Surprising Consequences of Innocuous Mobile Transaction Reminders of Credit Card Use
Bibliographic record
Abstract
Excessive credit card use has been a serious concern across the world since the introduction of the payment method. In South Korea, credit card companies and the government collaborated on a behavioral intervention, the transaction reminder service, to help consumers better manage their credit. Credit card transactions trigger text message confirmations sent to users’ mobile phones, increasing the salience and memory of expenses and resulting in more controlled spending. Experimenting in an institutional setting in which one group receives reminders and the other does not, the authors combined difference-in-differences methodology with inverse probability treatment weighting to assimilate random assignment. The empirical findings show that this intervention counterintuitively brings an overall increase in spending. This increase is substantial among those who had been light to medium spenders before the implementation, whereas historically high spenders experience little to no change after receiving the transaction reminders. The results are consistent with a theory that users reallocate the mental effort of remembering their past spending (mental recordkeeping) to digital devices, leading to higher spending due to poor recall. These findings attest to the value of evaluating a policy before scaling it broadly.
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.
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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".