scholarly journals Causality between Output and Income Inequality across U.S. States: Evidence from a Heterogeneous Mixed Panel Approach

2020 ◽  
pp. 20
Author(s):  
Stephen M Miller
Keyword(s):  
Panel Data ◽  
Income Inequality ◽  
Causal Relationship ◽  
Gini Coefficient ◽  
Personal Income ◽  
Mean Deviation ◽  
Cross Sectional ◽  
Inequality Measures ◽  
Atkinson Index ◽  
Cross Sectional Dependence

In this paper, we investigate the causal relationship between output, proxied by personal income, and income inequality in a panel data of 48 states from 1929 to 2012. We employ the causality methodology proposed by Emirmahmutoglu and Kose (2011), as it incorporates possible slope heterogeneity and cross-sectional dependence in a multivariate panel. Evidence of bi-directional causal relationship exists for several inequality measures -- the Atkinson Index, Gini Coefficient, the Relative Mean Deviation, Theil’s entropy Index and Top 10% -- but no evidence of the causal relationship for the Top 1 % measure. Also, this paper finds state-specific causal relationships between personal income and inequality.

Does Clientelism Affect Income Inequality? Evidence from Panel Data

2018 ◽  
pp. 1-24
Author(s):  
Ebney Ayaj Rana ◽  
Mustafa Kamal
Keyword(s):  
Panel Data ◽  
Income Inequality ◽  
Management Strategies ◽  
Political Regime ◽  
Cross Sectional ◽  
Fiscal Management ◽  
Long Run ◽  
Negative Effect ◽  
Cross Sectional Dependence ◽  
The Relationship

This paper studies the determinants of income inequality in a panel of countries to provide empirical evidence to the relationship between income inequality and clientelism. Using different panel data techniques, especially group mean fully modified OLS estimator, and also allowing for control variables, cross-sectional heterogeneity and cross-sectional dependence, we find that in the long run, clientelism exerts a significant negative effect on income equality. The overall results of the study have implications for fiscal management strategies and political regime choice.

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

Detecting common breaks in the means of high dimensional cross-dependent panels

2021 ◽  
Author(s):  
Lajos Horváth ◽  
Zhenya Liu ◽  
Gregory Rice ◽  
Yuqian Zhao
Keyword(s):  
Panel Data ◽  
Common Factors ◽  
Real Data ◽  
Change Points ◽  
High Dimensional ◽  
Asymptotic Results ◽  
Cross Sectional ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Cross Sectional Dependence

Abstract The problem of detecting change points in the mean of high dimensional panel data with potentially strong cross–sectional dependence is considered. Under the assumption that the cross–sectional dependence is captured by an unknown number of common factors, a new CUSUM type statistic is proposed. We derive its asymptotic properties under three scenarios depending on to what extent the common factors are asymptotically dominant. With panel data consisting of N cross sectional time series of length T, the asymptotic results hold under the mild assumption that min {N, T} → ∞, with an otherwise arbitrary relationship between N and T, allowing the results to apply to most panel data examples. Bootstrap procedures are proposed to approximate the sampling distribution of the test statistics. A Monte Carlo simulation study showed that our test outperforms several other existing tests in finite samples in a number of cases, particularly when N is much larger than T. The practical application of the proposed results are demonstrated with real data applications to detecting and estimating change points in the high dimensional FRED-MD macroeconomic data set.

scholarly journals Pandemics and Income Inequality: What Do the Data Tell for the Globalization Era?

2021 ◽  
Vol 9 ◽  
Author(s):  
Tiejun Chen ◽  
Giray Gozgor ◽  
Chun Kwong Koo
Keyword(s):  
Panel Data ◽  
Income Inequality ◽  
Least Squares ◽  
Gini Coefficient ◽  
Oecd Countries ◽  
Sensitivity Analyses ◽  
The World

This paper empirically investigates the effects of pandemics uncertainty on income inequality We consider a new measure of pandemics uncertainty, the World Pandemic Discussion Index (WPDI), and the post-tax (net) Gini coefficient We focus on the panel data of 141 countries from 1996 to 2020. The results from the Feasible General Least Squares estimations indicate that the WPDI is negatively related to income inequality in 107 non-OECD countries. However, the WPDI is positively associated with income inequality in 34 OECD economies. This evidence remains robust when considering different models, including several controls, and implementing various sensitivity analyses.

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