Income Determination and Income Discrimination in Shenzhen

Abstract

Shenzhen is not only one of the fastest-growing urban agglomerations in mainland China, Shenzhen also stands for a unique, most rapid and successful transformation into an urban market economy. How important are personal characteristics, social norms and policy-related discriminatory factors for income determination in such an extraordinary city? This paper estimates the income effect of non-productivityrelated discriminatory factors (like personal characteristics, social norms and policies), compared with productivity-related returns on human capital. The design of the Shenzhen Household Survey 2005 enables the inclusion of a large set of discriminating factors in a Mincer Becker type of income model, together with a unique look at the migrant population in this urban centre. The results show that the human capital approach holds. Strong evidence is also found of a significant influence of social norms and policies, which is particularly relevant in a developing and transitional economy, even in such an exceptional city. The most important discriminating factor appears to be the hukou registration system. The on-going existence of these non-productivityrelated discriminating factors can be regarded as indicative of an on-going process of transition towards a fully functioning market economy.

Migration and Discrimination in Urban China: A Decomposition Approach

“…Our headline figure from the Nopo decomposition is that discrimination against migrants account for roughly 25 percent of the native–migrant compensation gap. This is about half of what was found in previous studies (Deng, ; Gravemeyer et al ., ; Frijters et al ., ), whose B–O estimates varied between 49 percent and 60 percent. The main difference is thus in the differences in wages for urban and migrants groups outside of the common support, showing the importance of linearity and other comparability assumptions underlying the higher estimates of previous studies.…”

“…Econometric studies such as Gravemeyer et al . () using the B–O decomposition analysis found that 52.9 percent of the wage gap between workers with urban and rural hukou in Shenzhen in 2005 cannot be explained by differences in characteristics and is hence interpreted as discrimination. Meng and Zhang () find that 49.18 percent of the wage gap for Shanghai in 1995/6 is unexplained, and is attributed to discrimination.The authors use the Brown et al .…”

“…The detailed nature of the data also allows us to examine whether discrimination against migrants varies widely across Chinese cities. We extend on the recent papers that have already looked at compensation differences between migrants and non‐migrants (Meng and Zhang, ; Démurger et al ., ; Liu et al ., ; Deng, ; Frijters et al ., ; Gravemeyer et al ., ; Lee, ), by using a unique dataset that is more representative of the migrant population and by applying a new technique better suited for analysing wage differences across very different populations.…”

The effect of basic public service on urban-rural income inequality: a sys-GMM approach

“…These studies could be classified into three groups. The first group studies the impact of political strategies, such as the household registration system, the urban biased policy and the opening reform (e.g., D emurger, Li, & Yang, 2012;Gravemeyer, Gries, & Xue, 2011;Sicular et al, 2007). The second group studies individual and household characteristics, such as human capital, health, and family structure (Solinger, 1999;Young, 2013).…”

“…Further, some studies investigate how the household registration system affects urban-rural income inequality. The majority of them focus on the labour market discrimination caused by the household registration system, including wage discrimination and hiring discrimination (Gravemeyer, Gries, & Xue, 2011;Chen & Hoy, 2011;Demurger et al, 2012). 6 In this paper, we include the two key influential variables, household registration system and public services provision in the same analysis framework to discuss urban-rural income inequality.…”