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The effect of female and male health on economic growth: cross-country evidence within a production function framework

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Abstract

Adopting a production function-based approach, we model the role of health as a regular factor of production on economic growth, and use disaggregate measures of male and female health capital using principal component analysis. Allowing for the dynamics of TFP to be embedded in the production function, we estimate both in levels and in growth rates to distinguish between long- and short-run effects. We use appropriate panel cointegration methodology to control for endogeneity, cross-sectional dependence and heterogeneity. Our main finding is that while male and female health capital stocks have a significantly positive effect on level of output in the long-run, changes in gender-disaggregated health capital have a negative or insignificant effect on output growth in the short-run.

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Notes

  1. Note that while higher values of life expectancy and survival indicate better health, the opposite applies to mortality. Hence to make these three measures comparable, we take the inverse of mortality and then carry out the principal component analysis.

  2. The null of non-stationarity is rejected at the 5 per cent level of significance for FH, but when a trend is added to the constant in the ADF regression, the null cannot be rejected. For all five variables our preferred CIPS results are the ones which include both a constant and trend.

  3. Some robustness checks indicated that the results reported do not change when varying the number of lags included in the ADF regression.

  4. This latter statistic is analogous to the Im et al. (2003) test for a panel unit root applied to the estimated residuals of a cointegrating regression.

  5. The Ga and Gt test statistics are based on a weighted average of the individually estimated short-run coefficients and their t-ratio’s, respectively. The Pa and Pt test statistics pool information over all the cross-sectional units to test the null of no cointegration for all cross-sectional entity.

  6. The share of profits is by definition:

    \(\alpha =\frac{\frac{\partial \ln (Y)}{\partial \ln (K)}\times K}{Y}\approx \frac{\Delta Y}{\Delta K}\left( {\frac{K}{Y}} \right) \hbox { }\hbox { }\)

    where the numerator is the remuneration for capital measured the marginal product of capital (MPK) multiplied by capital stock, and (K / Y) is the capital–output ratio (KYRAT). It is to be expected that MPK will be higher in developing countries because of their lower capital stocks and so making \(\alpha \)higher. This effect will be partly offset by lower KYRATs in the developing countries. But in proportionate terms, the differences in MPKs are likely to be higher than KYRATs.

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Author information

Authors and Affiliations

  1. Department of Economics, University of Waikato, Private Bag 3105, Hamilton, 3240, New Zealand

    Gazi Hassan & Mark Holmes

  2. The University of Nottingham (Malaysia Campus), 43500, Semenyih, Selangor Darul Ehsan, Malaysia

    Arusha Cooray

  3. Centre for Applied Macroeconomic Analysis, Australian National University, Canberra, Australia

    Arusha Cooray

Authors
  1. Gazi Hassan
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  2. Arusha Cooray
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  3. Mark Holmes
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Corresponding author

Correspondence to Gazi Hassan.

Additional information

We would like to acknowledge helpful comments from an anonymous referee and Prof. John Gibson. Remaining errors are our responsibility.

Appendix: Data appendix

Appendix: Data appendix

Variable

Source

Per capita income (constant 2000 US$) (Y)

World Development Indicators (2011)

Capital stock dollars (K)

Bosworth and Collins (2003), from 2004 to 2009 World Development Indicators 2010 using perpetual inventory method

Labour force number (L)

Bosworth and Collins (2003), from 2004 to 2009 World Development Indicators 2010

Life expectancy, female (years)

World Development Indicators (2011)

Life expectancy, male (years)

World Development Indicators (2011)

Adult mortality rate, female (per 1000 female adults)

World Development Indicators (2011)

Adult mortality rate, male (per 1000 male adults)

World Development Indicators (2011)

Survival to age 65, female (% of cohort)

World Development Indicators (2011)

Survival to age 65, male (% of cohort)

World Development Indicators (2011)

Female health (FH) (principal component)

Computed by authors

Male health (MH) (principal component)

Computed by authors

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Hassan, G., Cooray, A. & Holmes, M. The effect of female and male health on economic growth: cross-country evidence within a production function framework. Empir Econ 52, 659–689 (2017). https://doi.org/10.1007/s00181-016-1088-2

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  • DOI: https://doi.org/10.1007/s00181-016-1088-2

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