efficiency evaluation of skilled nursing facilities

Journal of Medical Systems, Vol. 22, No. 4, 1998

Efficiency Evaluation of Skilled Nursing Facilities

Yasar A. Ozcan,1,4 Stephen E. Wogen,2 and Li Wen Mau3

This study employs Data Envelopment Analysis (DEA) to determine technical efficiencyusing skilled nursing facilities in the United States, using a 10% national sample of324 skilled nursing facilities stratified by ownership and size cluster groupings. Resultsshow that nonprofit and for-profit firms operate using significantly different modes ofproduction, thus allowing the best of the for-profits to achieve a level of technicalefficiency .86 times higher than the most efficient nonprofit homes. The best largernursing homes are .89 times more efficient than the best smaller facilities, alsoindicating a difference in production goals and technologies. A rationale for thesedifferences is sought through an analysis of DEA generated slacks and a logisticregression. Controlling for size and ownership in the DEA, a higher percentage ofMedicare patients leads to lower efficiency, while higher occupancy and greaterpercentage of Medicaid patients lead to greater efficiency. Regional characteristics donot impact efficiency. It is concluded that reimbursement policies should account fordifferences in organizational goals created by size and ownership differentials. The greatvariations in efficiency demonstrate tremendous potential for cost-savings throughimitation of efficient firms.

INTRODUCTION

The need to assess the efficiency of nursing homes is made obvious by the pleth-ora of research on the topic. Early studies estimated nursing home cost functionsto identify characteristics systematically associated with the cost of nursing homecare.(1-4) More recently, the literature has assessed the relative technical efficiencyof nursing homes using Data Envelopment Analysis (DEA), a nonparametric, mul-tiple input, multiple output technique.(5-9) This study expands upon the recent DEAnursing home literature.

1Department of Health Administration, Virginia Commonwealth University, P.O. Box 980203. Richmond,Virginia 23298-0203.

2CIGNA Health Care, New York, New York.3Evergreen Management University, Tainan, Taiwan ROC.4To whom correspondence should be addressed.

0148-5598/98/0800-0211$15.00/0 C 1998 Plenum Publishing Corporation

KEY WORDS: DEA; skilled nursing facilities; technical efficiency.

211

As purchasers of health care demand that costs be controlled and efficiencybe promoted, the nursing home industry, which now accounts for 9% of nationalhealth care expenditures, represents a prime target for reform efforts. Aggregatenursing home expenditures have risen more rapidly than those of other sectors ofthe health economy, from $4.9 billion in 1970 to $53.1 in 1990, and estimates predictthat costs will reach $131 billion by 2000.(10,11) The public's share of expendituresis substantial, with Medicaid alone accounting for 41% of outlays.(12)

Strains on both governmental and private spending will worsen in the comingdecades. The population age 65 and over is predicted to more than double fromits current 32 million by the year 2030. The population age 85 and over will expandmost rapidly. These individuals are among the most debilitated, with over 55%needing assistance with one or more activities of daily living, a factor shown toincrease nursing home costs substantially.(3,4,13) Coupling demographic trends withincreasing health care input prices and an overall increase in nursing home patientseverity created by earlier hospital discharges, the urgency for the development ofan efficient long term care system is clearly demonstrated. As organizations foldnursing home facilities into integrated networks and as payers, both public and pri-vate, establish reimbursement methods for these facilities, methods of determiningorganizational efficiency will be needed. DEA is a tool that can be used to suchan end.

This study expands upon the current nursing home DEA literature in two ways.First, DEA is applied to a national sample of skilled nursing facilities (SNFs). Pre-vious DEA studies have used state-specific samples, citing variations in case mix,level of care, and length of stay created by state-determined Medicaid policies.(14)

Although we recognize that Medicaid policies differ across states, we assume thatMedicare certification of SNFs assures that variations in quality and proceduresare not drastic. Further, because DEA allows the use of multiple outputs and be-cause outputs are measured in patient days, both case mix and length of stay dif-ferences are accounted for in the DEA model. Regional variations in efficiency arecontrolled for in a post hoc analysis of efficiency scores, as has been done in costfunction studies.(2)

The second significant contribution is an expansion of the work of Grosskopfand Valdmanis.(15) Their study allowed for the possibility that different types ofhospitals may face diverse operating environments and/or objectives that impacttheir resource allocation procedures. Nursing home DEA studies have controlledfor nonuniformity in objectives, particularly those created by profit status and size,through a regression model on DEA determined efficiency scores. The currentstudy, however, allows the technical efficiency scores of nursing homes to be de-composed into components(15) based on both size and ownership type, separately.From this analysis, a more direct comparison of the technical efficiency across or-ganizations with divergent goals (not-for-profit versus proprietary; small vs. large)can be made.

212 Ozcan, Wogen, and Mau

MARKET SEGMENTATION IN THE NURSING HOME INDUSTRY

Data envelopment analysis estimates a best frontier function based upon thosedecision making units (DMUs) that operate with "optimal" patterns of production.DMUs composing the frontier are deemed technically efficient with respect to theirpeers based upon actual performance, and are assigned an efficiency score of one.Technically inefficient units are assigned a score of less than 1 but greater than 0;the lower the efficiency score, the greater the technical inefficiency of the unit.These technically inefficient units require either relatively more weighted inputs toproduce the weighted outputs, or produce less weighted output per weighted inputthan those facilities on the frontier.

DEA assumes that all firms in the analysis share a common production surface,or frontier, and that technical inefficiencies are evaluated with respect to this fron-tier. Thus, it follows that to determine efficiency accurately, firms should be evalu-ated only against those organizations operating within a similar environment andwith similar goals and objectives. Previous studies applied to nursing homes haveplaced each DMU on a similar playing field; that is, potential factors affecting pro-duction goals, and ultimately resource usage have been explored. These studies thenuse regression analysis to distinguish the performance of the various providers.(5,6)

Several recent studies of the hospital industry have recognized the need tocluster the sample according to factors that could cause movement onto a differentproduction frontier.(15-17) The bases for cluster grouping have included owner-ship,(15) size,(17) and local market characteristics.(16) Choice of cluster groupings de-pends largely upon a theoretical determination of factors motivating production,such as profit orientation; that is, what factors influence the position of the organi-zation's efficiency frontier? Based upon theories of nonprofit ownership and scaleeconomies supported by the literature discussed below, the impacts on efficiencyof two cluster grouping variables, ownership type and size, are analyzed in thisstudy.

Ownership Type and Efficiency

Cost function studies have historically found higher costs in nonprofit institu-tions.(1-4) This cost differential could reflect either lower efficiency, higher qualityof care or higher input prices (see Feldstein's "philanthropic wage" theory(18)) ofthe nonprofits. However, most authors concluded that at least some of the differ-ential is due to inefficiency.(3,4) The DEA studies confirm this finding; only Sexton(5)

finds that nonprofits may be more efficient, although the result is only marginallystatistically significant. Potential reasons for his finding include a lack of qualityand case-mix controls and a small sample (52 Maine nursing homes). Chattopad-hyay and Heffley(8) by incorporating Activities of Daily Living (ADL) scores as aconstraint in the DEA, find that for-profit homes are more efficient after controllingfor case-mix.

213Evaluation of Skilled Nursing Facilities

The theoretical rationale for the greater efficiency of proprietary firms arisesfrom property rights theory.(15,19,20) The for-profit decision maker maximizes utilityover wealth and nonpecuniary benefits. Because the nonprofit firm is limited in theamount of wealth the decision-maker can hold, a constraint is introduced that forcesthe nonprofit provider to choose nonpecuniary benefits at the expense of wealth.Without profit incentives, nonprofits are less likely to reach productive efficiency.Instead of emphasizing efficiency and cost minimization, the nonprofits will maxi-mize budgets because, in the long run, they are constrained to spend their entirebudget—the so-called public choice theory.(7,15,21) Many alternative models of non-profit hospital behavior exist. Some focus on utility maximization,(22) in which anoptimal level of both quality and quantity are chosen. Others see nonprofits asmaximizers of pecuniary gains for physicians,(23) or the status of administrators.(24)

Davis(25) develops a model specifically for the nursing home industry. He arguesthat although nursing homes have limited control over entry, they can exercise con-siderable control over patients once admitted. Therefore, proprietary homes, be-cause of disincentives to raise rates for patients seeking entry, often reduce directcosts. He notes that lower costs may indicate decreased quality. A synthesis of stud-ies comparing quality in for-profits and nonprofits found ambiguous results, withseveral findings of no significant difference.(26) Some studies have found that for-profit homes demonstrate greater efficiency, controlling for quality.(8,27) Elwell(28)

concluded that nonprofit nursing homes were significantly more likely to spend ondirect patient care, although it is not known if the additional resources led to greaterquality. Ullman(29) in a study of process quality, concluded that ownership and ef-ficiency are unrelated.

Nursing Home Size and Efficiency

The concept of economies of scale suggests that larger homes facing decreasingaverage costs over the relevant range of outputs should experience efficiency ad-vantages. Potential reasons for efficiencies include specialization of labor and/or anability to negotiate lower input prices. Beyond a threshold, diseconomies may arisedue to managerial inefficiency. Because nationally many nursing homes are smallon average (more than two thirds have less than 100 beds), there is reason to believethat many facilities are operating in a range of decreasing average costs.(10) It isalso plausible that homes whose growth has been constrained through certificateof need (CON) regulations have substituted other inputs (e.g., labor), creating in-efficiency.(30)

Reviews of cost function studies have reported minimal relationships betweensize, measured in patient beds, and efficiency.(2,3,31,32) The DEA literature providesmixed results. Nyman(6) finds that size had positive impacts on efficiency up to athreshold of 170 beds. Chattopadhyay and Heffley(8) also find a parabolic efficiencyfunction, indicating an optimal size for efficiency. Sexton(5) finds a negative rela-tionship between size, occupancy and efficiency that he attributes to potential con-


gestion within the facility. These mixed results may be in part due to the effectsof a regional sample in each of these studies.

Market Segmentation Within the Nursing Home Industry

Given the above discussion, it is possible to differentiate various market seg-ments defined in part by size and ownership status. The performance within thesedivisions could differ, thereby necessitating cluster groupings in technical efficiencyevaluation. A recent study examines the effects of strategic group membership onnursing home performance and strategic behavior.(9) Seven strategic groups areidentified, across which are significant variations in firm size, the percentage ofproprietary firms, the percentage of Medicare/Medicaid patients, and occupancyrate. There are also significant variations in efficiency across these groupings, with,for example, segments composed largely of proprietary facilities with a high per-centage of Medicare patients having higher average efficiency.

Given the evidence, both theoretical and empirical, of the existence of strategicsegmentation based on goal orientation and resource commitments, efficiencyshould be evaluated for particular cluster groups of the nursing home market asdelineated based on size and ownership status.

METHODS

This study draws upon previous studies to calculate the efficiency,(5,7-9,15) how-ever, applies to skilled nursing homes when they are assumed to share a similarproduction surface (pooled).

Scores of pooled frontier efficiency estimates are then compared within clus-tered sample by ownership status, and then by size.

The Data

Data was obtained from the Health Care Financing Association's MinimumCost Data Set of national skilled nursing homes, covering the period of September1990 to October 1991. The data set includes the most current cost reports, financialand other information from the Medicare skilled nursing facilities. Data for areacharacteristics were obtained from the Area Resources File for 1992.

A 10% national sample of 330 skilled nursing facilities (SNFs) in the UnitedStates was chosen using stratified, proportionate random sampling. Bed size andownership type were the stratification variables. Six observations were omitted dueto missing data, making the final sample 324 SNFs.

The final sample consists of 210 for-profit and 114 nonprofit facilities. Non-profit nursing homes include public skilled and voluntary nursing facilities. Smallfacilities (n = 189) are defined as those with between 50 and 100 beds; the mediumsized group (n = 135) have between 100 and 250 beds. Nursing homes with bed

Evaluation of Skilled Nursing Facilities 215

sizes not within this range were excluded from the study because we could notobtain a reasonable national sample representing the stratum we used in this study.

The DEA Model

Each skilled nursing home is viewed as a producer of multiple outputs and aconsumer of multiple inputs. The DEA model uses two outputs—the sum of totalinpatient days of Medicare and Medicaid, and total private-pay inpatient days. Thetotal Medicare and Medicaid inpatients include all residents with any share of thebill paid by Medicare or Medicaid; that is, those residents in receipt of governmentfunds. Private pay inpatients encompass all residents with out-of-pocket expendi-tures, family support, private insurance, or continuing community care, and a re-sidual category for all others.(33)

The outputs are disaggregated into two categories to account for patient case-mix. Similar approaches have been used in other DEA studies.(5,8) Justification forthis distinction is found in studies demonstrating a negative relationship betweenthe proportion of public pay residents and expenditures.(3,28)

Input variables include number of beds, participating full-time-equivalents onthe payroll (FTEs) and operational expenses. The number of beds has traditionallybeen used as a measure of size in both DEA and conventional average cost stud-ies.(6,1-4) The number of FTEs is a measure of labor inputs. Because this study usesthe number of FTEs rather than the number of hours worked (the input measureused in previous DEA studies), it is implicitly assumed that two homes with similarFTE requirements utilize a similar number of employee hours. Operating expensesaccount for additional capital and labor inputs of support services. [Operationalexpenses include expenses of nursing administration, central services and supply,pharmacy, medical records and library, social services, administrative and general,plant operations, maintenance and repair, laundry and linen services, housekeeping,dietary, capital related, movable equipment, cafeteria, and maintenance of person-nel.]

An input oriented DEA model was used to evaluate the selected 324 skillednursing facilities (SNFs). In addition, DEA generated slacks were examined foreach of the output/input variables. Slacks are generated by inefficiently used inputsor a lack of produced outputs, and thus represent either surpluses (inputs) or short-ages (outputs) in production compared to the optimally producing SNFs. Slackscan be used to determine how input usage should be changed to reach the levelof technical efficiency of the optimal producers.

Post Hoc Analysis

Post hoc logistic regression analysis is used to examine determinants of nursinghome efficiency. Because a normal distribution cannot be assumed, a logistic ratherthan linear multiple regression is applied using the DEA model's efficiency scoresas a dichotomous dependent variable. Efficient SNFs will be assigned a score of


one; inefficient ones receive a score of zero. All independent variables, with theexception of region, are dichotomous, with a value of one representing a "high"percentage, and zero a "low" percentage. Based on a univariate analysis, it wasdetermined that a division based on the 75 percentile value for each variable couldbe used to identify the high and low category.

Five independent variables are included in the logistic regression. The first ispercentage of Medicare patients. A greater percentage of Medicare patients(>10%) may lead to a more severe case mix, thereby decreasing efficiency. Thisresult has been demonstrated in previous studies.(8) However, prospective reim-bursement may also provide incentives to improve efficiency, creating a positiverelationship between percent Medicare and efficiency.(9) The second measure is thepercentage of Medicaid patients, which could have similar implications as percent-age Medicare, although several studies have found a negative relationship.(5,8) Theeffects of this factor may be confounded by state determined reimbursement forMedicaid, data that are difficult to obtain for a study of national scope. High Medi-caid SNFs are identified as those facilities with more than 72% Medicaid recipients.

The third factor, occupancy rate, has been seen to have a positive impact onefficiency.(6) Nyman(6,7) predicted a positive relationship because, as occupancy in-creases, firms are more likely to reach their target level of staffing, while thosewith lower occupancy are below their targets and are therefore overstaffed. Sex-ton,(9) however, concludes that occupancy rate, holding beds constant, is negativelyrelated to efficiency, due to potential overcrowding within the facility. High occu-pancy rate SNFs are defined as those with occupancy above 95.8%.

The final two variables control for environmental characteristics. Region is di-vided into four areas—northeast, south, west, and central—to control for regulatoryand environmental characteristics that may influence a nursing home's efficiency.Each variable is defined as 0 if the SNF is not in the region, and one if it is locatedthere. To avoid over-identification of the model, the central group is used as thereference category. The percentage of the population above 84 years in age isthought to be negatively related to efficiency due to a higher severity of illness.(6,8)

A high population 84 and over is considered to be a population with more than12,506 persons in the county in which the skilled nursing facility is located.

RESULTS

Table I presents the descriptive statistics of the output and input variables bysize and profit orientation. As expected, the output means for the nonprofit sizecategories and their corresponding for-profit size homes are similar, with one ex-ception. The nonprofit small size category has a surprisingly low number of self-paypatients (mean = 134.4) compared to the for-profit category (mean = 1197.2). Itis possible, as hypothesized by Nyman,(6) that small nonprofit SNFs may be facingexcess demand for Medicaid patients, making quality a minimal concern; thus, fewerself-pay patients are willing to admit themselves to these facilities. Or given thepotential charitable mission of the nonprofit facility, they may admit government



Table I. Descriptive Statistics of DEA Inputs/Outputs by Facility Size andOwnership [Mean (Std. Dev.)]

Nonprofit

Variable

OutputsSelf-pay inpatient days

Government-payinpatient days

InputsBeds

FTEs

Operational Expenses(in $1000s)

Small(n = 65)

134.3(87.97)

1131.1(87.55)

68.52(12.96)

66.49(30.59)

265.56(180.91)

Medium(n = 49)

2312.9(172.86)2608.7(194.34)

148.00(36.93)

156.19(87.12)

563.65(339.63)

For-Profit

Small(n = 24)

1197.2(89.58)959.7(80.33)

68.69(15.14)

59.33(28.70)

222.21(123.74)

Medium(n = 86)

2275.7(193.17)2465.2(216.09)

146.95(42.20)

120.13(51.49)

431.24(327.82)

pay patients with a more severe case mix, which would also explain the higher op-erational expenses for the small, nonprofit, vis-a-vis the for-profit.

The input variables are similar for both the nonprofit and for-profit SNFs ineach size category. Consistent with the literature, nonprofit SNFs have higher ex-penses; means for the nonprofit and for-profit small groups are $265.56 thousandand $222.21 thousand, respectively. The nonprofit and for-profit medium homeshave respective operational expense means of $563.65 thousand and $431.24 thou-sand. Nonprofit SNFs also tend to have more FTEs than their for-profit counter-parts, although these differences are less pronounced.

Table II presents the results of the DEA efficiency analysis. The for-profit andmedium SNFs both appear more efficient than their counterparts, nonprofit andsmall, respectively. The average efficiency of the for-profits is .840 and for the non-

Table II. Efficiency by Profit Status and Facility Sizea

Avg. EfficiencyStandard deviation

Wilcoxon Tests ofefficiency differences

Z-testProb(Z)

Profit Status

For-Profit(n = 210)

.840(.151)

Nonprofit(n = 114)

.803(.178)

-1.85(0.064)

Size

Small(n = 189)

.803(.173)

Medium(n = 135)

.859(.139)

3.04(0.002)

aThe null hypothesis is that the distribution of the differences in efficiency betweenfor-profit and nonprofit or small versus medium size nursing homes is zero.


Table III. Slack Analysis of Inefficient Skilled Nursing Facilities

Ownership

Variable

OutputsSelf-pay inpatient days

Government-payinpatient days

InputsBeds

FTEs

Operational Expenses($1000s)

For-Profit(n = 175)

907.6(233.18)

325.8(41.24)

1.13(6.72)

13.43(21.05)

42.64(104.10)

Nonprofit(n = 105) t

980.2 0.26(220.34)

412.4 0.66(102.68)

0 . 5 7 - 0 . 8 8(3.82)

20.88 1.76*(40.15)

66.17 1.53(134.91)

Small(n = 165)

1569.7(280.02)

540.1(134.06)

0.10(0.98)

12.70(21.07)

44.36(79.88)

Size

Medium(n = 115) t

23.8 -7.07***(17.36)

97.5 -3.98***(40.90)

2.09 2.40**(8.87)

21.27 2.16**(38.68)

61.64 1.10(155.26)

*Significant at the 10% level.**Significant at the 5% level.

** *Significant at the 1% level.

profits .803, a statistically significant difference. The Wilcoxon statistic, a nonpara-metric, distribution free test, is used to test for statistical significance because anormal distribution of efficiency scores cannot be assumed. The differential in ef-ficiency between the size categories is also significant (p < .002); the efficiency ofthe small SNFs is .803; medium facilities have average efficiency of .859.

Consistent with the previous literature, nonprofit SNFs must reduce, on aver-age, a greater number of FTEs (20.88) than the for-profits (13.43). Operationalexpenses are also greater for the nonprofits, although this difference is insignificant.As expected, the slacks for medium nursing homes are lower than those for smallfacilities. Noteworthy, however, is the similarity between FTE and operational ex-pense input slack values for the for-profit and small, and the nonprofit and mediumnursing homes. The bed capacity of the facilities is appropriate, on average, possiblydue to CON regulations. Some evidence that nursing facilities may substitute towardother inputs when CON restricts bed size exists.(30)

On the output side, the slacks (shortages) for self-pay inpatient days are greaterthan the slacks for government days for all but the medium facilities. Both thefor-profit and small nursing homes need, on average, to increase their self-pay daysat a rate three times greater than government pays to become efficient. The non-profit SNFs must increase private pay days at a rate twice that of government pay.The shortages in patient days are not statistically different by ownership group;however, there is a significant difference in slacks between the small and mediumgroups, with the medium group requiring less additions to patient days than thesmall facilities (Table III).


Based on the efficiency analysis, evidence exists that various groups do facedifferent performance ratings. These distinctions must be accounted for when ana-lyzing determinants of efficiency; that is, post hoc analyses should use DEA effi-ciency scores as the dependent variable in a regression. Use of raw DEA efficiencyscores simply identifies determinants of efficiency for the group that dominates thepooled frontier. In the current analysis, for instance, 82% of the nursing homesare for-profit. Table IV presents the results of the logistic regression using DEAscores.

The explanatory power of the model is high (-2 Log Likelihood of 29.412; p =(0.0001). The most significant factor is the percent of Medicare inpatients. A nurs-ing home with a high percent of Medicare patients (>10%; the sample mean) is.322 times less likely to be efficient than those with a low percentage of Medicarepatients. The percentage of Medicaid patients, in contrast, has a positive impacton efficiency; nursing homes with a high percentage of Medicaid patients (>72%)are 2.67 times more likely to appear efficient. Also significant was the occupancyrate of the facility. Those nursing homes with above average occupancy (>95.8%)are 2.09 times as likely to have greater efficiency scores.

The controls for the population over 84 years and region were both insignifi-cant. The finding of insignificance for the regional controls suggests that the useof a national rather than single state sample may not impact the analysis, controllingfor ownership and profit status.

DISCUSSION

This study, by expanding upon the traditional DEA analysis, allows a directcomparison of efficiency across ownership type and size cluster groupings for a na-tional sample of 324 skilled nursing facilities. The analysis allows us to determineefficiency differentials across cluster groups when the groups are assumed to facethe same frontier.

Table IV. Predictors of Efficiency, Logistic Regression

Variable

InterceptPercent MedicaidPercent MedicareOccupancy ratePopulation over 84NortheastSouthCentral

ParameterEstimate

-2.2870.982

-1.1320.7370.347

-0.0930.7080.267

Wald Chi-Square

18.184***8.790***4.851**4.776**0.7850.0261.5790.163

StandardizedEstimate

0.235-0.2710.1740.081

-0.0250.1900.051

Odds Ratio

0.1022.6710.3222.0891.4150.9112.0311.305

**Significant at 5% level.***Significant at 1% level.

Consistent with much of the previous literature, this study found for-profit fa-cilities to be more efficient than nonprofits, when allowed to face the same frontier.However, it is generally accepted that these groups face different technological useand goal orientations.(9,19-25) On average, the production processes of the nonprofitSNFs are less technically efficient than those of the for-profits. Given the generaldivergence of goals of the nonprofit facilities,(22,24) and relative agreement uponthe goal of the for-profits (stockholder wealth maximization) this finding comes asa surprise. Potentially, because the nonprofit SNFs operate at a lower "benchmark"of efficiency, it may be easier to attain by a majority of the firms. However, it isimportant to note that this "benchmark" represents only production efficiency; thelevel of production efficiency could be lower due to a higher quality of care ormore intensive patient mix. The for-profits, in contrast, have a large number offacilities that excel in cost minimization, creating a greater variation in average ef-ficiency for this group.

Reasons nonprofit SNFs may operate at a lower level of technical efficiencymay include a higher number of public pay patients, lower number of self-pay pa-tients, and higher operational expenses and employment levels. Evidence linkingthe percentage of public pay patients to lower efficiency is mixed. Prospective pay-ment for Medicare should improve efficiency. Several studies have indicated nega-tive relationships between the proportion of public pay patients, efficiency, andexpenditures (possibly due to a lower quality of care for these patients).(3,8,9,28) How-ever, using Medicare as a case-mix index, a greater percentage of Medicare patientscould lead to higher resource intensity.(13) This study confirms the latter hypothesis.In fact, high Medicare homes are more than .322 times more likely to be inefficient.

This finding, although at odds with previous studies, is important because sizeand ownership are controlled for in the DEA rather than simply in post hoc re-gression analysis. If, as in this study's sample, for-profits dominate the pooled fron-tier, the percentage of Medicare patients would have an artificially negativecoefficient if for-profits have a lower than average Medicare census. Because ofthe excess demand for nursing home beds, for-profit facilities in an attempt to maxi-mize stockholder value, have incentives to reduce high complexity, Medicare census.

Another important variable in the ownership status efficiency differential isthe potential variation in quality. Consistent with both the property rights and publicchoice theories, nonprofit providers have higher expenses and labor capital. Doesthis greater resource commitment translate into superior quality or simply ineffi-ciency? DEA may control for the quality differential to some extent, assuming, afacility that uses poor inputs can also be assumed to have poorer quality outputs.Thus, there seems to be some justification for attributing at least a portion of theefficiency difference to efficiency itself and not quality concerns. However, onestudy did find that nursing homes with more resources per patient do have betterpatient outcomes.(31) Two other studies that attempt to sort differences in efficiencyand quality confirm this result.(9,34) Considerable concern over differentials in nurs-ing home quality, both across ownership status and payer type, does exist.(8,25,34)

Perhaps nonprofits correct contract failures in the nursing home market.(35) Thatis, the nonprofit SNF monitors quality not observable by the purchaser; the for-


profit, due to its cost minimization strategy, may have an inherent conflict of interestin providing high quality care.

Analysis of the size impacts on efficiency indicate that the medium firms aremore efficient. The excess operating expenses are of particular concern for thisgroup. In fact, the slack value is not significantly different from that of the mediumgroup. This is surprising since operating expenses, an indicator of firm size, wouldnaturally be so much greater in magnitude for the medium size firms, as seen inTable I. This low efficiency for the small group may be driven, in part, by the lownumber of self-pay days found among the small, nonprofit cluster group. Again, ahigher percentage of Medicare patients is found to decrease efficiency.

Two other variables in the regression analysis have significant positive impactson efficiency—occupancy rate and percentage Medicaid. SNFs with high occupancyrates are 2.09 times more likely to be efficient, consistent with Nyman's hypothesisthat higher occupancy allows the firm to staff more efficiently.(6,7) Several studieshave found a higher percentage of Medicaid patients associated with lower effi-ciency.(2,8) The current study concludes the opposite. Again, this finding could beconfounded by regional variations in Medicaid policy across a national sample. Yet,the regional control variables in the regression were insignificant, indicating thatsuch confounding factors could have canceled out across the sample.

One further point deserves mention. The slack variable for beds was minimal.This could indicate the positive impact that certificate of need laws have had onlimiting excess capacity in the nursing home market. By limiting beds in a highdemand market, occupancy rates of homes increased, thereby increasing efficiencyas well. Potential negative implications of CON are also visible, however. The highslack values for expenses and labor FTEs could indicate that nursing homes havesubstituted away from capital inputs toward these other inputs.(30)

Based upon the above discussion, several areas are identified as policy con-cerns. First, any reimbursement system should account for inherent variations innursing home goals that may impact efficiency, particularly for the nonprofit SNFs.The need for case-mix to be accounted for in reimbursement has already beenraised.(13) The impacts of the percentage of Medicare patients on efficiency is evi-dent in this study. Case-mix and efficiency may be confounding factors that bothmust be considered when developing a reimbursement schedule. Second, concernover the great variations and low overall level of efficiency among small firms shouldbe examined. When such a wide deviation exists, there are almost undeniable ef-ficiency and/or quality problems. In an industry where small firms are so prevalent,there is potential for tremendous cost savings. Finally, although not a direct impli-cation of this study, the role of quality must be ascertained. If the nonprofit doesnot improve quality, but simply engages in conspicuous consumption without directbenefits for individual patients, the nonprofit is not performing its function as acorrector of contract failure. In this instance, promoting for-profit ownership in thenursing home industry may actually improve efficiency and reduce aggregate ex-penses without impacting quality.


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