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The demand for foreign workers by foreign firms: evidence from Africa

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Abstract

Foreign workers play a crucial role in channelling resources and information flows both within the boundaries of firms and between foreign firms and the host country economy. In this study we employ a novel firm-level database (UNIDO Africa Investor Survey 2010) in order to investigate the factors that determine the employment of foreign workers by foreign firms in Sub-Saharan African countries. We shed light on important firm-level as well as host–home country characteristics which shape the demand of foreign workers in developing countries. We show that differences between investors are largely explained by the role played by economic and institutional distance between home–host countries as well as by firm-level heterogeneity in the degrees of knowledge intensity and local embeddedness.

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Notes

  1. UNCTAD (2015) reports that in 2014 investments toward the African continent represented 4.4% of the total reaching 54 billion $. Despite of the current stagnant pattern due to the financial crisis, over the last 3 years, FDI inflows increased by almost six times.

  2. Perez-Villar and Seric (2015) find using the same dataset employed in this work that institutional distance deters backward linkages between foreign and domestic firms in Africa; the authors find a stronger effect for MNEs originating from rich countries.

  3. In this regard, Belderbos and Heijlties (2005) found that firms building local partnerships are less likely to appoint a home country national as a managing director.

  4. According to Bartlett and Ghoshal (1992), the main challenge for internationalized firms is to create a learning organization able to develop and integrate knowledge throughout their cross-border activity. Evidence shows that MNC's subsidiaries rely heavily on knowledge created at the parent company level (Caves 1996). Hence, the knowledge transfer from the parent company to the controlled affiliates becomes a crucial competitive factor.

  5. We are grateful to an anonymous referee for useful suggestions in this respect.

  6. We also employ as an alternative the proxy of multinational firms’ knowledge intensity employed by Keller and Yeaple (2013), i.e. the measure of the importance of Analyzing Data provided by the O*NET database of the US Department of Labour.

  7. Gaur et al. (2007) argue that in the presence of a distant institutional environment between the origin and the destination country of the firm, age could have the opposite relation and lead to an over-time increase in the use of parent country managers and workers. Over time, the local legitimacy of foreign firms increases the ability to overcome local environment hurdles using home country managers.

  8. Burundi, Burkina Faso, Cameroon, Cape Verde, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Niger, Nigeria, Mozambique, Rwanda, Senegal, Uganda, United Republic of Tanzania and Zambia.

  9. This type of workers do not transfer within the same firm, but make their own job arrangements.

  10. We explicitly take into account in our analysis the possibility that foreign workers might be immigrants already residing in the country rather than intra-firm expatriates. In our parametric analysis we controls for the stock of immigrants residing in the host countries.

  11. Data on host country characteristics are not available for Cape Verde, Niger and Rwanda. Hence, in estimations when these characteristics are controlled for these countries are excluded from the analysis.

  12. We adopt the World Bank definition and classify a source country as South when GNI per capita is lower than 12.616$.

  13. Coniglio et al. (2015) investigate the heterogeneous labour market effects of North–South versus South–South FDI in Sub-Saharan Africa. In particular their study finds that although South investors tend to demand a higher number of workers, generally they tend to generate mostly low-skilled employment and pay lower wages. In particular, their study finds that Chinese investors employ a significant share of foreign workers at all skills levels.

  14. These macro sectors are defined aggregating the 59 sub-sectors available in the data, corresponding to the ISIC 2-digit classification.

  15. Given the nature of the data—i.e. firm-level observations of investors from more than 80 origin countries toward 19 host countries belonging to Sub-Saharan Africa (a relatively homogeneous area of the world)—the identification of dyadic variables is problematic if we include in our estimates both country of origin and host country fixed effects. Hence when our focus is on home–host country heterogeneity (Hypothesis 1–3 in particular) we include more aggregated area of origin fixed effects, namely Europe, China, India, South Africa, SSA, MENA Other Asia, North America, Latin America and Other. The inclusion of more refined origin country fixed effects does not changes the sign and magnitude of the main parameters of interest but makes the estimated coefficient less precise (higher standard errors).

  16. Lagged values are measured as the value of asset at the beginning of the last financial year. Note that when the dependent variable employed in the estimation is the share of foreign highly skilled workers, the issue of endogeneity remains potentially relevant. Skilled workers, including foreign ones, are more likely to be employed by firms to develop and manage the technological content of firms’ operations including patents and copyrights assets. Hence, the results in terms of direction of causality of the estimations using the high-skilled variable should be interpreted with the appropriate caution.

  17. We thank an anonymous referee for suggesting the use of sectoral level measures of knowledge intensity.

  18. For a detailed description on the measurement of the index we refer to Oldenski (2012) or more specifically to Costinot et al. (2011) who use the occupational characteristic "making decisions and solving problems" as we do here.

  19. As an alternative, we also estimates of a negative binomial model where the dependent variable is specified as a count—number of foreign workers—rather than a share. The results are qualitatively in line with those presented here and are available upon request from the authors.

  20. Given the high correlation between the different dimensions of distance we report only estimates which include the three measures separately.

  21. Also note that when the distance between host and home countries is high, the temporary deployment of staff from the headquarters or other units to the local subsidiary might be less convenient than recruiting foreign workers directly at the subsidiary.

  22. We find a weak evidence that the effect of time since entry is non-linear and decreasing over time as suggested by the positive coefficient of age squared.

  23. Note that the stock of immigrants as a percentage of native population is rather low in all Sub-Saharan African countries. In year 2005 the percentage of foreigners ranges from 0.19 in Madagascar to 2.17 in Ghana. As mentioned before, immigrants in SSA countries are mainly low skilled workers coming from neighboring countries.

  24. Similar results are obtained using as an alternative the level of corruptions (measured as the share of firms paying bribes to public officials in the host country). Results are available from the authors upon request.

  25. In the AIS 2010 dataset information on backward linkages are available only for firms in the manufacturing sector and not for those operating in the service sector.

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Authors and Affiliations

  1. University of Bari “Aldo Moro”, Bari, Italy

    Nicola D. Coniglio & Rezart Hoxhaj

  2. NHH Norwegian School of Economics, Bergen, Norway

    Nicola D. Coniglio

  3. CERGE-EI, Prague, Czech Republic

    Rezart Hoxhaj

  4. UNIDO, Vienna, Austria

    Adnan Seric

Authors
  1. Nicola D. Coniglio
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  2. Rezart Hoxhaj
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  3. Adnan Seric
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Corresponding author

Correspondence to Nicola D. Coniglio.

Appendix

Appendix

See Tables 7, 8, 9, and 10.

Table 7 Foreign firms by country of destination.
Full size table
Table 8 Foreign firms by areas of origin and destination country.
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Table 9 Robustness check of host-home countries economic, institutional and geographical distances (excluding China and India)
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Table 10 Robustness check of extra-firm and intra-firm transaction costs (excluding China and India)
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Coniglio, N.D., Hoxhaj, R. & Seric, A. The demand for foreign workers by foreign firms: evidence from Africa. Rev World Econ 153, 353–384 (2017). https://doi.org/10.1007/s10290-016-0272-y

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