Interventions to bolster benefits take-up: Assessing intensity, framing, and targeting of government outreach
Bibliographic record
Abstract
= 542,804 low-income households), we test whether more proactive communication, varying message framing, and more precise targeting can boost take-up of tax-based benefits in California above and beyond traditional light-touch approaches. Our interventions focused on extremely vulnerable households, most with no prior-year earnings, who were at risk of missing out on two crucial benefits: the 2021 expanded Child Tax Credit and pandemic-relief Economic Impact Payments. Light-touch outreach consistently increased take-up of these benefits by 0.14 to 2 percentage points-a 150% to over 500% relative increase-regardless of message, sample, timing, or modality. These light-touch approaches resulted in over $4 million disbursed, with a highly cost-effective return of $50 to over $8,000 per $1 spent. However, higher-touch proactive outreach, varying messaging, and more precise targeting yielded minimal additional benefits, with proactive outreach even showing negative returns. These findings demonstrate that light-touch outreach can effectively shift behavior among very vulnerable households in contexts with reduced compliance burdens, but also underscore an urgent need to rethink the role of higher-touch strategies in closing take-up gaps in social safety net programs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".