Encarnación Álvarez-Verdejo
Dados Biográficos
| ID | 4270774 |
|---|---|
| NOME | Encarnación Álvarez-Verdejo |
| PRENOMES | Encarnación |
| SOBRENOME | Álvarez-Verdejo |
| ASSINATURA | ÁLVAREZ-VERDEJO E |
| AFILIAÇÕES | Universidad de Granada |
| ORCID | 0000-0002-0473-6037 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 5 |
| TOTAL DE CITAÇÕES | 3 |
| TOTAL COMO AUTOR | 5 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2014 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 1 |
A Practical Guide to Proper Estimation and Inference of the Gini Index by Avoiding often Encountered Methodological Pitfalls
The Gini index is the most widely-used measure of inequality. Unfortunately, its computation is subject to error. Researchers and practitioners often fall into common methodological pitfalls, leading to inaccurate estimates and inferences, and ultimately hindering efforts to reduce inequality and improve societal quality of life. This paper clarifies the challenges of non-parametric estimation of the Gini index more comprehensively than previous …
Innovation and Poverty-Related Income Gaps in Europe
This article examines how innovation dynamics interact with income inequality and poverty-related vulnerability across European countries, with attention to the persistent North – South divide. Using harmonized data from Eurostat and the Global Entrepreneurship Monitor, income inequality is measured through the S80/S20 income quintile share ratio, an indicator sensitive to disparities affecting lower-income groups. The analysis combines descripti…
Exploring and Correcting the Bias in the Estimation of the Gini Measure of Inequality
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 est…
On Estimating the Poverty Gap and the Poverty Severity Indices With Auxiliary Information
Many poverty measures are estimated by using sample data collected from social surveys. Two examples are the poverty gap and the poverty severity indices. A novel method for the estimation of these poverty indicators is described. Social surveys usually contain different variables, some of which can be used to improve the estimation of poverty measures. The proposed estimation methodology is based on this idea. The variance estimation and the con…
Efficient Estimation of the Headcount Index
Efficient Estimation of the Headcount Index
Exploring and Correcting the Bias in the Estimation of the Gini Measure of Inequality
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 est…
Efficient Estimation of the Headcount Index
On Estimating the Poverty Gap and the Poverty Severity Indices With Auxiliary Information
Many poverty measures are estimated by using sample data collected from social surveys. Two examples are the poverty gap and the poverty severity indices. A novel method for the estimation of these poverty indicators is described. Social surveys usually contain different variables, some of which can be used to improve the estimation of poverty measures. The proposed estimation methodology is based on this idea. The variance estimation and the con…
Exploring and Correcting the Bias in the Estimation of the Gini Measure of Inequality
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 est…
A Practical Guide to Proper Estimation and Inference of the Gini Index by Avoiding often Encountered Methodological Pitfalls
The Gini index is the most widely-used measure of inequality. Unfortunately, its computation is subject to error. Researchers and practitioners often fall into common methodological pitfalls, leading to inaccurate estimates and inferences, and ultimately hindering efforts to reduce inequality and improve societal quality of life. This paper clarifies the challenges of non-parametric estimation of the Gini index more comprehensively than previous …
Innovation and Poverty-Related Income Gaps in Europe
This article examines how innovation dynamics interact with income inequality and poverty-related vulnerability across European countries, with attention to the persistent North – South divide. Using harmonized data from Eurostat and the Global Entrepreneurship Monitor, income inequality is measured through the S80/S20 income quintile share ratio, an indicator sensitive to disparities affecting lower-income groups. The analysis combines descripti…
Income, Poverty, and Inequality (4 obras) · Econometrics (3 obras) · Estimation (3 obras) · Estimator (3 obras) · Mathematics (3 obras) · Monte Carlo method (3 obras) · Statistics (3 obras) · Computer Science (2 obras) · Economic Growth and Productivity (2 obras) · Economic inequality (2 obras)