The project aims to identify gaps in available data sets and key data requirements for constructing better competitiveness indicators at different levels. A key aim is to analyze the combined use of three types of resource: census type quantitative (e.g. national tax authority) data, quantitative survey (e.g. EFIGE survey) data and qualitative (interview based) information. Integrating these approaches may allow a deeper understanding of a wide range of topics related to competitiveness. Indeed, in terms of competitiveness analysis, our primary focus is firm performance. However, we approach this from a wide angle. Hence, data analyzed should cover not only traditional balance sheet figures but include areas such as:
- Trade statistics, internationalization (outsourcing, direct investment, etc.),
- Labor statistics (skill composition, remuneration, on the job training, flexibility)
- R&D, innovation
- Non-tangible assets
- Regional and local dimensions
- Creation of new firms (entrepreneurship) and attraction of foreign investment (FDI)
- Stakeholders (entrepreneur/owner characteristic, social capital, state/local government)
- Customers/suppliers, position in value chains
Naturally, the mapping and matching of data cannot be carried out abstractly, i.e. without having clearly in mind (i) the type of research questions that should be addressed by using the data and (ii) the policy indicators that should be constructed by using the data. One of the work packages of the project will be precisely devoted to laying down this conceptual ground, so as to build an analytical framework within which to address all the data issues. The project also aims to create a more systematic connection between research results and the developments of indicators to be used for policy purposes.
The framework for data assessment will have to be derived from a broader analysis of competitiveness and its measures. Proposals will cover three areas: First, how to netter manage existing data (regarding data collections of the past), with special attention to linking and matching datasets. Second, how to better collect data, improve on collection methodology and extended the coverage in terms of new variables. Third, what sort of new types of data shall be collected in the future and what pilot project may be initiated – based on lessons from previous programs such as EFIGE, Innovation surveys, global value chain projects.
- Mapping existing datasets: screen national sector and micro-level datasets regarding geographical coverage, time span, representativeness with a special focus on areas where no standard set of variables exist, such as non-tangible assets and innovation.
- Consistency issues of different datasets: benchmark on existing research to understand the extent to which some country and year-specific competitiveness-related indicators can be derived from data contained within available dataset or from a cross-reference use of available datasets at different levels of aggregation.
- Conditions and requirements to match different data sets: on the basis of the pilot indicators identify the extent to which datasets relevant for competitiveness can be matched within country and across countries and map data gaps.
- Research directions towards better competitiveness indicators: investigate how novel data or combination of datasets can be used to introduce novel research areas and design new research directions leading towards better competitiveness indicators.
- Benchmarking: identify steps to enhance quality and availability of existing data and suggest new methods and sources of data collections