Price and saliency in health care: When can targeted nudges change behaviors?
Bibliographic record
Abstract
This paper takes advantage of a natural experiment to examine the relationship between the price and saliency of health services. A large employer e-mailed individually targeted health education encouraging high-value care to high-risk employees. Weeks before the program launched, a company reorganization affecting about a quarter of employees resulted in employees in that group not receiving the intervention. Using event study, difference-in-differences, and triple differences methods, I find that costlier services are associated with relatively less utilization and that prior use was associated with relatively more utilization following the campaigns. In all cases, the targeted nudges either increased or did not affect utilization, suggesting that while these interventions may increase health care consumption choices for some lower-cost preventative services or for some services previously utilized, it is unlikely to reduce health care costs in the short-run. This research may inform employer, governmental, and health insurer choices concerning low-cost interventions seeking to shift health behaviors, and may also be relevant in other settings in which targeted informational nudges are deployed.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".