A geographic identification of multidimensional poverty in rural China under the framework of sustainable livelihoods analysis

• A method for identification of multidimensional poverty was established.
• The method better reflects the impact of multiple factors on people’s livelihood.
• The method targets at counties both in economic and multidimensional poverty.
• Natural basis is still the key determination for China’s multidimensional poverty.
• The method can improve the effectiveness and sustainability of poverty reduction.

Developing methods of measuring multidimensional poverty and improving the accuracy of poverty identification have been hot topics in international poverty research for decades. They are also key issues for improving the quality and effectiveness of rural poverty reduction programs in China. So far, selection and integration of poverty indicators remains the main difficult for measurement of multidimensional poverty. Guided by the sustainable livelihoods framework developed in the UK by the Department for International Development (DFID), an index system and an integration method for geographical identification of multidimensional poverty were established, and they were further used to carry out a county-level identification of poverty in rural China. Additionally, comparisons were made of the identification results with counties having single-dimension income poverty in rural areas and poor counties designated by the Chinese central government. The results showed that a total of 655 counties, with 141 million rural residents, were identified as multidimensionally poor. They are concentrated and conjointly distributed geographically, and evil natural conditions are their common features. In comparison to the income poor and the designated poor counties, the multidimensionally poor counties were not only worse in single-dimensional and composite scores, but also having multiple disadvantages and deprivations. By identifying the disadvantage and deprived dimensions, the measurement of multidimensional poverty should be very helpful for each county to work out and implement antipoverty programs accordingly, and it would make contribution to improve the sustainability of poverty reduction. Hopefully, this research may also shed light on multidimensional poverty measurement for other developing countries.