Abstract
The objective of this paper is to determine whether health (measured by life expectancy at birth) contributes to economic growth and the functional form in which it influences per capita income. This links our study to the debate between neo-classical and endogenous growth theorists on whether investment in human capital can sustain growth indefinitely. Data on 216 countries for the period 1980–2009 has been obtained from World Development Indicators dataset. This enables us to focus on a period characterized by globalization and demographic transition manifested in the form of population graying. Our findings confirm the importance of investment in human capital. But, in contrast to conclusions of endogenous growth models, we find evidence that benefit from increasing longevity tapers off. We conclude by pointing out that it is necessary to extend this study further by incorporating other dimensions of health that are not captured by life expectancy.
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Notes
Quesnay’s Tableau èconomique (1759) and Turgot’s Reflections on the formation and distribution of wealth (1776) may be cited in this context (see Meek 1962).
The second generation growth models differ from neo-classical growth models by arguing that technological progress is endogenous and varies between countries. However, it also takes into account the process of international technology transfer and its obstacles—so that the rate of technological progress becomes linked to global conditions (and not solely local conditions, as assumed in the AK growth models).
Pritchett and Summers (1996) estimate the effect of income on health, measured by infant and child mortality as well as life expectancy. The existence of an impact of health on economic growth with similar magnitudes has been verified for different time periods and countries, including Latin America and Mexico (Preston 1975; Deaton 2001).
This was done in order to observe the statistical impacts made by the different health measures on economic growth.
For instance, QALY-adjusted life expectancy appears to be a more relevant variable for our purpose as it captures quality of life dimension. Unfortunately, we have country-wise data on only 1 year.
The Kaiser–Meyer–Olkin statistic was 0.771, Bartlett’s Test statistic was 1,274.32 (p = 0.00); one eigenvalue with value of 3.98 explaining 79.61 % of the variation was extracted.
Introducing lags normally reduces sample size as lags of the first observations are generally not available. In our case, annual data on LGNI is available from 1960. This was added to the existing sample preventing sample size from falling substantially.
The classification system is described in http://data.worldbank.org/about/country-classifications (accessed on 31 December 2012). The critical income levels are calculated using the World Bank Atlas method (http://data.worldbank.org/about/country-classifications/world-bank-atlas-method, accessed on 31 December 2012) and announced by the World Bank every year. We have used the levels for 2010, the last year of our study period.
We should also note that the coefficients of the quadratic form in remaining three groups are insignificant.
The proportion of individuals in relevant age group enrolled in educational institutions (colleges, universities and polytechniques) offering post-secondary education.
Hausman tests indicate the LSDV model as the preferred variant.
In Model 3, value of slope for Low middle income countries (the reference category) is given by coefficient of LLE (i.e. 2.12). Values of slope for remaining three groups of countries may be obtained by adding values of slope dummies to coefficient of LLE. Note that coefficient of slope dummy for Low income countries is not significantly different from zero (as z = − 0.25), so that slope of these countries is 0 + 2.12 = 2.12. Slopes for High medium income and High income countries are, respectively, 2.12–1.48 = 0.64 and 2.12–2.52 = −0.40.
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Acknowledgments
The authors would like to express their gratitude to two anonymous reviewers, Caspar Worm Hansen, Department of Economics, University of Southern Denmark, Ankush Aggarwal and, particularly, Arup Mitra, Institute of Economic Growth, Delhi, for their comments and suggestions. The authors are responsible for the remaining errors.
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Husain, Z., Dutta, M. & Chowdhary, N. Is Health Wealth? Results of a Panel Data Analysis. Soc Indic Res 117, 121–143 (2014). https://doi.org/10.1007/s11205-013-0337-4
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DOI: https://doi.org/10.1007/s11205-013-0337-4