Annex 4 analytical methods

In this estimated/calibrated general equilibrium model, the supply-side is based on input-output relationships among industries, while the demand side is fully specified under the hypothesis of monopolistic competition among industries, such that firms are price-setters, i.e. they consider a mark-up over marginal costs in their pricing decisions, and demand is defined considering the full set of industry-specific relative prices.

Production takes place considering an input/output production technology, in which the input mix is chosen optimally based on the relative prices of intermediate factor inputs. The telecommunication sector is isolated, detailed into its mobile, fixed telephony and internet subsectors, the latter disaggregated further in order to take into account changes in rules affecting digital identity investments, and included into the several production functions, such that a simulated investment decision affects each sector both directly and indirectly through the other sectors' responses. The impact in each sector is captured by a digital identity-specific variation in the telecommunication input, leading to production effects and substitution effects, the latter driven by relative price's changes.

A flexible trans-log production technology employing 16 factor inputs is adopted for describing the supply side: sectors are those of the two-digits NACE classification (Rev. 1.1) . The attractive feature of the trans-log functional form is that it imposes no a priori restrictions on substitution and price elasticity, that can be derived from the estimated parameters of the implied cost share functions.

On the demand side, following a quite standard approach, 58 sector-specific demand and price setting functions are analytically derived under the hypothesis of monopolistic competition.

The IO-DGEM thus provides an instrument that allows a scientific evaluation of the potential macroeconomic effects of changes in the use eID service at a high level of detail. For expositional convenience, and given the specific goals of the analysis for the European Digital Identity regulation, simulation results are summarized considering only output variations, labour input variations and price changes.

Given the limited sample size and the nonlinearity of the key output production functions and of the related cost shares, the Bayesian estimator is employed to parameterize the supply side of the model. The parameterization of the demand side is instead calibrated.

The instantaneous and cumulated effects on output and employment are evaluated in terms of both percentage deviations from control (i.e. a situation in which no investment occurs) and in terms of variations of volumes, i.e. output value effects (in Euros), and employment effects (in jobs).

The estimation requires detailed statistical information on sectoral outputs and inputs, i.e. industry by industry input-output tables, publicly provided by the Eurostat (European System of Accounts - ESA 95), while other variables and data are obtained from the Eurostat Structural Indicators and from the STAN - OECD database.

The model parameterization is obtained from the information provided by a panel of years and sectors. The time-period ranges from 1995 to 2014.  According to the 2-digit NACE classification systems, 58 production sectors are included in the estimates and in the model simulation (NACE-P is omitted because of data constraints). These 58 economic sectors cover all the economic activities, that is, only mentioning the macro-areas (1-digit NACE): Agriculture, hunting and forestry (A), Fishing (B), Mining and quarrying (C), Manufacturing (D), Electricity, gas and water supply (E), Construction (F), Wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods (G), Hotels and restaurants (H), Transport, storage and communication (I), Financial intermediation (J), Real estate, renting and business activities (K), Public administration and defense; compulsory social security (L), Education (M), Health and social work (N), Other community, social and personal service activities (O).The econometric analysis relies on the following set of data:

• values of the 1-digit 17 inputs used (including labour) at purchaser prices
• values of the 2-digit sectoral output at basic prices
• inputs’ prices (except labour)
• labour compensation

All this information is obtained by three main data sources:

• OECD – STAN STructural ANalysis Database;
• Eurostat - Industry, trade and services – Industry and construction Industry;
• ESA 95 Table – Input-output tables – Eurostat.

The model presents the impact of different policy scenarios in terms of “EU value added” and employment impact. The impact is simulated at different time scales (2 years, 5 years and 10 years).

The model can simulate the sectorial economic effects of policies/investments/exogenous shocks affecting any sector in the demand and supply-sides of the economy. In the recent past, previous model versions have been used to anticipate the effects of disruptions (e.g., cyber-attacks) in digital networks at the EU country-level, to evaluate the effects of terrorist actions on gas and oil pipelines at the EU level, and to anticipate the effects of broadband investments at the Italian level.

• Agriculture and rural development 
• Education and training 
• Taxation 
• Employment and social affairs 
• Regional policy 
• Competition 
• Digital economy and society 
• Business and industry 
• Research and innovation 
• Trade 
• Banking and financial services