Price and saliency in health care: When can targeted nudges change behaviors?
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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