Redemption Fees and Gates in the Lab

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1: Overview

This paper uses laboratory experiments to evaluate the effectiveness of two liquidity management tools — redemption fees and redemption gates — in reducing runs on money market funds (MMFs), explicitly accounting for preemptive run behavior where investors withdraw before a fee or gate is triggered to avoid being harmed by its imposition. The experimental design is based on a Diamond–Dybvig framework modified following Engineer (1989), in which four investors must decide whether to withdraw before learning their own liquidity type (patient or impatient), generating a setting where preemptive runs are theoretically possible even without fear of fund default. Three treatments are compared: a laissez-faire baseline, a gates treatment (withdrawals suspended after cash reserves are exhausted), and a fees treatment (a redemption fee charged on withdrawals once cash reserves are exhausted). Across 15-period session halves, redemption fees produce significantly lower withdrawal rates than both the baseline and gates treatments, with the gap emerging primarily after the first ten periods as participants adapt to the tool; gates, contrary to the theoretical prediction that they reduce the risk factor of the no-run equilibrium, do not lower withdrawal rates relative to the baseline — and in the full-session analysis, gates actually generate significantly higher withdrawal rates than the baseline, consistent with preemptive runs accelerating when investors fear losing access to their funds. The overall finding is that neither tool eliminates fund fragility, but fees offer a modest and delayed stabilizing effect while gates are counterproductive, lending empirical support to the SEC’s 2023 regulatory shift away from gates and toward fees in MMF regulation.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the experimental design and why does it explicitly study preemptive runs?

The experiment models a fund with four investors, each holding a demandable claim worth 1 ECU in period 1; the fund holds 2 ECUs in cash and a project that pays 2R ECUs in period 2 if allowed to mature but only 1 ECU if liquidated early, and investors must make their period-1 withdrawal decision before learning whether they are impatient (need period-1 funds) or patient (can wait), mirroring the Engineer (1989) setup where preemptive runs arise from the risk of being locked in rather than from fundamental insolvency concerns. The key feature is that an investor who expects fees or gates to be imposed faces an incentive to withdraw early to avoid either losing access (gates) or paying a fee (fees) at precisely the moment their liquidity need arises, which is exactly the preemptive run mechanism observed empirically during the COVID-19 MMF turmoil of spring 2020. Investors are sequentially asked whether they wish to withdraw in a random order without observing others’ choices, and they learn their type only in the evening of period 1 after having already made the morning withdrawal decision. In the treatment with gates, the fund suspends payouts entirely once its 2 ECU cash reserve is exhausted (i.e., after two withdrawals), forcing the third and fourth investors to wait for period 2 regardless of their type. In the treatment with fees, the fund charges a redemption fee on the third and fourth period-1 withdrawals instead of suspending them.

Q2. How does the theoretical risk factor framework generate the paper’s main hypothesis?

The paper uses the concept of the “risk factor of the no-run equilibrium” — defined as the probability p at which an investor becomes indifferent between staying invested and withdrawing when all other investors stay with probability p — to rank the three treatments by their predicted effectiveness: fees should generate the lowest risk factor and thus the highest tendency toward the no-run equilibrium, followed by gates, with the baseline highest. Fees dominate gates on the risk factor because fees still permit withdrawal in period 1 (albeit at a cost), meaning an impatient investor who remained invested can still access funds when needed, whereas under gates an impatient investor who is locked out has no recourse. This additional flexibility of fees means that the downside of remaining invested is smaller under fees than under gates, making the no-run equilibrium relatively more attractive under fees. The paper’s design tests whether this theoretical ranking carries through to actual investor behavior in the lab, where cognitive limitations, learning dynamics, and strategic uncertainty may produce deviations from the prediction.

Q3. What are the main experimental results on withdrawal rates?

Pooled over the first 15 periods of the first session halves (where no spillovers from prior experience occur), withdrawal rates are 30.3% in the baseline, 31.7% in gates, and 27.6% in fees; proportion tests confirm that fees produce significantly lower withdrawal rates than both baseline and gates at the 5% level, but no significant difference is found between baseline and gates — gate withdrawal rates are actually slightly higher than the baseline, contradicting the directional hypothesis. In the robustness check using both session halves (full 30 periods), the pattern sharpens: overall withdrawal rates are 30.3% (baseline), 33.4% (gates), and 25.4% (fees), with gates now significantly higher than baseline (p = 0.000) as well as significantly higher than fees, indicating that gates actively encourage preemptive withdrawal rather than deterring it. Withdrawal rates in the fees treatment exhibit a distinctive time pattern: they start higher than the other treatments in the first 5 periods (the Fees × Period interaction in the regression is negative and significant, while the main Fees coefficient is positive and significant, indicating an initially elevated but steeply declining trajectory), with the fee benefit materializing only from period 11 onward — consistent with the European Commission’s (2023) observation that European MMF investors more familiar with fees show less preemptive behavior than U.S. investors.

Q4. What does the regression analysis reveal about the treatment effects and dynamics?

Random-effects panel linear probability models of the binary withdrawal decision confirm that the fees treatment produces a significantly negative trend (Fees × Period coefficient negative and statistically significant) while the baseline shows no time trend and gates show no significant deviation from baseline trends, and that prior round experience — specifically the number of withdrawal requests in the immediately preceding round — is a strong positive predictor of withdrawal (approximately 6 percentage points per additional prior-round withdrawal request), while longer-run experience before the last round carries no significant predictive power. The inclusion of individual-level controls in Model (4) shows that higher risk tolerance is associated with significantly lower withdrawal rates (a surprising finding relative to prior experimental literature, which the authors suggest may reflect the preemptive nature of the decision making risk tolerance relevant through attitudes toward liquidity timing risk rather than through classic strategic risk). The regression analysis confirms that gates’ ineffectiveness is not explained by observable participant characteristics: the gates dummy is never significant and the gates-period interaction is not significantly different from the baseline, ruling out the possibility that session-level composition differences drive the null result for gates.

Q5. Does switching regulatory regime across session halves generate behavioral change?

Switching from the baseline to fees in the second session half produces a significant reduction in withdrawal rates in the final 5-period block consistent with Hypothesis 2, and switching from fees to gates in the second half produces a significant increase in withdrawal rates in the final 5-period block; however, switching from baseline to gates and from gates to fees produce no significant differences between session halves at the 5% level. The modest switching effects suggest that the fee benefit takes time to emerge regardless of prior regime experience — a finding consistent with the general pattern that fee effectiveness materializes only after participants have had multiple rounds of exposure. This regime-switching analysis also rules out a strong order effect as an explanation for the observed fee benefit: the fee advantage over baseline is present even when comparing within the same session halves and is not driven by participants carrying in stabilizing prior knowledge from the fees treatment.

Q6. What are the regulatory implications and how do the findings connect to the 2020 MMF turmoil?

The experimental findings directly inform the ongoing regulatory overhaul of MMF liquidity management tools, supporting the SEC’s 2023 decision to move away from gates toward mandatory swing pricing (which functions similarly to a fee) as the primary tool for U.S. MMFs, and providing micro-level behavioral evidence for why the 2014 fees-and-gates provisions failed to prevent the spring 2020 MMF runs even though they were in force. The preemptive run mechanism is empirically identified in the lab as a real and substantial phenomenon: withdrawal rates in the first round are if anything higher under fees than under the baseline, and the fee benefit only consolidates after participants have repeatedly experienced the tool, suggesting that investor familiarity is necessary for fee effectiveness — a condition that was likely not met in 2020. The finding that gates actively worsen run propensity in the full-session analysis provides the starkest regulatory implication: gates may be self-defeating by compressing investors’ effective option to wait, creating a focal first-mover advantage that accelerates exactly the run the gate is meant to stop.

Key Concepts

preemptive run : a run in which investors withdraw from a fund before their immediate liquidity need arises, driven by the strategic risk that fees or gates will be imposed at the exact moment they need liquidity; modeled here following Engineer (1989) and experimentally documented as a significant behavioral phenomenon that undermines both fees and gates.

risk factor of the no-run equilibrium : a measure based on risk dominance (Harsanyi and Selten 1988) defined as the probability p at which an investor becomes indifferent between withdrawing and remaining when all others stay with probability p; lower risk factor means the no-run equilibrium is more robust to coordination failure, and the paper predicts fees < gates < baseline in this ranking.

redemption gate : a liquidity management tool that suspends fund withdrawals once cash reserves are depleted, theoretically preventing fire sales but experimentally found to be ineffective and potentially counterproductive due to the preemptive run incentive it creates for investors who fear losing access to their funds.

redemption fee : a liquidity management tool that charges a cost on fund withdrawals during periods of redemption stress, internalizing liquidation losses into the withdrawing investor’s payoff; experimentally found to significantly reduce withdrawal rates relative to both baseline and gates, but only after a learning period of approximately 10 periods.

C92 | Macro Paper Warehouse