2014
DOI: 10.1007/s00168-014-0601-9
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How important is geographical agglomeration to factory efficiency in Japan’s manufacturing sector?

Abstract: In this paper, geographical spillover potential is modeled and empirically examined using factory-level data from Japan's Census of Manufactures. First, the efficiency of each factory is estimated using a non-parametric data envelopment analysis (DEA) model for each industry. Second, the geographical distances to the most efficient factory in the prefecture and Japan overall are estimated. Third, the determinants of the factories' performance are identified and estimated. We find that clustering occurs in each… Show more

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Cited by 4 publications
(3 citation statements)
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“…De acuerdo con estos resultados, se puede afirmar la existencia de dependencia espacial en los datos. Estos hallazgos concuerdan con estudios previos sobre ied, especialmente cuando los estados tienen características económicas similares, como es el caso de la zona del Bajío (Blonigen et al, 2004;Coughlin & Segev, 2000;Fukao, Kravtsova & Nakajima, 2014;Horn & Cross, 2016;Villaverde & Maza, 2015).…”
Section: Resultsunclassified
“…De acuerdo con estos resultados, se puede afirmar la existencia de dependencia espacial en los datos. Estos hallazgos concuerdan con estudios previos sobre ied, especialmente cuando los estados tienen características económicas similares, como es el caso de la zona del Bajío (Blonigen et al, 2004;Coughlin & Segev, 2000;Fukao, Kravtsova & Nakajima, 2014;Horn & Cross, 2016;Villaverde & Maza, 2015).…”
Section: Resultsunclassified
“…Under the framework of the new economic geography, economist Krugman (1991) revealed how transportation costs, factor flows and economies of scale affect the spatial agglomeration of economic activities through market transmission mechanisms, which solved problems that could not be answered by the traditional location advantage theory [23,24]. Since entering the 21st century, with the active participation of developing countries in the division of labor in the global manufacturing value chain, scholars have focused on the trends and determinants of industrial spatial agglomeration [25][26][27][28], the relationship between spatial agglomeration and economic development [29,30] and the impact of spatial agglomeration on environmental pollution [31][32][33] and productivity [34][35][36]. Many studies have confirmed the positive effect of industrial spatial agglomeration on economic development, productivity and technological innovation, but the degree of influence has been found to vary among different regions [37,38].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the year dummy variables were also added to the regressions to control for this variance among years. The industry variables were first classified into low, medium-low, and medium-high; high technology industries following the guidelines of OECD (2009) on industry classification were then coded as binary variables (Fukao, Kravtsova, & Nakajima, 2014;Onkelinx, Manolova, & Edelman, 2016;Santamaría, Nieto, & Barge-Gil, 2009). The OECD (2009) classifies manufacturing industries according to levels of technology intensity using data on R&D expenditure divided by value added.…”
Section: Control Variablesmentioning
confidence: 99%