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The impact of health worker migration on development dynamics: evidence of wealth effects from Africa

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Abstract

This article examines three relevant hypotheses on the effect of health worker migration on human development and economic prosperity (at the macro- and micro-levels) in Africa. Owing to the lack of relevant data on health human resource (HHR) migration for the continent, the subject matter has remained empirically void over the last decades despite the acute concern about health professional emigration. Using quantile regression, the following findings have been established. (1) The effect of HHR emigration is positive (negative) at low (high) levels of economic growth. (2) HHR emigration improves (mitigates) human development (GDP per capita growth) in low (high) quantiles of the distribution. (3) Specific differences in effects are found in top quantiles of human development and low quantiles of GDP per capita growth where the physician (nurse) emigration elasticities of development are positive (negative) and negative (positive), respectively. As a policy implication, blanket health-worker emigration control policies are unlikely to succeed across countries with different levels of human development and economic prosperity. Hence, the policies should be contingent on the prevailing levels of development and tailored differently across the most and least developed African countries.

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Notes

  1. According to Packer et al. [31], in one study, the waiting time for a heart bypass in the UK could last up to 6 months and cost the NHS between 15,000 and 19,000 pounds, whereas a large pool of well-qualified doctors in India will readily perform the surgery at a cost of 4,800 pounds. For clinics and hospitals in developing countries receiving these patients, their treatment brings in important revenue and desirable foreign exchange.

  2. Refer to Figure 2, page 18, of [31]. Africa has 25 % of the global disease burden with a share in the population of 13.76 %, but has only a 1.3 % share of health workers.

  3. (1) Do existing human development and economic prosperity levels matter in the impact of HHR emigration on development? (2) Are blanket common policies relevant irrespective of specific development characteristics? (3) To be effective, should immigration policies be contingent on the prevailing levels of development dynamics and tailored differently across countries with the best and worst development records?

  4. The data are collected from census offices of destination countries of African physicians and professional nurses working abroad, that is, those that practice abroad. Health professionals that emigrated from Africa but did not practice abroad are not considered.

  5. We believe presenting both cases of GDP growth is relevant on two counts. (1) The effect of physician emigration (exogenous variable of interest) in the analysis may be different across GDP growth and GDP per capita growth distributions. (2) We expect the elasticities of the control variables to be similar; hence using both measures is a means of robustness check.

  6. There are other justifications for the use of quantile regression. (1) It is rare to find normal distributions of variables in the real world, so the use of quantile regression merely provides a different algorithm for estimating the conditional mean. It should be kept in mind that, if the distribution of y for a fixed x is symmetric, then the conditional mean and the conditional median are the same thing. (2) Upper and lower quantiles could have significant policy implications. In other words, existing levels of development may respond differently to health worker migration, implying blanket immigration control policies may not be effective unless they are contingent on existing levels of development and tailored differently across the least and most developed countries. (3) The inherent variability in the data is worth studying, too.

  7. See Fernández-Villaverde [19] for a broad explanation. “This paper studies the relationship between population dynamics and economic growth. Prior to the Industrial Revolution increases in total output were roughly matched by increases in population. In contrast, during the last 150 years, increments in per capita income have coexisted with slow population growth. Why are income and population growth no longer positively correlated? This paper presents a new answer, based on the role of capital-specific technological change, that provides a unifying account of lower population growth and sustained economic growth” (p.1).

  8. See the signs in panel A and panel B in Table 1.

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Acknowledgments

The author is highly indebted to the editor and referees for their very useful comments.

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  1. HEC-Management School, University of Liège, Rue Louvrex 14, Bldg. N1, 4000, Liège, Belgium

    Simplice A. Asongu

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Appendices

Appendices

Appendix 1

See Table 6.

Table 6 Summary statistics
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Appendix 2

See Table 7.

Table 7 Correlation analysis and presentation of countries
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Appendix 3

See Table 8.

Table 8 Variable definitions
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Asongu, S.A. The impact of health worker migration on development dynamics: evidence of wealth effects from Africa. Eur J Health Econ 15, 187–201 (2014). https://doi.org/10.1007/s10198-013-0465-4

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