1995 State Data Book on Long-Term Care Program and Market Characteristics. Department of Social and Behavioral Sciences, University of California, San Francisco, CA. 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 of California, San Francisco, CA. Harrington, C., Swan, J.H., Griffin, C., Clemena, W., Bedney, B., Carillo, H., Shosak, S., 1998b. 1996 State Data Book 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 Care Program and Market Characteristics. Department of Social and Behavioral Sciences, University of California, San Francisco, CA. Hibbs, P., 1988. The economics of pressure ulcer prevention. Decubitus 1 (4), 32–38. Institute of Medicine, 1986, Improving the Quality of Care in Nursing Homes, Committee on Nursing Home Regulation. National Academy Press, Washington, DC. Institute of Medicine, 1996, Nursing Staffing in Hospitals and Nursing Homes: Is it Adequate? National Academy Press, Washington, DC. Kane, R., Ouslander, J., Abrass, I., 1989. Essentials of Clinical Geriatrics, 2nd Edition. McGraw-Hill, New York.

D.C. Grabowski / Journal of Health Economics 20 (2001) 549–569

569

Katz, S., 1963. Studies of illness in the aged: the index of ADL. Journal of the American Medical Association 195, 914–919. Lakdawalla, D., Philipson, T., 1999. The Rise in Old Age Longevity and the Market for Long-Term Care, National Bureau of Economic Research Working Paper Series. Working Paper No. 6547, Cambridge, MA. Manton, K.G., Corder, L.S., Stallard, E., 1993. Estimates of change in chronic disability and institutional incidence and prevalence rates in the US elderly population from the 1982, 1984, and 1989 National Long-Term Care Survey. Journal of Gerontology 48 (4), 153–166. McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models, 2nd Edition. Chapman & Hall, London. 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 quality of nursing home care. Journal of Health Economics 4 (3), 237–259. Nyman, J.A., 1988a. Excess demand, the percentage of Medicaid patients, and the quality of nursing home care. Journal of Human Resources 23 (1), 76–92. Nyman, J.A., 1988b. The effect of competition on nursing home expenditures under prospective reimbursement. Health Services Research 23 (4), 555–574. Nyman, J.A., 1989a. Analysis of nursing home use and bed supply: Wisconsin, 1983. Health Services Research 24 (4), 511–537. Nyman, J.A., 1989b. Excess demand, consumer rationality, and the quality of care in regulated nursing homes. Health Services Research 24 (1), 105–128. Nyman, J.A., 1993. Testing for excess demand in nursing home care markets. Medical Care 31 (8), 680–693. 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. Scanlon, W.J., 1980. A theory of the nursing home market. Inquiry 17, 25–41. 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. US Department of Health and Human Services, National Center for Health Statistics, Centers for Disease Control and Prevention. Swan, J.H., Harrington, C., Grant, L.A., 1988. State Medicaid reimbursement for nursing homes, 1978–1986. Health Care Financing Review 9 (3), 33–50. US Senate, Subcommittee on Long-Term Care, Senate Special Committee on Aging, 1974, Nursing Home Care in the United States: Failure in Public Policy. US Government Printing Office, Washington, DC. US Senate, Special Committee on Aging, 1986, Nursing Home Care: The Unfinished Agenda. Special Hearing and Report. US Government Printing Office, Washington, DC.