Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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This study was proposed to measure the extent of vulnerability to poverty as well the effect of socio-economic characteristics on household susceptibility to poverty using Feasible Generalized Least Squares (FGLS) estimation and logistic regression methods. The results revealed that, sizable fractions of non-poor households (51.3%) were vulnerable to poverty and 53.2 % of the sampled poor households have a probability of 50 percent and above to fall in to poverty in the near future again. Household livestock holding, crop diversification, Household head education level and household’s access to credit and their exposure to idiosyncratic shocks are found to be important variables in examining the determinants of rural household vulnerability to poverty. The results suggested that since poverty and vulnerability to poverty are different signs of the same coin, policies directed towards poverty reduction need to consider not only the current poor but also the vulnerability of current non-poor households.
Wondaferahu Mulugeta. 2016. \u201cRural Household Vulnerability to Poverty in South West Ethiopia: The Case of Gilgel Gibe Hydraulic Dam Area of Sokoru and Tiro Afeta Woreda\u201d. Global Journal of Human-Social Science - E: Economics GJHSS-E Volume 16 (GJHSS Volume 16 Issue E3): .
Crossref Journal DOI 10.17406/GJHSS
Print ISSN 0975-587X
e-ISSN 2249-460X
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Total Score: 73
Country: Ethiopia
Subject: Global Journal of Human-Social Science - E: Economics
Authors: Sisay Tola, Wondaferahu Mulugeta, Yilkal Wassie (PhD/Dr. count: 0)
View Count (all-time): 164
Total Views (Real + Logic): 3624
Total Downloads (simulated): 1773
Publish Date: 2016 12, Thu
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This study was proposed to measure the extent of vulnerability to poverty as well the effect of socio-economic characteristics on household susceptibility to poverty using Feasible Generalized Least Squares (FGLS) estimation and logistic regression methods. The results revealed that, sizable fractions of non-poor households (51.3%) were vulnerable to poverty and 53.2 % of the sampled poor households have a probability of 50 percent and above to fall in to poverty in the near future again. Household livestock holding, crop diversification, Household head education level and household’s access to credit and their exposure to idiosyncratic shocks are found to be important variables in examining the determinants of rural household vulnerability to poverty. The results suggested that since poverty and vulnerability to poverty are different signs of the same coin, policies directed towards poverty reduction need to consider not only the current poor but also the vulnerability of current non-poor households.
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