Club convergence and factors of income inequality in theEuropean Union

Abstract

The measurement of inequality and its determinants are major tasks within the economic literature. The main objectives of this paper are to identify patterns of convergence in the income inequality in European Union countries and to investigate the factors behind the formation of convergence clubs. For those purposes and considering the Gini index during the period 2007–2018, the club convergence approach and an ordered logit model are employed. The results show four clubs and four divergent countries. In addition, the analysis identifies certain factors, such as economic openness, public intervention and education, as the main drivers of inequality reduction. Finally, the results from the club convergence analysis for an alternative inequality measure, specifically the ratio 80:20, are similar to those achieved in the case of the Gini index.

Innovation dynamics and club convergence in innovation activity in China: A temporal perspective

“…To account for the heterogeneity of innovation capabilities across China's provinces, we employ the club convergence methodology developed by Phillips and Sul (2007, 2009), which has been widely used in many previous studies to test for economic and innovation convergence patterns at the national and regional levels (Barrios et al, 2019; Fischer & LeSage, 2015; Mazzola & Pizzuto, 2020; Monfort et al, 2013; Suárez‐Arbesú et al, 2022; Von Lyncker & Thoennessen, 2017). The implementation of Phillips and Sul’s (2007, 2009) convergence methodology allows us to test for the potential existence of multiple innovation equilibria across China's provinces.…”

“…This test is based on a non‐linear time‐varying factor model that analyses club convergence over time. The log t ‐test assesses how the idiosyncratic growth transition paths evolve over time in relation to a common growth component in the variable of interest (patent applications in our case) and has been frequently used in convergence studies with similar or fewer units of analysis (i.e., regions or countries) (e.g., Monfort et al, 2013; Suárez‐Arbesú et al, 2022) and/or shorter time periods (e.g., Barrios et al, 2019). We present the detailed operationalization of the Phillips and Sul (2007, 2009) log t ‐test, and the converging and merging procedure in Appendixes and , respectively.…”

“…This ordinal distribution consists of nonnegative values ranging from 1 to the number of clubs identified in each period. To investigate the factors that determine provinces' membership to a particular innovation club, we employ an ordered logit model, which allows us to analyse the relationship between the ordinal club membership and the predictor variables (e.g., Barrios et al, 2019; Suárez‐Arbesú et al, 2022). To calculate the models, we reverse the ordinal club membership distribution of provinces so that positive coefficients indicate increased probabilities of belonging to clubs with higher levels of innovation activity.…”