The Effect of Health on Economic Growth: A Production Function Approach
Introduction
Although labor quality, in the form of human capital, clearly contributes significantly to economic growth, most crosscountry empirical studies identify human capital narrowly with education. This practice ignores strong reasons for considering health to be a crucial aspect of human capital, and therefore a critical ingredient of economic growth. Healthier workers are physically and mentally more energetic and robust. They are more productive and earn higher wages. They are also less likely to be absent from work because of illness (or illness in their family). Illness and disability reduce hourly wages substantially, with the effect especially strong in developing countries, where a higher proportion of the work force is engaged in manual labor than in industrial countries. A substantial body of microeconomic evidence documents many of these effects (see Strauss & Thomas, 1998). The objective of this paper is to determine whether this micro evidence can be corroborated by macro evidence of an effect of population health on economic growth. Health, in the form of life expectancy, has appeared in many crosscountry growth regressions, and investigators generally find that it has a significant positive effect on the rate of economic growth (see Bloom and Canning, 2000, Bloom and Canning, 2003). (Table 1 reports a selection of the papers that include health as a determinant of economic growth and the magnitude of the effect on growth they find.) These regressions, however, do not indicate whether health directly benefits growth or whether it is merely a proxy for other missing or mismeasured factors (as suggested, for example, by Barro & Sala-I-Martin, 1995).
The main objective of this study is to include health in a well-specified aggregate production function in an attempt to test for the existence of an effect of health on labor productivity, and to measure its strength. Because human capital is multidimensional, however, we need a model of growth that includes all its major components. This helps ensure that we do not erroneously overestimate the contribution of one component by mistakenly attributing to it the contributions of those components we omit. In particular, there is a potential bias in estimates of the effect of health that rely on life expectancy data in that countries with high life expectancies tend to have older work forces with higher levels of experience. Considerable microeconomic evidence––dating back to Mincer (1974)––indicates that experience has an impact on workers’ earnings. By including the experience of the workforce directly into the model we control for this effect.
To this end we specify an aggregate production function that expresses a country’s output as a function of its inputs and the efficiency with which it uses these inputs. These inputs are physical capital, labor, and human capital in the three dimensions of education, experience, and health. Our model also considers the efficiency with which these inputs are used, that is, total factor productivity (TFP) allowing both for crosscountry differences in steady-state TFP and for technological diffusion. We estimate all the parameters of this production function using panel data for 1960–90 and obtain measures of the relative contributions of each of the inputs and of TFP to economic growth. An alternative approach would be to calibrate the model using microeconomic evidence for parameter values (see, for instance, Klenow & Rodriguez-Clare, 1997; Prescott, 1998; Young, 1994, Young, 1995). The potential advantage of estimation over calibration is that the microeconomic evidence measures the effect of improvements in an individual’s human capital on own earnings, ignoring the additional effects it might have on other individuals or on society as a whole. These additional effects, that is, externalities, might arise because people’s productivity depends on the productivity of their coworkers. When workers obtain more schooling, their earnings rise, but those of their coworkers may rise as well. By estimating the returns to human capital in aggregate, we let these returns differ from microeconomic estimates, which allows us to make inferences about the existence and magnitude of the externalities.
Our main result is that health has a positive and statistically significant effect on economic growth. It suggests that a one-year improvement in a population’s life expectancy contributes to a 4% increase in output. We also find that our estimates of the contributions of education and work experience are close to those found in microeconomic studies. Indeed, the differences between our parameter estimates and the averages found in microeconomic studies are usually statistically insignificant. Thus we find no evidence of the existence of externalities to human capital in the form of schooling and experience (or such externalities are too small for us to detect). While large crosscountry differences in life expectancy and average years of schooling explain a substantial proportion of the income gaps we observe between countries, crosscountry differences in average work experience are small, implying that work experience plays a relatively minor role in explaining income gaps.
Our model captures the direct effect of education and health on output. We do not investigate how education and health are themselves created––to do this would require a system of equations mapping out the development process. This implies however, we may miss the effect of increased education on health (Pritchett & Summers, 1996; Summers, 1992; and Younger, 2002), and of improved health on education (Balasz et al., 1986; Bhargava, Jukes, & Sachs, 2001a; Kremer & Miguel, 2001; Pollitt, 1997, Pollitt, 2001).
Section snippets
Theory
We assume that we can decompose economic growth into two sources: growth in the level of inputs and growth in TFP. We take our inputs to be physical capital, labor, and human capital.
We model output as a function of inputs and technology using the following aggregate production function:where Y is output or gross domestic product (GDP); A represents TFP; K is physical capital; L is the labor force; and human capital consists of three components, average years of
Data
We construct a panel of countries observed every 10 years over 1960–90. Output data (GDP) are obtained from the Penn World Tables (see Heston & Summers, 1994 for a description). We obtain total output by multiplying real per capita GDP measured in 1985 international purchasing power parity dollars (chain index) by national population.
We measure a country’s labor supply by the size of its economically active population using data from the International Labour Office (1997), which also gives
Estimation and results
We begin by estimating Eqn. (5) under the assumption that steady-state TFP levels are the same in every country. The results are reported in Table 3. The parameters of each regression are estimated by nonlinear least squares, and all contemporaneous growth rates of inputs are instrumented with their lagged growth rates. Time dummies (not reported) are included that act as proxies for the average worldwide level and growth rate of TFP.
The results in column (1) of Table 3 include only physical
Conclusion
Our model accounts for economic growth by the growth of factor inputs, technological innovation, and technological diffusion. Our main result, which is consistent with our theoretical argument and with the microeconomic evidence, is that health has a positive and statistically significant effect on economic growth. It suggests that a one-year improvement in a population’s life expectancy contributes to an increase of 4% in output. This is a relatively large effect, indicating that increased
Acknowledgements
The authors gratefully acknowledge financial support from the World Health Organization’s Economic Advisory Service. Earlier drafts of this paper were presented at the National Bureau of Economic Research Summer Institute and at meetings of the WHO Commission on Macroeconomics and Health (Working Group One). The authors are grateful for many helpful comments received during the course of those presentations.
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