Exploring and Correcting the Bias in the Estimation of the Gini Measure of Inequality
Datos Bibliográficos
| ID | 2331160 |
|---|---|
| Autores | Juan Francisco Muñoz Rosas (0000-0001-7427-6630, Universidad de Granada, autor de correspondencia), Pablo J Moya-Fernández (0000-0003-0980-3849, Universidad de Granada), Encarnación Álvarez-Verdejo (0000-0002-0473-6037, Universidad de Granada) |
| Año | 2025 |
| Volumen | 54 |
| Número | 1 |
| Páginas | 237-274 |
| Fecha de publicación | 2025-02-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Sociological Methods & Research (JOURNAL) |
| Identificadores de la revista | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Editorial | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/00491241231176847 |
| OpenAlex | W4378192371 |
| Idioma | EN |
| Citas recibidas | 2 |
| Referencias citadas | 48 |
The Gini index is probably the most commonly used indicator to measure inequality. For continuous distributions, the Gini index can be computed using several equivalent formulations. However, this is not the case with discrete distributions, where controversy remains regarding the expression to be used to estimate the Gini index. We attempt to bring a better understanding of the underlying problem by regrouping and classifying the most common estimators of the Gini index proposed in both infinite and finite populations, and focusing on the biases. We use Monte Carlo simulation studies to analyse the bias of the various estimators under a wide range of scenarios. Extremely large biases are observed in heavy-tailed distributions with high Gini indices, and bias corrections are recommended in this situation. We propose the use of some (new and traditional) bootstrap-based and jackknife-based strategies to mitigate this bias problem. Results are based on continuous distributions often used in the modelling of income distributions. We describe a simulation-based criterion for deciding when to use bias corrections. Various real data sets are used to illustrate the practical application of the suggested bias corrected procedures
Data mining · Distribution (mathematics) · Econometrics · Economic inequality · Estimator · Gini coefficient · Index (typography) · Inequality · Jackknife resampling · Measure (data warehouse) · Monte Carlo method · Range (aeronautics) · Statistics · Computer Science · Fiscal Policy and Economic Growth · Global Health Care Issues · Income, Poverty, and Inequality · Mathematics
Inequality and poverty in Malaysia
Mathematical methods of statistics
An Introduction to the Bootstrap
Methods of Measuring the Concentration of Wealth
Home-market economic development as a moderator of the self-selection and learning-by-exporting effects
The Small-Sample Bias of the Gini Coefficient
About Capital in the Twenty-First Century
Working from home and income inequality
On Estimating the Poverty Gap and the Poverty Severity Indices With Auxiliary Information
Decomposing the Gini Inequality Index
Pareto and the Upper Tail of the Income Distribution in the UK
On Gini's Mean Difference and Gini's Index of Concentration
Linking Input Inequality and Outcome Inequality
A Method of Measuring Inequality Within a Selection Process
Statistical Inference for Measures of Inequality With a Cross-National Bootstrap Application
Horizontal Inequality and Data Challenges
| Obras citantes distintas | 2 |
|---|---|
| Citas por año | 2 |
| Intervalo de citas | 2025 - 2026 (2) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 2 |