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, and hold across alternative market power measures including the Lerner index and branch market share.

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	Robustness: Lerner index and branch share confirm market power dampening	Table 9, pp. 15-16	Lerner x shock = -9.40*** bps; branch share x log(WFR) x shock (triple interaction) = +60.79*** bps; lagged HHI as IV yields -9.14** bps interaction

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 has no formal theoretical model. It builds on two prior mechanisms and documents a new interaction between them.

Deposit channel (Drechsler et al. (2017)). When the policy rate rises, banks with market power over local depositors need not match the rate increase: they hold the deposit spread wide and allow some depositors to leave without triggering mass outflows. This contracts aggregate deposits but does not immediately force pass-through to lending rates.

Wholesale funding substitution (Choi and Choi (2021)). As policy tightens, banks can replace contracting retail deposits with wholesale funding (repos, federal funds purchased, brokered deposits, time deposits from institutional investors). In concentrated markets, this substitution is cost-effective because raising deposit rates on a large base is expensive relative to borrowing wholesale at the margin. In competitive markets, thin markups force deposit repricing anyway, so wholesale funding adds cost pressure rather than providing insulation.

Central hypothesis. The two mechanisms interact: wholesale funding amplifies pass-through in competitive markets (funding costs track the policy rate closely) but dampens it in concentrated markets (market power absorbs wholesale cost increases into margins). The paper documents this interaction empirically across multiple policy-rate regimes.

Identification. The paper exploits cross-bank variation in HHI (measured from the FDIC Summary of Deposits) and WFR (from Call Reports), holding constant time trends via the monetary shock variable and location trends via MSA fixed effects. Bauer and Swanson (2023) high-frequency monetary surprises serve as the exogenous policy variable (see Method). Within each MSA, the identifying variation is cross-bank heterogeneity in market concentration and funding mix.

Method

The estimating equation (equation (1), p. 8) is a panel regression of loan-level mortgage rate changes on the monetary shock, the log wholesale funding ratio, and their interaction, with bank and MSA fixed effects:

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

where $\Delta r_{mbt}$ is the change in the loan-level mortgage rate at MSA $m$, bank $b$, quarter $t$; $\alpha_b$ is a bank fixed effect; $\alpha_m$ is an MSA fixed effect; $\Delta_t$ is the Bauer and Swanson (2023) monetary shock normalized to a 100 bps impact; and $\text{WFR}_{bt-1} = \text{wholesale funding}_b / \text{retail deposits}_b$ at quarter $t-1$. HH controls (interacted with the shock) include the borrower’s credit score, LTV, and debt-to-income ratio. Bank controls include number of branches, liquidity asset ratio, duration mismatch, liability interest rate, real estate loans ratio, commercial and industrial loans ratio, and MBS-to-asset ratio. Macro controls include the unemployment rate, house price index, and personal income per capita. Standard errors are clustered at the bank and quarter levels.

Policy shocks. The shock $\Delta_t$ aggregates changes in financial variables in a 30-minute window around FOMC announcements (10 minutes before to 20 minutes after), orthogonalized to Fed information effects following Bauer and Swanson (2023). An alternative sign-based decomposition from Jarocinski and Karadi (2020) is used in robustness checks (Table 8).

Market concentration. Local deposit market concentration in MSA $m$ at quarter $t$ is the Herfindahl-Hirschman Index constructed from the FDIC Summary of Deposits (p. 3):

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

where $dep_{mbt}$ is deposits of bank $b$ in MSA $m$ in year $t$. A lower HHI indicates a competitive market; a higher HHI indicates a concentrated market. The main results split the sample at the median HHI into competitive (HHI = 0) and concentrated (HHI = 1) subsamples.

The method builds on panel-regression with two-way fixed effects and instrumental-variables for the monetary shock. Robustness checks instrument for potential endogeneity of market structure using lagged HHI and WFR as instruments (Table 9, col 2; interaction coefficient -9.14** bps).

Empirical specifications

All headline results come from equation (1), estimated separately on competitive and concentrated subsamples. The focal coefficient is $\beta_3$, the interaction of log WFR and the monetary shock, capturing how wholesale funding reliance moderates policy pass-through.

Main pass-through (R1, R2; Table 4). The competitive subsample (HHI = 0, N = 24,539) yields a direct shock effect of 18.47*** bps (SE = 6.14) and a WFR interaction of 15.68* bps (SE = 8.69). The concentrated subsample (HHI = 1, N = 30,527) yields a baseline of 9.10 bps (SE = 11.05, insignificant) and a WFR interaction of -0.728** bps (SE = 0.318). For a $300,000 mortgage the 35 bps gap translates to approximately $67 per month.

ZLB vs non-ZLB (R3, R4; Table 5). The same regression is estimated separately for the ZLB period (2009-2015, cols 3-4) and non-ZLB periods (cols 1-2). The deposit floor constraint pins deposit rates at zero in competitive markets, removing the margin buffer and raising competitive pass-through to 38.80*** bps. In concentrated markets, forward guidance and quantitative easing compress long-term premia while market power prevents pass-through of cheap funding to borrowers, producing a large negative coefficient of -21.18*** bps.

Low vs high policy rates (Table 6). Estimated separately for low and high rate periods (split at the median federal funds rate). In competitive markets, the WFR interaction is largest at low rates (25.77*** bps) and remains large at high rates (18.37*** bps). In concentrated markets the baseline is positive at low rates (32.07*** bps) but deeply negative at high rates (-16.99*** bps), reflecting that banks at low rates lower mortgage rates to expand lending volume while using their pricing power to avoid fully passing the cost saving to borrowers at high rates.

Contractionary vs expansionary shocks (R5; Table 7). The sample is split into expansionary and contractionary periods. Within each, the model estimates separate coefficients for positive shocks ($\Delta_t > 0$, rate hikes) and negative shocks ($\Delta_t < 0$, rate cuts). Contractionary shocks produce 29.66*** bps pass-through in competitive markets (concentrated: 8.73***) with WFR adding 16.03*** bps in competitive markets. Expansionary shocks produce only 6.86*** bps in competitive markets and an insignificant -3.17 bps in concentrated markets, consistent with banks in concentrated markets rebuilding margins during easing.

Alternative market power measures (R6; Tables 8-9). The specification is replicated with (i) liability-side wholesale funding share, (ii) branch market share, and (iii) the Lerner index as market power proxies. The Lerner index specification (Table 9, col 4) yields a direct interaction of -9.40*** bps, providing the most direct evidence that price-setting ability, not just market concentration, dampens transmission. Branch share produces a positive triple interaction of +60.79*** bps (col 3; this is the Δt × Branch share × log(WFR) coefficient; the simpler Δt × Branch share = 73.35** bps), showing that physical presence amplifies rather than dampens transmission, distinct from market power measured by HHI or Lerner.

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.