Firm productivity, international trade and competition: using micro data to examine the dynamics of South African firms
| dc.contributor.advisor | Rankin, Neil | en_ZA |
| dc.contributor.author | Naughtin, Tasha Lynn | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics. | en_ZA |
| dc.date.accessioned | 2016-12-22T13:13:30Z | |
| dc.date.available | 2016-12-22T13:13:30Z | |
| dc.date.issued | 2016-12 | |
| dc.description | Thesis (DCom)--Stellenbosch University, 2016. | |
| dc.description.abstract | 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. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : Uitvoere is van belang vir ekonomiese groei. Uitvoer word met hoër vlakke van werksgeleenthede, innovasie en belegging geassosieer. Die Suid-Afrikaanse regering erken die rol van uitvoere vir die stimulasie van die ekonomie, soos gesien kan word in die Nuwe Groeipad, die Nasionale Uitvoerontwikkelingsprogram en die Mediumtermyn Strategiese Raamwerk 2014-2019. Ten spyte hiervan bestaan relatief min kennis oor die dinamiek van werklike uitvoerfirmas in Suid-Afrika. Bestaande Suid-Afrikaanse literatuur is beperk weens ʼn tekort aan toegang tot omvattende firma-vlak-paneeldata. Hierdie proefskrif oorkom hierdie probleem deur twee unieke bronne van substansiële, gedetailleerde data oor Suid-Afrikaanse firmas, oor tyd, bekom vanaf amptelike regeringsbronne, te analiseer. Hierdie is een van die eerste gevalle waartydens data van hierdie aard beskikbaar was vir analise in Suid-Afrika, en dus maak hierdie dit vir dié proefskrif moontlik om die Suid-Afrikaanse uitvoeromgewing op dieselfde vlak van detail, soos in internasionale literatuur gevind word, te bestudeer. Eerstens herbesin hierdie proefskrif oor die ‘gestileerde feite’ van uitvoer in die geval van Suid-Afrika in meer besonderhede. Teenstrydig met internasionale literatuur kom bestaande Suid-Afrikaanse navorsing tot die gevolgtrekking dat uitvoerders, oor die algemeen, nie meer produktief as nie-uitvoerders is nie. ʼn Aantal moontlike verduidelikings vir hierdie ontbrekende produktiwiteitspremie is in die literatuur voorgestel; gegewe die vorige tekort aan voldoende firma-vlak-data, oor tyd, is min van hierdie verduidelikings egter na behore getoets. Hierdie proefskrif kan nou sommige van hierdie verduidelikings toets deur gebruik te maak van dié twee amptelike firma-vlak-datastelle. Daar word bevind dat beide die aard van die data in vorige studies gebruik, sowel as die homogene hantering van uitvoerders ʼn beduidende rol in die versteking van Suid-Afrikaanse uitvoerders se produktiwiteitspremie speel. Tweedens maak hierdie proefskrif gebruik van ʼn relatief nuwe, sonder toesig masjienleer-tegniek om die robuustheid van die tradisionele klassifikasie van firmas en uitvoerder te toets. Navorsing wat van firma-vlak-data gebruik maak, klassifiseer firmas, en uitvoerders, normaalweg op a priori-aannames. Firmas word normaalweg volgens grootte, uitvoerdeelname, bestemming en produkte gegroepeer, en korrelasies word gerapporteer gebaseer op hierdie klassifikasies. Hierdie studie keer hierdie proses om, deur die data die bondels te laat identifiseer. Dit maak gebruik van bondel-analisetegnieke om klassifikasies van Suid-Afrikaanse vervaardigingsfirmas a posteriori te identifiseer. Die bevindings lig, onder andere, die bruikbaarheid van eksploratiewe tegnieke, soos bondeling vir die identifisering van potensiële heterogeniteit tussen firmas, veral binne groot firma-vlak-datastelle, uit. Laastens word die belangrikheid om firma- en uitvoerder-heterogeniteit vir beleidsdoeleindes te identifiseer, geïllustreer. Spesifiek maak hierdie proefskrif gebruik van substansiële firma-vlak-data, tesame met ʼn natuurlike eksperiment inherent tot die Suid-Afrikaanse belastingwetgewing, om die impak van ʼn spesifieke belastinginsentief op kleinsakebelegging en –groei te assesseer. Die bevindinge suggereer dat die insentief op kleinsakeondernemings nie die gewenste effek op kapitaalakkumulering, oor die algemeen, gehad het nie. Daar was egter onbedoelde voordele vir klein uitvoerders; ʼn resultaat wat belangrik is vir uitvoer-groeibeleid, en een wat gemis sou word indien alle kleinsakeondernemings as homogeen in die analise hanteer sou word. | af_ZA |
| dc.format.extent | xi, 174 pages ; illustrations, includes annexures | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/100085 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | |
| dc.rights.holder | Stellenbosch University | |
| dc.subject | UCTD | en_ZA |
| dc.subject | Economic development | en_ZA |
| dc.subject | Exports | en_ZA |
| dc.subject | Export trading companies – South Africa | en_ZA |
| dc.title | Firm productivity, international trade and competition: using micro data to examine the dynamics of South African firms | en_ZA |
| dc.type | Thesis | en_ZA |