As the labour market continues to evolve amid digital and green transitions, shifting demographics, and economic uncertainty, the effectiveness of Active Labour Market Policies (ALMPs) remains a central concern for policymakers across Europe and beyond. In response to this challenge, the EU-ALMPO project has launched a coordinated effort to strengthen evidence-based policymaking and advance analytical tools for evaluating ALMP effectiveness. At the heart of this effort lies Work Package 1, which aims to develop an analytical model and evaluation framework for understanding how ALMPs contribute to skills matching in dynamic labour markets.
One of the key components of WP1 is Task 1.3, led by Prof. Łukasz Sienkiewicz and his research team at Gdańsk University of Technology. This task provides the empirical backbone for the project by identifying the key determinants that shape ALMP effectiveness, particularly in supporting skills matching for different target groups under varied institutional and economic contexts.
Task 1.3 plays a pivotal role within WP1 by conducting a comprehensive meta-evaluation of existing ALMP impact studies, with a focus on answering fundamental questions: What works? For whom? Why? Under what conditions? The scope of the review is broad yet sharply focused. It covers both programmatic aspects—such as the type and design of ALMP interventions—and participant-level characteristics, including gender, age, educational attainment, and labour market status. A distinctive feature of the analysis is its attention to the timing of effects, distinguishing between short-term impacts, which may be affected by temporary lock-in, and more durable medium- or long-term outcomes.
Equally important is the systematic examination of negative or unintended effects that ALMPs may generate. These include well-documented phenomena such as displacement, substitution, deadweight loss, creaming, and carousel effects. By identifying not only what works but also where and why policies may fail or fall short, the task provides a more complete picture of ALMP performance.
To carry out this complex and nuanced analysis, the Gdansk Tech team has adopted a realist-informed evidence synthesis approach (Pawson, 2006). At its core is a thematic literature review—a qualitative methodology suited to fields like labour market policy, where heterogeneity in intervention types, target groups, and evaluation designs makes quantitative meta-analysis less appropriate (Thomas & Harden, 2008; Gough et al., 2012).
The literature review was informed by a transparent and systematic search strategy, drawing on:
- Leading academic databases such as Scopus, Web of Science, and EconLit.
- Grey literature from international organisations, including the OECD, ILO, World Bank, ETF, and IDB.
- National-level repositories and ALMP-specific portals.
Inclusion criteria ensured that only studies with measured outcomes relevant to employment or skills matching were considered. Both peer-reviewed articles and high-quality institutional reports were included, enabling the team to capture both academic and practice-oriented insights (Boaz et al., 2006).
To synthesise findings across studies, the team developed a meta-evaluation framework rooted in theory-based evaluation principles (Weiss, 1997; OECD, 2022). This framework enabled structured comparison along three core dimensions:
- Programme design and implementation (e.g., intensity, duration, delivery model)
- Timing of effects (short-, medium-, and long-term impacts)
- Target group and contextual heterogeneity (demographics, regional labour market conditions)
In addition to tracking positive outcomes, the framework captures a wide array of negative effects—both direct and indirect. These are categorised using established typologies in the labour market policy literature (Heckman et al., 1999; Brown & Koettl, 2015), ensuring conceptual rigour.
To ensure consistency and comparability, a detailed coding framework was developed following content analysis protocols (Mayring, 2000; EPPI-Centre, 2010). Each study was coded according to key analytical variables:
- ALMP type (e.g., training, job matching, employment incentives)
- Target group characteristics (e.g., youth, long-term unemployed, migrants)
- Outcome indicators (e.g., employment, skill alignment, wage gains)
- Contextual conditions (e.g., economic cycle, administrative capacity, benefit systems)
- Methodological robustness and quality assurance
A structured spreadsheet was used for data collection, enabling cross-tabulation and thematic synthesis of factors such as the relationship between programme type and observed outcomes, or between institutional capacity and negative effects.
While the study does not compute aggregate effect sizes, its strength lies in its qualitative depth and analytical granularity, which are critical for informing the design of complex labour market interventions.
The findings from Task 1.3 are directly feeding into the development of the analytical model and evaluation framework in Tasks 1.4 and 1.5. By identifying the most important success factors, common barriers, and recurring patterns across diverse evaluations, the Gdańsk University of Technology team is helping to build a more robust and context-sensitive understanding of ALMP effectiveness.
Ultimately, this work will provide policymakers with a clearer view of which interventions work best, in which settings, and for which populations. It will also help design smarter, fairer, and more responsive ALMPs that are better equipped to support workers and employers in navigating ongoing economic and technological change.
