- Email: [email protected]
The role of education in health system performance
ARTICLE IN PRESS
Economics of Education Review 27 (2008) 299–307 www.elsevier.com/locate/econedurev
The role of education in health system performance Michel Grignon Department of Economics and Department of Health, Aging, and Society, McMaster University, KTH 232, 1280 Main Street West, Hamilton, Ont., Canada L8S 4M4 Received 29 August 2005; accepted 30 November 2006
Abstract I investigate the role of education on health, using country-level data and the production frontier framework suggested by the World Health Organization (WHO) to assess performances of health care systems. I find that the impact of human capital on health is much smaller than suggested by the WHO frontier model, and the relationship exhibits diminishing return in the observed range of education. Taking into account the heterogeneity in this relationship generates a different ranking of the efficiency of health care systems internationally. This suggests that the method currently used by the WHO favors health care systems operating in countries that underinvested in education in the past. r 2007 Elsevier Ltd. All rights reserved. JEL Classification: I18 Keywords: Human capital; Rate of return; Economic impact; Efficiency
1. Introduction This study investigates the role of education in the production of health, and in particular, heterogeneity in the marginal impact of education on health depending on the baseline level of education. The work is motivated by two concerns. The first is the surprising logical implication of the education– health relationship as estimated by the World Health Organization (WHO) in its World Health Report 2000 (WHO, 2000), which posited a single education–health relationship for both highly developed and underdeveloped countries. The increasing marginal return of education on health found by the WHO implies that all countries would improve Tel.: +1 905 525 9140x23493; fax: +1 905 525 4198.
E-mail address: [email protected]. 0272-7757/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2006.11.001
population health substantially if they redistributed monies from their health care system to the education system. The second is the relative neglect of the shape of the education–health relationship in the empirical literature, which to date has emphasized identifying a causal effect of education on health (Grossman & Kaestner, 1997). I hypothesize that the marginal impact of education on health differs below and above a threshold level of education. I test this hypothesis using an aggregate, country-level population health production function. I estimate the impact of education on health separately in low- and high-education countries, where the threshold education level that divides the sample is set so as to minimize the sum of squared errors across the two equations. The results strongly support differing health returns to education in the low- and high-education samples.
ARTICLE IN PRESS 300
M. Grignon / Economics of Education Review 27 (2008) 299–307
I then illustrate the importance of this heterogeneity for assessing health system performance in the same spirit as WHO (2000). Failing to account for this heterogeneity overestimates the health possibility frontier among countries with high-educational levels, and therefore underestimates the performance of their health care systems. More generally, however, a better understanding of the shape of the relationship between education and health can inform policies guiding public spending in education and health. 2. The shape of the relationship between human capital and health: what do we know? 2.1. Individual-level analyses of the human capital– health relationship One strand of empirical literature concerning the return of education on health stems from Grossman’s seminal paper on health capital (Grossman, 1972). This empirical literature has attempted to disentangle the causal impact of education on health from a mere correlation between the two. The literature has paid relatively little attention to the shape of the education–health relationship (e.g., linear, convex, or concave). Although Grossman (1975) suggested that, unlike income, education continues to influence health even at high-education levels, this proposition is absent from more recent reviews by Grossman and Kaestner (1997) and Grossman (2000). There appears to be little systematic investigation of the form of the relationship based on individual-level studies. Some studies (e.g., Curtin & Nelson, 1999; Duleep, 1986; Ross & Mirowsky, 1999) offer suggestive evidence from disparate contexts, but Gilleskie and Harrison (1998) and Auld and Sidhu (2005) constitute perhaps the strongest individual-level studies that estimated the relationship between education and health at various levels of education while controlling for other factors (age, race, income, cognitive ability, and body mass index). These two studies suggest that the relationship between education and health is non-linear, and somewhat concave. 2.2. Aggregate analyses of the human capital– health relationship The literature on aggregate analyses of the link between human capital and health stems from a different tradition, started by Adelman (1963). This
literature focuses on the impact of health care services on health outcomes, and treats education as an ‘environmental’ factor to be controlled for along with income, life-style indicators, or geographical location. The studies generally estimate an aggregate health production function, where population health in each geographical area is measured by mortality (or sometimes the prevalence of a given condition), the density of physicians or hospital beds is the factor of interest, and education is measured as the average number of years of schooling. The specifications often deal inadequately with issues such as the time lags between the determinants and health outcomes (Grignon, 2001; SPRG, 2002), potential non-linearities (all these studies assume that the link between schooling and health is linear), and the potential endogeneity of education (though Auster, Leveson, & Sarachek, 1969 addressed potential endogeneity of health care expenditures). As summarized by Newhouse and Friedlander (1980), the effect of education often appears significant in this literature, mostly based on comparisons across regions of the United States. By contrast, the impact of medical care appears small and insignificant. Among developing countries, the health transition literature demonstrates that women’s literacy is strongly associated with reduced infant mortality (Caldwell, 1993). The more recent literature estimate production frontiers that allow for non-linearities in the relationship between education and health. In its World Health Report 2000, the WHO used repeated observations (yearly, from 1993 to 1997) on a set of 191 countries to estimate a translog two-factor (health expenditure and number of years of schooling) production function in which a quadratic relationship was estimated for each factor, as well as an interaction term (Evans, Tandon, Murray, & Lauer, 2000). This flexible functional form yields a better estimate of the rates of return of education on health at various levels of education. Because the focus of this literature is ‘‘performance’’ (or technical efficiency, Kumbhakar & Lovell, 2000) Evans et al. (2000) estimate a stochastic production frontier: instead of the average relationship between human capital and health, they calculate the maximum amount of health a country could be expected to produce given the average number of years of education of its population. Their estimates imply increasing returns of education so that the impact on health of increasing schooling by 1 year is larger in a country with an average of 12 years of
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
schooling than in a country with an average 5 years of schooling. This finding, however, has been heavily criticized and does not appear to be robust. Evans et al. (2000) employ a fixed-effect (or within) estimator, which decomposes the error term into a time invariant term, measuring the countryspecific deviation from the average linear relation between inputs and outputs, and a pure random error term, varying over time for the same country. Gravelle, Jacobs, Jones, and Street (2002), reestimate the production frontier using the same data as Evans et al. (2000) but using a ‘‘between’’ estimator (which pools data across years for each country) and obtain a diminishing rather than increasing rate of return of education on health. Moreover, their model appears to better fit the data (due mainly to the very small variation over time within countries in key variables, which is the only source of variation the within estimator exploits). In addition, Hollingsworth and Wildman (2003) show that, even when they use a within estimator, the production frontier among OECD countries exhibits diminishing returns of education on health. The impact on health of increasing population schooling from 4 to 5 years is more than double the impact of one more year of schooling from 8 to 9 years. Their estimates also suggest the kind of heterogeneity that is the focus of this analysis. In their analysis based on the within estimator, the coefficient for squared number of years of education is negative among OECD countries and positive among non-OECD countries. Since OECD countries also are characterized by a much higher level of education than non-OECD countries (8.93 years on average versus 5.30 years), there are good reasons to suspect this heterogeneity simply reflects a decreasing return of education on health beyond a threshold level of education. In this work, I directly test for such heterogeneity by allowing two distinct regimes in the relationship between human capital and health, separated by a cut-off level of human capital rather than a correlated, extraneous characteristic (such as being an OECD country or having a minimum level of income per capita). 3. Methods The primary objective of this analysis is to investigate the shape of the education–health relationship. I test whether the relationship between education and health differs across low- and high-
301
education countries, and determine the cut-off point in the distribution of education separating the two sets of countries. The relationship between education and health is homogeneous within each set, but differs across the two sets. As is described in more detail below, the study uses aggregate data on health, educational attainment and other health determinants from a crosssection of 111 countries. Following Hollingsworth and Wildman (2003), I estimate two sets of equations; however, instead of separating two sets of countries using an external threshold (being an OECD member) I separate countries based on their average levels of education. Because my main concern in this study is with the heterogeneity of the relationship between education and health, I keep the baseline simple: I use the translog specification with health outcome (H) on the lefthand side of the equation, a measure of health care (M), education (E) and its squared value (E2) on the right-hand side (other factors are included in the sensitivity analysis). The equation to be estimated using OLS is as follows: H i ¼ B0 þ B1 M i þ B2 E i þ B3 E 2i þ i . I test for heterogeneity using a standard misspecification test known as parameter inconstancy (Kennedy, 2003). The procedure is as follows: first, I split the population of 111 countries into two subsets, one comprised of countries with values for E below a threshold and one comprised of countries with values of E above the threshold. I estimate the model under varying levels of the threshold and identify the threshold value of E that minimizes the sum of SSR (sum of squared residuals) across both equations. The cut-off value is therefore the value that separates the data into two regimes that best fit a piece-wise quadratic relationship between health and the inputs. I then use a Chow test to test whether the coefficients associated with each regime are different, which implies differing input–health relationship above and below the threshold. Although more sophisticated non-parametric procedures can be used to identify such distinct regimes, I rely on this simpler approach both because non-parametric approaches would not work with only 111 observations and because I subsequently use the specified relationship to estimate a production frontier, I must keep the specification reasonably simple. To illustrate the impact of incorrectly assuming homogeneity in the relationship across low- and
ARTICLE IN PRESS 302
M. Grignon / Economics of Education Review 27 (2008) 299–307
high-education countries, I estimate two production frontiers, one for countries with educational attainment below the cut-off point and one for countries above, and compare the resulting efficiency rankings of national health care systems with those derived from the mis-specified model that assumes homogeneity. Among stochastic frontier estimates, the composed error model is the natural choice with crosssectional data. Following Kumbhakar and Lovell (2000), in my primary model I use a ‘‘normal halfnormal’’ hypothesis for the decomposable error term in which the purely random term is normal and the efficiency term follows a half-normal nonpositive distribution. They show that in most of the cases imposing a more flexible constraint on the distribution of the error term (such as truncated distributions) adds computation difficulties but does not yield substantially different estimates. However, as a sensitivity analysis (see Section 5.2), I reestimate the frontier under several distributional assumptions. In the composed error model, the following equation is estimated:
negative, and vi is the random component in the error term. The sum ei ¼ (ui+vi) is skewed, indicating potential inefficiency. This generates a health frontier that represents the maximum achievable level of H for a given country; the efficiency of the country’s health care system is the ratio of its observed level of H to the maximum achievable. For each country, technical inefficiency is given by TEi ¼ Eðexpðui Þ ei Þ (Kumbhakar & Lovell, 2000).
H i ¼ A0 þ A1 M i þ A2 E i þ A3 E 2i þ ui þ vi ,
4.2. Independent variables
where ui is the efficiency component in the error term of the regression, constrained to be non-
Education in a country is measured as the average number of years of schooling in the population aged
4. Data and variable specification The key features of the data and specification are presented here. More details are in Grignon (2006). 4.1. Dependent variable The output, health (H), is measured as disability adjusted life expectancy (DALE), defined as the life expectancy at birth minus the years of life ‘‘lost’’ due to impairment or disability when lived in a less than perfect health state (Murray & Lopez, 1996).
Table 1 Descriptive statistics, means of the main variables used in the estimation Variables
191 countries
Non missing years of education (N ¼ 111)
High education level (N ¼ 57)
Low education level (N ¼ 54)
Disability adjusted life expectancy (years)1 1997
56.8 442.4 6.0 7385.4 40.07 73.63
57.3 553.6 6.0 8783.6 40.74 74.40
66.1 945.1 8.3 13898.8 37.26 88.39
48.1 140.3 3.6 3054.6 44.75 64.12
79.07
80.78
91.60
72.12
94.45
96.21
101.39
89.56
0.01 0.02 0.03 0.02 0.03 0.03
+0.10 +0.03 +0.19 +0.28 +0.19 +0.20
+0.68 +0.62 +0.84 +0.77 +0.83 +0.87
0.51 0.59 0.49 0.24 0.48 0.52
Years of education 19952 Gini: last available year4 Improved sanitation facilities (% of population with access) 20005 Improved water source (% of population with access) 20005 Ratio of girls to boys in primary and secondary education (%) 19985 Governance: voice3 Governance: political stability3 Governance: government effectiveness3 Governance: regulatory quality3 Governance: rule of law3 Governance: control of corruption3
Sources: (1)World Health Organization (2000), World Health Report 2000 (2) Barro and Lee (2000), (3) Kaufmann, Kraay, and Mastruzzi (2003), (4) Deininger and Squire (unknown), (5) World Development Indicators (World Bank, 2004).
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
15 and over in 1995, as published by Barro and Lee (2000). Barro and Lee provide data for 112 countries in 1995, 111 of which are in the WHR00 panel. Health care resources are measured as annual health care expenditures. In addition, in sensitivity analyses, I use other controlling factors potentially correlated with education and health outcome including income per capita, income inequality, quality of governance, and environmental and sanitation measures (Greene 2004; Hildebrand & Van Kerm, 2005). I also test the use of female education, measured as the sex ratio in schools, as an alternative measure of education (Jamison, Sandbu, & Wang, 2001). All independent variables are entered as natural logarithms, except for the six governance variables, which take values between 4 and +4. Descriptive statistics for all variables are provided in Table 1. 5. Results
303
the threshold of 5.8, with a Fisher statistic F(4,103) equal to 6.45 which is significant at a level below 0.1%. Table 2 presents the coefficients estimated in each subset of countries defined by the threshold of 5.8 years of education (Grignon, 2006, provides the full set of results using all control variables, as well as the results of two robust regressions. Conclusions are not qualitatively affected by adding controls or running robust regressions). The strong negative value of the coefficient on square number of years of education among countries above the cut-off point suggests a non-linear relationship between education and health and Fig. 1 illustrates the heterogeneity in the impact of education on health at the sample mean level of health care expenditures. When the relationship is estimated over the full sample of 111 countries, the rate of return diminishes then stabilizes at a positive value, so the impact on health of adding 1 year of schooling is approximately the same at 10 years of education as
5.1. Baseline analysis—heterogeneity and the shape of the education– health relationship
Impact of schooling sample means
I tested for an education threshold in the range 4.09–7.25 years of education, which would split the data set into two subsets of reasonable size (neither below 30). The results indicate that 5.8 years of education is a reasonable cut-off point between two regimes of the relationship between education and health (detailed results are available in Grignon, 2006). The best cut-off from a linear model-fitting perspective is 5.8, though any value between 5.5 and 6.0 would be reasonable. The results suggest that after 6 years of education (the beginning of a secondary level in many countries, around the age of 12), there is a change in the relationship between education and health. The Chow test confirms that coefficients differ across models below and above
Estimated average DALE
80.00 70.00 60.00 50.00 40.00 30.00 using eq all using eq low using eq high
20.00 10.00 0.00 0
5 10 Years of schooling
15
Fig. 1. Simulated impact of years of schooling on disability-free life expectancy—regression results using all countries, loweducation countries only, and high-education countries only.
Table 2 OLS coefficients, Health (disability adjusted life expectancy) as a function of expenditure and education, trans-log specification, estimations run on two set of countries, across the cut-off value at 5.8 years of education (standard errors in parentheses)
Intercept Ln(health expenditure) Ln(education) [Ln(education)]2 N (countries with non-missing data) R2 (adjusted)
Low-education countries (educ o5.8)
High-education countries (educ 45.8)
All countries
2.68 (0.16) 0.24 (0.04) 0.24 (0.14) 0.12 (0.08) 54 0.60
0.59 (1.58) 0.05 (0.02) 4.10 (1.52) 0.94 (0.36) 57 0.38
3.12 (0.09) 0.11 (0.02) 0.25 (0.08) 0.04 (0.03) 111 0.68
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
it is at 4 years of education. However, when separate estimates are obtained for low- and higheducation countries, the marginal rate of return for low-education countries begins diminishing at around 3 years of education while the rate of return in high-education countries begins decreasing at around 6 years of education and is 0 at the base level of 9. These OLS estimates suggest that raising schooling above 3 years would not yield benefit in health among low-education countries, and raising it above 9 years would not yield benefit in health among high-education countries. As shown below (Section 5.2), frontier analysis yields the same conclusion among high-education countries, but the rate of return on maximum (rather than average) health is never zero among low-education countries. The model fits these data reasonably well even among the subset of high-education countries (R2 ¼ 38%). This contrasts with the findings of Hollingsworth and Wildman (2003) for OECD countries. In this case, the set of high-education countries includes most of the OECD countries, but also 32 non-OECD countries, indicating that heterogeneity and inconstancy in parameters derive from the level of education input rather than other differences such as income per capita.1 5.2. Implications of heterogeneity for assessing health system efficiency Studies linking education to health at the aggregate level often do so in the context of evaluating heath system performance. Such studies are more interested in the health frontier—maximum health outcome attainable—than in the average relationship. I therefore estimate two production frontiers, one each for low- and high-education countries as defined by the cut-off identified by the previous (OLS) analysis (Fig. 2).2 I then compare the country1 All OECD are in the high-education regime, except for Turkey and Portugal; Luxembourg is not in the sample as no information is available for 1995. 2 I applied to the frontier estimation the regime-testing procedure suggested by Kennedy to identify two regimes (split the sample using a cut-off point, estimate on each separate subsample, then calculate the log-likelihood). The results are less clear-cut than for the OLS procedure: for some cut-off points, convergence is not achieved, and there are local maximums. However, the cut-off corresponding to the overall maximum is close to the one selected by the OLS procedure. I therefore use the same cut-off point to re-estimate the frontier on two sets of countries.
Impact of schooling: frontiers Estimated maximum DALE
304
90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
using eq low using eq high
0
5 10 years of schooling
15
Fig. 2. Simulated impact of years of schooling on disability-free life expectancy—frontier results using low-education countries only, and high-education countries only.
Table 3 Values of coefficients estimated on both subsets of countries for the production frontiers Coefficient
Low-education countries (below 5.8) N ¼ 54
High-education countries (above 5.8) N ¼ 57
Intercept Expenditure Education Education, squared Sigma v (random term) Sigma u (efficiency term)
+3.35 +0.10 +0.29 0.05 0.028 0.276
+1.09 +0.04 +2.78 0.65 0.000 0.138
specific efficiency values and rankings derived from these two frontiers with those derived from a single frontier estimated on all countries simultaneously. Table 3 presents the results of the estimation of the production frontiers. In both cases, the coefficient for the squared value of education is negative, indicating diminishing returns of education on health. The impact of education on the maximum attainable level of health differs across the subsets. In low-education countries, adding 1 year of education on a base of 5.8 adds 1.02 years to DALE, and the marginal impact of education on health becomes negative above approximately 20 years of education. These findings differ from those of the OLS, and imply positive health returns at all observable average levels of education. In higheducation countries, adding 1 year of education on a base of 5.8 adds 1.06 years to DALE, but the rate of returns decreases steadily to 0 at 8.48 years (exp(2.78/1.30)). These results imply no gain in
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
population health to investing in education beyond an average of 9 years in high-education countries. Among high-education countries, the frontier is almost deterministic, with a very small variance for the pure random part of the error term (and consequently, a very high value for the ratio of the inefficiency variance to the random variance). This baseline estimation assumes the normal– half-normal distribution for the error term. The estimates are robust to both normal–exponential and normal–truncated distributional assumptions (see Grignon (2006) for details). 5.2.1. Impact on technical efficiency and countries rankings Table 4 presents the calculated levels of technical efficiency and the health system rankings based on the estimates from Table 3 under two assumptions: (1) separate regimes for low- and high-education countries; and (2) single regime among all countries. Overall, most efficiency scores are left unaffected by the treatment of heterogeneity: the correlation between the two arrays of efficiency scores is .95
305
and the Spearman’s rank correlation is .79. However, using the single-regime model dramatically affects the score and ranking of a subset of 21 countries. Countries such as Norway, the USA, New Zealand, Canada, and Sweden rank much higher under two-regime estimation which imputes low health returns to high average education levels. A distinguishing feature of this subset of 21 countries is their high level of education, with an average of 9.6 years (compared to an overall average of 6.0); for each country in this subset, exception of Kuwait, Paraguay, and Croatia, the average years of education exceeds 9 years, the level above which the health return is zero or negative. The two-regime ranking likely gives a fairer assessment of health system performance in higheducation countries than do rankings based on frontiers estimated over a heterogeneous set of countries. Note, however, that it still suffers from some other deficiencies of the production frontier approach as developed by the WHO and calls for further inquiry into aggregate health–education relationship.
Table 4 Comparison of efficiency when estimation is based on one frontier (no correction for heterogeneity) and when it is based on two frontiers (correction for heterogeneity) Country
Technical efficiency frontier using two subsets
Technical efficiency frontier using one set
Ranking, frontier using two subsets
Ranking, frontier using one set
Difference in ranking
Norway United States of America New Zealand Canada Sweden Switzerland Australia Germany Kuwait Finland Israel Belgium Netherlands Republic of Korea Croatia Denmark Poland Ireland Japan Paraguay Czech Republic
1.000 0.957
0.894 0.841
3 23
68 87
65 64
0.965 0.992 0.999 0.957 0.992 0.928 0.938 0.936 0.935 0.941 0.943 0.898 1.000 0.911 0.923 0.927 0.980 0.961 0.928
0.873 0.897 0.909 0.896 0.923 0.877 0.897 0.900 0.904 0.914 0.915 0.846 0.963 0.878 0.903 0.905 0.949 0.938 0.909
19 7 5 22 6 45 38 40 42 33 32 69 1 60 48 47 12 21 44
77 62 55 66 41 75 64 61 59 48 47 84 14 73 60 58 23 32 54
58 55 50 44 35 30 26 21 17 15 15 15 13 13 12 11 11 11 10
Only countries with a higher efficiency and ranking in the corrected estimation than in the un-corrected one are listed below; countries are sorted according to the difference in ranking across the two methods of estimation.
ARTICLE IN PRESS 306
M. Grignon / Economics of Education Review 27 (2008) 299–307
6. Conclusions Using data at the country level and a production frontier framework built upon that developed by the WHO in its World Health Report 2000, I show that the impact of education on health follows two different regimes across a threshold of 5.8 years of education on average. This should be further tested with other data, focusing on the functional relationship between schooling and health rather than on the mere presence of the effect, but it suggests that the marginal impact of schooling on health is smaller when the population has received 8 years of schooling on average. These results based on aggregated data are consistent with the limited individual-level studies showing stabilization or even a decrease in health between those having completed high school and those with more education. The second finding from this study is that the evaluation of efficiency of health care at the national level using country-level data must use more flexible specifications to allow heterogeneity in the education–health relationship, and more generally should be based on a thorough assessment of the estimated production function, including the role of human capital on health. Lastly, it should be noted that, while the present study focuses on the extent of two different regimes in the relationship between education and health at the country level, no attempt has been made to correct for reciprocal causality of health on human capital. In this I have followed a course well established in the field of health system performance assessment; however, correcting for such endogeneity in estimating the health production function would add to the robustness of the analysis and allow for more policyoriented recommendations based on its findings. Acknowledgements I express my gratitude to Bruce Colburn, Paul Contoyannis (McMaster University), Agne`s Couffinhal (World Bank, Washington DC), Claude Grignon (MSH, Paris), Jerry Hurley (McMaster University), Zeynep Or (IRDES, Paris), Simone Sandier (IRDES, Paris), and Peter Smith (CHE, York, England).
References Adelman, I. (1963). An econometric analysis of population growth. American Economic Review, 52, 314–339.
Auld, M. C., & Sidhu, N. (2005). Schooling, cognitive ability and health. Health Economics, 14, 1019–1034. Auster, R., Leveson, I., & Sarachek, D. (1969). The production of health, an exploratory study. The Journal of Human Resources, 4(4), 411–436. Barro, R., & Lee, J. -W. (2000). International data on educational attainment; updates and implications. NBER Working Paper Series 7911. National Bureau of Economic Research. Caldwell, J. C. (1993). Health transition: The cultural, social and behavioural determinants of health in the Third World. Social Science and Medicine, 36(2), 125–135. Curtin, T. R. C., & Nelson, E. A. S. (1999). Economic and health efficiency of education funding policy. Social Science and Medicine, 48, 1599–1611. Deininger, K. W., & Squire, L. (unknown). Measuring income inequality database, World Bank, accessed May 2006 on /http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/ EXTRESEARCH/0,,contentMDK:20699070pagePK:64214825~ piPK:64214943~theSitePK:469382,00.htmlS Duleep, H. O. (1986). Measuring the effect of income on adult mortality using longitudinal administrative record data. Journal of Human Resources, 21, 238–251. Evans, D. B., Tandon, A. C., Murray, J. L., & Lauer, J. A. (2000). The comparative efficiency of national health systems in producing health: An analysis of 191 countries. WHO, GPE Discussion Paper 29, Geneva. Gilleskie, D. B., & Harrison, A. L. (1998). The effect of endogenous health inputs on the relationship between health and education. Economics of Education Review, 17(3), 279–295. Gravelle, H., Jacobs, R., Jones, A. M., & Street, A. (2002). Comparing the efficiency of national health care systems. CHE Technical Paper Series 25. York, UK: University of York, Center for Health Economics. Greene, W. (2004). Distinguishing between heterogeneity and inefficiency: Stochastic frontier analysis of the WHO’s panel data on national health care systems. Health Economics, 13, 959–980. Grignon, M. (2001). Is the French system really the most efficient? A propos World Health Report 2000. Mimeo CREDES (IRDES), Paris, IRDES. Grignon, M. (2006). CHEPA. The role of education in health system performance—A Propos World Health Report 2000. Working Papers Series 06–03. Center for Health Economics and Policy Analysis, McMaster University. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80, 223–255. Grossman, M. (1975). The correlation between health and schooling. In E. Nestor (Ed.), Household production and consumption (pp. 147–211). Terleckyj, NY: Columbia University Press. Grossman, M. (2000). The human capital model. In Antony J. Culyer, & Joseph P. Newhouse (Eds.), Handbook of health economics (pp. 347–408). Amsterdam: North-Holland (Chapter 7). Grossman, M., & Kaestner, R. (1997). Effects of education on health. In J. R. Behrman, & N. Stacey (Eds.), The social benefits of education (pp. 69–124). Ann Arbor: University of Michigan Press (Chapter 4). Hildebrand, V., & Van Kerm, Ph. (2005). Income inequality and self-rated health status: Evidence from the European community household panel. SEDAP Research Paper, #127. McMaster University.
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307 Hollingsworth, B., & Wildman, J. (2003). The efficiency of health production: Reestimating the WHO panel data using parametric and nonparametric approaches to provide additional information. Health Economics, 12, 493–504. Jamison, D. T., Sandbu, M., & Wang, J. (2001). Cross country variation in mortality decline, 1962– 87: The role of country specific technical progress. CMH Working Paper No. WGI: 4. Geneva: WHO, Commission on Macroeconomics and Health. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2003). Governance Matters III: Governance indicators for 1996– 2002. World Bank Policy Research Department Working Paper. Kennedy, P. (2003). A guide to econometrics (5th ed.). Cambridge, MA: MIT Press. Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. Cambridge: Cambridge University Press. Murray, C. J. L., & Lopez, A. D. (1996). The global burden of disease, Harvard School of Public Health on behalf of the
307
WHO and the World Bank. Cambridge: Harvard University Press. Newhouse, J., & Friedlander, L. J. (1980). The relationship between medical resources and measures of health: Some additional evidence. The Journal of Human Resources, 15(2), 200–217. Ross, C. E., & Mirowsky, J. (1999). Refining the association between education and health: The effects of quantity, credential, and selectivity. Demography, 36(4), 445–460. Scientific Peer Review Group (2002). Efficiency Report of SPRG on HSPA (Health System Performance Assessment). Geneva: World Health Organization. World Bank (2004). World development indicators 2004. Washington, DC. World Health Organization (2000). The world health report 2000. Health performance: Improving performance. Geneva: World Health Organization.
Economics of Education Review 27 (2008) 299–307 www.elsevier.com/locate/econedurev
The role of education in health system performance Michel Grignon Department of Economics and Department of Health, Aging, and Society, McMaster University, KTH 232, 1280 Main Street West, Hamilton, Ont., Canada L8S 4M4 Received 29 August 2005; accepted 30 November 2006
Abstract I investigate the role of education on health, using country-level data and the production frontier framework suggested by the World Health Organization (WHO) to assess performances of health care systems. I find that the impact of human capital on health is much smaller than suggested by the WHO frontier model, and the relationship exhibits diminishing return in the observed range of education. Taking into account the heterogeneity in this relationship generates a different ranking of the efficiency of health care systems internationally. This suggests that the method currently used by the WHO favors health care systems operating in countries that underinvested in education in the past. r 2007 Elsevier Ltd. All rights reserved. JEL Classification: I18 Keywords: Human capital; Rate of return; Economic impact; Efficiency
1. Introduction This study investigates the role of education in the production of health, and in particular, heterogeneity in the marginal impact of education on health depending on the baseline level of education. The work is motivated by two concerns. The first is the surprising logical implication of the education– health relationship as estimated by the World Health Organization (WHO) in its World Health Report 2000 (WHO, 2000), which posited a single education–health relationship for both highly developed and underdeveloped countries. The increasing marginal return of education on health found by the WHO implies that all countries would improve Tel.: +1 905 525 9140x23493; fax: +1 905 525 4198.
E-mail address: [email protected]. 0272-7757/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2006.11.001
population health substantially if they redistributed monies from their health care system to the education system. The second is the relative neglect of the shape of the education–health relationship in the empirical literature, which to date has emphasized identifying a causal effect of education on health (Grossman & Kaestner, 1997). I hypothesize that the marginal impact of education on health differs below and above a threshold level of education. I test this hypothesis using an aggregate, country-level population health production function. I estimate the impact of education on health separately in low- and high-education countries, where the threshold education level that divides the sample is set so as to minimize the sum of squared errors across the two equations. The results strongly support differing health returns to education in the low- and high-education samples.
ARTICLE IN PRESS 300
M. Grignon / Economics of Education Review 27 (2008) 299–307
I then illustrate the importance of this heterogeneity for assessing health system performance in the same spirit as WHO (2000). Failing to account for this heterogeneity overestimates the health possibility frontier among countries with high-educational levels, and therefore underestimates the performance of their health care systems. More generally, however, a better understanding of the shape of the relationship between education and health can inform policies guiding public spending in education and health. 2. The shape of the relationship between human capital and health: what do we know? 2.1. Individual-level analyses of the human capital– health relationship One strand of empirical literature concerning the return of education on health stems from Grossman’s seminal paper on health capital (Grossman, 1972). This empirical literature has attempted to disentangle the causal impact of education on health from a mere correlation between the two. The literature has paid relatively little attention to the shape of the education–health relationship (e.g., linear, convex, or concave). Although Grossman (1975) suggested that, unlike income, education continues to influence health even at high-education levels, this proposition is absent from more recent reviews by Grossman and Kaestner (1997) and Grossman (2000). There appears to be little systematic investigation of the form of the relationship based on individual-level studies. Some studies (e.g., Curtin & Nelson, 1999; Duleep, 1986; Ross & Mirowsky, 1999) offer suggestive evidence from disparate contexts, but Gilleskie and Harrison (1998) and Auld and Sidhu (2005) constitute perhaps the strongest individual-level studies that estimated the relationship between education and health at various levels of education while controlling for other factors (age, race, income, cognitive ability, and body mass index). These two studies suggest that the relationship between education and health is non-linear, and somewhat concave. 2.2. Aggregate analyses of the human capital– health relationship The literature on aggregate analyses of the link between human capital and health stems from a different tradition, started by Adelman (1963). This
literature focuses on the impact of health care services on health outcomes, and treats education as an ‘environmental’ factor to be controlled for along with income, life-style indicators, or geographical location. The studies generally estimate an aggregate health production function, where population health in each geographical area is measured by mortality (or sometimes the prevalence of a given condition), the density of physicians or hospital beds is the factor of interest, and education is measured as the average number of years of schooling. The specifications often deal inadequately with issues such as the time lags between the determinants and health outcomes (Grignon, 2001; SPRG, 2002), potential non-linearities (all these studies assume that the link between schooling and health is linear), and the potential endogeneity of education (though Auster, Leveson, & Sarachek, 1969 addressed potential endogeneity of health care expenditures). As summarized by Newhouse and Friedlander (1980), the effect of education often appears significant in this literature, mostly based on comparisons across regions of the United States. By contrast, the impact of medical care appears small and insignificant. Among developing countries, the health transition literature demonstrates that women’s literacy is strongly associated with reduced infant mortality (Caldwell, 1993). The more recent literature estimate production frontiers that allow for non-linearities in the relationship between education and health. In its World Health Report 2000, the WHO used repeated observations (yearly, from 1993 to 1997) on a set of 191 countries to estimate a translog two-factor (health expenditure and number of years of schooling) production function in which a quadratic relationship was estimated for each factor, as well as an interaction term (Evans, Tandon, Murray, & Lauer, 2000). This flexible functional form yields a better estimate of the rates of return of education on health at various levels of education. Because the focus of this literature is ‘‘performance’’ (or technical efficiency, Kumbhakar & Lovell, 2000) Evans et al. (2000) estimate a stochastic production frontier: instead of the average relationship between human capital and health, they calculate the maximum amount of health a country could be expected to produce given the average number of years of education of its population. Their estimates imply increasing returns of education so that the impact on health of increasing schooling by 1 year is larger in a country with an average of 12 years of
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
schooling than in a country with an average 5 years of schooling. This finding, however, has been heavily criticized and does not appear to be robust. Evans et al. (2000) employ a fixed-effect (or within) estimator, which decomposes the error term into a time invariant term, measuring the countryspecific deviation from the average linear relation between inputs and outputs, and a pure random error term, varying over time for the same country. Gravelle, Jacobs, Jones, and Street (2002), reestimate the production frontier using the same data as Evans et al. (2000) but using a ‘‘between’’ estimator (which pools data across years for each country) and obtain a diminishing rather than increasing rate of return of education on health. Moreover, their model appears to better fit the data (due mainly to the very small variation over time within countries in key variables, which is the only source of variation the within estimator exploits). In addition, Hollingsworth and Wildman (2003) show that, even when they use a within estimator, the production frontier among OECD countries exhibits diminishing returns of education on health. The impact on health of increasing population schooling from 4 to 5 years is more than double the impact of one more year of schooling from 8 to 9 years. Their estimates also suggest the kind of heterogeneity that is the focus of this analysis. In their analysis based on the within estimator, the coefficient for squared number of years of education is negative among OECD countries and positive among non-OECD countries. Since OECD countries also are characterized by a much higher level of education than non-OECD countries (8.93 years on average versus 5.30 years), there are good reasons to suspect this heterogeneity simply reflects a decreasing return of education on health beyond a threshold level of education. In this work, I directly test for such heterogeneity by allowing two distinct regimes in the relationship between human capital and health, separated by a cut-off level of human capital rather than a correlated, extraneous characteristic (such as being an OECD country or having a minimum level of income per capita). 3. Methods The primary objective of this analysis is to investigate the shape of the education–health relationship. I test whether the relationship between education and health differs across low- and high-
301
education countries, and determine the cut-off point in the distribution of education separating the two sets of countries. The relationship between education and health is homogeneous within each set, but differs across the two sets. As is described in more detail below, the study uses aggregate data on health, educational attainment and other health determinants from a crosssection of 111 countries. Following Hollingsworth and Wildman (2003), I estimate two sets of equations; however, instead of separating two sets of countries using an external threshold (being an OECD member) I separate countries based on their average levels of education. Because my main concern in this study is with the heterogeneity of the relationship between education and health, I keep the baseline simple: I use the translog specification with health outcome (H) on the lefthand side of the equation, a measure of health care (M), education (E) and its squared value (E2) on the right-hand side (other factors are included in the sensitivity analysis). The equation to be estimated using OLS is as follows: H i ¼ B0 þ B1 M i þ B2 E i þ B3 E 2i þ i . I test for heterogeneity using a standard misspecification test known as parameter inconstancy (Kennedy, 2003). The procedure is as follows: first, I split the population of 111 countries into two subsets, one comprised of countries with values for E below a threshold and one comprised of countries with values of E above the threshold. I estimate the model under varying levels of the threshold and identify the threshold value of E that minimizes the sum of SSR (sum of squared residuals) across both equations. The cut-off value is therefore the value that separates the data into two regimes that best fit a piece-wise quadratic relationship between health and the inputs. I then use a Chow test to test whether the coefficients associated with each regime are different, which implies differing input–health relationship above and below the threshold. Although more sophisticated non-parametric procedures can be used to identify such distinct regimes, I rely on this simpler approach both because non-parametric approaches would not work with only 111 observations and because I subsequently use the specified relationship to estimate a production frontier, I must keep the specification reasonably simple. To illustrate the impact of incorrectly assuming homogeneity in the relationship across low- and
ARTICLE IN PRESS 302
M. Grignon / Economics of Education Review 27 (2008) 299–307
high-education countries, I estimate two production frontiers, one for countries with educational attainment below the cut-off point and one for countries above, and compare the resulting efficiency rankings of national health care systems with those derived from the mis-specified model that assumes homogeneity. Among stochastic frontier estimates, the composed error model is the natural choice with crosssectional data. Following Kumbhakar and Lovell (2000), in my primary model I use a ‘‘normal halfnormal’’ hypothesis for the decomposable error term in which the purely random term is normal and the efficiency term follows a half-normal nonpositive distribution. They show that in most of the cases imposing a more flexible constraint on the distribution of the error term (such as truncated distributions) adds computation difficulties but does not yield substantially different estimates. However, as a sensitivity analysis (see Section 5.2), I reestimate the frontier under several distributional assumptions. In the composed error model, the following equation is estimated:
negative, and vi is the random component in the error term. The sum ei ¼ (ui+vi) is skewed, indicating potential inefficiency. This generates a health frontier that represents the maximum achievable level of H for a given country; the efficiency of the country’s health care system is the ratio of its observed level of H to the maximum achievable. For each country, technical inefficiency is given by TEi ¼ Eðexpðui Þ ei Þ (Kumbhakar & Lovell, 2000).
H i ¼ A0 þ A1 M i þ A2 E i þ A3 E 2i þ ui þ vi ,
4.2. Independent variables
where ui is the efficiency component in the error term of the regression, constrained to be non-
Education in a country is measured as the average number of years of schooling in the population aged
4. Data and variable specification The key features of the data and specification are presented here. More details are in Grignon (2006). 4.1. Dependent variable The output, health (H), is measured as disability adjusted life expectancy (DALE), defined as the life expectancy at birth minus the years of life ‘‘lost’’ due to impairment or disability when lived in a less than perfect health state (Murray & Lopez, 1996).
Table 1 Descriptive statistics, means of the main variables used in the estimation Variables
191 countries
Non missing years of education (N ¼ 111)
High education level (N ¼ 57)
Low education level (N ¼ 54)
Disability adjusted life expectancy (years)1 1997
56.8 442.4 6.0 7385.4 40.07 73.63
57.3 553.6 6.0 8783.6 40.74 74.40
66.1 945.1 8.3 13898.8 37.26 88.39
48.1 140.3 3.6 3054.6 44.75 64.12
79.07
80.78
91.60
72.12
94.45
96.21
101.39
89.56
0.01 0.02 0.03 0.02 0.03 0.03
+0.10 +0.03 +0.19 +0.28 +0.19 +0.20
+0.68 +0.62 +0.84 +0.77 +0.83 +0.87
0.51 0.59 0.49 0.24 0.48 0.52
Years of education 19952 Gini: last available year4 Improved sanitation facilities (% of population with access) 20005 Improved water source (% of population with access) 20005 Ratio of girls to boys in primary and secondary education (%) 19985 Governance: voice3 Governance: political stability3 Governance: government effectiveness3 Governance: regulatory quality3 Governance: rule of law3 Governance: control of corruption3
Sources: (1)World Health Organization (2000), World Health Report 2000 (2) Barro and Lee (2000), (3) Kaufmann, Kraay, and Mastruzzi (2003), (4) Deininger and Squire (unknown), (5) World Development Indicators (World Bank, 2004).
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
15 and over in 1995, as published by Barro and Lee (2000). Barro and Lee provide data for 112 countries in 1995, 111 of which are in the WHR00 panel. Health care resources are measured as annual health care expenditures. In addition, in sensitivity analyses, I use other controlling factors potentially correlated with education and health outcome including income per capita, income inequality, quality of governance, and environmental and sanitation measures (Greene 2004; Hildebrand & Van Kerm, 2005). I also test the use of female education, measured as the sex ratio in schools, as an alternative measure of education (Jamison, Sandbu, & Wang, 2001). All independent variables are entered as natural logarithms, except for the six governance variables, which take values between 4 and +4. Descriptive statistics for all variables are provided in Table 1. 5. Results
303
the threshold of 5.8, with a Fisher statistic F(4,103) equal to 6.45 which is significant at a level below 0.1%. Table 2 presents the coefficients estimated in each subset of countries defined by the threshold of 5.8 years of education (Grignon, 2006, provides the full set of results using all control variables, as well as the results of two robust regressions. Conclusions are not qualitatively affected by adding controls or running robust regressions). The strong negative value of the coefficient on square number of years of education among countries above the cut-off point suggests a non-linear relationship between education and health and Fig. 1 illustrates the heterogeneity in the impact of education on health at the sample mean level of health care expenditures. When the relationship is estimated over the full sample of 111 countries, the rate of return diminishes then stabilizes at a positive value, so the impact on health of adding 1 year of schooling is approximately the same at 10 years of education as
5.1. Baseline analysis—heterogeneity and the shape of the education– health relationship
Impact of schooling sample means
I tested for an education threshold in the range 4.09–7.25 years of education, which would split the data set into two subsets of reasonable size (neither below 30). The results indicate that 5.8 years of education is a reasonable cut-off point between two regimes of the relationship between education and health (detailed results are available in Grignon, 2006). The best cut-off from a linear model-fitting perspective is 5.8, though any value between 5.5 and 6.0 would be reasonable. The results suggest that after 6 years of education (the beginning of a secondary level in many countries, around the age of 12), there is a change in the relationship between education and health. The Chow test confirms that coefficients differ across models below and above
Estimated average DALE
80.00 70.00 60.00 50.00 40.00 30.00 using eq all using eq low using eq high
20.00 10.00 0.00 0
5 10 Years of schooling
15
Fig. 1. Simulated impact of years of schooling on disability-free life expectancy—regression results using all countries, loweducation countries only, and high-education countries only.
Table 2 OLS coefficients, Health (disability adjusted life expectancy) as a function of expenditure and education, trans-log specification, estimations run on two set of countries, across the cut-off value at 5.8 years of education (standard errors in parentheses)
Intercept Ln(health expenditure) Ln(education) [Ln(education)]2 N (countries with non-missing data) R2 (adjusted)
Low-education countries (educ o5.8)
High-education countries (educ 45.8)
All countries
2.68 (0.16) 0.24 (0.04) 0.24 (0.14) 0.12 (0.08) 54 0.60
0.59 (1.58) 0.05 (0.02) 4.10 (1.52) 0.94 (0.36) 57 0.38
3.12 (0.09) 0.11 (0.02) 0.25 (0.08) 0.04 (0.03) 111 0.68
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
it is at 4 years of education. However, when separate estimates are obtained for low- and higheducation countries, the marginal rate of return for low-education countries begins diminishing at around 3 years of education while the rate of return in high-education countries begins decreasing at around 6 years of education and is 0 at the base level of 9. These OLS estimates suggest that raising schooling above 3 years would not yield benefit in health among low-education countries, and raising it above 9 years would not yield benefit in health among high-education countries. As shown below (Section 5.2), frontier analysis yields the same conclusion among high-education countries, but the rate of return on maximum (rather than average) health is never zero among low-education countries. The model fits these data reasonably well even among the subset of high-education countries (R2 ¼ 38%). This contrasts with the findings of Hollingsworth and Wildman (2003) for OECD countries. In this case, the set of high-education countries includes most of the OECD countries, but also 32 non-OECD countries, indicating that heterogeneity and inconstancy in parameters derive from the level of education input rather than other differences such as income per capita.1 5.2. Implications of heterogeneity for assessing health system efficiency Studies linking education to health at the aggregate level often do so in the context of evaluating heath system performance. Such studies are more interested in the health frontier—maximum health outcome attainable—than in the average relationship. I therefore estimate two production frontiers, one each for low- and high-education countries as defined by the cut-off identified by the previous (OLS) analysis (Fig. 2).2 I then compare the country1 All OECD are in the high-education regime, except for Turkey and Portugal; Luxembourg is not in the sample as no information is available for 1995. 2 I applied to the frontier estimation the regime-testing procedure suggested by Kennedy to identify two regimes (split the sample using a cut-off point, estimate on each separate subsample, then calculate the log-likelihood). The results are less clear-cut than for the OLS procedure: for some cut-off points, convergence is not achieved, and there are local maximums. However, the cut-off corresponding to the overall maximum is close to the one selected by the OLS procedure. I therefore use the same cut-off point to re-estimate the frontier on two sets of countries.
Impact of schooling: frontiers Estimated maximum DALE
304
90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00
using eq low using eq high
0
5 10 years of schooling
15
Fig. 2. Simulated impact of years of schooling on disability-free life expectancy—frontier results using low-education countries only, and high-education countries only.
Table 3 Values of coefficients estimated on both subsets of countries for the production frontiers Coefficient
Low-education countries (below 5.8) N ¼ 54
High-education countries (above 5.8) N ¼ 57
Intercept Expenditure Education Education, squared Sigma v (random term) Sigma u (efficiency term)
+3.35 +0.10 +0.29 0.05 0.028 0.276
+1.09 +0.04 +2.78 0.65 0.000 0.138
specific efficiency values and rankings derived from these two frontiers with those derived from a single frontier estimated on all countries simultaneously. Table 3 presents the results of the estimation of the production frontiers. In both cases, the coefficient for the squared value of education is negative, indicating diminishing returns of education on health. The impact of education on the maximum attainable level of health differs across the subsets. In low-education countries, adding 1 year of education on a base of 5.8 adds 1.02 years to DALE, and the marginal impact of education on health becomes negative above approximately 20 years of education. These findings differ from those of the OLS, and imply positive health returns at all observable average levels of education. In higheducation countries, adding 1 year of education on a base of 5.8 adds 1.06 years to DALE, but the rate of returns decreases steadily to 0 at 8.48 years (exp(2.78/1.30)). These results imply no gain in
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307
population health to investing in education beyond an average of 9 years in high-education countries. Among high-education countries, the frontier is almost deterministic, with a very small variance for the pure random part of the error term (and consequently, a very high value for the ratio of the inefficiency variance to the random variance). This baseline estimation assumes the normal– half-normal distribution for the error term. The estimates are robust to both normal–exponential and normal–truncated distributional assumptions (see Grignon (2006) for details). 5.2.1. Impact on technical efficiency and countries rankings Table 4 presents the calculated levels of technical efficiency and the health system rankings based on the estimates from Table 3 under two assumptions: (1) separate regimes for low- and high-education countries; and (2) single regime among all countries. Overall, most efficiency scores are left unaffected by the treatment of heterogeneity: the correlation between the two arrays of efficiency scores is .95
305
and the Spearman’s rank correlation is .79. However, using the single-regime model dramatically affects the score and ranking of a subset of 21 countries. Countries such as Norway, the USA, New Zealand, Canada, and Sweden rank much higher under two-regime estimation which imputes low health returns to high average education levels. A distinguishing feature of this subset of 21 countries is their high level of education, with an average of 9.6 years (compared to an overall average of 6.0); for each country in this subset, exception of Kuwait, Paraguay, and Croatia, the average years of education exceeds 9 years, the level above which the health return is zero or negative. The two-regime ranking likely gives a fairer assessment of health system performance in higheducation countries than do rankings based on frontiers estimated over a heterogeneous set of countries. Note, however, that it still suffers from some other deficiencies of the production frontier approach as developed by the WHO and calls for further inquiry into aggregate health–education relationship.
Table 4 Comparison of efficiency when estimation is based on one frontier (no correction for heterogeneity) and when it is based on two frontiers (correction for heterogeneity) Country
Technical efficiency frontier using two subsets
Technical efficiency frontier using one set
Ranking, frontier using two subsets
Ranking, frontier using one set
Difference in ranking
Norway United States of America New Zealand Canada Sweden Switzerland Australia Germany Kuwait Finland Israel Belgium Netherlands Republic of Korea Croatia Denmark Poland Ireland Japan Paraguay Czech Republic
1.000 0.957
0.894 0.841
3 23
68 87
65 64
0.965 0.992 0.999 0.957 0.992 0.928 0.938 0.936 0.935 0.941 0.943 0.898 1.000 0.911 0.923 0.927 0.980 0.961 0.928
0.873 0.897 0.909 0.896 0.923 0.877 0.897 0.900 0.904 0.914 0.915 0.846 0.963 0.878 0.903 0.905 0.949 0.938 0.909
19 7 5 22 6 45 38 40 42 33 32 69 1 60 48 47 12 21 44
77 62 55 66 41 75 64 61 59 48 47 84 14 73 60 58 23 32 54
58 55 50 44 35 30 26 21 17 15 15 15 13 13 12 11 11 11 10
Only countries with a higher efficiency and ranking in the corrected estimation than in the un-corrected one are listed below; countries are sorted according to the difference in ranking across the two methods of estimation.
ARTICLE IN PRESS 306
M. Grignon / Economics of Education Review 27 (2008) 299–307
6. Conclusions Using data at the country level and a production frontier framework built upon that developed by the WHO in its World Health Report 2000, I show that the impact of education on health follows two different regimes across a threshold of 5.8 years of education on average. This should be further tested with other data, focusing on the functional relationship between schooling and health rather than on the mere presence of the effect, but it suggests that the marginal impact of schooling on health is smaller when the population has received 8 years of schooling on average. These results based on aggregated data are consistent with the limited individual-level studies showing stabilization or even a decrease in health between those having completed high school and those with more education. The second finding from this study is that the evaluation of efficiency of health care at the national level using country-level data must use more flexible specifications to allow heterogeneity in the education–health relationship, and more generally should be based on a thorough assessment of the estimated production function, including the role of human capital on health. Lastly, it should be noted that, while the present study focuses on the extent of two different regimes in the relationship between education and health at the country level, no attempt has been made to correct for reciprocal causality of health on human capital. In this I have followed a course well established in the field of health system performance assessment; however, correcting for such endogeneity in estimating the health production function would add to the robustness of the analysis and allow for more policyoriented recommendations based on its findings. Acknowledgements I express my gratitude to Bruce Colburn, Paul Contoyannis (McMaster University), Agne`s Couffinhal (World Bank, Washington DC), Claude Grignon (MSH, Paris), Jerry Hurley (McMaster University), Zeynep Or (IRDES, Paris), Simone Sandier (IRDES, Paris), and Peter Smith (CHE, York, England).
References Adelman, I. (1963). An econometric analysis of population growth. American Economic Review, 52, 314–339.
Auld, M. C., & Sidhu, N. (2005). Schooling, cognitive ability and health. Health Economics, 14, 1019–1034. Auster, R., Leveson, I., & Sarachek, D. (1969). The production of health, an exploratory study. The Journal of Human Resources, 4(4), 411–436. Barro, R., & Lee, J. -W. (2000). International data on educational attainment; updates and implications. NBER Working Paper Series 7911. National Bureau of Economic Research. Caldwell, J. C. (1993). Health transition: The cultural, social and behavioural determinants of health in the Third World. Social Science and Medicine, 36(2), 125–135. Curtin, T. R. C., & Nelson, E. A. S. (1999). Economic and health efficiency of education funding policy. Social Science and Medicine, 48, 1599–1611. Deininger, K. W., & Squire, L. (unknown). Measuring income inequality database, World Bank, accessed May 2006 on /http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/ EXTRESEARCH/0,,contentMDK:20699070pagePK:64214825~ piPK:64214943~theSitePK:469382,00.htmlS Duleep, H. O. (1986). Measuring the effect of income on adult mortality using longitudinal administrative record data. Journal of Human Resources, 21, 238–251. Evans, D. B., Tandon, A. C., Murray, J. L., & Lauer, J. A. (2000). The comparative efficiency of national health systems in producing health: An analysis of 191 countries. WHO, GPE Discussion Paper 29, Geneva. Gilleskie, D. B., & Harrison, A. L. (1998). The effect of endogenous health inputs on the relationship between health and education. Economics of Education Review, 17(3), 279–295. Gravelle, H., Jacobs, R., Jones, A. M., & Street, A. (2002). Comparing the efficiency of national health care systems. CHE Technical Paper Series 25. York, UK: University of York, Center for Health Economics. Greene, W. (2004). Distinguishing between heterogeneity and inefficiency: Stochastic frontier analysis of the WHO’s panel data on national health care systems. Health Economics, 13, 959–980. Grignon, M. (2001). Is the French system really the most efficient? A propos World Health Report 2000. Mimeo CREDES (IRDES), Paris, IRDES. Grignon, M. (2006). CHEPA. The role of education in health system performance—A Propos World Health Report 2000. Working Papers Series 06–03. Center for Health Economics and Policy Analysis, McMaster University. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80, 223–255. Grossman, M. (1975). The correlation between health and schooling. In E. Nestor (Ed.), Household production and consumption (pp. 147–211). Terleckyj, NY: Columbia University Press. Grossman, M. (2000). The human capital model. In Antony J. Culyer, & Joseph P. Newhouse (Eds.), Handbook of health economics (pp. 347–408). Amsterdam: North-Holland (Chapter 7). Grossman, M., & Kaestner, R. (1997). Effects of education on health. In J. R. Behrman, & N. Stacey (Eds.), The social benefits of education (pp. 69–124). Ann Arbor: University of Michigan Press (Chapter 4). Hildebrand, V., & Van Kerm, Ph. (2005). Income inequality and self-rated health status: Evidence from the European community household panel. SEDAP Research Paper, #127. McMaster University.
ARTICLE IN PRESS M. Grignon / Economics of Education Review 27 (2008) 299–307 Hollingsworth, B., & Wildman, J. (2003). The efficiency of health production: Reestimating the WHO panel data using parametric and nonparametric approaches to provide additional information. Health Economics, 12, 493–504. Jamison, D. T., Sandbu, M., & Wang, J. (2001). Cross country variation in mortality decline, 1962– 87: The role of country specific technical progress. CMH Working Paper No. WGI: 4. Geneva: WHO, Commission on Macroeconomics and Health. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2003). Governance Matters III: Governance indicators for 1996– 2002. World Bank Policy Research Department Working Paper. Kennedy, P. (2003). A guide to econometrics (5th ed.). Cambridge, MA: MIT Press. Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. Cambridge: Cambridge University Press. Murray, C. J. L., & Lopez, A. D. (1996). The global burden of disease, Harvard School of Public Health on behalf of the
307
WHO and the World Bank. Cambridge: Harvard University Press. Newhouse, J., & Friedlander, L. J. (1980). The relationship between medical resources and measures of health: Some additional evidence. The Journal of Human Resources, 15(2), 200–217. Ross, C. E., & Mirowsky, J. (1999). Refining the association between education and health: The effects of quantity, credential, and selectivity. Demography, 36(4), 445–460. Scientific Peer Review Group (2002). Efficiency Report of SPRG on HSPA (Health System Performance Assessment). Geneva: World Health Organization. World Bank (2004). World development indicators 2004. Washington, DC. World Health Organization (2000). The world health report 2000. Health performance: Improving performance. Geneva: World Health Organization.