Estimating the joint distribution of global well-being: A copula-based approach (with Koen Decancq)
estimating the impact of neighborhood renewal programs on crime (WITH josé manuel alonso and rhys andrews)
Estimation OF inequality FROM grouped datA. ARXIV Working paper 1808.09831 (with José María Sarabia & Markus Jäntti)
Grouped data in form of income shares have been conventionally used to estimate income inequality due to the lack of availability of individual records. Most prior research on economic inequality relies on lower bounds of inequality measures in order to avoid the need to impose a parametric functional form to describe the income distribution. These estimates neglect income differences within shares, introducing, therefore, a potential source of measurement error. The aim of this paper is to explore a nuanced alternative to estimate income inequality, which leads to a reliable representation of the income distribution within shares. We examine the performance of the generalized beta distribution of the second kind (GB2) and related models to estimate different inequality measures and compare the accuracy of these estimates with the nonparametric lower bound in more than 5000 datasets covering 182 countries over the period 1867-2015. We deploy two different econometric strategies to estimate the parametric distributions, non-linear least squares and generalised method of moments, both implemented in R and conveniently available in the package GB2group. Despite its popularity, the nonparametric approach is outperformed even the simplest two-parameter models. Our results confirm the excellent performance of the GB2 distribution to represent income data for a heterogeneous sample of countries, which provides highly reliable estimates of several inequality measures. This strong result and the access to an easy tool to implement the estimation of this family of distributions, we believe, will incentivize its use, thus contributing to the development of reliable estimates of inequality trends.
Global inequality in length of life: 1950-2015. WIDER Working Paper 2017/192 (with Miguel Niño-Zarazúa)
This paper provides a broad picture of national, regional and global trends of inequality in length of life over the period 1950–2015. We use data on life tables from World Population Prospects to develop a comprehensive database of a battery of inequality measures for 201 countries at five-year intervals over the period under analysis. We estimate both absolute and relative inequality measures which have the property of being additively decomposable. This property makes the database remarkably flexible because overall inequality can be computed for any group of countries using only the information included in our database. The decomposition analysis reveals that differences in life expectancy between countries account for a very small portion of the observed changes in global inequality in length of life, evolution of which is large driven by within-country variation. Our estimates indicate that inequality in length of life has decreased sharply since 1950, a reduction that can be largely attributed to the substantial progress made in reducing child mortality worldwide. We also observe a degree of heterogeneity in the distributional patters of inequality in length of life across world regions.
GLOBAL INEQUALITY: HOW LARGE IS THE EFFECT OF TOP INCOMES? WIDER WORKING PAPER 2016/94 (WITH MIGUEL NIÑO-ZARAZÚA)
In this paper, we estimate the recent evolution of global interpersonal inequality and examine the effect of omitted top incomes on the level and direction of global inequality. We propose a methodology to estimate the truncation point of household surveys by combining information on income shares from household surveys and top income shares from tax data. The methodology relies on a flexible parametric functional form that models the income distribution for each country-year point under different assumptions on the omitted information at the right tail of the distribution. Goodness-of-fit results show a robust performance of our model, supporting the reliability of our estimates. Overall, we find that the undersampling of the richest individuals in household surveys generate a downward bias in global inequality estimates that ranges between 15 per cent and 42 per cent, depending on the period of analysis, and the assumed level of truncation of the income distribution.