I’m an Associate Professor of Economics at the University of Cantabria. I hold an MSc and PhD in economics from the University of the Basque Country, the University of Cantabria and the University of Oviedo. I was visiting scholar at the University of Reading (UK), the University of Antwerp (Belgium), the University of Helsinki (Finland), and UNU-WIDER.

My research interests include different aspects of the distribution of well-being, from the univariate to the multivariate setting. I am particularly interested in the estimation of inequality and poverty with limited information. My work has been published in leading academic journals including Journal of Urban Economics, Journal of the Royal Statistical Society, World DevelopmentReview of Income and Wealth, among others.

Research interests: 

–  Well-being
–  Inequality
–  Multivariate distributions

Google Scholar profile
Curriculum vitae (Last updated: June 2021)


1.  Vanesa Jorda, Jose Maria Sarabia, and Markus Jäntti. Inequality measurement with grouped data: Parametric and nonparametric methods. Journal of the Royal Statistical Society: Series A, In press, 2021.

Statistical software

2.  Emilio Gómez-Déniz, José María Sarabia, and Vanesa Jordá. Parametric Lorenz curves based on the beta system of distributions. Communications in Statistics- Theory and Methods, pages 1–20, 2021.

3.  José María Sarabia, Vanesa Jorda, Faustino Prieto, and Montserrat Guillén. Multivariate classes of GB2 distributions with applications. Mathematics, 9(1):72, 2021.

4  José María Sarabia and Vanesa Jorda. Lorenz surfaces based on the Sarmanov–Lee distribution with applications to multidimensional inequality in well-being. Mathematics, 8(11):2095, 2020.

5.   Alonso, JM., R. Andrews & V. Jorda (2019). Do neighbourhood renewal programs reduce crime rates? Evidence from England. Journal of Urban Economics, 110 (2019) 51–69 

6.   Jordá, V., B. López-Noval & J.M. Sarabia (2018). Distributional Dynamics of Life Satisfaction in Europe. Journal of Happiness Studies, in  press.

7.   Sarabia, E. Gómez-Déniz, F. Prieto & V. Jordá (2018). Aggregation of Dependent Risks in Mixtures of Exponential Distributions and Extensions. ASTIN Bulletin, in press.

8.   Jordá, V. & J.M. Alonso (2017) New Estimates on Educational Attainment Using a Continuous Approach (1970–2010). World Development, 90, 281-293.

Online dataset         Statistical software

9.   Sarabia, J.M., V. Jordá, & l. Remuzgo (2017). The Theil Indices in Parametric Families of Income Distributions—A Short Review. The Review of Income and Wealth, 63(4), 867-880.

10.   Sarabia, E. Gómez-Déniz, F. Prieto & V. Jordá (2016) Risk aggregation in multivariate dependent Pareto distributions. Insurance: Mathematics and Economics, 71, 154-163.

11.   J.M. Sarabia, F. Prieto & V. Jordá (2015) About the hyperbolic Lorenz curve. Economics Letters, 136, 42-45.

12.   Jordá & J.M. Sarabia (2015) Well-being distribution in the globalization era: 30 years of convergence. Applied Research in Quality of Life, 10 (1), 123-140.

13.   Jordá & J.M. Sarabia (2015) International convergence in well-being indicators. Social Indicators Research, 120 (1), 1-27.

14   J.M. Sarabia & V. Jordá (2014) Explicit expressions of the Pietra index for the generalized function for the size distribution of income. Physica A: Statistical Mechanics and its Applications, 416, 582-595.

15.   Jordá, J.M. Sarabia & F. Prieto (2014) On the estimation of the global income distribution using a parsimonious approach. Research on Economic Inequality, 22 115-145.

16.   J.M. Sarabia, F. Prieto & V. Jordá, (2014) Bivariate beta-generated distributions with applications to well-being data. Journal of Statistical Distributions and Applications, 1 (1), 1-15.

17.   J.M. Sarabia, V. Jordá & C. Trueba (2014) Estimating the income distribution in Latin America using limited information. International Advances in Economic Research, 20 (2), 231 – 232.

18.   J.M. Sarabia & V. Jordá (2014) Bivariate Lorenz Curves Based on the Sarmanov–Lee Distribution. Topics in Statistical Simulation, 114, 447-455.

19.   J.M. Sarabia, V. Jordá & C. Trueba (2014) The Lamé class of Lorenz curvesCommunications in Statistics – Theory and Methods. 46(11), 5311-5326.

20.   J.M. Sarabia, F. Prieto, C. Trueba & V. Jordá (2013) About the modified Gaussian family of income distributions with applications to individual incomes. Physica A: Statistical Mechanics and its Applications, 392 (6), 1398-1408.


Work in progress

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 SarabiaMarkus 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.


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.


  • Eighth Meeting of the Society for the Study of Economic Inequality (ECINEQ) Paris School of Economics, July 2019.
  • Royal Economic Society (RES) Annual Conference 2019, the University of Warwick, April 2019.
  • Sustainability and Development Conference, Michigan University, November 2018.
  • 35TH IARIW General Conference, Copenhagen, August 2018.
  • 7TH Workshop on Risk Management and Insurance (RISK), Santander, April 2018.
  • 32ND Annual Congress of the European Economic Association, Lisbon, August 2017.
  • Seventh Meeting of the Society for the Study of Economic Inequality (ECINEQ) , The Graduate Center, City University of New York, New York City, July 2017.
  • Development in the Face of Global Inequalities,  Institut Barcelona d’Estudis Internacionals (IBEI), May 2017.
  • APPAM International Conference, Inequalities: Addressing the Growing Challenge for Policymakers Worldwide. London School of Economics. June 2016.
  • Sixth Meeting of the Society for the Study of Economic Inequality (ECINEQ). Luxembourg, July 2015.
  •  Insurance: Mathematics and Economics (IME). University of Liverpool (England). June 2015.
  • XXIII Public Economics Meeting. Santander (Spain). January 2015.
  • X Winter School on Inequality and Social Welfare, Alba di Canazei (Italy). January 2015.
  •  XI Foro Internacional sobre Evaluación de la Calidad en la Investigación y la Educación Superior. Bilbao (Spain). July 2014.
  • XXI Encuentro Internacional de Profesores Universitarios de Matemáticas en Economía y Empresa (ASEPUMA). Málaga (Spain)., July 2014.
  • XV Iberian-Italian Congress of Financial and Actuarial Mathematics. Sevilla (Spain). October 2014.
  •  VI conference of the Spanish-Portuguese Association of Resource and Environmental Economics (AERNA). Gerona (Spain), September 2014.
  •  International Conference on Advances in Management, Economics and Social Science. Rome (Italy). June 2014.
  • XXI Public Economics Meeting. Gerona (Spain), January 2014.
  • Workshop on Economics of Energy Efficiency. Reus (Spain). December 2013.
  • XXXIX International Conference on Regional Science. Oviedo (Spain). November 2013.
  • International Conference on Statistical Distributions and Applications. Mt Pleasant (USA). October 2013.
  • Fifth Meeting of the Society for the Study of Economic Inequality (ECINEQ). Bari (Italy). July 2013.
  • International Conference on Applied Economics (ICOAE). Istanbul (Turkey). June 2013.
  •  7th International Workshop on Simulation. Rimini (Italy). May 2013.
  • XXVII International Conference in Applied Economics. Zaragoza (Spain). July 2013.
  • XV World Economic Meeting. Santander (Spain). June 2013.
  • 75th International Atlantic Economic Meeting.Vienna (Austria). April 2013.
  • XX Meeting of Public Economics. Sevilla (Spain). January 2013.
  • 10th International Conference on ordered Statistical Data and Their Applications. Murcia (Spain).
  • International Conference on Regional Science. Bilbao (Spain). November 2012.
  • XXV International Conference of Applied Economics. Santander (Spain). June 2011.
  •  XIII World Economic Meeting. San Sebastián (Spain). May 2011.