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Economic Growth and Infant Mortality in Developing Countries

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Abstract

This study investigates the effects of GDP per capita on infant mortality using panel data from 83 developing countries over a period of 40 years. Although economic growth broadly decreases infant mortality, the impact of economic growth on infant mortality for the periods of economic booms and slumps is asymmetrical. Positive economic growth may have weak, mixed effects on a reduction in infant mortality, but negative economic growth has a strong, adverse impact.

Cette étude explore les effets du PIB par tête sur la mortalité infantile, en mobilisant des données de panel recueillies dans 83 pays en développement, sur une période de 40 années. Bien que la croissance économique diminue, dans l’ensemble, la mortalité infantile, l’impact de la croissance économique sur la mortalité infantile en période de boum économique d’une part et en période de crise d’autre part, est asymétrique. Une croissance économique positive peut avoir des effets faibles et mitigés sur la mortalité infantile, alors qu’une croissance économique négative a des effets négatifs importants.

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Notes

  1. Such disagreement surfaced symbolically – though in a different, but closely related, context – in the conflict between globalization-skepticism in Stiglitz (2002) and pro-globalization advocacy in Bhagwati (2004), and in another recent controversy regarding the World Bank's Millennium Development Goals, that is, whether the number of poor people has been increasing or decreasing (World Bank, 2000; Bhalla, 2002; Sala-i-Martin, 2002, 2006; Chen and Ravallion, 2004; Ravallion, 2004; among others).

  2. See Yamazaki (2007) for thought-provoking discussion on the Dollar–Kraay regressions.

  3. See Deaton (2003) for an insightful survey with a broad perspective. Also see Smith and Haddad (2000) for a compact summary of past cross-country studies of child malnutrition.

  4. The conflicting results in the literature can be attributed to difference in the time lag between the dependent variable and the explanatory variable. The author thanks an anonymous referee of this journal for their comment on this point. However, few studies on the subject explicitly deal with it. Following the convention, the present study handles no time lag.

  5. For further discussion, see Greene (2008).

  6. The correlation between the three instruments and GDP per capita is satisfactory, as the following simple OLS regression shows (Figures in parentheses are t-statistics.): (Ln) GDP per capita=7.12 (110.6)+0.023 (6.9) CI+0.0084 (14.3) OPENC−0.00030 (−1.5) PPPI.

  7. Meanwhile, the Ruhm argumentation undergoes severe criticism. Recent papers by Economou et al (2008) and Miller et al (2009) presented statistical evidence contradictory to Ruhm's findings.

  8. These data are available beginning from 1960 on a consecutive annual basis for only a very small number of countries. Relying on observations in 1960 is not a good choice in order to maintain a satisfactory number of country observations.

  9. When a variable for GDP per capita is derived from any version of the Penn World Tables, virtually no study clearly specifies the type of GDP per capita variable employed in their statistical investigations. A few exceptions include a paper by Pritchett and Summers (1996) that uses RGDPCH. This is exactly why we employ RGDPCH, instead of the other five variables, from the Penn World Tables Version 6.2.

  10. Some may argue that another important determinant of health is health expenditure. However, the link between health expenditure and infant mortality can be quite weak (Filmer and Pritchett, 1999; McGuire, 2006; Thornton, 2002). Therefore, the attention of the present study is directed to seeking more fundamental determinants of the state of health rather than concentrating on health expenditure.

  11. The author is aware that the female-male life expectancy ratio is not an ideal measure of women's social status. The UNDP (1995), the World Bank (2001) and their websites provide some refined, alternative measures of women's social status, including the gender development index, the gender empowerment index, and female political participation. However, these data are available only for relatively recent points of time, and do not cover our concerned period. There seems to be no better alternative to our measures of women's social status for the present study and other longitudinal panel data studies.

  12. A crude, but useful definition of slums is ‘neglected parts of cities where housing and living conditions are appallingly poor’ (Asian Development Bank et al, 2005, p. 213).

  13. As a variable for food availability used in this study is calculated by cereal production divided by population, the author is aware that it is not an ideal measure. Nevertheless, there is to my knowledge no internationally comparable alternative.

  14. A popular instrumental variable for GDP per capita employed in Easterly (1999) and Pritchett and Summers (1996) is black market premium, which means the deviation of the official exchange rate from its purchasing power parity. Pritchett and Summers argue that black market premium is an inappropriate instrument for per capita GDP and limit their use of the variable in their investigation. This is because black market premium is highly correlated with social unrest, which can be a potential ‘third’ variable directly influencing both infant mortality and economic growth. The present study does not employ it either.

  15. With the borderline of 1 per cent annual average economic growth rate instead of 0 per cent, we also re-examined key regressions in Table 3. The estimated results are almost identical to our original results with the 0 per cent borderline. The results are not shown for reasons of space.

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Acknowledgements

I am indebted to Michael Bleaney, Hideki Nakamura, Takahiro Sato, Yasuyuki Sawada and Akihisa Shibata for their helpful comments. I am grateful to Claire Mainguy and two anonymous referees of this journal for their perceptive suggestions. I would like to thank Junko Nishiyama for research assistance. Financial support from the Grant-in-Aid for Scientific Research, Ministry of Education, Culture, Sports, Science and Technology, Japan is gratefully acknowledged (19730208). All remaining errors are mine.

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    Akira Nishiyama

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Appendices

Appendix A

Table A1

Table A1 Data description
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Appendix B

Table B1

Table B1 Two-SLS with different sets of instruments in Regression (2-10)
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Appendix C

Table C1

Table C1 Geographical distribution of economies (Regression (3-1))
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Nishiyama, A. Economic Growth and Infant Mortality in Developing Countries. Eur J Dev Res 23, 630–647 (2011). https://doi.org/10.1057/ejdr.2011.17

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