Nudging increases take‐up of employment services: Evidence from a large field experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it