Complementary policy fields in action: Local policies targeted at multi-problem NEETs
Renate Minas et al.
What the paper says
Different social policies may impact on the effectiveness of one another, yet the few existing results on such complementarities largely stem from macro-level studies and little is known about the actual character of such policy complementarities. A more direct and potentially illuminating approach to studying complementarity is instead to investigate a specific group needing support from several policy areas. One such group is young people who are neither in employment nor in education and training (NEET), and the heterogeneous character of NEETs as well as the complexity and interrelatedness of their problems has been frequently emphasized. We study both the extent and character of policy complementarities vis-à-vis this multi-problem group as well as the relationship between these polices and the local NEET rate. We use a mixed-methods approach in which a quantitative analysis of individual NEET risks using administrative data for the whole Swedish population provides the basis for a qualitative study among local representatives from organizations working with NEETs in 20 strategically selected municipalities. Using multiple informant methodology, we collected unique data on the organization of measures in the areas of education, employment, health and social services from local professionals representing the different policy areas. Analyses using fsQCA show that compared to municipalities with higher-than-expected NEET rates, municipalities with lower-than-expected NEET rates more often provide social welfare support measures and work with a reinforcement mechanism such as multi-professional teams. Policy complementary is thus a result of direct institutional design aiming to promote inter-sectoral coordination.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.