Imperfect Intermediation of Money-Like Assets: Stein & Wallen (2025)

What this is. The paper’s core results, the theoretical model of heterogeneous-elasticity intermediation, and the empirical specifications that test it: enough to know what was found and how, without reading the full 38 pages. To replicate or extend, read the original at doi.org/10.1111/jofi.13500.

TL;DR

Stein and Wallen (2025) study why the one-month T-bill rate regularly falls below the rate on the Fed’s reverse repurchase (RRP) facility, even though both are overnight-equivalent, credit-risk-free, government-backed instruments. The RRP-bill spread is a stark violation of the near-money premium logic of Nagel (2016), who shows T-bills command a liquidity premium precisely because they are more money-like. Stein and Wallen build and test a model with two types of investors: money funds (which have direct RRP access) and corporate treasurers (which do not). The model has three frictions: (i) money fund AUM is exogenously fixed (segmentation from outside investors, consistent with Bech and Klee (2011) showing limited access creates persistent rate wedges), (ii) corporate treasurers value T-bills for pledgeability as derivatives collateral, related to the Treasury richness documented by Fleckenstein and Longstaff (2024), and (iii) money funds substitute imperfectly and heterogeneously between T-bills and RRP, echoing the slow-moving capital mechanism of Duffie (2010). As the most-elastic money funds exhaust their T-bill holdings and reach a corner, the remaining less-elastic funds become marginal. Supply shocks then hit T-bill rates seven times harder than when funds are not constrained. Related evidence on law-of-one-price violations across segmented markets is provided by Siriwardane, Sunderam and Wallen (2025). The broader intermediary asset pricing literature, including He and Krishnamurthy (2013) who model aggregate intermediary wealth as the key state variable, typically focuses on a single representative intermediary; this paper shows that heterogeneity across intermediaries is equally important. Supply-side T-bill scarcity and its pricing consequences are documented in d’Avernas and Vandeweyer (2024). The mechanism described here was the key driver of the large yield dislocations during the 2023 debt-ceiling episode, when more than 90% of money fund AUM was already at a corner.

Core results

Magnitudes and significance are as reported. \*, \*\*, \*\*\* = 10%, 5%, 1%.

#	Result	Locator	Magnitude
R1	One-month T-bill rates fell well below expected RRP rates for most of June 2021 to May 2023, with the gap frequently exceeding 50 bps and spiking to over 160 bps during the March-April 2023 debt-ceiling uncertainty	Figure 1, p. 3186; Figure 3, p. 3199	Average RRP-bill spread 6 bps in Region 2 (non-scarce periods); 37 bps in scarce period (April 2022 to April 2023); spike to ~160 bps in March-April 2023
R2	Flows from outside investors into money funds explain very little of the variation in aggregate T-bill portfolio weight; virtually all substitution from T-bills to RRP is driven by active portfolio rebalancing by money fund managers	Figure 4, p. 3201	Counterfactual (investor-flows-only) portfolio share is an order of magnitude less variable than actual share; the two series are nearly uncorrelated
R3	Money fund investor flows are almost insensitive to fund returns: a 100 bp increase in returns raises fund AUM by only 5.9 percentage points per quarter	Table II, column (4), p. 3202	Coefficient 5.885*** (0.98); adjusted R-squared 14%; N = 4,449 fund-quarters
R4	Corporate derivatives exposure is associated with a significantly larger Treasury share in liquid assets: a one-standard-deviation increase in derivatives exposure raises the Treasury share by 1.8 percentage points, relative to a 7.6% mean	Table III, column (3), p. 3203	Coefficient 3.481** (1.57); adjusted R-squared 0.15; N = 2,730 firm-years, 2004-2021
R5	IV estimate of aggregate money fund elasticity of substitution: a 1 bp increase in the RRP-bill spread due to a supply-driven decrease in T-bills causes money funds to decrease their T-bill portfolio weight by 6.2%, or 79 bps of portfolio weight	Table IV, column (3), p. 3206	IV coefficient -6.19*** (2.24); first stage: 1% increase in Treasury supply decreases spread by 2.20*** (0.59) bps; N = 90 monthly observations
R6	Fund-level elasticities are highly heterogeneous: median elasticity is 7.4 (interquartile range 4.4 to 10.1); Treasury funds (most restrictive mandate) have average elasticity 5.4 vs. government funds 7.2 and prime funds 8.6	Figure 6, p. 3209	Kernel density of fund-level IV elasticities estimated on September 2013 to March 2021; Treasury funds significantly less elastic than government and prime funds
R7	When T-bills are scarce (April 2022 to April 2023), a 1% decrease in instrumented T-bill supply causes the RRP-bill spread to increase by 4.5 bps, about seven times the unconditional estimate of 0.66 bps	Table V, column (4), p. 3212	Interaction coefficient Scarce T-Bills x Delta T-Bill: -4.484** (1.83); unconditional IV: -0.656*** (0.21); full sample September 2013 to June 2024
R8	T-bills maturing before June 1, 2023 (the projected debt-ceiling date) yielded on average 153 bps less over May 2023 than T-bills maturing on June 1, 2023; average RRP-bill spread for pre-June maturities was 81 bps	Figure 10, p. 3215	Discontinuity in T-bill yield curve at June 1, 2023 maturity cutoff; T-bills maturing in May 2023 averaged ~4.4%, vs. RRP rate ~5.05%; June maturities showed a positive RRP-bill spread reflecting default risk

Overall (paper’s conclusion). Even in a simple and transparent setting, frictions in financial intermediation create economically significant and time-varying spreads between money-like assets. Heterogeneity in intermediary elasticity is the central mechanism: as more-elastic intermediaries are driven to corners, their departure leaves only less-elastic intermediaries as the marginal market participants, amplifying the rate impact of supply and demand shocks. This state-dependence has broad implications for evaluating QE programs and other policy interventions whose market impact is implicitly assumed to be constant over time.

Theory / model

The model (Section I, pp. 3191-3195) has a total supply of T-bills $S$ held by money funds or corporate treasurers. Money funds also have access to the RRP facility; treasurers cannot access RRP and can only hold T-bills or private repo. Aggregate money fund AUM is exogenously fixed at $A$.

Treasurers’ collateral demand. Corporate treasurers derive collateral benefits from T-bills. Their demand for T-bills is an increasing function of the RRP-bill spread (p. 3192):

$$X(r_b - r_p), \quad \text{with } X(\cdot) \text{ increasing}$$

where $r_b$ is the T-bill rate and $r_p$ is the (exogenous) RRP rate. Because private repo and RRP rates are nearly identical in the data, the model writes treasurer demand as $X(r_b)$ for simplicity.

Money fund preferences. Fund $i$ splits its portfolio between T-bills (weight $T_i \in [0,1]$) and RRP (weight $1 - T_i$) with utility (equation (1), p. 3192):

$$U_i = r_b T_i + r_p (1 - T_i) + V_i(T_i) \tag{1}$$

The term $V_i(T_i)$ is an increasing concave nonpecuniary benefit from holding T-bills, capturing idiosyncratic preferences (fund mandates, window-dressing, etc.). The paper uses the functional form (equation (2), p. 3192):

$$V_i(T_i) = \frac{T_i - \frac{1}{2} T_i^2}{b_i} \tag{2}$$

where $b_i$ is the elasticity parameter. Less elastic funds have smaller $b_i$; more elastic funds have larger $b_i$. The elasticity parameter $b_i$ is distributed uniformly on $[b_L, b_H]$ with $b_L > 0$.

Optimal portfolio. The first-order condition for an interior optimum is (equation (3), p. 3193):

$$(r_p - r_b) = V_i'(T_i) = \frac{1 - T_i}{b_i} \tag{3}$$

yielding fund $i$‘s optimal T-bill share in an interior solution (equation (4), p. 3193):

$$T_i^* = 1 - b_i (r_p - r_b) \tag{4}$$

Three regions. The model’s solution is characterized by three regions as T-bill supply $S$ shrinks:

• Region 1 (ample supply): $r_b \geq r_p$; all money funds hold only T-bills; market-clearing pins the spread at zero or negative: $X(r_b) = S - A$ (equation (5), p. 3193).
• Region 2 (moderate scarcity): Some funds are at an interior; the market-clearing condition is $X(r_b) = S - A\theta(r_b) T^{**}$ (equation (11), p. 3194), where $\theta(r_b)$ is the fraction of funds still holding T-bills (equation (12), p. 3194):

$$\theta(r_b) = \frac{b_U - b_L}{b_H - b_L} \tag{12}$$

The sensitivity of the T-bill rate to supply changes in Region 2b (Case 2b, where the most elastic fund is already at the corner) is (equation (13), p. 3194):

$$\frac{dr_b}{dS} = \frac{1}{X'(r_b) + A\theta\!\left(\frac{b_U + b_L}{2}\right)} \tag{13}$$

This sensitivity increases continuously ($\frac{d^2 r_b}{dS\, d\theta} < 0$) as fewer funds remain in the T-bill market.

• Region 3 (extreme scarcity): All money funds are at corners holding only RRP. Market clearing is entirely determined by treasurer collateral demand: $X(r_b) = S$ (equation (14), p. 3195), and the sensitivity is:

$$\frac{dr_b}{dS} = \frac{1}{X'(r_b)} \tag{15}$$

The transition from Region 2 to Region 3 is the key amplification mechanism documented empirically.

Method

The paper combines a partial-equilibrium theoretical model with instrumental-variable panel regressions. It builds on panel-regression and instrumental-variables as its core estimation primitives.

Instrument for the RRP-bill spread. The key endogeneity concern is that T-bill supply may respond to money fund demand. The paper instruments monthly changes in the RRP-bill spread using monthly log differences in the privately-held supply of all Treasuries (not just T-bills), on the grounds that Treasury auction timing follows a “regular and predictable” schedule (citing Garbade (2007)) and total Treasury supply is not driven by money fund demand dynamics (p. 3206-3207, Table IV).

First-stage specification (equation (23), p. 3207):

$$\Delta \text{Spread}_t = \alpha + \beta \Delta \text{Treasury}_t + \epsilon_t \tag{23}$$

Aggregate elasticity regression. The estimating equation for the aggregate T-bill portfolio weight of the money fund sector (equation (22), p. 3205):

$$\Delta w_{\text{Bills},t} = \alpha - \beta \Delta \text{Spread}_t + \epsilon_t \tag{22}$$

where $\Delta w_{\text{Bills},t}$ is the log difference in the aggregate T-bill portfolio weight. The IV estimate (Table IV, column (3)) delivers $\hat\beta = 6.19$.

Supply-shock amplification regression. The headline test of state-dependence (equation (24), p. 3212):

$$\Delta \text{Spread}_t = \alpha + \beta_0 \Delta \text{TBill Supply}_t + \beta_1 \text{Scarce TBills}_t + \gamma \Delta \text{TBill Supply}_t \times \text{Scarce TBills}_t + \varepsilon_t \tag{24}$$

The interaction term $\gamma$ captures the incremental sensitivity when T-bills are scarce. A continuous version replaces the dummy with Constrained Share (equation (25), p. 3213):

$$\Delta \text{Spread}_t = \alpha + \beta_0 \Delta \text{TBill Supply}_t + \beta_1 \text{Constrained Share}_t + \gamma \Delta \text{TBill Supply}_t \times \text{Constrained Share}_t + \varepsilon_t \tag{25}$$

where Constrained Share is the AUM-weighted fraction of funds with less than 5% T-bill weight and at least 10% weight in either RRP or T-bills.

Flow decomposition. To test the segmentation assumption, the paper decomposes dollar changes in T-bill holdings into an investor-flow component and a managerial rebalancing component (equations (16)-(20), pp. 3200-3201):

$$\Delta D_{\text{Bill},i,t} = w_{\text{Bill},i,t} A_{i,t} - w_{\text{Bill},i,t-1} A_{i,t-1} \tag{16}$$ $$\text{IFlow}_{\text{Bill},i,t} = w_{\text{Bill},i,t-1} (A_{i,t} - A_{i,t-1}) \tag{17}$$ $$\text{MFlow}_{\text{Bill},i,t} = \Delta D_{\text{Bill},i,t} - \text{IFlow}_{\text{Bill},i,t} \tag{18}$$

The counterfactual T-bill portfolio share driven only by investor flows is constructed as (equation (20), p. 3201):

$$\widetilde{\text{Ratio}}_t = \frac{\tilde{D}_{\text{Bill},t}}{\tilde{D}_{\text{Bill},t} + D_{\text{RRP},t}} \tag{20}$$

Corporate collateral regression. For the collateral-demand channel (equation (21), p. 3202):

$$w_{UST,i,t} = \alpha_i + \beta \, \text{Deriv}_{i,t-1} + \varepsilon_{i,t} \tag{21}$$

where $w_{UST,i,t}$ is firm $i$‘s Treasury holdings share and $\text{Deriv}_{i,t-1}$ is lagged derivatives exposure (absolute value of P&L on derivatives / total assets). Standard errors are clustered by firm; OLS with time, industry, and time-by-industry fixed effects.

Empirical specifications

Aggregate elasticity (R5, Table IV, p. 3206). Monthly time-series, September 2013 to March 2021 (N = 90). LHS: log change in aggregate money fund T-bill portfolio weight. RHS: change in the RRP-bill spread (instrumented by log change in privately-held Treasury supply). First stage F is implicit in the significant first-stage coefficient (-2.20 bps per 1% Treasury supply increase). Standard errors are robust to heteroskedasticity. The IV coefficient of -6.19 rises by almost an order of magnitude relative to OLS (-0.94), consistent with demand-side endogeneity biasing the OLS toward zero.

Supply-shock amplification (R7, Table V, p. 3212). Monthly time-series, September 2013 to June 2024 (N = 127; excludes May-June 2023 for debt-ceiling effects). LHS: change in RRP-bill spread. RHS: instrumented change in T-bill supply, scarce-T-bills indicator (April 2022 to April 2023), and their interaction. Unconditional IV: -0.656*** (0.21) bps per 1% supply change. Interaction with scarce indicator: -4.484** (1.83). Total effect when scarce: -(0.656 + 4.484) = -5.1 bps per 1% supply decrease (i.e., a 7x amplification). Column (5) with continuous constrained-share interaction shows that when 91% of funds are constrained (April 2023), a 1% supply decrease increases the spread by 6.0 bps, vs. 1.5 bps when 24% are constrained (May 2022).

Corporate collateral demand (R4, Table III, p. 3203). Annual firm-year panel, 193 large U.S. non-financial corporates, 2004-2021 (N = 2,730). LHS: Treasury holdings share (Treasury + agency securities / Treasury + agency + cash equivalents + money fund shares). RHS: lagged derivatives exposure (absolute P&L / total assets), firm size, industry and time fixed effects. One-standard-deviation increase in derivatives exposure (0.516%) is associated with a 1.8 percentage-point higher Treasury share (column (3)). Standard errors are clustered by firm.

Investor flow sensitivity (R3, Table II, p. 3202). Monthly and quarterly, September 2013 to June 2024 (N = 13,434 monthly; 4,449 quarterly). LHS: investor flows as a percentage of lagged fund AUM. RHS: contemporaneous fund return. Coefficient in AUM-weighted quarterly specification (column 4): 5.885*** (0.98). The economic implication: during the scarce period (April 2022 to April 2023) when the spread averaged 37 bps, a fund invested entirely in RRP would receive only 2.2 percentage points more quarterly inflows than a T-bill-invested fund.

Datasets used

Dataset	Role in paper	Wiki page
Crane Data LLC (monthly money fund holdings and AUM)	T-bill and RRP portfolio weights, fund-level elasticity estimation, AUM decomposition	Crane Data (licensed)
Federal Reserve (NY) - RRP program data	RRP counterparty identities, RRP take-up amounts, administered RRP rate	No page yet
Bloomberg (secondary market yields)	One-month T-bill yields, OIS rates for maturity adjustment	Bloomberg (licensed)
Federal Reserve (effective Fed Funds rate)	Maturity adjustment for expected one-month RRP return; monetary policy benchmarks	No page yet
U.S. Treasury / Federal Reserve (Treasury supply)	Privately-held outstanding Treasuries (instrument for T-bill supply shocks)	No page yet
Compustat (annual) via WRDS	Derivatives P&L, firm size, corporate bond and Treasury holdings for large non-financial firms, 2001-2021	WRDS (licensed)
Darmouni and Mota (2024) - corporate securities holdings	Cash and securities holdings for 200 largest U.S. public non-financial firms, 2001-2021	No page yet

Sample for the main money-market analysis: monthly, September 30, 2013 to June 30, 2024 (130 months). Sample for corporate collateral analysis: annual firm-year panel, 2004-2021.

When to read the full paper

Read the original if you are: studying the T-bill market microstructure or the Fed’s RRP facility design; analyzing how intermediary heterogeneity shapes asset price sensitivity to supply shocks; calibrating the impact of Treasury debt management decisions on short-term rates; or extending the framework to other asset classes where identifying which intermediaries are at corners is feasible. The exact model equations and Appendix A derivation of equation (13) are the load-bearing technical content.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6), December 2025. Wiley, paywalled. This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. Extract-only; the verbatim PDF is not hosted here.

Stein, Jeremy C., and Jonathan Wallen. “The Imperfect Intermediation of Money-Like Assets.” The Journal of Finance 80, no. 6 (December 2025): 3185–3221. DOI: 10.1111/jofi.13500. © 2025 the American Finance Association.