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

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)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(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.

A new approach in the DEA technique for measurement of productivity of decision-making units through efficiency and effectiveness

“…The DMUs 1,2,3,4,6,8,10,11,12,13,14,15,16 are known as efficient DMUs according to the results presented in Table 2.…”

“…Because, productivity is a combination of efficiency and effectiveness simultaneously, therefore, its evaluation will be more complete than the evaluation of effectiveness and efficiency separately [3]. The productivity is a subject of interest to many economists and policymakers, and it is crucial for economic growth and survival [4]. Productivity is also considered as an important element for the operations of the organization and its increase creates a competitive advantage for organization and is a vital issue for management.…”

“…They found no evidence of group convergence for bank productivity but found evidence of convergence in changing bank efficiency and technical change. Aduba and Asgari[4] using DEA and Malmquist productivity index and log-linear learning models, empirically estimated the productivity and technological learning of manufacturing industries. The result shows negative total factor productivity growth between 2000 and 2014.…”

Impact of learning through credit and value creation on the efficiency of Japanese commercial banks

“…The learning curve theory proposed and applied by Wright (1936), Arrow (1962) and others, has been used to quantify cost reduction in the form of changes in unit inputs (such as labor) required in the production process. These changes in unit input requirement not attributable to fluctuations in prices of variable inputs or scale economies, could be explained by the efficiency gain over time in the working process (Aduba and Asgari 2020). This efficiency gain has been linked to the improvement in experience with routines of the production tasks, information sharing, re-engineering and redesigning, efficient production scheduling, efficient supply chain management, and strategic decision making.…”

“…However, as literature on the learning curve advanced, other variants of outputs such as defect rate, quality, number of projects completed, total-factor productivity (TFP), service time and value-added (defined in financial term) were used to measure learning-by-doing in manufacturing and service organizations. Consequently, the learning curve has been applied across several industries: in pizza franchises using cumulative pizzas produced (Darr et al 1995), in the hotel industry using the number of available rooms (Baum and Ingram 1998), in factories using TFP (Lapré and Van Wassenhove 2001), in manufacturing at 3-digit ISIC using value-added (Aduba and Asgari 2020;Asgari and Yen 2009;Bahk and Gort 1993;Karaoz and Albeni 2005;Pramongkit et al 2000Pramongkit et al , 2002, in hospitals using the number of successful cardiac surgeries performed (Ramanarayanan 2011), and in many other industries.…”