Peer-Reviewed Publications

Palencia-Esteban, A.  and Salas-Rojo, P. (2023) Intergenerational Mobility and Life Satisfaction in Spain. Research on Economic Inequality, 30, 109-137. 

" This chapter explores the relation between personal well-being – measured with life satisfaction – and intergenerational mobility in Spain (2017). We start by applying machine learning techniques to overcome traditional data limitations and estimate intergenerational income mobility. Then, by means of several econometric specifications, we find the relation between personal well-being and intergenerational income mobility to be non-significant. This result is robust to several measures of educational and occupational mobility. Contrary to the comparison theory, if Spanish citizens derive well-being benefits or losses from intergenerational mobility, these effects are not permanent and dissipate with time. We find other variables, such as enjoying good health, higher income levels and marriage, to be positively associated with life satisfaction. Overall, personal well-being in Spain is more related to materialistic aspects rather than to the comparison of individuals’ current position against the previous generations’ socio-economic status."

Salas-Rojo, P.  and Rodríguez, J. G. (2022) Inheritances and Wealth Inequality: A Machine Learning Approach. Journal of Economic Inequality, 20, 27-51.

"This paper explores the relationship between received inheritances and the distribution of wealth (financial, non-financial and total) in four developed countries: the United States, Canada, Italy and Spain. We follow the inequality of opportunity (IOp) literature and − considering inheritances as the only circumstance− we show that traditional IOp approaches can lead to non-robust and arbitrary measures of IOp depending on discretionary cut-off choices of a continuous circumstance such as inheritances. To overcome this limitation, we apply Machine Learning methods (‘random forest’ algorithm) to optimize the choice of cut-offs and we find that IOp explains over 60% of wealth inequality in the US and Spain (using the Gini coefficient), and more than 40% in Italy and Canada. Including parental education as an additional circumstance −available for the US and Italy− we find that inheritances are still the main contributor. Finally, using the S-Gini index with different parameters to weight different parts of the distribution, we find that the effect of inheritances is more prominent at the middle of the wealth distribution, while parental education is more important for the asset-poor."

Salas-Rojo, P.  and Rodríguez, J. G. (2021) The Distribution of Wealth in the U.S. and Spain: The Role of Socioeconomic Factors. SERIEs. 12 (3): 423-451.

"The literature has typically found the distribution of covariates (education, labor status or income distribution) to not account for the remarkable cross-country wealth inequality disparities. Then, the different institutions and other latent factors have received all the credit. This paper confirms this result for the U.S. (2016) and Spain (2014), but also explores a different avenue, showing that the distribution of circumstances (parental education and the inheritances received) explain a remarkable share of the differences on wealth inequality of opportunity between both countries. By means of a counterfactual decomposition, we demonstrate that imposing the distribution of circumstances of the U.S. into Spain equalizes the opportunities in the latter country to accumulate total, financial and non-financial wealth. This result suggests that, to explain the cross- country differences on wealth accumulation, the literature should focus on the intergenerational wealth transmission factors, rather than on covariates disparities."

Cabrera, L., Marrero, G. A., Rodríguez, J. G. and Salas-Rojo, P. (2021) Inequality of Opportunity in Spain: New Insights from New Data. Hacienda Pública Española/Review of Public Economics. 237 (2): 153-185.

"Modern theories of justice consider Inequality of Opportunity (IO), the part of overall inequality explained by individual circumstances (factors beyond the individual control, like socioeconomic background), as the truly concept of unfair inequality. In addition, recent empirical studies have found that IO harms growth. Then, given the big increase in income inequality in Spain during the last decade (now one of the highest levels in the EU), how large is IO in Spain? By using a novel database from the Centro de Investigaciones Sociológicas (CIS) questionnaire on ‘Social inequality and social mobility in Spain’, we observe that the share of IO is 44% of overall inequality (Gini index). By circumstances, we find that about 90% of IO is due to parental education and occupation, the type of school attended, the gender of the household’s head and the size of the household. In addition, it is found that a large share of IO is channeled through the occupation and, especially, the level of education of the individual. These findings are consistent with the low levels of relative mobility in education and occupation observed in the database for Spain (2017)."

Collaborations

Salas-Rojo, P. (2024) Herencias y desigualdad en la riqueza, en "La Desigualdad en España" (Lengua de Trapo, Eds Javier Carbonell, Berna León y Javier Soria).

Delclós, C.,  Auciello, I., Lebrusán, I., Salas-Rojo, P. and Segú, M. (2023) Vivienda para vivir: de mercancía a derecho. Future Policy Lab Report.

Salas-Rojo, P. (2020) Herències i desigualtat en la riquesa. Revista econòmica de Catalunya, nº 82, 79-84 .

Under Review

Gil-Hernández, C., Salas-Rojo, P., Vidal, G. and Villani, D. (2024) Wealth Inequality by Social Classes in 21st-Century Europe. International Inequalities Institute Working Papers, 135 (Under Review at the European Sociological Review).

"Wealth is a central determinant of life chances and intergenerational status persistence in modern societies. Yet, sociologists traditionally overlooked its role in class measurement and inequality, while most economists focused on the elites. This article reconciles sociological and economic perspectives on class analysis by examining the relationship between classes and wealth inequality versus income. Drawing from the Luxembourg Wealth Study (2002-2018) in five European countries, we test whether occupational classes, based on the entire division of labour, keep up with rising economic inequality trends. In contrast to bold claims on class death or decomposition, inequality of outcomes in wealth accumulation is firmly rooted across big occupational classes in contemporary capitalism, potentially harming future equal opportunity and social mobility. Still, occupational classes better capture between-group income inequality and stratification than wealth, emphasizing the importance of economic resources beyond labour market attachment that spark advances in social class theory and measurement. This paper aims at reconciling the sociological and economic perspectives on class and inequality by examining the relationship between social classes, wealth, and income. Using data from the Luxembourg Wealth Study from 2002 to 2018 in five European countries (Finland, Germany, Greece, Slovakia, and Spain), we highlight the increasing significance of wealth as a determinant of socioeconomic status."

Brunori, P., Salas-Rojo, P. and Verme, P. (2023)  Estimating inequality with missing incomes. International Inequalities Institute Working Papers, 82. (Second R&R at the Review of Development Economics).

"The measurement of income inequality is affected by missing observations provoked by item non-responses, especially if they are concentrated at the tails of the objective income distribution. In this chapter we conduct an experiment to test how the different correction methods proposed by the statistical, econometric and machine learning literature address this measurement bias.  We take a baseline survey and artificially corrupt the data, employing several alternative non-linear functions that simulate patterns of income non-response. Applying the Gini index on these corrupted income data shows how biased inequality statistics can be when non-responses are ignored. The comparative assessment of correction techniques indicates that some methods can partially correct the missing data biases. In particular, we find that sample re-weighting produces inequality estimates quite close to reality in most simulated missing patterns. Other methods, such as modeling a Pareto-tail or imputing with predictive means matching or a LASSO regularized regression, perform well in some other patterns."

Brunori, P., Ferreira, F. and Salas-Rojo, P. (2023) Inherited inequality: a general framework and an application to South Africa. International Inequalities Institute Working Papers, 107 (Under Review at Economica).

"Scholars have sought to quantify the extent of inequality which is inherited from past generations in many different ways, including a large body of work on intergenerational mobility and inequality of opportunity. This paper makes three contributions to that broad literature. First, we show that many of the most prominent approaches to measuring mobility or inequality of opportunity fit within a general framework which involves, as a first step, a calculation of the extent to which inherited circumstances can predict current incomes. The importance of prediction has led to recent applications of machine learning tools to solve the model selection challenge in the presence of competing upward and downward biases. Our second contribution is to apply transformation trees to the computation of inequality of opportunity. Because the algorithm is built on a likelihood maximization that involves splitting the sample into groups with the most salient differences between their conditional cumulative distributions, it is particularly well-suited to measuring ex-post inequality of opportunity, following Roemer (1998). Our third contribution is to apply the method to data from South Africa, arguably the world’s most unequal country, and find that almost threequarters of its current inequality is inherited from predetermined circumstances, with race playing the largest role, but parental background also making an important contribution."

Working Papers / Work in Progress

Moramarco, D., Brunori, P., and Salas-Rojo, P. (2024) Strengths and Limitations of the Use of  Machine Learning in the Estimation of Inequality of Opportunity.

Palencia-Esteban, A.,  Salas-Rojo, P., Brunori, P. and Lodi, L. (2024) Measurement problems on transition performance indexes.

Palomino, J. C., Rodríguez, J. G., Salas-Rojo, P. and Sebastián, R. (2024) Technological change and wage inequality in Spain.

Brunori, P. Jordá, V., Salas-Rojo, P. (2024) Polarization of Opportunities in the US.

Valentini, A., Brunori, P., Ferreira, F. and Salas-Rojo, P. (2024) Playing the birth lottery in Europe and Latin America