Special Issue "Econometrics and Income Inequality"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 January 2018)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Prof. Martin Biewen

Universität Tübingen, Germany
Website | E-Mail
Interests: labor economics; income distribution; education economics; microeconometrics
Guest Editor
Prof. Emmanuel Flachaire

Aix-Marseille Université, France
Website | E-Mail
Interests: econometrics; income distribution

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the econometric analysis of income inequality and income distributions. Given the recent surge of inequality research, this Special Issue seeks to combine both theoretical and applied contributions which advance the econometric analysis of income inequality and income distributions. Possible topics include, but are not limited to, statistical inference for inequality measurement, inequality measurement with complex survey data, parametric or non-parametric modelling of income distributions, statistical decomposition methodology, methods to investigate the determinants of distributional change, causal inference in inequality measurement, and applications of such methods to substantive research questions in different fields of economics.

Prof. Martin Biewen
Prof. Emmanuel Flachaire
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charges (APCs) of 350 CHF (Swiss Francs) per published paper are fully funded by institutions through the Knowledge Unlatched initiative, resulting in no direct charge to authors. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Inequality measurement
  • Complex survey data
  • Incomplete data
  • Robust inference
  • Modelling of top tails
  • Decomposition methods
  • Wage inequality

Published Papers (16 papers)

View options order results:
result details:
Displaying articles 1-16
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Open AccessEditorial Econometrics and Income Inequality
Econometrics 2018, 6(4), 42; https://doi.org/10.3390/econometrics6040042
Received: 11 October 2018 / Accepted: 11 October 2018 / Published: 15 October 2018
PDF Full-text (171 KB) | HTML Full-text | XML Full-text
Abstract
It is well-known that, after decades of non-interest in the theme, economics has experienced a proper surge in inequality research in recent years. [...] Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available

Research

Jump to: Editorial, Review

Open AccessArticle Foreign Workers and the Wage Distribution: What Does the Influence Function Reveal?
Econometrics 2018, 6(3), 41; https://doi.org/10.3390/econometrics6030041
Received: 31 January 2018 / Revised: 31 August 2018 / Accepted: 31 August 2018 / Published: 7 September 2018
PDF Full-text (716 KB) | HTML Full-text | XML Full-text
Abstract
This paper draws upon influence function regression methods to determine where foreign workers stand in the distribution of private sector wages in Luxembourg, and assess whether and how much their wages contribute to wage inequality. This is quantified by measuring the effect that
[...] Read more.
This paper draws upon influence function regression methods to determine where foreign workers stand in the distribution of private sector wages in Luxembourg, and assess whether and how much their wages contribute to wage inequality. This is quantified by measuring the effect that a marginal increase in the proportion of foreign workers—foreign residents or cross-border workers—would have on selected quantiles and measures of inequality. Analysis of the 2006 Structure of Earnings Survey reveals that foreign workers have generally lower wages than natives and therefore tend to haul the overall wage distribution downwards. Yet, their influence on wage inequality reveals small and negative. All impacts are further muted when accounting for human capital and, especially, job characteristics. Not observing any large positive inequality contribution on the Luxembourg labour market is a striking result given the sheer size of the foreign workforce and its polarization at both ends of the skill distribution. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data
Econometrics 2018, 6(2), 30; https://doi.org/10.3390/econometrics6020030
Received: 1 January 2018 / Revised: 23 May 2018 / Accepted: 23 May 2018 / Published: 4 June 2018
PDF Full-text (323 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
It is sometimes observed and frequently assumed that top incomes in household surveys worldwide are poorly measured and that this problem biases the measurement of income inequality. This paper tests this assumption and compares the performance of reweighting and replacing methods designed to
[...] Read more.
It is sometimes observed and frequently assumed that top incomes in household surveys worldwide are poorly measured and that this problem biases the measurement of income inequality. This paper tests this assumption and compares the performance of reweighting and replacing methods designed to correct inequality measures for top-income biases generated by data issues such as unit or item non-response. Results for the European Union’s Statistics on Income and Living Conditions survey indicate that survey response probabilities are negatively associated with income and bias the measurement of inequality downward. Correcting for this bias with reweighting, the Gini coefficient for Europe is revised upwards by 3.7 percentage points. Similar results are reached with replacing of top incomes using values from the Pareto distribution when the cut point for the analysis is below the 95th percentile. For higher cut points, results with replacing are inconsistent suggesting that popular parametric distributions do not mimic real data well at the very top of the income distribution. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Open AccessArticle The Wall’s Impact in the Occupied West Bank: A Bayesian Approach to Poverty Dynamics Using Repeated Cross-Sections
Econometrics 2018, 6(2), 29; https://doi.org/10.3390/econometrics6020029
Received: 3 December 2017 / Revised: 7 May 2018 / Accepted: 22 May 2018 / Published: 30 May 2018
PDF Full-text (593 KB) | HTML Full-text | XML Full-text
Abstract
In 2002, the Israeli government decided to build a wall inside the occupied West Bank. The wall had a marked effect on the access to land and water resources as well as to the Israeli labour market. It is difficult to include the
[...] Read more.
In 2002, the Israeli government decided to build a wall inside the occupied West Bank. The wall had a marked effect on the access to land and water resources as well as to the Israeli labour market. It is difficult to include the effect of the wall in an econometric model explaining poverty dynamics as the wall was built in the richer region of the West Bank. So a diff-in-diff strategy is needed. Using a Bayesian approach, we treat our two-period repeated cross-section data set as an incomplete data problem, explaining the income-to-needs ratio as a function of time invariant exogenous variables. This allows us to provide inference results on poverty dynamics. We then build a conditional regression model including a wall variable and state dependence to see how the wall modified the initial results on poverty dynamics. We find that the wall has increased the probability of poverty persistence by 58 percentage points and the probability of poverty entry by 18 percentage points. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Decomposing Wage Distributions Using Recentered Influence Function Regressions
Econometrics 2018, 6(2), 28; https://doi.org/10.3390/econometrics6020028
Received: 31 December 2017 / Revised: 27 April 2018 / Accepted: 9 May 2018 / Published: 25 May 2018
Cited by 2 | PDF Full-text (1216 KB) | HTML Full-text | XML Full-text
Abstract
This paper provides a detailed exposition of an extension of the Oaxaca-Blinder decomposition method that can be applied to various distributional measures. The two-stage procedure first divides distributional changes into a wage structure effect and a composition effect using a reweighting method. Second,
[...] Read more.
This paper provides a detailed exposition of an extension of the Oaxaca-Blinder decomposition method that can be applied to various distributional measures. The two-stage procedure first divides distributional changes into a wage structure effect and a composition effect using a reweighting method. Second, the two components are further divided into the contribution of each explanatory variable using recentered influence function (RIF) regressions. We illustrate the practical aspects of the procedure by analyzing how the polarization of U.S. male wages between the late 1980s and the mid 2010s was affected by factors such as de-unionization, education, occupations, and industry changes. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle A Hybrid MCMC Sampler for Unconditional Quantile Based on Influence Function
Econometrics 2018, 6(2), 24; https://doi.org/10.3390/econometrics6020024
Received: 29 December 2017 / Revised: 25 April 2018 / Accepted: 26 April 2018 / Published: 4 May 2018
PDF Full-text (331 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we provide a Bayesian estimation method for the unconditional quantile regression model based on the Re-centered Influence Function (RIF). The method makes use of the dichotomous structure of the RIF and estimates a non-linear probability model by a logistic regression
[...] Read more.
In this study, we provide a Bayesian estimation method for the unconditional quantile regression model based on the Re-centered Influence Function (RIF). The method makes use of the dichotomous structure of the RIF and estimates a non-linear probability model by a logistic regression using a Gibbs within a Metropolis-Hastings sampler. This approach performs better in the presence of heavy-tailed distributions. Applied to a nationally-representative household survey, the Senegal Poverty Monitoring Report (2005), the results show that the change in the rate of returns to education across quantiles is substantially lower at the primary level. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Parametric Inference for Index Functionals
Econometrics 2018, 6(2), 22; https://doi.org/10.3390/econometrics6020022
Received: 13 December 2017 / Revised: 25 March 2018 / Accepted: 13 April 2018 / Published: 20 April 2018
PDF Full-text (312 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment
[...] Read more.
In this paper, we study the finite sample accuracy of confidence intervals for index functional built via parametric bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution, we propose a Generalized Method of Moment estimator that targets the quantity of interest, namely the considered inequality index. Its primary advantage is that the scale parameter does not need to be estimated to perform parametric bootstrap, since inequality measures are scale invariant. The very good finite sample coverages that are found in a simulation study suggest that this feature provides an advantage over the parametric bootstrap using the maximum likelihood estimator. We also find that overall, a parametric bootstrap provides more accurate inference than its non or semi-parametric counterparts, especially for heavy tailed income distributions. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Polarization and Rising Wage Inequality: Comparing the U.S. and Germany
Econometrics 2018, 6(2), 20; https://doi.org/10.3390/econometrics6020020
Received: 31 January 2018 / Revised: 12 March 2018 / Accepted: 22 March 2018 / Published: 11 April 2018
Cited by 1 | PDF Full-text (1340 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Since the late 1970s, wage inequality has increased strongly both in the U.S. and Germany but the trends have been different. Wage inequality increased along the entire wage distribution during the 1980s in the U.S. and since the mid 1990s in Germany. There
[...] Read more.
Since the late 1970s, wage inequality has increased strongly both in the U.S. and Germany but the trends have been different. Wage inequality increased along the entire wage distribution during the 1980s in the U.S. and since the mid 1990s in Germany. There is evidence for wage polarization in the U.S. in the 1990s, and the increase in wage inequality in Germany was restricted to the top of the distribution before the 1990s. Using an approach developed by MaCurdy and Mroz (1995) to separate age, time, and cohort effects, we find a large role played by cohort effects in Germany, while we find only small cohort effects in the U.S. Employment trends in both countries are consistent with polarization since the 1990s. The evidence is consistent with a technology-driven polarization of the labor market, but this cannot explain the country specific differences. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Decomposing the Bonferroni Inequality Index by Subgroups: Shapley Value and Balance of Inequality
Econometrics 2018, 6(2), 18; https://doi.org/10.3390/econometrics6020018
Received: 11 December 2017 / Revised: 20 March 2018 / Accepted: 23 March 2018 / Published: 2 April 2018
PDF Full-text (1487 KB) | HTML Full-text | XML Full-text
Abstract
Additive decomposability is an interesting feature of inequality indices which, however, is not always fulfilled; solutions to overcome such an issue have been given by Deutsch and Silber (2007) and by Di Maio and Landoni (2017). In this paper, we apply these methods,
[...] Read more.
Additive decomposability is an interesting feature of inequality indices which, however, is not always fulfilled; solutions to overcome such an issue have been given by Deutsch and Silber (2007) and by Di Maio and Landoni (2017). In this paper, we apply these methods, based on the “Shapley value” and the “balance of inequality” respectively, to the Bonferroni inequality index. We also discuss a comparison with the Gini concentration index and highlight interesting properties of the Bonferroni index. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle On the Decomposition of the Esteban and Ray Index by Income Sources
Econometrics 2018, 6(2), 17; https://doi.org/10.3390/econometrics6020017
Received: 18 January 2018 / Revised: 22 February 2018 / Accepted: 11 March 2018 / Published: 26 March 2018
PDF Full-text (285 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a simple algorithm based on a matrix formulation to compute the Esteban and Ray (ER) polarization index. It then shows how the algorithm introduced leads to quite a simple decomposition of polarization by income sources. Such a breakdown
[...] Read more.
This paper proposes a simple algorithm based on a matrix formulation to compute the Esteban and Ray (ER) polarization index. It then shows how the algorithm introduced leads to quite a simple decomposition of polarization by income sources. Such a breakdown was not available hitherto. The decomposition we propose will thus allow one to determine the sign, as well as the magnitude, of the impact of the various income sources on the ER polarization index. A simple empirical illustration based on EU data is provided. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Open AccessArticle Income Inequality, Cohesiveness and Commonality in the Euro Area: A Semi-Parametric Boundary-Free Analysis
Econometrics 2018, 6(2), 15; https://doi.org/10.3390/econometrics6020015
Received: 13 December 2017 / Revised: 22 February 2018 / Accepted: 8 March 2018 / Published: 21 March 2018
PDF Full-text (885 KB) | HTML Full-text | XML Full-text
Abstract
The cohesiveness of constituent nations in a confederation such as the Eurozone depends on their equally shared experiences. In terms of household incomes, commonality of distribution across those constituent nations with that of the Eurozone as an entity in itself is of the
[...] Read more.
The cohesiveness of constituent nations in a confederation such as the Eurozone depends on their equally shared experiences. In terms of household incomes, commonality of distribution across those constituent nations with that of the Eurozone as an entity in itself is of the essence. Generally, income classification has proceeded by employing “hard”, somewhat arbitrary and contentious boundaries. Here, in an analysis of Eurozone household income distributions over the period 2006–2015, mixture distribution techniques are used to determine the number and size of groups or classes endogenously without resort to such hard boundaries. In so doing, some new indices of polarization, segmentation and commonality of distribution are developed in the context of a decomposition of the Gini coefficient and the roles of, and relationships between, these groups in societal income inequality, poverty, polarization and societal segmentation are examined. What emerges for the Eurozone as an entity is a four-class, increasingly unequal polarizing structure with income growth in all four classes. With regard to individual constituent nation class membership, some advanced, some fell back, with most exhibiting significant polarizing behaviour. However, in the face of increasing overall Eurozone inequality, constituent nations were becoming increasingly similar in distribution, which can be construed as characteristic of a more cohesive society. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Statistical Inference on the Canadian Middle Class
Econometrics 2018, 6(1), 14; https://doi.org/10.3390/econometrics6010014
Received: 25 December 2017 / Revised: 2 February 2018 / Accepted: 8 March 2018 / Published: 13 March 2018
PDF Full-text (761 KB) | HTML Full-text | XML Full-text
Abstract
Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for
[...] Read more.
Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle Top Incomes, Heavy Tails, and Rank-Size Regressions
Econometrics 2018, 6(1), 10; https://doi.org/10.3390/econometrics6010010
Received: 19 November 2017 / Revised: 18 February 2018 / Accepted: 20 February 2018 / Published: 2 March 2018
PDF Full-text (1527 KB) | HTML Full-text | XML Full-text
Abstract
In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading
[...] Read more.
In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated), which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessArticle From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective
Received: 28 August 2017 / Revised: 17 January 2018 / Accepted: 22 January 2018 / Published: 25 January 2018
Cited by 1 | PDF Full-text (367 KB) | HTML Full-text | XML Full-text
Abstract
The underlying idea behind the construction of indices of economic inequality is based on measuring deviations of various portions of low incomes from certain references or benchmarks, which could be point measures like the population mean or median, or curves like the hypotenuse
[...] Read more.
The underlying idea behind the construction of indices of economic inequality is based on measuring deviations of various portions of low incomes from certain references or benchmarks, which could be point measures like the population mean or median, or curves like the hypotenuse of the right triangle into which every Lorenz curve falls. In this paper, we argue that, by appropriately choosing population-based references (called societal references) and distributions of personal positions (called gambles, which are random), we can meaningfully unify classical and contemporary indices of economic inequality, and various measures of risk. To illustrate the herein proposed approach, we put forward and explore a risk measure that takes into account the relativity of large risks with respect to small ones. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Open AccessFeature PaperArticle Inequality and Poverty When Effort Matters
Econometrics 2017, 5(4), 50; https://doi.org/10.3390/econometrics5040050
Received: 25 August 2017 / Revised: 21 October 2017 / Accepted: 23 October 2017 / Published: 6 November 2017
PDF Full-text (2100 KB) | HTML Full-text | XML Full-text
Abstract
On the presumption that poorer people tend to work less, it is often claimed that standard measures of inequality and poverty are overestimates. The paper points to a number of reasons to question this claim. It is shown that, while the labor supplies
[...] Read more.
On the presumption that poorer people tend to work less, it is often claimed that standard measures of inequality and poverty are overestimates. The paper points to a number of reasons to question this claim. It is shown that, while the labor supplies of American adults have a positive income gradient, the heterogeneity in labor supplies generates considerable horizontal inequality. Using equivalent incomes to adjust for effort can reveal either higher or lower inequality depending on the measurement assumptions. With only a modest allowance for leisure as a basic need, the effort-adjusted poverty rate in terms of equivalent incomes rises. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Review

Jump to: Editorial, Research

Open AccessReview Using the GB2 Income Distribution
Econometrics 2018, 6(2), 21; https://doi.org/10.3390/econometrics6020021
Received: 9 February 2018 / Revised: 29 March 2018 / Accepted: 4 April 2018 / Published: 18 April 2018
Cited by 1 | PDF Full-text (1564 KB) | HTML Full-text | XML Full-text
Abstract
To use the generalized beta distribution of the second kind (GB2) for the analysis of income and other positively skewed distributions, knowledge of estimation methods and the ability to compute quantities of interest from the estimated parameters are required. We review estimation methodology
[...] Read more.
To use the generalized beta distribution of the second kind (GB2) for the analysis of income and other positively skewed distributions, knowledge of estimation methods and the ability to compute quantities of interest from the estimated parameters are required. We review estimation methodology that has appeared in the literature, and summarize expressions for inequality, poverty, and pro-poor growth that can be used to compute these measures from GB2 parameter estimates. An application to data from China and Indonesia is provided. Full article
(This article belongs to the Special Issue Econometrics and Income Inequality) Printed Edition available
Figures

Figure 1

Back to Top