Cost Efficiency of Critical Access Hospitals

Transcription

1 Cost Effcency of Crtcal Access Hosptals I.Crstan Nedelea Graduate Student Department of Agrcultural Economcs and Agrbusness Lousana State Unversty 101 Ag. Admnstraton Bldg. Baton Rouge, LA Phone: Emal: J. Matthew Fannn, Ph.D. Assstant Professor Department of Agrcultural Economcs and Agrbusness Lousana State Unversty Agrcultural Center 101 Ag. Admnstraton Bldg. Baton Rouge, LA Phone: Emal: James N. Barnes, Ph.D. Assstant Professor and Drector Delta Rural Development Center Department of Agrcultural Economcs and Agrbusness Lousana State Unversty Agrcultural Center P.O. Box 620 Oak Grove, LA Emal: Selected Paper prepared for presentaton at the Southern Agrcultural Economcs Assocaton Annual Meetng, Orlando, FL, February 6-9, 2010 Copyrght 2010 by I. Crstan Nedelea, J. Matthew Fannn and James N. Barnes. All rghts reserved. Readers may make verbatm copes of ths document for non commercal purposes by any means, provded that ths copyrght notce appears on all such copes. 1

2 Introducton Relyng heavly on Medcare rembursement, small rural hosptals have been largely affected by the Medcare payment polces. Medcare has been the most mportant source of revenue for rural hosptals because rural communtes have a relatvely hgher proporton of the elderly compared to urban areas. Pror to 1983, Medcare rembursed hosptals accordng to the actual costs ncurred n provdng servces to Medcare benefcares. Ths cost-based rembursement gave hosptals lttle ncentves to control ther costs and encouraged both excessve servces and neffcently produced servces (Ganfrancesco 1990). In 1983, Medcare replaced cost-based rembursement wth the prospectve payment system 1 (PPS). The PPS system was desgned to promote effcency n hosptal operaton by rewardng hosptals that are able to keep ther costs below PPS rates and penalzng hosptals wth hgher costs 2. However, the rembursement changes caused by the PPS system led to the deteroraton of fnancal condtons of many small rural hosptals that commonly faled to cover costs on Medcare patents (Dalton et al. 2003). To allevate the negatve fnancal mpact of PPS on small rural hosptals, Congress created programs that exempted elgble rural hosptals from these payment polces. One of the most mportant programs, called Medcare Rural Hosptal Flexblty Program, was created by Congress n 1997 as part of the Balanced Budget Act. Ths program has establshed a natonal lmted servce hosptal program called Crtcal Access Hosptal (CAH). CAHs receve costbased rembursement 3 for npatent and outpatent servces delvered to Medcare benefcares 1 The PPS system pad a fxed fee based on the dagnoss related group (DRG) allowng varatons only for very serous cases that mght requre addtonal care and resources. 2 Under PPS system, hosptals are allowed to keep the surplus between the PPS rate and actual cost of provdng servces. Conversely, hosptals can lose money f ther costs exceed the PPS rate. 3 Under cost-based rembursement, hosptals were pad an nterm rate throughout the year, recevng retrospectve payments from Medcare for the dfference between nterm payments and total allowable cost at the end of ther fscal year. 2

3 and are desgned to address the needs of rural communtes where full servce hosptals are not fnancally vable (Capalbo et al. 2002). A rural hosptal qualfes as a CAH f t meets several requrements. Most mportantly the rural hosptal must be located at least 35 mles by prmary road, or 15 mles by secondary road, from the nearest full servce hosptal; use no more than 25 acute care beds at any one tme; the annual average length of stay cannot be greater than four days; and provde 24-hour emergency care servces. The PPS system reles on the assumpton that f a hosptal can ncrease ts net revenue by reducng costs, t wll seek ways to ncrease the effcency wth whch t uses ts resources (Sexton et al. 1989). Conversely, under cost-based rembursement a hosptal has an ncentve to ncrease costs n order to receve hgher revenues because Medcare pays for servces on a cost bass (McKay et al., 2002/2003). CAHs, whch receve cost-based rembursement, have been desgned to support small, solated rural hosptals that faced the threat of closure because of reduced patent volumes and rsng costs. However, even ths small rural hosptals need to be concerned wth the effcency of ther operatons because wastng resources s not n the nterest of ther long-term fnancal vablty. In addton, CAHs operate n an envronment where regulaton s geared to hosptal survval and prce competton s small. Vtalano and Toren (1996) ndcate that these two factors are not conducve to cost-mnmzng behavor for hosptals. Wth health care costs rsng at a rapd rate, understandng the factors that affect cost effcency of hosptals s crtcal. The prmary objectve of ths research s to examne the factors assocated wth cost neffcency of CAHs. Specfc objectve are: (1) estmate a stochastc fronter cost functon to assess the cost neffcency of CAHs; and (2) dentfy some of the factors that are lkely to nfluence the neffcency of CAHs. 3

4 Lterature revew Prevous studes of hosptal neffcency have found that hosptals do not use bestpractce or optmal combnatons of nputs to produce a gven level of output (McKay et al. 2002/2003). Emprcal studes of hosptal neffcency have appled fronter technques such as Data Envelopment Analyss or stochastc fronter analyss (SFA). SFA was developed ndependently by Agner, Lovell, and Schmdt (1977) and Meeusen and van den Broeck (1977). The frst study that used SFA to analyze US hosptal effcency was publshed by Zuckerman, Hadley, and Iezzony (1994). Usng data from 1,600 US hosptals, they estmated that neffcency accounts for 13.6 percent of total hosptal costs. In a dfferent study, Hadley, Zuckerman, and Iezzony (1996) looked at 1,435 acute-care US hosptals between 1987 and 1989 and found that hosptals facng greater fnancal pressure mproved ther effcency. Usng a stochastc costs fronter, Vtalano and Toren (1994) found that the average level of cost neffcency n New York nursng homes was 29 percent. Chronc excess demand, excessve manageral personnel and dseconomes of sze are suggested as causes of neffcency. In a smlar study, Vtalano and Toren (1996) appled SFA to 219 general care hosptals n New York n 1991 and estmated an average neffcency of 18 percent. They found that hosptals that rely more heavly on Medcare are more effcent and that rembursement restrctons may reduce neffcency. Rosko (1999) used SFA to estmate cost neffcency n US hosptals. Based on a sample of 3,262 hosptals from 1994, he calculated that mean neffcency s rangng from 20.2 percent to 25.5 percent dependng on the dstrbuton of error. The results show that fscal pressure from Medcare and Medcad creates ncentves for effcency. Model Specfcaton Ths study employs stochastc fronter analyss to estmate neffcency n CAHs. The stochastc fronter cost model may be specfed n a general form as: 4

5 TC = TC( Y, W ) + ε, = 1,2,..n (1) where TC represents total cost; Y s a vector of outputs; W s a vector of nput prces; and ε s a composte error term whch can be decomposed as: ε = v + u (2) where v captures random statstcal nose, assumed to be dstrbuted as N(0, σ 2 ); and u represents cost neffcency and s assumed to be postve. Furthermore, t s assumed that u has a specfc dstrbuton, dfferent of the dstrbuton of v, and that v and u are dstrbuted ndependently of each other and of regressors. Although the half-normal dstrbuton s the most frequently assumed, there s no economc crtera for the selecton of ths or other dstrbutonal forms for u (Schmdt and Sckles 1984). 4 The neffcency term u must be observed ndrectly from the estmates of ε (= v + u). Specfcally, for a half-normal dstrbuton, the expected mean value of neffcency, condtonal upon the composte resdual ε s (Jondrow et al., 1982): σλ φ( ε λ / σ ) ε λ E( u ε ) = (3) 2 (1 + λ ) Φ( ε λ / σ ) σ where σ = σ u + σ v. λ = σ u σ v ; φ (.) and Φ(.) are, respectvely, the probablty densty functon and cumulatve dstrbuton functon of the standard normal dstrbuton. In the estmaton of a stochastc fronter cost model, one must specfy a functonal form for the cost equaton. Hosptal applcatons of SFA have used a varety of functonal forms, the most popular beng the translog and the Cobb-Douglas cost functons (Rosko and Mutter 2008). Whle the man advantage of Cobb-Douglas s ts smplcty, f the structure of producton 4 Dstrbutons assumed for u are: half-normal, truncated-normal, exponental and gamma. 5

6 technology s more complex than ts Cobb-Douglas representaton, the unmodeled complexty wll show up n the error term, leadng to based estmates of the cost neffcency (Kumbhakar and Lovel 2000). The translog functonal form can mtgate ths problem. In a general form, the translog cost functon s expressed as: lntc = a λ lnw jl α lny + j lnw + l β lnw j j j ρ lny lnw + j η lny lny k m k ϕ PM + v + u (4) where TC s total cost; Y, outputs; W, nput prces; and PM represents product-mx varables, and v and u are as defned before. The translog cost functon n (4) s estmated wth the restrcton of lnear homogenety n nput prces mposed. Researchers have been nterested n explanng the factors that nfluence cost neffcency. The cost neffcency effects, u, could be specfed as (Battese and Coell 1995): u = z δ + w (5) where z s a vector of exogenous varables assocated wth neffcency effects; s a vector of parameters to be estmated; w s a random varable defned by the truncaton of the normal dstrbuton wth mean zero and varance σ 2, such that the pont of the truncaton s. The analyss of the effect of exogenous varables (z) on neffcency effects can be done n one or two stages. The two-stage approach ncludes a frst stage n whch one estmates the stochastc cost fronter model and predcts neffcency effects, gnorng z. Then, n the second stage, the neffcency estmates are regressed aganst the set of exogenous varables z. Kumbhakar and Lovel (2000) and Wang and Schmdt (2002) state that the two-stage procedure wll gve based results, because the model estmated n the frst stage s msspecfed. They 6

7 suggest that the soluton to ths bas problem s a one-stage procedure n whch equatons (1) and (5) are smultaneously estmated by maxmum lkelhood. We prefer the one-stage approach suggested by Battese and Coell (1995), Kumbhakar and Lovel (2000) and Wang and Schmdt (2002) to analyze cost effcency of CAHs. Our stochastc fronter cost model allows neffcency effects to be explctly modeled as a functon of exogenous varables, the parameters of whch are estmated smultaneously wth the stochastc cost fronter. The level of cost effcency of the th hosptal, CE, may be calculated as the rato of the fronter mnmum cost (where u =0 on the cost fronter) to observed total cost for the hosptal. Followng Coell et al. (2005), cost effcency of the th hosptal s defned as: CE = exp ( u ) (6) Ths measure ranges n value from zero to one, where the most effcent (low cost) hosptal takes a value of one. Smlarly, the amount by whch exp(u ) exceeds 1 s a measure of costneffcency of the th hosptal. Data The data used for ths study come prmarly from the 2006 Amercan Hosptal Assocaton Annual Survey of Hosptals and the 2006 Medcare Hosptal Cost Report. The unt of observaton for the analyss s the hosptal. The market area s defned as the county, a defnton used frequently n hosptal studes, as Rosko (1999, 2001) ponts out. Defnng the market area as the county allows us to use the Area Resource Fle for county level data. The dependent varable used n stochastc fronter cost model s represented by the total hosptal cost, calculated as the sum of total salary expenses, total captal related costs, and total other costs from the Medcare Cost Report (McKay and Dely 2008). The explanatory varables 7

8 consst of nput prces and outputs. In ther excellent revew of hosptal neffcency studes, Rosko and Mutter (2008) emphasze that most of the hosptal stochastc fronter studes ncluded both npatent and outpatent outputs. Followng McKay et al. (2002/2003) and Rosko (1999), we use as outputs the number of outpatent vsts, the number of admssons and the number of npatent days. Rosko and Mutter (2008) also pont out that, due to data constrants, the nput prce varables were smlar n each natonal study. Followng past practces, we use two nput prces n our analyss: the prce of labor, approxmated by the sum of hosptal payroll expenses and employee benefts dvded by the full-tme equvalent hosptal unt total personnel; and the prce of captal whch was approxmated by the sum of deprecaton expenses and nterest expenses (obtaned from Medcare Cost Report) dvded by the number of hosptal beds. The assumpton of lnear homogenety n nput prces s mposed by normalzng the cost equaton by the wage rate. Other varables nfluencng hosptal cost can be generally classfed as what Rosko and Mutter (2008) call product mx descrptors (.e., varables that reflect the heterogenety of output). In ths category we nclude the percent of outpatent vsts classfed as emergency ((emergency room vsts/ total outpatent vsts) 100), the percent of outpatent surgcal operatons ((outpatent surgeres/total outpatent vsts) 100), and the percent of npatent surgcal operatons ((npatent surgeres/admssons) 100). Two other varables ncluded n the cost functon are the number of hosptal beds set up and staffed and a varable for qualty to control for varaton n avalable hosptal servces. Folland and Hoffer (2001) state that falure to nclude a varable for qualty n the cost functon mght cause an omtted varable bas. We use as a qualty measure hosptal accredtaton status. Ths s represented by accredtaton by the 8

9 Jont Commsson on Accredtaton of Health Care Organzatons (JCAHO), a qualty measure commonly used n the lterature (see for example McKay and Dely 2008). A set of varables that may affect hosptal cost effcency s also ncluded n the stochastc fronter cost model. The prmary varables of nterest are those assocated wth the type of hosptal rembursement polces, ownershp status, and the degree of competton n a hosptal s market. Rosko (1999) suggests that cost neffcency may arse from the lack of regulatory pressure on hosptals to operate as effcently as possble. Medcare rembursement polces have an mpact on hosptal profts and can create ncentves for hosptals to operate more effcently. For example, rembursement polces under Medcare PPS create ncentves for reducng neffcency whle cost based rembursement gves hosptals lttle ncentves to control ther costs. We follow Vtalano & Toren (1996), McKay et al. (2002/2003), and Rosko (1999, 2001) and use two varables to reflect the regulatory pressure of publc payers: Medcare percent of dscharges ((Medcare dscharges / total dscharges) 100) and Medcad percent of dscharges ((Medcad dscharges / total dscharges) 100). Under PPS system, Medcare rembursement polces place fscal pressure on hosptals. Therefore the Medcare percent varable s expected to be nversely related wth neffcency when hosptals receve PPS rembursement. On the other hand, CAHs receve Medcare cost-based rembursement and have lttle ncentves to control ther costs. As a result, we expect that Medcare percent varable to be drectly assocated wth CAH cost neffcency. The effect of ownershp on hosptal effcency should be consstent wth Property Rghts Theory whch suggests that for-proft hosptals pursue proft maxmzaton (Rosko 1999). One way n whch for-proft hosptals ncrease ther profts s by reducng neffcency. The 9

10 ownershp status n ths analyss s ntroduced by usng dummy varables that defne publc/government owned hosptals, prvate non-proft hosptals and for-proft hosptals. Followng Rosko (1999, 2001) and Mutter and Rosko (2008) we use a Herfndahl- Hrschman ndex (HHI) to measure compettve pressure n the hosptal s market. HHI s calculated by summng the squares of the market shares of admssons for all of the hosptals n the county. Ths ndex equals one n monopolstc markets and approaches zero n markets wth hgh competton. Hgher HHI values reflect less compettve pressure, and hence ncreased effcency should be nversely related to HHI. Other varables that may be used to explan hosptal cost neffcency s county unemployment rate (from 2006 Area Resource Fle) and occupancy rate. The county unemployment rate s used as a proxy for the amount of uncompensated care provded by the hosptal. The occupancy rate s defned as the number of npatent days dvded by the cumulatve number of beds mantaned durng the year (number of hosptal beds 365 days). Ferrer and Valdmans (1996) found that hgher occupancy rates n rural hosptals helped enhance cost effcency. We also ncluded a dummy varable to represent whether the hosptal partcpates n a network. When a hosptal partcpates n a network, t has an agreement wth one or more hosptals for transfer of patents and sharng of resources and personnel. Ths allows hosptal to provde servces at lower costs by allocatng the treatment of patents across network members. Thus, t s expected that hosptals that partcpate n a network to be more effcent than the ones that does not. We started the analyss wth a sample of general medcal and surgcal hosptals located n non-metro areas n the U.S. n Ths sample also ncludes nformaton on CAHs whch s of prmary mportance for our study. Although there were 2,053 rural hosptals (out of whch

11 were CAHs) n the ntal sample, data ssues ncludng mssng nformaton and mplausble values reduced the number of hosptals elgble for the study to 1,561 rural hosptals (ncludng 688 CAHs). Table 1 presents defntons and summary statstcs for all varables used n emprcal estmaton for both the sample of non-cah rural hosptals and the CAH sample. Table 1. Varable defntons and summary statstcs CAH Sample (n=688) Non-CAH Sample (n=873) Cost Functon Varables Mean Std. Dev. Mean Std. Dev. tc Total hosptal cost($) 13,400, ,473, ,400, ,800, admh Total hosptal admssons , , pdh Total npatent days 2, , , , vtot Total outpatent vsts 28, , , , Pk Prce of captal($) 35, , , , w Prce of labor($) 49, , , , erpct % emergency room vsts suroppct % outpatent surgeres surppct % npatent surgeres jcaho 1 f accredted by JCAHO bdh # hosptal beds Effcency Varables gov Government owned hosptal nproft Non-proft hosptal fproft For-proft hosptal mcrpct % Medcare dscharges mcdpct % Medcad dscharges unemployment County unemployment % occup Occupancy rate netwrk 1 f h. partcpate n a network hh Herfndahl-Hrschman ndex Results We frst examned whether beng a CAH has any effect on hosptal effcency. Ths was done by estmatng a stochastc fronter model (wth a translog cost functon and assumng a half-normal dstrbuton for the neffcency error term) usng a sample of rural hosptals (N = 1,561) and ncludng n the neffcency equaton a dummy varable that s 1 f the hosptal s a 11

12 CAH and 0 otherwse. The estmated results show a postve and sgnfcant (p-value<0.01) coeffcent of CAH varable. Ths ndcates that CAHs mght be less effcent relatve to other rural hosptals. To nvestgate whether CAHs and non-cah rural hosptals can be modeled separately, we performed a Chow test to test the null hypothess of poolng the data (estmatng a stochastc cost fronter model usng the full sample of rural hosptals) aganst the alternatve of sortng the data (estmatng separate cost fronter models for CAHs and non-cah rural hosptals). The rejecton of the null hypothess (based on a Ch-square of and a p-value<0.05) ndcates that separate cost fronter models for CAHs and non-cah rural hosptals are approprate. Next, we performed a seres of lkelhood-rato tests to arrve at a preferred model usng the sample of CAHs. We started by testng H 0 : 0, where s a parameter of the log-lkelhood functon (Battese and Corra 1977). Ths s equvalent to testng whether SFA s more approprate than OLS as an estmaton technque. Acceptng H 0 mples that 0 and thus the devatons from the cost fronter are due entrely to statstcal nose. In ths case the parameters can be consstently estmated usng OLS. As ndcated n Tables 2, the null hypothess that 0 was rejected and the stochastc fronter cost model was used n emprcal analyss. We also tested whether a smpler functonal form such as Cobb-Douglas can more accurately represent the cost fronter. The null hypothess was that the parameters of all squared and nteracton terms n the translog cost functon equals zero (B j =0). The rejecton of the null hypothess, as shown n Table 2, ndcates that the translog functonal form can more accurately represent the cost fronter. 12

13 In SFA, an assumpton about the dstrbuton of the neffcency error term, u, must be made. The truncated normal dstrbuton for u, defned as u~ N + (µ, ), reduces to the halfnormal dstrbuton when µ=0. Therefore, we can test whether t s approprate to assume the half-normal dstrbuton for u by testng H 0 : µ=0. As Table 2 shows, we faled to reject H 0 and the half-normal dstrbuton for u was assumed n emprcal estmaton. Fnally, we tested whether the exogenous neffcency varables, as a group, have a sgnfcant mpact on cost neffcency. Ths s equvalent to testng H 0 : δ 1 =δ 2 =.=δ 8 =0. As Table 2 shows, the H 0 was rejected and ths mples that the exogenous neffcency varables belong n the model. Based on the above lkelhood rato tests, our preferred stochastc fronter model for the CAH sample uses the translog cost functon wth a half-normal dstrbuton for the neffcency error term. For comparson purposes, we also estmate a separate stochastc fronter model for non-cah rural hosptals mposng the same condtons as n the model that uses CAH sample. Table 2. Lkelhood-rato test of null hypothess for parameters of stochastc fronter translog cost model for CAH sample. Null Hypothess Tests Statstc Χ 2 (0.05) Decson µ = Fal to Reject B j = Reject = Reject δ 1 =δ 2 =.=δ 8 = Reject Table 3 summarzes the emprcal results obtaned from estmatng separate stochastc fronter models for CAHs and non-cah rural hosptals. Some of the estmated coeffcents of the output varables and nteracton terms are nsgnfcant and of unexpected sgn. Ths may be due to multcolnearty problems. When the Cobb-Douglas cost functon was used, the coeffcents of nput prce and output varables were sgnfcant and had the expected postve sgn. 13

14 In the translog cost functon, the squared and nteracton terms make the ndvdual estmated coeffcents dffcult to nterpret drectly. As an alternatve, these coeffcents can be used to calculate cost elastcetes. The cost elastctes of prce of captal and npatent days, evaluated at the mean, are larger for CAHs ( and ) than for non-cahs ( and ). On the other hand, the cost elastctes of admssons and outpatent vsts are larger for non-cahs ( and ) than for CAHs ( and ). The coeffcent of the accredtaton by JCAHO varable was found nsgnfcant n both samples of data. Whle the coeffcent of erpct varable was found nsgnfcant n the CAH sample, t was sgnfcant (p-value<0.01) and postve n the non-cah sample. The coeffcent of bdh varable s postve and hghly statstcally sgnfcant (p-value<0.01) n both samples; however, t s larger for CAHs mplyng that a reducton n bdh would decrease costs more for CAHs than for non-cah rural hosptals. Smlarly, the coeffcents of suroppct and surppct are postve and statstcally sgnfcant (p-value<0.05) n both samples, mplyng that an ncrease n surgcal operatons leads to an ncrease n hosptal costs. More mportant for our study are results for the neffcency effects varables presented at the bottom of Table 3. Whle the coeffcent of the government ownershp varable s negatve and sgnfcant (p-value<0.05) for CAHs, t s postve and sgnfcant (p-value<0.05) for non- CAHs. These results ndcate that government owned CAHs are more effcent relatve to nonproft CAHs whle government non-cahs are less effcent relatve to non-proft non-cahs. The postve and sgnfcant (p-value<0.05) coeffcent of for-proft varable for CAHs s contrary to what Property Rghts Theory (PRT) predcts. Contrary to what PRT predcts, our results ndcate that for-proft CAHs are more neffcent than non-proft CAHs. An explanaton for ths result could be the lack of constrant n payment mechansm. CAHs are fully rembursed 14

15 for the cost of provdng care to Medcare and Medcad benefcares and ths could dmnsh the attenton managers of for-proft CAHs mght pay to the mprovement of effcency. Our results for CAHs are consstent wth the fndngs of Ozcan et al. (1992) who found that for-proft hosptals are less effcent than non-proft hosptals whle government hosptals are more effcent than non-proft counterparts. It s wdely recognzed that hosptals respond to Medcare and Medcad payment mechansms (McKay et al. 2002/2003, Rosko and Mutter 2008). A large number of studes show that Medcare PPS payment mechansm places fscal pressure on hosptals. In these studes Medcare s nversely related to neffcency (Rosko 1999). CAHs receve cost-based rembursement and ths provdes lttle ncentve for cost control. Our man hypothess was that Medcare cost-based rembursement for CAHs may lead to neffcent operaton of these hosptals. The estmated results show that the coeffcent of mcrpct s nsgnfcant n CAH sample but postve and sgnfcant (p-value<0.01) n non-cah sample. The postve sgn of mcrpct n non-cah sample s unexpected and counterntutve. The coeffcent of mcdpct varable s postve and sgnfcant (p-value<0.05) for CAHs and nsgnfcant for non-cahs. The postve coeffcent of mcdpct ndcates that mcdpct mght lead to an ncrease n cost neffcency because CAHs tend to be located n less affluent areas wth a large number of Medcad and unnsured patents. The coeffcent of unemployment percent varable (a proxy for uncompensated care) s postve and sgnfcant for both CAHs and non-cahs; however, t s much larger (0.072) for CAHs than for non-cahs (0.040). An explanaton for the larger, postve effect of unemployment rate on CAH cost neffcency may resde n the combnaton of cost-based rembursement and the hgher levels of uncompensated care that CAHs provde. 15

16 The coeffcent of occupancy rate varable s negatve and hghly sgnfcant (pvalue<0.01) for both CAHs and non-cahs; however, the coeffcent s larger n absolute value for CAHs (4.92) than for non-cahs (4.04). Therefore, the results ndcate that an ncrease n occupancy rate leads to a decrease n cost neffcency. Ths s consstent wth Ferrer and Valdmans (1996), who found that occupancy rate n rural hosptals, s strong, postvely correlated wth cost effcency. The estmated coeffcent of netwrk has the expected negatve sgn and s statstcally sgnfcant (p-value<0.10) for CAHs and postve and sgnfcant (pvalue<0.01) for non-cahs. The negatve coeffcent of netwrk varable ndcates that CAHs that partcpate n a network wth other hosptals tend to be more cost effcent. Ths supports the recommendaton of Flex Program for CAHs to partcpate n networks wth other hosptals. Results presented n Table 3 also show that the estmated coeffcent of Herfndahl- Hrschman Index (hh) s negatve and sgnfcant (p-value<0.05) for CAHs and nsgnfcant for non-cahs. The negatve effect of hh on CAH cost neffcency may be contrary to what economc theory suggests. Economc theory predcts that f market competton s decreased (meanng that hh s ncreased), hosptals may not be cost mnmzers. In other words, f competton s ncreased (or hh s decreased) hosptals wll compete for patents by reducng costs. However, ths may not be always the case. Rosko (1996) states that payment mechansm must be taken nto account when evaluatng the mpact of competton on hosptal performance. He suggests that, pror to 1983, n the context of cost based rembursement, hosptals engaged n servce-based competton. Robnson and Luft (1985) and Noether (1988) support ths hypothess by suggestng that n more compettve markets hosptals employ more captal and equpment, produce more expensve medcal care and ncur hgher costs than hosptals n monopolstc markets. Therefore, the negatve coeffcent of hh, mplyng that as market 16

17 competton s decreased cost effcency of CAHs ncreases, s consstent wth the practce of servce-based competton. Conclusons In ths study, we attempted to answer to the followng two questons: Are CAHs less cost effcent than other rural hosptals? and f they are, Is Medcare cost-based rembursement the man cause of CAHs cost neffcency? Manly, we tred to analyze whether the polcy changes that created CAHs caused an ncrease n the cost neffcency of these hosptals or ther cost neffcency s brought about by other factors that are smlar to all rural hosptals. We estmated a mean effcency of CAHs of 1.25 whch gves an average cost neffcency of 25%. On the other hand, the estmated mean effcency of non-cah rural hosptals was whch mples an average cost neffcency of 17.6%. 5 Thus, the estmated results ndcate that CAHs are ndeed more cost neffcent than non-cah rural hosptals. However, the results do not ndcate that Medcare cost-based rembursement s the man cause of hgher cost neffcency of CAHs snce we found an nsgnfcant coeffcent of the Medcare percent of dscharges varable. There mght be other causes that can explan the hgher cost neffcency of CAHs such as the larger number of Medcad and unnsured patents and the hgher levels of uncompensated care that CAHs provde. A varable that we dd not consder because of the lack of data, but whch Vtalano and Toren (1994) found postvely correlated wth cost neffcency of nursng homes s the excessve manageral personnel. Therefore, poor manageral performance mght be another cause of ncreased cost neffcency of CAHs. An mportant polcy mplcaton of ths study s that regulatory changes amed at reducng cost neffcency of CAHs should focus on ncreasng occupancy rate n these hosptals. 5 Dfference between mean effcency of CAHs and that of non-cahs was statstcally sgnfcant at 0.01 level of sgnfcance (t-test=6.151). 17

18 One way n whch occupancy rate can be ncreased s by reducng the number of beds of CAHs, perhaps at the ntal levels provded under the BBA of 1997 (15 acute care beds, nstead of 25 as t s requred now). Another polcy mplcaton s that the requrement of Flex Program that CAHs should create networks wth larger hosptals s an example of good polcy snce ths leads to an ncrease n cost effcency of CAHs. Obvously, ths study has some mportant lmtatons. One of the most mportant lmtatons resdes n the strong dstrbutonal assumptons used wth cross-sectonal data. Schmdt and Sckles (1984) noted that maxmum lkelhood estmaton of the stochastc fronter model and the subsequent separaton of neffcency from statstcal nose, both requre strong dstrbutonal assumptons on each error component when cross-sectonal data are used. Furthermore, maxmum lkelhood estmaton requres an assumpton that neffcency error component be ndependent of all explanatory varables. Followng past lterature, we also assumed exogenous nput prces and exogenous outputs n the estmaton of stochastc fronter cost model. Another lmtaton was that the study was lmted to one year. It would be nterestng to examne changes n neffcency over tme, perhaps by comparng the neffcency of hosptals before and after the converson to CAH status. But ths mght be the subject of future research. 18

19 Table 3. Parameter estmates for fronter cost model CAH (n=688) Non CAH (n=873) Varable Coeffcent t rato Coeffcent t rato cons * ln(admh) * ln(pdh) ** ln(vtot) ** ln(pk) * * ln(admh)-sq ** ** ln(admh)*ln(pdh) * ** ln(admh)*ln(vtot) ** ln(pdh)-sq ** ln(pdh)*ln(vtot) * ln(vtot)-sq * ln(pk)-sq * * ln(admh)*ln(pk) * ln(pdh)*ln(pk) * ln(vtot)*ln(pk) * * erpct * suroppct ** ** surppct ** * jcaho bdh * * Ineffcency Effects gov ** ** fproft ** mcrpct * mcdpct ** unemployment * ** occup * * netwrk *** * hh ** σ * * γ * * *p<0.01, **p<0.05, ***p<

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