Centre for Economc and Fnancal Research at New Economc School Aprl 01 Estmatng ncome equty n socal health nsurance system Galna Besstremyannaya Workng Paper No 17 CEFIR /NES Workng Paper seres
Estmatng ncome equty n socal health nsurance system Besstremyannaya Galna 1 Center for Economc and Fnancal Research at New Economc School Aprl 01 Abstract The paper measures horzontal equty n health care access and utlzaton n Japan by estmatng the coeffcents for ncome groups n a mult-part model whch dstngushes between non-users of health care, the users of npatent and outpatent care. To account for consumer unobservable characterstcs, we apply a latent class approach. We address a retransformaton problem of logged health care expendture, usng generalzed lnear models. Our sample s the 009 data for 4,0 adult consumers (Japan Household Panel Survey). The coeffcents for ncome groups are nsgnfcant both n the bnary choce models for npatent/outpatent health care use, and n the models for health care expendture. Consumers separate nto two latent classes n the generalzed lnear model for outpatent health care expendture. Although the results reveal horzontal equty n health care access and utlzaton n Japan, horzontal nequty remans n health nsurance premums and the prevalence of catastrophc coverage. Keywords: health care demand, equty, ncome elastcty, generalzed lnear models, latent class, two-part model, four-part model, socal health nsurance JEL codes: I10, I18, G, R 1 Center for Economc and Fnancal Research at New Economc School, Offce 9, Nakhmovsky prospect, 47, Moscow, 117418. Emal: gbesstre (at) cefr.ru. I am ndebted to Ruben Enkolopov (New Economc School) for stmulatng dscusson and kn comments on earler versons of the paper. I apprecate generous help by Serge Golovan (New Economc School). I thank Dmtry Shapro (Unversty of North Carolna Charlotte) and Jaak Smm (Tokyo Insttute of Technology) for moral support. The cooperaton of Keo Unversty Jont Research Center for Panel Studes (Tokyo) n sharng the mcrodata of Japan Household Panel Survey (wave 1, 009) s gratefully acknowledged.
1. Introducton Guaranteeng equty for the poor s a major challenge for health care systems n developed countres. Overall, equty s an ethcal ssue related to the judgments about health care accessblty. At the same tme, an economc concept of horzontal equty deals wth an equal treatment for equal need (Wagstaff and van Doorslaer, 1991a; Culyer and Wagstaff, 1993) and means that persons n equal need of medcal care should receve the same treatment, rrespectve of whether they happen to be poor or rch (Wagstaff and van Doorslaer, 1991b). In practcal terms, there s a general agreement about strvng for mnmal varaton of [health care] use wth ncome (Newhouse et al., 1981) and ensurng equty for the poor (Wagstaff and van Doorslaer, 000b; Cutler, 00). Accordng to theoretcal predctons, a well-desgned socal health nsurance system may provde an equtable redstrbuton of medcal care between the rch and the poor (Zwefel and Breyer, 006). However, the actual performance of socal health nsurance systems wth respect to guaranteeng equty for the poor s an ultmately emprcal queston (Hurley, 000; van Doorslaer et al., 004; Rannan-Elya and Somanathan, 006; Wagstaff, 010). The most prevalent method for analyzng ncome equty measures coeffcents for ncome groups n the equaton for health care utlzaton, wth equalty of the coeffcents nterpreted as zero nequty (Wagstaff and van Doorslaer, 000a; Jones and Wldman, 008). The regresson method should also be regarded as the most general. Indeed, the non-rejecton of the null hypothess of equalty of coeffcents for ncome groups provdes a suffcent condton for zero nequty n terms of an alternatve approach, whch measures concentraton ndces (Wagstaff and van Doorslaer, 1991b; Wagstaff and van Doorslaer, 000a; Wagstaff and van Doorslaer, 000b). Regresson method commonly regards the state of health as the major covarate that should have a sgnfcant estmated coeffcent (.e., need explanatory varable). 3 However, owng to lmtatons of most mcrodata surveys, qualtatve parameters related to the state of health (e.g., self-assessed health) may mal fal to fully capture ndvdual s demand for health care. Therefore, ncorporatng consumer s unobservable characterstcs whch nfluence the decson about health care use, as well as the amount As s defned n The Dctonary of Health Economcs, equty relates n general to ethcal judgments about the farness of ncome and wealth dstrbutons, cost and beneft dstrbutons, accessblty of health servces, exposure to health-threatenng hazards (Culyer, 005). 3 Indeed, the healthy and the sck have dfferent ncome elastcty of health care expendture (Nyman, 006). 3
of health care purchased, s essental for rasng the precson of the estmatons of health care demand. The purpose of ths paper s to estmate ncome equty n health care access and utlzaton n Japanese socal health nsurance system. Despte recent concerns about the poor n Japan, the fndngs on ncome effect for health care demand are lmted and mxed. Income effect s nsgnfcant accordng to the results of some studes (Senoo, 1985; Sawano, 001; I and Ohkusa, 00a; Kawa, 007; Tokuda et al., 009; Kawa, 010), whle other studes fnd a postve and sgnfcant ncome effect (Bessho and Ohkusa, 006; Babazono et al., 008; Ish, 011). The nfluence of ncome s commonly studed through estmatng the sgnfcance of the coeffcent for ncome varable, whch mght not be the most applcable approach snce t captures only lnear effects. Therefore, we follow papers that use dchotomous varables for ncome groups (Bessho and Ohkusa, 006; Tokuda et al., 009). Such approach, whch ncorporates non-lnear ncome effects, allows a comparson of the values and sgnfcance of the coeffcents for dchotomous varables of ncome groups relatve to the reference group. Snce poverty lnes vary n each Japanese muncpalty (wth muncpalty nformaton unavalable n consumer survey data), we employ ncome quntles (Ish, 011) so that the lowest quntle approxmates the low ncome group (OECD, 009). 4 The novelty of the paper s twofold. Frst, we use the 009 data for npatent and outpatent health care expendture by 4,0 adult consumers from Japan Household Panel Survey, 5 whch enables an estmaton of a mult-part model, dstngushng between non-users of health care, the users of npatent and outpatent care (Duan et al., 1983). Second, we employ a latent class approach (Deb and Trved, 1997) that better encompasses unobservable consumer characterstcs than subjectve health assessment. The mult-part model comprses equatons for the bnary choce of seekng npatent/outpatent care, as well as equatons for the amount of npatent/outpatent expendture, gven the expendture s postve. The amount of health care expendture s commonly taken n logarthms, to solve the ssues of skewness and zero mass problem (.e., the fact that health care expendture s truncated at zero). Owng to the retransformaton problem n equatons wth logged dependent varable (Duan et al., 1983; Mannng, 1998; Mullahy, 1998), lnear models can yeld unbased predctons only when error terms are normal or homoscedastc. A soluton to the retransformaton 4 Note that quntle analyss s commonly used n the studes of horzontal equty of OECD countres (van Doorslaer, 004). 4
problem s the use of generalzed lnear models whch specfy the mean and varance functons of the dependent varable condtonal on covarates (Nelder and Weddernburn, 197; McCullagh and Nelder, 1989). Consequently, n case of non-normalty and heteroscedastcty of error terms n OLS models for health care expendture, we use Greene s (007) generalzed lnear models wth latent classes. The results of our estmatons ndcate that the coeffcents for ncome groups are nsgnfcant both n the bnary choce models for health care use and n the models for health care expendture. Consumers separate nto two latent classes n the generalzed lnear model for outpatent health care expendture, and n the OLS model for health care expendture of those who used npatent care. In the generalzed lnear model we fnd adequate goodness-of-ft for the nverse Gaussan dstrbuton famly. As for bnary choce models, consumers do not separate nto latent classes. Overall, the results of the estmatons reveal horzontal equty of health care access and utlzaton n Japanese health nsurance system. However, horzontal nequty may be found n health nsurance premums and the prevalence of catastrophc health care coverage. The remander of the paper s structured as follows. Secton outlnes varous dmensons of equty n Japanese socal health nsurance system. Secton 3 sets up the emprcal models for measurng the demand for health care wth need and non-need varables. Secton 4 descrbes the data of Japan Household Panel Survey. The fndngs of the emprcal estmatons wth the bnary choce models, OLS and generalzed lnear models wth latent classes, along wth the analyss of the goodness-of-ft are summarzed n secton 5. Secton 6 dscusses equty n Japanese socal health nsurance system. A revew of the studes on ncome effect for health care demand n Japan, dervatons of devance resduals and Anscombe resduals, normal probablty plot for standardzed devance resduals, as well as detals on the samplng procedure n Japan Household Panel Survey are presented n the Appendx. 5 The unque feature of ths recently launched household survey s the fact that t dstngushes between npatent and outpatent health care expendture, as well as between expendture covered and non-covered 5
. Equty n Japanese socal health nsurance system Mandatory and unversal socal health nsurance system n Japan celebrates ts semcentennal annversary. 6 The enrolment n one of the non-ntersectng health nsurance plans s oblgatory and depends on enrollee s age and status at the labor market. The major health nsurance plans nclude: 1) natonal health nsurance, whch s muncpalty-managed nsurance for self-employed, retrees, and ther dependents; ) government-managed nsurance for small frms employees and ther dependents, and 3) company-managed nsurance assocatons formed by frms wth over 700 employees for employees and ther dependents. The year 008 saw a creaton of a specal plan for the elderly (aged 70 and above). Japanese socal health nsurance system s equtable n terms of choce of health care facltes, the sze of nomnal consurance rate, and the prces charged by provders. The users of any health nsurance plan can choose any health care nsttuton, regardless of ts locaton or type (e.g., prvate/publc, hosptal, clnc or ambulatory dvson of hosptal). There are no gatekeepers, and only n 1996 an amendment to Health Insurance Law ntroduced mnor payments for turnng to a large faclty wthout referral. 7 Whle the amount of nsurance premums s determned by each of the health nsurance plans, the types of medcal servces and drugs to be offered wthn socal health nsurance and ther costs (.e., provder prces) are set by the Mnstry of Health, Labor, and Welfare (MHLW) n a bennally revsed unfyng fee schedule. 8 The schedule ensures equal prces for smlar types of health care nsttutons. The sze of nomnal consurance rate for non-elderly and non-nfant populaton (aged 3-69) vared n the 0-50% nterval, and became a flat value of 30% for enrollees of all health nsurance plans snce 003 (Fgure 1). by socal health nsurance. 6 See the seres Japan: Unversal Health Care at 50 Years n Lancet s ssue of September 17, 011. 7 A payment for the frst vst to a large hosptal (wth over 00 beds) wthout referral would normally vary from 1,570 yen to 5,50 yen. 8 Wth the excepton of obstetrcs, preventve care, cosmetology and a number of addtonal types of treatment, balance bllng,.e. chargng the patent over and above the rembursement from health nsurance (Ikegam and Campbell, 004), s prohbted n Japan (Ikegam, 006). 6
Natonal Health Insurance Company / Government Managed Insurance 50% 50% 40% 40% 30% 30% 0% 0% 10% 10% 0% 1961 1968 011 1963 heads of household dependents 0% 1961 1973 1984 1997 003 011 heads of household dependents Fgure 1. Nomnal consurance rates n 1961-011 Note: Nomnal consurance rate for npatent care of dependents was 0% n 1980-003. Although consumers pay out-of-pocket for the ncurred health care costs accordng to the nomnal consurance rate, they are compensated by ther nsurer n case of hghcost medcal expendture. The system of hgh-cost medcal benefts (catastrophc coverage) ams at enhancng ncome equty n health care access and utlzaton. Based on the amount of household ncome, consumers are compensated so that ther nomnal consurance rate become only 1% after a certan threshold value of ncurred health care expendture. As for the lowest ncome category, consumers face the cap of 35,400 yen a month, recevng the rest of the health nsurance care for free (Table 1). Owng to the system of hgh-cost medcal benefts, the values of the actual share of out-of-pocket expendture ncurred by an enrollee (effectve consurance rate) are almost twce lower than the nomnal consurance rate (Ikegam and Campbell, 1999; Ima, 00; Ikegam and Campbell, 004; Ikegam, 005). Table 1. Hgh-cost medcal benefts (catastrophc coverage) for Japanese consumers aged 3-69 Income category Hgh ncome (above 530,000 yen a month) General category Low ncome (exempt from resdence taxes) Caps on monthly out-of-pocket health nsurance expendture 150,000 yen + (health care expendture 500,000 yen)*1% <83,400 yen> 80,100 yen + (health care expendture 67,000 yen)*1% <44,400 yen> 35,400 yen <4,600 yen> Source: MHLW (011), Hese 18nen ryou sedo kakaku-kanren shryou, Kougaku ryou, kougaku kago gassan ryouyouh sedo-ntsute, Sanko shryou, p.17. Notes: Fgures n brackets correspond to the fourth hgh-cost medcal beneft wthn 1 months. All monetary values are reported accordng to the reform n October 008. The thresholds for resdence tax exemptons vary n each muncpalty. 7
It should be noted that the thresholds for the lowest ncome categores (those exempt from payng resdent taxes) are set at the muncpalty level. Therefore, the thresholds between the affluent (e.g., Tokyo metropoltan area) and the unprosperous muncpaltes (e.g., towns n Hokkado prefecture) may dffer up to tmes. Overall, the safety net and the thresholds are lkely to depend on the fscal stuaton n the muncpalty (Ikegam et al., 011). The studes of poverty and deprvaton n Japan have mxed results about ncome effect on the amount of health care expendture (See a revew n Appendx C). Overall, the ncome effect s rarely analyzed wth respect to ncome group. Even when ncome groups are ntroduced (e.g., Bessho and Ohkusa, 006; Tokuda et al., 009), threshold values for low and mddle-ncome groups are arbtrary chosen. 9 We beleve that employng ncome percentles (Ish, 011; OECD, 009) may be a better approach for a sample encompassng many unknown muncpaltes wth dfferent levels of poverty lnes. 3. Emprcal models Followng Gravelle et al. (006), we assume that ndvdual s welfare functon w( ) may be presented as w = w (y, x, c ), where s the ndex for consumer, y s the utlzaton of health care, x are consumer characterstcs, and c s access cost. Then, the reduced form equaton for health care utlzaton becomes y = f(x 1, x, s ), wth x 1 denotng need varables (.e., covarates that should have sgnfcant estmated coeffcents), 10 x standng for non-need varables (.e., covarates that should not have an effect on health care utlzaton), 11 and s ndcatng supply varables (e.g., per capta number of doctors or number of beds). Below we outlne econometrc models for health care access (the bnary choce of gong to clnc/hosptal) and utlzaton (the amount of health care expendture). To address ncome equty n health care access and ncurred health care costs (Culyer and Wagstaff, 1993), our emprcal analyss focuses on the examnaton of the estmated coeffcents for ncome groups. 9 Bessho and Ohkusa (006) separate consumers nto the followng ncome groups wth respect to annual household ncome: les than 1 mln. Yen, 1-3 mln. Yen, 3-5 mln. Yen, 5-7 mln. Yen, 7-10 mln. Yen, 10-15 mln. Yen, 15-0 mln. Yen, 0-30 mln. Yen, above 30 mln. Yen. At the same tme, Tokuda et al. (009) create categores: less than 1.48 mln. Yen, 1.48 4 mln. Yen, 4-6 mln. Yen, above 6 mln. Yen. Note that the annual CPI nflaton n the perod between the two studes less than 1%. 10 E.g., morbdty or self-assessed health. 11 E.g., ncome. 8
3.1 Mult-part models The four-part model dstngushes between non-users of health care, users of npatent and outpatent care. The model ncorporates bnary choce equatons and s estmated usng maxmum lkelhood method, wth each equaton of the model estmated separately owng to an addtve log-lkelhood functon (Duan et al., 1983). Let Pr( y >0 ) = F( y, x ' β 1 ) (1) Pr( npatent > 0 y >0 ) = F( npatent, x β ' ) () log (y y >0, npatent =0) = x ' β 3 + ε (3) log (y npatent >0) = x β ' 4 + ν (4) Eε =Eν = E (x ε ' )= E (x ν ' )=0, (5) where s the ndex for observatons, y denotes health care expendture, npatent ndcates npatent health care expendture, and x are covarates. The dependent varables n (3) and (4) are taken n logs due to the skewness of health expendture data and zero mass problem (.e., the fact that health care expendture s truncated at zero). The four-part model (1)-(5) s an extenson of the (1), (3), (5) two-part model (Duan et al., 1983; Duan et al., 1984) as specfed below: Pr(y >0)=F(y, x γ ' 1 ) (1) log (y ) = x ' γ + ξ (3) Eξ = E (x ξ ' )= 0, (5) where s the ndex for observatons, y denotes health care expendture, and x are covarates. 3. Generalzed lnear models Owng to the retransformaton problem n regressons wth logged dependent varable (Duan, 1983; Mannng, 1998; Mullahy, 1998), estmatng lnear models (3) and (4) can yeld unbased predctons only when error terms are normal or homoscedastc. More formally, n terms of notatons for equaton (3), f ε ~ N(0, σ ε I), then E(y x) = exp(x ' β 3 +0.5 σ I). If ε ε are not normal, but..d., then E(y x) = exp(x ' β 3 ) E(exp(ε)), and therefore, ^ E (y x)= exp(x ' 3 ) E(exp( )). However, the estmate of E(y x) becomes based n case of heteroscedastc errors. Indeed, when varance s some functon v( ) of 9
covarates, namely Var(ε)=v(x), the expresson for the expectancy of y condtonal on x becomes E(y x)=exp(x ' β 3 ) v(x). A soluton to the retransformaton problem n case of non-normal and heteroskedastc errors s the use of generalzed lnear models (Nelder and Wedderburn, 197; McCullagh and Nelder, 1989) for health care expendture data (Mullahy, 1998; Blough et al., 1999). Although there are other possble solutons, 1 the advantages of generalzed lnear models are mproved precson compared to OLS-methods and robustness of the estmate of the condtonal mean (Mannng and Mullahy, 001). Generalzed lnear models assume a partcular form of dstrbuton famly, whch requres postestmaton analyss about the goodness-of-ft. Generalzed lnear model specfes the mean and varance functons for y x by settng a famly of dstrbutons g( ), as well as the lnk functon f( ), so that f(e(y x)) = x ' β. We use LIMDEP 9.0 to analyze the models for nonnegatve dependent varables wth lognormal, gamma, Webull, and nverse Gaussan famles. Let f(e(y x))= x ' β (6) y x ~ g(y, x ' β, θ), (7) where f( ) denotes a logarthmc lnk functon, g( ) s a famly of dstrbuton, x are covarates, and θ are ancllary parameters. For each dstrbuton famly we examne the model ft, employng normalty test of Anscombe resduals (McCullagh and Nelder, 1989; Dobson, 00; Agrest, 007) and standardzed devance resduals (Davson and Ggl, 1989). 13 The comparson of the goodness-of-ft between OLS and generalzed lnear models s conducted wth the analyss of resduals (raw bas and mean squared error). 1 There are several alternatve ways to deal wth heteroscedastcty. Among them are Mannng s (1998) method, whch s partcularly easy to mplement f heteroscedastcty s present across mutually exclusve groups; sem-parametrc approaches and extenstons of generalzed lnear models (Basu and Mannng, 009). Recent revews of the appled lterature wth generalzed lnear models and other methods for modelng health care expendture may be found n Mhaylova et al. (011), Mullahy (009), Basu and Mullahy (009), Buntn and Zaslavsky (004). 13 See dervaton of model devance and devance resduals n the Appendx. 10
3.3 Latent class analyss 3.3.1 Bnary choce model wth latent classes The latent class approach (Deb and Trved, 1997; Deb and Holmes, 000) dvdes consumers nto unobservable classes of hgh and low users of health care to account for mmeasurable consumer characterstcs, not captured by self-assessed health and other varables. The bnary choce model (1) s extended to a latent class model n the followng way: Pr(y >0)=F(y, x β ' 1j ), (8) where s the ndex for observatons, j s the ndex for latent class (j =1 J), y s health care expendture, x are covarates related to the demand for health care, β 1j are coeffcents for j-th latent class. The estmatons are conducted n LIMDEP 9.0, whch determnes the most probable latent class by comparng posteror jont probabltes Pr(j ) for all j-s, wth the pror probablty F j of belongng to latent class j and posteror jont probablty Pr(j ) of belongng to latent class j calculated as: F j = J 1 j = 1 expϑ (1 + expϑ ) j j (9) Pr(j )= F Pr( j) J j= 1 j F Pr( j) j, (10) where Pr( j) s the densty functon of y gven observaton belongs to class j. Equatons ()-(4) are transformed nto a latent class model n a smlar way. 3.3. Generalzed lnear models wth latent classes For generalzed lnear models that ft the data, equatons (6)-(7) are extended as follows: f(e(y x))= x ' β j (11) y x ~ g(y, x ' β j, θ j ), (1) where f( ) denotes a logarthmc lnk functon, g( ) s a famly of dstrbuton, x are covarates, j s the ndex for latent class (j = 1 J), y s health care expendture, β j are coeffcents, θ j are ancllary parameters. The pror and posteror class probabltes are calculated accordng to (9) and (10). 11
3.3.3 Specfcaton tests Greene (007) proposes the followng statstcs to test between H o : a latent class (unrestrcted) model and H a : a model wthout latent classes (restrcted model) : L = (lnl u -lnl R ) ~ χ ( -1) (k 1) ) (J +, (13) where lnl u s loglkelhood of the unrestrcted model, lnl R s loglkelhood of the restrcted model, J s the number of latent classes, and k s the number of covarates. Although the statstcs L corresponds to the general logcs of lkelhood rato test for nested models, Greene (007) argues that the valdty of the statstcs needs to be further nvestgated, and the use of conventonal nformaton crtera s more preferable n the appled analyss. 14 Therefore, to choose between the models wth and wthout latent classes, we use both Greene s (007) LR test as specfed n (13) and nformaton crtera (AIC and BIC). 4. Data The paper uses the data of Japan Household Panel Survey. The survey was establshed n 009 as a natonally representatve annual survey of adults. Respondents aged above 0 answer a wde range of questons on ther labor actvty, ncome and expendture, soco-demographc characterstcs, anthropometry, health, and health-related lfestyles. There are a number of unque features of ths longtudnal survey for the purposes of the analyss of health care demand. Frst, health care utlzaton s reported at the ndvdual level. 15 Second, health care utlzaton s dvded nto health care n outpatent and npatent facltes. Fnally, health care expendture s subdvded nto the expendture covered and uncovered by health nsurance. The partcpaton n our analyss s modeled through dchotomous varables healthcare for usng any health care faclty (corresponds to eq.1 n 3.1), and npatent care for seekng care n an npatent faclty gven consumer used some health care faclty (eq.3 n 3.1). The ntensty varable expendture s out-of-pocket payments for health care covered by health nsurance (eq. 3 and 4 n 3.1). We construct dchotomous varables group 1 through group 5 for quntles of the annual dsposable (after-tax) household ncome (wth the upper quntle group 5 treated as a reference category). Fve nteracton terms (ncome group*log of annual 14 Greene (007) Testng for the Latent Class Model. In: LIMDEP. Verson 9.0. Econometrc modelng gude. Vol.1. E17.10.5. 15 Whle n Japanese Panel Survey of Consumers and Keo Household Panel Survey health care expendture s reported at the household level. 1
dsposable ncome) are added to the lst of regressors to estmate ncome elastcty n each quntle. Indvdual characterstcs are age, gender, bnary varables for graduate educaton, and employment. Health status s taken nto account wth a bnary varable for low health condton, Ben-Sra s (198) psychologcal dstress ndex (PDI), and body mass ndex (BMI). Bnary varables for drnkng, smokng, sports, and checkups reflect health-related lfe styles. The bnary varables for desgnated cty and other ctes capture health care supply whch s generally better n Japanese urban areas (rural areas,.e., towns and vllages become a reference category). We add a dummy for Natonal Health Insurance, snce sometmes there are addtonal hgh-cost medcal benefts for the poor n ths health nsurance plan. We use a subsample of non-elderly consumers (aged below 70), snce Japanese elderly have lower nomnal consurance rates 16 and specal thresholds for hgh-cost medcal benefts (Table ). 16 Snce 007 nomnal consurance rate s 10% for aged above 75 and 0% for aged 70-74. 13
Table. Descrptve statstcs of our sample Varable Defnton Obs Mean St.Dev. Mn Max healthcare = 1 f out-of-pocket expendture for health care covered by health nsurance s nonnegatve n 008; 0 otherwse 3563 0.61 0.49 0.00 1.00 npatent care expendture = 1 f out-of-pocket expendture for npatent care covered by health nsurance s nonnegatve n 008 gven ntensty equals 1; 0 otherwse out-of-pocket expendture for health care covered by health nsurance n 008, thousand yen 3563 0.05 0. 0.00 1.00 3563 41 117 0 400 ncome dsposable household ncome n 008, thousand yen 919 51 38 0 10000 age years of age as of January 31, 009 3563 46.64 14.41 19.84 69.99 gender =1 f female; 0 f male 3563 0.51 0.50 0.00 1.00 educaton = 1 f completed junor college, college or unversty 3563 0.41 0.49 0.00 1.00 work = 1 f was employed last month 3555 0.74 0.44 0.00 1.00 desgnated cty = 1 f lves n a desgnated cty, 0 otherwse 3563 0.6 0.44 0.00 1.00 cty = 1 f lve n a non-desgnated cty, 0 otherwse 3563 0.64 0.48 0.00 1.00 lowhcond PDI BMI =1 f self-assessed health condton s reported as not very healthy or not at all healthy ; 0 f self-assessed health condton s reported as very healthy, rather healthy or average health 3555 0.09 0.9 0.00 1.00 physologcal dstress ndex, calculated as the sum of responses to the questons on the recent presence of the below twelve condtons (each response s gven on a four-pont scale, where one refers to often, two means sometmes, three mples almost never, and four stands for never ): headache or dzzness; palptaton or shortness of breath; senstve stomach and ntestnes; backache or shoulder pan; get tred easly; catch a cold easly often feel rrtated; trouble gettng to sleep; feel reluctant to meet people; less concentraton on work; dssatsfed wth present lfe; anxety over the future. 3401 34.4 7.15 13.00 48.00 weght( kg) body mass ndex = 10000 heght ( cm ) 3379.57 3.4 14.69 75.31 smokng = 1 f currently smokes; 0 otherwse 3546 0.9 0.45 0.00 1.00 drnkng = 1 f drnks moderately or heavly; 0 otherwse 357 0.6 0.49 0.00 1.00 NHI = 1 f Natonal Health Insurance; 0 otherwse (other health nsurance plan) 3563 0.9 0.46 0.00 1.00 checkup gym = 1 f had nonnegatve expendture for varous checkups n 008 (apart from checkups at work); 0 otherwse = 1 f had nonnegatve expendture for dong sports, gong to gym, and buyng supplements n 008; 0 otherwse 3466 0.37 0.48 0.00 1.00 3403 0.34 0.47 0.00 1.00 14
5. Emprcal results 5.1 Bnary choce model for health care utlzaton Accordng to the results of the test for normalty of errors (Greene, 007), 17 we use probt model for bnary choce equatons of the four-part model (eq.1 and eq.). For each equaton we estmate a model wth two latent classes. The pror probabltes for latent class membershp are sgnfcant and Greene s (007) lkelhood rato test rejects the null hypothess of the model wthout latent classes. Yet, n each case we could not conclude that consumers separate nto two latent classes wth respect to ther bnary choce of seekng health care. 18 Indeed, AIC and BIC for the models wth and wthout latent classes are close. Moreover, margnal effects for most of explanatory varables n each latent class are nsgnfcant. Consequently, for each equaton we estmate probt model wthout latent classes (Table 3). The results reveal that wth the excepton of the forth ncome quntle n eq.1, the coeffcents for margnal effects for ncome groups are nsgnfcant n both eq.1 and eq.. Moreover, most of other non-need varables are nsgnfcant. Age s the only sgnfcant determnant of the bnary choce for seekng npatent care. In case of any type of health care, the sgnfcant covarates are age, gender, graduate educaton, and some lfestyle varables: body mass ndex and the bnary varable for checkups. 17 Greene (007). A Test for Normalty n the Probt Model In: LIMDEP 9.0. Econometrc modelng gude. Vol.1. E18.60. 18 The result s smlar to the prevous fndng wth the 000-007 data on Japanese women, where consumers dd not separate nto latent classes n the bnary choce model for seekng health care (Besstremyannaya, 011). 15
Table 3. Margnal effects n the bnary choce equatons (1) and () of a four-part model (1) () Healthcare Inpatent care constant 0.847 (0.5306) -0.3458 (0.046)* age 0.005 (0.0006) *** 0.001 (0.0003)*** group 1 * ln(ncome) -0.006 (0.0356) -0.0149 (0.0136) group * ln(ncome) 0.065 (0.1570) 0.056 (0.0664) group 3 * ln(ncome) 0.769 (0.840) -0.0095 (0.1187) group 4 * ln(ncome) -0.5591 (0.03) *** 0.0814 (0.0859) group 5 * ln(ncome) -0.049 (0.0579) 0.0179 (0.03) PDI 0.0000 (0.00004) -0.0000 (0.00001) BMI 0.000 (0.00004)*** 0.00003 (0.0000) gender 0.0601 (0.0169) *** -0.0069 (0.0071) educaton 0.0901 (0.0177) *** -0.0039 (0.0076) lowhcond -0.0001 (0.000) -0.00004 (0.0001) smokng -0.00001 (0.0001) -0.00004 (0.00004) drnkng -0.00001 (0.0001) 0.00003 (0.00004) NHI 0.0051 (0.0193) -0.0018 (0.0079) checkup 0.000 (0.0001) *** 0.0000 (0.0000) gym -0.00005 (0.00005) -0.0000 (0.0000) work -0.00005 (0.000) -0.00003 (0.0001) desgnated cty -0.079 (0.0316) -0.0187 (0.0100) cty -0.038 (0.083) -0.031 (0.01) group 1-0.4811 (0.588) 0.701 (0.48) group -0.9751 (1.665) -0.059 (0.5395) group 3 -.7679 (.103) 0.354 (0.8946) group 4 4.4861 (1.6576) *** -0.5533 (0.718) Log lkelhood -77.81-697.60 Observatons 538 538 Notes: *** p< 0.01, ** p< 0.05, *p< 0.1. Robust standard errors n parentheses. Margnal effects are evaluated at sample means. Group 1, group, group 3, group 4 and group 5 denote dchotomous varables for log(ncome) quntles, wth group 1 standng for the lowest quntle, and group 5 ndcatng the hghest quntle. 5. Modelng health care expendture wth logged dependent varable The post-estmaton analyss wth an ordnary least squares model for equaton (3) reveals that the errors are non-normal and heteroscedastc. Consequently, we experment wth generalzed lnear models wth four dstrbuton famles: lognormal, gamma, Webull, and nverse Gaussan. The results of the resdual analyss ndcate that nverse Gaussan dstrbuton provdes the best model ft n terms of the bas, mean squared error, and Anscombe resduals (Table 4, Fg.-3). 16
Table 4. Model comparson Lnear Model Generalzed lnear models (a) (b) lognormal (c) gamma (d) Webull dstrbuton dstrbuton dstrbuton (e) nverse Gaussan dstrbuton Mean raw bas (resdual) -.48 -.48 60.58-38.39 1.49 Mean squared error 48.06 48.06 15.17 13.4 11.13 Normalty test, 0.00 0.00 0.00 0.57 Anscombe resduals Notes: In lnear model the ftted values are calculated wth the smearng factor. Snce the general form of Webull famly does not lead to convergence, we use Raylegh dstrbuton (.e., the scale parameter n Webull dstrbuton equals two). Normalty test reports the p-value for jont probablty n skewness/kurtoss test wth the null hypothess of the standard normal dstrbuton. As for standardzed devance resduals, standardzed resduals, and Person resduals, the null hypothess of normalty s not accepted n all the generalzed lnear models. Dchotomous varables for ncome groups are excluded from the lst of covarates n generalzed lnear models snce they nfluenced convergence (namely, the margnal effects for these varables were huge). Although the dstrbuton of standardzed devance resduals s close to normal (See Fg.4 n Appendx A), the skewness/kurtoss test rejected the null hypothess of normalty. 0 0 0 Resduals verses ftted values 1 5 0 0 RESIDUAL 1 0 0 0 5 0 0 0-5 0 0 1 5 3 8 6 1 8 4 1 0 7 1 3 0 YF IT T E D Fgure. Resduals verses ftted values for the generalzed lnear model wth nverse Gaussan dstrbuton 17
7.1 7 N ormal probablty plot for A nscombe resduals 5.5 4 ANSCOMBE 3.9 1. 9.6 6 -.9 6 -.9 6.6 6. 9 3.9 1 5. 5 4 7. 1 7 Qu a n tle N-Q_ p lo t o f ANSCOM BEv s. N(.9 0 3 7, 1.1 5 7 ) Fgure 3. Quntles of Anscombe resduals verses quntles of normal dstrbuton for the generalzed lnear model wth nverse Gaussan dstrbuton 5.3 Income equty n a model wth latent classes 5.3.1 Consumers who used only outpatent care We estmate a generalzed lnear model wth nverse Gaussan dstrbuton and two latent classes, and fnd that the coeffcents for latent class probabltes are sgnfcant (Table 5). Accordng to the results of Greene s (007) LR test for nested models, H o of unrestrcted model (wth latent classes) s not rejected. Smlarly, the comparson of nformaton crtera demonstrates that the model wth latent classes s preferred to the model wthout latent classes. Consequently, we may conclude that consumers separate nto two latent classes wth respect to ther outpatent expendture. The frst latent class (183 observatons) s relatvely young adults: mean age 44.06, standard devaton 1.46. Only 5% of them have low health condton. The average annual outpatent health care expendture of the frst latent class s 61,377 yen, however, the standard devaton of ths varable s hgh: 197,163 yen. The second latent class contans 857 observatons for relatvely older adults: mean age 50.98, standard devaton 13.64. The prevalence of low health condton n the second latent class s 15.5%. The average annual outpatent health care expendture of the second latent class s 60,435 yen, whch s close to the value of ths varable n the frst latent class. 18
However, the standard devaton of the varable n the second latent class s tmes smaller than n the frst latent class: 80,301 yen. The coeffcents for ncome groups are nsgnfcant n each of the latent classes. Ths mples that smlarly to our fndngs for the bnary choce models, there s horzontal equty n ncome n each of the latent classes. The need varables (age and low health condton) are sgnfcant covarates n each latent class. The fndngs on horzontal equty n Japan may be contrasted to the estmatons of log health care expendture of the US elderly n a lnear model wth two latent classes, where the coeffcents for the lowest ncome quartle are sgnfcant n each class (Deb and Trved, 011). Table 5. Estmatng health care expendture wth a generalzed lnear model wth nverse Gaussan dstrbuton and latent classes (consumers who used only outpatent care) Whole sample Latent class 1 Latent class constant -0.90 (0.689)*** 3.8837 (.3896) -0.7179 (0.8433) age -0.069 (0.0037)*** -0.0493 (0.0083)*** -0.044 (0.0040)*** group 1 * ln(ncome) 0.049 (0.0157) -0.3648 (0.880) -0.016 (0.1057) group * ln(ncome) -0.0060 (0.0095) -0.96 (0.53) -0.041 (0.0959) group 3 * ln(ncome) 0.0079 (0.0143) -0.347 (0.50) -0.0315 (0.0935) group 4 * ln(ncome) -0.006 (0.0118) -0.379 (0.381) -0.0446 (0.0900) group 5 * ln(ncome) -0.004 (0.0103)** -0.310 (0.39) -0.015 (0.0858) PDI 0.0005 (0.0003) -0.000 (0.010)* -0.0066 (0.0060) BMI 0.0001 (0.0005) -0.011 (0.09) -0.0069 (0.0113) gender -0.1475 (0.093) -0.096 (0.168) 0.95 (0.0937)** educaton -0.185 (0.1045)* -0.355 (0.189) -0.0767 (0.0938) lowhcond -0.007 (0.085) -1.450 (0.5503)*** -0.4783 (0.1374)*** smokng 0.0011 (0.0115) 0.859 (0.579) 0.1836 (0.095)* drnkng -0.0009 (0.0009) 0.3505 (0.373) 0.0955 (0.088) NHI 0.091 (0.140) 0.0003 (0.95) 0.1038 (0.0953) checkup 0.0005 (0.0006) -1.8385 (0.93)*** 0.3071 (0.1093)*** gym 0.0005 (0.0005) 0.3348 (0.198) -0.90 (0.0877)*** work -0.0001 (0.011) -0.357 (0.66) 0.3887 (0.1140)*** desgnated cty 0.0975 (0.1876) 0.6591 (0.4855) 0.396 (0.1646) cty -0.0763 (0.1768) 0.9931 (0.4660)** 0.1640 (0.1595) Log lkelhood -6871.34-5089.93-5089.93 Observatons 1040 183 857 Scale parameter n the dstrbuton 4.8499 4.046 (0.1978)*** 8.0837 (0.395)*** Pror probablty for class membershp 0.968 (0.0359)*** 0.703 (0.0359)*** Notes: The dependent varable s annual health care expendture. The Table reports coeffcents for covarates n condtonal mean functon, and robust standard errors n parentheses. *** p< 0.01, ** p< 0.05, *p< 0.1. 19
5.3. Consumers who used npatent care The results of the heteroscedastcty test ndcate that the errors n the ordnary least squares models for health care expendture of consumers who used npatent care (eq.4) are homoscedastc. Consequently, we do not use generalzed lner models and employ an OLS model wth latent classes. Snce the subsample of npatent care users s 141 consumers, we keep the mnmal number of covarates. Namely, the regressors are age, gender, the bnary varable for low health condton, and the dummes for ncome quntles. The results of the estmatons reveal nsgnfcance of ncome groups n each latent class (Table 6). In other words, horzontal equty s found for health care expendture of Japanese consumers who used npatent care. Table 6. Estmatng a latent class lnear model for consumers who used npatent health care Whole sample Latent class 1 Latent class constant 319.4091 (103.468)*** 11.9158 (735.3916)*** 4.464 (5.67) age -1.716 (1.8454) -7.3837 (10.1811)*** 1.9350 (0.8366)** gender -1.0436 (5.1148) -303.7585 (378.759) 19.39 (1.534) group 1 7.3974 (51.8004) 347.67 (507.6971) -0.3974 (5.9890) group 11.3969 (5.159) -80.399 (478.733) -5.9836 (3.4999) group 3-61.4197 (57.7608) -81.7384 (1505.5319) 0.4613 (38.3753) group 4 4.6317 (57.4747) 476.0366 (608.97) 33.6805 (6.9913) lowhcond 0.0761 (0.384) 43.8034 (336.8508) 4.888 (4.9714)* Log lkelhood -118.58-91.14-91.14 Observatons 141 19 1 Pror probablty for class membershp 0.1568 (0.0439)*** 0.843 (0.0439)*** Note: The dependent varable s logarthm of annual health care expendture for the subsample that used npatent care. The Table reports coeffcents for covarates and robust standard errors n parentheses. *** p< 0.01, ** p< 0.05, *p< 0.1. 6. Dscusson Our estmatons, whch account for unobservable consumer heterogenety through a latent class approach, reveal horzontal equty n health care access and the amount of out-of-pocket expendture for health care covered by Japanese socal health nsurance. Overall, the presence of horzontal equty n health care access and utlzaton n Japan s smlar to the fndngs on equtable or pro-poor non-specalst care utlzaton n OECD countres (van Doorslaer et al., 004). Moreover, n terms of total health care expendture of consumers, socal health nsurance system n Japan s found to be more equtable than n Germany (Ikegam et al., 011). However, there are other aspects where Japanese socal health nsurance system demonstrates ncome nequty: health nsurance premums and catastrophc coverage (Ikegam et al., 011; Hashmoto et al., 011; HGPI, 009). 0