Research on Participatory Poverty Index in North Jiangsu

In terms of current life style, living and production conditions and hygienic and educational condition, we select 8 indices, such as annual net income of farmers per capita, annual grain yield per capita, total power of agricultural machinery per capita, dropout rate of school children and so on, to establish index system of determining the poor village in North Jiangsu. By selecting Lianqun Village in Suining County of Xuzhou City, Mawa Village in Siyang County of Suqian City, Chuanxing Village in Guanyun County of Lianyungang City, Xiaozhu Village in Hongze County of Huai'an City, Fengda Village in Xiangshui County of Yancheng City as the representative villages, after the discussion and consultation of the masses and the village cadres of all villages, we get the measuring results of weight. Through the field survey, investigation and interview in the selected regions, we get the relevant data, and then we conduct standardization processing, so as to get the index value that can comprehensively reflect the characteristics of poverty. According to the index data that have been standardized, by using participatory poverty index formula for calculation, we get the values that can explain the poverty degree of the respondents. We sequence the representative poor villages in this region according to the poverty degree from high to low, and the result is as follows: Mawa Village, ianqun Village, Chuanxing Village, Xiaozhu Village, and Fengda Village. It indicates that in terms of the operability of theory and technique, the participatory poverty index can better recognize the poor villages, so that it lays solid foundation for rationally and effectively using the limited poverty alleviation resources.

Evaluation of regional water security using water poverty index

Water security is a widely concerned issue in the world nowadays.A new method,water poverty index(WPI),was applied to evaluate the regional water security.Twelve state farms in Heilongjiang Province,Northeastern China were selected to evaluate water security status based on the data of 2006 using WPI and mean deviation grading method.The method of WPI includes five key indices:resources(R),access(A),capacity(C),utilization(U)and environment(E).Each key index further consists of several sub-indices.According to the results of WPI,the grade of each farm was calculated by using the method of mean deviation grading.Thus,the radar images can be protracted of each farm.From the radar images,the conclusions can be drawn that the WPI values of Farm 853 and Hongqiling are under very safe status,while that of Farm Raohe is under safe status,those of Farms Youyi,597,852,291 and Jiangchuan are under moderate safe status,that of Farm Beixing is under low safe status and those of Farm Shuangyashan,Shuguang and Baoshan are under unsafe status.The results from this study can provide basic information for decision making on rational utilization of water resources and regulations for regional water safety guarantee system.

Targeted Poverty Alleviation Lies in Precisely Identifying the Poverty–Stricken Population: An Analysis of the Targeting Effect of the Rural Subsistence Allowance Policy

Poverty–stricken populations must be identified precisely in the fight against poverty to implement the strategy of building a moderately prosperous society in all respects by 2020. The analysis based on the household survey in 2013 shows that the targeting accuracy is not high based on the standard of income and the accuracy is higher based on the standard of multidimensional poverty index. But the latter still has a low coverage rate. To gradually achieve integration of the rural poverty line and the rural subsistence allowance line, standards applied to identifying households entitled to subsistence allowances should be changed from the standard of income to multidimensional poverty indexes. A unified standard of subsistence allowances and a unified method for identifying related households should be developed. At the same time, coverage and funding of subsistence allowances should be extended and increased to better meet people's basic needs.

Is Microcredit Targeted to Poor People？ Evidences From a Cambodian Microfinance Institution

This study extends research on the social performance of microfinance institutions. The research methodology is based on Grameen Progress out of Poverty IndexTM （PPITM） for Cambodia applied to a sample of borrowers randomly extracted from a Cambodian microfinance institution＇s loan portfolio. Dataset has been directly collected through in-house interviews. Main questions discussed here are： （1） Is microcredit targeted to poor people？ （2） Has the poverty rate of the sample changed in last six months？ and （3） What percentage of male vs. female clients is poor？ We found an average poverty likelihood of about 8.1%, estimated at the day of the interview, steady over a period of six months and not statistically different between male and female borrowers. This evidence might be related to business geographical location or targeting. Actually, PPI too much relies on asset ownership rather than on cash flows and saving capacity. Despite the general wisdom microcredit is targeted to the ＂poorest among the poor people＂, this is utterly consistent with a sound and safe （micro）banking activity, aimed at sustainable results. Here comes a call for a triple bottom line performance evaluation on microflnance institutions： economic, social and environmental effects of their activities

The poverty dynamics in rural China during 2000–2014: A multi-scale analysis based on the poverty gap index

As the largest developing country in the world, China's rural areas face many poverty-related issues. It is imperative to assess poverty dynamics in a timely and effective manner in China's rural areas. Therefore, we used the poverty gap index to investigate the poverty dynamics in China's rural areas during 2000–2014 at the national, contiguous poor areas with particular difficulties and county scales. We found that China made significant achievements in poverty alleviation during 2000–2014. At the national scale, the number of impoverished counties decreased by 1428, a reduction of 97.28%. The rural population in impoverished counties decreased by 493.94 million people or 98.76%. Poverty alleviation was closely associated with economic development, especially with industrial development. Among all 15 socioeconomic indicators, the industrial added value had the highest correlation coefficient with the poverty gap index(r = –0.458, p<0.01). Meanwhile, the inequality of income distribution in the out-of-poverty counties has been aggravated. The urban-rural income gap among the out-of-poverty counties increased by 1.67-fold, and the coefficient of variation in rural per-capita income among the out-of-poverty counties also increased by 9.09%. Thus, we argued that special attention should be paid to reducing income inequality for sustainable development in China's rural areas.