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What Works for Active Labor Market Policies? A Meta-Analysis of RCT Findings

Eduardo Levy Levy-Yeyati et al.

Economia, the Journal of Latin American and Caribbean Economic Association (LACEA)2025https://doi.org/10.31389/eco.450article
ABDC B
Weight
0.37

Abstract

The past 10 years have witnessed a flurry of Randomized Controlled Trials (RCTs) that shed new light on the impact and cost-effectiveness of Active Labor Market Policies (ALMPs) that aim to improve workers’ access to new jobs and better wages. This paper presents the first systematic review of 102 ALMP RCTs comprising a total of 668 estimated impacts. We find that (i) a third of these estimates are positive and statistically significant (PSS) at conventional levels; (ii) programs are more likely to yield positive results when GDP growth is higher and unemployment is lower; (iii) programs aimed at building human capital, such as vocational training, independent worker assistance and wage subsidies, show significant positive impact; and (iv) program length, monetary incentives, individualized follow up and activity targeting are all key features in determining the effectiveness of the interventions. JEL Classification Codes: J21; J48; E24

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https://doi.org/https://doi.org/10.31389/eco.450

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@article{eduardo2025,
  title        = {{What Works for Active Labor Market Policies? A Meta-Analysis of RCT Findings}},
  author       = {Eduardo Levy Levy-Yeyati et al.},
  journal      = {Economia, the Journal of Latin American and Caribbean Economic Association (LACEA)},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.31389/eco.450},
}

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What Works for Active Labor Market Policies? A Meta-Analysis of RCT Findings

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 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.