Mechanism for the Diagnosis of Labour Market Needs

• July 21, 2025
• admin

An online tool for monitoring and anticipating labour market skills needs with an innovative skills data analysis tool.

The Mechanism for the Diagnosis of Labour Market Needs as a tool of good practice

In June 2025, the ‘Labour Market Diagnostic Mechanism’ was introduced as a good practice in all the practices of the European Commission’s European Network of Public Employment Services (PES), including practices reflecting EU employment policy in the context of the Europe 2020 strategy.

What is it?

The Greek labour market is evolving rapidly. Within this framework, the Labour Market Diagnostic Mechanism, an online labour market monitoring tool that combines big data analysis with job demand and skills data, has been developed, offering solutions for more effective employment services and labour market policies. The tool helps anticipate skills needs, with a view to improving labour market information and enhancing the services provided to public employment services counsellors, jobseekers and employers.

It operates under the supervision of the DYPA (Public Employment Service) and the Unit of Experts for Employment, Social Security, Welfare and Social Affairs (MEKY) of the Greek Ministry of Labour and Social Security and is a pioneering initiative that brings innovation to the monitoring and forecasting of skills needs.

The Facility, which was upgraded in 2024, uses data from the Ergani job register, the Hellenic Statistical Authority (ELSTAT) and the ESCO European Skills Classification System to map the most sought-after skills by sector, demographic group and region. This tool was initially tested in the IT and communications and pharmaceutical sectors, with plans to expand to all economic sectors by the end of 2025. The initiative is supported by the European Social Fund (ESF) and is part of the Human Resources Development and Social Cohesion programme 2021-2027.

Why is it good practice?

The Mechanism is an excellent practice for both the Greek and the European labour market for the following reasons:

• Data-based analysis: It combines data from multiple sources (Ergani, ELSTAT, ESCO) to provide accurate skills forecasts, overcoming the challenges of traditional surveys based on questionnaires. This allows for a rapid and reliable mapping of labour market needs.
• Reinforcement of DYPA services: The tool helps employment advisers to provide more targeted services by improving the match between unemployed and vacancies.
• In 2024, 861 advisors and 53 senior DYPA officials received training in the use of the Facility, enhancing the efficiency of their services.
• Adaptation of Educational Programmes: The results of the Facility lead to the reform of curricula in the vocational education and training (VET) institutions of DYPA, ensuring that educational offers are relevant to current and future market needs.
• Support for Enterprises and Employees: By using the European ESCO classification and validation through Employers Focus Groups, the Facility helps businesses identify the required technical skills and invest in the training of their employees.
• Flexibility and Adaptability: The tool has a user-friendly interactive interface that allows for the selection of demographic or local data, making it accessible to different audiences, such as policy makers, employment advisers and students.
• Contribution to Policy Making: the Mechanism provided documentation for the update of the National Strategy for Upskilling and Connecting to the Labour Market, which received a positive assessment from the National Labour Force Skills Council .

Results and Benefits

The Facility has already delivered significant results:

• Improved Employment Services: DYPA trained consultants use skills forecasts to better match jobs and the unemployed.
• Targeted Policies: The Facility’s references support evidence-based active labour market policies, reducing imbalances between skills supply and demand.
• Strengthening Vocational Training: DYPA VET institutions receive regular information on skills needs, allowing for the adaptation of their programmes.
• Labour market resilience: The tool helps prevent unemployment and strengthen labour market resilience through preventive measures.

Lessons and Success Factors

The success of the Mechanism is due to the simplified presentation of complex data, the regular training of consultants and the validation of results through employer focus groups. A key lesson is the need for continuous training of users of the tool and its adaptation to more user-friendly formats, such as the provision of unemployment indicators by occupation and region.

Conclusion

The Labour Market Needs Diagnosis Mechanism is an exemplary practice that brings Greece closer to a modern, evidence-based and flexible labour market. By using advanced technologies and data, the tool not only improves DYPA services but also strengthens the link between education, employers and employees, contributing to a more resilient and competitive economy. Its extension to all sectors by the end of 2025 will further strengthen the dynamics of the Greek labour market, making the Mechanism a model for other countries.