The new DSGE model for Europe that is being built goes beyond the state of the art by improving the representation of i) the decision of the public sector on R&I, ii) the public instruments and institutional settings that incentivize R&I, iii) the quality of R&I by sector and country, iv) the role of human capital in the production of knowledge and its importance in hosting innovation produced elsewhere, v) the mechanisms through which knowledge is diffused across countries and sectors, vi) the role of financial constraints of countries and sectors in supporting R&I. Moreover, it studies the implication of skill/unskill augmenting technical change in a DSGE model with growth. Essentially, it combines the Comin medium-frequency cycles literature with the skill augmenting technical change one.
E3ME is a computer-based model of the world’s economic and energy systems and the environment. It was originally developed through the European Commission’s research framework programmes and is now widely used in Europe and beyond for policy assessment, for forecasting and for research purposes. E3ME’s historical database covers the period 1970-2014 and the model projects forward annually to 2050. The main data sources for European countries are Eurostat and the IEA, supplemented by the OECD’s STAN database and other sources where appropriate. For regions outside Europe, additional sources for data include the UN, OECD, World Bank, IMF, ILO and national statistics. Gaps in the data are estimated using customized software algorithms.
The main dimensions of E3ME are:
- 59 countries – all major world economies, the EU28 and candidate countries plus other countries’ economies grouped
- 43 or 69 (Europe) industry sectors, based on standard international classifications
- 28 or 43 (Europe) categories of household expenditure
- 22 different users of 12 different fuel types
- 14 types of air-borne emission (where data are available) including the six greenhouse gases monitored under the Kyoto protocol
The E3ME model has three components (modules): energy, environment and economy. Each data set has been constructed by statistical offices to conform with accounting conventions. Exogenous factors coming from outside the modelling framework are shown on the outside edge of the chart as inputs into each component. For each region’s economy the exogenous factors are economic policies (including tax rates, growth in government expenditures, interest rates and exchange rates). For the energy system, the outside factors are the world oil prices and energy policy (including regulation of the energy industries). For the environment component, exogenous factors include policies such as reduction in SO2 emissions by means of end-of-pipe filters from large combustion plants. The economy module provides measures of economic activity and general price levels to the energy module; the energy module provides measures of emissions of the main air pollutants to the environment module, which in turn can give measures of damage to health and buildings. The energy module provides detailed price levels for energy carriers distinguished in the economy module and the overall price of energy as well as energy use in the economy.
Besides these 3E components, the E3ME uses measures of endogenous technological progress. The measures are based on cumulative gross investment, quality adjusted by using data on R&D expenditures, to adopt a measure of technological progress. They cover both process and product innovation and thus affect both price and non-price competitiveness in a sector, featuring for example in econometric equations for prices, international trade and industrial employment. The main difficulties with this approach is the diversity of R&D levels across sectors and accounting for knowledge spillovers. As part of the MONROE project, Cambridge Econometrics is improving the measures of technological progress, and the link between R&D and productivity growth in particular. This is done by bringing R&D spillovers, patents citations and indicators of human capital into the measures of technological progress (technology indices).
PACE (Policy Analysis based on Computable Equilibrium) is a multi-sector, multi-region computable general equilibrium (CGE) model of global production, consumption, trade and energy use. It is established in economic research and policy consulting.
PACE is implemented in MPSGE (Mathematical Programming System for General Equilibrium Analysis), a subsystem of GAMS (General Algebraic Modeling System) for solving the MCP (mixed complementarity problem). The model covers 23 regions and 36 sectors with a focus on energy-intensive industries. It runs until 2050 in five-year time steps and solves for a sequence of market equilibria. The model consists of a set of equations (i.e. market clearing, zero profit, and income balance conditions) that describe the world economy. For each year, the solution algorithm finds the set of prices and quantities that solves these equations.
Zero-profit conditions and market clearing conditions follow directly from the assumptions of profit maximization of firms, perfect competition among them, utility maximization of consumers, constant returns to scale in production, and homotheticity of consumer preferences. The latter class of conditions determines the price of each output good as the unit cost to produce this good. This cost equals the marginal and (given constant returns to scale) the average cost of production.
Each region consists of a representative consumer and representative producers (one for each production sector). The consumer chooses a bundle of consumption goods that maximizes her utility given her preferences and her budget. The budget is determined by her income received from selling the primary production factors (labour, capital and fossil-fuels) that she owns. The model also assumes that each region can obtain a certain amount of emission permits in each period. Final demand of the representative consumer is modelled as a constant elasticity of substitution (CES) composite good which combines an energy aggregate with a non-energy aggregate (analogue to the production structure described below). Substitution patterns within the non-energy aggregate are reflected by a Cobb-Douglas function. The energy aggregate consists of several energy goods combined with a constant elasticity of substitution.
The producers choose bundles of production goods that maximize their profits given their production possibilities. The production possibilities are determined by technologies, which efficiently transfer certain amounts of input goods and production factors into certain amounts of unique, sector specific output goods. Production factors entail labour, capital, (both perfectly mobile between sectors within a region) and sector specific resources for agriculture, oil and gas extraction, and coal and other mining. The labour supply is fixed for each region. There is full mobility among sectors and no unemployment. The production functions are nested CES functions with the following nesting structure: At the top level, non-energy inputs are employed with an aggregate of energy, capital and labour. At the second level, a CES function describes the substitution possibilities between the energy aggregate and the aggregate of labour and capital. At the third level, capital and labour (and if applicable: sector specific resources) are combined with a constant elasticity of substitution. Moreover, at the third level, the energy aggregate consists of electricity and a fossil fuel input. The latter input is further split into coal, gas and oil associated with different elasticities of substitution and with emission permits in fixed proportions (in the presence of a carbon pricing scheme). The CES specification allows producers to substitute fossil fuel inputs by other inputs as a reaction to an increasing carbon price. The extent of substitution, however, is limited by the choice of the cost minimizing input bundle given the elasticities of substitution.
Within the MONROE project, knowledge capital, which is gathered by investments in research and innovation, will be introduced as an additional primary production factor. This will enable the representation of endogenous technological progress in order to assess the impact of R&I policies.
EU-EMS (European Economic Modelling system) is a flexible modelling system build by PBL Netherlands Environmental Assessment Agency that has modular structure that allows to choose model form that fits the best policy question at hand. The model system database includes the representation of 62 countries of the world and one Rest of the world region. It has detailed regional dimensionality for EU28 countries and includes them as consisting of 276 NUTS2 regions. Sectoral and geographical dimensions of the model are flexible and can be adjusted to the needs of specific policy or research question. Mathematical forms chosen for modelling of demand, supply and production are also flexible and can be modified according to the needs of the modeler.
EU-EMS model extension developed in the MONROE project combines the modelling of sectoral structural change via sector-specific technological progress and dynamic changes in consumer preferences with representation of changes in regional labour supply by education type. Labour supply in the model is modelled as the combination of regional demographic projections of Eurostat with modelling of catch-up in education levels and participation rates between different EU regions. Endogenous growth part of the model includes three parts: (1) knowledge production with modelling of the number of patents as a function of private and public R&D, number of researchers, specific capital stocks (ICT etc.) and the stock of accumulated regional and rest of the world knowledge. (2) knowledge adoption that is modeled as sector and region specific catch-up process that depends upon the stock of human capital and R&D. (3) knowledge spillover effects between regions that transfer knowledge via trade, migration and FDI flows.
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 trade of goods and services (which is subject to trade costs), factor mobility and knowledge spill-overs. The model includes New Economic Geography (NEG) features such as monopolistic competition, increasing returns to scale and migration.
Endogenous growth in the model results in technological changes via product and process innovations. Product innovations in the model are captured via endogenous number of the varieties of various final consumption goods purchased by households. Process innovations are represented by the changes in productivity and share parameters of the sectoral production functions. Technological change in the model can be both capital and labour biased and results in either reduction or increase in labor demand. The model includes representation of sectoral employment by three levels of education and regional voluntary unemployment that is captured by the Beveridge curve.
The final version of the GEM-E3-MONROE model will be a large scale applied CGE model that will cover the whole world aggregated in 33 countries/regions and 50 economic activities. All countries and sectors will be linked with endogenous bilateral trade transactions. The model will have a detailed representation of the manufacturing sectors of clean energy technologies and knowledge spillover matrices linked to R&D expenditures for these technologies. The simulation time period of the model will be up to 2050 in five-year time steps. Firms decision on R&D that increases sectoral productivity and on the training of its employees will be endogenous. Households will decide endogenously on their consumption, savings, labour supply and education level. The overall spending of public R&D will be exogenous but the decision amongst different energy technologies will be endogenous. The labour market will feature unemployment for five skills/education categories and the labour supply will be based on empirical estimations of the wage curve for each EU member state and key trade partners (USA, China, India). An age cohort satellite model will be linked with the core GEM-E3-MONROE model ensuring that labour productivity applies to the educated cohort that enters into the labour market. The model will have an explicit link between human capital and the absorptive capacity of knowledge spillovers.
The model will be calibrated to the latest IO statistics. In its current version the model is calibrated to GTAP v9 database with base year 2011. With the publication of GTAP v10 (June 2018) the model will be recalibrated to 2014 base year. The model will have a representation of taxes and subsidies and in particular R&D tax incentives.