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Trade, Technology and Wage Inequality in the South African Manufacturing Sectors

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Abstract

This paper advances on previous work on the effects of trade and technical change on labour markets within the framework of Heckscher-Ohlin trade theory. First, we employ dynamic heterogeneous panel estimation techniques not previously used in this context, which separate Heckscher-Ohlin-based long run relationships from short run dynamics that are heterogeneous across sectors. Second, we provide evidence for an unskilled labor abundant developing country that allows comparison of the results against developed country evidence. Third, we consider the appropriateness of alternative approaches and examine endogeneity issues in the impact of technology and price changes on factor returns. For South African manufacturing we find that output prices increase most strongly in sectors that are labor intensive. Our results further suggest that trade-mandated earnings increases are positive for labor, and negative for capital. By contrast technology has mandated negative earnings increases for both factors. We also find that separation of different demand side factors collectively constituting globalization is useful in understanding the impact of trade, and taking account of endogeneity is important in isolating factor and sector bias of technological change.

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  • Johannes Fedderke & Yongcheol Shin & Prabhat Vaze, 2003. "Trade, Technology and Wage Inequality in the South African Manufacturing Sectors," Edinburgh School of Economics Discussion Paper Series 106, Edinburgh School of Economics, University of Edinburgh.
    • Handle: RePEc:edn:esedps:106
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    Cited by:

    1. Rattsø, Jørn & Stokke, Hildegunn, 2009. "Wage inequality, comparative advantage and skill biased technical change in South Africa," Proceedings of the German Development Economics Conference, Frankfurt a.M. 2009 34, Verein für Socialpolitik, Research Committee Development Economics.
    2. Ana Barreira & Rui Nuno Baleiras, 2000. "Cycles On Public Expenditure Composition Within the European Union," Regional and Urban Modeling 283600004, EcoMod.
    3. Jørn Rattsø & Hildegunn E. Stokke, 2013. "Trade, Skill Biased Technical Change and Wage Inequality in South Africa," Review of International Economics, Wiley Blackwell, vol. 21(3), pages 419-431, August.
    4. Sitikantha Pattanaik & Silu Muduli & Soumyajit Ray, 2020. "Inflation expectations of households: do they influence wage-price dynamics in India?," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 13(3), pages 244-263, September.
    5. George Clarke & James Habyarimana & David Kaplan & Vijaya Ramachandran, 2008. "Why isn't South Africa growing faster? Microeconomic evidence from a firm survey," Journal of International Development, John Wiley & Sons, Ltd., vol. 20(7), pages 837-868.
    6. Hildegunn Ekroll Stokke & Jørn Rattsø, 2004. "Ramsey model of barriers to growth and skill-biased income distribution in South Africa," Working Paper Series 4604, Department of Economics, Norwegian University of Science and Technology, revised 07 Feb 2005.
    7. Thurlow, James, 2006. "Has trade liberalization in South Africa affected men and women differently?," DSGD discussion papers 36, International Food Policy Research Institute (IFPRI).

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    More about this item

    Keywords

    trade; total factor productivity; Stolper-Samuelson theorem; mandated factor earnings changes; dynamic heterogeneous panel data; pooled mean group estimation;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • F16 - International Economics - - Trade - - - Trade and Labor Market Interactions

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