PUBLIC VS. PRIVATE HEALTH CARE SERVICES DEMAND IN ITALY*

Transcription

1 PUBLIC VS. PRIVATE HEALTH CARE SERVICES DEMAND IN ITALY* Danele Fabbr^ Chara Monfardn^ Abstract: In ths paper we use data comng from the new Italan Survey on Health Ageng and Wealth (SHAW) to analyse physcan servces utlzaton n Italy explctly acknowledgng the exstence of two dfferent classes of provders: publc and prvate. We consder vsts by a specalst physcan as the measure of ndvdual servces utlzaton. In partcular we assess the relatve mportance of varables lke ncome, educaton, prvate nsurance and supply characterstcs as determnants of the utlzaton of such servces, whle controllng for ndvdual health and need. We do that by estmatng some alternatve count data regresson models of whch we dscuss the relatve advantages and dsadvantages and the entaled dfferent nterpretaton of the results. JEL: C34, C35, C51, D12, I11 Ths verson: December 2002 ^ Danele Fabbr and Chara Monfardn are at the Department of Economcs - Unversty of Bologna. * We are grateful to Agar Brugavn, Tullo Jappell, Raffaele Mnac, Guglelmo Weber and the partcpants to AIEL-2002 and AIES-2002 conferences for helpful comments and dscussons. Data used n ths research are avalable at: Usual dsclamers apply. Correspondng author: Danele Fabbr, Department of Economcs - Unversty of Bologna, Pazza Scaravll, Bologna, Italy. Telephone: , fax: , emal address: dfabbr@economa.unbo.t.

2 1 INTRODUCTION Understandng the underlyng process of the demand for health servces s a key to a better assessment of the forces that ncrease health care expendture. Ageng and technologcal change play a major role n ths context wth cohorts lvng longer that consume ncreasng amounts of ntensve, prevously unavalable, treatments. Despte attenuatng effects due to the lower dsablty of margnal survvors, health care expendture s unanmously deemed to ncrease far beyond exstng GDP shares [see Cutler - Shener (1998)]. In ths turmol publc polces are repeatedly nvoked to cap expendture sprallng and to deal wth related equty ssues. Lookng at OECD data (see Fgure 1) t seems that physcan servces were a vctm of ths course of events. In all other OECD countres, n partcular n Italy, total expendture on physcan servces (ncludng both GPs and specalsts) stablsed and then started to decrease as a share of total expendture on health n the last ten years. A possble nterpretaton of ths trend reles on the low technologcal content of such servces compared to others lke hosptal treatments, pharmaceutcals and nstrumental dagnostc checks. Moreover patent tme s a basc nput n the producton of these servces. It could be that technologcal nnovaton postvely affected the organsaton of these servces reducng the number of vsts to obtan, for example, drugs or checks prescrptons. As a matter of fact cappng expendture n ths chapter revealed to be relatvely easy. Fgure 1: Total per capta expendture on physcan servces as a share of total per capta expendture on health.* 24% 22% 20% 18% 16% 14% 12% 10% Italy USA OECD (wthout USA) * Expendture (publc and prvate) on physcan servces ncludes expendture on professonal health servces provded by general practtoners and specalsts. Includes expendture on servces of osteopaths. Orgnal data are denomnated n US$/PPP. The OECD average s calculated as a smple mean of the followng countres rates: Australa, France, Germany, Iceland, Italy, Netherlands, Swtzerland. Source: Our elaboraton on OECD Health Data 2000, OECD, Pars, In order to motvate our nterest for the Italan case study t s relevant to notce how dd that happened n terms of publc vs. prvate nvolvement. Accordng to OECD data (see Fgure 2) we have no evdence of a shft occurrng from publc to prvate expendture for physcan servces n the USA and other OECD countres. In the USA we actually observe a slght decrease n the prvate share. Italy s a relevant excepton. 1 The share of prvate expendture for physcan servces remaned unchanged, at around 30%, from 1980 to 1992, ncreasng qute 1 Smlar trends are to be found n France. 2

3 rapdly thereafter and reachng more than 44% by In the meanwhle publc per capta expendture (see Fgure 3), constantly ncreasng snce 1980 at the same pace as other OECD countres, from 1992 stablsed at around 200 US$/PPP. Ths evdence suggests that a dramatc change took place n Italy approxmately around Snce then larger shares of expendture for physcan servces are fnanced out-of-pocket as a result of two renforcng dynamcs: frst, access to publc provders are ncreasngly subject to sgnfcant co-payments (tckets); second, ndvduals ncreasngly rely on prvate professonals. Fgure 2: Prvate per capta expendture on physcan servces as a share of total per capta expendture on pyhscan servces.* 80% 70% 60% 50% Italy USA OECD (wthout USA) 40% 30% 20% * Expendture on physcan servces ncludes expendture on professonal health servces provded by general practtoners and specalsts. Includes expendture on servces of osteopaths. Orgnal data are denomnated n US$/PPP. The OECD average s calculated as a smple mean of the followng countres rates: Australa, France, Germany, Iceland, Italy, Netherlands, Swtzerland. Source: Our elaboraton on OECD Health Data 2000, OECD, Pars, Fgure 3: Publc per capta expendture on physcan servces.* Italy USA OECD (wthout USA) * Expendture on physcan servces ncludes expendture on professonal health servces provded by general practtoners and specalsts. Includes expendture on servces of osteopaths. Values denomnated n US$/PPP. The OECD average s calculated as a smple mean of the followng countres rates: Australa, France, Germany, Iceland, Italy, Netherlands, Swtzerland. Source: Our elaboraton on OECD Health Data 2000, OECD, Pars,

4 In ths evolvng context some broad polcy ssues emerge. Frst of all, how can we fgure out the future evoluton of the demand and expendture for physcan servces n Italy? The observed and foreseeable reducton of publc fnancng to physcan servces has some mpact n terms of equalty of access? These knd of questons motvate our nterest for a mcroeconometrc analyss of the demand for physcan servces n Italy. A remarkable feature of the market for medcal professonal consultatons n Italy s the presence of two broad dstngushable classes of provders: publc, hghly regulated, specalsts, and prvate, less regulated, ones. We want to account for ths pecularty n our analyss, an ssue whch has been largely neglected n the lterature. Usually the demand for medcal consultatons s modelled as an aggregate demand rrespectve of the type of provder. There are actually good reasons to separately model the counts for publc specalsts n face of those for prvate specalsts. In our case study we notce that prvate consultaton s typcally of hgher accuracy, mples lower watng tmes at the cost of hgher out-of-pocket payment comparng to publc ones. Therefore aggregatng the two counts would lead to msleadng nterpretaton of the estmated elastctes. On the supply sde t s pretty relevant to realse that the role of physcan ncentves affect utlsaton. Indrect evdence of ths s provded by Table 1. In countres where general practtoners (GPs) are pad fee-for-servce, per-capta consultatons are slghtly more than n countres where they are pad accordng to captaton, but are almost double than n countres where GPs are salared. Ths provdes a strong, addtonal ratonale for our analyss of health servce utlsaton n whch we wll emphasse the role played by dfferent types of provder. Table 1: Per-capta general practtoners' consultatons across some European countres Subgroup mean Fee-for-servce Belgum 7.9 France 6.2 Germany Captaton Italy 6.7 Netherlands 5.7 Unted Kngdom Salary Fnland 4.0 Iceland 4.9 Norway 3.8 Portugal 3.2 Sweden Source: Our elaboraton on OECD Health Data 1999, OECD, Pars, 1999 Jmenez-Martn - Labeaga - Martnez-Granado (2002), usng data from the European Communty Household Panel (ECHP), estmate a model of demand for physcan servces for several European countres, Italy ncluded. However they do not dstngush between publc and prvate provders. We wll refer to ther results whle commentng on ours. On the equty ssue Van Doorslaer - Koolman - Puffer (2002), workng on an EU sample comng from the ECHP and agan dsregardng the prvate vs. publc ssue, suggest that "the rch appear to receve a hgher share of specalst vsts than expected on the bass of ther need". Ths result holds for Italy as well. In our vewpont t s nterestng to understand how does ths result emerge n terms of access to dfferent types of specalsts. We use the new Italan Survey on Health Ageng and Wealth (SHAW), conducted n the year 2001, to analyse physcan servces utlsaton explctly acknowledgng the exstence of two dfferent classes of provders: publc and prvate. We focus on vsts by a specalst 4

5 physcan. Actually t s reasonable to presume that the dynamcs we descrbed above largely nvolve ths segment and less so GPs vsts. In the year before the survey (year 2000), ndvduals can consume ths servce gong publc, prvate or both. Our am s to evaluate the determnants of ndvdual utlsaton for both classes of provders. In partcular we wsh to assess the relatve mportance of varables lke ncome, educaton, prvate nsurance and supply characterstcs as determnants of the utlsaton of such servces, whle controllng for ndvdual health and need. The paper s organsed as follows. In the next secton we qualtatvely revew the exstng econometrc lterature on health-care servces utlsaton. Secton 3 descrbes the data and some nsttutonal detals of our case study. The major emprcal results are reported n secton 4. Secton 5 concludes the paper wth suggestons for future research. 2 MODELS FOR HEALTH SERVICE UTILIZATION It has become generally accepted n the lterature on the demand for health care that the demand for certan types of these servces depends on two dfferent decson processes. In the Grossman tradton, as far as the demand for health care s essentally seen as the result of patents ntertemporal utlty maxmsaton, utlsaton s prmarly patent determned, though condtoned by the health-care delvery system. In the agency approach, physcans play an actve role n assessng the amount of servces that patents should consume, up to the pont of dstortng demand accordng to ther own preferences. These two perspectves lead to two dfferent streams of econometrc modellng tradtons: one-step models n the Grossman tradton [see Duan et al. (1983) and Cameron et al. (1988)] and two-step models n the agency tradton [see Mannng et al. (1981) and Pohlmeer - Ulrch (1995)]. In ths paper we model counts of specalst vsts by relyng on both tradtons. The Grossman model and the agency perspectve on patent-physcan relatonshp provde dfferent, despte complementary explanatons for the demand for health care. We look at them n sequence. 2.1 THE GROSSMAN MODEL The Grossman model [see Grossman (1982)] emphasses the role played by patents' choce lookng at health and wealth as two nterrelated assets the values of whch are optmally controlled over tme by the ndvdual. In the case of health, the margnal utlty of holdng a margnal unt of stock has a consumpton and an nvestment component, whch together must always be equal to ts margnal user cost. Ths conssts of the nterest rate, health captal deprecaton and a possble change n the value of the health captal over tme. In ths context the demand for health care servces s a derved demand, n that servces are not consumed per se but serve to mantan or mprove upon a certan health status. The typcal form of the ndvdual demand functon for health care servces that emerges from the Grossman model s gven by: M ( t) = f [ H ( t), w( t), p ( t), age( t), E( t), X ( t)] m The demand for health care servces (for smplcty we call them medcal servces) at tme t, M(t), s endogenously codetermned 2 wth the latent varable "health status", H(t), and t s affected by the wage rate, w(t), a prce vector for medcal servces, p m (t), ndvdual age, age(t), 2 The Grossman model s determnstc, so that desdered health stock always equal actual health stock, gven constrants. Therefore the demand for health servces, whch adjust exstng health stock net of deprecaton, s postvely lnked, one-to-one, wth endogenous health stock. 5

6 the level of educaton, E(t), and a vector of envronmental effects, X(t). A hgher wage lowers the margnal ncentve to hold health as an asset for consumpton use, thus depressng the demand for medcal care. By way of contrast t ncreases the opportunty cost of sck tme, hence renforcng the ncentve to hold health as an asset. Assessng the mpact of wage on medcal servce demand s therefore an emprcal matter. The mpact of prces s negatve lke that of better educaton. Ths last one should lower the demand for nvestment n health because t contrbutes to lower health stock deprecaton 3. Demand for medcal care should ncrease wth ageng, because t s not optmal to let health stock declne n step wth deprecaton. 2.2 THE AGENCY APPROACH In the agency approach, physcans play an actve role n assessng the amount of servces that patents should consume as far as they typcally act a double role: performng checks on the status of patent's health stock and, condtonal on checks, supplyng treatments amed at restorng health stock to a desred level. Sgnfcant nformaton asymmetry may provde physcans the opportunty to nfluence demand through ther role as health evaluators. Ths nformatonal advantage s exploted provded that physcan's objectve functon dffers from patent's. In ths respect t s common to assume that physcans do not only follow Hppocratc oath (for example maxmsng ndvdual health), but derve utlty also from ncome and lesure. Therefore, when ncome or lesure are talored to specfc procedures and/or servces, physcans wll dstort demand to perform more remuneratve, or less tme consumng, procedures/servces, f the margnal benefts of a specfc procedure outweghs the assocated margnal costs. In ths framework a large body of emprcal research s devoted to test the so called suppler nduced demand (SID) hypothess. The SID hypothess states that [McGure - Pauly (1991)] n the face of negatve ncome shocks, physcans may explot ther agency relatonshp wth patents by provdng excessve care. Income shocks examned n the lterature arse from three dfferent sources. A frst source s varaton n the physcan/populaton densty across areas: ncreased densty lowers the ncome of exstng stock of physcans, and t wll lead to ncreased utlsaton of medcal procedures n an nducement-type model. Income shocks may also emerge as the consequence of an exogenous change n demand due to epdemologcal shfts, evoluton of needs, varaton n tastes. However, the most common source s varaton n fees pad to physcans, generally by government payers. The nducement model has tradtonally been tested by assessng how these three alternatve changes n the envronment facng physcans affect the utlsaton of medcal procedures 4. Despte each of these testng strateges face mportant problems they are qute convergent n suggestng that physcans, to some extent, do actually manage demand accordng to economc ncentves. 2.3 THE BASIC FRAMEWORK FOR MODELS OF VISITS' COUNTS Econometrc models for count data The class of econometrc models of health servce demand we consder here s that concerned wth dscrete counts of medcal vsts. In ths case excess zeroes s the most relevant 3 In a more general model, Ehrlch - Chuma (1990) show that the mpact of educaton may go n both drectons. 4 Representatve studes that use physcan densty changes to proxy for ncome shocks are Fuchs (1978) and Cromwell - Mtchell (1986). Gruber - Owngs (1996) use exogenous demand changes, whle Yp (1998) examnes fee changes. 6

7 modellng ssue 5. From a purely statstcal vewpont, the problem bascally conssts n buldng enough flexblty nto the econometrc model to account for the excess probablty mass concentrated n the zero counts. Tacklng ths problem has major econometrc and economc mplcatons. In general terms the problem of bult-n-flexblty can be addressed n ether a sngle process perspectve (determnng both null and postve counts), or n a double process perspectve (one generatng the zeroes vs. the postves and one determnng the postves provded that a postve has been already generated). In the context of our study, ths amounts to say that n a sngle process approach all the vsts counts, zeroes ncluded, are drven by the same process. On the other hand, when a double process s envsaged, contact process (to access to medcal treatment or not?) s dstngushed from utlsaton (gven that the frst answer s YES, how much to consume?). From an economc vewpont, the double process perspectve has a natural appeal n the health economcs lterature as far as t dstngushes the two-part character of the decson-makng process n health care demand [Stoddart - Barer (1981)]. Whle at the frst stage t s the patent who decdes whether or not she needs medcal attenton and therefore to access a physcan (contact analyss), n the second stage the health care provders together wth the patent determne the ntensty of the treatment (frequency analyss). Ths modellng approach has, gven certan condtons, a sound structural nterpretaton [see Santos Slva - Wndmejer (2001)] whch motvated ts broad adopton n emprcal studes. Moreover t provdes a unfyng emprcal framework for the two above-mentoned theores of health care demand. A Grossman-lke nterpretaton mght be called for explanng the contact decson, whle an agency perspectve could be nvoked for the nterpretaton of the frequency decson. In the sngle process approach, the smplest econometrc model for count data s based on the Posson dstrbuton, whch s charactersed by the property of equdsperson. Ths mply that, condtonally to the covarates ntroduced n the model, the mean of the count varable s equal to ts varance and makes the Posson model unsutable n most emprcal applcaton, where the above-mentoned excess zeroes dsplayed by the count varable makes the condtonal varance to exceed the condtonal mean. The most popular parametrc model accountng for overdsperson s based on the Negatve Bnomal dstrbuton, whch can be seen as a generalsaton of a Posson process. The alternatve way of dealng wth the excess zeros les n the second modellng approach, represented by the hurdle model. Ths modfcaton of the basc model was frstly ntroduced by Mullahy (1986) and thereafter receved a great deal of attenton n the emprcal analyss of the usage of medcal servces. The hurdle model can be nterpreted as a two part model, n whch a bnary model for the decson of use, determnng the probablty of crossng a zero threshold, s combned wth a truncated count data model on postve counts, explanng the extent of use condtonally to some use. 6 We present n the Appendx the econometrc detals of the Negatve Bnomal (NB) and the Double Hurdle (H) model we use later n the paper for our emprcal applcaton, where the demand for physcan servces s measured by counts of utlsaton,.e. number of publc and prvate vsts consumed by the ndvduals n our sample. 5 Smlar methodologcal problems arse whle consderng contnuous demand measures lke expendture [see Newhouse and The Insurance Experment Study Group (1993)]. 6 For the sake of completeness t has to be notced that, on a purely statstcal ground, there s no clear evdence that econometrc models based on the two process approach should be preferred to those relyng on a sngle process approach. Actually t has been shown [Deb - Trved (1997, 2002)] that suffcently flexble specfcaton, based on latent class analyss, let sngle process models better ft the emprcal dstrbuton of vsts' counts. 7

8 2.3.2 Overvew of emprcal evdence on the determnants of specalst vsts A common feature n the lterature on models for vst counts s the lack of control for medcal servces' prces. Ths s due to unavalablty of detaled data on sngle vsts outlays. As far as surveys are desgned to gather total number of vsts per tme perod, no data are avalable on each vst payment 7. Therefore, monetary opportunty costs are typcally captured by prvate nsurance status varables [lke n Pohlmeer and Ulrch (1995) and Deb - Trved (1997)] or, more precsely, by ndvdual consurance rates [lke n Deb - Trved (2002)]. 8 Usually the avalablty of prvate nsurance s found to postvely affect contact choce but not frequency choce [Pohlmeer - Ulrch (1995)]. Smlar effects are found for co-payment rates: hgher copayment rates result n a lower probablty of contact whle frequency s unaffected [Deb - Trved (2002)]. These results are coherent wth a Grossman nterpretaton but less so wth an agency perspectve. Comng to the results concernng other typcal regressors n models for vsts' counts t must be notced that some predctons of the Grossman model are frequently contradcted by emprcal evdence 9. In partcular, good health status s found to be negatvely related to the number of vsts. Ths results s coherently consstent across all the papers we revewed despte dfferences n econometrc specfcaton. Educaton, typcally measured as years of schoolng, s usually found to ncrease vsts counts [see Deb - Trved (1997, 2002)]. Pohlmeer - Ulrch (1995) show that hgher educaton reduces contact decson for GPs vsts whle ncreases t for specalsts, n both cases not affectng frequency. Santos Slva - Wndmejer (2001) fnd that educaton postvely affects contacts and negatvely affects frequency for specalst vsts. Evdence concernng the mpact of ncome and age tends to be more coherent wth the theory. The theoretcal partton between the two processes n the two-part approach underpns the choce and nterpretaton of typcal regressors coeffcents ntroduced n each of the two-part components. Take for nstance the paper by Pohlmeer - Ulrch (1995). They estmate two dstnct two-part models for general practtoners vsts and for specalst vsts on a sample of employed Germans. They control for sex, ncome, age, educaton, chronc condtons, physcan densty n place of resdence, plus a set of other covarates. It s nterestng here to notce the results on physcan densty. The two-part model estmates show that physcan densty does not affect the contact choce whle t has a postve mpact on the frequency decson. The authors note that "whle physcan densty proxes an avalablty effect for the patent at the frst stage, t captures both demand and suppler response at the second stage. we are nclned to nterpret ths fndng as some evdence of suppler-nduced demand". Lkewse also other common covarates to the two parts are gven dfferent nterpretaton n the contact and frequency analyss. 3 DATA AND INSTITUTIONAL SETTING Our data source s the new Survey on Health Ageng and Wealth (SHAW) collected n The data are descrbed n Brugavn, Jappell and Weber (2002) and are downloadable at the followng address: The survey focuses on ndvduals aged 50 or more. The dataset ncludes a wde range of mcro-level nformaton on soco-economc characterstcs of ndvduals and households, ncludng specfc varables on 7 Pohlmeer - Ulrch (1995) argue that the mpact of prces may be neglected gven that for many western health care systems the drect prce of medcal servces s close to zero. 8 Introducng nsurance status varables rases endogenety problems. See Cameron et al. (1988), Wndemejer - Santos Slva (1997), Vera Hernandez (1999). 9 Wagstaff concludes that "the majorty of the model's structural parameters are n fact of the 'wrong sgn'" [Wagstaff (1986), p. 216]. 8

9 workng and lvng condtons as well as varables on health condton and health care utlsaton. We nclude n our sample householders and ther spouse when the latter s aged 50 or more. Accordngly, the total sample conssts of 1664 ndvduals. We model as a dependent varable the number of vsts to a specalst physcan. These nclude optcans, dentsts and any other physcan specalsed n a certan feld. In performng our analyss of vst counts separately for publc and prvate specalsts we dropped observatons dsplayng mssng values or unrealstc counts (greater than 20) for the dependent varables. As a result, we work on a sample of 1598 ndvduals for the analyss of prvate specalst vsts and a sample of 1608 observatons for publc specalst vsts. Table 2 shows the tabulatons for the two counts n our dependent varables. Zero counts are about 60% for prvate specalst consultatons, and about 3 pont percent hgher n the dstrbuton of publc ones. Alternatve, prvate vsts partcpaton rates s around 40%, whle the correspondng fgure for publc vsts s 37%. 647 and 597 ndvduals are observed wth at least one vst to a publc and a prvate specalst respectvely. Prvate consultatons are more frequent on our sample due to larger ncdence of hgher counts vs. 2.9 on postve counts -. Contact decson process leads to smlar sample means partcpaton rates across provders' types, whle the second stage process dfferentates condtonal frequences of vsts across types. Ths provdes a frst evdence that the process underlyng the contact decson s dfferent from the second stage process. Table 2: Tabulatons of specalst vsts n our sample PUBLIC PRIVATE Count Freq. Percent Cum. Freq. Percent Cum Total Postves Mean Varance St. dev. Mean Varance St. dev. Full Sample Postve counts Partcpaton rate A frst ndcaton of overdsperson n the data s obtaned when the sample varance of the dependent count varable s found to be greater than ts sample mean. After ncluson of regressors, the Posson model sample condtonal varance wll decrease wth respect to the sample varance, whle the sample average of the condtonal mean wll be equal to the sample mean f a constant s ncluded among the regressors. Cameron and Trved pont out that f the sample varance s more than twce the sample mean - ths s true n our data for both publc and prvate vsts - the data are lkely to exhbt overdsperson even after ncluson of regressors, as n cross-secton data regressons usually explan less than half of the varaton of the dependent varable. Explanatory varables are conventonal predsposng varables and varables capturng the access to medcal servces. Table 3 contans a descrpton of the varables used n ths pece of emprcal work (see Table A1 n Appendx 1 for some descrptve statstcs of these varables). 9

10 We tred to keep our specfcaton as parsmonous as possble, whle mmckng smlar specfcaton n the lterature. In ths respect our specfcaton s very close to Deb - Trved (1997) and qute smlar to Pohlmeer - Ulrch (1995) thus allowng us to make useful comparsons. It should be notced that publc specalsts are pad accordng to admnstered prces, whle prvate ones are free to set prces accordng to compettve pressures comng from close substtutes. Ths feature would suggest that controllng for out-of-pocket payments would be qute relevant n our case study. SHAW collects nformaton on total amount pad out-of-pocket for the cumulatve count of vsts, both specalst and generc, n each type of provder. However no-response rate was qute large (23% for publc and 17% for prvate vsts). Moreover averagng outlays across multple vsts could severely dstort results. We preferred, at ths stage, not to use payments nformaton n the modellng exercse. Table 3: Descrpton of varables Varable Descrpton Dependent Publc specalst vsts Number of vsts to a publc specalst n the year before survey (2000) Prvate specalst vsts Number of vsts to a prvate specalst n the year before survey (2000) Explanatory Famly ncome Monthly famly ncome, net of ncome taxes and socal nsurance rates Educaton Number of year of educaton Unemployed =1 f the person s unemployed Female =1 f the person s female Sngle =1 f the person s unmarred or wdow Age Age n years Chronc condtons =1 f the person suffers from chronc condtons Physcal lmtatons =1 f the person has a condton that lmts actvtes of daly lfe Poor self-perceved health =1 f self-perceved health s poor Excellent self-perceved health =1 f self-perceved health s excellent Hearng troubles =1 f the person suffers from hearng troubles Eyesght troubles =1 f the person suffers from eye troubles Never smoked =1 f the person never smoked n hs lfe Alcohol consumpton =1 f the person consumes alcohol regularly Prvate health nsurance =1 f the person s covered by prvate health nsurance Central regon =1 f the person lves n central regons Southern regon =1 f the person lves n southern regons Publc exp. per-capta Publc expendture per capta n the resdng Local Health Authorty Avalablty of prvate hosptals =1 f prvate hosptals are present n the resdng Local Health Authorty area Physcans per bed n prvate Rato of physcan per bed n prvate hosptals operatng n the resdng Local Health Authorty area Physcans per bed n publc Rato of physcan per bed n publc hosptals operatng n the resdng Local Health Authorty area Physcan densty Authorsed physcan per 1000 nhabtants n place of resdence Populaton Total populaton n place of resdence (n thousands of nhabtants) 4 RESULTS 4.1 THE NEGATIVE BINOMIAL ESTIMATES We start our emprcal analyss by estmatng NB models on the number of publc and prvate specalst consultatons respectvely. The Maxmum Lkelhood estmaton results 10 reported n Table 4 reveal that the Posson dstrbuton s ndeed rejected by the data, as the nestng parameter φ s found to be sgnfcantly dfferent from zero. Ths confrms the stylsed facts on overdsperson of the data emerged by the descrptve analyss. 10 The estmaton has been obtaned usng STATA 7. 10

11 The man fndngs concernng the role of the nserted explanatory varables are the followng. Famly ncome appears to be an mportant determnant of the number of prvate consultatons, wth hgher ncome famles ncreasng ther utlsaton of prvate servces. On the contrary, the demand of publc specalst vsts s not affected by the famly ncome varable. The level of schoolng has a sgnfcant postve mpact on both prvate and publc counts. Ths educaton effect result agrees wth the conventonal reason that educaton makes ndvduals more nformed consumers and sgnals that more educated people are orented towards a more frequent use of medcal care servces. Table 4: Estmates of the negatve bnomal model PUBLIC PRIVATE Coef. Std. Err. z Coef. Std. Err. z Famly ncome *** Famly ncome_sq *** Educaton *** ** Educaton_sq *** Unemployed Female * *** Sngle Age Age_sq Chronc condtons *** *** Physcal lmtatons *** Poor self-perceved health ** Excellent self-perceved health *** Hearng troubles Eyesght troubles ** Never smoked ** *** Alcohol consumpton ** Prvate health nsurance *** Central regon *** Southern regon *** Publc expendture per-capta Avalablty of prvate hosptals *** Physcans per bed n prvate *** Physcans per bed n publc ** Physcan densty *** Populaton/ Populaton/100_sq Constant Ln(alpha) Alpha Number of obs Wald ch2(27) Prob > ch Pseudo R Log lkelhood Holdng a prvate health nsurance ncreases the consultatons of prvate specalsts. Ths s a common result n the appled lterature whch s coherent wth fours stores. The frst one relates to prce elastctes (beng double nsured allows to access prvate health care at lower out-of-pocket payments). Accordng to the second explanaton, ths could also be the effect of an adverse selecton process makng the frequent health servces users to look for supplementary 11

12 coverage and cost rembursement. 11 A thrd key of nterpretaton s represented by moral hazard where ncentves by the patent and the physcans for over-treatment algn aganst the nsurer. The last possble explanaton has to do wth suppler nduced demand n a wde sense. Pohlmeer - Ulrch (1995) fnd no evdence of such behavour as the prvate nsurance dummy s only sgnfcant n the frs stage -.e. contact decson - of ther hurdle model. Turnng to the demographc varables, we fnd that ndvdual s age play no role n both equatons. The effect of ths varable s usually found to be negatve untl some age (whch vares from 33 to 52 n dfferent studes), and ncreasng thereafter. We observe coherent coeffcent sgns n the publc vst equaton, but these parameters are not enough precsely estmated. 12 Women appear to seek more medcal care than men, as usually evdenced n emprcal studes. In our context, ths s true both for prvate and publc specalst consultatons. The health status measures dsplay the usual emprcal lnk wth the degree of utlsaton of medcal care. Ths ncreases when chronc condtons or physcal lmtatons are present, the level of self-perceved health s poor and n presence of eyesght troubles (prvate vsts), and decreases wth excellent self-assessed health (publc vsts). Indvduals who never smoked seek less both publc and prvate medcal consultatons. Customary consumers of super-alcoholc drnks use less prvate doctor vsts. Regonal-specfc unobservable factors make publc specalsts less accessble n central and southern Italy than n northern Italy. Ths evdence has been already notced by Van Doorslaer - Koolman - Puffer (2002) for the aggregate utlsaton of specalst vsts. We show however that t does not hold for the prvate provders. We could nfer that ths effect has to do wth local government "falures", beng prvate consultaton purchased on compettve local markets. The effect of the sze of the communty of resdence, amed at proxyng the opportunty costs of vstng a physcan, turns out not to be sgnfcant. The varables whch proxy the accessblty to the two knd of medcal servces show the expected sgn, wth the rato of physcans per bed n prvate (publc) hosptals exhbtng a negatve effect on the number of vsts demanded from publc (prvate) physcans. Fnally a hgher physcan densty ncreases the number of publc specalsts consultatons. 4.2 THE HURDLE MODEL ESTIMATES The Maxmum Lkelhood estmaton results of the two parts of the model (probt at the frst stage, truncated negatve bnomal at the second one) are contaned n Tables 5 and A frst look at both tables reveals that the frst stage model exhbts a better ft than the second one. Some of the varables whch were non sgnfcant n the NB specfcaton turned out as beng mportant determnants of the contact decson. It may be temptng to nterpret ths fndng as an evdence n favour of the Grossman model for the explanaton of the contact decson. However, as Pohlmeer - Ulrch (1995) pont out, household data are better suted to quantfy the determnants of the contact decson, whle the frequency of use also depends on supply-sde factors on whch observable nformaton s lmted. Also, the number of observatons s consderably reduced n the second part of the model. Despte ths, there s a number of relevant comments concernng dfferences between the parameters across the two stages and wth the NB model, whch does not dstngush between the two parts. 11 Followng ths nterpretaton, a problem of endogenety of the prvate nsurance varable can be envsaged. 12 Ths result mght be due to the substtuton of hosptal admssons for specalsts' vsts. We are grateful to Tullo Jappell for suggestng ths nterpretaton of the age coeffcent. 13 In order to mplement estmaton wth the truncated negatve bnomal dstrbuton we resorted to the STATA ado fle provded by Hlbe (1999) on the Stata Techncal Bulletn. 12

13 The regressors mostly exert on the modelled probablty of contactng a publc/prvate specalst a smlar effect to what was found n the NB model. To hgher famly ncome corresponds hgher probablty of contactng a prvate specalst. The ncome varable s now sgnfcant also n determnng a less probable contact wth a publc specalst. Ths result marks a clear dstance between our analyss and that conducted by Jmenez-Martn - Labeaga - Martnez-Granado (2002). Accordng to them the contact decson for specalst vsts n Italy s postvely, despte decreasngly, affected by ncome. 14 We show that ths holds for prvate specalsts only. We report no sgnfcant mpact of ncome on the frequency decson. Consstently wth our prevous fndngs, more educated ndvdual tend to have hgher probablty of contactng a publc physcan. It has to be notced that ths set of varables turns out not to be relevant n the second stage model. Pohlmeer and Ulrch (1995) fnd the same result on both the counts of general practtoner and specalst vsts. Ths means that once the knd of provder s chosen, ncome and educaton do not affect the frequency behavour. Table 5: Estmates of the double hurdle model: frst stage PUBLIC PRIVATE Coef. Std. Err. z Coef. Std. Err. z Famly ncome ** *** Famly ncome_sq *** * Educaton *** Educaton_sq *** Unemployed * Female *** Sngle Age * Age_sq ** Chronc condtons *** *** Physcal lmtatons *** Poor self-perceved health Excellent self-perceved health *** Hearng troubles Eyesght troubles ** Never smoked * ** Alcohol consumpton * Prvate health nsurance Central regon *** Southern regon *** Publc expendture per-capta ** Avalablty of prvate hosptals *** Physcans per bed n prvate *** Physcans per bed n publc * Physcan densty *** Populaton/ ** Populaton/100_sq ** Constant * Number of obs Wald ch2(27) Prob > ch Pseudo R Log lkelhood Wthn a dfferent framework Van Doorslaer - Koolman - Puffer (2002) reach a smlar concluson. 13

14 Age has sgnfcant negatve, despte decreasng, mpact on the propensty to contact a publc specalst. Age, however, does not seem to affect the frequency decson. Ths result was concealed n the NB model specfcaton. Women do not show to contact and to vst more frequently publc specalsts. On the contrary the female dummy have a sgnfcant mpact on both steps for prvate consultatons. Health status varables tend to play a major role n the contact decson and for the publc specalsts. Accordng to our evdence t seems that, once a patent choose a prvate specalst, the frequency of vsts does not depend on hs health. Dfferently from the NB model, the sze of the communty of resdence postvely affects the decson of contactng a publc specalst: ndvduals lvng n a bgger town reveal to face a lower opportunty cost of contactng a publc specalst. On the same lne of reasonng, regonal effects, whch, accordng to the NB model, proved to be relevant for the publc consultatons, not for the prvate, show to be manly relevant for the contact decson and less so for the frequency decson. These two results mght reflect government neffcences, compared to the performance of compettve markets for prvate consultatons, n provdng equal access to specalst vsts. Table 6: Estmates of the double hurdle model: second stage PUBLIC PRIVATE Coef. Std. Err. z Coef. Std. Err. z Famly ncome Famly ncome_sq Educaton Educaton_sq Unemployed Female ** Sngle Age Age_sq Chronc condtons ** Physcal lmtatons ** Poor self-perceved health * Excellent self-perceved health ** Hearng troubles Eyesght troubles * Never smoked ** Alcohol consumpton * Prvate health nsurance ** Central regon ** Southern regon Publc expendture per-capta Avalablty of prvate hosptals * Physcans per bed n prvate Physcans per bed n publc *** Physcan densty ** Populaton/ Populaton/100_sq Constant Constant Alpha LR test aganst Posson, ch 2 (1) Number of obs Model ch 2 (27) Prob > ch Pseudo R Log Lkelhood

15 It s nterestng to remark the mpact of holdng a prvate health nsurance. Ths has no mportance n the contact of ether knd of specalsts, but postvely affects the frequency of prvate specalst vsts. Followng a lne of reasonng suggested by Pohlmeer - Ulrch (1995) ths last evdence s plausbly due to a suppler nduced demand effect snce only the frequency equaton descrbes the outcome of the jont decsons of the physcan and the patent, whle the contact equaton reflect patents' decsons only. Comng to the supply-sde varables, ther coeffcents dsplay dfferent sgnfcance and magntude n the two stages, as the hurdle model allows to dsentangle ther effect on the contact decson and the number of vsts respectvely. Publc per-capta expendture affects postvely the decson to contact a publc specalst, but not the number of referrals. The second measure of accessblty, represented by the number of doctors per bed n publc hosptals s sgnfcant n the second part of the model and negatvely related to the number of vsts provded by prvate specalsts. A hgher physcan densty ncreases both the contact and frequency of use of publc specalsts. 5 DISCUSSION AND CONCLUSIONS Snce 1992 larger shares of expendture for physcan servces n Italy are fnanced out-ofpocket. Ths s a result of two renforcng dynamcs: frst, access to publc provders are ncreasngly subject to sgnfcant co-payments (tckets); second, ndvduals ncreasngly rely on prvate professonals. These evolvng patterns motvate our nterest for a mcroeconometrc analyss of the demand for physcan servces n Italy. Exstng econometrc models perform aggregate demand analyss,.e. model the overall counts of physcan vsts or specalst vsts consumed by ndvduals as explaned by covarates lke ncome, out-of-pocket payments, consurance rates, health condtons. In case patents, wthn an health-care delvery system lke the Italan, could receve the same servce by two dfferent classes of provders, say publc vs. prvate, major problems of nterpretaton arse n performng aggregate demand estmaton. We used the new Italan Survey on Health Ageng and Wealth (SHAW) to analyse physcan servces utlsaton explctly acknowledgng the exstence of two dfferent classes of provders: publc and prvate. We consdered vsts by a specalst physcan as the measure of ndvdual servces utlsaton. Our am was to evaluate the determnants of ndvdual utlsaton for both classes of provders. The econometrc evdence we found confrmed that the prvate and publc counts are drven by dfferent processes. Therefore, examnng the two servces separately allowed us to make some nference and to gan some hnts on the specfc determnants for each class of provders. Generally speakng, our analyss confrmed that the knd of observatonal data on vsts counts typcally used n the lterature are better suted to quantfy the determnants of the contact decson, whle the frequency of use also depends on supply-sde factors on whch observable nformaton s lmted. It s rash to say that ths evdence s n favour of a Grossman-lke explanaton of the demand for health care servces. Actually, t s hard to beleve that physcans play no role n determnng vsts counts. However, n the health economcs lterature, t s common to notce that ndvdual condtons generally play a major role n explanng behavoural and medcal outcome varaton [see Slber - Rosenbaum - Ross (1995)]. Accordng to our results age does not play any strong role n determnng the utlsaton of medcal consultatons. We only found a very small negatve effect on the decson to contact a publc physcan. Puttng ths result on a polcy perspectve we mght be tempted to observe that ageng does not seem to be a major problem for the publc fnancng of such expendtures. 15

16 We also found that holdng a prvate health nsurance has no mportance n determnng the contact of ether knd of specalsts, but postvely affects the frequency of prvate specalst vsts. We nterpreted ths last evdence as due to a suppler nduced demand effect. A deeper analyss of such an effect s needed than the one we conducted here. We beleve that ths wll be a major ssue n the next future, gven the foreseeable reducton of publc drect provson of specalst consultatons and the enlargement of the doubly-nsured segment of the populaton. A predctable effect s an ncreasng tendency towards the ntegraton of nsurance companes and medcal provders amed at reducng ex-post moral hazard behavour of physcans. Comng to the equty of access ssue our separate analyss for publc and prvate specalsts proved to be qute frutful. Van Doorslaer - Koolman - Puffer (2002) show that lookng at aggregate demand for medcal servces mght conceal redstrbutng effects n the consttuent components of ths demand, namely GPs and specalsts, as far as one may offset the other. In ther context they show that, n Italy, GPs access s slghtly pro-poor, whle access to specalst s clearly pro-rch. Our analyss extends ths results to the consttuent part of the demand for specalst vsts. Namely we fnd that beng rcher ncreases the propensty to contact a prvate specalst and consstently decreases the propensty to contact a publc specalst. It s common to retan that prvate specalst are of hgher qualty. Therefore we conclude that n the Italan natonal health servce access to better specalst consultaton s pro-rch, wth publc provson manly guaranteeng access to specalsts consultaton for the poorer. Moreover we found some ndrect evdence of government falures to guarantee equal access opportunty to medcal consultaton across the country. Central and southern regons seem to suffer some ratonng compared to the northern regons. We found no evdence of ths dfferental effect for prvate consultaton. Incdentally t must be notced the doubly-nsured segment of the populaton s manly concentrated n the northern regons. From the analyss performed n ths paper we receved a strong ndcaton of the mportance of modellng the two counts, correspondng to prvate and publc specalst vsts, as drven by dfferent processes. Next step n ths drecton conssts n consderng the two processes as jontly dependent, descrbng ther nterrelaton n an analogous way as the seemngly unrelated regresson model. The resultng bvarate framework s ndeed the approprate one to take nto account and evaluate substtuton/complementarty relatonshps whch are lkely to exsts between the two classes of provders. 16

17 6 REFERENCES Brugavn A. - Jappell T. - Weber G. (2002), "The Survey of Health, Agng and Wealth", CSEF Workng Paper n 86, Unverstà d Salerno. Cameron A.C. - Trved P.K. (1998), Regresson Analyss of Count Data, Cambrdge Unversty Press, Cambrdge. Cameron A.C. - Trved P.K. - Mlne F. - Pggott J. (1988), "A mcroeconometrc model of the demand for health care and health nsurance n Australa", Revew of Economc Studes, vol. 55, pp Cromwell J. - Mtchell J. (1986), "Physcan-nduced demand for surgery", Journal of Health Economcs, vol. 5, pp Cutler D.M. - Shener L. (1998), "Demographcs and medcal care spendng: standard and non-standard effects", NBER Workng Paper n Deb P. - Trved P. (1997), "Demand for medcal care by the elderly: a fnte mxture approach", Journal of Appled Econometrcs, vol. 12, pp Deb P. - Trved P. (2002), "The structure of demand for health care: latent class versus two-part models", Journal of Health Economcs, vol. 21, pp Duan N. - Mannng W.G. - Morrs C.N. - Newhouse J.P. (1983), "A comparson of alternatve models for the demand for medcal care", Journal of Busness and Economc Statstcs, vol. 2, pp Fuchs V. (1978), "The supply of surgeons and the demand for operatons", Journal of Human Resources, vol. 13, pp Grossman M. (1982), "The demand for health after a decade", Journal of Health Economcs, vol. 1, pp Gruber J. - Owngs M. (1996), "Physcan fnancal ncentves and cesarean secton", Rand Journal of Economcs, vol. 27, Ehrlch I. - Chuma H. (1990), "A model of the demand for longevty and the value of lfe extenson", Journal of Poltcal Economy, vol. 98, pp Hlbe J. (1999), "Zero-truncated Posson and negatve bnomal regresson", Stata Techncal Bulletn, vol. 1/99, pp.37-40; Reprnts Vol 8, pp Jmenez-Martn S. - Labeaga J.M. - Martnez-Granado M. (2002), "Latent class versus two-part models n the demend for physcan serveces across the European Unon", Health Economcs, vol. 11, pp Mannng et al. (1981), "A two-part model of the demand for medcal care: prelmnary study from the health nsurance study", n Health, Economcs, and Health Economcs ed. by Sheffler R.M. - Rosster, L.F., Amsterdam, North-Holland, pp McGure T.G. - Pauly M.V. (1991), "Physcan response to fee changes wth multple payers", Journal of Health Economcs, vol. 10, pp Mullahy J. (1986), "Specfcaton and testng of some modfed count data models", Journal of Econometrcs, vol. 33, pp Newhouse and The Insurance Experment Study Group (1993), Free for All? Lessons from the RAND Health Insurance Expendture, Cambrdge, Harvard Unversty Press. Pohlmeer W. - Ulrch V. (1995), "An econometrc model of the two-part decsonmakng process n the demand for health care", Journal of Human Resources, vol. 30, pp Santos Slva J.M.C. - Wndmejer F. (2001), "Two-part multple spell models for health care demand", Journal of Econometrcs, vol. 104, pp

18 Slber J.H. - Rosenbaum P.R. - Ross R.N. (1995), "Comparng the contrbutons of groups of predctors: whch outcomes vary wth hosptal rather than patents characterstcs?", Journal of the Amercan Statstcal Assocaton, vol. 90, pp Stoddart G.L. - Barer M.L. (1981), "Analyses of the demand and utlzaton through epsodes of medcal care", n Health, Economcs and Health Economcs, ed. by, van der Gaag J. -Perelman M., Amsterdam, North-Holland. Van Doorslaer E. - Koolman X. - Puffer F. (2002), "Equty n the use of physcan vsts n OECD countres: has equal treatment for equal need been acheved?", n Measurng Up: Improvng health systems performance n OECD countres, OECD, Pars, France. Vera Hernandez A.M. (1999), "Duplcate coverage and demand for health care. the case of catalona", Health Economcs, vol. 8, pp Wagstaff A. (1986), "The demand for health - Some new emprcal evdence", Journal of Health Economcs, vol. 5, pp Wndmejer F. - Santos Slva J.M.C. (1997), "Endogenety n count data models: an applcaton to demand for health care", Journal of Appled Econometrcs, vol. 12, pp Yp W.-C. (1998), "Physcan response to medcare fee reductons: changes n the volume of Coronary Artery Bypass Graft (CABG) surgeres n the Medcare and prvate sectors", Journal of Health Economcs, vol. 17, pp

19 APPENDIX 1 Table A1: Descrptve statstcs for the regressors Varable Mean Std. Dev. Mn Max Famly ncome Famly ncome_sq Educaton Educaton_sq Unemployed Female Sngle Age Age_sq Chronc condtons Physcal lmtatons Poor self-perceved health Excellent self-perceved health Hearng troubles Eyesght troubles Never smoked Alcohol consumpton Prvate health nsurance Central regon Southern regon Publc exp. per-capta Avalablty of prvate hosptals Physcans per bed n prvate Physcans per bed n publc Physcan densty Populaton Populaton_sq

20 APPENDIX 2: THE NEGATIVE BINOMIAL AND DOUBLE HURDLE MODELS Havng avalable a sample of N ndependent observatons ( y, x ), where y denote the count varable of nterest and x a set of covarates, the startng pont for count data analyss s the Posson regresson model, defned by the condtonal densty: f P ( y µ e x; β) = µ y = 0,1,2,... (1) y! y where: µ exp( ' = xβ ), µ > 0. The property of equdsperson mpled by the Posson dstrbuton means that: E ( y x ) = V ( y x ) = µ To tackle wth overdsperson we resort to the Negatve Bnomal (NB) dstrbuton. Ths can be derved as a compound Posson process where the parameter of the Posson dstrbuton ncludes a gamma dstrbuted random varable reflectng ndvdual heterogenety: y Posson( µ ν ) wth ν ~ Gamma( α, λ) 15, wth α = λ, and the negatve bnomal ~ dstrbuton s obtaned by ntegratng over ν : f NB ( y x ; α, β) e = 0 ( µν ) ( µν ) y! Γ( y + α) α = y Γ( α ) Γ( + 1) µ + α y g( ν ) dν α y µ µ + α (2) where = exp( x ' β ) as above, and the condtonal mean and varance are gven by: µ E ( y x ) = µ 2 ( y x ) = µ + V φµ where φ = α 1 > 0 s an overdsperson parameter, makng the varance greater than the mean, as observed n many data sets. The parameters ( α, β ) can be estmated by the maxmzng numercally the log-lkelhood functon correspondng to the densty above (estmaton s automatcally mplemented n some statstcal packages, lke STATA). Ths s the most common mplementaton of the Negatve Bnomal Model, NB2 n the termnology of Cameron - Trved (1998). The addtonal parameter characterzng the NB dstrbuton makes t more flexble than the Posson, to whch t reduces when φ = 0. In most applcatons, NB regresson models are lkely to provde more effcent estmators than those based on Posson dstrbuton, as falure of the assumpton of equdsperson has smlar consequences to falure of the homoskedastcty assumpton n the lnear regresson model [Cameron - Trved, 1998]. 15 The densty functon for the postve contnuous varable ν s gven by: α 1 α ν λ t a 1 g( ν ) = exp( λν ), where λ > 0, α > 0 and Γ( α ) = Γ( α) e t dt = ( a 1)!, a >