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Foreign direct investment and wage inequality in host country

ZHOU Yunbo1 CHEN Cen1 TIAN Liu1

(1.Collaborative Innovation Center for China Economy,Nankai Institute of Economics, Center for Studies of Political Economy of Nankai University 300071)

【Abstract】Based on the theoretical analysis of the effects of foreign direct investment (FDI) on wage inequality, the paper calculates contributions of relevant factors, particularly FDI, to wage inequality in enterprise-level by using the regression based decomposition approach (Shapley value). Theoretical analysis shows that the wage gap between foreign and local firms which caused by FDI through labor transfer and technology spillover effects presents an inverted U-shaped feature. Empirical analysis shows FDI has great influence on China’s wage inequality which contributed over 10 percent to the wage gap, however,there is a sharp decline in the overall trend. The chief reason is that the wage gap between foreign and local firms which caused by FDI through labor transfer and technology spillover has entered the downward stage. Thus, in order to reduce the wage gap between enterprises, longstanding market-access barriers in China should be dismantled to attract more companies with high technological levels.

【Keywords】 FDI; wage inequality; Theil index; Shapley value decomposition; FDI; Wage Inequality; Theil Index; Sharply Value Decomposition;


【Funds】 The phased achievement of the New Century Excellent Talent Program of Ministry of Education (No.:NCET—13—0311); and the phased achievement of the major program of the Humanities and Social Science Research Base of Ministry of Education (12JJD790050) The phased achievement of the major subject cultivation program under the special fund programs for basic scientific research operations of Nankai University (NKZXZD1404)

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    [1]. ① According to the regulations of the National Bureau of Statistics, Foreign Direct Investment (FDI) discussed in this paper includes three forms, namely, exclusively foreign-funded enterprises, Sino-foreign cooperative venture, and Sino-foreign joint venture. [^Back]

    [2]. ① The data from the National Bureau of Statistics show that the wage income in 2013 accounted for 56.9% of the per capita disposable income of China’s residents. [^Back]

    [3]. ② According to Rozelle (2012), the Gini coefficient of the residents’ income in the countries caught in the “middle income trap” after the World War II is about 0.47 on the average. The data from the National Bureau of Statistics show that in 2008, China’s income gap reached its peak, the Gini coefficient of the residents' income was 0.491, and after that the figure went down to 0.469 in 2014. [^Back]

    [4]. ① The Gini coefficient or variation coefficient is generally adopted to measure China’s income gap. However, the Gini coefficient focuses on measuring the overall income gap and has the disadvantages of uncertainty, one-sidedness, and incomparability while variation coefficient focuses on the differences between the average level of the subgroups and the overall income level. The advantage of the Theil index is that it can better measure the differences between the groups. [^Back]

    [5]. ① Assume f(w)=wlnw-w+1, f'_w=lnw>0, f(1)=0, so f(w)>0; assume g(w)=1-w+lnw, g'_w=1/w-1<0, g(1)=0, so g(w)<0, namely, w(1-w+lnw)<0, then η*<1. [^Back]

    [6]. ② Assume h(w)=3w-2lnw-3, then h(1)=0, h'_w(w)=3-2/w>0, so h(w)>0, namely η**-η*>0。 [^Back]

    [7]. ① Given the specific η, the fitted value of A_0 can be obtained. [^Back]

    [8]. ① According to the enterprise registration type of Chinese Industrial Enterprises Database, the foreign-funded enterprises mentioned in this paper mainly includes: Sino-foreign joint venture, Sino-foreign cooperative venture, exclusively foreign-funded enterprises, joint stock company with foreign investment, and Hong Kong-, Macao-, and Taiwan-invested enterprises. [^Back]

    [9]. ② According to Ding (2010), we have categorized the following into the monopolistic industry: petroleum and natural gas mining, petroleum processing, coking and nuclear fuel processing, coal mining and washing, nonferrous metal and ores selection, tobacco products, electric power, thermal production and supply, gas production and supply, and water production and supply. [^Back]

    [10]. ① We delete the samples as follows. The observation value of some important indices are missing and does not meet the “above-scale” standard and comply with the accounting principle and the extreme value of key indices. [^Back]

    [11]. ② As the data of the out-of-factory price indices of some industrial products are missing, we have substituted the indices of other mining industries, agricultural and sideline food processing industry, printing industry, the copying of the recorded media, universal equipment manufacturing industry, waste resources and waste materials recycling and processing industry with the same-period indices of the nonferrous metal picking industry, food manufacturing industry, paper making and paper products industry, special equipment manufacturing industry, art work and other manufacturing industries. [^Back]

    [12]. ③ We conduct matching and identifying over the enterprises based on the method of Brandt (2012). [^Back]

    [13]. ④ Due to space limitations, the results of the multicollinearity test are not listed in this paper. Readers who are interested in it can ask the author for the information. [^Back]


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This Article



Vol 50, No. 12, Pages 128-142

December 2015


Article Outline


  • 1 Introduction
  • 2 Literature review
  • 3 Theoretical model
  • 4 Data and empirical analysis
  • 5 Regression and decomposition results
  • 6 Conclusion and policy enlightenment
  • Footnote