Poverty remains one of the most serious chronic dilemmas facing civilization and economic development in the 21st century. How to accurately measure, identify and alleviate poverty have been urgent topics on different geographical scales for decades. Based on census data at the county level from 2000 to 2010 in China, principal component analysis was used to establish an integrated multidimensional poverty index (IMPI) for geographical identification of poverty-stricken counties using an indicators system guided by a sustainable livelihoods framework. Further cluster analysis, spatial analysis and a self-organizing map show obvious spatiotemporal heterogeneity of multidimensional poverty across the 2311 counties in China. The results demonstrate that the counties with higher IMPI are concentrated and conjointly distributed in southwest China, north of central China and southeast of northwest China in mountainous regions and plateaus. Longitudinal comparisons demonstrate that the degree of multidimensional poverty has relatively decreased across China from 2000 to 2010, but regional disparities continue to expand and new aspects are emerging. In addition, compared with 2000, the number of counties with multidimensional poverty in 2010 increased in northeast China and decreased in central China. Many counties have experienced generally increased levels in certain domains of poverty. The relative contribution of each indicator to the IMPI also provides important references for formulating and implementing poverty policy. Quantile regression was utilized to explore the application of the IMPI in assessing environmental inequality. The result indicates that many poverty-stricken and developed counties are exposed to poor air quality. The accurate identification of geographical and spatiotemporal patterns of poverty in China can lead to the implementation of anti-poverty strategies. This paper also offers new insights into poverty measurement for other developing countries.
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This study is supported by the project of The National Key R&D Program of China (No. 2017YFB0503500).
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Li, G., Cai, Z., Liu, J. et al. Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications. Soc Indic Res 144, 1099–1134 (2019). https://doi.org/10.1007/s11205-019-02072-5
- Multidimensional poverty
- Spatiotemporal dynamics
- Quantile regression
- Self-organizing map (SOM)
- Environmental problem