Bank Market Power and Monetary Policy Transmission: Enkhbold (2026)

What this is. The paper’s core results, the empirical specification, and the identification strategy: enough to know what it found and how, without reading the full paper. To replicate or extend, read the original at the DOI link.

TL;DR

The paper studies how the interaction between local deposit market concentration and bank wholesale funding reliance (WFR) shapes the transmission of monetary policy surprises to mortgage rates. Using US bank- and loan-level data from 2000 to 2019, and building on the deposit channel of Drechsler et al. (2017) and the wholesale funding analysis of Choi and Choi (2021), the paper shows that in competitive markets (low HHI) a 100 bps policy shock raises mortgage rates by 34 bps for banks with high WFR; wholesale funding amplifies pass-through because it ties funding costs directly to market rates. In concentrated markets (high HHI), the same shock produces near-zero pass-through: banks use market power to hold deposit rates steady and absorb the cost change in margins rather than passing it to borrowers. The 35 bps differential translates to approximately $67 per month on a $300,000 mortgage ($24,000 over the loan life). Wang et al. (2022) show that market power dampens transmission in a structural model; this paper adds WFR as an interacting channel and traces heterogeneity across the interest rate cycle. These contrasts sharpen at the zero lower bound and during contractionary episodes. Robustness results vary by measure: HHI and Lerner estimates indicate dampening, while branch-share interactions are positive and alternative shock and funding measures change the wholesale-funding interaction.

Core results

Magnitudes and significance as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point to the source PDF.

#	Result	Locator	Magnitude
R1	Competitive markets: 100 bps shock transmits 34.1 bps to mortgage rates for high-WFR banks	Table 4, p. 9	Direct effect 18.47*** bps; WFR interaction adds 15.68* bps; total ~34.1 bps for high-WFR competitive banks
R2	Concentrated markets: near-zero pass-through; WFR interaction is negative	Table 4, p. 9	Baseline 9.10 bps (insig., t=0.83); WFR interaction = -0.728** bps/log-unit; net pass-through near zero
R3	Zero lower bound amplifies competitive pass-through to 38.8 bps	Table 5, p. 10	Competitive baseline at ZLB = 38.80*** bps (vs 23.14*** non-ZLB); WFR interaction = 10.05*** bps
R4	ZLB reverses concentrated-market pass-through to -21.2 bps	Table 5, p. 10	Concentrated ZLB = -21.18*** bps (vs -1.81* non-ZLB); sign reversal vs competitive ZLB (+38.8 bps)
R5	Asymmetric transmission: contractionary shocks pass through 4x more than expansionary	Table 7, p. 13	Contractionary: competitive 29.66*** bps, concentrated 8.73***; expansionary: competitive 6.86*** bps, concentrated -3.17 (insig.)
R6	Market-power robustness results vary by proxy	Table 9, pp. 14-15	Lerner x shock = -9.40*** bps; branch share x shock = +73.35** bps and branch share x log(WFR) x shock = +60.79*** bps; lagged HHI as IV yields -9.14** bps interaction
R7	Wholesale funding reliance and funding levels covary differently with policy rates across market structures	Table 2, p. 6	Corr(log WFR, federal funds rate): 0.346* competitive, 0.221* concentrated; corr(wholesale funding liabilities, federal funds rate): -0.352* competitive, -0.116* concentrated
R8	Policy-rate and concentration interaction predicts shifts between retail deposits and wholesale funding	Table 3, p. 7	FFR x HHI: -0.00346*** for retail deposits/liabilities, +0.00575*** for wholesale funding/liabilities, and +0.0000143*** for WFR/liabilities
R9	Pass-through differs between low- and high-rate regimes	Table 6, p. 11	Low rates: competitive 17.43*** baseline + 25.77*** WFR interaction; concentrated 32.07*** + 4.41***. High rates: competitive 7.26*** + 18.37***; concentrated -16.99*** + 1.83***
R10	Wholesale-funding interactions vary with the direction of policy shocks	Table 7, p. 13	Contractionary: +16.03*** bps competitive and +2.12*** concentrated; expansionary: -2.46 bps competitive (insig.) and -3.79*** concentrated
R11	Alternative shock identification preserves positive concentrated-market interaction but changes other coefficients	Table 8, p. 14	Jarociński-Karadi shock: direct effect 30.96*** bps competitive and 8.75*** concentrated; WFR interaction -58.92*** competitive and +2.08** concentrated
R12	Liability-based wholesale funding measure gives different interaction magnitudes	Table 8, p. 14	WFR interaction = +143.9* bps competitive; +5.14 bps concentrated (insig.)
R13	Triple interaction supports concentration dampening at higher wholesale funding reliance	Table 9, col. 1, pp. 14-15	HHI SD x policy shock x log(WFR) = -16.79*** (SE = 3.92)
R14	Instrumenting market structure and funding retains a negative concentration interaction	Table 9, col. 2, pp. 14-15	Second lags of HHI and WFR as instruments: HHI SD x policy shock = -9.14** (SE = 3.99)
R15	Branch market share produces positive shock interactions	Table 9, col. 3, pp. 14-15	Policy shock x branch share = +73.35** (SE = 29.83); triple interaction with log(WFR) = +60.79*** (SE = 6.05)
R16	Lerner-index market power is negatively associated with pass-through	Table 9, col. 4, pp. 14-15	Policy shock x Lerner = -9.40*** (SE = 2.92); triple interaction = +1.45 (SE = 1.71, insig.)

Overall (paper’s conclusion). Market concentration dampens monetary policy transmission by allowing banks to absorb policy shocks in margins. Wholesale funding reliance amplifies this gap: in competitive markets it ties funding costs to market rates and forces pass-through; in concentrated markets it provides a substitute for unraised deposit rates, further cushioning borrowers from the policy change. The Federal Reserve’s ability to affect household mortgage costs depends on local banking market structure throughout the interest rate cycle.

Theory / model

The paper does not specify a formal theoretical model. It combines the deposit channel of Drechsler et al. (2017) with wholesale-funding substitution discussed by Choi and Choi (2021). When market power lets banks hold deposit rates below policy rates, depositors leave and banks replace some retail funding with wholesale funding. Because wholesale costs track policy rates more closely, this replacement affects mortgage-rate pass-through differently in competitive and concentrated markets (Introduction, pp. 1-2; §3, pp. 6-7).

The tested hypothesis is that wholesale funding reliance amplifies pass-through in competitive markets, where banks have limited room to absorb cost changes, while deposit-market power can weaken or reverse that pass-through in concentrated markets. The paper tests the claim using within-MSA differences across banks and time variation in high-frequency monetary-policy surprises. A separate second-lag IV specification addresses endogeneity of market structure and funding choice (Table 9, p. 15).

Method

The paper estimates fixed-effects panel regressions. Equation (1) is the main loan-level mortgage-rate specification (p. 8):

$$\begin{aligned} \Delta r_{mbt} ={}& \alpha_b + \alpha_m + \beta_1 \Delta i_t + \beta_2 \log(\text{WFR}_{bt-1}) + \beta_3 \log(\text{WFR}_{bt-1}) \times \Delta i_t \\ &+ \Gamma \text{HH Controls}_{mbt-1} + \Xi \text{HH Controls}_{mbt-1} \times \Delta i_t \\ &+ \Pi \text{Bank Controls}_{bt-1} + \Lambda \text{Bank Controls}_{bt-1} \times \Delta i_t \\ &+ \Psi \text{Macro Controls}_{mt-1} + \Omega \text{Macro Controls}_{mt-1} \times \Delta i_t + \epsilon_{mbt} \tag{1} \end{aligned}$$

Here $\Delta r_{mbt}$ is the mortgage-rate change for loans in MSA $m$ originated by bank $b$ in quarter $t$; $\alpha_b$ and $\alpha_m$ are bank and MSA fixed effects; $\Delta i_t$ is a 100 bps monetary shock; and $\text{WFR}_{bt-1}$ is wholesale funding divided by retail deposits. Household controls include credit score, LTV, and debt-to-income ratio. Bank controls include branch count, liquid assets, duration mismatch, liability interest rate, real-estate and commercial-and-industrial loan shares, equity-to-asset ratio, and MBS-to-asset ratio. Macro controls include unemployment, income per capita, and house prices. The authors cluster standard errors by bank and quarter (equation (1) notes, pp. 8-9).

The policy shocks are changes in financial variables in a 30-minute FOMC announcement window, from 10 minutes before to 20 minutes after the announcement. Bauer and Swanson (2023) orthogonalize the measure to Fed information effects; Table 8 also uses the sign-based Jarociński and Karadi (2020) measure (pp. 3, 13-14). Deposit-market concentration is the local deposit HHI:

$$\text{HHI}_{mt} = \sum_{b \in m} \left(\frac{\text{dep}_{mbt}}{\sum_{b' \in m} \text{dep}_{mb't}}\right)^2$$

The baseline results split observations into competitive and concentrated markets using the HHI classification. Table 9 replaces or instruments the concentration and funding variables to assess robustness and endogeneity. The baseline design is a reduced-form panel specification using high-frequency monetary shocks; the table 9 lagged-variable instrument check is a distinct identification check, not the source of the baseline policy shock.

Empirical specifications

Funding-composition specification (Table 3, p. 7). For changes in deposit/liability shares, wholesale-funding/liability shares, and WFR/liabilities, the paper estimates:

$$\Delta y_{mbt} = \alpha_b + \alpha_m + \beta_1 \text{FFR}_t + \beta_2(\text{FFR}_t \times \text{HHI}_{m,t-1}) + \beta_3 \text{HHI}_{m,t-1} + \Gamma \text{HH Controls}_{mb,t-1} + \Xi \text{Bank Controls}_{b,t-1} + \epsilon_{mbt}$$

The table notes report bank and MSA fixed effects and bank-clustered standard errors. The samples contain 39,785-40,381 bank-MSA-quarter observations depending on the outcome. Table 2’s 2000Q1-2019Q4 correlations are descriptive, with 0.346* versus 0.221* correlations between log WFR and the federal funds rate in competitive versus concentrated markets (p. 6).

Main mortgage pass-through and heterogeneity (Tables 4-7, pp. 9-13). Equation (1) is estimated for competitive and concentrated market samples (Table 4), then by non-ZLB/ZLB periods (Table 5), low/high federal-funds-rate regimes (Table 6), and contractionary/expansionary shocks (Table 7). The shock-direction split estimates separate terms for positive and negative policy shocks. Tables 4-7 use bank and MSA fixed effects and standard errors clustered by bank and quarter. The reported sample sizes are 24,539 and 30,527 in Table 4; 17,281, 22,473, 10,067, and 12,182 across Table 5; 16,888, 20,100, 10,454, and 14,554 across Table 6; and 28,295 or 33,710 in Table 7. The source reports the actual coefficients in R1-R5 and R9-R10.

Alternative policy-shock and funding measures (Table 8, p. 14). The baseline regression is re-estimated for the competitive and concentrated subsamples using liability-based WFR and the Jarociński-Karadi shock. It retains bank and MSA fixed effects and bank-quarter clustered standard errors; sample sizes range from 27,351 to 34,664. The alternative shock yields direct coefficients of 30.96*** and 8.75*** bps and interactions of -58.92*** and 2.08** bps in competitive and concentrated markets, respectively (R11). The liability-based WFR interaction estimates are 143.9* and 5.14 bps, the latter insignificant (R12).

Market-power and endogeneity specifications (Table 9, pp. 14-15). Table 9 estimates an expanded interaction model with HHI and WFR, and substitutes branch market share or the Lerner index. Its general HHI specification is:

$$\begin{aligned} \Delta r_{mbt} ={}& \alpha_b + \alpha_m + \beta_1 \Delta i_t + \beta_2 \log(\text{WFR}_{bt-1}) + \beta_3 \log(\text{WFR}_{bt-1})\times\Delta i_t \\ &+ \beta_4 \text{HHI}_{m,t-1} + \beta_5 \text{HHI}_{m,t-1}\times\Delta i_t + \beta_6 \text{HHI}_{m,t-1}\times\log(\text{WFR}_{bt-1}) \\ &+ \beta_7 \text{HHI}_{m,t-1}\times\Delta i_t\times\log(\text{WFR}_{bt-1}) + \Gamma \text{HH Controls}_{mb,t-1} \\ &+ \Xi \text{HH Controls}_{mb,t-1}\times\Delta i_t + \Pi \text{Bank Controls}_{b,t-1} + \Lambda \text{Bank Controls}_{b,t-1}\times\Delta i_t \\ &+ \Psi \text{Macro Controls}_{m,t-1} + \Omega \text{Macro Controls}_{m,t-1}\times\Delta i_t + \epsilon_{mbt} \end{aligned}$$

The HHI SD x shock x log(WFR) coefficient is -16.79*** (SE 3.92) in column 1 (R13). Column 2 uses second lags of HHI and WFR as instruments and reports HHI SD x shock = -9.14** (SE 3.99), as also recorded in R14. Columns 3-4 replace HHI with branch market share and the Lerner index. These specifications use bank and MSA fixed effects and bank-quarter clustered standard errors; N is 61,143, 25,324, 69,292, and 62,015 by column. The branch-share and Lerner coefficients are summarized in R6 and R15-R16. Their signs differ by proxy: branch-share interactions are positive, while the Lerner shock interaction is negative. The table’s notes list household, bank, and macro controls as in equation (1).

For column 3, the market-power interactions use branch market share in place of HHI:

$$\begin{aligned} \Delta r_{mbt} ={}& \alpha_b + \alpha_m + \beta_1 \Delta i_t + \beta_2 \log(\text{WFR}_{bt-1}) + \beta_3 \log(\text{WFR}_{bt-1})\times\Delta i_t \\ &+ \beta_4 \text{BranchShare}_{bt-1} + \beta_5 \text{BranchShare}_{bt-1}\times\Delta i_t + \beta_6 \text{BranchShare}_{bt-1}\times\log(\text{WFR}_{bt-1}) \\ &+ \beta_7 \text{BranchShare}_{bt-1}\times\Delta i_t\times\log(\text{WFR}_{bt-1}) + \Gamma \text{HH Controls}_{mbt-1} + \Xi \text{HH Controls}_{mbt-1}\times\Delta i_t \\ &+ \Pi \text{Bank Controls}_{bt-1} + \Lambda \text{Bank Controls}_{bt-1}\times\Delta i_t + \Psi \text{Macro Controls}_{mt-1} + \Omega \text{Macro Controls}_{mt-1}\times\Delta i_t + \epsilon_{mbt} \end{aligned}$$

Column 4 substitutes the Lerner index, retaining the same main effects, pairwise terms, triple interaction, and controls:

$$\begin{aligned} \Delta r_{mbt} ={}& \alpha_b + \alpha_m + \beta_1 \Delta i_t + \beta_2 \log(\text{WFR}_{bt-1}) + \beta_3 \log(\text{WFR}_{bt-1})\times\Delta i_t \\ &+ \beta_4 \text{Lerner}_{bt-1} + \beta_5 \text{Lerner}_{bt-1}\times\Delta i_t + \beta_6 \text{Lerner}_{bt-1}\times\log(\text{WFR}_{bt-1}) \\ &+ \beta_7 \text{Lerner}_{bt-1}\times\Delta i_t\times\log(\text{WFR}_{bt-1}) + \Gamma \text{HH Controls}_{mbt-1} + \Xi \text{HH Controls}_{mbt-1}\times\Delta i_t \\ &+ \Pi \text{Bank Controls}_{bt-1} + \Lambda \text{Bank Controls}_{bt-1}\times\Delta i_t + \Psi \text{Macro Controls}_{mt-1} + \Omega \text{Macro Controls}_{mt-1}\times\Delta i_t + \epsilon_{mbt} \end{aligned}$$

Datasets used

Dataset	Role in paper	Wiki page
Fannie Mae Single-Family Loan Performance Data	Loan-level mortgage rates, borrower characteristics (FICO, LTV, loan purpose, MSA)	No page yet
Freddie Mac Single-Family Loan-Level Dataset	Loan-level mortgage rates, originator identity	No page yet
FDIC Summary of Deposits (SOD)	Branch-level deposits; used to construct HHI for each bank-MSA-quarter	No page yet
Federal Reserve Call Reports	Bank-level wholesale funding, assets, liabilities, branch count (quarterly)	No page yet
HMDA (Home Mortgage Disclosure Act)	Loan-level origination data; used to construct mortgage market HHI for robustness	No page yet

Sample: 2000Q1 to 2019Q4. Working sample: 27 largest US banks with assets over $1 billion, approximately 40,000 bank-MSA-quarter observations. Mortgages restricted to 30-year fixed-rate single-family loans acquired by Fannie Mae and Freddie Mac (30-year, fully amortizing, full documentation, conventional).

When to read the full paper

Read the original if you are: studying heterogeneous monetary policy transmission across bank types (Tables 4-7 carry the full coefficient estimates); designing monetary policy that accounts for local banking market structure (the ZLB and asymmetric results in Tables 5-7 are particularly relevant to forward guidance); extending the baseline specification with additional controls or alternative periods; comparing with shadow bank transmission (see Enkhbold (2024) for the companion paper on traditional vs shadow banks); or replicating the deposit-market concentration and wholesale funding interaction documented by the paper.

Attribution and rights

Source: peer-reviewed, Journal of Banking and Finance 187 (2026) 107690. Crown Copyright 2026, published by Elsevier B.V. All rights reserved, including text and data mining. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. No CC licence is in effect; redistribution requires publisher permission. Reproduced here as extract-only commentary.

Enkhbold, Amina. “Monetary policy transmission, bank market power, and wholesale funding reliance.” Journal of Banking and Finance 187 (2026): 107690. DOI: 10.1016/j.jbankfin.2026.107690.