Browsing by Author "Naughtin, Tasha Lynn"

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    Firm productivity, international trade and competition: using micro data to examine the dynamics of South African firms
    (Stellenbosch : Stellenbosch University, 2016-12) Naughtin, Tasha Lynn; Rankin, Neil; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.
    ENGLISH SUMMARY : Exports matter for economic growth. Exporting is associated with higher levels of employment, innovation, and investment. The South African government recognises the role of exports in stimulating the economy as evident in the New Growth Path, the National Exporter Development Programme, and the Medium-Term Strategic Framework 2014-2019. Despite this, relatively little is known about the dynamics of actual exporting firms in South Africa. Existing South African literature is limited due to the lack of access to comprehensive firm-level panel data. This thesis overcomes this by analysing two unique sources of substantial, detailed data on South African firms over time obtained from official government sources. This is one of the first instances in which data of this kind has been available for analysis in South Africa, and therefore it enables this thesis to study the South African exporting environment at the level of detail seen in the international literature. Firstly, this thesis re-examines the ‘stylised facts’ of exporting in the case of South Africa in more detail. In contrast to the international literature, existing South African research concludes that exporters are, in general, no more productive than non-exporters. A number of possible explanations for this missing productivity premium have been suggested in the literature, however given the previous lack of sufficient firm-level data over time, few of these explanations have been adequately tested. This thesis is now able to test some of these explanations by making use of the two official firm-level datasets. It finds that both the nature of the data used in previous studies, as well as the homogeneous treatment of exporters, play a significant role in hiding South African exporters’ productivity premium. Secondly, this thesis employs a relatively novel unsupervised machine learning technique to test the robustness of the traditional classification of firms and exporters. Research using firm-level data usually classifies firms, and exporters, based on a priori assumptions. Firms are generally grouped by size, export participation, destination and products and correlations are reported based on these classifications. This study reverses the process through letting the data identify clusters. It uses cluster analysis techniques to identify classifications of South African manufacturing firms a posteriori. The findings highlight, among other things, the usefulness of exploratory techniques such as clustering for identifying potential heterogeneity among firms, particularly within large firm-level datasets. Finally, the importance of identifying firm- and exporter-heterogeneity for policy purposes is illustrated. In particular, this thesis makes use of the substantial firm-level data, in conjunction with a natural experiment inherent in the South African tax legislature, to assess the impact of a specific tax incentive on small business investment and growth. The findings suggest that the incentive on small businesses did not have the desired effect on capital accumulation in general. However, there were unintended benefits for small exporters, a result that is important for export-growth policy and one that would have been missed had all small firms been treated as homogenous in the analysis.