IDEAS home Printed from https://ideas.repec.org/a/spr/apjors/v4y2020i2d10.1007_s41685-019-00131-w.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s41685-019-00131-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s41685-019-00131-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Ryota Hattori & Eiji Maeda, 2000. "The Japanese Employment System," Bank of Japan Research Papers 2000-01-28, Bank of Japan.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.
    18. 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.
    19. Katsuya TAKII, 2011. "Persistent Productivity Differences Between Firms," Discussion papers 11048, Research Institute of Economy, Trade and Industry (RIETI).
    20. Yosuke Okada, 2005. "Competition and Productivity in Japanese Manufacturing Industries," NBER Working Papers 11540, National Bureau of Economic Research, Inc.
    21. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masayuki MORIKAWA, 2019. "Price Competition vs. Quality Competition: Evidence from a Survey," Discussion papers 19075, Research Institute of Economy, Trade and Industry (RIETI).
    2. Morikawa, Masayuki, 2021. "Price competition vs. quality competition: Evidence from firm surveys," Journal of Economics and Business, Elsevier, vol. 116(C).
    3. Wei Shao & Yangyang Sun & Xiao Bai & Muhammad Abubakr Naeem & Farhad Taghizadeh‐Hesary, 2023. "Zombie enterprises, crowding out effect, and total factor productivity: Empirical evidence from Chinese manufacturing listed companies," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4512-4531, October.
    4. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    5. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    6. Ravelojaona, Paola, 2019. "On constant elasticity of substitution – Constant elasticity of transformation Directional Distance Functions," European Journal of Operational Research, Elsevier, vol. 272(2), pages 780-791.
    7. Peter Fernandes Wanke & Rebecca de Mattos, 2014. "Capacity Issues and Efficiency Drivers in Brazilian Bulk Terminals," Brazilian Business Review, Fucape Business School, vol. 11(5), pages 72-98, October.
    8. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    9. George Halkos & Roman Matousek & Nickolaos Tzeremes, 2016. "Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 47-77, January.
    10. Duk Hee Lee & Il Won Seo & Ho Chull Choe & Hee Dae Kim, 2012. "Collaboration network patterns and research performance: the case of Korean public research institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 925-942, June.
    11. Mehdiloozad, Mahmood & Zhu, Joe & Sahoo, Biresh K., 2018. "Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data," European Journal of Operational Research, Elsevier, vol. 265(2), pages 644-654.
    12. Subhash C. Ray & Lei Chen, 2015. "Data Envelopment Analysis for Performance Evaluation: A Child’s Guide," Springer Books, in: Subhash C. Ray & Subal C. Kumbhakar & Pami Dua (ed.), Benchmarking for Performance Evaluation, edition 127, chapter 0, pages 75-116, Springer.
    13. Zhou, Xiaoyan & Zhang, Jie & Li, Junpeng, 2013. "Industrial structural transformation and carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 57(C), pages 43-51.
    14. Aurélie Corne & Olga Goncalves & Nicolas Peypoch, 2020. "Evaluating the performance drivers of French ski resorts: A hierarchical approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(3), pages 389-405, April.
    15. Chen, Xiaodong & Miao, Zhuang & Wu, Ge & Zhu, Pengyu, 2024. "City-level green growth accounting: Evidence from China's thirteen urban agglomerations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    16. Rut Atayde & Rafael Garduño & Eduardo Robles & Pluvia Zúñiga, 2021. "Market competition and firm productivity and innovation: Responses in Mexican manufacturing industries," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(4), pages 1185-1214, August.
    17. Alessandra Cepparulo & Gilles Mourre, 2020. "How and How Much? The Growth-Friendliness of Public Spending through the Lens," European Economy - Discussion Papers 132, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    18. Mohsen Afsharian & Anna Kryvko & Peter Reichling, 2011. "Efficiency and Its Impact on the Performance of European Commercial Banks," FEMM Working Papers 110018, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    19. Miningou, Élisé Wendlassida & Vierstraete, Valérie, 2013. "Households' living situation and the efficient provision of primary education in Burkina Faso," Economic Modelling, Elsevier, vol. 35(C), pages 910-917.
    20. Tao Ding & Ya Chen & Huaqing Wu & Yuqi Wei, 2018. "Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach," Annals of Operations Research, Springer, vol. 268(1), pages 497-511, September.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:apjors:v:4:y:2020:i:2:d:10.1007_s41685-019-00131-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.