中国超大城市常住外来和常住户籍人口居住－就业的空间错位--基于北京、上海和广州的实证

以北京、上海、广州作为中国超大城市的代表，探讨中国超大城市常住外来和常住户籍人口居住－就业空间错位的差异及其影响因素。总体而言，常住外来人口的居住－就业空间错位要高于常住户籍人口，且不同户籍属性人口的错位形态存在差异。分行业来看，公共管理和社会组织等公共服务业常住外来人口居住－就业空间错位程度相对较高，而制造业、居民服务和其他服务业常住户籍人口居住－就业空间错位程度相对较高。由计量分析发现，住房价格水平、就业集聚规模、高端产业比重和地区地方财政支出对不同户籍属性人口就业－居住空间分布差异均产生相似的影响；而租赁户数比重和低端产业比重对不同户籍属性人口的就业－居住空间分布则产生差异性的影响。

Jobs-Housing Spatial Mismatch of Permanent Immigrants and Local Registered Residents in Chinese Metropolises:An Empirical Study Based on Beijing,Shanghai and Guangzhou

Based on the population census data and the economic census data of Beijing, Shanghai and Guangzhou, this paper empirically analyzes the differences between permanent immigrants and local registered residents on the jobs?housing spatial mismatch in Chinese metropolises. Overall, the jobs?housing spatial mismatch among the permanent immigrants is more severe than that among the local registered residents, and people with different household registrations exhibit different spatial mismatch patterns. Specifically, the permanent immigrants excessively concentrate in the inner suburbs while local registered residents show excessive concentration of residences in the outer suburbs, although jobs are excessively concentrated in the central city for both the immigrants and the local residents. Besides, people from different sectors also exhibit different spatial mismatch patterns. Compared to the local residents, the permanent immigrants show higher spatial mismatch in such sectors as public management and social organization, education, while they show lower spatial mismatch in such sectors as manufacturing, services to households and other services. Furthermore, the empirical results show that opportunities of jobs, opportunities of housing and regional characteristics are all significant factors to explain the jobs?housing spatial distribution patterns of both permanent immigrants and local residents. Although most of the factors indicate the same influences on both permanent immigrants and local registered residents, the impacts of two variables, namely the proportion of local lease housing and the share of low?end industries, are different on people with different household registrations.