Multidimensional Poverty and Inequality: Insights from the Upper Blue Nile Basin, Ethiopia

被引:17
作者
Abeje, Misganaw Teshager [1 ,2 ]
Tsunekawa, Atsushi [3 ]
Haregeweyn, Nigussie [4 ]
Ayalew, Zemen [5 ]
Nigussie, Zerihun [3 ]
Berihun, Daregot [6 ]
Adgo, Enyew [5 ]
Elias, Asres [7 ]
机构
[1] Tottori Univ, United Grad Sch Agr Sci, 4-101 Koyama Minami, Tottori 6808553, Japan
[2] Bahir Dar Univ, Inst Disaster Risk Management & Food Secur Studie, POB 79, Bahir Dar, Ethiopia
[3] Tottori Univ, Arid Land Res Ctr, 1390 Hamasaka, Tottori 6800001, Japan
[4] Tottori Univ, Int Platform Dry Land Res & Educ, 1390 Hamasaka, Tottori 6800001, Japan
[5] Bahir Dar Univ, Coll Agr & Environm Sci, POB 79, Bahir Dar, Ethiopia
[6] Bahir Dar Univ, Coll Business & Econ, POB 79, Bahir Dar, Ethiopia
[7] Tottori Univ, Fac Agr, 4-101 Koyama Minami, Tottori 6808550, Japan
基金
日本科学技术振兴机构;
关键词
Multidimensional Poverty Index; Inequality; Correlation Sensitive Poverty Index; Drought-prone; Upper Blue Nile basin; PERSON GENERATED INDEX; ACCESS; DYNAMICS; SERVICES; GENDER; GROWTH; RIGHTS; WATER;
D O I
10.1007/s11205-019-02257-y
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
As stated in the 2018 global Multidimensional Poverty Index (MPI) report, Ethiopia has the second largest multidimensionally poor population in Africa (after Nigeria). The global MPI was created to measure household's multiple deprivations, but little systematic study has been carried out on the application of MPI measures on a smaller scale and vis-a-vis other measures of poverty. In addition, most of the few existing studies ignore any measure of inequality amongst the multidimensionally poor. This study explored multidimensional poverty in three different drought-prone agro-ecological settings of the Upper Blue Nile basin, Ethiopia. A preliminary participatory exercise was carried out at the study sites to select important indicators and then a structured survey was administered to 390 systematically and randomly selected households. The Alkire-Foster method was used to analyse multidimensional poverty and verified it with Correlation Sensitive Poverty Index (CSPI). Multidimensional poverty incidence, adjusted head count ratio and inequality were significantly different between study sites (p < 0.001). Results indicated a high incidence (88%, 82% and 80%), intensity (52%, 55% and 56%), MPI (46%, 45% and 45%) and inequality (53%, 60% and 63%) of poverty in Aba Gerima, Guder and Dibatie study sites, respectively. A high level of divergence was revealed between the MPI and CSPI in terms of identifying the poor. The living standard and land and livestock ownership dimensions contributed the most to MPI. The case study signifies the importance of inclusion of land and livestock indicators for the national MPI. Besides, it implies that researchers and policymakers need to account for smaller scale contextualised indicators and location differences when studying and designing anti-poverty interventions.
引用
收藏
页码:585 / 611
页数:27
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