Leading team: Centro Studi Luca d’Agliano (LdA)


The investigation of existing and new indicators of competitiveness, suitable to capture different competition strategies, on the one side, and working at different levels of aggregation (i.e. microeconomic, industry, macroeconomic) calls for a deep and comprehensive knowledge of the existing datasets and their characteristics. WP2 will therefore map the existing datasets used to build the existing indicators of competitiveness.

This step is the natural starting point to provide the necessary background for the work to be carried out in the other WPs, i.e. retrieving consistent competitiveness measures from existing data (WP3), assessing potential problems in matching the existing data (WP4), identifying possible improvements in existing data to build new indicators of competitiveness (WP5), pointing out a potential lack of information and therefore suggesting new data collections (WP6). Accordingly, WP2 will map the existing data, providing information on the three above-mentioned levels of aggregation (micro, industry, macro) on different topics, at the regional, national and European level.

Specifically, at the micro level, different types of resources will be taken into account: census type quantitative (e.g. national tax authority) data, quantitative survey data (e.g. EFIGE survey), commercial data bases (like Amadeus), qualitative (interview based) information , and linked employer/employee data. Integrating these approaches may allow for a deeper understanding of a wide range of topics related to competitiveness. Firm level data analyzed should cover not only traditional balance sheet figures, but include areas such as: trade statistics, internationalization (outsourcing, foreign direct investment, etc.) labor statistics (skill composition, remuneration, on the job training, flexibility), R&D, innovation, stakeholders (entrepreneur/owner characteristic, social capital, state/local government), intangibles (knowledge diffusion, social capital, intangible assets), customers/suppliers, position in value chains.

The mapping exercise will also include sectoral data (like trade statistics, industry statistics – e.g. EUKLEMS) and aggregate statistics, like balance of payments data. Data on regional and local conditions will also be considered. Finally, as global value chains pose a major problem to account for firm performance, this work package will map the current attempts undertaken within the EU or at the international level to improve data collection on GVS, and how participation in the latter may affect competitiveness.