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Productivity and technological progress of the Japanese manufacturing industries, 2000–2014: estimation with data envelopment analysis and log-linear learning model

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  • Joseph Junior Aduba

    (Ritsumeikan University)

  • Behrooz Asgari

    (Ritsumeikan Asia Pacific University)

Abstract

How has the Japanese manufacturing sector fared in productivity and technological learning in recent years? To answer this, we summarized the manufacturing industry into 3-digit sub-sector (25 sub-sectors) and evaluated the entire manufacturing industry. Our study covers 15 years of production cycles (2000–2014). Using data envelopment analysis and loglinear learning models, we empirically estimated the productivity and technological learning of these industries. The result shows negative (− 0.6%) total factor productivity (TFP) growth between 2000 and 2014. TFP was particularly affected by 2001, and 2008/2009 financial crisis. TFP regress also deepened in recent years (2011–2014) which we blamed on both internal and external shocks in the system. We showed that positive TFP observed in other years resulted from technical progress and efficiency improvement. Industry-level results were consistent with the annual mean result which suggest a common economic downturn. Estimated progress ratios from learning models show that individual industry exhibits unique learning rates, with some industries showing technological learning (i.e., decreasing unit cost of production) between 2000 and 2007 and others between 2010 and 2014. Industries viz. production machinery, electrical devices and circuit, chemical, pharmaceutical, and food manufacturing showed sustained learning between 2001 and 2013, implying huge cost saving as outputs expand. The overall result, however, showed that learning got worst and was lost at some point between 2008 and 2014. We conclude that productivity differentials explained by learning rates show that technological progress and innovations in Japanese manufacturing were capital intensive and cost inefficient and that Japanese manufacturing industry has not fully regained its competitiveness as the world’s leading manufacturing hub. We argued that for productivity improvement in Japanese manufacturing industries, there is a need for policy thrust to restore and ensure sustained learning within and across the industries.

Suggested Citation

  • Joseph Junior Aduba & Behrooz Asgari, 2020. "Productivity and technological progress of the Japanese manufacturing industries, 2000–2014: estimation with data envelopment analysis and log-linear learning model," Asia-Pacific Journal of Regional Science, Springer, vol. 4(2), pages 343-387, June.
    • Handle: RePEc:spr:apjors:v:4:y:2020:i:2:d:10.1007_s41685-019-00131-w
    DOI: 10.1007/s41685-019-00131-w
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    1. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    2. Chad Syverson, 2011. "What Determines Productivity?," Journal of Economic Literature, American Economic Association, vol. 49(2), pages 326-365, June.
    3. Serguey Braguinsky & Atsushi Ohyama & Tetsuji Okazaki & Chad Syverson, 2015. "Acquisitions, Productivity, and Profitability: Evidence from the Japanese Cotton Spinning Industry," American Economic Review, American Economic Association, vol. 105(7), pages 2086-2119, July.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Alan Ahearne & Naoki Shinada, 2005. "Zombie firms and economic stagnation in Japan," International Economics and Economic Policy, Springer, vol. 2(4), pages 363-381, December.
    6. Tomoe Moore & Ali Mirzaei, 2016. "The Impact of the Global Financial Crisis on Industry Growth," Manchester School, University of Manchester, vol. 84(2), pages 159-180, March.
    7. Kyoji Fukao & Hyeog Ug Kwon, 2006. "Why Did Japan'S Tfp Growth Slow Down In The Lost Decade? An Empirical Analysis Based On Firm‐Level Data Of Manufacturing Firms," The Japanese Economic Review, Japanese Economic Association, vol. 57(2), pages 195-228, June.
    8. Tsutomu Miyagawa & Yukie Sakuragawa & Miho Takizawa, 2005. "Productivity and the Business Cycle in Japan: Evidence from Japanese Industry Data," Hi-Stat Discussion Paper Series d05-108, Institute of Economic Research, Hitotsubashi University.
    9. Tsutomu Miyagawa & Yukie Sakuragawa & Miho Takizawa, 2005. "Productivity and the Business Cycle in Japan -Evidence from Japanese Industry Data -," Discussion papers 05022, Research Institute of Economy, Trade and Industry (RIETI).
    10. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    11. Taegeun Byun & Kabsung Kim & Hyejin Choi, 2012. "Comparative Analysis of the Total Factor Productivity of Manufacturing in Northeast Asian Metropolitan Areas," Growth and Change, Wiley Blackwell, vol. 43(1), pages 167-177, March.
    12. TAKII Katsuya, 2011. "Persistent Productivity Differences Between Firms," Discussion papers 11048, Research Institute of Economy, Trade and Industry (RIETI).
    13. Yosuke Okada, 2005. "Competition and Productivity in Japanese Manufacturing Industries," NBER Working Papers 11540, National Bureau of Economic Research, Inc.
    14. Ryota Hattori & Eiji Maeda, 2000. "The Japanese Employment System," Bank of Japan Research Papers 2000-01-28, Bank of Japan.
    15. Carlo Milana & Leopoldo Nascia & Alessandro Zeli, 2013. "Decomposing multifactor productivity in Italy from 1998 to 2004: evidence from large firms and SMEs using DEA," Journal of Productivity Analysis, Springer, vol. 40(1), pages 99-109, August.
    16. Okada, Yosuke, 2005. "Competition and productivity in Japanese manufacturing industries," Journal of the Japanese and International Economies, Elsevier, vol. 19(4), pages 586-616, December.
    17. Yihan Liu & Niklas Westelius, 2017. "The Impact of Demographics on Productivity and Inflation in Japan," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 1-16, June.
    18. Arup Mitra & Hajime Sato, 2007. "Agglomeration Economies In Japan: Technical Efficiency, Growth And Unemployment," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 19(3), pages 197-209, November.
    19. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    20. Sangho Kim, 2016. "Factor Determinants Of Total Factor Productivity Growth For The Japanese Manufacturing Industry," Contemporary Economic Policy, Western Economic Association International, vol. 34(3), pages 572-586, July.
    21. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, April.
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    Cited by:

    1. Joseph Jr. Aduba & Hiroshi Izawa, 2021. "Impact of learning through credit and value creation on the efficiency of Japanese commercial banks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-37, December.
    2. Yanying Wang & Qingyang Wu, 2024. "Robots, firm relocation, and air pollution: unveiling the unintended spatial spillover effects of emerging technology," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.

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    More about this item

    Keywords

    Efficiency; Productivity; Total-factor-productivity; Learning-by-doing; Technological learning; Manufacturing Industry;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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