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The learning curve and the yield factor: the case of Korea's semiconductor industry

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  • Sangho Chung

Abstract

This article attempts to find out how the Korean economy has grown so rapidly in a short time span of less than 30 years. For that purpose, it focuses on the development of the Korean semiconductor industry-more specifically, the memory-chip segment of the industry-as a case in point. To test the hypothesis that the learning curve effects have been significant in the memory-chip industry, 'yield factor' (the ratio of sellable chips to total chips in a wafer) in semiconductor production is used as a measure of the learning progression. That is, by tracing how the yield factor for each generation of memory chips has increased, one is able to see how well the Korean chip makers have exploited the learning effects. This article improves the learning-by-doing modelling by introducing a richer set of yield data; and the unit of analysis employed throughout the article is at the firm level, which is not common in the literature dealing with East Asian development as well as the economics of technology, thus enhancing understanding of the industry dynamics.

Suggested Citation

  • Sangho Chung, 2001. "The learning curve and the yield factor: the case of Korea's semiconductor industry," Applied Economics, Taylor & Francis Journals, vol. 33(4), pages 473-483.
    • Handle: RePEc:taf:applec:v:33:y:2001:i:4:p:473-483
    DOI: 10.1080/00036840122474
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    Citations

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    Cited by:

    1. Liu, Wen-Hsien & Chyi, Yih-Luan, 2006. "A Markov regime-switching model for the semiconductor industry cycles," Economic Modelling, Elsevier, vol. 23(4), pages 569-578, July.
    2. Manda, A.B. & Uzsoy, Reha, 2021. "Managing product transitions with learning and congestion effects," International Journal of Production Economics, Elsevier, vol. 239(C).
    3. J H Chen & T S Jan, 2005. "A system dynamics model of the semiconductor industry development in Taiwan," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1141-1150, October.
    4. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
    5. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    6. Lundmark, Robert, 2008. "Empirical specification of cost reductions associated with accumulated knowledge in the Swedish kraft paper industry," Forest Policy and Economics, Elsevier, vol. 10(7-8), pages 460-466, October.
    7. M. Aubry & P. Renou-Maissant, 2013. "Investigating the semiconductor industry cycles," Applied Economics, Taylor & Francis Journals, vol. 45(21), pages 3058-3067, July.
    8. Gurkan Calmasur & Meryem Emre Aysin, 2020. "Regional Technological Learning in Turkish Cement Industry," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 204-216.

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