Competitiveness and social cohesion, two key pillars for a sustainable and prosperous European Union, are the focus of JRC's regional economic analysis and modelling.

Economic impact assessment of regional policies is a complex task. It affects a wide range of macroeconomic variables, including GDP, employment, productivity, budget deficit and trade balance, which are interrelated and affected by a large number of external factors.

A large part of the EU budget is devoted to promote inclusive development - the KCTP aims at supporting policy makers in assessing the impact of their choices, following two lines of research:

Regional Economic modelling with the RHOMOLO Model
Evaluation of historical and future socio-economic trends at regional and sub-regional scale with the LUISA Territorial Modelling Platform
The combination RHOMOLO - LUISA allows to capturing territorial and economy-wide effects of investments and reforms.  They can be used to evaluate general-equilibrium and second-order effects of policies – at the most appropriate level of aggregation: national, regional, and urban or at high spatial resolution (100 x 100 Mt)

Regionalisation of economic and demographic projections

Economic and demographic projections are important for territorial impact assessment. The European Commission periodically releases projections at country-level, which are used, for instance, to assess the economic and budgetary impacts of the ageing population in Europe. However, this level of geographical breakdown does not seem sufficient to assess policy domains with a strong territorial dimension.

The Knowledge Centre for Territorial Policies provides regionalised demographic and economic projections that are consistent with the reference EU projections. The regionalisation relies on a set of linked equations, which integrate assumptions regarding future regional growth, and estimated in a dynamic and recursive fashion while ensuring full consistency with the country totals from the reference projections. Assumptions of growth are based on available historical data. The general scheme of the regionalisation exercise is presented in next figure:

The regionalisation outputs are originally available at NUTS3 level, but these are then aggregated at NUTS2 level, which is the most relevant regional level for EU policy-making, since notably at this level the eligibility of regions for support from the EU cohesion policy is defined.

The economic and demographic projections resulting from the regionalisation exercise are integral components of the LUISA Territorial Modelling Platform.

The results from a regionalisation exercise, which was carried out in 2016 by applying two different regional growth scenarios - trend and convergence – are available for download (zip file).
A comprehensive description of this regionalisation exercise, including results and discussion, is available here.

Application: Regionalising the 2015 Ageing Report’s projections

This regionalisation exercise relied on two main data inputs:

historical (2000-2011) data available at NUTS3 level, published by Eurostat including GVA, employment and population data, and
available projections at national level of GDP employment and population data.
As starting point, demographic and economic projections with country-level detail were taken from data that were presented in the 2015 Ageing Report (European Commission, 2015), produced by the Directorate General for Economic and Financial Affairs (DG ECFIN) and Eurostat (ESTAT). Original country level projections covering the period 2015-2060 were downscaled to NUTS3 level detail. The set of regionalised variables included GDP, employment and population.

Two different regional growth scenarios corresponding to two rather distinct future trajectories of growth were considered to regionalise the reference projections:

A trend scenario, where sectors in regions would experience growth rates of GVA and employment similar to those in the previous years at the beginning of the projection period and then would slowly shift to the national mean by 2035. Afterwards, only national sector growth rates were assumed. Within this approach, the downscaling of each variable was made independently which means that they did not influence each other. Regional values of GVA integrating all the sectors were converted to GDP assuming that ratios between regional and national values for both parameters were constant.
A convergence scenario that assumed that growth rates would converge in the future, thereby leading to more similar regional levels of GDP per capita and productivity in the long-run. Basically, the levels of GDP per capita in a given year are negatively correlated with the subsequent growth rates, thus allowing lagging economies to grow at faster paces than wealthier ones. The convergence scenario was implemented in an integrated system where all variables were tied influencing each other dynamically in time as explained in figure.

In the trend scenario, the regions that are currently most developed benefit the most. This leads to further geographical concentration of production, employment and population. On the other hand, the convergence scenario promotes a more balanced economic growth, with less developed regions growing faster, generating more employment and attracting more inhabitants than in the trend scenario.

In the context of evaluating the regional impact of EU Cohesion Policy, the NUTS3 regional projections were aggregated to NUTS2 levels and classified according to their eligibility for Structural Founds as:

More developed regions: NUTS2 regions with GDP per capita above 90 % of the EU average;
Transition regions: NUTS2 regions with GDP per capita between 75 % to 90 % of the EU average;
Less developed regions: NUTS2 regions with GDP per capita below 75 % of the EU average.

The analysis of the evolution of different variables under the two scenarios shows that transition and less developed regions perform better in the convergence scenario, while more developed regions perform better in the trend scenario. The main outputs of the analysis are:

GDP per capita and productivity tend to catch-up with time in both scenarios, but the impact is considerably more pronounced in the convergence scenario
By 2060 productivity almost converge in all regions under the convergence scenario.
Under the convergence scenario transition and less developed regions account for a higher share of total GDP, employment and population by 2060 than what the trend scenario would warrant.
Employment and population growth is particularly diverse between scenarios and types of regions, and the behaviour is constrained by the need to fit results aggregated to national values with the imposed reference projections. Still, a more pronounced catch-up effect is visible in the convergence scenario.

The different regional growth and development trends under the two scenarios have dissimilar consequences for the eligibility of regions for EU funds. The most visible effect of the trend scenario is the increase in the number of less developed regions, which is compensated by a decrease in the number of both transition and more developed regions in the long run. On contrary, under the convergence scenario, the number of more developed and transition regions increases at the expense of a reduction in the number of less developed regions.

Regional Modelling

Investment Policies and Reforms

The Rhomolo model is the spatial CGE model with a focus on EU regions and sectors used for impact assessment of investment policies and structural changes (in labour productivity, total factor productivity and transport costs).

RHOMOLO covers all EU regions, disaggregating their economies into NACE rev.2 sectors, entailing a constant effort on data updating and maintenance. All the monetary transactions in the economy are included in the model as results of agents making optimising decision. Goods and services are consumed by households, government and firms, and are produced in markets that can be perfectly or imperfectly competitive. Spatial interactions between regions are captured through costly trade matrices of goods and services, factor mobility through migration and investments, and knowledge spill-overs. This makes RHOMOLO particularly well suited for analysing policies related to investments in human capital, transport infrastructure and innovation.

RHOMOLO is built following the same micro-founded general equilibrium approach as the national DSGE model QUEST, developed by the Directorate-General for Economic and Financial Affairs (DG ECFIN), and is often used in combination with it to provide a breakdown of results by region and sector, taking duly into account the specificities of each model.

For more information about the model, see the technical documentation or visit the RHOMOLO homepage on the EU Science Hub.

The RHOMOLO model is designed for policy impact assessment. The explicitly modelled spatial dimension makes it a unique tool for territorial impact assessment. Spatial interactions between regional economies are captured through trade of goods and services (which is subject to trade costs), income flows, factor mobility and knowledge spillovers, making RHOMOLO particularly well suited for simulating human capital, transport infrastructure, R&D and innovation policies.

Labour market and Employment

In the age of globalisation and the knowledge economy, skill mobility is perceived as one of the key factors for fully unlocking the labour market potential. Assessing the social and macroeconomic impacts of increased skill mobility is an important though also challenging task, which requires a holistic approach. The Regional Holistic Model (RHOMOLO) has been applied to better understand the relationship between education, skills, migration and economic growth. Two key channels of labour market adjustment -- upward skill mobility and spatial skill mobility -- are presented and explained in particular detail. By performing numerical simulations and conceptual analysis of labour market integration, we aim to facilitate understanding of the advantages and limitations of the approach taken in RHOMOLO, and its potential for education, skills and employment policy impact assessment. The results from our analysis suggest that a holistic approach is indeed crucial for capturing all the direct and indirect, short- and long-run effects, and it has a wide potential for assessing region-, sector- and skill-specific macroeconomic and social effects of policies aiming at integration e.g. of marginalised communities, such as Roma or refugees, into the EU labour markets.

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