Commitment-Flexibility Trade-Off and Withdrawal Penalties
Bibliographic record
Abstract
Withdrawal penalties are common features of time deposit contracts offered by commercial banks, as well as individual retirement accounts and employer-sponsored plans. Moreover, there is a significant amount of early withdrawals from these accounts, despite the associated penalties, and empirical evidence shows that liquidity shocks of depositors are a major driving force of this. Using the consumption-savings model proposed by Amador, Werning and Angeletos in their 2006 Econometrica paper (henceforth AWA), in which individuals face the trade-off between flexibility and commitment, we show that withdrawal penalties can be part of the optimal contract, despite involving money-burning from an ex ante perspective. For the case of two states (which we interpret as “normal times” and a “negative liquidity shock”), we provide a full characterization of the optimal contract, and show that within the parameter region where the first best is unattainable, the likelihood that withdrawal penalties are part of the optimal contract is decreasing in the probability of a negative liquidity shock, increasing in the severity of the shock, and it is nonmonotonic in the magnitude of present bias. We also show that contracts with the same qualitative feature (withdrawal penalties for high types) arise in continuous state spaces, too. Our conclusions differ from AWA because the analysis in the latter implicitly assumes that the optimal contract is interior (the amount withdrawn from the savings account is strictly positive in each period in every state). We show that for any utility function consistent with their framework there is an open set of parameter values for which the optimal contract is a corner solution, inducing money burning in some states.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".