Market segmentation, resource misallocation and labor share of income

WANG Songtao1,2 WEN Simei3 ZHU Tengteng1

(1.Business School of Shantou University )
(2.Research Institute for Guangdong-Taiwan Business Cooperation of Shantou University)
(3.College of Economics & Management, South China Agricultural University)

【Abstract】Factor market segmentation is an important problem in current economic development of Chinese market that not only affects economic growth, but also income distribution. In this paper, we establish a neoclassical production model based on market segmentation and conclude that the factor market segmentation will aggravate resource misallocation, and when the capital-labor relationship is substitutive, it will reduce labor share of income. Using Chinese industrial enterprises databases(1998–2007) to build the panel data of factor market segmentation indexes and resource misallocation indexes of 279 cities and through empirical study, we find that factor market segmentation significantly exacerbates resource misallocation of Chinese industrial enterprises and reduces the labor share of income.

【Keywords】 factor market segmentation; resource misallocation ; labor share of income; income distribution;

【DOI】

【Funds】 Philosophy and Social Sciences Planning Project of Guangdong Province (GD13YLJ01) National Natural Science Foundation of China (71473089) Project of Soft Science Research Project of Guangdong Province (2015A070704057) Scientific Research Project of Shantou University (STF13011) Art and Human Sciences Research Fund Project of Shantou University (SR13004)

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(Translated by ZHANG Liang)

    Footnote

    [1]. (1) he meaning of segmentation, in this article, is not totally in a physical sense; instead, wherever there is market fraction or flow cost can be called market segmentation. [^Back]

    [2]. (1) This is an ideal situation, that is, it is assumed that there are no other factors influencing price changes. [^Back]

    [3]. (2) Factor market segmentation is only one factor that causes resource misallocation; in real life, total factor productivity in different regions is different, which will also cause different capital stock per capita. The model in this article assumes that each region’s total factor productivity is the same, but in the empirical analysis, we regard the real return on capital as the control variable, which helps to control differences in total factor productivity to some extent. [^Back]

    [4]. (3) As return on capital is higher in regions with low capital stock per capita, the order of regions should be inversed when calculating return on capital Gini coefficient (Lorenz curve). [^Back]

    [5]. (1) As the sample size is quite large across the cities, the Gini coefficient calculated by Lorenz curve fitting (then calculating integration or parameter) is nearly the same with the Gini coefficient calculated by the discrete method; since the calculation volume of 2,780 samples is relatively big, in real practices, the discrete method is used uniformly for programming calculation. [^Back]

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This Article

ISSN:1005-3425

CN: 42-1348/F

Vol , No. 01, Pages 13-25+79

January 2016

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Article Outline

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Abstract

  • 1 Introduction and literature review
  • 2 Theoretical analysis
  • 3 Econometric specification and data description
  • 4 Empirical results
  • 5 Conclusion and implication
  • Appendix:
  • Footnote

    References