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• W2586899777 abstract "Though the rate of poverty in Ghana has consistently declined over the years, some parts of the country stillrecord substantially high figures [1], and this is a major concern for stake holders. Previous research to identify causal factorshas commonly used the binary logit or probit models. These models, however, mask the effect of important intermediateinformation during the binary transformation of the response variable. This has the potential to misestimate the probability ofpoverty. In this study, the ordered probit model was used, thus creating a framework that includes the ordinal nature ofpoverty severity. The model was based on the round 6 dataset of the Ghana Living Standards Survey. Our findings show thatpoor and extremely poor were negatively affected by rural location, illiteracy, and Savannah ecological zone. Policies toeradicate poverty must therefore aim at optimizing these significant variables contributions to welfare conditions in thecountry." @default.
• W2586899777 created "2017-02-17" @default.
• W2586899777 creator A5035418341 @default.
• W2586899777 creator A5077499050 @default.
• W2586899777 date "2018-08-24" @default.
• W2586899777 modified "2023-09-22" @default.
• W2586899777 title "Household Poverty-Risk Analysis and Prediction Using Bayesian Ordinal Probit Models" @default.
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• W2586899777 doi "https://doi.org/10.5923/j.statistics.20160606.09" @default.
• W2586899777 hasPublicationYear "2018" @default.
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