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Significant drivers of growth in Africa

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Abstract

We employ bootstrap techniques in a production frontier framework to provide statistical inference for each component in the decomposition of labor productivity growth, which has essentially been ignored in this literature. We show that only two of the four components (efficiency changes and human capital accumulation) have significantly contributed to growth in Africa. Although physical capital accumulation is the largest force, it is not statistically significant on average. Thus, ignoring statistical significance would falsely identify physical capital accumulation as a major driver of growth in Africa when it is not.

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Notes

  1. For example, Sickles (2012) advocates for the existence of separate production frontiers. Similarly, Grosskopf and Self (2006) analyze Southeast Asian economies in isolation from the rest of the world.

  2. An alternative specification is the human-capital augmented Solow model where human capital enters the production function as an additional ordinary input, next to physical capital and raw labor (Mankiw et al. 1992). However, this type of formulation is not micro-founded (see Acemoglu 2009, chap. 3 and chap. 10 for a discussion on this issue).

  3. Note that \({{\varvec{E}}}_{bc}^{1}\) is \(\left( N\times 2\right)\), whereas \({{\varvec{E}}}_{bc}^{R}\) is \(\left( 4N\times 2\right)\).

  4. Note that the statistical inference that we have discussed here is provided with respect to each component for each country in the decomposition (9).

  5. These education data are an update of widely used previous compilation of Barro and Lee (2001).

  6. Whereas these estimates are in line with the standard finding of diminishing returns to education, an anonymous referee pointed out the work of Colclough et al. (2009) suggesting that this pattern may have changed in recent years in developing countries. In particular, the authors argue that the returns to primary education may have decreased in these countries, due to both demand and supply side factors. On the other hand, they show that the rates of return to secondary and higher education have increased in recent years in a number of developing countries. If we were to employ higher returns to education, it is likely that the results would show even larger contributions from human capital.

  7. We would like to thank an anonymous referee for pointing this out.

  8. Note that this is a crude approximation of the average productivity evolution over the entire period on a yearly basis under the assumption of constant productivity growth.

  9. Note that the confidence bounds cannot be provided for productivity measures since they are actual data points, not point estimates.

  10. Percentages are obtained by subtracting 1 from the index and multiplying by 100. Because of compounding, the average contributions of individual components do not, of course, sum to the average productivity change.

  11. We note that bootstrap techniques have been applied for inference regarding aggregate efficiencies of countries (see Henderson and Zelenyuk 2007), but not with respect to the components of decomposition.

  12. Delgado et al. (2014) find an insignificant impact of human capital in growth regressions. Similarly, Acemoglu et al. (2014) find much lower (and sometimes insignificant) impacts of human capital accumulation on growth. Our finding may imply that the production function framework is a better way to capture the empirical impact of human capital accumulation.

  13. Much of African investment goods are imported from abroad such that their high value presumably reflects high taxation and transportation cost as well as the monopoly power of the few domestic importing firms.

  14. See Guariso and Verpoorten (2014) for a recent survey on different channels through which armed conflicts impact education.

  15. Note that in each of the these time intervals, the assumption that technological regress is precluded has been maintained; for example in the 1990–1999 analysis, the countries have an access to technologies defined by observations back to the 1970s.

  16. Note the averages for 1970–1979 and 1970–2007 are roughly identical.

  17. We would like to thank a referee for drawing our attention to this point. See also Weil (2008) for a discussion on the possible explanations of a lack of technology transfer from rich to poor countries.

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Acknowledgments

An earlier version of this paper circulated under the title “Explaining African Growth Performance: A Production-Frontier Approach”. We would like to acknowledge useful comments from Steve O’Connell, Andrew Warner and three anonymous referees. We also wish to thanks participants at a CRED Workshop in Namur, the 2010 African Economic Conference in Tunis, and the 2011 CSAE Conference 2011 in Oxford. The usual disclaimer applies.

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Authors and Affiliations

  1. Cologne Graduate School in Management, Economics and Social Sciences, University of Cologne, Albertus-Magnus-Platz, 50923, Cologne, Germany

    Oleg Badunenko

  2. Department of Economics, Finance and Legal Studies, University of Alabama, Tuscaloosa, AL, 35487-0224, USA

    Daniel J. Henderson

  3. Department of Economics, Centre of Research in the Economics of Development (CRED), Center for Research in Finance and Management (CeReFiM), University of Namur, Rempart de la Vierge 8, 5000, Namur, Belgium

    Romain Houssa

  4. Center for Economic Studies (CES), University of Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Romain Houssa

  5. CESifo, Poschingerstr. 5, 81679, Munich, Belgium

    Romain Houssa

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  1. Oleg Badunenko
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Correspondence to Oleg Badunenko.

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Badunenko, O., Henderson, D.J. & Houssa, R. Significant drivers of growth in Africa. J Prod Anal 42, 339–354 (2014). https://doi.org/10.1007/s11123-014-0400-4

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