Nudging increases take‐up of employment services: Evidence from a large field experiment
Bibliographic record
Abstract
Abstract When people lose their job, labor market programs help them get back to work. But administrative burdens can hinder enrollment in such programs. We report results from a mixed‐method project to increase enrollment in employment services during the first 3 months of the COVID‐19 pandemic. First, we interviewed jobseekers and frontline staff to uncover administrative burdens. Second we worked with staff to co‐design a behavioral “nudge” intervention. Finally, in a large field experiment ( N = 14,008), we evaluate the impact of this intervention on participation in employment services. We present two main findings. First, reducing administrative burden triples enrollment in the program within the first 30 days. Second, we test two motivational frames—one emphasizing social norms, another using checklist messaging. We find that message framing drives engagement with communications, such as email open rates and website click‐throughs. However, framing generates no statistically significant difference in enrollment rates. Our results demonstrate the potential for applied behavioral science to improve implementation of labor market policy. We also contribute to current debates about the effectiveness of nudging to increase take‐up of public services.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".