There is considerable variation in health care utilization and spending. Geographic Variation in Health Care: The Role of Private Markets

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1 TOMAS J. PHILIPSON Univesity of Chicago SETH A. SEABUY AND Copoation LEE M. LOCKWOOD Univesity of Chicago DANA P. GOLDMAN Univesity of Southen Califonia DAIUS N. LAKDAWALLA Univesity of Southen Califonia Geogaphic Vaiation in Health Cae: The ole of Pivate Makets ABSTACT The Datmouth Atlas of Health Cae has documented substantial egional vaiation in health cae utilization and spending, beyond what would be expected fom such obsevable factos as demogaphics and disease seveity. Howeve, since these data ae specific to Medicae, it is unclea to what extent this finding genealizes to the pivate secto. Economic theoy suggests that pivate insues have stonge incentives to estain utilization and costs, while public insues have geate monopsony powe to estain pices. We ague that these two diffeences alone should lead to geate egional vaiation in utilization fo the public secto, but eithe moe o less vaiation in spending. We povide evidence that vaiation in utilization in the public secto is about 2.8 times as geat fo outpatient visits (p < 0.01) and 3.9 times as geat fo hospital days (p = 0.09) as in the pivate secto. Vaiation in spending appeas to be geate in the pivate secto, consistent with the impotance of public secto pice estaints. Thee is consideable vaiation in health cae utilization and spending acoss geogaphic aeas in the United States, but little evidence of coesponding diffeences in health outcomes o satisfaction with cae. 1 This vaiability is often cited as evidence that cuent levels of health cae spending eflect flat-of-the-cuve medicine, that is, teatment fo which the maginal benefit of an additional unit of cae is appoximately zeo. 1. The main data souce used to document egional vaiations is the Datmouth Atlas of Health Cae, which can be found at (accessed Januay 15, 2010). 325

2 326 Bookings Papes on Economic Activity, Sping 2010 Intepeted this way, these findings have damatic implications fo the potential to incease the poductivity of health cae spending, and fo this eason they have figued pominently in the policy debate. Howeve, the evidence on egional vaiation is almost exclusively limited to the public secto, because it elies on Medicae data. Less is known about the coesponding pattens in the pivate secto. A veneable liteatue in economics has agued that pivate fims and thei manages have stonge incentives to estain costs and boost efficiency than thei public countepats. 2 In the health insuance context, Medicae does not face competition ove pemiums that might othewise estain its costs, and unlike pivate secto fims, Medicae does not have diect esidual claimants whose standad of living impoves with the efficiency of the entepise. To develop the implications of these incentive diffeences, this pape povides a theoetical and empiical analysis of how egional vaiation in health cae diffes acoss the public and the pivate sectos. We fist examine conceptually how pivate effots to contol costs within a egion, though selection of povides, might tanslate into diffeences in cae acoss egions. In paticula, ou analysis implies that utilization contols within egions in the pivate secto should lead to lowe egional vaiation in the pivate secto than in Medicae. Howeve, the implications fo vaiation in spending ae less clea, because Medicae may also be able to bette contol pices though its geate monopsony powe. If the pivate secto contols utilization while the public secto contols pices, the esult is an ambiguous pediction fo vaiation in spending. We examine these implications empiically using individual-level data on patients with heat disease, compaing utilization and spending on patients who have pivate insuance with that on simila patients within Medicae. Data on the fome come fom a lage database of pivate secto medical claims, and on the latte fom the Medicae Cuent Beneficiay Suvey. Both datasets include patient-level demogaphics and co-mobidities, which allow us to identify egional vaiation distinct fom individual chaacteistics such as health. The focus on heat disease helps 2. Alchian and Demsetz (1972) showed a geate incentive fo shiking and inefficiency in public entepise, whee manages and employees own standads of living ae unaffected by poo pefomance. De Alessi (1974a, 1974b) obseved that inefficient pivate fims disappea, wheeas inefficient public fims can last fo long peiods. Spann (1977) agued that pivate fims typically poduce simila goods and sevices at much lowe cost than thei public countepats.

3 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 327 mitigate the confounding impact of egional diffeences in health status on ou analysis. Ou main object of inteest is the egional vaiation in utilization and spending acoss sectos that cannot be explained by vaiation in patient chaacteistics. Ou data suggest geate vaiation in utilization in the public secto: ou main analysis suggests that vaiation in the public secto is about 2.8 times as geat fo outpatient visits (p < 0.01) and 3.9 times as geat fo hospital days (p = 0.09) as in the pivate secto. Thee is some evidence of geate vaiation fo the numbe of hospitalizations in the public secto, but this evidence is less obust. Pesciption dug utilization seves as ou placebo case of insuance that was pivately povided in both samples duing the peiod investigated. Significantly, and unlike othe types of medical cae, dug utilization exhibits less vaiation among Medicae patients. On the othe hand, thee is geate spending vaiation in the pivate secto, suggesting the potential impotance of monopsony powe in the public secto. The pape poceeds as follows. Section I povides the conceptual analysis of how diffeing cost-contol measues within a egion might lead to diffeences in egional vaiation in utilization and spending. Section II epots ou empiical analysis compaing egional vaiation in the public and the pivate sectos. Section III discusses how ou findings elate to the existing liteatue on health cae vaiation and the esulting policy implications. Section IV discusses some limitations of ou analysis and pesents seveal obustness tests. Section V concludes. I. A Simple Analysis of egional Vaiation in Utilization and Spending This section pesents a simple analysis of how pivate and public incentives inteact to ceate diffeent degees of egional vaiation in health cae utilization and spending between the public and the pivate secto. 3 A key assumption is that pivate insues have stonge incentives to estain costs and utilization than a public insue such as Medicae. This assumption is based on the liteatue demonstating that, unlike public entepises, 3. This analysis is geneal enough to include seveal possible souces of egional diffeences, and in paticula it allows fo such diffeences to be efficient. Howeve, diffeences in liability (Kessle and McClellan 1996, 2002a, 2002b, Baicke and Chanda 2007) o poductivity (Chanda and Staige 2007), fo example, may imply diffeences in efficient levels of cae.

4 328 Bookings Papes on Economic Activity, Sping 2010 pivate fims have to estain costs in ode to compete on pice, and pivate fims inefficiencies have diect impacts on the welfae of thei ownes and employees (Alchian and Demsetz 1972, De Alessi 1974a). Fo example, pivate payes may explicitly manage cae and exet pessue on povides though utilization eview and case management. They can also selectively contact with lowe-cost povides, stee patients to pefeed povides, and exclude inefficient doctos o hospitals. In addition, pio authoization of lage expenditues is pevalent in the pivate health insuance secto, a pactice that allocates majo spending decisions to the paye athe than the povide. Finally, pivate payes can stee patients towad efficient cae though benefits management fo example, by not coveing cetain sevices unless cetain clinical citeia ae met. In what follows, we use the shothand of utilization estictions (U) to efe to all these pactices. We intepet U as a limit on the povision of teatments whose costs exceed thei benefits. This may still lead to egional vaiation in utilization, because thee is substantial heteogeneity among appaently simila patients in the efficacy of diffeent teatments. Excessive cae fo one patient may be cost-effective fo anothe. I.A. Causes of Sectoal Diffeences within egions We fist conside the level of utilization in both the pivate and the public sectos. Define y* as the efficient utilization level, that at which maginal benefit equals maginal cost. Following the ealie liteatue, we assume that pivate insues have stonge incentives to limit utilization that ises above this level. They do this though U, which we assume places an uppe bound on utilization, y U y*, and pefectly eliminates inefficient utilization above that level. 4 The assumption of full efficiency is an analytical simplification; the positive pedictions do not depend on it, and we do not emphasize the nomative pedictions. Within any egion thee is a distibution of povides, who vay in the level of cae they would povide to an identical patient. We chaacteize this distibution using the cumulative distibution function F(y) fo the andom utilization vaiable Y. Pivate payes U pocedues limit utilization and theeby tuncate the suppot of the povides paticipating in thei plans. This esults in the pivate mean utilization level, µ = E(Y Y y U ). 4. Impefect U has qualitatively simila theoetical implications. The diffeence is one of degee athe than natue.

5 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 329 Figue 1. Vaiation in Spending Given Public Monopsony Powe Maginal cost (MC) MC MC * MC p* MC p µ µ p Aveage utilization Souce: Authos model descibed in the text. This constained pivate secto mean is thus lowe than the unconstained public secto mean, µ p E(Y). Now conside a pue incease in utilization, holding health status fixed. This can be epesented as a ightwad shift in the function F(y). Assuming the efficient level of utilization emains fixed, the esult is a geate diffeence in mean utilization acoss the two sectos, µ p µ. In othe wods, in egions with povides who have geate tendencies towad inefficiency, the diffeence in utilization between sectos will be lage. The second key assumption is the pesence of geate monopsony powe in the public secto. The esult is geate estaint of pices, as opposed to utilization, in the public secto. This affects the analysis of vaiation in spending, which combines the utilization effect and the pice effect. If the govenment pays below-maket pices though the execise of eithe monopsony powe o diect pice egulation, the cost cuves will diffe acoss sectos. The esult is depicted in figue 1. Aveage spending pe patient in the pivate secto may exceed that in the public secto, if equilibium maginal cost in the public secto, MC p *, is less than equilibium maginal cost in the pivate secto, MC*. I.B. Causes of Sectoal Diffeences acoss egions Next conside how mean utilization fo each secto might vay acoss egions. Define the joint distibution G(µ p, µ) of mean utilization levels acoss egions. Specifically, suppose that the undelying distibution F(y)

6 330 Bookings Papes on Economic Activity, Sping 2010 Figue 2. Compaing Diffeences in Utilization acoss Sectos µ µ = µ p egion egion ' µ(') Δµ µ(') < µ p (') Δµ < Δµ p µ() µ p () Souce: Authos model descibed in the text. Δµ p µ p (') µ p diffes acoss egions. Figue 2 illustates how one might then chaacteize the elationship between changes in the public mean and the mean diffeence between sectos: d ( ) = p μ μ p dμ dμ 1 p. dμ Fo example, conside the case of nomally distibuted public secto utilization, Y N(µ p, σ 2 ). In this case, mean utilization in the pivate secto follows fom the fomula fo the mean of a tuncated nomal andom ϕα ( vaiable, µ = µ p +σλ(α), whee λα ( ) ) Φ( is the invese Mills atio and α ) p y μ α = U. This implies that the slope of the pivate mean as a function σ of the public mean is less than unity o, equivalently, that the betweensecto diffeence ises with the public mean: 5 dμ 0 1 p dμ p d ( μ μ) 0. p dμ 5. We use the fact that the deivative of the invese Mills atio with espect to α is stictly between zeo and 1, λ (α) (0,1). (Sampfod 1953).

7 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 331 When the public secto povides moe cae above the efficient level, this aises the between-secto diffeence. This in tun implies that the vaiance in the egional means in the public secto will exceed the vaiance in the egional means in the pivate secto: V(µ p ) > V(µ). This simple famewok leads to seveal testable empiical pedictions: Pivate povision should lead to lowe mean utilization and less vaiance in mean utilization acoss egions, but not necessaily lowe mean spending. In addition, the diffeence in utilization between sectos is likely to ise with the mean level of public utilization. Note that all these pedictions hold patient health status constant. II. Empiical Analysis of egional Vaiation acoss Sectos In this section we descibe ou empiical analysis of egional vaiation in the public and pivate sectos aimed at testing the implications discussed above. II.A. Data and Empiical Specification We compae egional vaiation between a sample of pivately insued patients and a sample of Medicae patients. The pivate data come fom a lage database of health insuance claims. The data captue all health cae claims, including pesciption dugs and inpatient, emegency, and ambulatoy sevices, by employees and etiees while they ae enolled in the health plans of 35 Fotune 500 fims. The analytical database integates component datasets of medical claims, phamacy claims, and enollment ecods. This allows us to calculate spending and utilization fo all sevices povided to the patients ove ou study peiod. The enollment ecods allow us to identify basic demogaphics of the patients, including age, sex, and some infomation on income. 6 Impotantly fo ou puposes, the data also include infomation on aea of esidence, coded by metopolitan statistical aea (MSA) and 3-digit zip code. This allows us to analyze health cae spending and utilization pattens at diffeent levels of geogaphic aggegation. Ou Medicae sample is taken fom the Medicae Cuent Beneficiay Suvey (MCBS), which is administeed to a nationally epesentative sample of aged, disabled, o institutionalized Medicae beneficiaies. 6. Ou poxy fo income is median household income at the 3-digit zip code level; this is taken fom the 2000 Census.

8 332 Bookings Papes on Economic Activity, Sping 2010 espondents, whethe living in the community o esiding in health cae facilities, ae inteviewed up to 12 times ove a 4-yea peiod. Institutionalized espondents ae inteviewed by poxy. Thee is ovesampling of the disabled unde 65 yeas of age and of the oldest old (85 yeas of age o olde). The MCBS uses a otating panel design with limited peiods of paticipation. Each fall a new panel is intoduced with a taget sample size of 12,000 espondents, and each summe a panel is etied. The MCBS data include detailed infomation on self-epoted health status, health cae use and expenditue, insuance coveage, and demogaphic chaacteistics. Additional Medicae claims data fo beneficiaies enolled in fee-fo-sevice plans ae also incopoated to povide moe accuate infomation on health cae use and expenditue. The MCBS data do not include actual claims data on pesciption dugs; all infomation on pesciption dug spending and utilization in the MCBS is self-epoted. This leads to a known undecount of dug spending in the MCBS. 7 Both datasets include infomation on medical claims that is used to compile utilization, spending, and baseline health infomation. That is, although the MCBS contains a suvey component, all data on spending and utilization ae compaed with Medicae s administative claims data (Eppig and Chulis 1997). Howeve, since Medicae does not cove pesciption dugs ove ou sample peiod, this validation pocedue applies to medical cae but not dugs. Finally, fo both datasets we use infomation fom 2000 to The one exception is pesciption dug utilization and spending: to abstact fom the complexities of Medicae Pat D s intoduction, we eliminate the 2006 data fo these vaiables. To mitigate diffeences in health status acoss sectos and egions, we condition inclusion in the sample on a diagnosis of ischemic heat disease (IHD). 8 We also use the diagnosis codes on medical claims to identify whethe patients wee teated fo any of 30 diffeent conditions in a 7. When estimating the cost of Medicae Pat D (fo example), the Congessional Budget Office scaled the epoted MCBS pesciption dug spending up by 33 pecent fo the noninstitutionalized population (Chistensen and Wagne 2000). 8. Also called myocadial ischemia, IHD is chaacteized as educed blood flow to the heat. In the pivate data we identify patients with IHD as those with at least one inpatient o outpatient claim with a pimay diagnosis ICD-9 code of 410.xx, 411.xx, 412.xx, 413.xx, o 414.xx. In the public data we identify patients based on self-epots of eve diagnosed with heat disease. See the online data appendix (available on the Bookings Papes webpage at unde Confeences and Papes ) fo moe infomation.

9 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 333 calenda yea. 9 The claims-based measues of the numbe of diseases ae available in both the MCBS and the pivate health insuance data. 10 This is impotant because unmeasued diffeences in seveity acoss egions could lead to spuious positive coelation between sectos. The pimay geogaphic unit of analysis fo ou study is the MSA. An altenative candidate would be the hospital efeal egion (H), used by the Datmouth Atlas. Howeve, Hs ae not epoted in eithe of ou datasets, and the pivate secto data do not contain 5-digit zip codes, which ae equied to constuct an individual s H. We estict ou sample to the 99 MSAs whee we have the lagest samples. MSAs ae somewhat lage than Hs, and this may compess the vaiation fo both sectos in ou data. Ou final sample contains 240,028 pivate patients and 24,800 public patients. 11 Since thee ae many fewe public patients, it is impotant to coect fo the effects of sample size on ou estimates. We deive and epot these coections in detail below. Table 1 epots some summay statistics compaing demogaphic chaacteistics in the public and the pivate samples. As one would expect, the aveage age in the pivate sample is lowe than in the sample of Medicae patients, most of whom ae olde than 65. The pivate sample contains a geate faction of males, in pat because it is influenced by cuent o past employment status. (The pivate sample contains both active wokes and etiees eceiving benefits fom thei cuent o past employes.) Aveage income is also highe in the pivate sample. The geate vaiance in income fo the public sample is likely due to the fact that income is epoted individually in the MCBS, but imputed at the local level in the pivate sample. 9. The specific conditions consideed ae essential hypetension, congestive heat failue, diabetes, asthma, hypecholesteolemia, ulce, depession, chonic obstuctive pulmonay disease, allegic hinitis, migaine, athitis, chonic sinusitis, anxiety disode, cadiac disease, vascula disease, epilepsy, gastic acid disode, glaucoma, gout, hypelipidemia, iitable bowel syndome, malignancy, psychotic illness, thyoid disode, heumatoid athitis, tubeculosis, angina, human immunodeficiency vius infection, anemia, and stoke. Most co-mobidities ae elatively uncommon, except fo the ones involving heat disease (o isk factos fo heat disease). 10. The MCBS also contains self-epots of a numbe of distinct health conditions, as well as the individual s self-epoted geneal health status (coded 1 to 5, with 1 indicating poo and 5 indicating excellent). Ou egession analysis elies on the claims-based, athe than self-epoted, disease measues fo both the public and the pivate samples. Moe details appea in ou online data appendix. 11. See the footnotes to tables 1 and 2 fo a few sample size issues specific to cetain vaiables.

10 334 Bookings Papes on Economic Activity, Sping 2010 Table 1. Selected Patient Demogaphic and Health Chaacteistics a Pivate Public Standad Standad Patient chaacteistic Mean deviation Mean deviation Age Pecent male Income (thousands of 2004 dollas) Pecent with heat disease in yea No. of advese health conditions Souces: Data on pivate patients come fom a modified vesion of the Ingenix Touchstone poduct. Data on public patients come fom the MCBS. a. Histoy of heat disease is self-epoted in the public sample and identified using medical claims in the pivate sample. The pivate sample has 240,028 obsevations and the public sample 24,800 obsevations. The table also compaes the health of individuals in the two samples. Since both samples ae limited to individuals with a histoy of heat disease, we include a vaiable indicating the faction of individuals who ae diagnosed with heat disease in a paticula yea. In all cases, the pesence of disease is taken fom claims athe than fom self-epoted data. The incidence of heat disease is simila in the two samples: 0.32 in the pivate sample and 0.37 in the public sample. In addition, the table epots the aveage numbe of advese health conditions (out of the total of 30, including heat disease) pe patient. As with heat disease, the health conditions ae detemined using the ICD-9 diagnosis codes fom medical claims in both the public and the pivate samples. Unsupisingly, the eldely individuals in the public sample ae much sicke on aveage, with 2.9 advese health conditions in the yea compaed with 1.4 in the pivate sample. As a matte of couse, the public and the pivate samples ae dawn fom diffeent populations. We include a numbe of contols and analyses designed to mitigate and test fo the impact of these diffeences, but heteogeneity acoss samples emains a possibility. Late we discuss the souces of heteogeneity, the methods we have employed to addess them, and thei possible implications fo the analysis. II.B. Desciptive Statistics Table 2 pesents some desciptive statistics fo health cae spending and utilization in the public and the pivate samples aggegated ove all egions and patient chaacteistics. We pesent not only the mean and the standad deviation but also the 25th-pecentile, median, and 75th-pecentile values. Ou utilization measues (all measued as yealy aveages pe patient)

11 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 335 Table 2. Distibutions of Spending and Utilization Measues a Standad 25th 75th Measue Sample Mean deviation pecentile Median pecentile Utilization (numbe pe patient pe yea) Hospitalizations Pivate Public Hospital days Pivate Public Outpatient visits Pivate Public b Dug pesciptions c Pivate Public Spending (thousands of 2004 dollas) Total spending Pivate Public Inpatient spending Pivate Public Outpatient spending Pivate Public Pesciption dug Pivate spending c Public Souce: Authos calculations. a. Figues ae yealy aveages duing ( fo dug pesciptions) fo patients with a histoy of heat disease, which is self-epoted in the public sample and identified using medical claims in the pivate sample. Except whee noted othewise, the pivate sample has 240,028 obsevations and the public sample 24,800 obsevations. b. Suvey esponses (used to coss-validate the claims data) wee incomplete in 3,769 cases, so that the public sample has 21,031 obsevations. c. Because obsevations fom 2006 ae omitted, the pivate sample has 231,802 obsevations and the public sample 21,140 obsevations. Numbe of pesciptions is in tems of 30-day-equivalents. include the numbe of hospitalizations, total hospital days acoss all hospitalizations, the numbe of outpatient visits, and the numbe of 30-dayequivalent pesciptions in both samples. Fo spending, we ecod total (inpatient plus outpatient), inpatient, and outpatient spending, as well as spending on pesciption dugs. Utilization, in tems of hospitalizations, hospital days, and outpatient visits, is lowe fo the pivate patients. Spending fo this goup also tends to be lowe. Total medical spending fo individuals in the pivate plans is $8,401 pe yea, compaed with $10,245 fo the Medicae patients about a 20 pecent diffeence. The exception to the patten is pesciption dugs, fo which both utilization and spending ae geate among pivate patients. Figues 3 and 4 povide a boad sense of the vaiation pesent in ou samples. Figue 3 epots fo both samples the estimated kenel densities of MSA-level deviations fom the mean fo both hospital days and

12 336 Bookings Papes on Economic Activity, Sping 2010 Figue 3. Kenel Density Estimates of egional Fixed Effects fo Selected Utilization Measues a Density Hospital days Pivate Public Deviation fom sample mean Density Outpatient visits Pivate 0.1 Public Deviation fom sample mean Souce: Authos calculations. a. Estimated kenel densities of the deviation of mean hospital days and outpatient visits pe patient pe yea acoss MSAs. outpatient visits. Each data point undelying the kenel estimate is the diffeence between an MSA-level mean and the oveall sample mean. Fo both vaiables, the distibutions appea to be tighte fo the pivate than fo the public sample. Howeve, these distibutions ae based on aw, unadjusted numbes that do not account fo disease o othe covaiates.

13 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 337 Figue 4. Kenel Density Estimates of egional Fixed Effects fo Selected Spending Measues a Density Inpatient spending Pivate Public Deviation fom sample mean Density Outpatient spending 0.4 Public 0.3 Pivate Deviation fom sample mean Souce: Authos calculations. a. Estimated kenel densities of the deviation of mean inpatient and outpatient spending pe patient pe yea acoss MSAs. Figue 4 epeats this execise fo inpatient and outpatient spending. Hee the findings ae decidedly moe mixed. Fo outpatient spending the distibution appeas to be slightly tighte fo the public sample. The figue fo inpatient spending is hade to intepet visually, as the diffeences in the densities ae small and asymmetic. In any event, the diffeences

14 338 Bookings Papes on Economic Activity, Sping 2010 Figue 5. Hospital Days: elationship between Public and Pivate Deviations fom the Mean a Deviation fom sample mean: pivate Deviation fom sample mean: public Souce: Authos calculations. a. Each obsevation pais the deviation fo a single MSA fom mean hospital days pe patient pe yea in the public sample with that fo the same MSA fo the pivate sample. The line epesents the fitted odinay least squaes egession line. obsevable visually between the spending and the utilization distibutions suggest the possible impotance of public secto pice estaints, which would lowe spending vaiation even with geate vaiation in utilization. Finally, figue 5 plots the elationship between deviations fom the MSA-level means fo public and pivate hospital days. This is the empiical analogue to the theoetical elationship in figue 2. The figue suggests that mean pivate hospital days incease slightly with mean public hospital days, but much less than one fo one. This is consistent with thee being less egional vaiation in the pivate secto; we test this hypothesis moe fomally in the following analyses. II.C. Famewok fo Estimating egional Vaiation We ae paticulaly inteested in the between-msa vaiance in spending and utilization fo the public and the pivate samples. We begin with the simplest possible appoach that evaluates the vaiance between MSAs in the sample means. We then move to estimating the vaiance in egessionadjusted means, which we estimate fom egessions that contol fo vaious factos that might also influence spending and utilization. In both cases we account fo the elative bias that is ceated by the substantial diffeences in

15 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 339 sample size acoss sectos: because the public samples ae much smalle than the pivate samples, thee is geate sampling vaiance in the public secto estimates and thus geate vaiation in the MSA-level means fo Medicae patients. To estimate the tue between-secto diffeences in egional vaiation, we estimate and emove the vaiability that is due to sample size diffeences alone. Fomally, the obseved egional vaiation within a secto is due to the tue vaiation and the sampling vaiance in estimating that vaiation. Denote by µ the tue mean fo egion and by µˆ the coesponding sample estimate, whethe unconditional o egession-adjusted. The sample mean is equal to the tue mean plus sampling eo, accoding to μˆ = μ + z. The sampling eo z has zeo mean, and the covaiance of the sampling eo acoss egions is E(z, z s ) = σ s. Define μ 1 μ 1, the gand = mean acoss egions. Similaly, define the coesponding sample analogue, μˆ 1 μˆ. Finally, define the aveage sampling eo acoss egions, 1 = z 1. The object of inteest is the degee of egional vaiation in = z 1 the tue MSA means, V 1 (, which has the sample analogue V μ μ) 2 = = 1( μˆ μˆ ) The obseved vaiation V is a biased estimato of V, as a esult of sampling eo in the estimates. Moeove, this bias is likely to be lage fo ou public secto estimates because of the smalle public secto sample size, which yields noisie estimates of public secto utilization. Howeve, we can ecove a consistent estimate of the bias and coect fo it, accoding to E V ( ) = V + E z z = ( ) In the appendix we show how to estimate this bias fom sample vaiances and covaiances. Ou fomula woks fo both the case of unconditional sample means and the case of egession-adjusted means. In the simple case without covaiance acoss egions o zeo aveage eo acoss egions, this expession simply states that the obseved vaiation is the tue.

16 340 Bookings Papes on Economic Activity, Sping 2010 vaiation plus the aveage squaed standad eo. Moe geneally, the moe pecisely the sample means ae estimated, the smalle is the bias coection. In sum, the object of inteest in ou analysis is V, which we estimate as V Bias fo both the public and the pivate secto. Using these estimates of egional vaiation, we epot both the atio of public to pivate vaiation and the diffeence between public and pivate vaiation. We constuct standad eos aound these by means of a bootstap pocedue, which samples individuals with eplacement within each MSA, so that each bootstap sample contains exactly the same numbe of individuals in each MSA as the oiginal sample. 12 The bootstap pocedue eflects the natue of ou sample design. We egad the set of MSAs as fixed but each sample within an MSA as a andom sample of that MSA s population. Statistically, ou set of MSAs appoximates a population, but we have samples within each MSA. Ou egession-adjusted estimates employ a model with egional fixed effects that contols fo disease seveity and demogaphics. 13 Fo each secto s we estimate Y its = α s + its β s + δ ts + δ s + its 0 X. Hee Y its epesents some measue of utilization o spending by patient i in egion at time t and in secto s. The vecto X includes the following demogaphic chaacteistics fo each patient: age, age squaed, sex, income, income squaed, age and age squaed inteacted with sex, as well as dummy vaiables fo each of the advese health conditions listed above. The tems δ ts and δ s ae secto-specific fixed effects fo yea and MSA, espectively. The secto-specific vaiance in the fixed effect δ s is the egession-adjusted analogue to the vaiance in the MSA-level sample means. As a geneal matte, the covaiates have elatively little pedictive powe within MSAs but a fai amount between MSAs. Acoss all specifications, fo instance, the MSA means of the covaiates explain about 50 to 70 pecent of the between-msa vaiation in utilization and spending, in the sense of The altenative block-bootstap that samples MSAs with eplacement geneates nealy identical infeences fo statistical significance in ou analysis, and so does a flat bootstap. 13. A possible altenative is a andom-effects model, but the Hausman test ejected this moe efficient model in favo of the fixed-effects model in the majoity of cases we analyzed.

17 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 341 Table 3. egional Vaiation in Mean Utilization a Obseved vaiation b Coected vaiation c Utilization measue Pivate Public Pivate Public Diffeence, public minus pivate atio of public to pivate Unconditional means Hospitalizations (0.003) (0.204) Hospital days ** 2.870* (0.199) (1.017) Outpatient visits *** 2.735*** (0.502) (0.323) Dug pesciptions d *** 0.405*** (3.896) (0.043) egession-adjusted means e Hospitalizations (0.002) (0.430) Hospital days * 3.907* (0.124) (1.684) Outpatient visits *** 2.841*** (0.322) (0.379) Dug pesciptions d * 0.780** (3.130) (0.106) Souce: Authos calculations. a. Numbes in paentheses ae standad eos on the diffeence between public and pivate vaiation o the atio of public to pivate vaiation and ae bootstapped within MSAs, sepaately fo public and pivate patients, with 200 bootstap daws. Fo both sectos, then, the numbe of patients in each egion in each bootstapped sample is the same as the numbe of patients in the oiginal sample. Asteisks indicate diffeences statistically significantly diffeent fom zeo o atios statistically significantly diffeent fom 1 at the ***1 pecent, **5 pecent, and *10 pecent level. b. Vaiance in the egional means o fixed effects of the utilization vaiables. c. Unbiased measue of the tue vaiance in the egional means o fixed effects coected fo sampling eo, as descibed in the text and the appendix. All diffeences and atios ae based on these numbes. d. 30-day-equivalents. e. Estimates of egional fixed effects on each utilization vaiable fom a egession that includes as othe independent vaiables yea fixed effects, quadatic specifications of patient age and income, patient sex, sex inteacted with age, and dummy vaiables fo 30 sepaate types of disease. II.D. egional Vaiance in Utilization and Spending Table 3 epots the estimated egional vaiance in fou utilization measues: numbe of hospitalizations, numbe of hospital days, numbe of outpatient visits, and numbe of pesciption dugs (in tems of 30-dayequivalents). Again, pesciption dug coveage is povided by the pivate secto in both populations thoughout the sample peiod, and theefoe we do not expect to see simila diffeences fo pesciption dugs as fo the othe measues.

18 342 Bookings Papes on Economic Activity, Sping 2010 The table shows oveall between-msa vaiation in the public and the pivate sectos. The obseved vaiation (fist two columns) is computed as the aveage MSA-level deviation fom the oveall mean. The top panel epots the vaiation based on unconditional means; in the bottom panel, both the oveall mean and each MSA-level mean ae egession-adjusted, as descibed above. The coected vaiation (second two columns) is computed by subtacting the expected bias due to sampling eo, as descibed above. The next column shows the absolute diffeence between the public and pivate vaiances, and the last column the atio of the vaiances. Asteisks indicate statistically significant diffeences fom zeo fo the diffeences, and fom unity fo the atios. Vaiation in hospital days is about thee times, and vaiation in outpatient visits about two times, highe in the public secto. These diffeences ae statistically significant at the 10 pecent level o highe and appea egadless of whethe we adjust fo covaiates (although the magnitudes diffe somewhat). On the othe hand, pesciption dug utilization exhibits statistically less vaiation in the Medicae population; this is impotant because, again, even Medicae patients obtain thei pesciption dug insuance pivately in ou sample. Finally, thee is no statistically significant diffeence in the vaiation fo hospitalizations. It is likely that moe statistical powe is needed to pin down this vaiance, in one diection o the othe. Oveall, these esults povide evidence suggesting highe vaiance in the public secto, but fo a few of the outcomes ou statistical tests lack the powe to geneate definitive esults. Table 4 epots the estimated egional vaiance in fou spending measues: total spending, inpatient spending, outpatient spending, and pesciption dug spending. The egession-adjusted estimates indicate that outpatient spending exhibits only about 35 pecent as much vaiation in the public secto as in the pivate secto. Inpatient spending exhibits oughly equal vaiation in the two sectos. Finally, pesciption dug spending vaies less fo Medicae patients. With that exception, these esults ae quite diffeent fom the utilization esults and suggest that pice estaints play a ole in the public secto. In spite of geate vaiation in utilization, the public secto exhibits less vaiation in spending. III. Compaisons with Existing Liteatue egional vaiation in spending and utilization in the public secto has been well documented in a liteatue that is almost 40 yeas old and well accepted by the academic community. In that sense, ou contibution is to

19 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 343 Table 4. egional Vaiation in Mean Spending a Obseved vaiation b Coected vaiation c Spending measue Pivate Public Pivate Public Diffeence, public minus pivate atio of public to pivate Unconditional means Total medical * spending (0.981) (0.207) Inpatient spending (0.416) (0.247) Outpatient spending (0.355) (0.266) Pesciption dug *** 0.329*** spending (0.049) (0.060) egession-adjusted means d Total medical spending (0.728) (0.258) Inpatient spending (0.324) (0.298) Outpatient spending * 0.349** (0.261) (0.272) Pesciption dug *** 0.300*** spending (0.043) (0.082) Souce: Authos calculations. a. Spending is measued in 2004 dollas. Numbes in paentheses ae standad eos on the diffeence between public and pivate vaiation o the atio of public to pivate vaiation and ae bootstapped within MSAs, sepaately fo public and pivate patients, with 200 bootstap daws. Fo both sectos, then, the numbe of patients in each egion in each bootstapped sample is the same as the numbe of patients in the oiginal sample. Asteisks indicate diffeences statistically significantly diffeent fom zeo o atios statistically significantly diffeent fom 1 at the ***1 pecent, **5 pecent, and *10 pecent level. b. Vaiance in the egional means o fixed effects of the spending vaiables. c. Unbiased measue of the tue vaiance in the egional means o fixed effects coected fo sampling eo, as detailed in the text and the appendix. All diffeences and atios ae based on these numbes. d. Estimates of egional fixed effects on each spending vaiable fom a egession that includes as othe independent vaiables yea fixed effects, quadatic specifications of patient age and income, patient sex, sex inteacted with age, and dummy vaiables fo 30 sepaate types of disease. compae this with vaiation in the pivate secto, athe than to establish the existence of public secto vaiation. Table 5 summaizes a few epesentative papes fom this vast liteatue. 14 John Wennbeg and Alan Gittelsohn (1973) povide an ealy example. Thei study analyzed vaiation acoss hospital sevice aeas, a pecuso to the Hs typically analyzed in the moden Datmouth Atlas 14. Fo useful summaies fom both the economic and the clinical liteatues on geogaphic vaiation in health cae, see Wennbeg and Coope (1998), Phelps (2000), Fishe and othes (2003a, 2003b), Chanda and Staige (2007) and Sutheland, Fishe, and Skinne (2009).

20 Table 5. Key Findings on Vaiation in egional Health Cae Spending Using Medicae Data a Geogaphic Study aggegation Summay Key findings Wennbeg and Gittelsohn (1973) Cutle and Sheine (1999) Fishe and othes (2003a) Siovich and othes (2006) Chanda and Staige (2007) Fowle and othes (2008) Hospital sevice aea H H MSA H H Studied geogaphic vaiation in utilization and spending in Vemont. Calculated shae of egional vaiation in Medicae spending attibutable to egional diffeences in health and demogaphics. Compaed patients acoss egions holding othe chaacteistics constant. Compaed vaiation in intensity of teatment with physician peceptions of quality of cae. Specified a model of patient teatment choice with poductivity spilloves and tested the model using teatment choices and health outcomes of heat attack patients. Used a patient suvey to compae local vaiation in spending and utilization with patient peceptions of quality. Found wide vaiations appaently due to diffeences in pactice style athe than in population health. Hospital days in highest-use aea wee 1.5 times that in lowest. egional diffeences in demogaphics can explain about 70 pecent of egional diffeences in Medicae spending, but significant diffeences emain unexplained. Patients in highe-spending egions eceived appoximately 60 pecent moe cae. The inceased utilization mostly aose fom moe fequent physician visits. Medicae spending pe capita in highest intensity quintile was 1.58 times that in lowest. Pattens of which patients benefit and which lose fom intensive medical cae ae consistent with poductivity spillove model. egional diffeences in spending wee not systematically elated to diffeences in patient peceptions of cae quality.

21 Wennbeg and othes (2008) H Summaized Datmouth Atlas findings on geogaphic vaiation in Medicae spending and thei implications. ettenmaie and Saving (2009) Sutheland, Fishe, and Skinne (2009) State H Studied how state ankings in medical spending pe capita change when diffeent definitions of spending ae used, such as Medicae only o total spending by all payes. Updated Datmouth Atlas findings on geogaphic vaiation in Medicae spending and thei implications. Chenew and othes (2010) H Compaed H-level vaiation in medical spending between Medicae and lage fims. Gottlieb and othes (2010) H Examined ole of Medicae pices in diving geogaphic vaiations in health cae. Souces: Liteatue cited. a. H = hospital efeal egion; MSA = metopolitan statistical aea. Thee states spent moe than 20 pecent above the national aveage of $46,412. Convesely, thee states spent 25 pecent below the national aveage o less. Intequatile atio (75th pecentile ove 25th) is 1.26 fo Hs. Found a state-level coelation between Medicae spending and total spending of 0.21, and that vaiation in Medicae spending exceeds vaiation in pivate spending. Inpatient days pe beneficiay in highest cost quintile wee 1.50 times that in lowest; physician visits in highest cost quintile wee 1.36 times that in lowest. Found substantial egional vaiation in spending, geate fo lage fims than fo Medicae (coefficient of vaiation 0.21 v. 0.16). Coelation between pivate and public inpatient utilization was Pices explain a small potion of vaiation in spending. The 80th pecentile of pice-adjusted Medicae Pat B spending was 1.37 times the 20th pecentile.

22 346 Bookings Papes on Economic Activity, Sping 2010 of Health Cae studies. The table also lists a couple of impotant studies that use states o MSAs. It is impotant to ecognize this diffeence when compaing ou MSA-level analysis with H-level analyses elsewhee, and it is impotant fo futue wok to assess the potential implications of this diffeence. The 2008 Datmouth Atlas of Health Cae epots that aveage spending on health cae in the last 2 yeas of life (fo deaths occuing fom 2001 to 2005) anged fom a high of $59,379 in New Jesey to $32,523 in Noth Dakota (Wennbeg and othes 2008). This ange, fom 28 pecent above to 30 pecent below the national aveage, is simila to the ange of quantity utilization epoted acoss MSAs by MedPac: fom 39 pecent above the national aveage in Miami to 25 pecent below in ual Hawaii (Medicae Payment Advisoy Commission 2009). These vaiations ae not fully explained by factos such as age, insuance coveage, aveage income, and ates of illness o disease. David Cutle and Louise Sheine (1999) investigate the extent to which vaiation in spending acoss Hs can be explained by egional diffeences in illness, in the demand fo health (fo example, as measued by income and ace), o in exogenous diffeences in the stuctue of medical cae makets (fo example, in the atio of genealists to specialists). They find that egional demogaphics can explain about 70 pecent of the vaiation in medical spending acoss egions, but the unexplained vaiation emains lage. Fo example, when diffeences in demogaphics and the illness of the population ae accounted fo, binging Medicae spending down to the level of the 10th-pecentile egion would educe total spending by 15 pecent. Pehaps the existing study most closely elated to ous is that of Michael Chenew and othes (2010), who compae H-level vaiations in Medicae against those in a sample of lage fims in the Thomson eutes (Medstat) MaketScan Commecial Claims and Encountes Database. They estimate that the geogaphic vaiation in pivate secto spending is geate than that in Medicae spending (coefficient of vaiation of 0.21 vesus 0.16). This is consistent with ou findings fo spending. They focus less on vaiation in utilization, although they do epot a positive coelation between Medicae and non-medicae inpatient days. IV. Limitations of Ou Analysis Thee ae seveal empiical questions that ou data cannot addess but that should be addessed in futue wok. The populations of pivately insued and publicly insued patients diffe, because the latte have often opted out

23 PHILIPSON, SEABUY, LOCKWOOD, GOLDMAN, and LAKDAWALLA 347 of pivate health insuance options. The empiical implications of this ae not clea a pioi. Fee-fo-sevice Medicae patients ae likely to be sicke than thei countepats in pivate Medicae health maintenance oganizations (HMOs), because HMOs attempt to select healthie patients (Mogan and othes 1997). On the othe hand, the pivately insued noneldely may also be healthie than the noneldely oveall, if pivate health insues select against the sickest patients fo simila easons. The link between health insuance and employment in the noneldely population adds futhe complexity, as those who ae eligible fo employment-based health insuance may be iche o healthie, o both, than thei pees. Finally, the fact that ou pivate secto data ae based only on employees of lage (Fotune 500) fims adds a futhe dimension of selection. We an seveal supplementay analyses to investigate some of these issues, but ou data lack the powe to each definitive conclusions acoss the boad. Fist, we naowed the age ange of ou compaisons, to mitigate some of the diffeences in health status. We compaed 60- to 64-yea-olds in the commecially insued population with 66- to 70-yea-olds in the feefo-sevice Medicae population. As this estiction futhe educes the sample, we limit ou analysis to the 70 MSAs fo which we have at least 25 obsevations in both samples. Table 6 epots the esult fo the samples with the naow age anges. Geneally, the point estimates based on these esticted age anges ae simila to those based on the full sample, but the pecision of the estimates declines enough to eliminate statistical significance. The point estimates indicate that vaiation in the public secto is about 5.1, 3.4, and 1.2 times that in the pivate secto fo hospital days, outpatient visits, and hospitalizations, espectively. As in the analysis based on the full sample, vaiation in pesciption dug use is smalle in the public secto, about 53 pecent as lage as vaiation in the pivate secto. Next we investigated the issue of selection based on employment by compaing ou pivately insued sample with Medicae patients who also have coveage fom an employe. If an individual has such coveage, we know that he o she was employed and pivately insued at one point. oughly 35 pecent of Medicae enollees in ou sample also have employe-povided coveage. They ae slightly younge (aveaging 77 yeas, compaed with 79 yeas fo those without such coveage), iche (aveage income is 58 pecent highe), and moe likely to be male (52 pecent vesus 40 pecent) than the aveage Medicae enollee. Having employe coveage is associated with vey small diffeences in the faction of total expenses paid fo by Medicae: Medicae pays 39 pecent

24 348 Bookings Papes on Economic Activity, Sping 2010 Table 6. egional Vaiation in egession-adjusted Mean Utilization, Patients Aged 60 to 70 a Obseved Coected vaiation b vaiation Diffeence, c Utilization public minus measue Pivate Public Pivate Public pivate atio of public to pivate Hospitalizations (0.010) (0.921) Hospital days (0.886) (4.134) Outpatient visits (2.316) (1.695) Pesciptions ** (11.090) (0.209) Souce: Authos calculations. a. The pivate sample is esticted to patients aged 60 to 64 and the public sample to patients aged 66 to 70. Both samples ae esticted to include only the 70 MSAs with at least 25 obsevations in both samples. The pivate sample has 67,414 obsevations and the public sample 3,568 obsevations. Numbes in paentheses ae standad eos on the diffeence between public and pivate vaiation o the atio of public to pivate vaiation and ae bootstapped within MSAs, and sepaately fo public and pivate patients, with 200 bootstap daws. Fo both sectos, then, the numbe of patients in each egion in each bootstapped sample is the same as the numbe of patients in the oiginal sample. Asteisks indicate diffeences statistically significantly diffeent fom zeo o atios statistically significantly diffeent fom 1 at the ***1 pecent, **5 pecent, and *10 pecent level. b. Vaiance in the egional means o fixed effects of the utilization vaiables. c. Unbiased measue of the tue vaiance in the fixed effects coected fo sampling eo, as detailed in the text and the appendix. All diffeences and atios ae based on these numbes. of the expenses of those without employe coveage and 38 pecent of those with such coveage. The lack of a dispaity is due to the fact that once an eldely Medicae beneficiay eties, the employe-povided coveage becomes seconday to Medicae. In ou data just 9 pecent of individuals in the Medicae sample with employe coveage ae woking, so fo the vast majoity Medicae is the pimay paye. It thus seems easonable to assume that Medicae is the pimay dive of esouce allocation fo these individuals. A numbe of MSAs ae left with vey small samples afte this estiction, so we limit ou analysis to the 77 MSAs whee we have at least 50 obsevations in both samples. These esults ae pesented in table 7. Again, the point estimates ae simila to those based on the full sample, but the pecision of the estimates declines enough to eliminate much of the statistical significance. The point estimates indicate that vaiation in the public secto is about 4.1, 3.8, and 1.6 times that in the pivate secto fo hospital days, outpatient visits, and hospitalizations, espectively. The geate vaiation in outpatient visits in the public sample is statistically significant at the 1 pecent level. The othe