Testing the Effectiveness of a Future Selves Intervention for Increasing Retirement Saving: Evidence from a Field Experiment in Mexico
Bibliographic record
Abstract
<title>Abstract</title> One psychological barrier impeding saving behavior is the inability to fully empathize with one’s future self. Future self interventions have improved savings by helping people overcome this obstacle. Despite the promise of such interventions, previous research has focused predominantly on hypothetical contexts and western settings where the target sample has been predominantly undergraduate. Do interventions that encourage people to more concretely consider their future selves during retirement still have a positive effect on behavior in consequential, real-world savings decisions? Using a field experiment in Mexico (<italic>N</italic> = 7,603), where less than 1% make a voluntary savings contribution annually, we developed a low-cost, easy-to-implement intervention to test whether concrete thinking about one’s future life improves recurring retirement savings signups relative to a status quo, control group. We find that future self decision aids significantly improved the likelihood of signing up for an automatic recurring savings plan by nearly four times compared to the control.
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How this classification was reachedexpand
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 | 0.013 | 0.010 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".