Household wealth proxies for socio-economic inequality policy studies in China

被引:2
|
作者
Lam, Jacqueline C. K. [1 ,2 ,3 ,4 ]
Han, Yang [1 ]
Bai, Ruiqiao [1 ]
Li, Victor O. K. [1 ,2 ]
Leong, Jeff [5 ]
Maji, Kamal J. [1 ]
机构
[1] Univ Hong Kong, Dept Elect & Elect Engn, Pok Fu Lam, Hong Kong, Peoples R China
[2] Univ Cambridge, Judge Business Sch, Energy Policy Res Grp, Cambridge, England
[3] Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England
[4] MIT, Energy Initiat, CEEPR, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[5] Univ Cambridge, Fac Econ, Cambridge, England
来源
DATA & POLICY | 2020年 / 2卷
关键词
China; data privacy and security; fine-grained resolution; household wealth proxies; socio-economic status; environmental inequality;
D O I
10.1017/dap.2020.4
中图分类号
C93 [管理学]; D035 [国家行政管理]; D523 [行政管理]; D63 [国家行政管理];
学科分类号
12 ; 1201 ; 1202 ; 120202 ; 1204 ; 120401 ;
摘要
In China, one percent of the richest population holds more than one-third of the wealth, while the poorest 25% shares no more than two percent of the total. The country's rapid economic development has resulted in increasing socio-economic disparities, and a rapidly deteriorating environment. This puts the Chinese citizens, especially the most vulnerable and deprived socio-economic status (SES) groups, at high risks of environmental inequality (EI). In most SES-based EI studies conducted in China, household wealth has often been overlooked, though it potentially serves a good economic indicator to capture the socio-economic effect of environmental change in China. Nevertheless, existing SES databases in China are of low spatial resolution and are insufficient to support fine-grained EI studies at the intra-city level in China. The core research challenge is to develop a representative household wealth proxy in high-spatial resolution for China. This study highlights the research gaps and proposes anew household wealth proxy, which integrates both fine-grained data/features such as daytime satellite imagery and easily accessible wealth indicators such as house prices. We also capitalize on everyday economic activity data retrieved from personal mobile phones and online transaction/social platforms in the composition of our wealth proxy to achieve a higher accuracy in estimating household wealth at fine-grained resolution via machine learning. Finally, we summarize the challenges in improving both the quality and the availability of Chinese socio-economic datasets, while protecting personal privacy and information security during the data collection process for household wealth proxy development in China.
引用
收藏
页数:21
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