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.
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20 September 2019
In the original publication of the article, the equation 12 was published incorrectly and the footnote was missing. The correct version of equation 12 and footnote is as below.
Notes
All variables except number of employees are measured in yen.
The value of \(\lambda\) indicates the technical biases associated with production expansion. \(\lambda = 1\) indicate neutrality in technological progress whereas \(\lambda > 1\), suggests that capital labour ratio proportionally increases as output expands (see Pramongkit et al. 2000; Karaoz and Mesut 2005).
Published annually from 2007 onward and downloadable at http://www.meti.go.jp/english/report/index_whitepaper.html#monodzukuri.
References
Adhikari DR (2005) National factors and employment relations in Japan. Japan Institute of Labour Policy and Training, Tokyo
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
Andress FJ (1954) The learning curve as a production tool. Harvard Business Review (January–February), pp 87–97
Argote L (2013) Organizational learning; creating, retaining, and transferring knowledge, 2nd edn. Springer, New York
Arrow K (1962) The economic implications of learning-by-doing. Rev Econ Stud 29(3):155–173
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 3:101–119
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):71–99
Badiru BA (1992) Computational survey of univariate and multivariate learning curve models. In: IEEE transaction on engineering management, pp 176–188
Baily MN, Hulten C, Cambell D (1992) Productivity dynamics in manufacturing plants, Brookings papers: microeconomics. University of Maryland, Maryland
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:078–1092
Barreto-Gomez TL (2001) Technological learning in energy optimization models and deployment of emerging technologies. Thesis (doctoral). Swiss Federal Institute of Technology, Zurich
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):2086–2119
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):167–177
Carlsson B (1996) Technological systems and economic performance. In: Dodgson M, Rothwell R (eds) The handbook of industrial innovation. Edward Elgar, Broadheath, pp 33–53
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 2(6):429–444
Coelli TJ, Rao PD, O’Donnel CJ, Battese GE (2005) An introduction to efficiency and productivity analysis. Springer Science, New York
Cooper WW, Seiford LM, Tone K (2007) Data envelopment analysis; a comprehensive text with models, applications, references, and DEA-solver software. Springer, New York
Fare R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress and efficiency change in industrialized countries. Am Econ Rev 84(1):63–83
Fukao K (2013) Explaining Japan’s unproductive two decades. Asian Econ Policy Rev 8(2):193–213
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):195–228
Hattori R, Maeda E (2000) The Japanese employment system (summary). Bank of Japan Monthly Bulletin, Tokyo
Ikemoto Y (1986) Technical progress and the level of technology in Asian countries. Dev Econ XXXIV(4):368–390
Jackson D (1998) Technological change, the learning curve and profitability. Edward Elgar Publishing Limited, Cheltenham
Jajri I (2007) The determinant of total factor productivity growth in Malaysia. J Econ Cooper 28(3):41–58
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
Jurgen E, Kadokawa K (2010) The evolution of regional labor productivities in Japanese manufacturing, 1968–2004. Reg Stud 44(9):1189–1205
Karaoz M, Mesut A (2005) Dynamic technological learning trends in Turkish manufacturing industries. Technol Forecast Soc Chang 27(7):866–885
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, Tokyo
Kim S (2016) Factor determinants of total factor productivity growth for the Japanese manufacturing industries. Contemp Econ Policy 34(3):572–586
Kim S, Lee K (2015) Returns to scale, markup and total factor productivity for the Japanese manufacturing industry*. Korea World Econ 16(2):195–222
Krawiec F, Thornton J, Edesses M (1980) An investigation of learning and experience curve. Solar Energy Research Institute, Colorado
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, Tokyo
Liu Y, Westelius N (2016) The impact of demographics on productivity and inflation in Japan. International Monetary Fund (IMF-Working Paper-WP/16/237)
Mahadevan R (2002) A DEA approach to understanding the productivity growth of Malaysia’s manufacturing industries. Asia Pac J Manag 19:587–600
Maisom A, Arshard M (1992) Pattern of total factor productivity growth in Malaysia manufacturing industries, 1973–1989. Universiti Pertanian Malaysia, Serdang
Majundar S, Asgari B (2017) Performance analysis of listed companies in the UAE-Using DEA Malmquist index approach. Am J Oper Res 7(2):133–151
METI (2010) Japan’s Manufacturing Industry. Ministry of Economy Trade and Industry, Tokyo
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
Mitra A, Sato H (2007) Agglomeration economies in Japan: technical efficiency, growth, and unemployment. RURDS 19(3):197–209
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-022
Moore T, Mirzaei A (2016) The impact of the global financial crisis on industry growth. Manch Sch 84(2):159–180
Najmabadi F, Lall S (1995) Developing industrial technology; lessons for policy and practice. The World Bank, Washington, DC
OECD (2001) Measurement of aggregate and industry-level productivity growth. Organization for Economic Co-operation and Development, Paris
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, Paris
OECD (2011b) China’s emergence as a market economy: achievements and challenges. Organization for Economic Corporation and Development (OECD), Beijing
Okada Y (2005) Competition and productivity in Japanese manufacturing industries. J Jpn Int Econ 19(4):586–616
Platt L, Wilson G (1999) Technological development and the poor/marginalized; context. Intervention and participation. Technovation 19(6–7):393–401
Pramongkit P, Shawyun T, Boonmark S (2000) Analysis of technological learning for Thai manufacturing. Technovation 20(4):189–195
Rogers M (1998) The Definition and Measurement of Productivity. Melbourne Institute Working Paper No. 9/98, Parksville
Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65–94
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):2319–2828
Syverson C (2011) What determines productivity? J Econ Lit 49(2):326–365
Takii K (2011) Persistent productivity differences between firms. RIETI discussion paper series 11-E-048. The Research Institute of Economy, Trade, and Industry
Taylor ML (1961) The learning curve—a basic cost prediction tool. Natl Assoc Acc Bull 21–26
The Economist (2009) The collapse of manufacturing. http://www.economist.com/node/13144864. Retrieved 11 May 2017
Tim C (1996) A guide to DEAP version 2.1: a data envelopment analysis computer program-CEPA working papers. University of New England, Armidale
Tomiura A (1997) Productivity in Japan’s manufacturing industry. Int J Prod Econ 52(1–2):239–246
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 Agency
World Bank (2012) The role of emerging-market economy demand during the post-2005 boom. World Bank, Washington, DC
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
Wright TP (1936) Factors affecting the cost of airplanes. J Aeronaut Sci 3:122–128
Wyer R (1953) Learning curve helps figure profits, control cost. Natl Assoc Cost Acc Bull 35(4):490–502
Yelle LE (1979) The learning curve: historical review and comprehensive survey. Decis Sci 10(2):302–328
Yoshino N, Taghizadeh-Hesary F (2015) Japan’s lost decade: Lessons for other economies. Asian Development Bank Institute (ADBI Working Paper 521), Tokyo
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.
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Appendices
Appendix A: Test for return to scale production technologies
Panel A: Output-value added | |||||||
---|---|---|---|---|---|---|---|
lnva | Coef. | St.Err. | t value | p value | (95% Conf. Interval) | Sig | |
lnl | 6.924 | 0.713 | 9.71 | 0.000 | 5.526 | 8.323 | *** |
lnk | 0.425 | 0.053 | 8.07 | 0.000 | 0.322 | 0.528 | *** |
Constant | − 8.912 | 1.073 | − 8.30 | 0.000 | − 11.016 | − 6.809 | *** |
Mean dependent var | 14.621 | SD dependent var | 1.393 | ||||
Number of obs | 375.000 | Chi square | 2140.085 | ||||
Prob > Chi2 | 0.000 | Akaike crit. (AIC) | − 49.022 |
lnva | Coef. | Std.Err. | z | p > z | (95% Conf. Interval) | |
---|---|---|---|---|---|---|
IRS/DRS test | 7.349 | 0.665 | 11.050 | 0.000 | 6.046 | 8.653 |
Chi2 | 91.15 | Prob > Chi2 | 0.000 |
Panel B: Output-revenue | |||||||
---|---|---|---|---|---|---|---|
lnR | Coef. | St.Err. | t value | p value | (95% Conf. Interval) | Sig | |
lnl | 6.004 | 0.788 | 7.61 | 0.000 | 4.458 | 7.549 | *** |
lnk | 0.499 | 0.054 | 9.16 | 0.000 | 0.392 | 0.606 | *** |
Constant | − 4.923 | 1.194 | − 4.12 | 0.000 | − 7.264 | − 2.582 | *** |
Mean dependent var | 16.189 | SD dependent var | 1.414 | ||||
Number of obs | 375.000 | Chi square | 1333.981 | ||||
Prob > Chi2 | 0.000 | Akaike crit. (AIC) | − 430.461 |
lnR | Coef. | Std.Err. | z | P > z | (95% Conf. Interval) | |
---|---|---|---|---|---|---|
IRS/DRS test | 6.503 | 0.740 | 8.790 | 0.000 | 5.052 | 7.953 |
Chi2 | 55.27 | Prob > Chi2 | 0.0000 |
Appendix B: Estimated technical efficiency using VRS production technology assumption
Manufacturing industry | 00 | 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Business oriented machinery | 77 | 76 | 68 | 64 | 64 | 53 | 54 | 46 | 42 | 50 | 49 | 39 | 36 | 34 | 37 |
Ceramic, stone and clay products | 43 | 40 | 38 | 35 | 34 | 27 | 28 | 26 | 26 | 31 | 30 | 25 | 26 | 25 | 27 |
Chemical and allied products | 84 | 83 | 81 | 75 | 80 | 72 | 75 | 75 | 78 | 85 | 89 | 87 | 81 | 81 | 79 |
Electrical machinery, equipment and supplies | 100 | 100 | 100 | 100 | 100 | 63 | 72 | 64 | 59 | 69 | 70 | 64 | 66 | 69 | 74 |
Electronic parts, devices and electronic circuits | 75 | 64 | 65 | 59 | 58 | 43 | 45 | 44 | 40 | 50 | 48 | 46 | 47 | 44 | 49 |
General-purpose machinery | 67 | 66 | 62 | 60 | 64 | 36 | 40 | 37 | 39 | 44 | 40 | 36 | 36 | 32 | 37 |
Information and comm. electronic equipment | 93 | 99 | 96 | 99 | 94 | 86 | 89 | 87 | 90 | 97 | 95 | 65 | 61 | 57 | 51 |
Iron and steel | 60 | 57 | 54 | 52 | 57 | 54 | 59 | 62 | 70 | 63 | 74 | 68 | 55 | 56 | 62 |
Production machinery | 64 | 57 | 51 | 54 | 58 | 47 | 51 | 48 | 45 | 43 | 45 | 39 | 39 | 35 | 43 |
Transport equipment | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Beverages, tobacco and feed | 75 | 75 | 69 | 70 | 65 | 47 | 46 | 41 | 46 | 60 | 61 | 42 | 43 | 41 | 43 |
Food | 75 | 83 | 83 | 77 | 76 | 69 | 72 | 68 | 75 | 94 | 92 | 85 | 86 | 83 | 88 |
Furniture and fixtures | 75 | 77 | 74 | 71 | 66 | 54 | 60 | 52 | 56 | 68 | 59 | 58 | 62 | 56 | 59 |
Leather tanning, leather products and fur skins | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Lumber and wood products | 79 | 80 | 88 | 76 | 69 | 59 | 72 | 53 | 55 | 66 | 60 | 58 | 60 | 59 | 62 |
Miscellaneous manufacturing industries | 71 | 79 | 66 | 63 | 51 | 41 | 48 | 50 | 54 | 63 | 49 | 39 | 44 | 37 | 39 |
Printing and allied industries | 63 | 65 | 58 | 51 | 49 | 39 | 41 | 37 | 39 | 54 | 45 | 38 | 40 | 36 | 35 |
Pulp, paper and paper products | 41 | 40 | 39 | 36 | 34 | 27 | 29 | 28 | 29 | 38 | 35 | 30 | 30 | 30 | 32 |
Textile mill products | 45 | 43 | 39 | 37 | 37 | 25 | 30 | 27 | 29 | 53 | 45 | 27 | 29 | 26 | 27 |
Fabricated metal and products | 53 | 54 | 53 | 45 | 51 | 43 | 45 | 44 | 44 | 56 | 51 | 45 | 48 | 44 | 48 |
Non-ferrous metals and products | 43 | 42 | 38 | 36 | 37 | 36 | 51 | 53 | 46 | 49 | 59 | 49 | 45 | 43 | 46 |
Petroleum and coal products | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
Plastic products | 71 | 67 | 57 | 52 | 55 | 46 | 48 | 42 | 41 | 46 | 41 | 37 | 41 | 38 | 40 |
Ruber products | 39 | 46 | 40 | 33 | 32 | 26 | 28 | 27 | 29 | 31 | 30 | 26 | 27 | 23 | 25 |
Industry average | 70 | 70 | 67 | 64 | 64 | 54 | 58 | 55 | 55 | 63 | 61 | 54 | 54 | 52 | 54 |
Appendix C: Summary of Malmquist productivity index by industrial groups*
2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Panel A: High tech industries | ||||||||||||||
Business oriented machinery | ||||||||||||||
EFFCH | 0.981 | 0.887 | 0.939 | 1.016 | 0.825 | 1.016 | 0.850 | 0.911 | 1.197 | 0.985 | 0.783 | 0.935 | 0.947 | 1.082 |
TECHCH | 0.961 | 1.098 | 1.112 | 1.053 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.942 | 0.974 | 1.044 | 1.070 | 1.003 | 0.982 | 0.947 | 0.862 | 0.835 | 1.199 | 0.940 | 0.863 | 1.036 | 0.976 |
Electronic dev. and electronic circuits | ||||||||||||||
EFFCH | 0.848 | 1.019 | 0.911 | 0.984 | 0.741 | 1.050 | 0.872 | 1.012 | 1.256 | 0.839 | 0.772 | 1.026 | 0.946 | 1.150 |
TECHCH | 0.961 | 1.098 | 1.123 | 1.043 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.815 | 1.118 | 1.023 | 1.027 | 0.901 | 1.014 | 0.971 | 0.957 | 0.876 | 1.021 | 0.927 | 0.947 | 1.036 | 1.037 |
Info. and comm. Electronic equipment | ||||||||||||||
EFFCH | 0.707 | 0.915 | 0.901 | 1.032 | 0.863 | 1.027 | 0.968 | 0.954 | 1.186 | 0.846 | 0.840 | 0.997 | 0.885 | 0.993 |
TECHCH | 0.984 | 1.098 | 1.089 | 1.072 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.695 | 1.004 | 0.982 | 1.106 | 1.050 | 0.992 | 1.078 | 0.902 | 0.828 | 1.030 | 1.009 | 0.921 | 0.968 | 0.896 |
Pharmaceutical industries | ||||||||||||||
EFFCH | 0.970 | 0.942 | 0.858 | 0.993 | 0.847 | 1.050 | 0.962 | 1.013 | 1.308 | 0.886 | 0.792 | 1.067 | 0.955 | 1.077 |
TECHCH | 0.998 | 1.098 | 1.085 | 1.078 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.968 | 1.034 | 0.930 | 1.070 | 1.030 | 1.014 | 1.072 | 0.959 | 0.913 | 1.078 | 0.951 | 0.985 | 1.045 | 0.972 |
Panel B: Medium high-tech industries | ||||||||||||||
General-purpose machinery | ||||||||||||||
EFFCH | 0.950 | 0.891 | 0.948 | 1.025 | 0.661 | 1.090 | 0.942 | 1.040 | 1.121 | 0.929 | 0.890 | 0.990 | 0.893 | 1.172 |
TECHCH | 0.961 | 1.098 | 1.114 | 1.050 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.913 | 0.978 | 1.056 | 1.077 | 0.803 | 1.053 | 1.050 | 0.984 | 0.782 | 1.131 | 1.069 | 0.915 | 0.977 | 1.057 |
Production machinery | ||||||||||||||
EFFCH | 0.892 | 0.903 | 1.045 | 1.079 | 0.811 | 1.092 | 0.932 | 0.938 | 0.954 | 1.056 | 0.861 | 0.996 | 0.900 | 1.231 |
TECHCH | 0.961 | 1.098 | 1.116 | 1.048 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.857 | 0.992 | 1.166 | 1.130 | 0.985 | 1.055 | 1.039 | 0.887 | 0.666 | 1.285 | 1.033 | 0.920 | 0.985 | 1.110 |
Electrical machinery, equip. | ||||||||||||||
EFFCH | 0.963 | 0.945 | 1.099 | 0.853 | 0.517 | 1.180 | 0.902 | 0.920 | 1.313 | 0.873 | 0.743 | 1.125 | 1.019 | 1.106 |
TECHCH | 0.961 | 1.098 | 1.121 | 1.044 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.925 | 1.037 | 1.233 | 0.890 | 0.629 | 1.140 | 1.005 | 0.871 | 0.916 | 1.063 | 0.892 | 1.039 | 1.115 | 0.997 |
Chemical | ||||||||||||||
EFFCH | 0.970 | 0.942 | 0.858 | 0.993 | 0.847 | 1.050 | 0.962 | 1.013 | 1.308 | 0.886 | 0.792 | 1.067 | 0.955 | 1.077 |
TECHCH | 0.998 | 1.098 | 1.085 | 1.078 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.968 | 1.034 | 0.930 | 1.070 | 1.030 | 1.014 | 1.072 | 0.959 | 0.913 | 1.078 | 0.951 | 0.985 | 1.045 | 0.972 |
Transport equipment | ||||||||||||||
EFFCH | 1.026 | 0.995 | 0.894 | 0.940 | 0.814 | 1.073 | 0.982 | 0.958 | 1.278 | 0.876 | 0.838 | 1.070 | 0.878 | 1.001 |
TECHCH | 0.982 | 1.098 | 1.097 | 1.067 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 1.007 | 1.093 | 0.981 | 1.004 | 0.989 | 1.036 | 1.095 | 0.906 | 0.892 | 1.066 | 1.006 | 0.988 | 0.961 | 0.903 |
Panel C: Medium low-tech industries | ||||||||||||||
Iron and steel | ||||||||||||||
EFFCH | 0.913 | 0.976 | 0.934 | 1.132 | 0.888 | 1.017 | 1.046 | 1.171 | 0.924 | 1.001 | 0.876 | 0.910 | 1.038 | 1.117 |
TECHCH | 1.016 | 1.098 | 1.075 | 1.087 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.927 | 1.071 | 1.004 | 1.230 | 1.079 | 0.982 | 1.166 | 1.107 | 0.645 | 1.218 | 1.052 | 0.840 | 1.136 | 1.008 |
Petroleum and coal products | ||||||||||||||
EFFCH | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
TECHCH | 1.108 | 1.050 | 1.026 | 1.104 | 1.189 | 1.013 | 1.100 | 0.905 | 0.716 | 1.196 | 1.180 | 0.974 | 1.000 | 0.925 |
TFPCH | 1.108 | 1.050 | 1.026 | 1.104 | 1.189 | 1.013 | 1.100 | 0.905 | 0.716 | 1.196 | 1.180 | 0.974 | 1.113 | 0.925 |
Plastic products | ||||||||||||||
EFFCH | 0.937 | 0.850 | 0.903 | 1.074 | 0.828 | 1.040 | 0.890 | 0.972 | 1.123 | 0.878 | 0.916 | 1.094 | 0.925 | 1.061 |
TECHCH | 0.961 | 1.098 | 1.116 | 1.044 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.000 | 0.902 |
TFPCH | 0.901 | 0.933 | 1.008 | 1.121 | 1.006 | 1.004 | 0.992 | 0.920 | 0.784 | 1.069 | 1.100 | 1.010 | 1.012 | 0.957 |
Rubber products | ||||||||||||||
EFFCH | 1.178 | 0.888 | 0.806 | 0.976 | 0.803 | 1.084 | 0.969 | 1.065 | 1.091 | 0.978 | 0.840 | 1.045 | 0.870 | 1.088 |
TECHCH | 0.981 | 1.098 | 1.095 | 1.069 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.000 | 0.902 |
TFPCH | 1.155 | 0.975 | 0.883 | 1.044 | 0.976 | 1.047 | 1.080 | 1.008 | 0.762 | 1.191 | 1.009 | 0.965 | 0.952 | 0.982 |
Non-ferrous metals and products | ||||||||||||||
EFFCH | 0.843 | 0.914 | 0.888 | 1.054 | 0.979 | 1.405 | 1.040 | 0.890 | 1.116 | 0.921 | 0.785 | 0.975 | 0.922 | 1.186 |
TECHCH | 1.009 | 1.098 | 1.076 | 1.086 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 0.979 | 0.902 |
TFPCH | 0.851 | 1.003 | 0.955 | 1.144 | 1.190 | 1.357 | 1.159 | 0.842 | 0.779 | 1.121 | 0.942 | 0.901 | 1.009 | 1.070 |
Fabricated metal and products | ||||||||||||||
EFFCH | 1.014 | 0.982 | 0.858 | 1.090 | 0.870 | 1.067 | 0.977 | 0.994 | 1.265 | 0.870 | 0.802 | 1.094 | 0.935 | 1.092 |
TECHCH | 0.961 | 1.098 | 1.123 | 1.041 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.013 | 0.902 |
TFPCH | 0.974 | 1.078 | 0.964 | 1.134 | 1.057 | 1.030 | 1.088 | 0.941 | 0.883 | 1.059 | 0.963 | 1.010 | 1.024 | 0.985 |
Panel D: Low-tech industries | ||||||||||||||
Food | ||||||||||||||
EFFCH | 1.042 | 0.953 | 0.851 | 0.960 | 0.798 | 1.049 | 0.930 | 1.144 | 1.329 | 0.877 | 0.854 | 1.051 | 0.902 | 1.083 |
TECHCH | 0.961 | 1.098 | 1.126 | 1.039 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 0.941 | 0.902 |
TFPCH | 1.001 | 1.046 | 0.958 | 0.997 | 0.970 | 1.013 | 1.037 | 1.083 | 0.927 | 1.067 | 1.025 | 0.971 | 0.987 | 0.977 |
Beverages, Tobacco and Feed | ||||||||||||||
EFFCH | 0.961 | 0.902 | 0.978 | 0.988 | 0.766 | 0.961 | 0.872 | 1.133 | 1.419 | 0.841 | 0.680 | 1.100 | 0.918 | 1.161 |
TECHCH | 1.009 | 1.098 | 1.077 | 1.085 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 0.949 | 0.902 |
TFPCH | 0.970 | 0.990 | 1.054 | 1.072 | 0.931 | 0.928 | 0.972 | 1.072 | 0.991 | 1.023 | 0.817 | 1.016 | 1.004 | 1.047 |
Textile mill products | ||||||||||||||
EFFCH | 0.957 | 0.902 | 0.956 | 0.995 | 0.693 | 1.164 | 0.910 | 1.055 | 1.874 | 0.843 | 0.599 | 1.080 | 0.885 | 1.067 |
TECHCH | 0.961 | 1.098 | 1.120 | 1.041 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.000 | 0.902 |
TFPCH | 0.919 | 0.990 | 1.072 | 1.036 | 0.842 | 1.124 | 1.014 | 0.998 | 1.308 | 1.026 | 0.719 | 0.998 | 0.969 | 0.962 |
Lumber and wood products | ||||||||||||||
EFFCH | 0.976 | 1.083 | 0.861 | 0.950 | 0.862 | 1.193 | 0.747 | 1.030 | 1.216 | 0.897 | 0.968 | 1.043 | 0.984 | 1.046 |
TECHCH | 0.961 | 1.098 | 1.126 | 1.039 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 0.999 | 0.902 |
TFPCH | 0.938 | 1.189 | 0.970 | 0.987 | 1.047 | 1.152 | 0.832 | 0.974 | 0.849 | 1.092 | 1.162 | 0.963 | 1.077 | 0.944 |
Furniture and fixtures | ||||||||||||||
EFFCH | 0.967 | 0.942 | 0.963 | 1.003 | 0.798 | 1.094 | 0.885 | 1.066 | 1.223 | 0.872 | 0.973 | 1.082 | 0.900 | 1.067 |
TECHCH | 0.961 | 1.098 | 1.126 | 1.039 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.002 | 0.902 |
TFPCH | 0.929 | 1.033 | 1.084 | 1.042 | 0.970 | 1.057 | 0.987 | 1.009 | 0.854 | 1.061 | 1.169 | 1.000 | 0.985 | 0.963 |
Pulp, paper and paper products | ||||||||||||||
EFFCH | 0.943 | 1.000 | 0.867 | 0.951 | 0.795 | 1.087 | 0.931 | 1.084 | 1.330 | 0.842 | 0.821 | 1.099 | 0.997 | 1.087 |
TECHCH | 0.999 | 1.098 | 1.087 | 1.076 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.002 | 0.902 |
TFPCH | 0.942 | 1.098 | 0.942 | 1.024 | 0.966 | 1.050 | 1.038 | 1.026 | 0.928 | 1.025 | 0.986 | 1.015 | 1.091 | 0.981 |
Printing and allied industries | ||||||||||||||
EFFCH | 1.026 | 0.900 | 0.872 | 0.981 | 0.788 | 1.044 | 0.909 | 1.056 | 1.386 | 0.830 | 0.837 | 1.066 | 0.907 | 0.972 |
TECHCH | 0.961 | 1.098 | 1.121 | 1.045 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.000 | 0.902 |
TFPCH | 0.986 | 0.988 | 0.978 | 1.025 | 0.958 | 1.008 | 1.013 | 0.999 | 0.968 | 1.011 | 1.005 | 0.984 | 0.993 | 0.877 |
Leather tan., products and fur skins | ||||||||||||||
EFFCH | 0.546 | 0.963 | 0.967 | 1.805 | 0.788 | 0.782 | 1.135 | 0.784 | 1.314 | 0.985 | 0.847 | 1.026 | 0.954 | 1.418 |
TECHCH | 0.961 | 1.098 | 1.126 | 1.039 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 0.954 | 0.902 |
TFPCH | 0.525 | 1.057 | 1.089 | 1.874 | 0.958 | 0.756 | 1.265 | 0.742 | 0.917 | 1.199 | 1.017 | 0.948 | 1.044 | 1.279 |
Miscellaneous manufacturing industries | ||||||||||||||
EFFCH | 1.094 | 0.831 | 0.964 | 0.831 | 0.804 | 1.150 | 1.052 | 1.082 | 1.169 | 0.774 | 0.797 | 1.130 | 0.838 | 1.045 |
TECHCH | 0.963 | 1.098 | 1.113 | 1.058 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 0.999 | 0.902 |
TFPCH | 1.054 | 0.912 | 1.073 | 0.878 | 0.977 | 1.111 | 1.173 | 1.024 | 0.816 | 0.942 | 0.957 | 1.044 | 0.917 | 0.943 |
Ceramic, stone and clay products | ||||||||||||||
EFFCH | 0.913 | 0.957 | 0.933 | 0.818 | 0.880 | 1.084 | 0.927 | 1.024 | 1.168 | 0.945 | 0.797 | 1.080 | 0.944 | 1.105 |
TECHCH | 0.981 | 1.098 | 1.096 | 1.070 | 1.216 | 0.966 | 1.115 | 0.946 | 0.698 | 1.217 | 1.201 | 0.923 | 1.094 | 0.902 |
TFPCH | 0.895 | 1.050 | 1.022 | 0.875 | 1.070 | 1.047 | 1.033 | 0.968 | 0.815 | 1.151 | 0.957 | 0.997 | 1.034 | 0.997 |
About this article
Cite this article
Aduba, J.J., Asgari, B. Productivity and technological progress of the Japanese manufacturing industries, 2000–2014: estimation with data envelopment analysis and log-linear learning model. Asia-Pac J Reg Sci 4, 343–387 (2020). https://doi.org/10.1007/s41685-019-00131-w
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DOI: https://doi.org/10.1007/s41685-019-00131-w
Keywords
- Efficiency
- Productivity
- Total-factor-productivity
- Learning-by-doing
- Technological learning
- Manufacturing Industry