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).
E3ME model improvements in human capital and R&D
By improving the treatment of research and innovation in E3ME, a broader set of policies associated with research & innovation can be assessed with the model, including: increased public spending on R&D, increased private spending on R&D, human capital investments, collaborative programmes, open access to science, public training programmes, private training programmes, education spending, increased competition, and policy packages including several measures. The following improvements have been introduced in the various ways R&D is represented in the E3ME model:
Incorporation of spillover effects from R&D expenditure
Technology spillover matrices have been introduced into E3ME to better represent knowledge and rent spillovers in the economy. The spillover matrices are constructed using patent citation data from the United States Patent and Trademark Office (USPTO). We have extracted all patents in the five-year window 2012-2016, where the patents are classified using the International Patent Classification (IPC) system.
New econometric equation for R&D intensity by sector
In previous versions of E3ME, R&D had been treated as either exogenous, or set as a fixed share of investment in each sector. R&D has now been endogenised through the inclusion of a separate R&D equation. This endogenisation is central for analysing R&D and innovation policy on a macro level and allows for better representation of important links in the economy. For each sector, R&D expenditures is a function of output (QR), positively associated with R&D, relative prices of R&D (RDRP), negatively associated with R&D, and capital-to-labour ratio (KLR).
Improved link with investment
As R&D expenditure is now modelled endogenously, we have added a link in the model between the stock of knowledge (accumulated R&D, see below) and the level of investment. The relationship is assumed to be positive and the magnitude is determined for each sector and country by a set of econometric coefficients.
Improved link with human capital and skills degradation
The human capital variable is constructed as an index, taking values between 0 and 8 reflecting different educational attainments. The value 0 represents pre-primary education, and value 8 represents doctoral degrees. Eurostat data show the percentage of individuals, within a certain gender-age band group, having obtained Low, Medium or High attainment. The weighted average of educational attainment within each age-gender group features in the labour participation rate equation. To obtain a value of the human capital at the country level, a weighted average among age-gender groups, using population figures as weights, is used.
The representation of human capital is expanded by taking into account the skills depreciation rate, which represents the loss of skills incurred by workers during spells of unemployment. This has been incorporated in E3ME by using Eurostat data on long-term unemployment in order to calculate a share of long term unemployed in the unemployment variable. To this, a skills degradation coefficient of 5 percent is added.
Update of the E3ME database
The E3ME database for historical data was updated to account for the latest data in Europe. All time series were extended to cover the period up to 2016, in some cases incorporating figures for 2017. The input-output tables were updated to use a 2010 base year (the most complete year available from Eurostat).
The model’s parameters were re-estimated on the extended data set so that they factor in the most recent information available.
Estimating the link between degree of competition and R&D spending
We include a scenario about how an increase in competition within a sector may stimulate R&D spending. Although the treatment of competition has not been formally added to the E3ME model source code (mainly as there is no year-to-year measure of the degree of competitiveness in the model), the R&D routine was adapted to allow for additional changes in R&D expenditure due to increased competition.
Improved representation of product and process innovation
As a result of the model improvements outlined above, E3ME simulates several features relevant for R&I, which is represented by following feedbacks:
- Process innovation – an improvement in efficiency and knowledge accumulation indices increases capacity and potential supply, represented as the ‘normal’ output in the model.
- Product innovation – a higher quality product and capital accumulation may lead to higher levels of demand or command a higher price, so the indices feature in the model’s trade and price equations.