EU Economic Modelling System

EU-EMS is a dynamic spatial general equilibrium model. It has been developed by the PBL Netherlands Environmental Assessment Agency within the EU Horizon 2020 Research and Innovation Programme. It is being used for the policy impact support by the European Commission, European Investment Bank, European Institute of Innovation and Technology as well as EU Member States and European Neighbourhood Policy countries.

EU-EMS is a micro-founded macroeconomic model with a neoclassical equilibrium closure where supply and demand are balanced through a system of relative prices and behavioural functions. Policy-driven scenario perturbations are modelled as deviations from a benchmark equilibrium state of the economy affecting the optimal supply and demand behaviours of all the agents in all the economies. Policy shocks result in a reallocation of production and consumption, market transactions, goods and factors consistent with the new price system in the simulated counterfactual equilibrium. Policy appraisals are based on a comparison between the counterfactual and the benchmark equilibrium; an explicit modelling of financial transactions including a green finance, distributed ledgers, blockchain and SDGs. A particular attention is devoted to an explicit modelling of spatial spillovers, interactions and linkages between regional and sectoral economies and global value chains in EU regions, Member States and 35 non-EU countries including the European Neighbourhood Policy (ENP) countries.

EU-EMS can be used for ex-post policy evaluations and as well as for ex-ante policy impact assessment and provides sector-, region- and time-specific simulations to support EU policy evaluation of investments in innovation, education and human capital, green infrastructures and structural reforms across a wide array of policies. All direct, indirect and spatial spillover effects of public investments or EU policies are explicitly captured in EU-EMS. EU-EMS is suited for assessing distributional impact by income deciles (integrated with the EU-SILC micro-data), firm heterogeneity in terms of technology and productivity (efficiency, capital deepening, human resources, green technology).

The conceptual framework of the EU-EMS is founded in the microeconomic theory and modelling techniques; it draws on the long tradition of the dynamic spatial general equilibrium modelling. EU-EMS has gone through several adjustments to fit policy advice purposes, and is firmly grounded in an academic peer-review process. Being a Computable General Equilibrium model, it is featured by a complex system of a large number of nonlinear equations that are solved simultaneously.

All transactions in the global value chains included in regional and sectoral economies result of agents optimising their production, consumption, employment, savings, investment, trade, education and other decision-making. Goods and services are consumed by households, government and firms, and are produced by firms operating in imperfectly competitive markets. Spatial interactions between regions and reallocation between sectors are captured through the trade flows of goods and services as well as production factors capital and labour. The capital mobility is represented through inter-regional investment flows, labour mobility through the inter-regional migration of workers. Spatial dimensions are a key element of the EU-EMS in terms of trade, labour and capital mobility (in terms of investment flows), and the location decisions of firms. Trade activities between regions are determined by transport costs, which are of iceberg type and imply that a given share of the goods ‘melts’ during shipping. Thus, transport infrastructure projects imply reduced transport costs between and within regions, thereby increasing the competitiveness of regions and competition between regional producers and consumers.

The theoretical underpinning of the innovation process and the factor productivity growth follows Griffith et al. (2001) and Acemoglu et al. (2006), where firms invest into both innovation (knowledge production) and adoption of technologies from the global technology frontier. In this framework, the selection of high-skill workers and firms is more important for innovation production than for knowledge adoption. Regional economies at early stages of development pursue an investment-based strategy, which relies on existing firms and managers to maximise investment but sacrifices selection. Closer to the world technology frontier, economies switch to an innovation-based strategy with short-term relationships, younger firms, less investment, and better selection of firms and managers.

In EU-EMS, economies (regions within EU, countries outside EU) differ by the type of production sectors, which capture overall production activities in the region. There are regions that specialise in traditional sectors like agriculture, whereas others specialise in skill- and knowledge-intensive sectors such as finance and industry. Heterogeneous economic sectors are characterised by a different degree of agglomeration and its importance for innovation, as innovation activities tend to be highly concentrated. Traditional sectors do not experience any agglomeration effects whereas skill- and knowledge-intensive sectors do, that result in some sectors growing faster than others. In order to capture inter-sectoral differences in the innovation activity and performance, all economic sectors are modelled within six broad innovation-intensity groups following the Eurostat classification of the economic sectors according to their R&D intensity: (1) Traditional, (2) Low-tech industry, (3) Medium-tech industry, (4) High-tech industry, (5) Knowledge intensive services and (6) Other services (see Table 4). This classification follows the Eurostat’s definition, where groups “High-technology” and “Medium-high technology” into “High-technology” are merged. These aggregated innovation-intensity sectors are also used in the econometric analysis for the estimation of structural innovation parameters in the model.

EU-EMS is a global model with a great geographic detail – it includes 62 countries and the Rest of the world. The EU27 Member States are further disaggregated into 236 NUTS2 regions and each regional economy is disaggregated into 63 NACE Rev.2 economic sectors. Goods and services are consumed by households, government and firms and are produced in markets that differ in the competition intensity. The macro-financial module of the EU-EMS includes Real Business Cycle features such as monopolistic competition, increasing returns to scale as well as overlapping generations. Spatial interactions between regions are captured through trade of goods and services (which is subject to trade and transport costs), factor mobility and knowledge spillovers. This makes EU-EMS a particularly well suited modelling tool for analysing policies related to the human capital, R&I and innovation.

The EU-EMS database has been constructed by combining national, European and international data sources; it contains a detailed regional level (NUTS2 for EU27 plus 35 non-EU countries) multi-regional input-output (MRIO) table for the world.  The main datasets used for the construction of MRIO include the OECD database, the BACI trade data, the Eurostat regional statistics and national Supply and Use tables as well as detailed regional level transport database ETIS-Plus from the DG MOVE. The EU-EMS database has a detailed sectoral and regional dimensionality, EU27 Member States are disaggregated as 236 NUTS2 regions. Both sectoral and geographical dimensions of the model are flexible and can be adjusted to the needs of specific policy or research question. Transportation costs in EU-EMS are both good-specific and differentiated between the origin and destination regions. The inter-regional trade flows data at the level of NUTS2 are unique, as these data are not available from official statistical sources  (e.g. Thissen et al. 2018; Ivanova, Kancs and Thissen 2020).

• Savings rate of households in each region
• Household consumption share for each region & sector
• Substitution elasticities between goods from different sectors in each region
• Share of factor use (capital, labour) and intermediate inputs in production in each region & sector
• Substitution elasticities between different factors of production in each region & sector
• Total factor productivity
• Substitution elasticities between goods from different regions for each sector
• Share of exports from each region & sector
• Transport cost rate for each region pair & sector
• Savings flows between regions