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S. Li, C. Luo, Z. Wei, X. Yue
Appendix: the 1995 and 2002 household surveys: sampling methods and data description
Xin Meng, Junsen Zhang (2001)
The Two-Tier Labor Market in Urban China: Occupational Segregation and Wage Differentials between Urban Residents and Rural Migrants in ShanghaiJournal of Comparative Economics, 29
An Extension of the Blinder-oaxaca Decomposition to Non-linear Models
(2010)
Wage Structures and Inequality among Local and Migrant and Urban Workers
Zhong Zhao (2005)
Migration, Labor Market Flexibility, and Wage Determination in China: A ReviewLabor and Demography
R. Oaxaca (1973)
Male-Female Wage Differentials in Urban Labor MarketsInternational Economic Review, 14
J. Knight, L. Yueh (2008)
Segmentation or Competition in China's Urban Labour Market?Cambridge Journal of Economics, 33
R. Fairlie (2006)
An Extension of the Blinder-Oaxaca Decomposition Technique to Logit and Probit ModelsYale: Economic Growth Center Discussion Papers
X. Meng, J. Zhang
The two‐tier labor market in Urban China
T. Kong (2010)
Rural Urban Migration in China: Survey Design and Implementation
(2002)
* represent significant at 1%. 5% and 10% level, respectively. Appendix Table 3. Migration Decision in 2002 and 2007 (Cont.) All Male Female
Thomas Bauer, Mathias Sinning (2006)
An extension of the Blinder–Oaxaca decomposition to nonlinear modelsAStA Advances in Statistical Analysis, 92
J. Knight, Lina Song (2003)
Increasing urban wage inequality in ChinaEconomics of Transition, 11
Yang Du, A. Park, Sangui Wang (2005)
Migration and rural poverty in ChinaJournal of Comparative Economics, 33
O. Duncan, B. Duncan (1955)
A METHODOLOGICAL ANALYSIS OF SEGREGATION INDEXESAmerican Sociological Review, 20
N. Zhu (2002)
The impacts of income gaps on migration decisions in ChinaChina Economic Review, 13
Xiao-yuan Dong, L. Xu (2009)
Labor restructuring in China: Toward a functioning labor marketJournal of Comparative Economics, 37
Myeong-Su Yun (2003)
Decomposing Differences in the First MomentMicroeconomic Theory eJournal
S. Appleton, Lina Song, Qingjie Xia (2005)
Has China crossed the river? The evolution of wage structure in urban China during reform and retrenchmentJournal of Comparative Economics, 33
Yaohui Zhao (1999)
Leaving the countryside: rural-to-urban migration decisions in China.The American Economic Review, 89
R. Fairlie (1999)
The Absence of the African‐American Owned Business: An Analysis of the Dynamics of Self‐EmploymentJournal of Labor Economics, 17
(2002)
Appendix Table 4. Wage Equations of Natives and Migrants in
B. Gustafsson, Li Shi, T. Sicular (2008)
Inequality and Public Policy in China: Frontmatter
(2007)
Marginal Coef. Marginal Coef
A. Blinder (1973)
Wage Discrimination: Reduced Form and Structural EstimatesJournal of Human Resources, 8
Note: Numbers inside square brackets are standard errors
S. Démurger, M. Gurgand, Shi Li, Ximing Yue (2008)
Migrants as Second-Class Workers in Urban China? A Decomposition AnalysisEmerging Markets: Economics eJournal
Yaohui Zhao (2003)
The Role of Migrant Networks in Labor Migration: The Case of ChinaContemporary Economic Policy, 21
F. Cai (2008)
Approaching a Triumphal Span : How Far Is China Towards its Lewisian Turning Point?
Li Shi, Chuliang Luo, W. Zhong, Ximing Yue (2008)
Inequality and Public Policy in China: Appendix: The 1995 and 2002 Household Surveys: Sampling Methods and Data Description
The Total Number of Off-Farm Rural Laborers Reached 225.42 Millions at the End of
Purpose – The purpose of this paper is to study the dynamic change of the migrant labor market in China from 2002 to 2007 using two comparable data sets. Design/methodology/approach – To understand the factors behind the wage change, the authors use the Oaxaca‐Blinder decomposition (Oaxaca, 1973; Blinder, 1973) method to study the hourly wage change over this five‐year period. Findings – The focus is on the rural‐urban migration decision, the wage structure of migrants, the labor market segmentation between migrants and urban natives, and the changes of these aspects from 2002 to 2007. The paper finds that prior migration experience is a key factor for the migration decision of rural household members, and its importance keeps increasing from 2002 to 2007. The results show that there is a significant increase in wages among both migrants and urban natives over this five‐year period, but migrants have enjoyed faster wage growth, and most of the increase of wages among migrants can be attributed to the increase of returns to their characteristics. The authors also find evidence suggesting convergence of urban labor markets for migrants and for urban natives during this five‐year period. Research limitations/implications – In order to make the 2002 and 2007 data sets comparable, the authors had to restrict the observations with fixed residence only, and can only include seven cities. These limit the representativeness of the sample. When interpret the findings in this paper, it is important to keep this in mind. Originality/value – Due to the scarcity of data, there are few studies on the dynamics of the migrating population and the migrant labor market. Since the urban natives and migrants are still segmented in the labor market, the migrant labor market may have its own characteristics, and also, because of the increasing importance of the migrants in Chinese society, knowledge of the evolution of the migrant labor market is crucial for grasping the whole story behind the Chinese economic miracle.
China Agricultural Economic Review – Emerald Publishing
Published: Apr 29, 2014
Keywords: China; Employment; Labour use and migration
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