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Importing, Productivity and Absorptive Capacity in Sub-Saharan African Manufacturing and Services Firms

Abstract

This paper examines the relationship between importing and firm-level productivity in Sub-Saharan Africa. Using a recent firm-level survey for 19 Sub-Saharan African countries, the paper shows that there is a positive and significant relationship between importing and productivity for both manufacturing and services firms. Using a series of robustness tests, the paper finds that the importer-productivity relationship is robust in the case of manufacturing firms, but the results for services appear sensitive to the presence of extreme values. Finally, the paper shows that manufacturing firms with the highest levels of human capital show the strongest relationship between importing and productivity, a result consistent with a role for absorptive capacity in maximising the benefits of importing.

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Notes

  1. 1.

    An earlier literature at the aggregate level also considered the relationship between exporting and aggregate productivity or growth (see for example Michaely 1977; Feder 1983).

  2. 2.

    A non-linear relationship between firm size and productivity has been reported e.g. by the International Study Group on Exports and Productivity (ISGEP) (ISGEP) (2008).

  3. 3.

    White-collar workers include technical workers, supervisory and managerial staff, and clerical, administrative and sales staff. Blue-collar workers include production staff in the case of manufacturing firms, and manual staff in the case of services firms.

  4. 4.

    A firm is defined as foreign-owned if a direct investor that is resident of another economy has 10 % or more of the ordinary shares or voting power or the equivalent, an approach consistent with much of the existing literature.

  5. 5.

    For an introduction to quantile regression models see Buchinsky (1998) and Koenker and Hallock (2001).

  6. 6.

    In a production function context, the possibility of endogeneity of inputs arises. While this issue is difficult to deal with in a cross-section setting, recent developments with firm-level panel data allow one to control for endogeneity of inputs by assuming a monotonic relationship between some firm-level decision variable (for example investment) and the unobserved firm-level state variable productivity (see Olley and Pakes 1996).

  7. 7.

    For example, it would have been useful to be able to estimate three structural equations, (i) a production function, (ii) an import equation for the firm decision to import, and (iii) a skilled labor share equation. This would have allowed to control the endogeneity of both the import and the skilled labor share variables and to provide a better mechanism through which imported capital and skilled labor impact productivity.

  8. 8.

    For a brief introduction to matching techniques see Todd (2010). In the results reported below, we use single nearest neighbor matching.

  9. 9.

    The data used in this paper are confidential, but not exclusive. In order to gain access to the data, a confidentiality agreement with UNIDO will need to be signed.

  10. 10.

    Initially, also Cote d’Ivoire was part of the survey as the 20th country. During the data collection, however, the work in Cote d’Ivoire got stopped and the collected data did not get verified by the national supervisor. Given the incomplete execution of the sampling plan and the lack of quality assurance, we did not include Cote d’Ivoire in this study.

  11. 11.

    In Nigeria, this structure was doubled due to country size, creating a southern survey zone coordinated in Lagos, and a northern survey zone coordinated in Abuja.

  12. 12.

    Exceptions were Cape Verde, Lesotho and Burundi, where firms with 5 to 10 employees were also included.

  13. 13.

    We also estimated the basic model for each country separately. A lack of degrees of freedom for many countries resulted in many insignificant coefficients, but the coefficients tend to be positive. These results are available upon request. For all firms, we observe a negative and significant coefficient in only one of 19 countries (Niger), while we find significant and positive coefficients in those countries with the largest sample sizes (Cameroon, Ghana, Mozambique and Tanzania).

  14. 14.

    Results from the OLS single threshold model for manufacturing firms only are available upon request. The coefficients on the importer variable are positive and significant in both regimes, but are larger in the high regime.

  15. 15.

    Results from the OLS and Qreg single threshold models for services firms only are available upon request. The coefficients on the importer variable are positive and significant only in the low regime, while close to zero and insignificant in the high regime.

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Acknowledgments

The dataset used in this paper is an output of the project EERAF08043 “Survey of Enterprises in Selected ACP Countries”, funded by the European Commission. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the United Nations Industrial Development Organization. The authors are thankful for comments from the editor and three anonymous referees.

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Correspondence to Florian Kaulich.

Appendix 1: Regression results when removing foreign-owned firms

Appendix 1: Regression results when removing foreign-owned firms

Table 9 Ordinary least squares results when removing foreign-owned firms
Table 10 Robust regression results when removing foreign-owned firms
Table 11 Results using a matched sample of firms (Single nearest neighbour) when removing foreign-owned firms
Table 12 Interaction results when removing foreign-owned firms
Table 13 Threshold results when removing foreign-owned firms

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Foster-McGregor, N., Isaksson, A. & Kaulich, F. Importing, Productivity and Absorptive Capacity in Sub-Saharan African Manufacturing and Services Firms. Open Econ Rev 27, 87–117 (2016). https://doi.org/10.1007/s11079-015-9367-7

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Keywords

  • Importing
  • Productivity
  • Sub-Saharan Africa
  • Absorptive capacity
  • Human capital

JEL Classification

  • D24
  • F10
  • M20
  • L10