Health and economic development: evidence from non-OECD countries

ABSTRACT

This paper studies the empirical relationship between population’s health and real GDP dynamics in low- and middle-income countries. We employ a semi-parametric technique, which combines mixed panel data models and cluster analysis to account for unobserved heterogeneity, an important source of estimation bias in growth regressions. We estimate a version of the Solow growth model augmented with human capital, in the form of both education and health. Our estimates show that population s health, here proxied by the life expectancy at birth, has a positive, sizable, and statistically significant effect on both the level and the growth rate of the real per capita GDP.

KEYWORDS:

• Health
• education and human capital
• economic development and growth
• finite mixture models
• classification

JEL CLASSIFICATION:

• I15
• I25
• J24
• O41
• C14

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ See e.g. Griliches and Hausman (1986), Davidson and McKinnon (1993) and Wooldridge (2010).:

² We show in Appendix A1 that, with our data, this estimator performs better than both OLS and GMM.

³ Notice that, despite our empirical model can not identify per se any causal relationship between population’s health and GDP level/growth rate, which are, however somehow provided by the augmented Solow model, it is able to capture the mutual dependency between covariates and response variables, assuming that such a dependency may vary across countries.

⁴ See also de la Croix and Licandro (1999), Kalemli-Ozcan, Ryder, and Weil (2000), Boucekkine, de la Croix, and Licandro (2002, 2003), Chakraborty (2004), Cervellati and Sunde (2005), and Soares (2005).

⁵ See Bucci, Carbonari, and Trovato (2019) for the derivation of Equation (10)(10) $+\left(\frac{\gamma }{1-\alpha -\beta }\right)ln(h^{\ast })$(10) .:

⁶ The HC index is based on the average years of schooling (Barro and Lee 2013) and an assumed rate of return to education (based on Mincer equation estimates). Alternative measures for population health are the health adjusted life expectancy, the adult mortality rate or infant mortality. Data series for these variables, however, are available only for shorter duration and/or with respect to a limited number of countries.

⁷ See Alfò and Trovato (2004), Alfò, Trovato, and Waldmann (2008), Owen, Videras, and Davis (2009), Ng and Mclachlan (2014), Yu, O’Malley, and Ghosh (2014), Lu, Huang, and Zhu (2016), Alfò, Carbonari, and Trovato (2020). Notice that measurement error, omitted variable and varying parameters may be additional source of unobserved heterogeneity (and thus, model mis-specification).

⁸ Notice that the flexible BFMM allows to deal with non-trivial correlation structure. For instance, omitted covariates may affect both real GDP and aggregate health. It is well known that when responses are correlated (in our case, real GDP level and life expectancy), the univariate approach is less efficient than the multivariate one.

⁹ In clusterwise regressions, the standards errors are obtained by the bootstrap method based on 500 samples.

¹⁰ In the two systems of equations presented here, human capital in the form of education/schooling appears as a control only in Equations (15)(15) $E\left[ln{(y)}_{it,j=1}|u_{i1},X_{it}\right]=a_{i1}+{\beta }_{1}ln(s_e{)}_{it}+{\beta }_{2}ln(s_k{)}_{it}+{\beta }_{3}ln(n+g+\delta {)}_{it}$(15) and (17(17) $+{\xi }_{4}ln(s_h{)}_{it-1}$(17) ). We run regressions, available upon request, in which it appears even in the two equations for life expectancy with no significant change in our results. Given the importance of education, as a productive input in the augmented Solow model, we also estimate a three-equation model with human capital added as a third response variable. In this case, however, we obtain less accurate estimates.

¹¹ As a robustness check, we run our regression using the average years of education in working age population, as an alternative proxy for human capital. Qualitatively, our results do not change. Estimates are available upon request.

¹² Table B2 in the Appendix B1 reports, as a robustness check, the estimates for the two BFMMs using the infant mortality (source: UNICEF) as a proxy for aggregate health. Again, the main message of our analysis does not change.

¹³ See https://www.doingbusiness.org/en/rankings.

¹⁴ We do not produce the residual plots for life expectancy in the two models, since the variable is needed only for solving the endogeneity issue, thus reducing the bias in the estimation.

¹⁵ To study this possibility, Aghion, Howitt, and Murtin (2011) and Bloom, Canning, and Fink (2014) include initial health in the Acemoglu and Johnson (2007)’s regressions and find that, indeed, the negative causal effect vanishes. More specifically, Aghion, Howitt, and Murtin (2011) combine the Mankiw, Romer, and Weil (1992)’s approach (whereby output growth is correlated with the rate of improvement in human capital) with the Nelson and Phelps (1966)’s approach (whereby a higher level of health should spur growth by facilitating technological innovation), and look at the joint effect of health (level and accumulation) on economic growth. After running cross-country growth regressions over the period 1960–2000, they show that the level and the accumulation of health have significant positive effects on per capita income growth. Moreover, they find a weaker relationship between health and growth over the contemporary period in OECD countries. According to them, this result is explained by the fact that only gains in life expectancy below 40 years are significantly correlated with per capita income growth.

¹⁶ Chunling et al. (2010) warn that a potential substitution may occur between international health aid and domestic expenditure on health. Studying a sample of developing countries they find that the presence of programs aimed at providing Development Assistance for Health (DAH) to countries has a negative effect on domestic government spending on health, while having a positive and significant effect on domestic non-governmental health spending.

¹⁷ Studying a sample of Sub-Saharan African countries, Novignon, Olakojo, and Nonvignon (2012) find that health expenditure significantly improves life expectancy, and reduces death and infant mortality rates. Barenberg, Basu, and Soylu (2017) find similar results using Indian data in the periods 1983–1984 and 2011–2012. For a large sample of developing countries, Baldacci et al. (2008) explore the channels through which social spending can affect human capital and GDP growth. They find that health spending has a positive and significant impact on human capital, and thus supports higher growth. Ssozi and Amlani (2015) find that, although health expenditure in Sub-Saharan Africa has substantially increased since 2000, it has had a low impact on both life expectancy and infant mortality.

¹⁸ For the sake of brevity we do not report these regressions, which, however, are available upon request.

¹⁹ See Weil (2014), Tamakoshi and Hamori (2015) and Linden and Ray (2017).

²⁰ Results for the growth Equation (12)(12) $+\zeta \left(\frac{\gamma }{1-\alpha -\beta }\right)ln(h^{\ast })-\zeta ln\left(\frac{Y_0}{L_0}\right)+\zeta ln{A}_0+gt$(12) are available upon request.

²¹ The BIC is largely used in cluster analysis because it allows to compare models with different parametrization, different numbers of components, or both (see Fraley and Raftery 1998).