Title page 1

Contents 6

Foreword 4

Acknowledgements 5

Executive summary 9

1. Assessment and recommendations 10

1.1. High caseloads limit DYPA counsellors' ability to provide individualised support to vulnerable jobseekers 11

1.2. The current profiling tool should be made more data-driven to meaningfully identify jobseekers in need of additional support 11

1.3. Despite being compulsory, the profiling process is not always completed for the more vulnerable groups 12

1.4. A revised digital profiling tool can help counsellors better understand jobseekers' barriers and design tailored action plans 12

1.5. Digital tools can support the co-ordination of services for vulnerable clients 13

1.6. New functionalities to better support vulnerable jobseekers should be considered within DYPA's ongoing review of profiling processes 13

1.7. Relevant stakeholders should be involved early in the development process of the new digital profiling tool 13

1.8. Profiling must comply with data protection, transparency and fairness standards 14

1.9. Consolidate partnerships and strengthen data exchanges with other institutions 14

1.10. While the proposed tool can help streamline processes, it is essential that human judgement remains central 14

1.11. Face-to-face interactions remain essential 15

1.12. Put a monitoring and evaluation framework in place from the outset 15

1.13. Capacity constraints can be alleviated by contracting out some services to external providers 15

2. Digital solutions for vulnerable jobseekers: Evidence and approaches from OECD countries 18

2.1. Introduction 19

2.2. DYPA has dedicated services for vulnerable groups 19

2.2.1. Vulnerable clients in Greece face diverse challenges 19

2.2.2. DYPA has invested in internal processes and programmes targeted to vulnerable groups 20

2.2.3. DYPA has significantly upgraded its IT infrastructure and adopted new digital tools 21

2.2.4. DYPA still faces multiple challenges in reaching out and providing ALMPs to vulnerable groups 22

2.3. PES across the OECD employ a variety of digital tools that use data analytics 23

2.3.1. Digital tools can support outreach to vulnerable groups 23

2.3.2. Data analytics can help better understand the needs of vulnerable jobseekers 25

2.3.3. PES across the OECD use different types of profiling tools 26

2.3.4. Digital tools can support the referral of clients to appropriate services 29

2.3.5. Digital tools help provide employer services and job matching for vulnerable groups 30

2.4. Conclusion 32

References 33

Notes 36

3. Proposal for a Digital Tool to Identify Vulnerable Clients Needing Intensive Support 37

3.1. Introduction 38

3.2. Vulnerable jobseekers needing intensive support 38

3.2.1. Comprehensive administrative data provide the opportunity to identify vulnerable clients 39

3.2.2. DYPA currently takes a multi-faceted approach to identify those most in need of intensive support 40

3.2.3. Jobseekers needing intensive support share some common features 46

3.3. Services provided by DYPA to the most vulnerable jobseekers 53

3.3.1. DYPA provides a wide array of active labour market policies to support jobseekers 53

3.3.2. Participation in ALMPs varies across different groups of jobseekers 54

3.4. Proposal for the new tool 58

3.4.1. The new digital tool will pursue three key objectives 58

3.4.2. The tool will enhance the early identification of vulnerable jobseekers 58

3.4.3. The tool will support individualised counselling by highlighting jobseeker-specific risk factors and employment barriers 60

3.4.4. The tool will help counsellors referring jobseekers to tailored services 64

3.5. Implementation plan 65

3.5.1. Potential risks and mitigation measures 70

3.6. Conclusion 71

References 72

Annex 3.A. Data processing 73

Annex 3.B. Additional statistics 74

Notes 76

4. Proposal for Procuring Intensive Job Placement Support for Vulnerable Clients in Greece 77

4.1. Introduction 78

4.2. Vulnerable clients with additional support needs in Greece 78

4.3. International best practices on supporting vulnerable groups 79

4.3.1. Helping those furthest from the labour market requires co-ordinated steps towards employment 79

4.3.2. Pro-active outreach efforts are also needed to engage vulnerable individuals 80

4.3.3. Integrated service delivery can help to provide comprehensive and holistic support 81

4.3.4. Involving social economy actors can provide a bridge to unsupported employment 83

4.3.5. Targeted mental health support can help individuals to re-connect with the labour market 84

4.3.6. Post-placement follow-up support can help to cement labour market attachment 85

4.3.7. The good practices in OECD countries provide fertile ground for DYPA to integrate new services within its existing delivery model 86

4.4. International best practices on procuring intensive job placement support 87

4.4.1. Contracting out can facilitate innovation and flexibility to support diverse needs 87

4.4.2. Designing contracting models to support differing individual and institutional needs 88

4.4.3. Contracting authorities create market structures and provider competition 90

4.4.4. Strong performance frameworks are vital to manage service delivery 92

4.4.5. Robust data exchange platforms enable smooth service delivery 94

4.4.6. Effective employer engagement strengthens labour market links 95

4.5. Institutional and legal considerations for contracting out employment services in Greece 96

4.5.1. The "Jobs Again" DYPA legislation already enables partnerships in the delivery of existing employment services 96

4.5.2. DYPA has the ability to co-ordinate and deliver employment services at regional level 97

4.5.3. DYPA has built institutional capacity to deliver contracted-out training programmes 98

4.6. An outline of contracted-out employment services for vulnerable people in Greece 99

4.6.1. Programme 1: Intensive support for the long-term unemployed 100

4.6.2. Programme 2: Employment support for people with health conditions or disabilities 101

4.7. Detailed implementation plan 102

4.7.1. Indicative timeline 102

4.7.2. Steps to implement a pilot 103

4.7.3. Steps to evaluate a pilot 106

References 107

5. Monitoring and evaluation framework of the digital tool to identify clients needing intensive support 113

5.1. Introduction 114

5.2. Foundations of the Monitoring and Evaluation Framework 114

5.2.1. Monitoring and evaluation (M&E) frameworks are key to effective implementation, learning, and impact 115

5.2.2. A Theory of change and results chain are both the starting point and the backbone of an M&E Framework 115

5.2.3. An example results chain can guide the development of the M&E framework and inform stakeholder discussions 117

5.3. Monitoring indicators 118

5.3.1. The monitoring framework includes indicators related to each step of the results chain 118

5.3.2. The monitoring indicators require different types of data and could be visualised in a dashboard 120

5.4. Proposal for an evaluation plan 121

5.4.1. Defining outcomes and indicators to evaluate impact enables to plan for data collection and evaluation 122

5.4.2. A robust evaluation design is essential to assess the tool's impact 123

5.4.3. RCTs deliver the most credible evidence when carefully designed and implemented 124

5.4.4. Quasi-experimental methods can provide credible evidence when randomisation is not feasible 127

5.5. Conclusion 127

References 128

Annex 5.A. Key questions in planning an evaluation 130

Notes 131

Table 3.1. Several databases are used in the analysis 39

Table 3.2. Vulnerable and special groups often have lower levels of education and are more likely to have incomplete data in DYPA's records 41

Table 3.3. Jobseekers assessed as most job-ready have higher rates of employment within 12 months 45

Table 3.4. Vulnerable and special category jobseekers enter employment slower 45

Table 3.5. The new digital tool can directly draw on the results presented in this report 62

Table 3.6. The implementation of the tool requires diverse skill sets and varying levels of resources 69

Table 3.7. Key implementation risks for the new tool and mitigation measures 70

Table 4.1. Factors associated with long-term unemployment are grouped around three categories 78

Table 5.1. The proposed monitoring framework contains indicators for inputs, activities, outputs and outcomes 119

Table 5.2. Linking outcomes to indicators and data sources for the DYPA digital tool evaluation 123

Boxes 47

Box 3.1. Statistical methods to determine risk factors 47

Box 4.1. Estonia's advanced government data strategy supports modern, joined-up services 94

Box 4.2. DYPA has already established a legal basis for performance-related training contracts 99

Annex Table 3.A.1. Different data samples are used for different parts of the analysis 73

Annex Table 3.B.1. Demographic characteristics, educational and occupational backgrounds by vulnerability 74

Annex Table 3.B.2. Profiling availability and average scores by type of vulnerability 75

Annex Table 5.A.1. Key questions in planning an evaluation 130