The Beneficent and Maleficent Effects of Simplification on Retirement Savings
Bibliographic record
Abstract
Abstract Considerable research suggests that making information simpler is better. Simplification improves the efficiency of information extraction and lowers psychological frictions, leading to its popularity with policymakers and practitioners worldwide. However, it remains unclear when and how simplification can be utilized most effectively, or if there are contexts where simplification may produce unintended maleficent effects. Using two large-scale field experiments (N = 126,673), we test whether simplifying account statements helps encourage retirement savings in Mexico. We partner with two retirement firms, one ranked high in rate of returns and the other ranked lower. We find that simplifying retirement account statements improves contribution rates for consumers in the high-ranking firm but reduces contribution rates for consumers in the low-ranking firm. Five follow-up experiments provide evidence consistent with a fluency amplification account. Simplifying information improves processing fluency, making it easier to accurately recall firm rank relative to the control, which amplifies behavior bidirectionally: High-ranking (low-ranking) firm consumers more accurately recall their firm’s rank, subsequently increasing (decreasing) contributions. However, if simplification is harnessed in ways that improve processing fluency and lower perceived switching costs, then simplification can improve retirement savings for everyone either by boosting contributions or encouraging people to switch to higher performing alternatives.
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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 | 0.034 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".