WT__ 2 WT 4WORLD BANK TECHNICAL PAPER NUMBER 274 March I q95
Health Expenditures in Latin America
Ramesh Govindaraj, Christopher J. L. Murray,and Gnanaraj Chellaraj
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WORLD BANK TECHNICAL PAPER NUMBER 274
Health Expenditures in Latin America
Ramesh Govindaraj, Christopher J. L. Murray,Gnanaraj Chellaraj
The World BankWashington, D.C.
Copyright (© 1995The International Bank for Reconstructionand Development/THE WORLD BANK1818 H Street, N.W.Washington, D.C. 20433, U.S.A.
All rights reservedManufactured in the United States of AmericaFirst printing March 1995
Technical Papers are published to communicate the results of the Bank's work to the development com-mnLMity with the least possible delay. The typescript of this paper therefore has not been prepared in accor-dance with the procedures appropriate to formal printed texts, and the World Bank accepts no responsibili-ty for errors. Some sources cited in this paper may be informal documents that are not readily available.
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TIhe complete backlist of publications from the World Bank is shown in the annual Index ofPublications, which contains an alphabetical title list (with full ordering information) and indexes of sub-jects, authors, and countries and regions. The latest edition is available free of charge from theDistribution Unit, Office of the Publisher, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433,U.S.A., or from Publications, The World Bank, 66, avenue d'1ena, 75116 Paris, France.
ISSN: 0253-7494
Ramesh Govindaraj is a research associate and Christopher J. L. Murray is an associate professor, bothat the School of Public Health at Harvard University. Gnanaraj Chellaraj is a consultant in HealthEconomics to the Middle East and North Africa Country Department II at the World Bank.
Library of Congress Cataloging-in-Publication Data
Govindaraj, Ramesh, 1961-Health expenditures in Latin America / Ramesh Govindaraj,
Christopher J. L. Murray, Gnanaraj Chellaraj.p. cm. - (World Bank technical paper, ISSN 0253-7494; no.
274)Includes bibliographical reference (p. ).ISBN 0-8213-3142-X1. Medical care, Cost of-Latin America. 2. Medical care, Cost
of-Latin America-Statistics. I. Murray, Christopher J. L.II. Chellaraj, Gnanaraj, 1958- . III. Title. IV. Series.RA410.55.L29G68 1995388.4'33621'098-dc2O 95-3945
CIP
- 111 -
Contents
Foreword ....................................... iv
Acknowledgments ................................. v
Chapter I. Introduction ............................ 1
Chapter II. Past Studies of National Health Expenditures ..... . . . . 3
Chapter III. Definitions, Methods, and Materials ..... . . . . . . . . . 5
A. Definitions ............. ... 5
B. Methods ................................ . 61. Currency Conversions ...... . . . . . . . . . . . . . . 62. Disaggregation of Government Expenditures ..... . . 6
C. Materials .......... . .. . .. .. . .. . .. . .. . .. . . . 8
Chapter IV. Estimating Out-Of-Sample .................. . . 11
A. Estimating Public Sector Expenditure .12B. Estimating Private Health Expenditure .13
Chapter V. Results .15
A. Regional Health Expenditures .15B. Time Trends .16C. Disaggregated Data on Government Health Expenditures . . .. 18
Chapter VI. Discussion and Conclusions .21
A. Tracking Health Expenditures .21B. Income and Other Determinants of Health Expenditure .... . 21C. How Do We Expect Health Expenditures to Change
with Income Per Capita ......... .. . .. . . .. . . .. . . 23D. What Does Health Expenditure Buy? ....... . . . . . . . . . . 24
Bibliography ............... . .................... . 27
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
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Foreword
The preparation of the World Bank's World Development Report1993: Investing in Health (WDR) included a substantial effort to assembleintemationally comparable statistics on a broad range of health and healthsystem indicators. Much of this effort involved compiling data from existingsources. It also involved substantial analytical efforts to improve the qualityand comparability of data (e.g. for trends in under-5 mortality rates and forlevels of health expenditures). Appendix A of the WDR contains the resultingstatistical tables. In particular, that Appendix reports the first estimates thathave been assembled on global public and private health expenditures.
This Discussion Paper presents an expanded and updated version of theWDR estimates of 1990 health expenditures for the countries of Latin Americaand the Caribbean (LAC). While the population cut-off for inclusion in theWDR was 3 million, these tables include almost all countries in the region.This update of the WDR expenditure assessments results in a substantialupward revision in the estimated percentage of GNP spent on health in LatinAmerica in 1990. Thus, while the WDR estimated that 4% of GNP was spenton health in the countries of LAC, these revisions suggest that the figure wasaround 6%. Development of policies to contain cost escalation becomescorrespondingly more salient.
Preparation of this document was enormously aided by collaborationwith staff and consultants of the Pan-American Health Organization (PAHO).If the document proves of value, we expect to update it every few years,ideally in continued collaboration with PAHO and the Inter-AmericanDevelopment Bank.
Sri-Ram AiyerDirector
Technical DepartmentLatin America and the Caribbean
September 1994
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Acknowledgments
The authors gratefully acknowledge the contribution of colleagues atHarvard University, The World Bank (particularly the WDR 1993 team), andvarious U.N. agencies, whose input and assistance made this study possible.Special thanks are due to participants from the World Bank, PAHO, and IDB(in particular Philip Musgrove, Emesto Castagnino, Rubdn Sudrez, CdsarVieira and Pamela Henderson) at a series of meetings on Latin-Americanhealth expenditures in Washington, D.C.
Chapter I. Introduction
Measurement of health expenditures is important from severalperspectives. The satisfactory realization of the objectives of health planners -effective planning and management of health programs; intra-sectoral andintersectoral priority-setting and resource allocation; assessment of thedistributional impact of health programs; planning an optimal public-privatemix in the provision and financing of health services; and undertaking researchinto the determinants of changes in health status - is dependent, to a significantextent, on the availability of reliable, up-to-date data on health spending. Theabsence of standardized and systematic national health expenditure estimates,thus, is often a stumbling block in undertaking economic analyses of the healthsector at the intra-national and cross-national levels. As health care is financedby a multitude of sources in most countries, tracking overall health spending,without a sustained and coordinated effort, is often a difficult task. In LatinAmerican countries, many of which have sizable health spending throughvarious government, parastatal, and social security institutions, this task isparticularly complicated.
The purpose of this study, commissioned by the Latin America andCaribbean Technical Department of the World Bank, is to document, in detail,the levels and trends in health spending in the Latin American countries. Assuch, it represents an extension of a study on health expenditures undertakenby the authors as background for the World Development Report (WDR)1993, which was the first comprehensive and systematic effort to compareglobal health spending (Murray, Govindaraj, and Chellaraj, 1993). We haveattempted, as part of this study, to estimate health spending in many smallerLatin American countries, that were excluded from the WDR backgroundpaper (see Appendix 1 for list of countries included in database). In addition,we present a more detailed breakdown of spending by ministries of health inthese countries.
The objectives of this study are five-fold:
a) To access existing information on national public, private and socialinsurance health expenditures from governments, international agenciesand ad-hoc studies;
b) To explore the relationships between national health expenditures andimportant socio-economic variables, using econometric models, for out-of sample prediction and analysis of health expenditures;
c) To use the available information on health expenditures and thepredictive equations derived from (b) to ascertain the level of national
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health expenditures in every country in Latin America for 1990;
d) Where possible, to analyze patterns of expenditure disaggregated byactivity, type, and source of finance; and
e) To carry out analysis on time series data on health expenditures (in thedecade preceding 1990) for countries where such data are available.
This study has benefited substantially from interactions and severalrounds of discussion with the team from PAHO, responsible for estimatinghealth spending in the Latin American and Caribbean region for the 1994annual report on "Health Conditions in the Americas", and the Inter-AmericanDevelopment Bank . Indeed, these efforts should be seen as a potentiallycontinuing collaborative relationship between these institutions in monitoringon a regular basis the spending on health in Latin America.
It should be noted, however, that this study differs, in importantrespects, both from the study on health expenditures for the WorldDevelopment Report (WDR), 1993, and from the PAHO study. Thus, theestimates in this study represent a more up-to-date and comprehensiveassessment of spending in this region compared to the WDR (see Appendix 18and 19 for a description and explanation of the differences between theestimates of this study and WDR 1993). The study is also not strictlycomparable with the PAHO study, as the definitions of the various categoriesof the health sector; the methods used (e.g., the predictive regression equationsare based on a global, rather than a regional analysis); and the sources ofinformation (e.g., on expenditures and of the independent variables used in theregressions), are somewhat different. These differences have persisted, despiteattempts made to resolve them. The differences are, however, not dramatic,and it is hoped that future collaborations will help in explicating them.
The paper is structured as follows: Section II provides a brief review ofprevious literature on health expenditures; Section III describes the definitions,methods and materials used in this study; Section IV discusses the regressionsundertaken for the prediction of public and private sector health expenditures;Section V summarizes the key results of the study; and Section VI discussesthe results and the main conclusions. Details of the definitions and methods,and of the results, are provided in the appendices.
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Chapter HI. Past Studies of National Health Expenditures
For a detailed review of previous studies on national and internationalhealth expenditures, readers are referred to the WDR 1993 background paper(Murray, et al., 1993b). To provide the context for this study, however, fourthemes from past studies, that we have alluded to elsewhere (Murray,Govindaraj and Musgrove, 1994), should be highlighted. First, theinformation on health expenditure has evolved considerably in the past threedecades in industrialized countries but not in developing countries. Abel-Smith(1963, 1967) was the first to try to standardize cross-national data by definingthe constituent components of health services, listing the main sources offinance, and laying down a standard classification of expenditures which heapplied to several industrialized countries. His efforts were followed by aseries of comparative studies, including the development of an standardized,annual database on OECD health expenditures.
The development of health expenditure data for developing countrieshas been less successful. WHO, PAHO, USAID and the Sandoz Institute forHealth and Socioeconomic Studies have attempted to improve information bypromoting household surveys and publishing manuals for estimating nationalhealth expenditures. Despite these efforts, most estimates of national healthexpenditure come from ad hoc studies or development agency missions tocountries, often conducted over brief periods of time. Consequently, theunpublished literature from agencies such as the World Bank remains animportant but difficult to obtain source of expenditure estimates for developingcountries. Regional reviews drawing largely on these sources have beenprepared for Asia (Griffin, 1992), Africa (Vogel, 1989), and Latin America(McGreevy, 1992).
Second, many cross-sectional studies have explored the determinants ofnational health expenditure, particularly in OECD countries. Taken together,these studies show that income per capita explains most of the variance inhealth expenditure per capita; Newhouse (1977), for example, found that 90%of the variance in OECD health expenditure was explained by GDP per capita.Some studies report that other variables such as reimbursement methods,institutional variables and the inpatient/outpatient mix can explain some of thevariance in health expenditure. Nevertheless, the strongest factor in nearly allstudies, including those few which examine developing countries, has beenincome per capita. Most studies have also found that health expenditure has anincome elasticity greater than one. On this basis, Newhouse (1987) concludesthat health expenditure in OECD countries must be purchasing caring (whichis more of a luxury) rather than curing (which seems to be more of anecessity). However, Parker, Maguire and Yule (1987) take issue with theempirical observation that health expenditure has an income elasticity greater
%I
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than one and challenge the interpretation of health care as a luxury good.
Third, most studies at the household level in developed countries do notshow a greater than unitary elasticity for health expenditure with respect toincome. The discrepancy between the relations at the national level and at thehousehold level has been attributed in Canada to non-price rationing, so thatconsumers do not buy health care to the point of zero marginal utility when theprice is zero to the consumer (Culyer, 1988). Relaxing such non-pricerationing with increasing aggregate income leads to a much greater incomeelasticity (even exceeding unity as empirical studies have shown) at the macrolevel than at the level of the household (Gerdtham, et al., 1991). As we haveargued elsewhere (Murray, et al., 1994), the implication that high-incomeconsumers are more rationed than those with lower incomes seems far lesspersuasive than the explanation that social insurance plays a greater role infinancing large health care expenditures with increase in household income.This claim seems to find some substantiation in a health sector study onMexico, currently being undertaken by the World Bank (Chellaraj, 1994). Theresults of this study show that, in Mexico, the total share of out-of-pockethealth expenditures declines significantly, among the rich more than the poor,with increases in insurance coverage through government social securityinstitutes and private insurers.
Finally, few studies on either OECD countries or developing countrieshave examined public health expenditures and private health expenditures andtheir determinants separately. Musgrove's study (1983), using householdsurvey data from six Latin American countries, is a noteworthy exception. Inthese countries, private care had a higher income elasticity than public sectorhealth expenditures, suggesting that private care is a luxury relative to publiccare and that consumption shifts from public to private, ceteris paribus, ashousehold income rises. This may be partly attributed to differences in real orperceived quality which make private and public health care only imperfectsubstitutes. The finding that higher income shifts expenditure to the privatesector is not generally observed at the aggregate level, when countries outsideLatin America are also studied.
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Chapter IH. Defmitions, Methods. and Materials
A. Defrintions
Assessment of health spending requires a consistent definition ofexpenditure and agreement on how to group spending by different agents andexpress its value in internationally comparable terms. Further, defining healthexpenditure requires defining health, the set of health promoting activities, andthe subset of activities that promote health to be included in the health sector.These and other related issues are discussed by us in Murray, et al., 1993b,and, therefore, not reiterated here. We present below, for the convenience ofthe reader, some key definitions relevant to this paper.
The operational definition of health expenditures used in this study,along the lines of the previous study, includes all expenditures incurred onpreventive and curative health services for individuals, and on population-based public health programs, as well as some programs with a direct impacton health status (e.g. family planning programs, nutrition programs, and healtheducation but not other kinds of education). Programs that only indirectlyaffect health, such as relief and food programs, and environmental programsrelated to water and sanitation, were excluded. We had hoped to be able toestimate health expenditure according to who pays for it and also who providesit. Categorizing health expenditure according to both provision and financingof services (represented as the two axes of the matrix) by the government,parastatal agencies and the private sector defines a 3X3 matrix. In this matrix,typically, data were available for the total financing provided by each of thethree sub-sectors. The breakdown of government financing for servicesprovided by the government itself, by parastatal agencies and by the privatesector was also often available. However, data were rarely found for the othercells in the matrix. The study was therefore restricted to the financing ofhealth services by the various sectors.
Domestic expenditures for each country are classified as government,parastatal, or private sector spending (see Appendix 2). Total healthexpenditures comprise these expenditures and external assistance. Governmenthealth expenditure is defined as spending on health by the government atvarious administrative levels or by institutions wholly controlled by thegovernment. Parastatal expenditures consist of the health components of socialsecurity and social insurance programs, and the expenditures on health byother parastatal agencies. It should be noted here that, in some rare instances(e.g. in the case of the Instuto Assist. Medica Collectivo [IAMCs] - mutualfund agencies in Uruguay), the decision to classify an organization asparastatal or private was difficult. In such cases, decisions had to be made,somewhat arbitrarily, based on the extent of the organization's funding through
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private or public sources.
Public expenditures are defined as the sum of government andparastatal expenditures (and external aid), to permit comparisons with theOECD countries where expenditures on health-related social insurance andsocial security programs are not distinguished from government expenditure.Private sector health expenditure refers to spending by all non-governmentalentities, including individuals, households, private corporations and non-profitorganizations. Private expenditures are the sum of private institutional andindividual expenditures (including both direct or out-pocket costs and purchaseof insurance by institutions and individuals or households)
B. Methods
1. Currency Conversions
The base year for the study is 1990. For countries with estimates priorto 1990 but no data for 1990, it was assumed that spending on health as ashare of GDP was the same in 1990 as in the year of the most recent estimate.Estimates of expenditure in 1990 local currency have been converted into USdollars (US$) using 1990 official exchange rates. The results were alsocalculated in "International dollars" (I$) using purchasing-power parity (PPP)ratios from the World Bank's modification of the United Nations InternationalComparisons Program (ICP Phase V, 1985). In calculating expenditures ininternational dollars, external assistance, which is primarily paid in US. dollarsor other hard currency, was assumed to fund only tradable goods, so it wasnot corrected for purchasing power parity.
Purchasing power parity ratios calculated specifically for the healthsector would be preferable to those based on total GDP, since GDP PPP ratesmeasure "non-health consumption foregone" rather than the more appropriate"health care output". However, while a complete set of GDP purchasingpower parity ratios for all the countries of the world have been estimated bythe World Bank, as noted above, health-sector PPPs are available only for thecountries for which these figures were estimated in ICP Phase IV and V (ICPproject, 1980 and 1985). Therefore, only the GDP PPPs have been used inestimating national health expenditures in international dollar terms.
2. Disaggregation of Government Expenditures
An attempt was initially made to be comprehensive in the categorizationof the government health expenditures, i.e. attempts were made to trackallocations to all major government health programs. Given the extremepaucity of disaggregated data and other limitations that made cross-countrycomparability difficult, the number of functional categories had to be
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restricted. Besides the fact that many countries do not provide in their budgetsa breakdown of expenditures by individual programs, the categorization ofexpenditures often varies from one country to the other. In fact, definitions areoften inconsistent even for those expenditure categories, e.g. secondary care,where one might expect congruity across countries and over time. Therefore,only those categories of expenditures were retained in this study that lentthemselves to a reasonably consistent definition across countries.
As part of the study, govemment health expenditures for each countrythat had a reasonable level of disaggregation were classified:
a. by activity
i) MCH/Family Planning services - All govemment expenditures onmatemal and child health and population/family planning programs;
ii) Primary Health Care services - Services (preventive and curative)rendered at the first level of health care delivery, e.g. at rural andurban dispensaries, health centers, polyclinics or outpatient departmentsof hospitals (excluding MCH/FP services);
iii) Secondary services - Services rendered by primary and secondary levelhospital inpatient departments and tertiary care centers;
iv) Administration - Administrative expenditures at the central and locallevels;
v) Other - All other programmatic activities and services, such asimmunization, vertical disease control programs, vector controlprograms, nutrition programs, health education programs, etc., and
b. by type
i) Capital expenses - All investments involved in the creation of physicalcapital, either at initial set-up or during augmentation of healthprograms and services, such as buildings, machinery, other equipment,vehicles, etc.
ii) Recurrent expenses - Periodic expenditures involved in running theprograms and services, such as1. Salaries - Includes personal compensation in the form of
salaries, wages, and other allowances,2. Drugs and supplies - includes pharmaceutical and drug supplies,
and other hospital and clinic supplies needed for treatingpatients, and
3. Others - All other recurrent expenditures, such as transport andtravel, including pr diem payments (but not reimbursement ofexpenses), personnel training, maintenance, utilities, and othermiscellaneous expenditures.
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C. Materials
Considerable effort was invested in obtaining data on public, parastataland private health expenditure directly from governments, supplemented withreports and data from the World Health Organization, PAHO, the WorldBank, the International Labor Organization, Regional Development Banks(i.e., the IDB and CDB), and the United Nations Statistics Division as well asthe published literature. The collection includes material from numerousreports, articles and budgets, much of which is not published.
Comparability across data sources was a significant problem for allthree subsectors. For several countries there is a wide divergence in the quotedexpenditure figures for the same year across data sources, and over fairly shortperiods of time (which may be explained by radical changes in the levels ofspending from one year to the next for some countries, but seem very unlikelyfor others). Discussions with the country officers at the World Bank or withpeople familiar with those countries led to a choice of which estimate wasmost plausible (see Appendix 3 for sources of chosen expenditure estimates).
The order of selection of sources of government health expenditureswas, in general, government budgets, followed, in descending order, by WorldBank studies, GFS data, and ad-hoc studies. There were some exceptions tothis general algorithm. For example, for some Latin American countries,where hyper inflation and changes in currency rendered sensible calculations ofhealth expenditures impossible, data from one of the other sources were used,even if the data were not the most recent available. Similarly, if, for example,the World Bank studies had captured local expenditures which neither thebudget nor the GFS were successful in capturing, the expenditure figures fromthe World Bank study were chosen over the budget. The primary source ofinformation on social security programs was the ILO, with some augmentationthrough World Bank and ad-hoc studies on social security programs. Theorder in which a selection was made between the various sources ofinformation on private expenditures was adjusted household survey estimates(the adjustment procedure is discussed below), World Bank studies, nationalaccounts data, and ad-hoc studies - the latter two, where no other source wasavailable.
Information on government health expenditures was available, for theyears ranging from 1982 and 1990, for a total of 34 countries, i.e., almost allthe Latin American countries with the exception of some of the very smallislands. These islands have been left out of the dataset since accurateinformation was not available for them on even the determinants that havebeen used to predict health expenditures. Their exclusion, however, is unlikelyto make a significant difference in the overall regional expenditures, given thesmall levels of health expenditures in these countries. The information for themajority of the countries on which studies on government expenditures were
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available (often from multiple sources for some countries) was post-1986. Ourfinal data set (which includes the sources that in our assessment offered themost detailed and, possibly, the most accurate estimates of government healthexpenditures), consisted of information on fifteen countries from GovernmentBudgets, for ten other countries from the Government Financial Statistics(GFS) published by the International Monetary Fund, and for an additionalseven countries from World Bank country studies.
Information on parastatal spending was similarly available for most ofthe major Latin American countries. For some Caribbean islands (e.g.Antigua and Barbuda, St. Vincents, Suriname, etc.), however, data on healthexpenditures through the various social securityinstitutes were not available. In these cases, our estimates are probably the
lower bound of health spending in the public sector, although it may be thathealth-related social security in these countries is not substantial. In our dataset, information on eighteen countries was obtained from ILO studies, for anadditional eight countries from World Bank health sector studies, and for afurther two countries from ad-hoc sources.
Even using multiple sources, reasonable data on private sector spendingwere available for only nineteen countries for the period 1975 to 1991. In ourfinal data-set, data on seven countries were compiled from household surveys,for one country (Costa Rica) from a WHO country study, for two countriesfrom the National Accounts surveys, and for an additional nine countries fromWorld Bank studies (some of which have used data from household surveys).As we have pointed out elsewhere (Murray, et al., 1993b), though, even whenthese assessments were based on surveys--either institutional or at thehousehold level--many estimates were suspect.
Household surveys, although widely acknowledged to provide the mostreliable assessment of private spending on health, often exhibited systematicsampling and non-sampling bias. For several reasons, including non-representative sampling, many household surveys in developing countries mayoverestimate per capita private consumption. However, private healthexpenditures as a share of total private expenditure may not be biased if theincome elasticity across households is close to one (and any bias in the data isindependent of income). Household survey results were therefore adjusted byapplying the percentage of household spending on health from these surveys tototal private consumption numbers from the national accounts, to estimateprivate sector financing. Since figures were not available for severalcountries, however, we had to predict the numbers for these countries basedon a regression equation.
It is important to re-emphasize here that the data-set used for privatehealth regressions in this study differs, to some extent, from the one used inthe WDR 1993 study, in that estimates for certain countries have been
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updated, and, further, only those estimates which included household andprivate institutional spending have been used in the new set of regressions.Also, the criteria for selecting estimates for countries was far more stringent,in that estimates that could not be verified were excluded.
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Chapter IV. Estimating Out-Of-Sample
One of the objectives of this study is to estimate total healthexpenditures in 1990 for every country in Latin America. Although publicsector data were available for all the major countries, for fifteen countries inLatin America, as noted, there was no information on private sectorexpenditures. Predictive equations were, therefore, developed to estimate, outof sample, the private sector expenditures, as needed (see Appendix 5). Thesepredictive equations are based on a sample of countries from the whole world(the data set, as noted, is an updated version of the one used for the study onhealth expenditures for the WDR 1993), rather than only the countries of theLatin America region, since the larger sample size was expected to providebetter results. A dummy variable for Latin America was introduced to captureany regional differences in health expenditures from the overall globalspending pattern.
In constructing the private sector predictive equation, it was assumedthat private sector expenditure is a function of public sector expenditure, butnot vice-versa. This hypothesis is grounded in the belief that while mostgovernments are probably largely unaware of the magnitude of the privatehealth sector, or at least do not take it into account in determining publicbudgets (and, therefore, the public sector expenditure may not be a function ofprivate sector expenditures), the health services that people are willing to buyfor themselves, in contrast, may depend on what the public sector is alreadyfinancing. Hence, in examining the determinants of public sectorexpenditures, we have not included private sector expenditures as one of thepredictors.
We have argued that the private sector is sensitive to the size ofgovernment financing of health services, and that, therefore, public sectorexpenditures needs to be used as an independent variable in the private sectorequation. There are however two reasons why observed private spendingcannot simply be regressed on observed public expenditure. First, the globalprivate sector estimates span 16 years from 1974 to 1990. Estimates of publicsector expenditure are not always available for the same years. Second, ifprivate sector expenditure is a function of GDP per capita, other socio-economic variables, and public sector health expenditure, while public healthexpenditure is also a function of GDP per capita, the parameter estimates fromOLS regression will be biased.
To deal with both problems, we had to first develop public sectorregressions (see Appendix 4) to predict public sector expenditure in the sameyear as the private sector expenditure estimate, effectively creating aninstrumental variable for public sector health expenditure (the public and theprivate sector regressions were also used in analyzing the determinants of such
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expenditures). The independent variables, GDP per capita and governmentconsumption as a share of GDP, were also taken from the same year as theprivate sector estimate in generating the instrumental variable. We haveassumed, in effect, that the functional relationship between the share of GDPspent by the public sector on health and GDP per capita and governmentconsumption has not changed over the last 16 years. A discussion on theregressions for public sector health expenditures is now presented.
A. Estimating Public Sector Expenditure
We examined the relation between public sector health expenditure andGDP per capita, government consumption as percent GDP, privateconsumption as percent GDP, life expectancy at birth, the infant mortalityrate, percent urban population, average years of schooling completed, literacy(which was not correlated with the average schooling variable), hospital bedsper capita, and regional dummy variables. In addition to these variables, weadded a dummy variable for former British colonies which gainedindependence after World War II and another for former French colonies, onthe assumption that colonial history might play a significant role in explainingthe variance in public health expenditures. Regressions were estimated inboth US dollars and International dollars; in each case the dependent variable,public sector health spending, was measured both per capita and as a percentof GDP. The independent variables were derived primarily from sources atthe World Bank, with some augmentation from the IMF (Government andInternational Financial Statistics) and other UN agencies.
For the per-capita specification, univariate tests with the differentindependent variables showed closer association with the logarithm ofexpenditure than with expenditure itself. Strong univariate relations wereobserved, among others, for public sector expenditures per capita as a functionof GDP per capita in US and International dollar terms (RA2 of 0.91 and 0.85,respectively), and of health status indicators such as infant mortality and lifeexpectancy at birth. However, as argued in the WDR 1993 background paper,close relations between public sector expenditure denominated in per capitaterms and income per capita are not so impressive as one might assume, sinceeven randomly generated expenditure shares can suggest a close fit betweenper capita expenditure and per capita income. A more exacting test of therelation between public health expenditure and income as well as otherindependent variables that are highly collinear with income is to examinepublic health expenditure as a share of GDP, which is the specification used inthe regressions.
We tested the most general model first, using all the independentvariables. Non-significant independent variables were dropped until the mostparsimonious form was generated. Groups of independent variables were F-
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tested, and retained if the F-test was significant. Four parsimoniousregressions were estimated for the share of GDP: linear forms withindependent variables in US$ and in I$, and double-log forms withindependent variables in US$ and in I$.
For prediction, we chose the form with the highest adjusted R2. Thisequation:
Public Health Expenditure as % GDP = 0.02 + 8.54E-7 GDP per Capita+ 0.09 Government Consumption as % GDP - 0.03 Dummy for MEC- 0.02 Dummy for OAI - 0.02 Dummy for LAC - 0.03 Dummy for SSA
shows public expenditure on health as a share of GDP to be a linear functionof GDP per capita in I$, government consumption as a percent of GDP, anddummy variables for MEC, OAI and SSA (which are indistinguishable fromone another) and LAC (All coefficients are non-zero at the 0.01 confidencelevel). The adjusted RA2 was 0.79. Higher income was associated with ahigher share of income spent on health; the elasticity from the double-log formwas 1.43 (1.34 in US$). Governments that consumed a larger share of GDPin total also had higher expenditure on health. The significant dummyvariables indicate greater regional differences in share of GDP spent on healththan can be explained by income per capita alone. However, the infantmortality rate and life expectancy at birth were not related to public sectorhealth expenditure. Thus the equation says nothing about the causal relationsbetween expenditure and health status (we will return to this question in thediscussion section).
B. Estimating Private Health Expenditure
Based on the same arguments as the public sector regressions, privatesector health expenditures as a percent of GDP (rather than private healthexpenditures per capita) was analyzed as the dependent variable. As before,regressions were run using US dollar and PPP-adjusted incomes. Allindependent variables were from the same year as the private expenditureestimate, for each country. The steps used in obtaining the parsimoniousmodels were, essentially, similar to the public sector regressions. In additionto the variables included in the public sector regressions, however, we added adummy variable (opecdum) for countries that have revenues from oil exportsthat made up a greater than 50 percent share of their GDP, since suchrevenues provide an accessible source of financing for public sector socialprograms, including health. The expectation was that a well financed publichealth system would decrease the level of private spending and, therefore, thatthe coefficient on this dummy variable in the private sector regression wouldbe negative.
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As for the public sector expenditures, four parsimonious regressionswere estimated for private expenditures as a share of GDP: linear forms withindependent variables in US$ and in 1$, and double-log forms withindependent variables in US$ and in I$. Using the updated version of ourprivate sector dataset, we obtained a reasonably good relationship betweenprivate health expenditures as a share of GDP and the independent variables inour dataset - a result different from our previous study (Murray, et al.,1993b). What is striking, however, is that there is still no relationship at theaggregate national level between income and private health expenditures. Aswe had shown in our previous study (WDR 1993 background paper), theglobal income elasticity of private health expenditures is 1.03 (i.e.,indistinguishable from unity). In other words, the share of GDP privatelyspent on health is nearly constant over the range of GDP per capita. Thisresult is further confirmed in this analysis. Also, notably, public sector healthexpenditure was not significant in any of these regressions. The dummyvariables for colonial history, meant to capture potential institutional effects,were also not significant.
The equation used in predicting private health expenditures was, as forpublic expenditures, the one with the highest adjusted R2. The equation
Private Health Spending as % GDP = -3.2 - 3.7 Government consumption as% GDP - 0.4 Human Capital Stock - 0.7 Dummy for OPEC+ 0.07 Beds per capita
shows private expenditure on health as a share of GDP to be a function ofgovernment consumption as a percent of GDP, the human capital stock(schooling) variable, beds per capita, and the dummy variable for the oilproducing countries (All coefficients are non-zero at the 0.01 confidencelevel). The adjusted RA2 was 0.29. As expected, governments that consumeda larger share of GDP in total also had lower private expenditure on health.The significant (negative) coefficient on the dummy variable for the oilproducing states suggests that, as anticipated, these countries have a lowerlevel of private health spending than would be otherwise predicted. Countrieswith a higher bed per capita, on average, seem to have higher private healthexpenditures. Also, countries with, on average, a higher number of years ofschooling (humcap) have a lower level of private expenditures.
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Chapter V. Results
The regional spending on health care in Latin America and theCaribbean in 1990 was estimated by combining observed values with thosepredicted by the regressions for the public and private subsectors. Theseregressions estimates were used for 15 countries in the private sector, but theyaccount for only about one percent of estimated total expenditure in LatinAmerica, because the great bulk of spending occurs in countries for which datawere available and it was not necessary to predict values from the equations.Estimates of public, private and total health expenditure are provided inAppendices 9-11 for every country: estimates derived from the regressionanalysis are in bold face.
A. Regional Health Expenditures (A Comparative Perspective)
Global spending on health care in 1990 was a little over U.S.$ 1.7trillion, which constituted a little more than 8 percent of global GDP (seeAppendix 6). Of this, almost 86 percent of spending occurred in thedemographically developed countries of the world (i.e. the established marketeconomies and the former socialist economies of Eastern Europe). Spending inthe Latin American and Caribbean region constitutes just more than 4 percentof global spending and approximately 36 percent of spending in thedemographically developing regions of the world. As a share of GDP, healthspending in the LAC region is about 6.3 percent, which is significantly greaterthan the spending in China (3.5%), the Middle-Eastern Crescent (3.6%), Sub-Saharan Africa (4.2%), Asia (4.5%), and somewhat more than the spending inIndia (6%). In general, even as a share of GDP, spending in the developingregions is significantly lower than in the market economies, which averagedalmost 9.3% in 1990.
When expenditures are corrected for purchasing parity, global spendingamounted to over 1.9 trillion International dollars (see Appendix 7).Adjustment for purchasing parity increases expenditures in developing regionssubstantially (422 versus 193 billion dollars), without significantly changingthe health spending in the developed countries. Health spending in the LatinAmerican region in international dollars is about 6 percent of global healthexpenditure, and about 28 percent of the total spending on health in thedeveloping regions.
While globally, the public sector (in US or International dollars)accounts for 60 percent of the total health spending with private sectorfinancing constituting the other 40 percent, in the LAC region the public sectorshare of total health spending (despite the sizable social security spending) isonly 49 percent (see Appendices 8 and 9). Thus, spending by both the public
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sector and the private sector is about 3.1 percent of GDP. This is in keepingwith the trend in developing regions for the private sector to play asignificantly greater role (inversely correlated with aggregate income, as notedby us in the WDR 1993 background paper) in the financing of health care.External aid plays a very small role in total health financing in the LACregion, accounting for just less than 1 percent of the total health expenditure(see Appendix 9). Aid, however, is a significant component of total healthexpenditures for certain countries (see Appendix 10) in the Latin Americanregion (e.g. Guyana ), although the dependence on aid is nowhere assignificant as it is in Sub-Saharan Africa, where aid flows as a percent of totalregional health expenditures comprise between 9-10 percent, and, for someindividual countries is greater than 50 percent of total expenditures.
Per capita total health spending in 1990 in the LAC region (seeAppendices 6, 7, and 12) was about $162 (280 International dollars), whichwas significantly greater than spending in any other developing region of theworld, and even the former socialist economies (which spent, on average,US$142 or I$ 240 per capita). For example, China and India spent only about$11 and $20, respectively on health (although the amount does go up whenconverted to International dollars, i.e., I$72 and I$61, respectively). Given thehigh income levels and the high share of GDP spent on health in the marketeconomies, however, their spending of $1869 (I$ 1793) per capita dwarfsspending in the other regions of the world (see Appendices 6 and 7).
B. Time Trends
The trends in government health expenditures, where such data isavailable, vary among the countries of Latin America and the Caribbean (seeAppendix 13). It should be noted that these trends represent comparisons ofonly the government health expenditures, except for a few countries (e.g.Colombia, Paraguay, and Peru) where, in the absence of disaggregatedinformation for previous years, the estimate for 1990 maintains comparabilityby including parastatal expenditures. There are at least five discerniblepatterns of health expenditure during the period 1980-90. These are,respectively, a rising, falling, or constant trend; or a pattern of spending whereexpenditures grew, initially, followed by a drop, or vice versa. Thus, forexample, countries, such as Peru, Paraguay, and Venezuela, experiencedsteady declines in health expenditures as a proportion of GDP. Others, suchas Ecuador, Jamaica, and Mexico, have had consistently increasingexpenditures. Yet others, such as El Salvador, and St.Vincents, have had arelatively constant level of expenditures over the ten year period.Expenditures as a proportion of GDP, in countries like Argentina, theDominican Republic, and Uruguay, declined initially (e.g. from 1.57% of theGDP in the Dominican Republic in 1980 to 1.48% in 1983), before climbingback (e.g. to about 1.64% of the GDP in the Dominican Republic) in 1990.
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However, other countries, such as Barbados, Belize, and Trinidad,experienced declines in health expenditures as a proportion of GDP by 1990,following a rise in the 1980s.
It is important, however, to emphasize a few caveats vis-A-vis thesetrends. First, these estimates of government expenditures were obtained fromdifferent sources, and, therefore, may not be strictly comparable. Second, theperiod between 1980-90 may or may not be representative of the general long-term trend in these countries. Third, even if these general patterns areaccurate, different countries have different absolute levels of expenditures,and, thus, it is difficult to make any generalizations about countries which fallinto any of the above five categories. Furthermore, changes in governmenthealth expenditures might have been adequately substituted by correspondingchanges in social security spending. Unfortunately, this is difficult to ascertainfrom the available information. Finally, it is not entirely clear that theseperiods of fluctuations in health spending in these countries always correspondto any obvious socio-economic or political events, although, for somecountries, the debt crisis and structural adjustment, obviously, have played arole.
Despite these caveats, certain observations can be made on the patternsof government health expenditures. It is striking that the Caribbean countries,with one or two minor exceptions, experienced no major declines in healthexpenditures as a proportion of GDP. It is tempting to make an associationbetween this observation, and the fact that, with the exception of Jamaica,these countries did not experience serious economic problems unlike the LatinAmerican countries. It is also possible that these countries, unlike the LatinAmerican countries, have made a deliberate attempt to keep their levels ofhealth expenditures at a certain level, in spite of other problems. Thus it is,for example, that, despite economic problems, health expenditures as aproportion of GDP actually increased in Jamaica as a result of the emphasisgiven to the sector by successive Jamaican governments.
It should be noted, though, that a few countries in Latin America toohave experienced steady increases in health expenditures despite economicproblems, e.g. Nicaragua and Panama. It is possible that the nature of thepolitical regime in these countries is an explanatory factor. For example, inNicaragua, health expenditures as a proportion of GDP rose from 3.2% in1980 to 4.9% in 1990, as a result of the emphasis given to the health sector bythe then-Nicaraguan govemment. A similar situation existed in Panama duringmost of the 1980s and the health expenditure as a proportion of GDP in 1990stood at about 5.2%.
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C. Disaggregated Data on Government Health Expenditures (1990)
Appendices 9-11 show total health expenditures broken down intopublic sector, private sector and external assistance for each country in LatinAmerica. Figures are provided in US dollars, International dollars, and percentof GDP terms. Total health expenditures as a share of GDP ranges from 9.7percent in Guyana and 9.6 percent in Argentina, to 3.7 percent in Ecuador and3.1 per cent in Peru. In terms of per capita total health spending in US$, therange is from almost $600 in the Bahamas and the Cayman Islands, to lessthan $50 in countries like Bolivia, Ecuador, Guatemala, and Haiti. Evenadjusting for purchasing power, a number of countries still spend less than I$100 per capita (e.g. Bolivia, Honduras, Peru and Haiti).
We present below the results of government health expendituresdisaggregated into various categories (see Appendix 14). It should be notedthat the sample sizes differ for each expenditure category, and, further, thatthe countries represented in each sample are different.
Local and provincial expenditure on health as a proportion of totalgovernment health expenditures varies widely across countries. For example,in Colombia, over 90 percent of the expenditures (excluding transfers from thecentral government) are at the provincial and local levels, with the centralgovernment having a negligible role in the financing of health care services.In many countries, e.g. Argentina, public sector spending on health at thelocal and provincial levels constitutes a significant majority of all public sectorhealth expenditures. On the other hand, in countries such as Chile, localexpenditures are less than 10 percent of total public sector health expenditures.It should be emphasized that it is very likely that significant expenditures at thelocal level are often missed in assessments of public health expenditures, dueto the difficulty in tracking them. Thus, estimates of local expenditures forseveral countries where such data is collected are likely to be conservative.Given the observed wide variability in the share of local expenditures,however, it seems meaningless to calculate any average figure for suchexpenditures, that might be applied to other countries where local expendituresmight be expected but for which no information exists.
Other ministries (i.e., other than the Ministries of Health) also spend asignificant amount in providing health services. These expenditures includespending such as by Ministries of Defense for providing medical care to thearmed forces, or by Education Ministries for providing school health services(but not social security health expenditures). It is probable that spending byother ministries for health care services is missed in many countries, and,further, even in countries with such information, the estimates are likely to beconservative. Unfortunately, however, there is as wide a variation in theseexpenditures, across countries, as there is in local health spending. Thus, it is
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very difficult, without personal knowledge of a country, to predict the level ofthese investments in any particular case.
Capital expenditures comprise, on average, about 15-20 percent of totalgovernment health expenditures in our sample of 16 countries. 7 countries hadcapital expenditures of less than 5 percent; 5 countries had spending between5-15 percent; while 4 countries had capital expenditures of greater than 15percent of total government health expenditures. While there is variationacross countries, in the majority of countries in our sample (i.e., 12/16),capital expenditures were less than 15 percent of total health expenses. Thisobserved pattern, however, give little indication of the level of capitalinvestments across countries and over time, as capital investments varydramatically between countries and from year to year.
Expenditures on Primary Health Care Services (PHC) constitute, onaverage, about 18 percent of recurrent expenditures of the government,according to government budgets. In our sample of 12 countries, PHCexpenditures of 10-20 percent of recurrent expenses were the mostrepresented. 2 countries had PHC expenditures of less than 5 percent; 3countries had expenditures on PHC between 5-10 percent; 3 countries hadexpenditures on PHC between 10-15 percent; while 4 countries hadexpenditures on PHC greater than 15 percent - of which only 2 countries had agreater than 35 percent share for expenditures on PHC services, none of themmajor countries in Latin America. While this is a small sample of countries,the data does indicate that, despite a decade and a half of rhetoric, the majorityof health expenditures are not for primary health services. To an extent, thismay reflect the higher levels of investment required in establishing andoperating facilities for secondary and tertiary level services.
Salaries, in our sample of 17 countries, constituted, on average, about55 percent of total recurrent expenditures. This feature is very consistent withestimates from other countries, regardless of income and expenditure levels.Further, this pattern is stable over time. Most countries in our sample hadsalaries ranging between 50-75 percent of recurrent expenditures. Of the 17countries, 12 countries had expenditures on salaries of between 40 and 70percent; 2 countries had expenditures on salaries as a proportion of recurrentexpenditures of greater than 70 percent; while 3 countries had salariesconstituting between 10-40 percent of total recurrent expenditures.
The portion of recurrent expenditures constituted by drugs and supplieswas about 19 percent, in our sample of 17 countries. Unlike salaries, however,there is a wide variation in spending on drugs and supplies across countries.Of the 17 countries, 1 had expenditures on drugs of under 10 percent; 11 hadexpenditures between 10 and 20 percent; 3 had expenditures of between 20 and30 percent; and 2 countries had drug expenditures as a proportion of recurrentexpenditures of greater than 30 percent.
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In this sample, there is no clear relationship between the income of the countryand expenditure on drugs and supplies. A rich country is just as likely to spenda small (or large) proportion of its recurrent expenditures on drugs as a poorcountry. The claim that poor countries are more likely to spend a significantproportion of their recurrent budgets on procuring drugs does not hold in thisadmittedly small sample. This may indicate a tendency in most countries tohave consumers buy drugs from private sources, rather than provide themthrough the government health services. Of course, if such a tendency wereuniform, there might still be a relationship; the lack of a relation suggests thatthe differences across countries are random.
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Chapter VI. Discussion and Conclusions
A. Tracking Health Expenditures
Our study of health expenditures in Latin America has demonstrated thesignificant gaps in the knowledge of health expenditures, particularly (but notexclusively) in the private sector. Although information on countries of thisregion are somewhat better than in some other developing regions, there is stillmuch work to be done in this area. As noted in our earlier study (Murray, etal., 1993b), the World Health Organization (and PAHO), the World Bank, andthe regional Banks (Inter-American Development Bank and the CaribbeanDevelopment Bank) have not, to date, devoted sufficient resources tomaintaining a database on national health expenditures. If costly ad hocstudies are to be avoided in the future, data collection needs to be moresystematized, and immediate attention needs to be directed towards this issue.
As noted, measurement of private sector expenditures is particularlyinadequate in the developing world. Even for those countries with detailed ad-hoc studies, the data are subject to doubt. One way forward would be thedevelopment of national health accounting akin to the OECD healthexpenditure database. However, the majority of developing countries probablycannot institute such information systems in the near future. Rapid assessmenttechniques therefore need to be developed and implemented, in conjunctionwith an international database on government expenditures, to fill theinformation gap in the short-term.
The efforts of the International Monetary Fund and the ILO - thatalready maintain a database of government and parastatal expenditures,respectively - are steps in the right direction. This work could besupplemented by the other multilateral agencies, at little extra cost, so as toenable the creation of a coordinated and comprehensive database on healthexpenditures. It is, thus, important that the momentum generated by thecollaboration between the World Bank, PAHO, and the Inter-AmericanDevelopment Bank on this Harvard study be sustained in the future.
B. Income and Other Determinants of Health Expenditure
The data reviewed in this study suggest that private health spendingrelative to GDP is unrelated to income. In other words, the income elasticityof private health expenditures is indistinguishable from unity. This is animportant observation, since it suggests that the relationship of public andprivate health expenditures with income is essentially different. While, asprevious studies have also found, public health expenditures as a share of GDPrises (at an even faster rate) with increasing income, no such correlation is
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seen in the case of private spending. Previous studies that have studied healthexpenditure as a homogenous entity (by running a regression for total asopposed to individual public and private health expenditures) have, for obviousreasons, failed to make this differentiation between private and public sectorexpenditures.
Furthermore, our analysis demonstrates that private expenditure as ashare of GDP is unrelated to health status indicators, geographical region, orpublic health expenditure (see Appendix 5). It is particularly surprising to findno association with public expenditure, since it was expected, a priori, toprovide an alternative to private expenditures. Apparently public and privatespending are not simply substitutes, because they finance services which differin kind, or quality, or in utilization by different population groups. Asdiscussed above, private expenditures are a function of the size of government,the average years of schooling, the number of hospital beds per capita, and thedummy variable for the oil-producing countries. The association of theeducation variable with private health expenditures is reassuring since itconforms to our hypothesis that education influences people's understanding oftheir health needs and their demand for health care. The negative andsignificant coefficient on the dummy variable is also interesting. This suggests,as hypothesized, that oil resources represent an easy source of revenues that isused to fund publicly financed health services, resulting in declining privatehealth expenditure.
It should be emphasized that the proportion of variance in privateexpenditures explained by the identified determinants is comparatively small(although the results are much more significant than our previous study). Itmay well be that private health expenditure are determined in part byhistorical, cultural and institutional factors not captured in this analysis.Furthermore, errors or mis-specifications in the data may reduce the statisticalsignificance of the variables tested. It is conceivable that further refinement ofthe dataset (through more carefully carried out private health expendituresurveys) might improve the results.
We have shown that, in contrast to private spending, public healthexpenditure has an income elasticity substantially greater than one. Moreover,income has a dominant impact on public health expenditures as a share ofGDP, and explains a large proportion (albeit not the entire amount) of thevariance in the dependent variable (see Appendix 4). We have examined theincome elasticities of the public and private sectors. What, however, is theincome elasticity of total health expenditure? Is total health expenditure aluxury good? It is important to note that total health expenditure also includesexternal assistance that flows primarily to low income countries (Michaud andMurray, 1993).
For all developing countries with observed data (not derived from our
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estimating equations), a double-log regression of total health expenditure percapita against income per capita gives an elasticity of 1.003, which isindistinguishable from unity. In other words, the share of GDP spent onhealth does not increase with income. As noted above, however, average totalhealth expenditure in EME is substantially higher than in all other regions, soa regression including these countries shows health care to be a luxury good.Compared to the pattern in poorer countries, high health expenditure in EMEis not accounted for simply by higher average income.
C. How Do We Expect Health Expendituresto Change with Income Per Capita?
More income means more resources with which to deal with healthproblems. We have argued elsewhere (Murray, et al., 1994), however, thatthere may be two separate factors involved in the "health problems" whichgenerates demand for health care: observed or objective health status andperceived or subjective health status. Numerous interview surveys in poordeveloping countries have recorded higher rates of self-reported morbidity anddisability in rich than in poor households (Murray, et al., 1993a). We hadargued that such counter-intuitive patterns of reported morbidity may be atleast partly explained by changing expectations of health status. Ifexpectations of good health increase faster than actual health status--becausepeople have more access to health care, or because more education makesthem understand more about health--then perception of ill health may increasewith income. The result will be increasing expenditure which is only looselyrelated to objective health problems.
Moreover, if, as we suspect, perceived health status is more of a luxurywhereas treatment for objective health problems is more of a necessity, thenthe elasticity of the combined tendencies to spend might increase with incomeper capita as "health status" comes to be more a matter of subjectiveperception. However, as a population ages and develops chronic healthproblems that are costly to treat, even objective health status may generatepressure, with increasing income, to spend an increasing share of income onhealth care. Furthermore, the relation between total health expenditure andincome per capita will be affected by the effect of the expanding role for thepublic sector in financing health care, that makes public spending respond toperceived health status and the demand for health services from the populationand not only to objective needs. Any understanding of what accounts for healthexpenditure and how it is related to health status that goes beyond thesuperficial, therefore, will have to disentangle these effects.
In order to illustrate some of these issues, and examine the incomeelasticities at different levels of aggregate income, we undertook separateregressions between income and (public and private) health expenditures, forcountries with low (<$635), middle ($635-$7199), and high income. The
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income elasticity for public sector expenditures were, respectively, 1.03, 1.46and 2.04 for the three groups of countries. Interestingly, private healthexpenditures show the exact opposite trend. The elasticities for the threegroups were 1.26, 0.95, and 0.66, respectively. A similar set of results areseen when the regressions are run for the different regions, ordered accordingto income. These results, while not presented here in the interest of brevity,are available with the authors for the interested reader.
D. What Does Health Expenditure Buy?
The relations studied here raise the perennial question about whathealth expenditures actually purchase - in particular whether they buyimproved objective health ("curing") or something more subjective ("caring"),or whether they are largely wasted through inefficiency in the production ofservices and the choice of which services to provide. Using the improvedestimates of national health expenditures including external assistance providedin this study, we can examine some relations between total health expendituresand measures of health status for Latin America. For example, life expectancyat birth can be analyzed in comparison to health expenditures per capita.Appendices 15 and 16 shows life expectancy at birth plotted against totalhealth expenditures per capita and as a share of GDP. The per capita plotdemonstrates the rise of life expectancy (at a decreasing rate) with increase intotal health expenditures, while the plot of health expenditures as a share ofGDP seems essentially random.
Another such analysis is shown in Appendix 17. Over the last decade,there has been considerable attention directed to those countries, such as SriLanka, China, Costa Rica, Cyprus, and Cuba that have "good health at lowcost" (Halstead et al., 1985). Analyses of good health at low cost are usuallybased on good health at low income, as a comparable set of total healthexpenditures have not been available before. Using the relationship for lifeexpectancy and total health expenditures as a share of GDP, we can identifythose countries in Latin America with higher or lower than expected lifeexpectancy for their total health expenditures. Accordingly, GDP per capitaand a Human Capital variable summarizing schooling levels were used topredict both observed total national health expenditure (as a share of GDP) andlife expectancy at birth, for 58 countries worldwide. Once again the globalrelationships were used because of the larger sample size. The equations were:
Total Health Expenditure as % GDP = -0.0485 + 0.0119 Natural Log GDPper Capita - 0.0055 Natural Log Human Capital and
Life Expectancy at Birth (Years) = 41.98 + 3.120 Natural Log GDP perCapita + 5.316 Natural Log Human Capital.
Estimates of expenditures derived from the regressions reported earlier
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were not used in this exercise, which was limited chiefly by the availability ofestimates for private health spending and the human capital variable. (Humancapital was just significant at the 0.05 confidence level in explaining healthexpenditure; otherwise all variables were significant at the 0.01 level. In asimilar analysis for 73 countries in WDR, 1993, human capital did notcontribute significantly to explaining health spending.) The values ofexpenditure and life expectancy predicted from these equations were thencompared to the observed values, and the differences or residuals plotted inthe figure.
The result shows for each country whether it spends more or less thanmight be expected, given its income and education level, and whether itspopulation lives longer or less than might be anticipated. Although income,education and health expenditure are not the only factors influencing lifeexpectancy, this comparison indicates, roughly, whether health expenditure ina given country is buying increased life to the same degree as in othercountries with similar resources and human capital. Points in the upper right(e.g. Costa Rica, Chile, Colombia, etc.) and lower left quadrants (i.e., Peru)correspond to countries showing a systematic relation between more healthexpenditure and longer life: that is, spending on health appears to be buyingmore years of life. Countries in the upper left quadrant (e.g. Paraguay,Venezuela, etc.) achieve gains in life expectancy without spending so much--their health expenditure appears to translate more effectively into improvedhealth status. The data do not indicate, of course, whether this occurs forreasons directly related to how resources are spent in health care, or becausethe population takes better care of its health through diet and other habits andtherefore needs less medical care to achieve the same result. Countries shownin the lower right quadrant (i.e. Bolivia) are in the opposite situation, withshorter than expected life despite spending more on health care than would beexpected on the basis of income and schooling (see Appendix 17).
Similar relations could be explored using other indicators of healthstatus such as child mortality. The most interesting comparison would relatehealth expenditure to the total burden of disease in a country, including theeffects of disability as well as premature mortality, as measured in DALYs(Murray, 1994). We cannot provide an analysis parallel to that above,however, for two reasons. First, the disease burden has so far been estimatedonly for the eight regions of the world and for a few individual countries.Secondly, even the regional estimates now available describe only the burdenof disease remaining as a result of everything that has been done, includingexpenditure on health care, to improve health. In fact, comparison shouldreally be made to the reduction in disease burden which can be attributed tohealth spending, which implies comparison to the situation that would exist inthe absence of that expenditure. Such information, unfortunately, is notcurrently available.
- 27 -
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Appendices
-33 - Appendix 1
Countries in the Latin America and Caribbean region
Anguilla*Antigua and Barbuda
Argentina
Aruba*
BahamasBarbados
Belize
BoliviaBrazil
British Virgin Islands
Cayman IslandsChile
Colombia
Costa RicaCuba*
Dominica
Dominican Rep.Ecuador
El Salvador
Falkland Islands*French Guiana*
Grenada
Guadeloupe*
GuatemalaGuyana
Haiti
HondurasJamaica
Martinique*
Mexico
Montserrat*
Netherlands Antilles*
NicaraguaPanama
ParaguayPeru
Puerto Rico*
St. Kitts and Nevis
St. Lucia
St. VincentSuriname
Trindad and Tobago
Turks and Caicos*
UruguayVenezuela
Virgin Islands*
* indicates countries not included in study dataset
-34 - Appendix 2
Definition of Subsectors
* Government health expenditure = Spending on health by the government atvarious administrative levels or by institutions wholly controlled by the government.
* Parastatal expenditures = Health components of social security and socialinsurance programs, and the expenditures on health of other parastatal agencies.
* Public expenditures = Sum of government and parastatal expenditures.
* Private sector health expenditure = Spending by all non-governmental entities,including individuals, households, private corporations and non-profitorganizations, i.e. the sum of private institutional and individual expenditures(including both direct or out-pocket costs and purchase of insurance by institutionsand individuals or househords).
I Total health expenditures = Government + Parastatal + Private + foreign aid.
nscaain Lxl)enuirurcs in Larin America - sources ot E;xpenditure JEstimates Chosen
Country Government Expenditures Parastatal Expenditures Private ExpendituresYear Source Year Source Year Source
Latin America&Caribbean
Antigua and Barbuda 1990 Budget N.A. 1990 PredictedArgenlina 1988 GFS 1990 Cetrangolo 1985 WBBahamas 1986 GFS 1986 ILO 1990 PredictedBarbados 1989 Budget 1986 ILO 1990 PredictedBelize 1983 Budget 1986 ILO 1980 H.Hold SurveyBolivia 1990 GFS 1985 ILO 1990 H.Hold SurveyBrazil 1989 WB 1986 WB 1987 H.Hold SurveyBritish Virgin Islands 1988 WB N.A. 1990 PredictedCa% man lslands 1988 WB N.A. 1991 H.Hold SurveyChile 1988 Budget 1988 WB 1992 WBColombia 1986 GFS 1980 WB 1985 WBCosta Rica 1988 WB 1988 WB 1986 WHODominica 1985 Budget 1986 ILO 1990 PredictedDominican Rep. 1989 GFS 1986 ILO 1984 WBEcuador 1990 GFS 1986 WB 1984 WBEl Salvador 1987 WB 1986 ILO 1975 Nat.Accts.Grenada 1985 Budget 1986 ILO 1990 PredictedGuatemala 1989 GFS 1986 ILO 1990 PredictedGuvana 1983 Budget 1986 ILO 1990 PredictedHaiti 1986 Budget 1986 ILO 1985 WBHonduras 1986 Budget 1986 ILO 1990 Nat.Accts.Jamaica 1993 WB 1993 WB 1993 WBMexico 1990 WB 1990 WB 1989 H.Hold SurveyNicaragua 1986 WB 1986 ILO 1990 PredictedPanama 1982 Budget 1986 ILO 1986 H.Hold SurveyParaguay 1990 Budget 1990 WB 1990 PredictedPeru 1984 Budget 1990 Nat.SS.Rep. 1990 H.Hold SurveySt. Kitts and Nevis 1987 GFS 1986 ILO 1990 Predicted gSt. Lucia 1986 Budget 1985 ILO 1990 Predicted 0L
St. Vincent 1988 GFS N.A. 1990 Predicted xSuriname 1988 Budget N.A. 1990 PredictedTrindad and Tobago 1988 Budget 1986 ILO 1990 PredictedUruguay 1992 WB 1992 WB 1992 WBVenezuela 1986 GFS 1986 ILO 1992 WBNote: N.A. implies N.ot Available
- 36 - Appendix 4
Public Sector Regression Equation
. Reg pubgdp gdpcap asia govcon afr lac mec
Source I SS df MS Number of obs - 106--------- +------------------------------ F( 6, 99) - 70.09
Model I .034143298 6 .00569055 Prob > F = 0.0000Residual I .008037183 99 .000081184 R-square - 0.8095---------+------------------------------ Adj R-square - 0.7979
Total I .042180481 105 .000401719 Root MSE - .00901
pubgdp I Coef. Std. Err. t P>Itl [95% Conf. Interval]
gdpcapus 1 8.54e-07 1.56e-07 5.475 0.000 5.45e-07 1.16e-06asiadum I -.0238509 .0035661 -6.688 0.000 -.0309268 -.016775goveon I .0927046 .0141226 6.564 0.000 .0646822 .120727afrdum I -.0248389 .0033358 -7.446 0.000 -.0314579 -.0182199lacdum I -.0191125 .0033532 -5.700 0.000 -.0257659 -.0124591mcdum I -.027473 .0034568 -7.947 0.000 -.0343321 -.0206139cons I .0210799 .0035832 5.883 0.000 .0139701 .0281897
pubgdp: Public health expenditures as a share of GDPgdpcap: GDP per capitaasia: Dummy variable for OAIgovoon: Governmnent consumption as a share of GDPafr: Dummy variable for africalac: Dummy variable for Latin Americaec: Dummy variable for NEC
- 37 - Appendix 5
Private Sector Regression Equation
* Reg priperx govper bedcap humcap opec
Source j Ss df MS Number of obs - 46---------------------------------------- F( 4, 41) - 5.65
Model I 4.09952455 4 1.02488114 Prob > F s 0.0010Residual I 7.4323142 41 .181275956 R-square = 0.3555--------- +------------------------------ Adj R-square - 0.2926
Total I 11.5318388 45 .256263083 Root MSE = .42577
-------------------------------------------------------------------- __-------_
priperx I Coef. Std. Err. t P>jtl (95% Conf. Interval]--------- +…___________________________________________________________________
govper I -3.6894 1.260262 -2.927 0.006 -6.234551 -1.144249bedcap I .0725247 .02609 2.780 0.008 .0198349 .1252146humcap I -.3718042 .1061984 -3.501 0.001 -.5862761 -.1573323
opecdum I -.674999 .3132419 -2.155 0.037 -1.307604 -.0423942-cons I -3.183827 .2268098 -14.037 0.000 -3.641879 -2.725775
--------------------------------------------------------- __------------------_
priperx: Private health expenditure as a share of GDPgovper: Government consumption as a share of GDPbedcap: Hospital beds per capitahumcap: Human capital stock variableopecdum: Dummy variable for oil-producing states
Regional Total Healtli Expenditures in 1990 United States D)ollars
I '990 'I) Ibic 199(1 Ivriv atc I 99"( Aid I k iws 'I o1aI 'I otal l ikallth I K xpenditllrcs1'99() ( liI) IlcVallth F\PCIil1UTeS I Iiilih ENpcnditures ior I Icaltlh I-ealhl Expcnditurces lcr (Capita
tE{I(ill)N (NijilionM 190 UISSS) (N1ilIlio, 190( IJSs) 'milliiin l() )t ISS) Nlillion i(9)9) I NS ) (Million 1990 USS) -.",) As% I P 1')JiSS)
Lstablishicd Markct Icolfnonlics 15,974.547 905,998 577.287 0 1,483,285 9.29% 1,869
Nliddlc IKasIcrn (Ccswiti 1,248,990 25,414 18,887 330 44,631 3.57% 88
I-otriorl\' Socialist Iconolliics 1,380,409 34,864 14.250 0 49,114 3.56% 142ol' Liir.
India 291,561 3,499 13,703 286 17,488 6.00% 20
(lina 365,557 7,494 5,248 77 12.819 3.51 % 11
00
Other Asia aid Islands 817.304 13,972 22.303 542 36,817 4.50% 53
Stub-Saliaran Africa 275,580 5,102 5,432 1,072 11.607 4.21% 22
|Latin Anicrica and tht (icarihhcan 1,109,135 34,104 34,598 542 69,243 6.24% 162
TO( AI 21,463.083 1,030,447 691,708 2.848 1.725,003 8.04% 354
x
0s
Regionial Total Health Expenditures in 1990 International Dollars
1990 Public 1990 Private 1990 Aid Flows Total Total llealth ExpenditLres1990 GDP Healti Expenditures Healil Expenditures for Health Healtil Expenditures Pler Capita
REGION (Million 1990( lt (Million 1990 Sy (Million 1990 IS) (Million 1990 IS) (Million 1990 IS As% (3DP 1990 1$
Establisied Market Economics 15,202,504 864,110 565,850 0 1,429,961 9.3% 1,793
Middle Eastern Cresceit 2,091,124 41,722 34,076 330 76,128 3.6% 167
Formerly Socialist Economies 2,214,726 58,849 22,950 0 81,799 3.6% 240of' Europe
India 878,687 10,544 41,298 286 52,128 6.0% 61
China 2,346,464 48,103 33,685 77 81,864 3.5% 72
()herAsiaand Islands 1.777,383 24,131 42,936 542 67,608 4.5% 111
Sub-Saharan Africa 669,148 11,278 12,466 1,072 24,858 4.2% 50
I,alin Amierica and the Carihbean 1,990,934 60,308 58,533 542 119,382 6.2% 280
tD
x
Regional Total Health Expenditures as Percent of Regional Total Health Expenditures
Public Health Exp. Private Health Exp. Aid Flows as %as % Total as % Total Total Health
REGION Health Expenditures Health Expenditures Expenditures
Established Market Economies 61% 39%
Middle Eastem Crescent 56% 43% 1%
Fomnerly Socialist Economies 71% 29%of Europe
India 20% 78% 2%
China 58% 41% 1%
Othier Asia and Islands 38% 61% 1%
Sub-Saharan Africa 44% 47% 9%
[Latin America and the Caribbean 49% 50% 1%
Health Expenditures in Latin America
1990 Total Health Expenditure Health Expenditures as a percentage of GDP Development Assistance for Health(ofricial exchant.e rate dollars) Aid Flows
Aid FlowsRegion and Economy Millions Per Capita Total Public Sector Private Sector Total Aid Flows Aid Flows as % total
(1990US$) (1990US$) 1990 1 99( 199() (Mill. 1990US$) per capita Health Exp.1 99( 1990 1990
Latin America&Caribbean 69,243 162 *6.2% 3.1% 3.10/%) 542 1.3 0.8%
Antigna and Barbuda 21 262 5.0% 2.9% 2.1% 0.7 8.7 3.3%Argentina 10.0)7) 312 9.6% 5.9% 3.7% 10.9 0.3 0.1%Bahamas 15O 580 4.8% 2.6% 2.2% 0.0 0.0 0.0%Barbados 13( 400 6.2% 3.3% 2.9% 1.6 6.1 1.5%Beli7e 22 118 5.8% 3.4% 2.4% 2.4 12.8 10.8%IBolivia 247 34 5.5% 2.4% 3.1% 36.8 5.1 14.9%Brazil 3 3 445 222 6.4% 2.8% 3.6% 84.1 0.6 0.3%lBritishi Virein Islands 6 536 4.7% 2.7% 2.0% 0.0 0.0 0.0%( ayntial Islanlds I i 701 4.0% 2.0% 2.0% 0.0 0.0 0.0%( hilc 2,017 153 7.3% 3.4% 3.9% 9 7 0.7 0.5%(,ololnhia 2.'. 16 65 5.1% 3.0% 2.2% 26.0 0.8 1.2%(tosta Rica 523 186 9.2% 7.6% 1.6% 4.4 1.6 0.8% 4D)onilic:j 14 195 8.2% 6.4% 1.8% 2.0 27.9 14.3%l)(1i1illican Relp 417 59 5.7% 2.1% 3.6% 10.9 1.5 2.6%ILcuaLclhr 40)2 39 3.7% 2.6% 1.1% 30 7 3.0 7.6%1-1 Salvatdor ThO 58 5.9% 2.6% 3.3% 44 1 8.5 14.7%(irena.a I1' 133 5.9% 4.3% 1.6% 04 4.7 3.5%(itlat1iilalfa -,2 37 5.0% 2.1% 2.9% 31.5 3.4 9.2%(iuv;lia 31 39 9.6% 8.8% 0.9% 14.7 18.4 47.5%Ial il 173 27 7.0% 3.2% 3.8% 32.8 5.1 19.0%
llu lLd.iras . 1 65 5.7% 2.9% 2.8% 20 3 4.0 6.1%Jamaica 37' 154 9.4% 3.6% 5.8% 19.0 7.8 5.1%Mexico t1(l46 155 5.5% 3.1% 2.4% 65.3 0.8 0.5%Nicaralua 1t) 31 7.9% 6.7% 1.2% 26.6 6.9 22.4%I'aniama 417 173 8.7% 5.5% 3.2% 14.7 6.1 3.5%ParaLIuav 213 49 3.9% 1.2% 2.7% 10.3 2.4 4.8%Peru l.28(0 59 3.1% 1.1% 2.0% 29.3 1.4 2.3%St. Kitts and Nevis i(I 239 6.8% 4.3% 2.4% 1.2 29.9 12.5% >St. Lucia 27 179 7.6% 5.6% 2.0% 0.4 2.4 1.3% tSi. Vincenit 12 110 6.1% 4.0% 2.1% 0.3 2.7 2.5%Suriname 5q 133 4.1% 1.2% 2.9% 1.7 3.8 2.9% XTIrindadandlobago 235 190 4.8% 2.9% 1.9% 1.4 1.1 0.6% XUruguay 07) 219 8.3% 6.4% 1.9% 5.3 1.7 0.8% sVenezuela 2,0(9) 102 4.2% 2.0% 2.2% 2.5 0.1 0.1%N( ) tI-: oItd f)ce de I "es pr'nt I i Ie ( aIue
HIaIth Exwntditu..s In LOtin Anie"k Iin 199 US Dollars
Total HealthCountry Population 1990 CDP 1990 Pubblc Heath Fx%piditures 1990 Pnw Health E dines_ 1990 Aid Flows 1990 Total Health Expenditures Public Private Aid Flows Expenditure
1000) (1990 USS) Expeiditue (1990 USS) Expenditure (1990U) Expen,diure (1990USS) Expendbture (1990 US$) as % tobla as% total as % total PerCapitaAs An.oDP As %1G3P As %GDP As q6GDP (1990 US$)
Latin America&Cairbbean 426.893 1,109,134,962,133 307% 34,103,174,850 3 12°o 34,597,889,249 005% 541,603040 624% 69.242.667,139 4925% 49 97% 078% 162
Aiirigt .and Ba1b1id. 79 418.703.692 2 69% 11.258,519 2.10% 8.792.778 0 16% 684,500 4 95% 20,735.796 54 30% 42 40% 3.30% 262Argeirilna 32.293 105.437.652 631 5 85% 6 166 794 735 3 70% 3 901.193.147 0 01% 10.890.0W0 9 56% 10 078.877,883 61 19% 38 71% 0 11% 312R.ahamas 258 3100000000 263% 81 394.905 2.20% 68,200000 0 483% 149594905 5441% 4559% 000% 580Barbados 257 1.647.300.000 3 24% 53.454.061 2.90% 47.771.790 0 10% 1.570.350 6 24% 102.796.111 52 00% 46 47% 1 53% 400Blhze 188 382,450 048 275% 10.525.769 241% 9202,979 063% 2,398.200 579% 22126,948 4757% 41 59% 10 84% 118llolin-a 7 171 4.478,220,499 1 60% 71 724.438 310% 138 824 835 0.82% 36 770 440 5 52./ 247 319 714 29 00% 5613% 14.87% 34P-razl lS0 348 521 607.637.091 2 76% 14 374 721 003 3 64% 18 986 517,990 0 02% 84 131 000 6 41% 33,445 369,993 42 98% 56 77% 0 25% 222Bnuihli b poii lol,iiids 12 137.800 000 2 67% 3.679 260 2.00% 2 756.000 0 4 67% 6 435 260 57 17% 42 83% 0 00% 536a-an- Istand 22 382.500,000 2 03% 7 774 167 2 00% 7 650,000 0 4 03% 15 424 167 50 40% 49 60% 0 00% 701Clhd, 13 173 27.790 737,347 3 32% 923 011 422 3 90% 1 083 638.757 0 03% 9,681 500 7 26% 2,016,531.678 45 77% 53 75% 0 48% 153Colombia 32 345 41,123.004 586 2 91% 1 197.137 600 2 17% 892 369,200 0 06% 26,049,300 5 14% 2115 556 100 56 59% 42 18% 1 23% 55C-ia R-ca 2 807 5 702.391 276 7 50% 427 894 324 1 60% 91238 260 0 080/ 4 366 400 918% 523 498 985 81 74% 17 43% 0 83% 186D.iUUUca 72 171 555 556 5 23% 8 974 339 1.80% 3 088 000 1 17% 2 005 500 8 20% 14 067.839 63 79% 21 95% 14 26% 195Douiiomiinaii Rop 7 074 7 304 319 730 1 96% 142 980 955 3 60% 262 955.510 0 15% 10 890.400 5 71% 416 826 866 34 30% 63 09% 2 61% 59Ecu^ador l10 284 1O 875.528,435 2 31% 251 437.697 1 10% 120 1 1S 206 0 28% 30 668 180 3 70m/ 402.221.083 62 51% 29 86% 7 62% 39FltS.ador 5.213 5112813.382 1 74% 89174887 326% 166589319 086% 44096462 586% 299860668 2974% 5556% 1471% 58Oire-ada 91 203.703.751 4 10% 8 346 506 1.63% 3 320.371 0 21% 423.250 5 94% 12 090.127 69 04% 27 46% 3 50% 133Guaiammuoolo 9.197 6 787.540 280 1 64% 111 093,016 2.93% 198 874 930 0 46% 31 543 950 5 03% 341 511.897 32 53% 58 23% 9 24% 37Ilmnitad 798 319,924,463 4 22% 13 493 361 0.85% 2.719,358 4 58% 14 659,000 9 65% 30 871.719 43 71% 8 81% 47.48% 39Hamt. 6 472 2,467100.058 1 84% 45 304 209 3 83% 94 539.023 1 33% 32 752,650 7 00% 172 595 882 26 25% 54 77% 18 98% 27Vl,nd..ra. 5.105 5830.000128 257% 149959335 2 76% 160908.004 035% 20265 200 568% 331,132,538 4529% 4859% 6 12% 65Jaimiaica 2.420 3,967.956.668 3 10% 123,006 657 5 80% 230 141,487 0 48% 18 955,050 9 38% 372 103 193 33.06% 61 85% 5.09% 154\tnSmnm l84 154 237.748 337.662 3 10% 7 370 198 468 2 36% 5 610 860 769 0 03% 65 288.000 5 49% 13 046 347,236 56 49% 43 01% 0 50% 155Nicaragula 3 853 1 500,000000 4 90% 73 500.000 1.24% 18 600 000 1 77% 26 592 780 7 91% 118 692 780 61 92% 15 67% 22 40% 31Piano 2.418 4 815.797 760 5 18% 249 361 876 3 18% 153.142.369 0 31% 14.715 300 8 66% 417 219 545 59 77% 36 71% 3 53% 173Pmag-iar 4 314 5.477 268,680 0 98% 53 410 689 2.72% 148 981 708 0 19% 10 263 950 3 88% 212 656.348 25 12% 70 06% 4 83% 49Porn 21 663 40 838 444 372 1 06% 433 641 140 2 00% 816 768 887 0 070/ 29 260 298 3 13% 1 279 670 325 3 89/% 63 83% 2 29% 59Si K1l1o mid .\.. 40 141.555 591 3 48% 4 925 388 2.44% 3 453.956 0 85% 1 196 850 6 76% 9 576 194 51 43% 36 07% 12 50% 239SI lucia 150 352 500.000 5 53% 19 484 673 2.00% 7 OS0 000 0 10% 353 000 7 63% 26 887.673 72 47% 26 22% 1 31% 179Si \ 107 191.185 150 3 90% 7 449 915 2.10% 4 014 888 015% 291 250 6 15% 11 756 054 63 37% 34 15% 2 48% 110Sii--u-on 447 1 438 994,449 1 09% 15,697 152 2.92% 42 018 638 0 12% 1.704.680 4 13% 59 420.470 26 42% 70 71% 2 87% 133Tmidad aid
T
oobugo 1 236 4.890,823.499 283% 138 617.702 1.94% 94,881 976 003% 1412000 480% 234 911 678 59 01% 4039% 060% 190lr-unla) 3.094 8217.979,465 632% 519 376 302 1 88% 154.498.014 006% 5,257,250 826% 679 131 566 7648% 2275% 077% 219Veizsi"eld 19738 48273235.887 1 96% 944370380 220% 1 062011 190 001% 2466350 4 16% 2008847919 4701% 5287% 012% 102NOTE Bot1tan1 donins prrduri,d cmld c
it
it(D0~
X<
C
li-:l8h E,p-ndit.-e In Latin A-1r-c In 1990 HIm-ri.n.lll D.11-r
CounU ~~~~~~Pqupdto _ 1- °° DP _ -I NJPbbc He,ahh Fxpend,t, 1 es _ 90ou P-z1, Hculff Ex,ptndIturs____ Ics. 0 AJd Fl.-. 1°°OTmJo H-alRh Expend it-, ToUd HealGh E,p-admnu Per Capda
mm H0 (1°O [,,IM Intol,Hl ~as GDP (1-o nlwl'lS) as-.DP -(1°O IPI'lS) u°(iDP (I Q fl IrS) 5° GDP 1-lToOlm;lS) (1 990ULSS)
L~UAb.lsn,- 426.8-3 1 990 933SOS5875 60 307 761,860 58,533,474,557 541 603 040 119 382 154,957 280 162
Anr,"a and Elrb,, 79 418 7038692 11,258,519 2 69% 8,792 778 2.10°/ 684 500 0 16Y% 20 735.796 4 95% 262 262Argelilula ~~~~~32 293 141.247,140 000 8 261 205,534 S 65% S 226 144 180 3 70% 10 890 000 0 01% 13 498 239 714 9 56% 418 312
BhdlA-m 258 3 100 000 000 81 394 905 2 63% 66 200 000 2 20%/ 0 149 594 905 4 83% 560 5808Rb d.A.s 257 2 6334 150 000 85 476,850 3 24% 76 390,350 2 90% 1 570 350 0 10% 163 437 SS0 6 24% 636 400B:h7e 166 699 129 243 19 241,397 2 75% 16 823,300 2 41% 2 398 200 0 63% 38,462 897 S 79% 205 118Robena 7 171 1 3 677 160 000 219 057 559 1 50% 423 992 580 3 10% 36 770 440 0 82% 679 820.579 S 52% 95 34Br,zI 150 348 694.700 160 GOO 19 144 890 278 2 76% 25 287 085 824 3 64% 64 131 000 0 02% 44 516 107 102 6 41% 296 2228n.l10,V.gul. 1,1-& 1 2 137 800 000 3 679,260 2 67% 2 756 000 2.00°b 0 6 435 260 467% 536 536
Cdysildn IsI,.sid, ~~~22 382 S00 000 7 774 167 2 03% 7 650 000 2 00% 0 15 424 167 4 03% 701 701<~~~luJ. ~~~~~13 173 78 906 270,000 2 620 707,309 3 132% 3 077 344,530 3 90S% 9 681 S00 0 03% 5 707 733 339 7 26% 433 153t.]1.walil,u ~~~~32 345 158 624 160000 4617 730 930 2 91% 3 442 144 706 2 17% 26 049 300 0 06% 8 065924,936 514% 250 65
6..l-RG 2 807 74 140 350.000 1061 059 337 7 50% 226.245 600 1 60% 4 366 400 0 08% 1 291,671 337 9 18% 460 186
D-."nlCd 72 310 125.844 16 223 167 S 23% S 582 265 1 80°/ 2 OOS S00 1 17% 23,810 933 8 20% 331 195E>.Iulci - Kp 7 074 20 076 000 000 392 984 667 1% 96% 722 736 000 3 60% 10 APO 400 0 15% 1 126 611 067 S 71% 159 59E-A,.ld. 10 264 38 430 810 000 8838 504 353 2 31% 4 24 450 609 1 1 0Ss 30 668 16QC 0 28% 1 343,623 142 3 70% 131 391.1 SuAd.l, S 213 9 558 640.000 166 716 557 1 74% 311 446 401 3 26% 44 096 462 0 86% 522 259 420 S 86% 100 58Cr-'Iadu 91 389 377 967 15.954.274 4 10% 6 346 861 1.63%/ 423 250 0 21% 22 724 385 S 94% 250 133
, M^-iAa 9,197 25 843 570 000 422,986,830 1 64% 757,216 601 2 93% 31 543 950 0 46% 1 211,747 381 5 03% 132 37,uI!-IIn ~~~ ~~~~798 1 639 760 000 69 159,681 422% 13 937 960 0 85%/ 14 659000O 4 58% 97,756.641 9 65% 123 39
1 jd10 ~~~~~~~~6,472 6 447 870 DOO 118,404,460 1 84°6 247 081 722 3 83% 32 752 650 1 33% 3-98 238 832 7 00% 62 27Hm,.-iua 5.105 8 066,660 000 207.490 728 2 57% 222.639.816 2 76% 20 265 200 0 35% 450,395,744 S 68% 88 65J u-dCa 2,420 7 122 400 000 220 794 400 3 10% 413 099 200 S 80% 16 955 OS0 0 48% 652 848 650 9 38% 270 154xle'ico 84 154 515 640 360 000 15 984 851 160 3 10% 12 169 1 12.496 2 38% 65 288 000 0 03% 28 219,251 656 5 49% 335 155
ulLArlpla ~~~~~~3 853 7 354 900 000 360 390 100 4 90% 91 200 760 1 24% 26 592 780 1 77% 478 183 640 7 91% 124 31p|d- 2 418 9 768 720 000 SOS 824 OSS S 18% 310,645 296 3168% 1 4715 300 0 31% B31 184 651 8 66% 344 173
INr.lelluE ~~~~~~4,314 12 916 540 000 1 25 953 527 0 98% 351 329 886 2 72% lO 263 950 0 19% 487,547 365 31 88% 113 49P-nl ~~~~~~~~21 6363 56 892 000 OO0 604 105 080 1 06% 1 137 840 000 2 00% 29 260 298 0 07% 1 771 205 378 3 13% 82 59
S, K.11n.,]d o.40 141 SSS 591 4 925 388 3 48% 3 453 956 2"4% 1 196 650 0 85% 9 576 194 6 77% 239 2399 I ~~~ ~ ~~~~~~150 7 76 733 807 42 934 480 5 53% 15 534 676 2 00% 113S3000 0 10% 58 822 156 76-3% 392 179
%' .l-nil 107 498 3,33 422 19 418 568 3 90% 10 465 002 2 10% 2 5 0 15% 30 174 820 815 282 110simulSlilic 447 1 747 080 000 ~~~~~~~~~~~19 057 877 1 09% 51 01 4 736 2 92°%170 8 0 12% 71 777 293 4 1 3% 16113
I mld.d.,rMdT.A,-, 1 236 10 619 490 000 30981 889 2 83% 206 01 8 106 1 94% 1 41 2000 0 03% 508 411 995 4 80% 411 190I ni-uo 3 094 18 192 720 000 1 149 779 904 63 -2% 342 023 136 1 88% 5 25,7 250 (I 0f% 1 497 060 290 8 26% 484 219% -m171el' 19,738 130 251 000 000 2 548 103 190 1 96% 2 865 522 000 2 20% 2 466 350 001% 54186,091 540 4 16% 274 102'-t, RArdt-c uld-11,e pr-dMctd v.Jlu M,der I[LIAILA 5 L)P fig,-c ::I, s..be E- b$ lgip-e -v1 -bud .Iild -or .. .....e.l.les of tdbl-
>>-
- 44 - Appendix 12Health Expendituires in Latin America
Country Total Health Expenditure Per Capita(1990 Int'l $) (1990 US$)
Latin America 280 162&Caribbean
Antigua and Barbuda 262 262Argentina 418 312Bahamas 580 580Barbados 636 400Belize 205 118Bolivia 95 34Brazil 296 222British Virgin Islands 536 536Cayman Islands 701 701Chile 433 153Colombia 250 65Costa Rica 460 186Domin1ica 331 195Dominican Rep. 159 59Ecu]ador 131 39El Salvador 100 58Grenada 250 133Guatemala 132 37Guyana 123 39Haiti 62 27Honduras 88 65Jamaica 270 154Mexico 335 155Nicaragua 124 31Panama 344 173Paraguay 113 49Peru 82 59St. Kitts and Nevis 239 239St. Lucia 392 179St. Vincent 282 110Suriname 161 133Trindad and Tobago 411 190Uruguay 484 219Venezuela 274 102
- 45 -Appendix 13
TIME TRENDS - GOVERNMENT HEALTH EXPENDITURE AS % GDP
1980 1983 1987 1990
United States 3.91% 4.41% 4.68% 5.24%
Latin America&Caribbean
Anguilla - 2.85% -
Antigua and Barbuda - - - 2.69%
Argentina 1.51% 1.46% 1.43% 2.53%Bahamas 3.08% 3.21% 2.96% 2.62%Barbados 3.36% 3.12% 3.88% 3.24%Belize 2.26% 2.75% 2.39% 2.71%Bolivia 2.70% 2.20% - 1.60%Brazil 1.29% 1.68% 1.33% 1.57%British Virgin Islands - - 3.07% 2.67%Cayman Islands - - - 2.03%Chile 2.56% 2.92% 2.27% 2.21%Colombia 2.16% 2.19% 2.00% 2.91%Dominica - 5.64% 4.60% 4.95%Domninican Rep. 1.57% 1.48% 1.49% 1.64%Ecuador - 1.08% 1.43% 1.59%El Salvador 1.29% 1.29% 1.24% 1.24%Grenada - - - 4.10%
Guatemala 1.90% 1.10% 1.10% 1.16%Guyana 3.34% 4.45% 2.81% 4.08%Haiti 1.35% 1.80% - 1.84%Honduras - 3.07% 2.39% 1.89%Jamaica 1.82% 2.78% - 2.78%Mexico 2.10% 2.10% 2.30% 3.10%Nicaragua 3.20% 4.90% 4.80% 4.90%Paraguay - 2.06% 1.12% 0.98%Peru 2.90% - 2.90% 1.06%St. Kitts and Nevis - 4.30% 3.48%St. Lucia - - - 5.53%
St. Vincent 3.74% 3.83% 4.00% 3.90%Suriname - - 1.91% 1.09%Trindad and Tobago - 2.95% 4.47% 2.83%Uruguay 1.07% 0.85% 0.99% 1.28%Venezuela 2.28% 2.04% 1.55% 1.15%
Government Health Expenditures, by expenditure type - Latin America & Caribbean
Country _ _ Percentage of recurrent health expenditures: Capital ExpendituresPHC Salaries Drugs and As percentage of total
Other Supplies health expenditures
Anguilla 1.0% 53.5% 17.0% 3.5%Antigua - 74.5% 11.5% 0.2%Bahamas 76.9% 16.1% 7.1%Barbados - 61.6% 23.1% 3.2%Belize 8.0% 58.0% 15.6% 13.2%Chile - 60.2% 27.1% 4.3%Costa Rica - 65.2% 18.3% 0.3%Dominica 31.1% 67.9% 13.4% 12.6%Dominican Republic 3.5% 35.6% 10.4% 23.2%Ecuador 12.8% - - 5.8%Guatemala 16.5% 41.0% 28.8% 44.6%Guyuna 42.1% 56.8% 19.7% 26.7%Honduras 8.6% 39.2% 37.8% 35.3%Jamaica 12.0% 40.6% 12.1% 3.2%St. Kitt's and Nevis 58.3% 11.5% 37.2%St. Vincent - 65.3% 10.4% 13.4%Trinidad and Tobago 7.1% 69.3% 6.8% 2.0%TurksandCaicos 11.9% 49.7% 10.1%
5
[Plot o~flif~e expectancy at birth and health expenditure per capit
80 ___
75 [ C(M BAR
ANTJAM PAN URU
CHI STLTRI ARG
70 VEiV GRE MEX STK
~~~~COLCL >BELSUR
m PSE%/ AR> ECU BRA
2 65 NIC HONGUY
a ~~GUAELx G PERa)
W60
HAI
BOL C
500 100 200 300 400 500
Total health expenditure per capita
lPlot of life expectancy at birth and health expenditure as percentage of GDPI
80
75 BAR DOM COS
ANTURU PAN JAM
TRI CHI STL ARG
] - 70 .- VEN STVMEX GRE STK
t ~~~~~~~~~~COL_ ~~~~~~SUR BEL
PAR DMR
ECU BRA
65 HON NIC GUY
BOLS
PER ~~~GUAEL
60 K
HAI55
BOL
50 - - _ l _ _._ _-
2% 4% 6% 8% 10%Total health expenditure as percentage of GDP
Life expectancies and health expenditures in selected countries:deviations from estimates based on GDP and schooling
Deviation from predicted life expectancy at birth (years)15
12 Costa Rica
9 ~~~~~~~~~~~~~~~~~~Argentina °El Salvador A
Paraguay Colombia 0 OChile 0 Panama
6 Venezuela C) CX) OHondurasE uador 0 Guatemala
3 B razil0Haiti
0
-3 0Peru
-6 0Bolivia
-9 D I_I_I_>
-2 0 2 4 6
Deviation from predicted percentage of GDP spent on health
Health Expenditures in Latin America - Comparison of WDR 1993 and Present Study
Health Expenditures as a percentage of GDP
Region and Economy Total Public Sector Private Sector
Present Study WDR 1993 Present Study WDR 1993 Present Study WDR 1993
Latin America&Caribbean 6.2% 4.0% 3.1% 2.4% 3.1% 1.6%
Argentina 9.6% 4.2% 5.9% 2.5% 3.7% 1.7%Barbados 6.2% 5.0% 3.3% 3.3% 2.9% 1.7%Belize 5.8% 5.8% 3.4% 3.4% 2.4% 2.4%Bolivia 5.5% 4.0% 2.4% 2.4% 3.1% 1.6%Brazil 6.4% 4.2% 2.8% 2.8% 3.6% 1.4% ,Cayman Islands 4.0% 3.8% 2.0% 2.0% 2.0% 1.8% 0
Chile 7.3% 4.7% 3.4% 3.4% 3.9% 1.4%Colombia 5.1% 4.0% 3.0% 1.8% 2.2% 2.2%Dominican Rep. 5.7% 3.7% 2.1% 2.1% 3.6% 1.6%Ecuador 3.7% 4.1% 2.6% 2.6% 1.1% 1.6%El Salvador 5.9% 5.9% 2.6% 2.6% 3.3% 3.3%Guatemala 5.0% 3.7% 2.1% 2.1% 2.9% 1.6%Haiti 7.0% 7.0% 3.2% 3.2% 3.8% 3.8%Honduras 5.7% 4.5% 2.9% 2.9% 2.8% 1.6%Jamaica 9.4% 5.1% 3.6% 3.4% 5.8% 1.7%Mexico 5.5% 3.2% 3.1% 1.6% 2.4% 1.6%Nicaragua 7.9% 8.6% 6.7% 6.7% 1.2% 1.9%Panama 8.7% 7.1% 5.5% 5.5% 3.2% 1.6%Paraguay 3.9% 2.8% 1.2% 1.2% 2.7% 1.6% 3
Peru 3.1% 3.2% 1.1% 1.9% 2.0% 1.3%Uruguay 8.3% 4.6% 6.4% 2.5% 1.9% 2.1%Venezuela 4.2% 3.6% 2.0% 2.0% 2.2% 1.6% t
NOTE: Boldface denotes predictcd value
-51 - Appendix 19
Explanations for Changes in Expenditure Estimates from WDR 1993
Country and Suib-Sector Reasoii for Change
Government
Argentina Addition of local expenditures (previously unavailable data)Colombia Addition of local expenditures (previously unavailable data)Jamaica Availability of more recent and comprehensive WB studyMexico Availability of more recent and comprehensive WB studyUruguay Availability of more recent and comprehensive WB study
Parastatal
Jamaica Availability of more recent social security estimate from WBMexico Availability of more recent social security estimate from nWBPeru Availability of more recent national social security estimate
Private
Argentina Availability of more recent and comprehensive WB studyBarbados New predictionBolivia Availability of more recent, national household surveyBrazil Availability of more recent, national household surveyCayman Islands Availability of more recent, national household surveyChile Availability of more recent and comprehensive WB studyDominican Republic Availability of inore recent, national household surveyEcuador Availability of more recent and comprehensive W'B studyGuatemala New predictionHonduras Availability of Inore recent, national household surveyJamaica Availability of more recent and comprehensive WB studyMexico Availability of inore recent, national household survey
Nicaragua New predictionPanama Availability of more recent, national household surveyParaguay New predictionPeru Availability of more recent, national household surveyUruguay Availability of more recent and comprehensive V1B studyVenezuela New prediction
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