| _version_ | 1866902082256109568 |
|---|---|
| author | David Clarence Gray |
| author_facet | David Clarence Gray |
| contents | <p>This paper provides a generalized structure to evaluate income inequality and poverty, focusing on income distribution and applying a Bayesian approach to derive various poverty measures. A parametric model from income distribution and samples from the posterior distribution were used to formulate poverty indices. Explicitly, it scrutinizes the Foster Greer Thorbecke (FGT) poverty index which assesses poverty by incorporating its incidence, depth, and severity. We evaluate poverty using discrete and continuous FGT indices, applying a Bayesian mixture model with lognormal components in the liberal context. This approach offers robust poverty estimates by integrating prior knowledge and handling data uncertainties. The analysis reveals significant income inequality across different counties in Liberia, underlining disparities in income distribution. The posterior distributions for the FGT indices provide comprehensive insights into poverty levels, emphasizing the need for targeted policy to address income inequality.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_21474_IJAR01_20835 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | BAYESIAN MIXTURE APPROACH TO INCOME INEQUALITY AND POVERTY INDICES OF LIBERIA A STUDY USING HIES DATA OF 2016-2017 David Clarence Gray <p>This paper provides a generalized structure to evaluate income inequality and poverty, focusing on income distribution and applying a Bayesian approach to derive various poverty measures. A parametric model from income distribution and samples from the posterior distribution were used to formulate poverty indices. Explicitly, it scrutinizes the Foster Greer Thorbecke (FGT) poverty index which assesses poverty by incorporating its incidence, depth, and severity. We evaluate poverty using discrete and continuous FGT indices, applying a Bayesian mixture model with lognormal components in the liberal context. This approach offers robust poverty estimates by integrating prior knowledge and handling data uncertainties. The analysis reveals significant income inequality across different counties in Liberia, underlining disparities in income distribution. The posterior distributions for the FGT indices provide comprehensive insights into poverty levels, emphasizing the need for targeted policy to address income inequality.</p> <p> </p> |
| title | BAYESIAN MIXTURE APPROACH TO INCOME INEQUALITY AND POVERTY INDICES OF LIBERIA A STUDY USING HIES DATA OF 2016-2017 |
| url | https://doi.org/10.21474/IJAR01/20835 |