Gini Variance Estimation of Grouped Data

Bibliographic Data

ID21512945
AuthorsSean Dalby (0009-0005-0493-7268, The George Washington University Washington DC USA, corresponding author), Huixia Judy Wang (0000-0002-5195-8564, Statistics Department Rice University Houston Texas USA)
Year2026
Publication date2026-04-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueOxford Bulletin of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0305-9049 • E-ISSN: 1468-0084
PublisherWiley (PUBLISHER • GB)
DOI10.1111/obes.70079
OpenAlexW7160118835
LanguageEN
References cited16

We propose a jackknife variance estimator for the Gini Index based on grouped data. It only requires access to group means and counts, and is computationally fast, modifying an existing algorithm that exploits the Gini's connection with regression modelling. After reviewing the group‐level point estimator, we discuss its asymptotic normality and our jackknife's consistency. We then conduct a multiplicative random effects simulation, comparing the jackknife's results to those of an off‐the‐shelf method for variance estimation in this setting. The jackknife is more stable across within‐group variations and more closely approximates the true group‐level variance, although cases of extreme inequality may require very high sample sizes to achieve desired accuracy. We conclude with thoughts for future research

Estimation · Estimator · Grouped data · Jackknife resampling · Multiplicative function · Point estimation · Advanced Statistical Methods and Models · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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