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ABSTRACT
South Africa is currently suffering from high unemployment. As export activity is generally associated with higher economic growth, productivity, employment levels and wages, this papers investigates if increasing export activity is one of the solutions to the high unemployment levels in South Africa. By using newly made available administrative data on the population of South African firms, this paper aims to deepen the understanding of the wage premium, employment premium and employment growth of exporters relative to non-exporters (as well as within exporters). By following the methodology of Bernard and Jensen, the results show that South African manufacturing exporters do have an employment premium and wage premium. But advocating increased export activity is not enough, as exporting firms are heterogenous. Furthermore, employment growth of exporters (measured by estimating first-difference regressions) is faster than non-exporters, but growth in jobs is skewed towards higher skilled, more experience workers.
Acknowledgements
All the authors would like to thank the United Nations University World Institute for Development Economics Research and National Treasury for funding support. Also, they would like to thank the South African Revenue Service for the data. Any opinion, finding and conclusion, or recommendation expressed in this material is that of the authors and the NRF does not accept any liability in this regard.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 It is important to note that a proportion of the data was lost when using the conjunction table to merge the CIT, IRP5 and customs data, therefore there is a possibility of selection bias in the data.
2 The IRP5 data was limited in terms of worker characteristics (no variables on race, education, skills and experience).
3 The customs data only shows direct exports, therefore indirect exports are not captured by the data (this is a limitation faced by other similar studies).
4 Entering firms are firms that did not export in t − 1, but exported in t.
5 Exiting firms are firms that exported in t − 1, but not in year t.
6 Continuing firms are firms that exported in year t − 1 and t.
7 As the total number of firms declined in 2014, all regressions (see section 4.3) were estimated with and without data for 2014. The results excluding 2014 are available on request.
8 As found by Bernard and Wagener (Citation1997) and Brambilla et al. (Citation2015).
9 As shown by, for example, Bernard et al. (Citation2007), Isgut (Citation2001), Schank et al. (Citation2007), Fafchamps (Citation2007), Brambilla et al. (Citation2010), Amiti and Davis (Citation2012), and Brambilla et al. (Citation2015).
10 As seen in Alcalá and Hernández (Citation2010), Serti et al. (Citation2010), Rankin and Schöer (Citation2013), Matthee et al. (Citation2016b).
11 An analysis was done to see whether the high employment propensity of firms exporting to international markets relative to African markets were influenced by income level of the market (GDP of export destination) as well as product sophistication (relative price per product), but the regression results were insignificant.
12 To calculate employment growth without short-term fluctuations, the period 2010–13 was used. As 2014 had fewer observations in the data, it was excluded from these regressions.
13 As there were no variables on education, skills and experience of workers, this study used age (above and below 30) and income (earning above and below R6500) as indications of skills and experience.
14 Owing to data availability problems, the estimations were for the period 2010–2013 only. The short-term, year-on-year estimations were inconsistent (probably due to the presence of occasional exporters).