Distribution of Labour Productivity in Japan over the Period 1996–2006
Author:
Souma Wataru1, Ikeda Yuichi2, Iyetomi Hiroshi3, Fujiwara Yoshi1
Affiliation:
1. ATR/NiCT CIS Applied Network Science Laboratory , Kyoto 2. Hitachi Ltd ., Hitachi Research Laboratory , Ibaraki 3. Department of Physics , Niigata University , Ikarashi , Niigata
Abstract
Abstract
The distribution of labour productivity is investigated by analyzing the longitudinal micro-level data set which contains the detailed financial conditions of large numbers of Japanese companies over the period 1996–2006. The authors show that the distribution of labour productivity in both the high and low productivity ranges follows a power law distribution. The generalized beta function of the second kind, which asymptotically reproduces a power law function, is applied to explain the distribution of labour productivity. By comparing the power law exponents that characterize high and low productivity ranges, the authors show that for manufacturing industries, inequality in the low productivity range is larger than that in the high productivity range. For the manufacturing industries, the authors also clarify that the change of inequality in the low productivity range has strong correlation with GDP. In addition, by comparing the power law exponents of the high productivity range in the manufacturing and non-manufacturing industries, the authors show that the inequality of the non-manufacturing industry is higher than that of the manufacturing industry.
Publisher
Walter de Gruyter GmbH
Subject
General Economics, Econometrics and Finance
Reference22 articles.
1. Aoyama, H., H. Yoshikawa, H. Iyetomi, and Y. Fujiwara (2008). Productivity Dispersion: Fact, Theory and Implications. RIETI Discussion Paper: 08-E-035. arXiv:0805.2792. 2. Aoyama, H., H. Yoshikawa, H. Iyetomi, and Y. Fujiwara (2009a). Labour Productivity Superstatistics. To appear in Progress of Theoretical Physics: Supplement. arXiv:0809.3541v1. 3. Aoyama, H., Y. Fujiwara, H. Iyetomi, Y. Ikeda, and W. Souma (2009b). Super-statistics of Labour Productivity in Manufacturing and Nonmanufacturing Sectors. To appear in Economics E-Journal (in this volume). arXiv:0901.1500v1. 4. Aw, BY., X. Chen, and MJ. Roberts (2001). Firm-level Evidence on Productivity Differentials and Turnover in Taiwanese Manufacturing. Journal of Development Economics, 66 (1): 51–86. 5. Bartelsman, E.J., and P.J. Dhrymes (1998). Productivity Dynamics: US Manufacturing Plants, 1972–1986. Journal of Productivity Analysis, 9 (1): 5–34.
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Statistical Properties of Labor Productivity Distributions;Frontiers in Physics;2022-02-18 2. Why Does Production Function Take the Cobb–Douglas Form?;Statistical Properties in Firms’ Large-scale Data;2021 3. Why does production function take the Cobb–Douglas form?;Evolutionary and Institutional Economics Review;2020-07-04 4. Endogenous Firm Dynamics and Labor Flows via Heterogeneous Agents ✶ ✶Support from the John D. and Catherine T. MacArthur Foundation, the National Science Foundation (0738606), the Small Business Administration (SBAHQ-05-Q-0018), and the Mercatus Center at George Mason is gratefully acknowledged. I have no relevant or material financial interests that relate to the research described in this paper or the associated model. Earlier versions of this work were presented at research institutions (Aix-en-Provence, Arizona State, Brookings, Carnegie Mellon, Emory, Esalen, Essex, George Mason, Georgia, Georgia Tech, James Madison, Leicester, Leiden, Limerick, Nanyang Technological University, New School for Social Research, Office of Financial Research, Oxford, Queen Mary and Westfield, Sant' Anna (Pisa), Santa Fe Institute, Turino) and conferences (Eastern Economic Association, INFORMS, Society for Computational Economics, Southern Economic Association) where comments from attendees yielded significant improvements. For helpful feedback on the manuscript I am grateful to Zoltan Acs, Luis Amaral, Brian Arthur, David Audretsch, Bob Axelrod, Bob Ayres, Eric Beinhocker, Margaret Blair, Pete Boettke, David Canning, Kathleen Carley, John Chisholm, Alex Coad, Herbert Dawid, Art DeVany, Bill Dickens, Kathy Eisenhardt, Joshua Epstein, Doyne Farmer, Rich Florida, Duncan Foley, Xavier Gabaix, Chris Georges, Herb Gintis, Joe Harrington, John Holland, Stu Kauffman, Steve Kimbrough, Paul Kleindorfer, Blake LeBaron, Axel Leijonhufvud, Bob Litan, Francesco Luna, Jim March, Michael Maouboussin, Greg McRae, Benoit Morel, Scott Moss, Paul Omerod, J. Barkley Rosser Jr., Martin Shubik, Gene Stanley, Dan Teitelbaum, Leigh Tesfatsion, Sid Winter and several people who are no longer with us: Per Bak, Michael Cohen, Ben Harrison, Steve Klepper, Sam Kotz, and Benoit Mandelbrot. The late Herb Simon inspired and encouraged the work. Anna Nelson and Omar Guerrero each advanced the work through their Ph.D. dissertations. Thanks are due Miles Parker and Gabriel Balan for implementing the model in Java, first in Ascape and then in Mason. Errors are my own.;Handbook of Computational Economics;2018 5. Endogenous Dynamics of Multi-Agent Firms;SSRN Electronic Journal;2015
|
|