Impact of global crises
on regional labour markets

By 2050, the labour force in the EU will shrink by 35 million people. At the same time, member states are stepping up their decarbonisation efforts and investing more and more in digitalisation. Demographic change and the green and digital transformation - together with possible unplanned economic or demographic changes and government intervention - will significantly change the EU labour market. All of this will have a direct impact on unemployment rates, wage levels and the overall economic health of EU regions. Crucially, these changes will be unevenly distributed between regions.

The research project ‘Overlapping crises (re)shaping the future of regional labour markets’ (OVERLAP) examines in detail how overlapping crises will influence regional labour markets at NUTS 3 level (this European spatial level corresponds to the German districts and independent cities). Prognos is working on the project on behalf of ESPON - an EU-funded research programme.

What specific effects will global challenges and changes have on regional labour markets?

ESPON commissioned Prognos to analyse the impact of global crises on regional labour markets. As part of OVERLAP, we use established techniques and innovative methods to forecast and analyse the impact of demographic change and the green and digital transformation on regional labour markets up to 2040.

The aim is to gain a better understanding of demographic dynamics at NUTS 3 level and to analyse the impact of EU policies or shocks on these labour markets. The European Union categorises territorial units into three levels. Smaller regions or cities are categorised at NUTS 3 level. In Germany, for example, this corresponds to districts and independent cities. The project therefore analyses the effects at the smallest level and thus provides detailed information.

OVERLAP aims to help policy makers improve their strategies and harmonise territorial developments with sectoral objectives.

This is how we proceed

In the first phase of the project, a top-down approach is being pursued. Using a dynamic input-output model, we are investigating how demographic, digital and green change will affect the size of the labour force and employment levels up to 2040.

In this analytical phase, we model each identified trend - decarbonisation, digitalisation and demographic change - in isolation using the DINOS model. This allows us to include additional trends later in order to check whether there are non-linearities between the various developments. For example, whether possible negative employment consequences of decarbonisation are mitigated by the introduction of digital technologies.

The results at national level are then transferred to the NUTS 3 level using a regionalisation model. These figures are also supplemented by information on the labour force at national level. For example, the average gender share in an economic sector can be used to determine whether male or female workers are more affected by current trends.

In the second phase of the project, detailed microsimulation models will be developed for four selected areas: Italy, Hungary, Poland and Greece. These models can be used to make predictions or conduct counterfactual exercises. They generate data based on an initial configuration of a population and apply biological, institutional and behavioural rules to study the dynamics within the population over time.

The advantage of this approach is that it provides a standardised framework for assessing economic, social and environmental changes as well as political measures on households and individuals. A bottom-up perspective also allows any aggregate to be analysed. This means that not only regional dynamics can be analysed, but also what happens between and within groups.

In the third phase of the project, the modelling assumptions are examined, refined and adapted in a joint process with selected political decision-makers and policy recommendations are developed on the basis of the resulting findings.

Links and downloads

Further information about the project (ESPON, website)

Project team: Markus Hoch, Neysan Khabirpour, Dr Jan-Philipp Kramer, Philipp Kreuzer, Jan Limbers, Lorenzo Pelizzari

Last update: 25.09.2024