Dimitris Kallioras (p), George Petrakos, Paschalis Arvanitidis, Yiannis Saratsis

Adam Ploszaj

Though malleable – meaning different things to different people and in different contexts – the concept and the phenomenon of inequality encompasses discrete, yet overlapping and interrelated, types. Indeed, inequality segregations, both actual and perceived, among individuals, social groups and spatial entities can be analyzed through segmentation that refers to income, race, health, education and gender. Considering that a complex set of theoretical propositions, ideological preoccupations, and institutional arrangements are confronted with the hard evidence of frequent market and policy failures, understanding the relations among the types of inequality is, apparently, an extremely important task that may provide valuable insight both to academic theory and to policy-making. This is especially so in the light of the paradigm shift that is taking place, most emphatically in Europe, as inequality is perceived not only as a problem of (re-)distribution (i.e., inequality of outcomes) but also as a problem of processes (i.e., inequality of opportunities, inequality of membership, inequality of treatment). The paper adopts a holistic and integrated approach and compiles a matrix that relates the various types of inequality to each other in an interactive and dynamic manner, in a multi-level (i.e., micro-, meso-, and macro-) and poly-parametric context. Particularly, the paper is structured around interrelated research lines that investigate the causalities among the various types of inequality aiming at identifying path-dependent or cumulative causation processes that may cause multiple types of inequality to coexist, and even to reinforce each other, in different social groups and spatial entities, and may cause asymmetries between levels of aggregation (e.g., more people may be left behind in places left behind). Towards analyzing the interactions and the interdependencies among the various types of inequality, the paper is going to provide a thorough analyses of bodies of literature that lies within the scientific fields of economics, sociology, political sciences and geography. While tractability of the existing theoretical and empirical models limits the scope and the complexity of interdependencies among the various types of inequality, the paper is going to scrutinize the issue, thus informing the design of the pre-distributive and re-distributive policy interventions aiming to reduce inequality.

Luca Serafini (p), Emanuela Marrocu, Raffaele Paci

Dimitris Kallioras

This study examines the effect of EU-funded projects related to Smart Specialisation Strategies (S3) and Industry 4.0 (14) on the performance of Italian firms that received European Regional Development Funding (ERDF) between 2014 and 2020. By reviewing data on employment, turnover, and other economic indicators, this research analyses the effect of S3 projects on Italian firms’ performance. The study assesses the degree to which S3 is linked to recipient businesses’ success and analyses the processes by which S3 can improve company performance. The findings of this research contribute to a better understanding of the effectiveness of S3 in promoting regional economic growth and identifying the factors that influence the success of ERDF-funded firms. Additionally, the analysis focuses specifically on the S3 that features Industry 4.0 (I4) with the aim of understanding whether the projects related to these new technologies have a higher impact on the firms that undertake such activities. To assess if the projects related to S3 or I4 have an additional effect with respect to the other ERDF-funded projects, the analysis uses an event study difference-in-difference method, using different control groups to assess the impact of the projects. The analysis results offer further evidence regarding the impact of the European Fund on firms’ performance, enhancing our understanding of how the Cohesion Policy is effectively providing support to businesses within the EU.

Adam Ploszaj (p), Agnieszka Olechnicka, Katarzyna Skorniewska, Teresa Darlinska

Luca Serafini

The Russian aggression against Ukraine, especially its escalation since February 2022, has multidimensional implications for the World and Europe, not only in terms of international relations and security but also in social and economic terms. This geopolitical shock affects social and economic inequalities, creating new ones and modifying old ones (e.g., pressure on social and healthcare systems in countries and regions to which large groups of war refugees from Ukraine have flowed). One aspect of this situation, so far not explored in a systematic way, is the impact of Russian aggression against Ukraine on the functioning of higher education institutions. In particular, the situation of universities in regions close to Ukraine, as well as the Russian Federation and Belarus, is worth examining. Such institutions are potentially most vulnerable to the consequences of the interruption or significant reduction of existing cooperation. This is due to two reasons. First, by spatial proximity, institutions in border regions have relatively more relations with their neighbors on the other side of the border than institutions further away from the border. These universities attract students from across the eastern border. The studies related to borderlands (i.e., research that requires cross-border collaboration, visits, and exchanges) are most common in the case of these universities. Second, border regions are often also peripheral regions in a socio-economic sense. At the same time, institutions in these usually peripheral regions were already facing unfavorable conditions compared to those in more central regions. Thus, the current situation may not only have created new inequalities but, more importantly, exacerbated those that already existed before. Taking these observations as a starting point, in this study, we answer the following question: How has the Russian invasion of Ukraine affected inequalities between institutions of higher education and science in particular in the regions neighboring Ukraine, Russia, and Belarus?

The main empirical material used in this study is survey data from a random sample of university employees in 10 countries bordering Ukraine, Russia, and Belarus: Finland, Estonia, Latvia, Lithuania, Poland, Slovakia, Hungary, Romania, Bulgaria, and Moldova. The analysis will compare universities in NUTS 3 and NUTS 2 regions bordering Ukraine, Belarus, and Russia with universities in other regions in the analysed countries. This will allow us to assess the role of spatial proximity to the shock under analysis (Russian invasion).