2021
DOI: 10.1111/rssa.12701
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Linearization and Variance Estimation of the Bonferroni Inequality Index

Abstract: The study of income inequality is important for predicting the wealth of a country. There is an increasing number of publications where the authors call for the use of several indices simultaneously to better account for the wealth distribution. Due to the fact that income data are usuallyinequality measures, inference, influence function | 1009 DONG et al. | INTRODUCTIONNobel Prize-winning economist, Joseph Stiglitz, stated that income inequality is an important measure for forecasting the wealth of a country… Show more

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Cited by 3 publications
(2 citation statements)
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“…However, it has been re‐discovered 40 years later by Piesch (1975) and Nygård & Sandström (1981). The Bonferroni index shares a lot of properties with the Gini index, but it is more sensitive to the left tail of the income distribution than the Gini index (Pizzetti, 1951; Dong et al , 2021). Furthermore, several extensions and interpretations proposed for the Gini index hold also for the Bonferroni index (see Tarsitano, 1990, for a comprehensive review).…”
Section: Family Of Gi(ab) and Counterexamplesmentioning
confidence: 99%
See 1 more Smart Citation
“…However, it has been re‐discovered 40 years later by Piesch (1975) and Nygård & Sandström (1981). The Bonferroni index shares a lot of properties with the Gini index, but it is more sensitive to the left tail of the income distribution than the Gini index (Pizzetti, 1951; Dong et al , 2021). Furthermore, several extensions and interpretations proposed for the Gini index hold also for the Bonferroni index (see Tarsitano, 1990, for a comprehensive review).…”
Section: Family Of Gi(ab) and Counterexamplesmentioning
confidence: 99%
“…The linearised variables are then used in the expression of the variance estimator of the total estimator for estimating the sampling variance (see, e.g. Dong et al , 2021). The Graf method can be applied to almost all sampling designs as long as the expression of the variance estimator of the total estimator under the sampling design is known.…”
Section: Estimation From a Samplementioning
confidence: 99%