Labour market and wage inequalities between permanent rural migrants and urban-born residents in China
Labour market and wage inequalities between permanent rural migrants and urban-born residents in China
复制标题
中国劳动力市场和永久农民工与城市出生居民之间的工资不平等
DOI：
10.1080/00036846.2021.1925079
复制
发表时间：
2022
影响因子：
2.2
通讯作者：
Kawata Keisuke
中科院分区：
文献类型：
--
作者：
Liu Yang;Kawata Keisuke
这项研究提供了一些关于劳动力市场在决定个人工资水平和随之而来的工资不平等方面的作用的第一批经验证据。与关注人力资本因素的传统收入方程不同，本研究遵循最新的工资理论，即纳什谈判模型，发现不仅人力资本，而且劳动力市场也是决定个人水平工资的重要因素。此外，本研究还考察了城市出生的工人和常住农民工之间的工资不平等，他们构成了中国城市所有农村移民的相当大的一部分，但在以前的学术研究中被忽视了。与以往研究中的临时性农民工不同，永久性农民工享有与城市出生工人相同的公民权利和社会福利。然而，我们仍然发现，这些永久性移民（分类为人才，土地，和“支持”的移民）面临的劣势，在获得高工资。基于我们的理论模型，通过改进的双盲-瓦哈卡分解，我们发现农民工和城镇居民之间的工资不平等是由劳动者的教育程度和劳动力市场的紧张程度以及父亲的就业状况、住房价格等因素共同造成的。
This study provides some of the first empirical evidence on the role of the labour market in determining individual-level wages and the consequent wage inequalities. Unlike traditional earnings equations that focus on human capital factors, this study follows recent wage theory, the Nash bargaining model, and finds that not only human capital but also the labour market are important factors in determining individual-level wages. Further, this study examines wage inequalities between urban-born workers andpermanentrural migrant workers, who constitute a sizable part of all rural migrants in urban China but have been ignored in previous academic studies. Different fromtemporaryrural migrants examined in previous studies,permanentrural migrants share the same citizenship rights and social welfare as urban-born workers. However, we still find that these permanent migrants (classified as talent, land, and ‘supported’ migrants) face disadvantages in acquiring high wages. Using a developed version of the two-fold Blinder–Oaxaca decomposition based on our theoretical model, we find that the wage inequalities between permanent rural migrants and urban residents are caused by both the workers’ education and labour market tightness, as well as the father’s employment status, housing prices, and other factors.