COST
COMPARISONS NONPROFIT
OF FORPROFIT HOSPITALS
AND
CARSON W. BAYS
Department
of Economics, University of Illinois at Chicago Chicago, Illinois 60680. U.S.A.
Circle.
Box 4348.
Abstract-The special organizational and institutional structure of the hospital industry has implications for cost differences between forprofit and nonprofit hospitals. These implications are developed and tested on a panel of data on California hospitals for which as estimate of the cost of admitting physician services could be made. Forprofit hospitals in general are significantly less costly than nonprofits after accounting for differences in case mix, but the interpretation of this result is complicated by the possibility of systematic overtreatment of certain case types by independent. or nonchain forprofits. The paper argues that the more appropriate comparison is between nonprofits and chain forprofits. The latter type of hospital has a distribution of cases which is similar to that of nonprofits but is less costly than both nonprbfit and nonchain forprofit hospitals.
INTRODUCTION
Rapid inflation in the cost of hospital care has focused interest on various structural and institutional aspects of the industry and their implications for cost and efficiency. Hospitals differ from other economic organizations in several important respects. First, the industry is a predominantly nonprofit one and, as such, is not subject to the same complex of market forces which compel competitive forprofit firms to produce efficiently. Second, widespread insurance coverage substantially alters the economic incentives of both users and providers of hospital care. Third. the institutional separation of hospitals and admitting physicians results in a situation in which physicians are primarily responsible for decisions on the allocation of hospital capital and personnel. but are only indirectly accountable for the cost of those decisions. This paper discusses these factors and tests some of the implications by comparing the cost performance of a group of forprofit and nonprofit hospitals. ORGANIZATIONAL A. Owwship
STRUCTURE AND COST
starus
Approximately 1O”, of U.S. hospitals are forprofit firms which account for 5”, of total patients beds. The largest proportion of other hospitals (50%) are private. nongovernmental. “voluntary” or nonprofit [l]. These hospitals have no owners in a formal sense, but rather a group of trustees which establishes general policy and is the final governing authority. Management of the hospital IS the responsibility of a hired administrator aho must carry out general policy, deal Lvith the medical staff. and perform routine management chores. Insuring efficient behavior by the * Of course. a nonprofit hospital could be taken over by a forprofit one. but most conversions of ownership have been in the opposite direction [3]. t Empirical support for the superior efficiency of forprofit firms has been found for firms supplying health Insurance [4]. Support from D.H.E.W grant HSOWI is acknowledged.
manager requires a more elaborate set of administrative rules and controls than in a forprofit firm in which a portion of the firm’s profit can be assigned to the manager as an incentive for efficiency [2]. Entry into the industry is sharply limited by such controls as Certificate of Need laws, and the fact that take-over of an inefficient nonprofit hospital would not allow the new management to extract a profit residual removes the discipline of potential entry.* These factors suggest that nonprofit hospitals will be less efficient than forprofit hospitals supplying a similar complement of services. This proposition has not been previously tested directly,? and Manning [S] has concluded that inefficiency from nonprofit status will be slight as long as nonprofits face competition from forprofit hospitals. An interesting change occurred within the forprofit sector of the hospital industry during the previous decade. Historically, forprofit hospitals have been small institutions which were owned by the physicians who practised there. During the late 1960’s a number of hospitals were built or acquired by large corporate chains, the ownership of which are broadly held. These hospitals have grown in both number and average size while the total number of forprofit hospitals has been declining. This suggests that if the forprofit form is demonstrably more efficient, it should be this type of hospital which should exhibit it. B. Health
insurance
Approximately 909~ of the population has some form of health insurance. This has two general effects upon hospital behavior. On the demand side. the existence of insurance coverage substantially lowers the patient’s direct cost of treatment and therefore increases the quantity demanded. There has been substantial variation in the empirical estimates of the price elasticity of demand for hospital care but it is clear that quantity is responsive to price and that insurance has increased total demand for care [6]. In addition. hospital insurance causes a supply response which results in higher cost. higher “quality” care being provided [7-91.
220
w.
CARSON
Table I. Case mix proportions Disease category
* Significantly t Significantly
for various
different different
hospital
groupmgs
All forprofit
Chain forprotit
Nonchain forprotit
0.0234t 0.059 It
0.0201 0.0602
0.0253+
0.0741 0.0171 0.0052 0.0 103
0.0197 0.0050 0.0087
0.0146* 0.0027 0.0101
0.0’27 0.0063 0.0079
0.0383*
0.02871
0.0383’
0.0232+
0.1193 0.0776*
0.1147 0.1051t
0.1241 0.0959
0. IO92 0.1 105+
011135 0.1097 0.1785 0.0118 0.0507 0.0066 0.0001* 0.0361 0.0943: 0.01388 0.027 1
0.1179 0.1188 0.1541 0.0147 0.0494 0.0047 0.0013 0.0357 0. I206t 0.0074t 0.0110
0.1058 0.1 176 0.1425 0.0146 0.0639* 0.0055 0.0000 0.0428 0.1123 0.0119* 0.0172
0. I 249 0.1 195 0.1608 0.0 147 0.041 I 0.0042
Nonprofit
Infective Neoplasms Metabolic. diabetes. endocrine Hematology Mental CNS, eye. ear. other nervous Heart. hypertension. vascular Respiratory Mouth, stomach. intestinal Genitourinary Pregnancy, etc. Skin Muscuioskeletal Congenital Childhood Symptoms Fractures, trauma Special services Births
BAYS
0.0158.*
from nonchain ffom nonprofit
0.0585
0.00’1~ 0.0316 0.1254t 0.004st 0.0074
forprofit at 95% level. at 95% level.
C. Role of admitting physicians Hospital care is a joint product of the inputs of the physician and of the hospital, but the physician is the one who decides-within rather broad limits--the type and amount of hospital inputs which will be devoted to a given case. The impact of this arrangement on hospital behavior has been investigated by several economists. Pauly and Redisch [lo] depict the hospital as a physicians’ cooperative of which admitting physicians are the de facto administrators. Shalit [ll] suggests that the appropriate paradigm within which to view hospital behavior is as a doctor-hospital cartel which is organized and managed to maximize physician income. Others have suggested that the relationship between hospital administrator and the medical staff be considered in a game theoretic context [5.12]. Arrow [ 131 has argued that the institutional separation of physician rind hospital is a consequence of the special trust relation between doctor and patient which is necessary because of the uncertainty surrounding medical care, and this theme has been recently reiterated by Harris [12]. The physician is expected to act as agent of the patient in diagnosis and treatment and should be concerned only with * Granfield [I41 treats this as the reason for the overcapitalization which is widely believed to characterize the hospital industry. However. the physician has an incentive to overutilize both hospital capital and ancillary personnel to the extent that they are both complementary to the physician input. t Several studies correct for size and/or facilities mix differences [l&21] but these institutional differences may account for as little as one fourth of the variation in case mix among hospitals [Xl.
medical need in allocating inputs. As a result. the cost to the physician of hospital inputs will be significantly less than their true social opportunity cost. so that there is a strong incentive toward overutilization of hospital inputs.* Nevertheless. the medical staff has a group interest in preventing such overutilization because an increase in the cost of the hospital component of treatment will lower the profit-maximizing price which the physician can separately charge the patient. Preventing noncooperative usage of the hospital by physicians will be more difficult as the number of admitting staff increases. or more specifically, as the share of hospital care attributable to each physician decreases [ 151. D. Case mix and cost Previous cost comparisons of forprofit and nonprofit hospitals have not adjusted for differences in the types of cases treated in hospitals of different ownership? but such differences may be important for two reasons. First. forprofits may selectively admit (or equip themselves to treat) only those cases which can be treated at acceptably high price-cost margins. Second, even if forprofits do not practice this explicit “cream skimming”. they may admit a more narrow range of cases than nonprofits to decrease the likelihood of noncooperative behavior among physicians in the utilization of hospital inputs. III. Estimation of a cost ftmction for hospitals Under the standard theory of the firm. cost per unit is a function of firm size, factor prices. the rate of plant utilization. and the level of managerial efficiency. The multiservice nature of the hospital requires that case mix be treated as a cost determinant as well.
Cost comparisons
of forprofit and nonprofit
Sarnplr
Data on hospital size, utilization. ownership. and finances are available from the American Hospital Association (AHA) annual surveys. but the necessity of controlling for case mix complicates the selection of a sample for testing since hospital specific data on cases are not generally available because of confidentiality considerations. This made it necessary to solicit the participation of a group of hospitals in the study by asking their permission to purchase their admissions data from their abstracting services. 46 short term general California hospitals (18 forprofit and 28 nonprofit) agreed to release their diagnostic and treatment records for 1971 and 1972. Of these 92 potential observations. 28 were seriously incomplete leaving a usable sample of 64 observations of which 30 were of forprofits (12 from 1971 and 18 from 1972). and 34 were of nonprofits (1.5 from 1971 and 19 from 1972). None of the hospitals were involved in teaching. A summary of the case mix measures for nonprofit, nonchain forprofit. and chain forprofit hospitals is presented in Table 1 and reveals some interesting differences between ownership types. The entries in the table are the average proportions of admissions by hospital type in each of the 19 broad diagnostic categories of the International Classification of Diseases-Amended (ICDA). One way analysis of variance was performed on various pairs of hospital types and the case mix categories tihich differ significantly are indicated. The proportions of admissions in 6 of the 19 categories in forprofit hospitals differ significantly from the respective proportions in nonprofit% The categories in which these differences occur represent approximately 34’6 of forprofit admissions. However. these differences are due entirely to the * A frequent allegation is that forprofit hospitals survive by systematically producing lower quality care than nonprofits. Most comparisons of hospital quality have centered on the extent of accreditation and the number of facilities and services provided. but ideally, quahty should be measured as a dimension of output rather than by the extent and variet) of inputs. The hospital death rate. although crude, should reflect gross differences in the quality of care received after accounting for differences in case m;x and severity [X]. For the sample of hospitals used in this study. there are no significant differences in death rates among ownership types after accounting for case mix. severity. and patient age. 4 The actual method for making this imputation is straightforward but tedious. The RVS divides procedures into five medical specialities each having different “unit” values which have different dollar conversion factors. The relative unit values are based on the median dollar charges for various treatments computed from an extensive survey of California physicians. Converting them into an arbitrary metric was probably an attempt on the part of the California Medical Association to maintain the fiction that the RVS could not be used as a price-fixing device. The dollar conversion factors for each speciality and year were provided b\ the statistical staff who collected the ortginal data. If ph&ians price their services rationally. then the imputed value of a given treatment or procedure should reflect the requisite level of skill. the time required. and the dif?icult> of the procedure. Another stud! offers some confirmation of this: for a group of New 1’ork surgeons there was a high and significant positive correlation (r = 0.97) brtkveen the California RVS and the observed operatinE room time [26].
hospitals
221
nonchain forprofit segment of the forprofit group: there are no significant differences in case proportions between nonprofits-and chain forprofits. Differences in output specialization between hospitals are shown by a comparison of the percentage of total admissions accounted for by the largest diagnostic proportions. The 19 ICDA proportions were ranked in decreasing order of size by hospital and then one way analysis of variance was performed on the sums of the largest 4, 6. and 10 proportions for various pairs of hospital types (Table 2). Nonchain forprofits have significantly greater output specialization than nonprofits while the percentages for chain forprofits are slightly lower than those of nonprofits. but the latter differences are not significant at the 759; level. These differences in case mix and output specialization confirm the need to treat chain and nonchain forprofits as distinct organizational types.*
With the exception of Feldstein’s study of the British National Health Service [24], previous hospital cost studies have omitted the cost of admitting physicians because of the absence of direct data on physician charges by hospital. For this study an estimate of these costs was made in the following way. The case abstracts contain data on treatments actually performed in addition to the initial and subsequent diagnoses on each patient. These treatments were weighted by the relative value scale (RVS) published by the California Medical Association [25], and the resulting estimate of total physician cost by hospital was added to the hospital cost data from the AHA surveys.? It is important to stress that this measure of physician charges is an imputation and therefore may contain some biases. One problem is that the RVS categories are not the same as the ICDA classification system used in recording operations and treatments on the patient abstracts. Of course. both classification schemes cover the same procedures. but they do not map together in any simple or consistent fashion. In some instances it was necessary to use a single fee index category for more than one ICDA group. In other cases, several RVS categories were included in a single ICDA classification so that the median RVS index was used. Because the price weights based on the RVS are medians (both in terms of the original survey from which they are derived. and with respect to the choice of the price weights for the ICDA categories which cover more than one RVS category), the resulting estimate of physician services probably understates the Table 2. Percentage of cases accounted for by largest 4. 6. and 10 diagnostic categories: Various hospital groupings Sum of categories Largest Largest Largest
4 6 10
* Si_gnificantlx Iebel. + SignificantI>
Nonprofit
All forprofit
56* 73* 87’
Chain forprofit
60t 76t 88
different
from
nonchain
different
from nonprofit
53’ 72” 86’ forprofit at 95”,
Nonchain forprofit 63 79t 89t at 959, level.
actual differences in physician cost between an average hospital and one which treats cases of greater than average complexity for each diagnostic category. This problem is ameliorated to some extent by limiting the sample to hospitals without a teaching program. Sprcijcarion
The cost function is of the general form: C=f(M.S,T:U,O)
(1)
where C = total cost per case (includes both hospital cost and imputed physician charges), M = mix of cases treated, S = hospital size, T= source of payment (third party of self pay), U = rate of plant utilization, 0 = hospital ownership.*
remibursemsnt programs lessen hospital incentives for cost minimization. The rate at which hospital facilities are utilized is measured as the number of cases per bed per year. CASEFLOW = (average daily available per number of beds censusaverage year) x (365;mean length of stay). CASEFLOW is entered in a quadratic specification.: Since the data are for two years combined a dummy variable. YEAR, is entered to allow for a separate intercept for the 1972 observations.4 Finally. two binary dummy variables are entered for ownership status. PROF takes the value 1 of the hospital is organized as a forprofit firm and CPROF equals 1 if it is part of an investor-owner chain of forprofit hospitals. A statistical summary of the sample is in Table 3. cost
Empiricul
rrsults
Variations in case mix over hospitals are accounted for by aggregating data from individual patient records into the nineteen broad diagnostic categories of the ICDA. These were converted into proportions and then combined by grouping those proportions which had similar coefficient estimates in a regression of average total cost on the entire vector of nineteen proportions. This procedure results in five composite variables for case mix (Ml-MS).t Hospital size is measured as the average number of beds available per year (BEDS). The percentage of self-pay admissions (SELF) is entered as an independent variable to test the hypothesis that third party
Equation (1) was estimated for the entire sample and for the nonprofit and forprofit hospitals separately using ordinary least squares and the results are presented in Table 4.,, One rather surprising result for the pooled model (regression 1) is the positive coefficient estimate for BEDS. Several previous studies have found evidence of scale economies in hospitals [16-l&21. 32-343 and. although other studies are not unequivocal on the point [19. 14. 301, this would imply a negative coefficient on BEDS. The fact that the inclusion of the estimated value of admitting physician input in the hospital cost function results in an implication of cost rising with size suggests that previous findings of scale economies in hospitals may
* Hospital factor costs are assumed constant over the sample and are therefore omitted from the equation. Hospital specific data on wage and salary levels of employed personnel were not obtainable for the sample hospitals but ail of them are located in two urban areas of the same state and compete in similar factor markets, t Initial estimates of the model included the entire vector of case mix proportions (with one proportion omitted to prevent definitional singularity of the data matrix) but collinearity among the proporttons was severe. The proportions were then combined both to conserve degrees of freedom and to make the presentation and discussion of results more manageable. The grouping method is that suggested by Feldstein [24] and developed by Lave et al. [27] in which the dependent variable is initially regressed on the entire vector of proportions (minus one). The parameter estimates are therefore estimates of the relative average cost of each diagnostic category. Those which are insignificantly different from zero are dropped and those with similar estimated average cost are combined. Any procedure for reducing multicollinearity by other than introducing new information must be regarded with some trepidatton. Obviously, no new information is added when variables are combined and although estimates of the new variables may be “sharper” there is a risk that the estimated coefficients wtll be biased. Two justifications are offered for using the procedure here. First. the I9 broad ICDA diagnostic categories are themselves aggregations 01 approximately 1000 different diagnoses. The I9 broad groups are basically anatomical categortes (infective diseases, skin disease. etc.) rather than isoresource categories. Combining them by their implied relative costliness is a theoretically plausible way of reducing their number to manageable size. Second. the major conclusions regarding cost differences between ownership types are not appreciably altered when alternative case mix specifications are
used. These included reducing the number of case mtx regressors by factor analytic techniques. grouptng of the original proportions based upon then loadings on the tirst principle component of the proportions. and including the full vector of case mix proporttons as a measure of case mix. A detailed listing of the ICDA composition of the case mix measures is available from the author on request. : Various nonlinear transformations of BEDS and SELF were tested but the linear specification of the variables produces a superior fit. 3 Analysis of covariance techmques [2X] confirmed that restricting the remaming varuibles to be equal over the 2 years IS approprtate. Cross section dummies were added to the equation and the resulting decrease In the restdual sum of squares compared to the pooled mode1 with onlv a ttme dummy was not significant by the F-test. A modthed von Neuman ratto test 1391 revealed no sigmficant senal correlation. When the equations are estimated for each year separately the I values are naturally lower but the major results are unchanged. II The possibility of a simultaneity problem m using OLS to estimate hospital cost functions has typtcally been ignored in previous cost studies by assummg that hospital output is exogenous to the decision makers because hospitals accept ail cases up to capactty and that Insurance mittgates the impact of average cost on quanti!y demanded [30]. However. there is evidence that both case mix and case flow may he endogenously determmed [31] and thts would seem especially true of forprofits tf they engage in cream skimming. The dataset was not suffictently rtch to allow estimation of a stmultaneous equattons model. Instead. each equation was estimated using the rank numbers of the independent variables as instruments on the assumption that the ranks would be independent oi the error term. Results were broadly atmilar to those presented.
Cost comparisons
of forprofit
Table
3. Statistical
and nonprofit summary
hospitals
223
of sample
Mean (Standard deviation)
c* Admissions Caseflow Length stay Self
of
Beds N
* Total
Nonprofit
Forprofit
Chain profit
Nonchain profit
994.3 (98.49) 7104 (4286) 47.28 (8.81) 6.0 1 (1.52) 0.067 (0.090) 153.7 (77.06) 34
960.1 (173.8) 4628 (2763) 47.8 I (13.71) 5.42 (1.02) 0.045 (0.091) 101.8 (58.46) 30
916.0 (141.8) 5864 (2236) 48.07 (15.25) 5.38 (0.71) 0.042 (0.049) 128.0 (43.90) 11
982.73 (184.99) 3786 (12807) 48.04 (15.25) 5.39 (1.36) 0.052 (0.118) 81.4 (61.1) 19
cost per admission
(includes
imputed
have resulted from misspecification of the underlying production function by omitting the physician input [35]. The coefficient estimates for the case flow variables are significant, have plausible signs, and imply that average cost declines with decreases in the number of cases per bed per year up to 65 and increases thereafter. The estimated coefficient for the proportion of admissions paid by the patient has the expected negative sign which is consistent with the hypothesis that a higher proportion of self-paying patients imposes Table 4. Coefficient Independent variables statistics BEDS CSFL CSFLSQ SELF YEAR Ml M2 M3 M4 M5 PROF CPROF CONSTANT R’ R: * Nonprofit
estimates
(1) All hospitals 0.433 (2.04) -21.05 (3.07) 0.161 (2.41) - 407.67 (2.09) 54.60 (2.12) 841.58 (3.48) 732.96 (1.46) 1371.17 (2.42) 2022.80 (0.70) -473.49 (0.91) 33.55 (1.01) - 117.21 (2.84) 1327.69 0.58 64 slope:
+ forprofit
value of admitting
services).
greater cost-minimizing constraints on hospitals. However, there is a relatively high and significant positive correlation between SELF and ICDA diagnostic category for normal births (r = 0.62) and a negative correlation between SELF and the average cost of an operation (r = -0.43) suggesting that SELF may be acting as a proxy for case severity rather than simply as a measure of incentives to control overall hospital cost. The dummy variables for forprofit status imply that forprofits in general are no less costly than nonprofits,
and r values for average
(2) Nonprofit
(3) Forprofit
0.632 (3.14) -28.61 (3.28) 0.256 (2.89) 24.04 (0.13) 77.13 (3.64) 67.34 (0.27) 298.72 (0.68) -551.31 (1.00) -2928.12 (1.08) - 1251.49 (2.19)
-0.063 (0.13) - 25.75 (2.66) 0.198 (2.08) -407.56 (0.96) 68.99 (1.34) 1046.84 (2.02) 1764.65 (1.52) 1770.93 (1.87) 9040.46 (1.80) -1115.18 (1.80)
1807.03 0.74 34
-112.44 (1.84) 1468.60 0.62 30
slope.
physician
total cost per case (4) All hospitals with differential slopes
0.273 (1.37) -21.45 (3.50) 0.173 (2.89) -450.78 (2.54) 62.32 (2.68) 750.70 (3.34) 644.89 (1.43) - 148.95* 2010.03t (0.19) (2.04) 17632.44+ - 7508.00* (3.54) (2.10) - 772.93 (1.62) - 237.48 (2.62) -81.49 (2.20) 1566.16 0.66 64
224
CARSON W. BAYS
but that chain forprofits are significantly less costly than other types of hospitals. Estimating the model for the nonprofits and forprofits separately reveals some interesting differences. The coefficient estimate for BEDS is now positive and significant only for the nonprofit group. The estimates for the case flow variables are unchanged in sign and significance but the poirit estimates imply that the optimal case flow rate varies between ownership types-55 for nonprofits and 65 for forprofits. There are also substantial differences in the estimated values of SELF and in some of the case mix measures. Indeed. the differences suggest that there may be significant structural differences in the cost functions of the nonprofit and forprofit groups and this is confirmed by a standard F-test on the homogeneity of the slope coefficients over the two groups [28]. These differences were investigated further by estimating the pooled model with multiplicative class dummies for all of the independent variables in a regression of the form C = a1 + a2D + b,X, + b2(DX,).. + b,,Xlo + b,,(DXlo) + e. The only DXi variables which were significant in this regression were two of the case mix indices, M3 and M4. The pooled sample was then estimated again with separate multiplicative dummies on these two variables to produce separate slope estimates for the nonprofit and forprofit groups (regression 4). The F-test was applied again and confirmed the homogeneity of the remaining slope coefficients between the two groups. A Chow test [36] was used to compare nonchain and chain forprofits (there were only 11 observations on chain hospitals) and revealed no significant structural differences in the cost functions of the two forprofit hospital types. Allowing separate slope estimates for the third and fourth case mix indices produces a dramatic change in the size and significance of the estimated coefficient for forprofit ownership. It is now negative and highly significant with an estimated value of over $200 while the size and significance of the chain forprofit coefficient is approximately the same as in the other regressions. The separate slope estimates for the two case mix variables imply that these types of cases (M3 is the proportions of infective diseases and special services while M4 contains central nervous system and genitourinary diseases) have much greater positive impact on cost in forprofit than nonprofit hospitals. but is is unclear a priori why forprofits should devote more resources to the treatment of certain disease types than do nonprofits. This may be a consequence of cream skimming by forprofits or perhaps simply reflects their systematic overtreatment of certain case types compared to nonprolits.*
* Investigating this further will probably require hospital data on price cost margins, insurance coverage. and intensity of treatment by diagnostic category which were not obtainable for this study. The dataset did include several possible proxy measures of overall intensity of treatment by hospital. however. There were no significant differences among nonprofit, nonchain forprofit. and chain forprofit hospitals in terms of average length of stay. the percentage of operated cases. the percentage of cases with multiple diagnoses. or the average cost of surgical operations.
CONCLL SIOU
This paper has discussed some cost implications of organizational differences among short term general hospitals and has tested them on a panel of data on California hospitals. The sample is limited and was not randomly selected. so generalizations must be made cautiously, but the results are suggestive. Forprofits as a group appear to be significantly less costly than nonprofits after accounting for differences in case mix, but the interpretation of this finding is confounded by the fact that this result emerges only after adjusting for the possibility that forprofits may treat certain cases types more intensively than do nonprofits. Moreover, chain forprofit hospitals-those which are part of broadly-owned hospital corporations-have case loads which are not significantly different from those of nonprofits. but appear to be less costly than nonprofits and nonchain forprofits as well. The latter finding lends support to the contention of Steinwald and Neuhauser [3] that forprofit hospitals managed by the physician-owners (which is typically the case for noncham forprofits) will be Iess efficient than a typical forprofit firm because the physicians must divide their labor between management and medical practice. It also suggests that chain forprofits may be exploiting economies of scale in management which are not attainable by a single forprofit institution. Additional research on chain forprofits is clearly warranted. In particular, it would be useful to investigate possible differences in the distribution of hospital output over admitting physicians among hospital types to determine if this is a source of their appar-
ently superior cost performance. If the typical physician in a chain forprofit hospital accounts for a larger share of total hospital output than in other hospital types, this may facilitate staff cooperation in minimizing the overutilization of hospital inputs. REFERENCES
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Cost comparisons
of forprofit
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A.E.R.
60. 379. 1970.
31. Rafferty J. A. Patterns of hospital use: An analysis of short-run variations, J.P.E. 880, 154. 1971. 32. Evans R. G. ‘Behavioral’ cost functions for hospitals. Can. J. Econ. 4, 198. 1971. 33. Feldstein. P. An Empirical Investigutiorr of the Mrrryirtul Cosr of Hospital Services. Univ. Chicago Press. Chicago. 1961. 34. Ro K. K. Determinants of hospital costs. Yolr Econ. Essa_vs 11, 185. 1968. 35. Bays C. W. Specification error in the estimation of hospital cost functions. Rec. econ. Stat. forthcoming. 36. Chow G. C. Tests of eaualitv between sets of coefficients in two linear regressions. Econometrica 27. 591. 1960.
COMPARISONS NONPROFIT
OF FORPROFIT HOSPITALS
AND
CARSON W. BAYS
Department
of Economics, University of Illinois at Chicago Chicago, Illinois 60680. U.S.A.
Circle.
Box 4348.
Abstract-The special organizational and institutional structure of the hospital industry has implications for cost differences between forprofit and nonprofit hospitals. These implications are developed and tested on a panel of data on California hospitals for which as estimate of the cost of admitting physician services could be made. Forprofit hospitals in general are significantly less costly than nonprofits after accounting for differences in case mix, but the interpretation of this result is complicated by the possibility of systematic overtreatment of certain case types by independent. or nonchain forprofits. The paper argues that the more appropriate comparison is between nonprofits and chain forprofits. The latter type of hospital has a distribution of cases which is similar to that of nonprofits but is less costly than both nonprbfit and nonchain forprofit hospitals.
INTRODUCTION
Rapid inflation in the cost of hospital care has focused interest on various structural and institutional aspects of the industry and their implications for cost and efficiency. Hospitals differ from other economic organizations in several important respects. First, the industry is a predominantly nonprofit one and, as such, is not subject to the same complex of market forces which compel competitive forprofit firms to produce efficiently. Second, widespread insurance coverage substantially alters the economic incentives of both users and providers of hospital care. Third. the institutional separation of hospitals and admitting physicians results in a situation in which physicians are primarily responsible for decisions on the allocation of hospital capital and personnel. but are only indirectly accountable for the cost of those decisions. This paper discusses these factors and tests some of the implications by comparing the cost performance of a group of forprofit and nonprofit hospitals. ORGANIZATIONAL A. Owwship
STRUCTURE AND COST
starus
Approximately 1O”, of U.S. hospitals are forprofit firms which account for 5”, of total patients beds. The largest proportion of other hospitals (50%) are private. nongovernmental. “voluntary” or nonprofit [l]. These hospitals have no owners in a formal sense, but rather a group of trustees which establishes general policy and is the final governing authority. Management of the hospital IS the responsibility of a hired administrator aho must carry out general policy, deal Lvith the medical staff. and perform routine management chores. Insuring efficient behavior by the * Of course. a nonprofit hospital could be taken over by a forprofit one. but most conversions of ownership have been in the opposite direction [3]. t Empirical support for the superior efficiency of forprofit firms has been found for firms supplying health Insurance [4]. Support from D.H.E.W grant HSOWI is acknowledged.
manager requires a more elaborate set of administrative rules and controls than in a forprofit firm in which a portion of the firm’s profit can be assigned to the manager as an incentive for efficiency [2]. Entry into the industry is sharply limited by such controls as Certificate of Need laws, and the fact that take-over of an inefficient nonprofit hospital would not allow the new management to extract a profit residual removes the discipline of potential entry.* These factors suggest that nonprofit hospitals will be less efficient than forprofit hospitals supplying a similar complement of services. This proposition has not been previously tested directly,? and Manning [S] has concluded that inefficiency from nonprofit status will be slight as long as nonprofits face competition from forprofit hospitals. An interesting change occurred within the forprofit sector of the hospital industry during the previous decade. Historically, forprofit hospitals have been small institutions which were owned by the physicians who practised there. During the late 1960’s a number of hospitals were built or acquired by large corporate chains, the ownership of which are broadly held. These hospitals have grown in both number and average size while the total number of forprofit hospitals has been declining. This suggests that if the forprofit form is demonstrably more efficient, it should be this type of hospital which should exhibit it. B. Health
insurance
Approximately 909~ of the population has some form of health insurance. This has two general effects upon hospital behavior. On the demand side. the existence of insurance coverage substantially lowers the patient’s direct cost of treatment and therefore increases the quantity demanded. There has been substantial variation in the empirical estimates of the price elasticity of demand for hospital care but it is clear that quantity is responsive to price and that insurance has increased total demand for care [6]. In addition. hospital insurance causes a supply response which results in higher cost. higher “quality” care being provided [7-91.
220
w.
CARSON
Table I. Case mix proportions Disease category
* Significantly t Significantly
for various
different different
hospital
groupmgs
All forprofit
Chain forprotit
Nonchain forprotit
0.0234t 0.059 It
0.0201 0.0602
0.0253+
0.0741 0.0171 0.0052 0.0 103
0.0197 0.0050 0.0087
0.0146* 0.0027 0.0101
0.0’27 0.0063 0.0079
0.0383*
0.02871
0.0383’
0.0232+
0.1193 0.0776*
0.1147 0.1051t
0.1241 0.0959
0. IO92 0.1 105+
011135 0.1097 0.1785 0.0118 0.0507 0.0066 0.0001* 0.0361 0.0943: 0.01388 0.027 1
0.1179 0.1188 0.1541 0.0147 0.0494 0.0047 0.0013 0.0357 0. I206t 0.0074t 0.0110
0.1058 0.1 176 0.1425 0.0146 0.0639* 0.0055 0.0000 0.0428 0.1123 0.0119* 0.0172
0. I 249 0.1 195 0.1608 0.0 147 0.041 I 0.0042
Nonprofit
Infective Neoplasms Metabolic. diabetes. endocrine Hematology Mental CNS, eye. ear. other nervous Heart. hypertension. vascular Respiratory Mouth, stomach. intestinal Genitourinary Pregnancy, etc. Skin Muscuioskeletal Congenital Childhood Symptoms Fractures, trauma Special services Births
BAYS
0.0158.*
from nonchain ffom nonprofit
0.0585
0.00’1~ 0.0316 0.1254t 0.004st 0.0074
forprofit at 95% level. at 95% level.
C. Role of admitting physicians Hospital care is a joint product of the inputs of the physician and of the hospital, but the physician is the one who decides-within rather broad limits--the type and amount of hospital inputs which will be devoted to a given case. The impact of this arrangement on hospital behavior has been investigated by several economists. Pauly and Redisch [lo] depict the hospital as a physicians’ cooperative of which admitting physicians are the de facto administrators. Shalit [ll] suggests that the appropriate paradigm within which to view hospital behavior is as a doctor-hospital cartel which is organized and managed to maximize physician income. Others have suggested that the relationship between hospital administrator and the medical staff be considered in a game theoretic context [5.12]. Arrow [ 131 has argued that the institutional separation of physician rind hospital is a consequence of the special trust relation between doctor and patient which is necessary because of the uncertainty surrounding medical care, and this theme has been recently reiterated by Harris [12]. The physician is expected to act as agent of the patient in diagnosis and treatment and should be concerned only with * Granfield [I41 treats this as the reason for the overcapitalization which is widely believed to characterize the hospital industry. However. the physician has an incentive to overutilize both hospital capital and ancillary personnel to the extent that they are both complementary to the physician input. t Several studies correct for size and/or facilities mix differences [l&21] but these institutional differences may account for as little as one fourth of the variation in case mix among hospitals [Xl.
medical need in allocating inputs. As a result. the cost to the physician of hospital inputs will be significantly less than their true social opportunity cost. so that there is a strong incentive toward overutilization of hospital inputs.* Nevertheless. the medical staff has a group interest in preventing such overutilization because an increase in the cost of the hospital component of treatment will lower the profit-maximizing price which the physician can separately charge the patient. Preventing noncooperative usage of the hospital by physicians will be more difficult as the number of admitting staff increases. or more specifically, as the share of hospital care attributable to each physician decreases [ 151. D. Case mix and cost Previous cost comparisons of forprofit and nonprofit hospitals have not adjusted for differences in the types of cases treated in hospitals of different ownership? but such differences may be important for two reasons. First. forprofits may selectively admit (or equip themselves to treat) only those cases which can be treated at acceptably high price-cost margins. Second, even if forprofits do not practice this explicit “cream skimming”. they may admit a more narrow range of cases than nonprofits to decrease the likelihood of noncooperative behavior among physicians in the utilization of hospital inputs. III. Estimation of a cost ftmction for hospitals Under the standard theory of the firm. cost per unit is a function of firm size, factor prices. the rate of plant utilization. and the level of managerial efficiency. The multiservice nature of the hospital requires that case mix be treated as a cost determinant as well.
Cost comparisons
of forprofit and nonprofit
Sarnplr
Data on hospital size, utilization. ownership. and finances are available from the American Hospital Association (AHA) annual surveys. but the necessity of controlling for case mix complicates the selection of a sample for testing since hospital specific data on cases are not generally available because of confidentiality considerations. This made it necessary to solicit the participation of a group of hospitals in the study by asking their permission to purchase their admissions data from their abstracting services. 46 short term general California hospitals (18 forprofit and 28 nonprofit) agreed to release their diagnostic and treatment records for 1971 and 1972. Of these 92 potential observations. 28 were seriously incomplete leaving a usable sample of 64 observations of which 30 were of forprofits (12 from 1971 and 18 from 1972). and 34 were of nonprofits (1.5 from 1971 and 19 from 1972). None of the hospitals were involved in teaching. A summary of the case mix measures for nonprofit, nonchain forprofit. and chain forprofit hospitals is presented in Table 1 and reveals some interesting differences between ownership types. The entries in the table are the average proportions of admissions by hospital type in each of the 19 broad diagnostic categories of the International Classification of Diseases-Amended (ICDA). One way analysis of variance was performed on various pairs of hospital types and the case mix categories tihich differ significantly are indicated. The proportions of admissions in 6 of the 19 categories in forprofit hospitals differ significantly from the respective proportions in nonprofit% The categories in which these differences occur represent approximately 34’6 of forprofit admissions. However. these differences are due entirely to the * A frequent allegation is that forprofit hospitals survive by systematically producing lower quality care than nonprofits. Most comparisons of hospital quality have centered on the extent of accreditation and the number of facilities and services provided. but ideally, quahty should be measured as a dimension of output rather than by the extent and variet) of inputs. The hospital death rate. although crude, should reflect gross differences in the quality of care received after accounting for differences in case m;x and severity [X]. For the sample of hospitals used in this study. there are no significant differences in death rates among ownership types after accounting for case mix. severity. and patient age. 4 The actual method for making this imputation is straightforward but tedious. The RVS divides procedures into five medical specialities each having different “unit” values which have different dollar conversion factors. The relative unit values are based on the median dollar charges for various treatments computed from an extensive survey of California physicians. Converting them into an arbitrary metric was probably an attempt on the part of the California Medical Association to maintain the fiction that the RVS could not be used as a price-fixing device. The dollar conversion factors for each speciality and year were provided b\ the statistical staff who collected the ortginal data. If ph&ians price their services rationally. then the imputed value of a given treatment or procedure should reflect the requisite level of skill. the time required. and the dif?icult> of the procedure. Another stud! offers some confirmation of this: for a group of New 1’ork surgeons there was a high and significant positive correlation (r = 0.97) brtkveen the California RVS and the observed operatinE room time [26].
hospitals
221
nonchain forprofit segment of the forprofit group: there are no significant differences in case proportions between nonprofits-and chain forprofits. Differences in output specialization between hospitals are shown by a comparison of the percentage of total admissions accounted for by the largest diagnostic proportions. The 19 ICDA proportions were ranked in decreasing order of size by hospital and then one way analysis of variance was performed on the sums of the largest 4, 6. and 10 proportions for various pairs of hospital types (Table 2). Nonchain forprofits have significantly greater output specialization than nonprofits while the percentages for chain forprofits are slightly lower than those of nonprofits. but the latter differences are not significant at the 759; level. These differences in case mix and output specialization confirm the need to treat chain and nonchain forprofits as distinct organizational types.*
With the exception of Feldstein’s study of the British National Health Service [24], previous hospital cost studies have omitted the cost of admitting physicians because of the absence of direct data on physician charges by hospital. For this study an estimate of these costs was made in the following way. The case abstracts contain data on treatments actually performed in addition to the initial and subsequent diagnoses on each patient. These treatments were weighted by the relative value scale (RVS) published by the California Medical Association [25], and the resulting estimate of total physician cost by hospital was added to the hospital cost data from the AHA surveys.? It is important to stress that this measure of physician charges is an imputation and therefore may contain some biases. One problem is that the RVS categories are not the same as the ICDA classification system used in recording operations and treatments on the patient abstracts. Of course. both classification schemes cover the same procedures. but they do not map together in any simple or consistent fashion. In some instances it was necessary to use a single fee index category for more than one ICDA group. In other cases, several RVS categories were included in a single ICDA classification so that the median RVS index was used. Because the price weights based on the RVS are medians (both in terms of the original survey from which they are derived. and with respect to the choice of the price weights for the ICDA categories which cover more than one RVS category), the resulting estimate of physician services probably understates the Table 2. Percentage of cases accounted for by largest 4. 6. and 10 diagnostic categories: Various hospital groupings Sum of categories Largest Largest Largest
4 6 10
* Si_gnificantlx Iebel. + SignificantI>
Nonprofit
All forprofit
56* 73* 87’
Chain forprofit
60t 76t 88
different
from
nonchain
different
from nonprofit
53’ 72” 86’ forprofit at 95”,
Nonchain forprofit 63 79t 89t at 959, level.
actual differences in physician cost between an average hospital and one which treats cases of greater than average complexity for each diagnostic category. This problem is ameliorated to some extent by limiting the sample to hospitals without a teaching program. Sprcijcarion
The cost function is of the general form: C=f(M.S,T:U,O)
(1)
where C = total cost per case (includes both hospital cost and imputed physician charges), M = mix of cases treated, S = hospital size, T= source of payment (third party of self pay), U = rate of plant utilization, 0 = hospital ownership.*
remibursemsnt programs lessen hospital incentives for cost minimization. The rate at which hospital facilities are utilized is measured as the number of cases per bed per year. CASEFLOW = (average daily available per number of beds censusaverage year) x (365;mean length of stay). CASEFLOW is entered in a quadratic specification.: Since the data are for two years combined a dummy variable. YEAR, is entered to allow for a separate intercept for the 1972 observations.4 Finally. two binary dummy variables are entered for ownership status. PROF takes the value 1 of the hospital is organized as a forprofit firm and CPROF equals 1 if it is part of an investor-owner chain of forprofit hospitals. A statistical summary of the sample is in Table 3. cost
Empiricul
rrsults
Variations in case mix over hospitals are accounted for by aggregating data from individual patient records into the nineteen broad diagnostic categories of the ICDA. These were converted into proportions and then combined by grouping those proportions which had similar coefficient estimates in a regression of average total cost on the entire vector of nineteen proportions. This procedure results in five composite variables for case mix (Ml-MS).t Hospital size is measured as the average number of beds available per year (BEDS). The percentage of self-pay admissions (SELF) is entered as an independent variable to test the hypothesis that third party
Equation (1) was estimated for the entire sample and for the nonprofit and forprofit hospitals separately using ordinary least squares and the results are presented in Table 4.,, One rather surprising result for the pooled model (regression 1) is the positive coefficient estimate for BEDS. Several previous studies have found evidence of scale economies in hospitals [16-l&21. 32-343 and. although other studies are not unequivocal on the point [19. 14. 301, this would imply a negative coefficient on BEDS. The fact that the inclusion of the estimated value of admitting physician input in the hospital cost function results in an implication of cost rising with size suggests that previous findings of scale economies in hospitals may
* Hospital factor costs are assumed constant over the sample and are therefore omitted from the equation. Hospital specific data on wage and salary levels of employed personnel were not obtainable for the sample hospitals but ail of them are located in two urban areas of the same state and compete in similar factor markets, t Initial estimates of the model included the entire vector of case mix proportions (with one proportion omitted to prevent definitional singularity of the data matrix) but collinearity among the proporttons was severe. The proportions were then combined both to conserve degrees of freedom and to make the presentation and discussion of results more manageable. The grouping method is that suggested by Feldstein [24] and developed by Lave et al. [27] in which the dependent variable is initially regressed on the entire vector of proportions (minus one). The parameter estimates are therefore estimates of the relative average cost of each diagnostic category. Those which are insignificantly different from zero are dropped and those with similar estimated average cost are combined. Any procedure for reducing multicollinearity by other than introducing new information must be regarded with some trepidatton. Obviously, no new information is added when variables are combined and although estimates of the new variables may be “sharper” there is a risk that the estimated coefficients wtll be biased. Two justifications are offered for using the procedure here. First. the I9 broad ICDA diagnostic categories are themselves aggregations 01 approximately 1000 different diagnoses. The I9 broad groups are basically anatomical categortes (infective diseases, skin disease. etc.) rather than isoresource categories. Combining them by their implied relative costliness is a theoretically plausible way of reducing their number to manageable size. Second. the major conclusions regarding cost differences between ownership types are not appreciably altered when alternative case mix specifications are
used. These included reducing the number of case mtx regressors by factor analytic techniques. grouptng of the original proportions based upon then loadings on the tirst principle component of the proportions. and including the full vector of case mix proporttons as a measure of case mix. A detailed listing of the ICDA composition of the case mix measures is available from the author on request. : Various nonlinear transformations of BEDS and SELF were tested but the linear specification of the variables produces a superior fit. 3 Analysis of covariance techmques [2X] confirmed that restricting the remaming varuibles to be equal over the 2 years IS approprtate. Cross section dummies were added to the equation and the resulting decrease In the restdual sum of squares compared to the pooled mode1 with onlv a ttme dummy was not significant by the F-test. A modthed von Neuman ratto test 1391 revealed no sigmficant senal correlation. When the equations are estimated for each year separately the I values are naturally lower but the major results are unchanged. II The possibility of a simultaneity problem m using OLS to estimate hospital cost functions has typtcally been ignored in previous cost studies by assummg that hospital output is exogenous to the decision makers because hospitals accept ail cases up to capactty and that Insurance mittgates the impact of average cost on quanti!y demanded [30]. However. there is evidence that both case mix and case flow may he endogenously determmed [31] and thts would seem especially true of forprofits tf they engage in cream skimming. The dataset was not suffictently rtch to allow estimation of a stmultaneous equattons model. Instead. each equation was estimated using the rank numbers of the independent variables as instruments on the assumption that the ranks would be independent oi the error term. Results were broadly atmilar to those presented.
Cost comparisons
of forprofit
Table
3. Statistical
and nonprofit summary
hospitals
223
of sample
Mean (Standard deviation)
c* Admissions Caseflow Length stay Self
of
Beds N
* Total
Nonprofit
Forprofit
Chain profit
Nonchain profit
994.3 (98.49) 7104 (4286) 47.28 (8.81) 6.0 1 (1.52) 0.067 (0.090) 153.7 (77.06) 34
960.1 (173.8) 4628 (2763) 47.8 I (13.71) 5.42 (1.02) 0.045 (0.091) 101.8 (58.46) 30
916.0 (141.8) 5864 (2236) 48.07 (15.25) 5.38 (0.71) 0.042 (0.049) 128.0 (43.90) 11
982.73 (184.99) 3786 (12807) 48.04 (15.25) 5.39 (1.36) 0.052 (0.118) 81.4 (61.1) 19
cost per admission
(includes
imputed
have resulted from misspecification of the underlying production function by omitting the physician input [35]. The coefficient estimates for the case flow variables are significant, have plausible signs, and imply that average cost declines with decreases in the number of cases per bed per year up to 65 and increases thereafter. The estimated coefficient for the proportion of admissions paid by the patient has the expected negative sign which is consistent with the hypothesis that a higher proportion of self-paying patients imposes Table 4. Coefficient Independent variables statistics BEDS CSFL CSFLSQ SELF YEAR Ml M2 M3 M4 M5 PROF CPROF CONSTANT R’ R: * Nonprofit
estimates
(1) All hospitals 0.433 (2.04) -21.05 (3.07) 0.161 (2.41) - 407.67 (2.09) 54.60 (2.12) 841.58 (3.48) 732.96 (1.46) 1371.17 (2.42) 2022.80 (0.70) -473.49 (0.91) 33.55 (1.01) - 117.21 (2.84) 1327.69 0.58 64 slope:
+ forprofit
value of admitting
services).
greater cost-minimizing constraints on hospitals. However, there is a relatively high and significant positive correlation between SELF and ICDA diagnostic category for normal births (r = 0.62) and a negative correlation between SELF and the average cost of an operation (r = -0.43) suggesting that SELF may be acting as a proxy for case severity rather than simply as a measure of incentives to control overall hospital cost. The dummy variables for forprofit status imply that forprofits in general are no less costly than nonprofits,
and r values for average
(2) Nonprofit
(3) Forprofit
0.632 (3.14) -28.61 (3.28) 0.256 (2.89) 24.04 (0.13) 77.13 (3.64) 67.34 (0.27) 298.72 (0.68) -551.31 (1.00) -2928.12 (1.08) - 1251.49 (2.19)
-0.063 (0.13) - 25.75 (2.66) 0.198 (2.08) -407.56 (0.96) 68.99 (1.34) 1046.84 (2.02) 1764.65 (1.52) 1770.93 (1.87) 9040.46 (1.80) -1115.18 (1.80)
1807.03 0.74 34
-112.44 (1.84) 1468.60 0.62 30
slope.
physician
total cost per case (4) All hospitals with differential slopes
0.273 (1.37) -21.45 (3.50) 0.173 (2.89) -450.78 (2.54) 62.32 (2.68) 750.70 (3.34) 644.89 (1.43) - 148.95* 2010.03t (0.19) (2.04) 17632.44+ - 7508.00* (3.54) (2.10) - 772.93 (1.62) - 237.48 (2.62) -81.49 (2.20) 1566.16 0.66 64
224
CARSON W. BAYS
but that chain forprofits are significantly less costly than other types of hospitals. Estimating the model for the nonprofits and forprofits separately reveals some interesting differences. The coefficient estimate for BEDS is now positive and significant only for the nonprofit group. The estimates for the case flow variables are unchanged in sign and significance but the poirit estimates imply that the optimal case flow rate varies between ownership types-55 for nonprofits and 65 for forprofits. There are also substantial differences in the estimated values of SELF and in some of the case mix measures. Indeed. the differences suggest that there may be significant structural differences in the cost functions of the nonprofit and forprofit groups and this is confirmed by a standard F-test on the homogeneity of the slope coefficients over the two groups [28]. These differences were investigated further by estimating the pooled model with multiplicative class dummies for all of the independent variables in a regression of the form C = a1 + a2D + b,X, + b2(DX,).. + b,,Xlo + b,,(DXlo) + e. The only DXi variables which were significant in this regression were two of the case mix indices, M3 and M4. The pooled sample was then estimated again with separate multiplicative dummies on these two variables to produce separate slope estimates for the nonprofit and forprofit groups (regression 4). The F-test was applied again and confirmed the homogeneity of the remaining slope coefficients between the two groups. A Chow test [36] was used to compare nonchain and chain forprofits (there were only 11 observations on chain hospitals) and revealed no significant structural differences in the cost functions of the two forprofit hospital types. Allowing separate slope estimates for the third and fourth case mix indices produces a dramatic change in the size and significance of the estimated coefficient for forprofit ownership. It is now negative and highly significant with an estimated value of over $200 while the size and significance of the chain forprofit coefficient is approximately the same as in the other regressions. The separate slope estimates for the two case mix variables imply that these types of cases (M3 is the proportions of infective diseases and special services while M4 contains central nervous system and genitourinary diseases) have much greater positive impact on cost in forprofit than nonprofit hospitals. but is is unclear a priori why forprofits should devote more resources to the treatment of certain disease types than do nonprofits. This may be a consequence of cream skimming by forprofits or perhaps simply reflects their systematic overtreatment of certain case types compared to nonprolits.*
* Investigating this further will probably require hospital data on price cost margins, insurance coverage. and intensity of treatment by diagnostic category which were not obtainable for this study. The dataset did include several possible proxy measures of overall intensity of treatment by hospital. however. There were no significant differences among nonprofit, nonchain forprofit. and chain forprofit hospitals in terms of average length of stay. the percentage of operated cases. the percentage of cases with multiple diagnoses. or the average cost of surgical operations.
CONCLL SIOU
This paper has discussed some cost implications of organizational differences among short term general hospitals and has tested them on a panel of data on California hospitals. The sample is limited and was not randomly selected. so generalizations must be made cautiously, but the results are suggestive. Forprofits as a group appear to be significantly less costly than nonprofits after accounting for differences in case mix, but the interpretation of this finding is confounded by the fact that this result emerges only after adjusting for the possibility that forprofits may treat certain cases types more intensively than do nonprofits. Moreover, chain forprofit hospitals-those which are part of broadly-owned hospital corporations-have case loads which are not significantly different from those of nonprofits. but appear to be less costly than nonprofits and nonchain forprofits as well. The latter finding lends support to the contention of Steinwald and Neuhauser [3] that forprofit hospitals managed by the physician-owners (which is typically the case for noncham forprofits) will be Iess efficient than a typical forprofit firm because the physicians must divide their labor between management and medical practice. It also suggests that chain forprofits may be exploiting economies of scale in management which are not attainable by a single forprofit institution. Additional research on chain forprofits is clearly warranted. In particular, it would be useful to investigate possible differences in the distribution of hospital output over admitting physicians among hospital types to determine if this is a source of their appar-
ently superior cost performance. If the typical physician in a chain forprofit hospital accounts for a larger share of total hospital output than in other hospital types, this may facilitate staff cooperation in minimizing the overutilization of hospital inputs. REFERENCES
1. American Hospital Association. Hospiral Srati.stics 1978. pp. 3-4. Chicago. 1979. 2. Clarkson K. Some implications of property rights In hospital management. J. Law Econ. 15, 363. 1972. 3. Steinwald B. and Neuhauser D. The role of the proprietary hospital. Law conremp. h-oh/ 35. 817 1970: 4. Frech H. E. The property rights theorv of the firm: Empirical results from a natural exper;ment. J. polit. Econ. 84, 143. 1976. 5. Manning W. G. Comparative efficiency in short-term general hospitals. Unpublished Ph.D. dissertation, Stanford University. 1973. 6. Feldstein M. S. Econometnc studies of health economics. In Frontiers of QuuntitafrorEconomics (Edited by Intriligator M.D. and Kendrick D. A.). pp. 384-8. North Holland. Amsterdam. 1974. 7. Feldstein M. S. The welfare loss of excess health insurance. J. polir. Econ. 81. 251. 1973. M. S. Quality change and the demand for 8. Feldstein hospital care. Economrrricu 45. I68 I. 1978. 9. Newhouse J. Toward a theory of nonprofit instltutions: an economic model of a hospital. &I. rcon. Rro. 60, 64. 1970. hospi10. Pauly M. V. and Redisch M. The not-for-profit tal as a physician’s cooperative. Am. rcon. Rev. 63. 87. 1973. I I. Shalit S. S. A doctor-hospital cartel theory. J. Btts~n. 50, 1. 1977.
Cost comparisons
of forprofit
12. Harris J. E. The Internal organization of hospitals: Some economic implications. Bell J. Eron. 8. 467. 1977. 13. Arrow K. J. Uncertainty and the welfare economics of medical care. .4m. ecori. Rer. 53. 941. 1963. 14. Granfield M. E. Resource allocation within hospitals: An unambigious analytical test of the A-J hypothesis. Appl. Ecou. 7. 241. 1975. 15. Pauly M. V. Medical staff characteristics and hospital costs. J. Hum. Res. 13, 76. 1978. of hospital 16. Berry R. Returns to scale in the production services. Hlth Sew. Rex 2, 123. 1967. 17. Carr W. J. and Feldstein P. J. The relationship of cost to hospital size. Irtquirj, 4. 45. 1967. E. W. Analysis of cost variations among 18. Francisco short-term general hospitals. In Empirical Studies in Health Economics (Edited by Klarman H. E.). pp. 321-37. Johns Hopkins Press. Baltimore. 1970. 19. Ingbar M. L. and Taylor L. D. Hosprtal Costs rn Massachuserts. Harvard Uni,. Press. Cambridge. 1968. in cost among hospitals of 20. Cohen H. A. Variations different sizes. Sth. Econ. J. 33. 355. 1963. cost curves with emphasis on Il. Cohen H. A. Hospital measuring patient care output. In Empirical Studies in Health Economics (Edited by Klarman H. E.). pp. 279-93. Johns Hopkins Press. Baltimore. 1970. 22. Lave J. R. and Lave L. B. The extent of role differentiatton among hospitals. H/t/r Serr. Res. 6, IS. 1971. 23. Roemer M.. Moustafa A. and Hopkins C. A proposed hospital quality index: Hospital death rates adjusted for case severity. Hkh Serv. Res. 3. 96. 1968.
and nonprofit
hospitals
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