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EU-EFIGE/Bruegel-UniCredit Dataset Summary

Core Content

The EU-EFIGE/Bruegel-UniCredit dataset is a unique firm-level database that provides detailed information on the international activities and internal policies of European manufacturing firms. It was developed as part of the EFIGE project (European Firms in a Global Economy: internal policies for external competitiveness), supported by the European Commission's 7th Framework Programme and coordinated by Bruegel. The dataset includes data from 15,000 firms across seven European countries (Germany, France, Italy, Spain, United Kingdom, Austria, and Hungary), with a focus on firms with more than 10 employees.

Main Points

• Scope and Purpose: The dataset is the first in Europe to combine firm-level data on international activities (exports, imports, FDI, outsourcing) with quantitative and qualitative information on various aspects of firm operations, including R&D, innovation, financial structure, and market behavior.

• Data Collection: The survey was conducted by GFK, a leading market research company, in 2010, covering the period from 2007 to 2009. It includes six broad sections with around 150 variables, capturing a wide range of firm characteristics and behaviors.

• Sampling and Representativeness:

  • The dataset includes a representative sample of firms by country, industry, and size class.
  • Sampling criteria were designed to ensure statistical representativeness, including proper stratification and oversampling of large firms.
  • A total of 135,000 firms were contacted across the seven countries to achieve the desired sample size.

• Validation and Comparison:

  • The dataset was validated by comparing it with balance sheet data from the Amadeus database, enabling the calculation of productivity measures.
  • Correlations were computed between EFIGE data and official statistics from Eurostat, showing high consistency for key variables like wages and productivity.
  • The dataset was also compared with national sources for export activity, with some discrepancies due to data availability and granularity.

• Internationalisation Categories:

  • Firms are classified into seven internationalisation categories based on their activities: non-active abroad, exporter, importer of materials, importer of services, global exporter, passive outsourcer, and active outsourcer.
  • Firms involved in more complex international activities tend to be larger, have higher turnover, and are more capital-intensive.

Key Variables and Indicators

• International Activities: Exports, imports, FDI, and international outsourcing.
• Firm Characteristics: Size (10–19, 20–49, 50–249, over 250 employees), turnover, number of employees, capital stock, and productivity.
• Competitiveness Measures: Total factor productivity (TFP), unit labor cost, and labor productivity.
• Validation Tools: Correlation analysis with Eurostat and OECD data, and comparison with national sources for export activities.

Data Structure and Usage

• The dataset includes a comprehensive set of variables and is structured to allow for in-depth analysis of how firms respond to globalization.
• The data was reorganized into a STATA-compatible format, with a DataMap provided to link variables to the original questionnaire.
• Weighting procedures were designed to ensure that the sample accurately represents the population of firms in each country.

Summary Statistics

• Total Sample: 14,759 firms.
• Average Turnover: €15,589.29 per firm (2008).
• Average Employees: 114.52 per firm.
• Average Capital Stock per Employee: €186.59.
• Total Factor Productivity (TFP): On average, 0.991 for the whole sample, with some categories showing higher productivity (e.g., FDI: 1.293).

Conclusion

The EFIGE dataset offers a valuable resource for researchers and policymakers to understand how European firms are adapting to global economic changes. It provides detailed, harmonized, and representative firm-level data across multiple dimensions, enabling analysis of the relationship between international activities and firm competitiveness. The dataset is particularly useful for studying productivity, labor costs, and the impact of globalization on different firm sizes and industries.