UNDERSTANDING URBAN POVERTY IN LARGE CHINESE CITIES USING MULTIPLE MEASUREMENTS AND HIERARCHICAL REGRESSION MODELS
HE Shen-jing1, ZUO Jiao-jiao2, ZHU Shou-jia3, LIU Yu-ting4
1. Integrated Geographic Information Center, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
2. Dali Urban Construction and Water Resources Bureau, Foshan 528231, China;
3. School of Geography and Ocean Science, Nanjing University, Nanjing 210093, China;
4. School of Architecture, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510641, China
Abstract：Based on a large-scale household survey conducted in low-income neighborhoods in six large Chinese cities, this paper applies multi-measurement and hierarchical linear regression models to present different poverty profile in different cities and to examine the determinants of poverty incidence. Referring to the theory of welfare triangle proposed by Rose 1986), this paper takes the influence of household, market, and state into account to examine the mechanism of poverty incidence in Chinese cities. The empirical results reveal salient intra-urban differences in terms of the variation of poverty intensity and composition among the six cities. Guangzhou and Xi'an suffer from the severe problem of relative deprivation and their low-income groups have very complex composition. The hierarchical linear regression models demonstrate that urban poverty incidence is contingent upon variations at individual, neighborhood, and city levels. In general, neighborhood with better economic status reduces the individual's risk of urban pauperization, while neighborhood with strong poverty culture or long poverty history tends to increase the risk of pauperization.
何深静, 左姣姣, 朱寿佳, 刘玉亭. 中国大城市贫困研究的多种测度与多层模型分析[J]. 人文地理, 2014, 29(6): 74-80,87.
HE Shen-jing, ZUO Jiao-jiao, ZHU Shou-jia, LIU Yu-ting. UNDERSTANDING URBAN POVERTY IN LARGE CHINESE CITIES USING MULTIPLE MEASUREMENTS AND HIERARCHICAL REGRESSION MODELS. HUMAN GEOGRAPHY, 2014, 29(6): 74-80,87.
