Tootfinder

Unbiased estimation in new Gini index extensions under gamma distributions
Roberto Vila, Helton Saulo
https://arxiv.org/abs/2506.00666 https://

Unbiased estimation in new Gini index extensions under gamma distributions
In this paper, we propose two new flexible Gini indices (extended lower and upper) defined via differences between the $i$-th observation, the smallest order statistic, and the largest order statistic, for any $1 \leqslant i \leqslant m$. For gamma-distributed data, we obtain exact expectations of the estimators and establish their unbiasedness, generalizing prior works by [Deltas, G. 2003. The small-sample bias of the gini coefficient: Results and implications for empirical research. Review of…

Inequality in the Age of Pseudonymity
Aviv Yaish, Nir Chemaya, Lin William Cong, Dahlia Malkhi
https://arxiv.org/abs/2508.04668 https://arxiv.org/pdf/2508.…

Inequality in the Age of Pseudonymity
Inequality measures such as the Gini coefficient are used to inform and motivate policymaking, and are increasingly applied to digital platforms. We analyze how measures fare in pseudonymous settings, as common to internet-based or blockchain-based platforms. One key challenge that arises is the ability of actors to create multiple fake identities under fictitious false names, also known as ``Sybils.'' While some actors may do so to preserve their privacy, we show that this can inadvertently di…

Novel measures and estimators of income inequality
Roberto Vila, Helton Saulo
https://arxiv.org/abs/2508.02965 https://arxiv.org/pdf/2508.02965

Novel measures and estimators of income inequality
In this paper, we propose new income inequality measures that approximate the Gini coefficient and analyze the asymptotic properties of their estimators, including strong consistency and limiting distribution. Generalizations to the measures and estimators are developed. Simulation studies assess finite-sample performance, and an empirical example demonstrates practical relevance.