布鲁盖尔-Mapping-competitiveness-with-European-data_194页_2mb

Summary of "Mapping Competitiveness with European Data"

Core Content

The document "Mapping Competitiveness with European Data" is a comprehensive analysis of the data landscape in Europe for assessing competitiveness. It is part of the MAPCOMPETE project, a European Union-funded initiative led by a consortium of research institutions, including Bruegel, Centro Studi Luca d'Agliano, CERS-HAS, IAW, Paris School of Economics, and Sciences Po. The report aims to evaluate the availability, accessibility, and matchability of data at both macro and micro levels to support comparative competitiveness analysis across European countries.

Main Views

The authors argue that while macro-level competitiveness indicators (e.g., unit labour costs, export share, real effective exchange rate) are generally available and computable across EU countries, they are often insufficient and misleading for policy purposes. These indicators are based on averages and do not capture the heterogeneity of firms in terms of size, productivity, innovation, and internationalisation strategies, which are crucial for understanding the true drivers of competitiveness.

The report highlights that micro-level data is available in many EU countries, enabling the construction of bottom-up indicators. However, access to such data is restricted, and cross-country comparability remains a challenge. These limitations are due to legal, administrative, and technical barriers, including unclear procedures, nationality restrictions, and data privacy laws.

Key Information

1. Competitiveness Indicators

• Macro indicators: Available for most EU28 countries, including unit labour costs, export shares, price indices, and real effective exchange rates.
• Micro indicators: Require access to firm-level data, which is often limited to national statistical institutes and not easily shared with external researchers.
• Data quality: Essential for accurate analysis and matching of datasets, but often lacks consistency across countries.
• Harmonisation: Needed for better comparability and integration of data sources.

2. Data Matching and Accessibility

• Data matching is a critical challenge for constructing comparable competitiveness indicators across countries.
• Legal and administrative constraints often prevent researchers from accessing micro-level data, even within the same country.
• Technical barriers include the complexity of data integration and the lack of a unified framework for data sharing.
• Distributed Micro-Data (DMD) is one approach to overcoming these barriers, allowing for the analysis of micro-level data without full access to raw data.

3. Cross-Country Datasets

• Several cross-country and matched datasets exist, such as:
  • EFIGE: Examines firm-level characteristics and success in global markets.
  • CompNet: A European Central Bank initiative focusing on competitiveness through micro-aggregated statistics.
  • KombiFiD: A German project providing firm-level data.
  • Data without Boundaries: A project promoting cross-border data sharing.
  • Eurostat's International Sourcing Survey: Offers insights into international operations and value chains.
• These datasets are limited in scope and availability, especially for long-term and comprehensive analysis.

4. Policy Recommendations

• Short-term solutions (workarounds):
  • Improve data matching techniques and anonymisation methods.
  • Use DMD approaches and imputation methods to enhance data utility.
  • Support multi-scope cross-country surveys to capture a broader range of competitiveness aspects.
• Long-term solutions:
  • Harmonise and standardise data collection and processing methods across EU countries.
  • Develop a common European statistical infrastructure with appropriate legal and administrative frameworks.
  • Reduce the burden on firms for data collection and sharing.
• EU support: Crucial for smaller member states to develop the necessary infrastructure and capabilities for micro-data analysis.

Conclusion

The report concludes that while the European data landscape offers significant opportunities for competitiveness analysis, there are major barriers to data access and matching. These challenges hinder the ability of researchers and policymakers to develop accurate, comprehensive, and comparable indicators. Therefore, improving data accessibility and harmonisation is essential for better economic policy-making and competitiveness assessment in Europe. The MAPCOMPETE meta-database, available at www.mapcompete.eu, serves as a valuable tool for researchers and policymakers by providing detailed information on data availability and computability for over 150 indicators.

Supporting Institutions

• National Bank of Belgium
• Banque de France
• Banco de España
• Deutsche Bundesbank
• Banca d'Italia
• Magyar Nemzeti Bank
• Italian National Institute of Statistics (ISTAT)

About the Authors

• Davide Castellani: Professor of applied economics at the University of Perugia, research fellow at CIRCLE and LdA. His work focuses on firm internationalisation and its impact on technology transfer and economic performance.
• Andreas Koch: Research fellow at IAW. His research examines structural changes at the intersection of regions, firms, and technological development.

Legal Notice

The report is funded by the European Union's Seventh Framework Programme (FP7) under grant agreement no 320197. The views expressed are those of the authors and do not necessarily reflect the views of the European Commission.

Executive Summary Highlights

• Competitiveness is central to Europe's structural policy and growth strategy.
• Macro indicators are insufficient for capturing the nuances of firm-level competitiveness.
• Micro-level data is essential but often not accessible or comparable across countries.
• Data harmonisation and accessibility are key to improving competitiveness analysis.
• MAPCOMPETE provides a framework for data assessment and a meta-database for researchers and policymakers.

Challenges

• Conceptual and methodological gaps in competitiveness measurement.
• Legal and administrative restrictions on micro-data access.
• Technical difficulties in data integration and matching.
• Limited cross-country comparability of micro-level datasets.

Opportunities

• New data sources and techniques (e.g., DMD, imputation) offer potential for more detailed analysis.
• EU-funded projects like EFIGE and CompNet contribute to firm-level competitiveness understanding.
• Collaboration and coordination among data providers can enhance data quality and accessibility.

Recommendations

• Develop common data standards and harmonise methodologies.
• Improve data matching and access through institutional collaboration.
• Enhance micro-data infrastructure in all EU countries.
• Promote EU support for smaller states to build data capabilities.
• Conduct more comprehensive and regular surveys to capture firm-level dynamics.