Dr. Thomas Schwab is an economist and works as a project manager for the Bertelsmann Stiftung. He specialises in European economic policy with a focus on cohesion of regions. His current focus is on the effects of the twin transition on regional disparities in Europe. For his data-driven work, he applies methods from econometrics and machine learning.
TS: Linking cohesion and the twin transition wasn’t exactly our choice alone but follows a strategic decision by the European Commission. Cohesion Policy is guided by the overarching ambition for Europe to become greener, smarter and fairer. This is reflected in the priorities outlined by the Commission for 2019-24 including the European Green Deal, a Europe fit for the digital age and an economy that works for the people. In this context, we undertook our study jointly with the Vienna Institute for International Economic Studies (wiiw) to look at how the twin transition, meaning the greening and digitalisation of European economies, will impact the core promise of the Cohesion Policy, which is reducing economic and social disparities between European regions.
NC: Yes, that’s right, but it’s important to note the wider context. Specifically, the 8th Cohesion Report released by the European Commission in February 2022 already recognised this risk of a widening gap between regions. While less developed and transition regions have been catching up, especially in Eastern Europe since 2001, their growth will likely slow because the structural factors behind it won’t be as important to future growth. For example, low cost advantages, returns on infrastructure investment and benefits from shifting employment away from agriculture may become relatively less significant. Meanwhile, regions around the Alps are generally already more developed and have higher overall growth potential. This geographic divide is the ‘playing field’ that the twin transition is stepping into. We therefore wanted to see how the twin transition would ‘change the cohesion game’ so to speak.
Nathan Crist is a project manager in the Europe’s Future Program at the Bertelsmann Stiftung working on the Europe’s Economy Project.
Our study looks at the overall growth potential of all EU NUTS-2 regions and then considers factors that will either ease or hinder their transition to a green, digital economy. We found that regions with economic specialisation in high tech, low carbon manufacturing or knowledge intensive services (which are generally also higher GDP per capita), already have the tools to ‘master’ the twin transition. In contrast, regions with a focus on carbon intensive manufacturing or agriculture, happen to have generally lower incomes and prosperity and don’t have as easy a path to a greener, digital economy. Existing disparities are therefore likely to widen as a result of the twin transition.
NC: Absolutely, and that really comes from the economic specialisation that urban areas, especially capital regions, tend to have. Apart from divides between core versus periphery and Alpine versus Eastern European and new Member States, there is also this urban-rural divide that is even recognisable in countries that face overall bigger hurdles. While many regions in Central and Eastern Europe are ‘falling behind’, the notable exceptions are the dynamic capital regions of Prague, Warsaw, Bratislava, Budapest and Bucharest which are actually some of the top performers in our forecasts alongside usual suspects like Stockholm, Upper Bavaria (which contains Munich) and Paris.
"Existing disparities are likely to widen as a result of the twin transition"
TS: We used a foresight approach because we want to look into the future of EU cohesion during the twin transition by using indicators which predict economic growth potential. Those indicators include high skilled labour, institutional quality, infrastructure quality, investment and innovation which are key for economic prosperity and growth (innovation being especially important for both a digital and green transition). We therefore collected a large amount of data and made comparisons across regions; the higher the values, the stronger the outlook for future growth.
TS: What is new to our study is that we developed these ‘readiness scores’ which determine how well regions are doing in transforming to a green and digital economy. We picked the indicators behind the scores based on rigorous literature research. Eventually, we ended up with indicators such as labour productivity and internet accessibility for the digital readiness score, as well as greenhouse gas emissions or number of road vehicles for the green readiness score. These indicators are not controversial per se, but reduction of complex reality in this economic model is and was. For example, we received feedback from a region that considered themselves more ‘ready’ than a neighbour and they therefore doubted our results. We checked the data again, but in the end we confirmed that the region was indeed performing worse in every single aspect. Research like this is always a reduction of complex reality, but I think we explain the most important aspects on how regions differ quite well.