Flachaire

Publications

Econometrics and Income InequalityJournal articleMartin Biewen and Emmanuel Flachaire, Econometrics, Volume 6, Issue 4, pp. 42, 2018

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. [...]

Measuring mobilityJournal articleFrank A. Cowell and Emmanuel Flachaire, Quantitative Economics, Volume 9, Issue 2, pp. 865-901, 2018

Our new approach to mobility measurement involves separating out the valuation of positions in terms of individual status (using income, social rank, or other criteria) from the issue of movement between positions. The quantification of movement is addressed using a general concept of distance between positions and a parsimonious set of axioms that characterize the distance concept and yield a class of aggregative indices. This class of indices induces a superclass of mobility measures over the different status concepts consistent with the same underlying data. We investigate the statistical inference of mobility indices using two well‐known status concepts, related to income mobility and rank mobility. We also show how our superclass provides a more consistent and intuitive approach to mobility, in contrast to other measures in the literature, and illustrate its performance using recent data from China.

Econometrics and Income InequalityBookMDPI Books, Martin Biewen and Emmanuel Flachaire (Eds.), 2018, 322 pages, MDPI, 2018

This is a reprint of articles from the Special Issue published online in the open access journal Econometrics
(ISSN 2225-1146) from 2017 to 2018 (available at: https://www.mdpi.com/journal/
econometrics/special issues/inequality)

Confidence Sets for Inequality Measures: Fieller-Type MethodsBook chapterJean-Marie Dufour, Emmanuel Flachaire, Lynda Khalaf and Abdallah Zalghout, In: Productivity and Inequality, William H. Greene, Lynda Khalaf, Paul Makdissi, Robin C. Sickles, Michael Veall and Marcel-Cristian Voia (Eds.), 2018, pp. 143-155, Springer International Publishing, 2018

Asymptotic and bootstrap inference methods for inequality indices are for the most part unreliable due to the complex empirical features of the underlying distributions. In this paper, we introduce a Fieller-type method for the Theil Index and assess its finite-sample properties by a Monte Carlo simulation study. The fact that almost all inequality indices can be written as a ratio of functions of moments and that a Fieller-type method does not suffer from weak identification as the denominator approaches zero, makes it an appealing alternative to the available inference methods. Our simulation results exhibit several cases where a Fieller-type method improves coverage. This occurs in particular when the Data Generating Process (DGP) follows a finite mixture of distributions, which reflects irregularities arising from low observations (close to zero) as opposed to large (right-tail) observations. Designs that forgo the interconnected effects of both boundaries provide possibly misleading finite-sample evidence. This suggests a useful prescription for simulation studies in this literature.

Inequality with Ordinal DataJournal articleFrank A. Cowell and Emmanuel Flachaire, Economica, Volume 84, Issue 334, pp. 290-321, 2017

The standard theory of inequality measurement assumes that the equalisand is a cardinal quantity, with known cardinalization. However, one often needs to make inequality comparisons where either the cardinalization is unknown or the underlying data are categorical. We propose an alternative approach to inequality analysis that is rigorous, has a natural interpretation, and embeds both the ordinal data problem and the well-known cardinal data problem. We show how the approach can be applied to the inequality of happiness and of health status.

Statistical Methods for Distributional AnalysisBook chapterFrank A. Cowell and Emmanuel Flachaire, In: Handbook of Income Distribution, A.B. Atkinson and F. Bourguignon (Eds.), 2015-11, Volume 2A, Ch.6, pp. 359-465, Elsevier, 2015

This Chapter is about the techniques, formal and informal, that are commonly used to give quantitative answers in the field of distributional analysis - covering subjects including inequality, poverty and the modelling of income distributions. It deals with parametric and non-parametric approaches and the way in which imperfections in data may be handled in practice.

Log-Transform Kernel Density Estimation of Income DistributionJournal articleArthur Charpentier and Emmanuel Flachaire, Actualité Économique (L'), Volume 91, Issue 1-2, pp. 141-159, 2015

Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.

Goodness of Fit: An Axiomatic ApproachJournal articleFrank A. Cowell, Russell Davidson and Emmanuel Flachaire, Journal of Business & Economic Statistics, Volume 33, Issue 1, pp. 54-67, 2015

An axiomatic approach is used to develop a one-parameter family of measures of divergence between distributions. These measures can be used to perform goodness-of-fit tests with good statistical properties. Asymptotic theory shows that the test statistics have well-defined limiting distributions which are, however, analytically intractable. A parametric bootstrap procedure is proposed for implementation of the tests. The procedure is shown to work very well in a set of simulation experiments, and to compare favorably with other commonly used goodness-of-fit tests. By varying the parameter of the statistic, one can obtain information on how the distribution that generated a sample diverges from the target family of distributions when the true distribution does not belong to that family. An empirical application analyzes a U.K. income dataset.

Political versus economic institutions in the growth processJournal articleEmmanuel Flachaire, Cecilia Garcia-Peñalosa and Maty Konte, Journal of Comparative Economics, Volume 42, Issue 1, pp. 212-229, 2014

After a decade of research on the relationship between institutions and growth, there is no consensus about the exact way in which these two variables interact. In this paper we re-examine the role that institutions play in the growth process using data for developed and developing economies over the period 1975–2005. Our results indicate that the data is best described by an econometric model with two growth regimes. Political institutions are the key determinant of which regime an economy belongs to, while economic institutions have a direct impact on growth rates within each regime. These findings support the hypothesis that political institutions are one of the deep causes of growth, setting the stage in which economic institutions and standard covariates operate.

Reference distributions and inequality measurementJournal articleFrank A. Cowell, Emmanuel Flachaire and Sanghamitra Bandyopadhyay, Journal of Economic Inequality, Volume 11, Issue 4, pp. 421-437, 2013

We investigate a general problem of comparing pairs of distributions which includes approaches to inequality measurement, the evaluation of “unfair” income inequality, evaluation of inequality relative to norm incomes, and goodness of fit. We show how to represent the generic problem simply using (1) a class of divergence measures derived from a parsimonious set of axioms and (2) alternative types of “reference distributions.” The problems of appropriate statistical implementation are discussed and empirical illustrations of the technique are provided using a variety of reference distributions.