The re la tionship between health care ex penditure and health out comes

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income (and health ex penditure as one ofthe studys dependent vari ables). Healthexpenditure as a share of GDP, gross na-tional product (GNP) or per cap ita healthexpenditure are com mon ly used. In ad-dressing prob lems of poor com parability

when using exchange rates, several studiesadopt ed purchasing power par ity (PPP) [ ,, , ] when using health expenditure

or income data. Ten of the studies useincome as an explanatory variable in ad-dition to health ex penditure, but there isa need to ac knowledge, as several studiesdo, that the cor relation be tween these two

variables is high [ ].In terms of oth er diet, socio-econom-

ic and life-style explanatory variables it ispossible to observe wide variations in thenum ber used (mean , range ). Thestudies utilising the most ex tensive num-ber of explanatory variables are Cochraneet al. [ ], which used seven health care

variables (such as physicians, nurs es, bedsetc), six dietary consumption vari ables(including those com mon ly used in sev-eral studies, e.g. alcohol consumption, to-bacco, fat intake) and four de mograph icand econom ic variables. Berger and Mess-er [ ] used explanatory variables. Someof the chosen explanatory variables alsoclearly reflect the hypothesis being in vesti-

gated, as exemplified by the use of pri vateand pub lic splits in health care ex pendi-tures [ ] and expenditure on phar maceu-ticals [ , , , ]. To capture pop ulationeffects a num ber of stud ies include an age-specific variable [ , , , ] or a popula-tion den sity variable [ , , ]. Some ve-ry specific explanatory variables includ-ed in the sam ple are decentralisation co-efficient, political rights [ ], propor tionof white-collar workers [ ] and NationalHealth Ser vice (NHS) financing of medi-cal ser vices [ ].

When examining the coun tries thatwere stud ied, it can be seen from . Ta-ble 1 that the vast ma jority (ten) stud ied

various combinations of OECD coun tries.Another three in cluded de veloped coun-tries or coun tries in West ern Europe, onestudy included both de veloping and de vel-oped coun tries, and two stud ies by the sa-me author anal ysed the Cana dian provin-ces [ , ]. It is interesting to note here thatthese two stud ies used data with a highdegree of homogeneity and con sistency,

outcomes. We also, how ever, examined pa-pers that ad dressed health in re lation toeconom ic growth. (b) Pa pers presentingempirical results obtained from mac ro-econometric mod els. (c) Papers explor-ing these issues at least for European coun-

tries or less specifically OECD countries.(d) Stud ies based on larger samples, i.e.those in cluding de veloping or tran sitionaleconomies, were also con sidered if they in-cluded European/OECD coun tries in thesample. By adopting this pro cedure we ini-tially identified potentially rele vant pa-pers. On closer examination were delet-ed either because they did not meet ourinclusion criteria, or because different ver-sions of same paper (with mar ginal differ-ences) existed. We finally retained pa-pers for re view/summary. This ap proachwas found to be use ful as it facilitated anoverview of the meth ods that have beenused, along with a sum mary of the prin ci-pal results. For ease of assimilation the rel-e vant data for each study are sum marisedin . Table 1 , which pro vides details ofthe dependent vari able(s), explanatory

variable(s), the coun tries stud ied, a briefdescription of the mod el, and the prin ci-pal results of each included study. Whilstwe acknowledge the wealth of literaturethat has examined variations in health out-

comes related to in come in equality (e.g.[ , , ]), we have not included this ty-pe of analysis in the re view as it falls out-side the scope of this pa per.

In terms of de pendent vari ables the vastma jority of studies utilise mortality rates(age-specific or infant mor tality in par tic-ular) and/or life ex pectancy. Life expectan-cy is used main ly at birth, but sev eral stud-ies assess life expectancy according to gen-der and at spe cific ages other than birth(e.g. , , years). One study [ ], how-ever, did use a health util ity variable (dis-ability-ad justed life expectancy or DALE)at birth and at age years, as well as po-tential years of life lost for circulatory dis-ease, cancer and res piratory disease.

All studies included some form ofhealth expenditure as one of the de pen-dent vari ables for the mod el used, withthe exception of Robalino et al. [ ], whichwas retained for in terest and com ment dueto its assessment of the im pact of fiscal de-centralisation on in fant mor tality, andGrubaugh and San terre [ ], which used

The second approach, adopt ed in thisstudy, con siders health as a pro ductionfunction which is ad dressed using aggre-gate or mac ro-level data. The basic tenetsof this approach are that health can be

viewed as an output, say of a health care

system, which is in fluenced by the inputsto that system. In par ticular researchersadopt ing this ap proach wish to in vesti-gate the re lationship between health careexpenditure, or med ical care resources asinputs, and health out comes as the out putof that system. Fur thermore, this is sue hasbecome a central question in the con textof health care cost-con tainment in mostde veloped coun tries in the past few de-cades.

However, it is the case that the dis tinc-tion be tween these ap proach es has be-come somewhat blurred, and there is a de-gree of overlap as many of the vari ables em-ployed in the two ap proach es are the same,and they are both cat egorised as produc-tion func tions. For the pur poses of our em-pirical analysis we focus our at tention onthe second approach due to the adop tionof macro-level variables in our pro ductionfunction. More over, Arrows impossibilitytheorem high lights the metho logical prob-lems in at tempt ing to move from the mi-cro-level to the mac ro level [ ]. This is par-

ticularly true in the health sec tor wheremany results confirm health as be ing a lux-ury good at a mac ro level, when it is a nor-mal good on a mi cro-level. Furthermore,analysis of this issue has dem onstrated an-alytically and empirically that consideringmicro-level results for health pol icy deci-sion mak ing at the mac ro-level may bemisleading [ ].

Pre vious re search

This section pro vides a re view of key stud-ies that have con sidered the re lationshipbetween health expenditure, among oth-er explanatory variables, and health out-comes, using macro-level data. In or derto iden tify potentially suitable studies wefirst searched for all the po tential rele vantpapers on these top ics using the In ternet(published articles, working papers, pub licreports) or ar ticles bibliograph ic details.Our in clusion cri teria were: (a) Papershad to focus main ly on the relationship be-tween health care ex penditures and health

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Table 1

Summa ry of data and re sults for pre vious stud ies (PYLL poten tial years of life lost, DALE disabil ity-ad just ed life ex pect an cy, PPP pur chas ing pow er par ity, GDP gross do mes tic prod uct, GNP gross na tion al prod uct, GLS gen eralised least squares, IMR infant mor tal ity rate)

Reference De pen dent vari able(s) (out puts) Ex plana to ry vari ables (in puts)

Babazono and

Hillman [3]

Perinatal mor tal ity, infant mor tal ity,

male life expect ancy at birth, fe malelife expect ancy at birth, male lifeexpect ancy at 80 years, fe male life ex-pect ancy at 80 years.

Total per cap ita health care spend ing; pub lic per cap ita health care spend ing (PPP);

in-patient beds per 1,000 pop ulation; ad missions per 100 pop ulation; average lengthof in-pa tient stay; num ber of physi cians per 1,000 pop ulation; phy sician con tacts percapita; phar maceutical expen diture per cap ita; non-health care spend ing per cap ita(PPP); percent age of pop ulation aged over 65 years.

Barlow andVissand jee [5]

Life expect ancy at birth (males,females and com bined).

Hypothet ical maximum fer tility; total fer tility rate; dai ly intake of an imal prod ucts;access to safe wa ter; per cap ita health ex pen diture (PPP and ex change rates); percapita GDP (PPP and exchange rates); pro por tion of adult pop ulation who are lit er-ate, ur ban pop ulation as a pro por tion of to tal pop ulation; pro por tion of pop ulationliving in trop ics.

Berger and Mess er[7]

Mortal ity rate per 1,000 pop ulation. GDP, health ex pen diture per cap ita in U.S.$1990; pop ulation aged over 65 years; to baccoconsump tion, al cohol con sump tion; fat con sump tion; female labour force par ticipation

rate; pro por tion of pop ulation aged over 25 years with post-sec ond ary education; Ginicoefficient; pro portion of to tal health ex pen ditures that are pub licly financed; pro por-tion of pop ulation el igible for in-pa tient care ben efits under a pub lic health scheme; pro-por tion of pop ulation el igible for am bulatory care ben efits under a pub lic scheme.

Cochrane et al. [8] Age-spe cific mortality rates (ma ter-nal, peri natal, infant, 1-4, 5-14, 15-24,25-34, 35-44, 45-54, 55-64 years per10,000 pop ulation).

Health care (physi cians, nurs es, acute hos pital beds, pae datricians, mid wives, GNPspent on health care); di etary con sump tion; cigarette con sump tion per cap ita per an-num; al cohol con sump tion in litres per cap ita per an num; calo ries per cap ita per day;pro tein per cap ita per day; fat in take per cap ita per day; sug ar per cap ita per day;demograph ic and eco nom ic (population den sity, GNP per capita; ed ucation in dex;inter vention in dex=per cent age of health ex pen diture cov ered by pub lic expen di-ture).

Crmieux et al. [9] Gender-spe cific infant mor tality;gen der-spe cific life expect ancy at bir-th and at age 65 years.

Public drug spend ing; private drug spend ing; non-drug health care spend ing; percapita income; pop ulation den sity; poverty; alcohol bev erages spend ing; gen der-spe-cific tobacco prod ucts spend ing; food and non-alcoholic beverages spend ing.

Crmieux et al. [10] Gen der-spe cific infant mor tality; gen-der-spe cific life expect ancy.

Total health care spend ing (private and pub lic); per capita physi cians, per cap ita in-come; den sity (pop ulation/area); ed ucation lev el; poverty; alcohol use; to bacco use;nutrition al data (meat and fat).

Elola et al. [11] Infant mor tality rate, PYLL females,PYLL males, life expect ancy males, lifeexpect ancy, females.

GDP per capita (U.S.$); health care ex pen diture per cap ita (U.S.$); propor tion of pop-ulation cov ered by health care sys tem; pub lic health ex pen ditures as pro por tion oftotal health ex pen diture; Gini coefficient..


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Countries stud ied and mod el de scrip tion Prin cipal re sults

21 OECD countries. Australia, Luxembourg and Turkey

were ex cluded due to mis sing data. Data are for 1988.Model: multiple linear re gres sion us ing step wiseanal ysis (due to small sam ple size).

Number of beds and non-health care spend ing are sig nificant for peri natal mor tal ity

(elasticities 0.52 and 0.48, re spec tively) and infant mor tality (elasticities 0.55 and0.35, re spec tively); length of stay is sig nificant for male life ex pect ancy at birth (elas tici-ty=0.6); length of stay (elas ticity=0.59) and pub lic health-care spend ing (elas ticity=0.38)are sig nificant for fe male life expect ancy at birth; non-health care spend ing is significant formale (elas ticity=0.5) and fe male (elas ticity=0.73) life ex pect ancy. Conclusion: only femalelife expect ancy at birth is af fected by health care ex pen diture.

76 and 77 de veloped and de veloping coun tries. Data arefor 1990. Model: multivariate re gression anal ysis usingfive-equa tion mod el.

Per cap ita income and lit eracy are strong pre dictors of life expect ancy, their in fluence ob-served on prox imal de terminants (fer tility, nu trition and wa ter). Health ex pen diture doesnot im pact on life ex pect ancy. Per cap ita con sump tion of an imal prod ucts has an in vert-ed-U relation ship with life ex pect ancy, lower fertility is associated with ma jor gains in lifeexpect ancy, located in the trop ics is associated with re ductions in life ex pect ancy

20 OECD countries 1960-1992. Mod el: regres sion anal y-sis using pan el data and cor rect ed stan dard er rors.

Five models are pre sent ed. Significant vari ables (co efficients) in most ex ten sive mod el are:health ex pen diture (0.1282), pop ulation aged over 65 years (0.3334), to bacco (0.1231),

alcohol (0.0477), fat (0.0126), fe male labour force (0.1226); Gini co efficient (0.096), pro-por tion of pop ulation el igible for in-pa tient care ben efits under a pub lic health scheme(0.0821), pro por tion of pop ulation el igible for am bulatory care ben efits under a pub licscheme (0.0224).

18 de veloped coun tries. Data used were for 1970, 1969or 1971. Mod el: regres sion anal yses of mor tal ity rates onseven vari ables found to have the great est ex plana torypower.

Seven input vari ables pro vide the most ex plana tory power: physicians (pos i tive associ ationwith ma ternal, peri natal, infant and age group 1524 age group mor tality), GNP (negativeassociation in most mor tal ity rates), cigarettes (pos itive as sociations for all mor talityrates); al cohol (most ly positive as sociations but neg ative as sociations for old er age groups);pop ulation den sity (positive as sociation for all but one mor tal ity rate), in ter vention in dex(most ly negative as sociations); sug ar con sump tion (neg ative as sociations with all mor talityrates). Mod el explains be tween 42% (514 age group) and 97% (in fant mor tality) of varia-tion in mor tality rates. Re sults have some anoma lies such as in creas ing physi cians as sociat-ed with high er mor tal ity rates, sug ar intake re duces mor tal ity rates etc.

Canadian provinces over the pe riod 19751998. Au-thors state that data are ho mogeneous in com par isonwith those de rived from in ternation al data sets. Mod el:cross-sec tion al time-se ries GLS for panel data with cor-rection for AR (1) autocorrelation with in pan els and het-eroskedas ticity across pan els. Canadian provinces areequal ly weight ed.

Public drug spend ing per cap ita and pri vate drug spend ing per cap ita are sig nificant for allhealth out comes (elas ticities: e.g. 0.108 for male in fant mor tal ity, 0.0.143 for fe male in fantmor tal ity, 0.001 for male life ex pect ancy and 0.009 for fe male life expect ancy). Total non-drughealth care spend ing is significant for male in fant mor tality, (0.51), male life ex pect ancy atbirth (0.017) and male life ex pect ancy at age 65 (0.051). Oth er significant vari ables (withexpect ed signs) in clude spend ing on al cohol and spend ing on to bacco, spend ing on foodand non-al coholic beverages, GDP per cap ita (not for in fant mor tal ity), den sity. Significantregion al variations also ex ist be tween provinces (high er private drug spend ing=bet ter healthout comes than pub lic drug spend ing). If provinces in creased drug spend ing to high est levels,584 fewer infant deaths would re sult and over 6 months life ex pect ancy at birth.

Canadian provinces over the pe riod 1978-1992. Au thorsstate that data are ho mogeneous in com par ison withthose de rived from in ternational data sets. Mod el: aggre-

gate pro duction func tion us ing GLS and provincial fixedeffects. Analyses given in orig inal values and logs.

Health ex pen diture is sig nificant for all out comes (elas ticities: 0.4 for male in fant mor tal-ity, 0.6 for fe male in fant mor tal ity, 0.05 for male life ex pect ancy and 0.024 for fe male lifeexpect ancy). Number of physi cians is also sig nificant in im proving all out comes. Oth er

significant vari ables (with ex pect ed signs) are: al cohol con sump tion and per cent age ofsmokers, den sity=neg ative impact fe male life expect ancy, poverty=neg ative impact oninfant mor tality, meat=pos itive impact on fe male life expect an cy, increased fat=neg ativeimpact on all health out comes ex cept fe male life expect ancy, high er income=high er lifeexpectan cies but not low er infant mor tality rates.

17 West ern European coun tries (Por tugal excluded).Data are for 1990 or 1991, or most re cent avail able atthe time of the study. Data are mean val ues accordingto health care sys tem (NHS or social security). Model:regres sion anal ysis using dum my variables for healthcare system type (NHS or so cial security). Relation shipbetween in fant mor tality and health ex pen diture wasinvestigat ed af ter con trolling for GDP.

Social security systems have sig nificant ly higher GDP and per cap ita health ex pen ditures.Health care ex pen diture ex plained 32% of vari ability in PYLL and 37% of life expect ancy forfemales. For GDP the figures were 26% and 23%, re spec tively. Health care ex pen diture wasa bet ter pre dictor of in fant mor tality (R2=0.45) than GDP (R2=0.38). Infant mor tality rateswould be low er for NHS systems at sim ilar levels of health care ex pen diture (mag nitude1113%).

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Table 1

Summa ry of data and re sults for pre vious stud ies (PYLL poten tial years of life lost, DALE disabil ity-ad just ed life ex pect an cy, PPP pur chas ing pow er par ity, GDP gross do mes tic prod uct, GNP gross na tion al prod uct, GLS gen eralised least squares, IMR infant mor tal ity rate)

Reference De pen dent vari able(s) (out puts) Ex plana to ry vari ables (in puts)

Grubaugh and

Rexford [16]

Infant mor tality (health ex pen diture

also assessed but not sum marisedhere).

Number of physi cians per cap ita; GDP; population den sity; real ed ucation ex pen di-

tures per cap ita; female labour force par ticipation rate; per cap ita real ex pen dituresof alcohol; per cap ita real ex pen diture on to bacco; time trend (for tech nology effect);coun try-spe cific non-system; health care sys tem dum my variable.

Hitiris and Pos net[17]

Health ex pen diture, crude mor tal ityrates

GDP; propor tion of pop ulation aged over 65 years; per cap ita healthexpen diture.

Leu [21] Age and sex-spe cific mortal ity rates ofadults; sex-spe cific post-neo natal mor-

tal ity (2nd to 12th months af ter birth).

GDP per capita; health ex pen diture; num ber of physi cians (lagged 10 years) and beds(lagged 10 years); ed ucation, ur ban isation; con sump tion of al cohol and to bacco;

pub lic financ ing of med ical services; NHS financ ing of med ical services; propor tion ofpop ulation aged un der 15 years; di rect democ racy.

Lichtenberg [22] Life expect ancy at birth. Per cap ita health care ex pen diture (pri vate and pub lic); medical innovation (newdrugs and phar maceutical R&D).

Miller and Frech [27] DALE at birth and at age 60 (19981999); life expect ancy at birth and atages 40 and 60 years (19971999);PYLL for circulatory disease, for can cer,and for res piratory disease; cause-spe-

cific mortality rates at par ticular ages:3554, 5564, 6574, and 75 years(19941996).

FEMALE, indicator vari able for a fe male out comes mea sure; GDPPC, gross domes ticprod uct per cap ita in PPP; PHPC pharmaceutical expen ditures per cap ita in PPP;HEPC other health ex pen ditures per cap ita in PPP; SMOKE, if female=1, the per cent-age of fe males aged 15 years or over who smoke; if fe male=0, the per cent age of ma-les aged 15 years or over who smoke; AL COHOL consump tion per cap ita; ALCOHOL

FEMALE, ALCOHOL inter action with FEMALE; OBESITY, propor tion of fe males withhigh body mass in dex.

Or [30] PYLL per 100,000 persons, aged up to69 years; all caus es except sui cides.

Total health ex pen diture per cap ita (PPP); propor tion of pub lic expen diture in to talhealth ex pen diture; GDP per cap ita (PPP); propor tion of white-col lar workers in to talwork force; NOx emissions per cap ita; alcohol con sump tion; to bacco consump tionexpen diture per cap ita (PPP); fat but ter con sump tion per cap ita; sug ar consump tionper cap ita.

Robalino et al. [32] In fant mor tality ratio. GDP; decentral isation co efficient; struc tural indicators (pol itics rights,

corrup tion, eth nicity); country effect dum my.

Shaw et al. [34] Life expect ancy at different ages (40,60, 65 years) for men and wom en in1997.

Gender; age; GDP; phar ma exp.; health exp.; be haviour variables (to bacco, but ter andvegeta bles con sump tion); pol lution proxy

Wolfe and Gabay [43] Gen der-spe cific life expect ancy atbirth and at age 60 years; in fant mor-tal ity; prenatal mor tality rate; med icalexpen diture.

Medical expen diture; pop ulation aged over 65 years, but ter con sump tion; road ac ci-dents; liv er cirrhosis (male and fe male); to bacco consump tion;employment in safe and risky in dus tries.

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Countries stud ied and mod el de scrip tion Prin cipal re sults

Panel data for 12 OECD countries (U.S. excluded). Data

are for 19601987. Mod el: multiple regression anal ysis.Actual and pre dicted per formance of the U.S. also anal-ysed us ing the se lected pan el and pub lished data forthe U.S.

Significant (co efficient) vari ables for in fant mor tality are: Num ber of physi cians (0.302),

GDP (0.386), alcohol (0.099), to bacco (0.145), time trend (0.145). The U.S. in fant mor tal-ity rate (pre dicted) was 17.2%, while the ac tual value was 12.8% if the U.S. pos sessed thehealth care sys tem and un observable non-sys tem struc ture of the typ ical OECD country.

20 OECD countries over 28 years (19601987). Mod el:regres sion anal yses on three mod els using linear andlog-linear form. Use of coun try-spe cific shift dum miesbased on a pooled sam ple of cross-sec tion and time-series data. PPP and ex change rates used.

Model 1 confirms the strong link be tween health ex pen diture and GDP (income elas ticityof health spend ing=1.026). Mod el 2 shows that pro por tion of age 65 above is sig nificant inexplain ing health ex pen diture (elas ticity=0.55). Mod el 3 shows that health ex pen diture hasa neg ative impact on mor tality (elasticity low at 0.08), and both pop ulation aged over 65years (elas ticity=0.350) and GDP (elas ticity=0.087) have a pos itive influence on mor talityrates. For a giv en level of health ex pen diture and GDP the UK has sig nificant ly higher mor-tal ity rates in the sam ple used.

19 OECD countries (not Luxembourg, Ice land, Japan,Portugal and Turkey). Data are for 1974. Mod el: regres-

sion anal yses using some lagged vari ables.

Only results for health out comes as de pen dent vari ables giv en here: Vari ations in adult mor-tal ity rates could not be ex plained by mod el. For post-neo natal mor tal ity (boys, girls), per

capita GDP (0.56, 0.71), ed ucation (1.3, 0.62) and pub lic spend ing (0.09, 0.06) weresignificant ex plana tory variables. Health ex pen diture was not sig nificant when in come iscontrolled for. Third par ty financ ing systems may have an ad verse in fluence on post-neo na-tal mor tality rates due to re duced up take.

U.S. over the pe riod 19601997. Mod el: aggre gateproduction func tion in corporat ing the ge omet ric lagmod el and logarithms, cor rections for se rial correla-tions.

10% rise in life expect ancy from 69.7 to 76.5 years. In creased health ex pen diture and drugapprovals ex plain about 100% of ob served long-run in crease in life ex pect ancy. Cost ofmed ical care per life year gained=$11,000; $1,345 for phar maceutical R&D. New drugs aremore cost-ef fective in increas ing life expect ancy

18 OECD countries. Model: cross-section re gres sionanal ysis with lagged vari ables in log-log.

Estimations with the mod el with life expect ancy at birth are non-sig nificant (ex cept con-stant and obe sity). For all of the oth er mod els with life expect an cy at ages 40, at 60, withDALE at birth and DALE at 60 years: pharmaceutical expen diture co efficient is always signifi-cant and the oth er health ex pen ditures nev er. Effects are high er for wom en (elas ticity from0.02 for DALE at birth to 0.09 for DALE at 60). With PYLL and mortality, re sults are dif ferent

according to the con sidered pa thol ogy. Obesity has also large ef fects. GDP and oth er healthcare expen ditures are non- sig nificant in all mod els except for can cer and res piratory mortal-ity mod els. Methodology seems non-ro bust (prob lems of collinear ity stressed by au thors),prob ably phar maceutical catch most of GDP and oth er HC expen diture ef fects.

21 OECD countries 19701992. Mod el: regres sionanal ysis with 483 ob servations us ing pan el data.

Health ex pen diture is sta tistically significant on health for wom en, in terms of pre maturedeath ap proximate by PYLL (0.18 in log), but non-sig nificant for men. If GDP removedfrom es timation (high collinear ity), both large ly significant, and still more for wom en. Pro-por tion of pub lic health ex pen diture is sig nificant for both men and wom en (0.17 and0.18 respec tively). Ma jor contribution to de crease in pre mature mor tality is (respec tivelyfor wom en and men): (a) Pro por tion of white-col lar (captur ing ed ucation and work): 0.80(w), 0.75 (m); (b) GDP per capita PPP: 0.34 (w), 0.44 (m); (c) alcohol: +0.20 (w) and +0.16(m); (d) propor tion of pub exp. In to tal health ex pen diture: 0.17 for both.

67 coun tries (LDC and OECD) 19701995. Model:

regres sion anal ysis using pan el data.

Fiscal decentral isation leads to de crease in IMR (decreas ing re turn ef fects with the GDP lev-

el). Elasticities are around 0.33 for rich er coun tries (those with more than U.S.$6,000 percapita). Other coefficient are also sig nificant.

19 OECD countries; 1980, 1985, 1990, 1997 data. Mod el:cross-sec tion anal ysis with lagged vari ables.

Pharmaceutical exp. leads to in crease life ex pect ancy at ages 60 and 65 years (elas ticitiesof 0.028 and 0.031, re spec tively). Per capita GDP impor tant pre dictor of life expect ancy atages 60 and 65 (elas ticities of 0.03 and 0.055 re spec tively). Estimat ed coefficients of oth erhealth care ex pen ditures are non-sig nificant

22 OECD countries in years 1960, 1970 and 1980.Data are con vert ed into rates of change. Mod el: linearstruc tural relations for si multaneous mod els (mod el1=health func tion of med ical expen diture and life-style;mod el 2=med ical expen diture is a func tion of life-style).

Increases in med i cal expen diture lead to im prove ments in all health out comes. Neg ativechang es in life-style lead to neg ative chang es in health out comes and in crease med icalexpen ditures. The in clusion of life-style vari ables must oc cur in order to de termine the pos-itive (ben eficial) link between health ex pen diture and health out comes. Increase in pro por-tion of pop ulation aged 65 or over, and high er occupation al risk are as sociated with high ermed ical expen diture.



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and bet ter hygiene (e.g. from a decline inwater and food-borne dis eases, improvedwater qual ity and sewage disposable sys-tems, diet, exercise, tobacco and alcoholconsumption), which have been sug gest-ed as the most im portant in the de termi-

nation of health out comes [ ].Taking account of these caveats and re-strictions, we un dertook econo metric anal-ysis of three dependent vari ables associat-ed with health out comes: life expectancyat birth of males ( M ) and females (F ), andinfant mor tality (I ). The chosen explana-tory variables, determined by examina-tion of the OECD health data set and inlight of the variables common ly applied inpre vious research (as out lined above), foreach of the three equa tions were: to tal (percapita) health expenditure (U.S. PPP), X ;health expenditure as pro portion of GDP, Y ; number of physicians (per , headof population), D; num ber of hospitalbeds (per , head of population), B;in-pa tient ad mission rate (per centage ofpopulation per an num), A; average in-pa-tient length-of-stay in hos pital (days perannum), H ; population coverage of healthcare system (per centage), C ; unemploy-ment rate, U ; alcohol consump tion (lit-res per cap ita per an num), S; expenditureon tobacco (U.S. PPP per capita per an-

num), T ; nutritional characteristics, suchas fruit con sumption (ki los per capita perannum), F ; nutrition (pro tein, per cap itaintake per an num), N ; and en vironmen-tal pollution, P (sulphur ox ide emission,measured in ki los per capita per an num).It was not pos sible to obtain cer tain vari-ables of interest, such as ed uca tional attain-ment for the EU pop ulation and ac tual cig-arette con sumption (as op posed to the uti-lised expenditure on cigarettes), a point ad-dressed fur ther in the dis cussion.

The analysis was applied to data of the EU countries of the pe riod ,

i.e. ! = obser vations. Vari ablesand data were ob tained from the OECDHealth Database [ ]. Data were analysedusing the econo metric software packageSHAZAM [ ]. The variables identifiedabove are assumed to exert some in fluenceon the de pendent vari ables. However, the-re may be specific characteristics in eachcoun try as well as latent vari ables, non-specified or di rectly quantifiable, whichmay exert di verging effects on different

countries. There fore in addition to the ex-planatory variables and the con stant term,our mod el includes a dum my variable foreach EU mem ber state (with the Unit edKingdom tak en as the stan dard and rep re-sented by the con stant term). Af ter taking

account of the ex pected non-lin earitiesand test ing for the spec ification form (us-ing the box Cox sta tistics) we have chosenthe log-linear functional form.

The mem ber-states of the EU make upa specific non-ran dom set and, there fore,the estimation con cerns a fixed-ef fects mod-el sub ject to stochastic disturbances. Con-sequent ly we applied an estimation meth-od that takes ac count of the open ness andinterdependence of the EU economieswithin the com mon mar ket and cor rectseconometric prob lems arising from the na-ture of the data in the sam ple by postulat-ing that the pooled set of coun try data iscross-sectionally correlated and time wiseautoregressive [ ].

Consistent estimates are de rived by sub- jecting the pooled ob ser vations to or di-nary least squares es timation to cal culatethe cor respond ing residuals. These areused to trans form the vari ables, removethe autocorre lation and, by ap plying gen-eralised least squares, ob tain asymp totical-ly efficient estimates of the re gression coef-

ficients and their vari ances. For the es tima-tion the data are trans formed, and there-fore the usu al goodness of fit statistics areinappropriate. We used in stead the R sta-tistic between observed and pre dicted val-ues of the dependent vari able.

Results

The pleth ora of correlated explanatory vari-ables led to prob lems of multi-collinear ity.However, following man ual stepwise pro-cedures we eliminated these prob lems byremoving from the set of the ex planatory

variables and coun try dum my variablesthose that pro vided estimated coefficientsof size and sign un acceptable by con ven-tion al statistical and econom ic criteria.The estimates of the re sulting par simo-nious mod el are presented in . Table 2.

The results suggest the following specif-ic points:

F Male life expectancy: Health expendi-ture, +X , num ber of physicians, +D,

nutrition, +N , and pollution, P , aresignificant determinants of male lifeexpectancy. But there is a statistical-ly significant level of heterogeneitybetween coun tries with Sweden thetop per former with . years, and

Portugal and Finland the worst with. years, with an EU average of. years in .

F Female life expectancy: Health expen-diture, +X , and num ber of physicians, +D, are the significant determinants offemale life expectancy. There is mar-ked heterogeneity between coun trieswith France the top per former with

. years, and Ireland the worst with

. , with an EU average of . yearsin .

F Infant mor tality: Again, health ex-penditure, X , and num ber of physi-cians, D, are the only significant de-terminants in the re duction in in fantmor tality. The top per former is Swe-den with an in fant mor tality of . perthousand, the worst is Por tugal with

. , with an EU average of . per thou-sand in .

. Table 3 presents the con tribution ofeach explanatory variable to the out come.

With the ex ception of in fant mor tali-

ty which, dur ing the pe riod un der re viewhas been more than halved by the sig nifi-cant con tribution of health ex penditure, X ,and med ical care (num ber of physicians, D), the pre dominant de terminants of bothmale and female life expectancy are tho-se contained in the con stant term, name-ly the unaccountable salient variables andcountry-specific characteristics. Thereforethe most im portant con clusion reachedby the analysis is that health care ex pendi-ture has made a rel atively marginal contri-bution to the im provements in life expect-ancy in the EU coun tries over the pe riodof analysis. It has added only . years tothe life expectancy of males and . yearsto that of females.

Discussion

As illustrated in this and pre vious stud ies,measuring the im pact of health expendi-ture on health out comes is a complex anddifficult issue, which is com monly exam-ined from ei ther a mi cro- or mac ro-per-


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Table 2

Results of the es tima tions

Life expect an cy, malescoefficient ( t ratio)

Life expect ancy, femalescoefficient ( t ratio)

Infant mor tal itycoefficient ( t ratio)

Inter cept 4.048 (219.000) 4.120 (427.160) 4.348 (23.922)

Expen diture 0.022 (8.828) 0.022 (17.081) 0.497 (23.466)

Physicians 0.029 (5.533) 0.034 (11.960) 0.380 (8.412)

Nutrition 0.006 (1.992)

Pollution 0.007 (5.106)

Austria 0.033 (5.346) 0.167 (3.489) 0.148 (2.566)

Belgium 0.330 (8.230) 0.255 (11.723) 0.315 (6.367)

Germany 0.357 (10.902) 0.298 (14187) 0.296 (3.832)

Denmark 0.172 (2.731) 0.260 (4.030)

France 0.258 (8.634)

Finland 0.255 (5.629) 0.342 (4.629)

Greece 0.169 (2.486)

Italy 0.108 (3.613) Ireland 0.145 (3.979) 0.250 (6.863)

Luxembourg 0.305 (11.693) 0.170 (7.706)

Portugal 0.382 (9.103) 0.190 (7.735) 0.280 (3.399)R2 Buse 0.763 0.733 0.720R2 observed/predicted

1.000 1.000 0.726

Table 3

Contribut ing fac tors to health out comes (%) (from: es timat ed co effi cients inTable 2)

Male lifeexpect an cy

Female lifeexpect an cy

Infant mor tal ity

% years % years % rate

Health ex pen diture 3.53 2.6 3.46 2.8 78.8 0.63

No. of physicians 2.14 1.6 2.46 1.9 27.8 0.22

Nutrition 0.74 0.5

Pollution 0.60 0.4

Constant Term 94.19 68.4 94.08 74.5 6.6 1.65

EU average 72.7 79.2 0.8

spective. Our ap proach has fol lowed thelatter, con sidering health as the out put ofa health care system, with vari ations beingexplained by an ar ray of healthcare inputsin con junction with a num ber of life-styleand en vironmental variables.

The results of our own em pirical studyconfirm McKeowns [ ] conclusions forlong-run anal yses, namely the relativelyweak impact of health care on life ex pect-ancy, and re search indicating the lim itedprogress of med icine in im proving healthsince the s in de veloped coun tries [ ,

, ]. Nevertheless, the more significantcontribution of health care ex penditure inimproving infant mor tality is consistentwith the opin ion of some health mac ro-economists [ ]. In terms of life expectan-cy improvements it is not ed that femaleshave gained marginal ly more than males,and anal ysis of the OECD health databaseconfirms that fe males generally maintain a- to -year ad vantage over men for the pe-

riod of analysis. The reasons for this maybe more com plex than can be dealt with inthis study, but in terms of health ex pendi-ture it is worth not ing that al most all massscreening programmes in de veloped coun-tries are tar getted at wom en (for example,breast and cer vical cancer), additional ex-penditure is in curred through child bear-

ing-re lated encoun ters with health caresystems, and be cause increased expendi-ture is linked with age ing women wouldbe expected to uti lise more health care be-cause of their lon gevity.

Below we highlight some im portant is-sues that affect the validity of the resultsof pre vious stud ies as well as our own find-ings.

Choice of health out comes

A key issue in stud ies with our ob jectiveis the weak robustness of available macro-econom ic indicators that can be used toapproximate pop ulation health sta tus. In-deed life expectancy and mor tality rates,common ly adopted by researchers, canonly par tially reflect the health sta tus ofa population and it is dif ficult to iden ti-fy feedbacks and causal ity links betweenhealth expenditures and health out comes,especially for de veloped coun tries. It wasfelt a priori that in fant mor tality would bea more rep resentative and re liable health

outcome than life ex pectancy as the lat-ter is more at tributable to fac tors not re lat-ed to the health care sys tem, whereas therisks associated with child birth and life inthe first year of an in fant are re duced bybetter health care fa cilities and pro cedures.This was borne out by our re sults.

In terms of al ternative outcome mea-sures, because life expectancy and infantmor tality are regarded as fairly crude prox-ies for health sta tus, which are not very sen-sitive to chang es in health care fi nancing

and de livery systems [ ], a number of oth-er measures are being de veloped. These in-clude amongst oth ers the QALY and HYEas outlined in the In troduction. Linked tothis research are a num ber of instrumentsthat mea sure health util ities, including theMedical Outcomes Study Short-Form and Sickness Impact Pro file which havebeen adapt ed for use in oth er languagesand cul tures [ ]. Although one study in-cluded in the re view the DALE [ ] wasused, it should be not ed that health sta tus



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measurements are still in the pro cess of development and not yet avail able for cross-coun try com parisons and/or tem poralanalysis (due to lim ited estimations). Thechoice of life expectancy and infant mor-tality to represent health out comes by us

and oth er researchers in this field of stu-dy is justifiable. Thus, although life expect-ancy and infant mor tality have their lim i-tations, oth ers support the view that pop u-lation-based mea sures of health out comehave a crucial place in the overall assess-ment of health ser vices. They are the ulti-mate validators of societal achievement inrespect of health and as such in vestmentin their study in ap propriate detail seemswarrant ed [ ].

Model mis-specification

Another ma jor difficulty in mod ellingthe relationship between health expendi-ture and health out comes is in the po ten-tial for mod el mis-specification. In deed,our anal ysis did not cap ture the im pact oflagged effects, which are par ticularly rele-

vant to life-style variables such as cigarettesmoking, alcohol consumption and pol lu-tion. Their im pact on health out comes, asshown in pre vious work, may take a num-ber of years and anal yses using lagging

and a large pan el of data would un doubt-edly increase the validity of the results offuture stud ies.

We also acknowledge that our cho senmodel could be better specified in termsof the chosen variables. For example, wecould not ob tain some vari ables of interestfrom the OECD Health Database, such aseducation at tainment and ac tual consump-tion of cigarettes in the EU coun tries. Ouruse of expenditure of cigarettes may nothave captured the true im pact of smokingin quan titative terms (al though ex pendi-ture rather than us age was employed in anum ber of stud ies included in the re view),and the im pact on health of ed ucation iswell established in Gross man-type stud ies.Future re search there fore should explorethe possibility of including these and oth ernon-health vari ables rele vant for this kindof modelling.

Furthermore, the re sults of our re viewindicate that vari ables on health care sys-tem or ganisation and fi nancing could beemployed further to test the ef ficiency of

various systems. In this re gard explanatory variables used may in clude centralised/de-centralised systems, Bis markian/Bev erid-gian approaches, share of pub lic health ex-penditure, share of pri mary care expendi-tures, pri mary pre vention, educational ca-

re expenditures, and the char acteristics ofhealth care financing.

Data qual ity

Some early empir ical testing of the re-lation ship between health ex penditureand health out comes start ed with cross-section cross-coun try estimations which,probably for reasons associated with da-ta heterogeneity and poor qual ity (e.g. theOECD start ed collect ing and pub lishingstatistical series on health only in the mid-dle s) did not pro vide any support tothe hypothesis of the link between healthexpenditure and health out comes. Indeed,pre vious stud ies have shown that mea sur-ing health out comes for any coun try willhave its limitations, and these lim itationswill be further exacerbated by the likeli-hood that a group of countries (suchas the EU over the pe riod of analysis) willhave some variations in the way that eachcountry defines and records data [ ]. AsMacbeth [ ] rather pessimistically points

out, this di versity of definitions in vali-dates many com parisons.

As indicated above, more re cent datawould help to in crease the re liability of ourown analysis. However, in con sidering theupdating of our data pan el we noted vari-ations in def initions for the OECD dataset between the ver sion we used and new-er versions, as well as gaps for some vari-ables/countries. In fu ture work we will ex-plore the pos sibility of using other sourcesto update and ex tend our dataset. By util-ising more re cent and ex tended variablesin our dataset the pres ent find ings can befurther explored and val idated. [For exam-ple, the Summer and Pre ston dataset,the Penn World Data (PWD) is avail ableat: http://bized.ac.uk/dataserv/pennhome.htm; the Sachs-Warn er dataset is availableat: http://www.nuff.ox.ac.uk/Eco nomics/Growth/datasets.htm; the East erly-Le-

vine dataset, the Bar ro-Lee dataset andthe World De velopment In dicators are available at: http://www.world bank.org/data; the WHO Eu ropean Health For

All Database (HFA), edition, is avail-able at: http://www.who.dk.]

Developed vs. de velopingcountries

Our re sults show a mar ginal but pos itiveeffect for health expenditure on the ex am-ined health out comes for de veloped na-tions (rep resented by EU), more so for in-fant mor tality than life expectancy, whichis consistent with ev idence con firming di-min ishing returns in the area of health ca-re in de veloped coun tries [ , ]. In con-trast, small amounts of health ex penditurein de veloping coun tries, and even in ter-mediate coun tries, would almost cer tain-ly have a bigger impact. In this re gard itwould be an in formative and in terestingexercise to link these re sults with the re-search field on growth and health, wherethis relationship [ ] has been stressed bymany au thors [ , ] for health expendi-ture on growth. Bau mol [ ] showed thatdue to pro ductivity differences betweenthe ser vices sector (low productivity ac-tivities) and the in dustrial sector (highproductivity activities) the value share ofthe former will increase over time, i.e. agrowing part of na tional income will bespent (and earned) through the low pro-

ductivity sector, as long as there is a de-mand for these ac tivities.

This well-known phe nom enon is cal-led Baumols disease and health ser vicesis one of the usu al applications of it. In-deed, the de mand for health ser vices (de-rived from the con cept of needs) is re cog-nised as unlimited as long as people ex-perience (exogenous) vari ations in theirhealth sta tus. Moreover, the low pro duc-tivity character of the health sec tor is en-hanced be cause the pro vision of health ser-

vices is sub ject to a decreasing returns toscale of . It would be an interesting issueto explore new re search to validate Bau-mols disease in health care in the light ofthe limited impact of care on glob al healthstatus in de veloped coun tries over the pastfew decades. As pre viously outlined, it isthe case that since the s in the de vel-oped coun tries there have been no cru-cial inno vations in health care, i.e. hav-ing a large impact on the pop ulation as awhole in these coun tries, while health careexpenditures have in creased dramatically.


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Hence the phe nom enon de scribed by Bau-mol could explain the large de creasing re-turn (con verging towards ) of health ca-re expenditure on health out comes sin-ce increases in expenditure are large ly tak-en up among ser vices (workforce) grow-

th and the de velopment of expensive prod-ucts which are ap plicable to small sub-pop-ulations. In con trast, the dis covery of new

vaccines or large effect drugs, such as pen-icillin, both pro ducing huge pos itive exter-nalities, induced in creasing returns basedon prod ucts (not ser vices). One may con-template that this could be again the casein the next decades with ge netics and asso-ciated ther apies.

In sum mary, in stud ies that have usedaggregate data to explore causal links be-tween health out comes and ex planatory

variables such as health ex penditure, thoseassociated with the health care sys tem, en-

vironmental and life-style, there are someconflicting results and method ological is-sues that need to be ad dressed, suggestingmore work needs to be done in this area[ ]. The above discussion regarding meth-odology and data sources should, in par tic-ular, help to im prove future stud ies.

Conclusions

This study examined the con tributionof health care expenditure to health out-comes. The find ings show that is sues in-

volved here are complicated because, first,health expenditure is only one of manyquan titative and qual itative factors thatcontribute to health out comes, and, sec-ond, health out comes are also qual itativeand quan titative, and only the lat ter maybe assessed by the available statistical andeconometric techniques. Tak ing accountof these con straints we searched for thedeterminants and their ef fects on threecon ventional health out comes, male andfemale life expectancy at birth and in fantmor tality, for which quan titative statisticalobser vations are available. In the pro cesswe were confront ed with the sev eral butexpected econometric obstacles and prob-lems but ar rived at rea sonable results with-in the con straints found in us ing OECDhealth data. In terms of the lim itations ofour em pirical study we acknowledge thatthe size of the available sample did not per-mit us to test for the pos sible existence of

any lag structure in the ex planatory vari-ables, potentially rele vant to expenditureon cigarettes, alcohol consumption and en-

vironmental influences. Given these cave-ats, the find ings of this study lead to theconclusion that while health care ex pendi-

tures are among the most im portant fac-tors in the low ering of infant mor tality,they make only a mar ginal contributionto the im provement of male and fe malelife expectancy. Our re sults are broad ly inline with those of oth er stud ies re viewedin this con tribution on de veloped coun-tries, but there are many caveats that needto be considered when in terpreting the re-sults of this and oth er stud ies, which wehave attempt ed to iden tify and discuss tothe ben efit of future stud ies in this area ofresearch.

Correspond ing au thor John Nixon

Centre for Reviews and Dissemination,University of York, York, YO10 5DD, UKe-mail: [email protected]

Acknowl edge ments

We express our thanks to Dr. Theo Hitiris, former-ly of the Department of Economics and RelatedStudies, University of York, for his input to theecono met ric analyses and con structive criticismsin the prep aration of this manuscript.

Conflict of in ter est: No information supplied

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The re la tionship between health care ex penditure and health out comes

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