How effective are behavioral interventions to increase the take‐up of social benefits? A systematic review of field experiments
Bibliographic record
Abstract
Abstract Non‐take‐up of social benefits is a significant policy issue caused by factors such as lack of awareness, compliance costs, and stigma. While public information campaigns, default options, and in‐person assistance are increasingly used, their effectiveness remains poorly understood. This study provides a systematic review of field experiments evaluating nudges and simple behavioral interventions on program take‐up. We analyzed 93 interventions from 35 studies published over nearly 20 years, predominantly focusing on major U.S. programs. We compared study characteristics, including sample and intervention types, and assessed study quality. Due to high heterogeneity, we did not conduct a meta‐analysis but used forest plots and thematic summaries instead. Most studies reported a positive impact on program take‐up, but not on program application. Two types of interventions were notable for their impact on program application and take‐up: 1) providing and framing information; and 2) providing assistance. We discuss the limitations of this review, including the cost and safety of nudges and the implications of focusing on field experiments. We conclude that further research is needed on simpler interventions outside the U.S., as well as on compliance and psychological costs. Additionally, improving the quality and transparency of field experiments is essential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".