Social Protection Unit Europe and Central Asia Region

1. Advanced Profiling of Unemployed in Public Employment ServicesA Critical Review of OECD Experiences and Applications for Western Balkans Vienna, March 4, 2014Artan Loxha Social Protection Unit Europe and Central Asia Region

2. Outline • Profiling in the context of activation • Best practice profiling methods in OECD • Statistical profiling and applications • Relevance for Western Balkans

3. Outline • Profiling in the context of activation • Best practice profiling methods in OECD • Statistical profiling and applications • Relevance for Western Balkans

4. Key elements of activation Mutual obligations principle Key elements of effective activation Activation models PROFILING • Individualized action-planning • Focus on high risk prioritization • Service integration between PES and SA • Enhanced performance-based sub-contracting Liberal model Social democratic model Continental corporatist model Enhanced responsibilities of the unemployed - Active job search and availability for work in return for income support Provision of income support - Access to income support and to public employment services Restricted ALMPs to incentivize jobseeker Operationalizing legislation through 4 main elements of activation Extensive services and high benefit levels and coverage Individual responsibility to mobilize own assets, with key state role

5. The traditional role of the PES Interventions HIGH Intensive counseling and special ALMPs  Vocational training Level of prioritization by caseworker Self-service and job matching  Traditional PES client: the unemployed LOW 1 Income support/Job matching Time

6. Reinventing the role of PES in the context activation Early interventions PROFILING Interventions 2 HIGH HIGH  Intensive counseling and special ALMPs   High risk group   Work-able vulnerable population Vocational training 1 Level of prioritization by caseworker   Middle risk group Distance from labor market  Self-service and job matching Low risk group  Traditional PES client: the unemployed LOW LOW 1 Income support/Job matching Time

7. Main uses of profiling $ Interventions Caseworker 2 HIGH HIGH  Intensive counseling and special ALMPs   High risk group  3  Vulnerable work-able population Vocational training Referral 1   Level of prioritization by caseworker Middle risk group Redistributing resources based on severity of profile Distance from labor market  Self-service and job matching Low risk group LOW LOW Client segmentation Targeting Resource planning

8. Profiling involves certain information asymmetries Caseworker Interventions 2 HIGH HIGH  Intensive counseling and special ALMPs   High risk group  3  Vulnerable work-able population Vocational training Referral 1   Level of prioritization by caseworker Middle risk group Distance from labor market  Self-service and job matching Low risk group LOW LOW Information asymmetries

9. Outline • Profiling in the context of activation • Best practice profiling methods in OECD • Statistical profiling and applications • Relevance for Western Balkans

10. Approach for studying OECD best practices

11. Methodology • Australia • Canada • Denmark • Finland • Germany • Ireland • Netherlands • Slovenia • South Korea • USA • Sweden • Switzerland • OECD activation country notes • EU PES-to-PES dialogue papers • Country-specific papers on profiling • Selected academic papers • Methodological notes on statistical profiling • Ireland, Department of Social Protection • Denmark, National Labor Authority • Sweden, Public Employment Service • (selected examples) • Technical description of JSCI (AUS) • Employee-focused Integration concept (GE) • The Dutch Work Profiler (NL) • Slovenian profiling system (SL)

12. Key approaches to profiling in OECD

13. Classifying profiling systems Degree of caseworker discretion Complexity of data flow and processing

14. Basic demographics 1. Data availability and processing • Complex data • Labor market data • Complexity of data and processing • Employment status • Duration • Special needs • Qualifications • Personal ID • Age • Gender • Children • Education level • Soft and hard skills • Motivation • Behavior • Health

15. 2. Degree of caseworker discretion HIGH • More likely to rely on caseworker-based diagnostics for segmenting jobseekers • Caseworker resistance to automation may be higher • More time-intensive and resource intensive • Requires higher capacity • However, caseworker’s discretion can be curtailed depending on how binding data processing is to their decision-making Degree of caseworker discretion • More likely to rely on administrative rules and regulations for segmenting jobseekers • Less caseworker resistance to introducing other analytical tools may help address different constraints LOW

16. Classifying profiling systems HIGH • Data-assisted • profiling • Caseworker-based profiling Degreeof caseworker discretion • Data-only • profiling • Rules-based • profiling Complexity of data flow and processing LOW HIGH LOW

17. Key trade-offs HIGH • Data-assisted • profiling • Caseworker-based profiling Degree of caseworker discretion Invest in more caseworkers Higher caseworker resistance to automation Invest in caseworkers and data • Data-only • profiling • Rules-based • profiling Invest in data acquisition Complexity of data flow and processing LOW HIGH LOW

18. Profiling systems in OECD

19. Outline • Profiling in the context of activation • Best practice profiling methods in OECD • Statistical profiling and applications • Relevance for Western Balkans

20. Statistical profiling: segmenting clients based on likelihood of work-resumption work-resumption • Outcomes HIGH 100 • Little chance of reemployment • Profiling model: • Binary or duration models • Data input: • MIS • Ad-hoc extra data • Better chance of reemployment Risk of remaining long-term unemployed • Improved chance of reemployment 2 1 • Best chance of reemployment LOW

21. Intervention strategies by client profile and support intensity Far Missed opportunities • Better chance of reemployment Directive Guidance Client Distance from Labour Market Frequency of Intervention • Improved chance of reemployment Reference to Personal Development Job Search Wasted resources • Best chance of reemployment Self-Serve Near High Low Intensity of Support

22. Ireland: statistical profiling for case management intensity

23. Sweden: statistical profiling for ALMP prioritization Segmentation based on risk groups Registration and initial interview Statistical profiling model Final caseworker decision 2 1 3

24. Assessment Support Tool

25. Australia: statistical profiling for steering private contractors

26. Australia: statistical profiling for steering private contractors

27. Outline • Profiling in the context of activation • Best practice profiling methods in OECD • Statistical profiling and applications • Relevance for Western Balkans

28. Relevance to the Western Balkans • New focus on activation • Descriptive profiling revealed high heterogeneity of clients in PES • Need to manage and focus scarce resources • Already have a functioning (little exploited) MIS • Can be integrated as part of a larger reform • Main challenge: define specific ALMPs for each client segment (taking heterogeneity into account)

29. Key implementation lessons • Data availability and nature of unemployment determine accuracy and feasibilty of profiling tool • Apply to critical spot in process management where profiling adds value, not just “another tool” • Pilot a lot on the ground, prepare clear guidelines to manage implications of tool on day to day case management • Reduce/manage perceptions of “de professionalization” of case workers, find where it adds value to their work

30. Contacts Artan Loxha Labor Market Consultant, World Bank aloxha@worldbank.org MatteoMorgandi Economist, World Bank mmorgandi@worldbank.org