Advertisement

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

  • Joseph Junior AdubaEmail author
  • Behrooz Asgari
Article
  • 27 Downloads

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.

Keywords

Efficiency Productivity Total-factor-productivity Learning-by-doing Technological learning Manufacturing Industry 

JEL Classification

D24 L60 O33 

Notes

Acknowledgements

We thank Japan Ministry of Economy, Trade and Industry (METI) for publishing and making data on manufacturing industries of Japan openly free for research. And Japan International Cooperation Agency (JICA) for generously providing scholarship fund to Mr. ADUBA Joseph Junior during his study at Ritsumeikan Asia Pacific University, under the ABE initiatives program.

References

  1. Adhikari DR (2005) National factors and employment relations in Japan. Japan Institute of Labour Policy and Training, TokyoGoogle Scholar
  2. Ahearne AG, Shinada N (2005) Zombie firms and economic stagnation in Japan. Institute of Economic Research Hi-Stat Discussion paper series; No. d05-95, Hitotsubashi University, Tokyo. http://hdl.handle.net/10086/13991. Retrived 15 June 2017
  3. Andress FJ (1954) The learning curve as a production tool. Harvard Business Review (January–February), pp 87–97Google Scholar
  4. Argote L (2013) Organizational learning; creating, retaining, and transferring knowledge, 2nd edn. Springer, New YorkGoogle Scholar
  5. Arrow K (1962) The economic implications of learning-by-doing. Rev Econ Stud 29(3):155–173CrossRefGoogle Scholar
  6. Asgari Behrooz, Gonzalez-Cortez Jose Luis (2012) Measurement of technological progress through analysis of learning rates: the case of the manufacturing industry in Mexico. Ritsumeikan J Asia Pac Stud 3:101–119Google Scholar
  7. Asgari B, Yen LW (2009) Accumulated knowledge and technical progress in terms of learning rate; a comparative analysis of the manufacturing industry and service industry in Malaysia. Asian J Technol Innov 17(2):71–99CrossRefGoogle Scholar
  8. Badiru BA (1992) Computational survey of univariate and multivariate learning curve models. In: IEEE transaction on engineering management, pp 176–188Google Scholar
  9. Baily MN, Hulten C, Cambell D (1992) Productivity dynamics in manufacturing plants, Brookings papers: microeconomics. University of Maryland, MarylandGoogle Scholar
  10. Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:078–1092CrossRefGoogle Scholar
  11. Barreto-Gomez TL (2001) Technological learning in energy optimization models and deployment of emerging technologies. Thesis (doctoral). Swiss Federal Institute of Technology, ZurichGoogle Scholar
  12. Braguinsky S, Ohyama A, Okazak T, Syverson C (2015) Acquisitions, productivity, and profitability: evidence from the Japanese cotton spinning industry. Am Econ Rev 105(7):2086–2119CrossRefGoogle Scholar
  13. Byun T, Kim K, Choi H (2012) Comparative analysis of the total factor productivity of manufacturing in Northeast Asian Metropolitan Areas. Growth Change 43(1):167–177CrossRefGoogle Scholar
  14. Carlsson B (1996) Technological systems and economic performance. In: Dodgson M, Rothwell R (eds) The handbook of industrial innovation. Edward Elgar, Broadheath, pp 33–53Google Scholar
  15. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 2(6):429–444CrossRefGoogle Scholar
  16. Coelli TJ, Rao PD, O’Donnel CJ, Battese GE (2005) An introduction to efficiency and productivity analysis. Springer Science, New YorkGoogle Scholar
  17. Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis; a comprehensive text with models, applications, references, and DEA-solver software. Springer, New YorkGoogle Scholar
  18. Fare R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress and efficiency change in industrialized countries. Am Econ Rev 84(1):63–83Google Scholar
  19. Fukao K (2013) Explaining Japan’s unproductive two decades. Asian Econ Policy Rev 8(2):193–213CrossRefGoogle Scholar
  20. Fukao K, Kwon HU (2006) Why did Japan’s TFP growth slow down in the lost decade? An empirical analysis based on firm-level data of manufacturing firms. Jpn Econ Rev 57(2):195–228CrossRefGoogle Scholar
  21. Hattori R, Maeda E (2000) The Japanese employment system (summary). Bank of Japan Monthly Bulletin, TokyoGoogle Scholar
  22. Ikemoto Y (1986) Technical progress and the level of technology in Asian countries. Dev Econ XXXIV(4):368–390CrossRefGoogle Scholar
  23. Jackson D (1998) Technological change, the learning curve and profitability. Edward Elgar Publishing Limited, CheltenhamGoogle Scholar
  24. Jajri I (2007) The determinant of total factor productivity growth in Malaysia. J Econ Cooper 28(3):41–58Google Scholar
  25. Japan Statistics (2014) Annual Report on the Consumer Price Index, Japan 2014. Statistical Bureau of Japan, Tokyo. http://www.stat.go.jp/english/data/cpi/report/2014np/pdf/2014np-e.pdf. Retrieved 8 May 2017
  26. Jurgen E, Kadokawa K (2010) The evolution of regional labor productivities in Japanese manufacturing, 1968–2004. Reg Stud 44(9):1189–1205CrossRefGoogle Scholar
  27. Karaoz M, Mesut A (2005) Dynamic technological learning trends in Turkish manufacturing industries. Technol Forecast Soc Chang 27(7):866–885CrossRefGoogle Scholar
  28. Kawakami A, Miyagawa T, Takizawa M (2011) Revisiting productivity differences and firm turnover: Evidence from product-based TFP measures in the Japanese manufacturing industries. RIETI Discussion Paper Series 11-E-064, TokyoGoogle Scholar
  29. Kim S (2016) Factor determinants of total factor productivity growth for the Japanese manufacturing industries. Contemp Econ Policy 34(3):572–586CrossRefGoogle Scholar
  30. Kim S, Lee K (2015) Returns to scale, markup and total factor productivity for the Japanese manufacturing industry*. Korea World Econ 16(2):195–222Google Scholar
  31. Krawiec F, Thornton J, Edesses M (1980) An investigation of learning and experience curve. Solar Energy Research Institute, ColoradoCrossRefGoogle Scholar
  32. Kwon HU, Inui T (2003) R&D and Productivity growth in Japanese manufacturing firms. Cabinet Office, Economic and Social Research Institute; ESRI Discussion Paper Series No. 44, TokyoGoogle Scholar
  33. Liu Y, Westelius N (2016) The impact of demographics on productivity and inflation in Japan. International Monetary Fund (IMF-Working Paper-WP/16/237)Google Scholar
  34. Mahadevan R (2002) A DEA approach to understanding the productivity growth of Malaysia’s manufacturing industries. Asia Pac J Manag 19:587–600CrossRefGoogle Scholar
  35. Maisom A, Arshard M (1992) Pattern of total factor productivity growth in Malaysia manufacturing industries, 1973–1989. Universiti Pertanian Malaysia, SerdangGoogle Scholar
  36. Majundar S, Asgari B (2017) Performance analysis of listed companies in the UAE-Using DEA Malmquist index approach. Am J Oper Res 7(2):133–151Google Scholar
  37. METI (2010) Japan’s Manufacturing Industry. Ministry of Economy Trade and Industry, TokyoGoogle Scholar
  38. Milana C, Nascia L, Zeli A (2013) Decomposing multifactor productivity in Italy from 1998 to 2004: evidence from large firms and SMEs using DEA. J Prod Anal 40:99–109.  https://doi.org/10.1007/s11123-013-0337-z CrossRefGoogle Scholar
  39. Mitra A, Sato H (2007) Agglomeration economies in Japan: technical efficiency, growth, and unemployment. RURDS 19(3):197–209Google Scholar
  40. Miyagawa T, Sakuragawa Y, Takizawa M (2005) Productivity and the business cycle in Japan; evidence from Japanese industry data. The Research Institute of Economy, Trade, and Industry (RIETI) Discussion Paper Series 05-E-022Google Scholar
  41. Moore T, Mirzaei A (2016) The impact of the global financial crisis on industry growth. Manch Sch 84(2):159–180CrossRefGoogle Scholar
  42. Najmabadi F, Lall S (1995) Developing industrial technology; lessons for policy and practice. The World Bank, Washington, DCGoogle Scholar
  43. OECD (2001) Measurement of aggregate and industry-level productivity growth. Organization for Economic Co-operation and Development, ParisCrossRefGoogle Scholar
  44. OECD (2011a) ISIC Rev. 3 Technology intensity definition; Classification of manufacturing industries into categories based on R&D intensities. OECD Directorate for science, technology, and industry, economic analysis, and statistics division, ParisGoogle Scholar
  45. OECD (2011b) China’s emergence as a market economy: achievements and challenges. Organization for Economic Corporation and Development (OECD), BeijingGoogle Scholar
  46. Okada Y (2005) Competition and productivity in Japanese manufacturing industries. J Jpn Int Econ 19(4):586–616CrossRefGoogle Scholar
  47. Platt L, Wilson G (1999) Technological development and the poor/marginalized; context. Intervention and participation. Technovation 19(6–7):393–401CrossRefGoogle Scholar
  48. Pramongkit P, Shawyun T, Boonmark S (2000) Analysis of technological learning for Thai manufacturing. Technovation 20(4):189–195CrossRefGoogle Scholar
  49. Rogers M (1998) The Definition and Measurement of Productivity. Melbourne Institute Working Paper No. 9/98, ParksvilleGoogle Scholar
  50. Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65–94CrossRefGoogle Scholar
  51. SriPoorni RS, Manonmani M (2014) Factors influencing productivity across the Southern States of India—an application of the discriminant function. Int J Commer Bus Manag 3(4):2319–2828Google Scholar
  52. Syverson C (2011) What determines productivity? J Econ Lit 49(2):326–365CrossRefGoogle Scholar
  53. Takii K (2011) Persistent productivity differences between firms. RIETI discussion paper series 11-E-048. The Research Institute of Economy, Trade, and IndustryGoogle Scholar
  54. Taylor ML (1961) The learning curve—a basic cost prediction tool. Natl Assoc Acc Bull 21–26Google Scholar
  55. The Economist (2009) The collapse of manufacturing. http://www.economist.com/node/13144864. Retrieved 11 May 2017
  56. Tim C (1996) A guide to DEAP version 2.1: a data envelopment analysis computer program-CEPA working papers. University of New England, ArmidaleGoogle Scholar
  57. Tomiura A (1997) Productivity in Japan’s manufacturing industry. Int J Prod Econ 52(1–2):239–246CrossRefGoogle Scholar
  58. US-EPA (2016) Cost reduction through Learning in manufacturing industries and in the manufacture of mobile sources. Working assignment No. 3-09. Fairfax: the United States Environmental Protection AgencyGoogle Scholar
  59. World Bank (2012) The role of emerging-market economy demand during the post-2005 boom. World Bank, Washington, DCCrossRefGoogle Scholar
  60. World Bank (2016) Data, The World Bank. Retrieved from The World Bank. http://data.worldbank.org/indicator/NV.IND.MANF.ZS?locations=JP. Retrieved 16 Feb 2017
  61. Wright TP (1936) Factors affecting the cost of airplanes. J Aeronaut Sci 3:122–128CrossRefGoogle Scholar
  62. Wyer R (1953) Learning curve helps figure profits, control cost. Natl Assoc Cost Acc Bull 35(4):490–502Google Scholar
  63. Yelle LE (1979) The learning curve: historical review and comprehensive survey. Decis Sci 10(2):302–328CrossRefGoogle Scholar
  64. Yoshino N, Taghizadeh-Hesary F (2015) Japan’s lost decade: Lessons for other economies. Asian Development Bank Institute (ADBI Working Paper 521), TokyoGoogle Scholar

Copyright information

© The Japan Section of the Regional Science Association International 2019

Authors and Affiliations

  1. 1.Graduate School of EconomicsRitsumeikan UniversityKusatsuJapan
  2. 2.Graduate School of ManagementRitsumeikan Asia Pacific UniversityBeppuJapan

Personalised recommendations