Do Large Banks Really Have Less Market Power?∗

Jason Lee†

September 2021

Abstract

Out of more than �ve thousand banks in the U.S., the four largest banks have a combinedmarket share of 35 percent of outstanding deposits. Yet, the way market power is typicallymeasured implies they have less than median levels of market power. What explains thiscounter-intuitive result? This paper shows that this puzzle stems from the way we commonlymeasure market power, which relies on the degree of local market concentration. By applyingthe Lerner index directly to deposit products, where bank deposit market power is measuredby the gap between insured deposit rates and risk-free rates, I �nd that the concentrationapproach is critically inaccurate. On average, the concentration measure predicts marketpower negatively in the cross-section. Examining bank mergers also con�rms that changesin local concentration have minimal impact in determining bank market power. All in all,assessing bank deposit market power can, and should, be based on the price informationdirectly rather than from market concentration.

∗I am indebted to Isil Erel (Chair), Jack Liebersohn, Victor Lyonnet, Itzhak Ben-David, and René Stulz for theirguidance and support throughout my PhD at The Ohio State University. I’m also grateful to Mark Johnson, ByungwookKim, Minsu Ko, Eugenia Lee, and participants of the OSU PhD seminar for helpful conversations. All errors are myown.

†The Ohio State University. Email: lee.7861@osu.edu.

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1 Introduction

Concentration is a staple measure used to assess bank market power. Yet, its implication leads

to an intriguing observation: Larger banks have less market power. Figure 1 portrays a clear

negative relationship between bank size and the concentration-based market power measure.

There are two potential explanations to this puzzling observation. First, contrary to our intuition,

large banks indeed have less market power. Second, the way we measure bank market power is

incorrect. The �ndings in this paper suggest that concentration is an inappropriate proxy for bank

deposit market power, and when measured correctly, large banks do have greater market power.

Economists and policymakers pay close attention to bank market power, the presence of which

has been shown to have real impacts to the economy in diverse ways. A greater degree of bank

market power leads to higher prices and lower quantities of its products, harming consumer

welfare (Maudos and de Guevara 2007; Dick 2008). Moreover, as commercial banks are one

of the main pillars of the �nancial system, bank market power has important macroeconomic

implications. Previous studies have documented the relationship between bank market power and

the stability of the �nancial system,1 and more recent literature has focused on banks’ market

power as one of the important drivers of the monetary policy transmission (Scharfstein and

Sunderam 2016; Drechsler, Savov, and Schnabl 2017; Wang et al. 2020).

Needless to say, accurate measurement of market power is a prerequisite for understanding

the e�ects of market power. Lerner (1934) elegantly demonstrates that, in theory, monopoly power

can be represented by the ratio of the divergence of price from marginal cost to price, commonly

referred to as the Lerner index. In practice, however, calculating the Lerner index from actual

data is an elusive task typically due to unobserved marginal cost. Economists have developed

various tools to get around this obstacle. Using concentration was one of those ways, which has

been discarded by the IO literature but still is the main approach in banking. Other tools include1There are two competing views. Under the competition-fragility view, bank competition encourages excessive

risk-taking, leading to �nancial fragility (Marcus 1984; Keeley 1990; Demsetz, Saidenberg, and Strahan 1996; Hellmann,Murdock, and Stiglitz 2000; Jiménez, Lopez, and Saurina 2013). On the other hand, the competition-stability viewargues that lack of competition leads to higher interest rates charged to the borrowers and causes moral hazardproblems (Boyd and De Nicoló 2005; Boyd, De Nicoló, and Jalal 2006; Carlson and Mitchener 2006).

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backing out product markup from a structural estimation of the demand system.

Using the special nature of commercial banks, the approach in this paper di�ers from others

in that the Lerner index is directly calculated without a complex estimation. The main idea is to

measure the Lerner index for the core bank products individually. In theory, market power is a

product-level concept, in which price and marginal cost are for that speci�c product. In that sense,

instead of measuring bank market power as a whole, the spread between loan and deposit interest

rates can be thought of as the combination of the loan markup and the deposit markup, which I

measure separately.

Using interest rate data from RateWatch, I calculate the Lerner index for savings and time

deposits; and conforming mortgage loans at the bank branch level. I �nd that the Lerner index for

banks has a sizable cross-sectional variation that is consistent with other proxies of bank market

power. Banks with high Lerner index set their interest rates less sensitive to changes in the federal

funds rate, and are larger in size. These cross-sectional relationships are strong and consistent

over time.2

A key advantage of the Lerner index over the concentration approach is that it can be measured

at the branch level, and hence does not involve de�ning a relevant market. This is crucial in at

least three ways. First, the Lerner index can account for the di�erences in the degrees of market

power among banks in the same market. I show that within-county variation in the Lerner index

is signi�cant and, in fact, much greater than its variation across counties. The concentration

approach, on the other hand, can only convey information about a market as a whole—in whatever

way a market is de�ned, which is only sensible if all banks in a given market have similar levels

of market power.

Second, with the technological development, a large part of bank operations is being shifted

online, for which de�ning a physical market is becoming harder and less relevant. For example,

online banks are signi�cantly growing their market presence, and it is futile—and will lead to an

inaccurate observation—to try to assign a geographic market to an online bank. For this reason,2An exception is the Zero Lower Bound period, in which the interest rate pass-through mechanically becomes

unreliable with no variation in the federal funds rate. Size is still strongly correlated in the ZLB period.

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the concentration approach is incapable of assessing online banks. In contrast, because the Lerner

index only requires information on price, it is easily applicable to online banks and services.

Lastly, it is free from the fundamental problem of market de�nition based on geographic

regions. What is the relevant size of a banking market? Is bank competition local, global, or

both? These questions need to be answered for accurately de�ning a market. In addition, market

de�nition involves somewhat arbitray borders. Two banks right across a boundary of a banking

market will directly compete against each other for a household living right on the borderline;

this type of competition cannot be accounted for by the market-based approach.

In addition to the methodological disadvantages, the concentration approach lacks empirical

accuracy. Having a reliable measure allows us to test the empirical performance of the HHI as

a measure of bank market power. The HHI approach has been most commonly used due to the

ease of implementation, but is criticized for its weak conceptual correspondence with market

power (Bresnahan 1989; Hausman and Sidak 2007; Berry, Gaynor, and Morton 2019; Syverson

2019; Eeckhout 2020). Nevertheless, banking literature and regulators still rely heavily on the

degree of local market concentration as a proxy for bank market power. Therefore, examining the

validity of the current approach is fundamentally important and has real-world implications.

Consistent with the concerns, I provide strong evidence against the reliability of the HHI. The

results are striking in that the HHI is strongly correlated with the Lerner index, but in the opposite

direction. Banks in more concentrated areas—which are predicted to have a greater degree of

market power according to the HHI—have on average lower Lerner index. Similarly, the HHI has

a strong negative correlation with bank size. The HHI would imply that larger banks have less

market power on average. These �ndings suggest that the HHI is more problematic than just

being a noisy representation of market power.

I turn to bank M&A events. M&A results con�rm the lack of grounds for the HHI approach.

This is particularly important because bank antitrust regulators rely on the changes in the HHI

to assess the M&A impact on market power.3 The Lerner index approach reveals that even3The current regulatory approach is summarized in the overview of the Bank Merger Competitive Review

(https://www.justice.gov/atr/bank-merger-competitive-review-introduction-and-overview-1995).

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though the M&As have a large impact on market power, the impact is not driven by the changes in

concentration. I show this in two ways. First, the changes in the Lerner index of the acquiring bank

branches are almost identical across regions regardless of the di�erential changes in local market

concentration. Even for the target branches in counties where there was no overlap between

the acquirer and the target pre-merger—hence expected to have no change in concentration—the

changes in the Lerner index are almost equal to branches in overlapping counties. Second, I show

that following an acquisition that increases concentration, other banks in the same counties where

acquiring bank branches have a presence do not see an increase in their Lerner index up to three

years after the event.

This paper relates to several strands of literature. First, I contribute to the studies of under-

standing bank market power by providing a reliable method of assessing bank market power

applying the Lerner index. Previous bank market power studies have relied on the HHI (Hannan

and Berger 1991; Petersen and Rajan 1995; Scharfstein and Sunderam 2016; Drechsler, Savov,

and Schnabl 2017), derive the Lerner index by estimating marginal costs with balance sheet data

(Berger, Klapper, and Turk-Ariss 2009; Carbó-Valverde, Rodríguez-Fernández, and Udell 2009;

Sha�er and Spierdijk 2020), or use structural estimation (Dick 2008; Egan, Hortaçsu, and Matvos

2017; Abrams 2019). Compared to the existing approaches, calculating the Lerner index for bank

products using product-level interest rates is preferable in that it is simple, straight to the theory,

and more importantly, measured without noise. The calculated Lerner index shows a strong

cross-sectional pattern that is consistent with interest rate pass-through and bank size.

Second, this paper contributes to the literature in the industrial organization particularly on

empirical studies on assessing �rm market power. Concentration measures such as the HHI have

been criticized as a proxy for �rm market power (Bresnahan 1989; Aghion et al. 2005; Hausman

and Sidak 2007; Syverson 2019; Eeckhout 2020). I con�rm the concerns empirically by showing

a strong deviation of the prediction of the HHI from bank market power. The IO literature has

largely moved away from using concentration and developed other sophisticated tools to estimate

the markup directly. One of the prominent approaches, for example, is a demand system structural

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estimation method developed by Berry, Levinsohn, and Pakes (1995) that has been used to study

the microeconomics of consumers and �rms in various industries, which has also been applied to

the banking market (Dick 2007; Dick 2008; Egan, Hortaçsu, and Matvos 2017; Egan, Lewellen, and

Sunderam 2018; Abrams 2019). My approach di�ers in that I directly calculate the markups without

resorting to statistical estimation by using the speci�city of the banking products. If available,

market power assessment should be made based on competitive benchmark price (Hausman and

Sidak 2007), and I use the property that the banking market is one of the few industries in which

competitive price is accessible.

Lastly, this paper is related to the studies of the e�ects of bank M&As on market power. As Erel

(2011) shows, the overall e�ect of bank M&As is complex and non-monotonic because mergers

result in both e�ciency gains and changes in market power. Looking at the changes in the Lerner

index focuses on the e�ect of bank M&As particularly on pricing power separate from e�ciency

gains. Consistent with Prager and Hannan (1998) and Kahn, Pennacchi, and Sopranzetti (2005),

I con�rm that M&As have an immediate and signi�cant impact on bank market power shown

by the increase in the Lerner index. The e�ect is persistent over time up to three years after

the merger event, which is di�erent from the results in Focarelli and Panetta (2003) who show

that the e�ect on prices is weakened in a longer horizon. Understanding the e�ects of M&As is

critical to establishing antitrust regulations. Liebersohn (2020) shows that the current regulatory

intervention—implemented by preventing too much increase in concentration—is e�ective. While

the empirical evidence in this paper suggests that the intervention is e�ective, the main e�ects

are unlikely driven by limiting the changes in concentration, but more because of the di�erences

in acquirer characteristics. The �ndings are closely related to Granja and Paixao (2019), who show

that banks set close to uniform prices across regions and that M&As result in convergence in

prices.

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2 Measuring bank market power

Predictions of market power theory have been empirically tested for numerous countries and

industries. The fundamental challenge in every empirical study of market power arises from the

fact that reliably measuring market power is di�cult. There is no consensus on a single simple

measure that adequately captures market power. This section brie�y reviews common empirical

measures of market power, with an emphasis on two particular measures: Concentration and the

Lerner index. I start by describing how the two measures are motivated, derived, and criticized in

the IO literature. Afterward, I discuss the applications in the banking literature and the approach

used in this paper.

2.1 Applications to banking research

Banks are intermediaries that primarily earn interest spread between lending and deposit rates.

Unlike non-�nancial �rms who typically have market power in the product market, banks are

special in that they also have signi�cant market power in the funding (deposit) market. Looking

at banks as an intermediary earning interest spread, bank operations are pro�table if they either

borrow at a low cost (low deposit rates) or lend at a high rate (high lending rates).

To this end, it is important to note that bank market power can be measured in two ways. One

approach is to measure market power at the bank-level as a whole. This is typically done by directly

estimating markups between loan rates and marginal funding rate. The other approach is to divide

bank market power into loan and deposit market powers. Loan market power can be de�ned as

the ability to raise the interest rates charged to the borrowers beyond the risk-adjusted return.

Similarly, deposit market power is the ability to pay lower deposit rates than the corresponding

competitive market rates, generally proxied by the Fed funds rate or T-bill rates depending on the

maturity of the deposit product.

Banking studies have commonly used concentration as a measure of market power. The

use of HHI goes back to the paper Hannan and Berger (1991). Antitrust regulators also rely on

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concentration to assess changes in banking market competition. A typical way to de�ne a banking

market is by geographic regions, usually at the county-level or MSA-level. HHI is calculated

with market shares based on deposits or lending within the market. Bank branches in more

concentrated areas—with high HHI—are thought to have greater market power.

However, the criticisms against concentration as a proxy for market power apply also to banks.

A critical issue in measuring concentration is de�ning a relevant market. Is a county or MSA a

close representation of a banking market? Is bank competition local or national? Answers to these

questions are unclear and potentially vary over time. It may be innocuous to assume that banks

in the past competed locally until the 1980s. Regulations restricted banks from expanding across

states, so they naturally competed locally. Concentration perhaps captures the market condition

pretty well in this environment.

However, notable changes made bank competition more complex. First, a series of banking

deregulation in the 1990s including the Riegle-Neal Act of 1994 made banks possible to operate

in multiple states. National bank charters appeared who are nowadays the key players of the

market, with the largest four banks accounting for more than 40% of the aggregate market. Large

national banks are known to operate di�erently than small regional banks (Stein, 2002). Also,

evidence shows that banks charge close to uniform rates across regions (Granja and Paixão, 2019).

Therefore, measuring local market concentration may correlate with the market power of local

banks at best, but its relationship with multi-market banks needs to be cautiously interpreted.

Second, technological progress has signi�cantly changed how banks and customers interact.

Traditionally, banking services—such as opening a deposit account, withdrawing cash, and apply-

ing for loans—were o�ered through visiting physical branches. Similarly, shopping around town

to get the best rates required time and e�ort. However, the advent of the internet changed the

geography of banks. More and more banking services shifted online. Consequently, reliance on

physical bank branches has sharply declined. FinTech �rms and online direct banks emerged and

they do not �t in the traditional way of bank competition. The trend has tremendously accelerated

with the supply of smartphones and apps since the 2010s. Customers nowadays can easily search

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for the best rates in their hands.

Lastly, de�ning a market based on a geographical region leaves branches near borders in

tricky positions. Consider two bank branches just across the street but in di�erent counties. For a

household residing on the street, the two banks will likely compete against each other directly

for the customer. The two branches are perhaps closer competitors against each other than other

branches on the other side of their corresponding counties. Geographic market de�nition cannot

be free from these problems.

These properties cast doubt on using concentration measures. Bank competition is moving

away further from the textbook Cournot competition. Accurately de�ning a banking market

has become extremely di�cult. Bank competition is now a mix of local, national, and online

competition. No one way of dividing geographic regions will accurately depict the banking market.

The Lerner index or markups are also used in banking studies (Berger et al. (2009), Carbó-

Valverde et al. (2009), Coccorese (2014), and Egan et al. (2017)). Marginal revenue and cost are

not directly observed, thus are estimated. Perhaps the reason this approach hasn’t been the main

method of measuring market power is that there is no consensus on what the right speci�cation

is. Also, estimations always induce errors. Without knowing the signi�cance or distribution of

the error, the inference becomes di�cult.

This paper proposes a simple deposit Lerner index as an alternative measure. The key idea is

to independently look at the deposit market power, which is the ability to raise deposits at a rate

lower than the market risk-free rate. Bank operation in the deposit market can be thought of as

borrowing at the deposit rate and lending at the Fed funds rate. The lending rate (the Fed funds

rate) is competitively set by the market—banks are price takers—but the deposit rate is a choice

variable that can vary across banks. To illustrate the idea, let the deposit pro�t function of bank i

be Πi = (1 +FFR) ∗Di − (1 + rDi ) ∗Di −C , where Di is the quantity of the deposit supply, rD

i

is the deposit rate, and C is the �xed cost of maintaining deposit franchise. Pro�t maximizing

implies the deposit Lerner index L = (FFR − rDi )/(1 + rD

i ) = ∂rDi /∂Di ∗ Di/(1 + rD

i ). The

right-hand side is equal to the inverse of the price elasticity of deposit demand.

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This approach gives several critical advantages over other measures. The �rst and obvious

is the simplicity. It is easy to calculate without any estimation noise. The usual concern for

using the Lerner index is accurately backing out marginal cost from the data. However, banks are

interest-spread earners; if we know the interest rates on loans (FFR) and deposits (rDi ), we can

accurately obtain markup, which eliminates the biggest empirical challenge of using the Lerner

index.

Second, calculating the deposit Lerner index does not require a market de�nition, which is

the biggest empirical challenge of concentration measures. Regardless of the location of the bank

branches, the Lerner index is calculated without specifying competitors or calculating market

shares as long as interest rates are observed. This allows us to assess the market power of not

only traditional brick-and-mortar commercial banks but also FinTech or online direct banks that

have no physical branch, to which market concentration based on the location does not apply.

Moreover, it can easily be obtained at the branch-level or aggregated at the bank-level, instead of

the county- or MSA-level, which allows more straightforward comparison across banks.

Third, by focusing on the deposit market, the measure is free from the potential error coming

from risk adjustment. Comparing an interest rate to a competitive benchmark rate involves

evaluating the riskiness of the borrower or the lender. For example, if evaluating loan market

power, it will be incorrect to calculate the markup by simply taking the di�erence between the

loan rate and the risk-free rate, because the di�erence includes compensation for risks as well

as the markup. The correct way should use risk-adjusted interest rates, obtaining which again

involves setting up the right model and induces estimation noise. Using deposits goes around this

issue if interest rates for insured deposits are used. Deposits are practically risk-free under the

FDIC deposit insurance; we have a set of reliable benchmarks for short-term risk-free rates such

as FFR and T-bill rates. Therefore, taking the di�erence between deposit and the market risk-free

rates is relatively harmless.

Lastly, the Lerner index has a closer association with market power conceptually than concen-

tration measures do. Provided both can be measured correctly without errors, the Lerner index

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captures market power more closely. It directly measures the gap between price and marginal

cost, which is the textbook de�nition of market power. On the other hand, concentration has

limited implications on market power, and in some cases even has a relation contradicting the

result predicted by the theory. Theoretically, the two measures may be consistent if markets are

well-de�ned in a Cournot-style competition. If banks operate within an island competing against

each other providing homogeneous goods, the Lerner index and concentration will line up and

proxy for bank market power well. As discussed previously, however, this is far from what the

banking market appears to be nowadays. In fact, as will be shown in the next section, the Lerner

index and HHI do not line up well empirically.

There are potential concerns for the deposit Lerner index. The deposit Lerner index assumes

that marginal cost is su�ciently represented by deposit rates and that costs of maintaining deposit

franchises are �xed costs. Managing deposit franchises is expensive (Begenau and Sta�ord (2019)).

A problem arises if a non-negligible part of the maintenance cost is variable costs and if such

variable costs vary across banks. The index will overestimate markups in those cases. A similar

concern is that markups may be necessary for banks to cover their �xed costs. A bank cannot

pay deposit rates too high if overall pro�t becomes negative due to high a �xed cost. In this

circumstance, markup does not necessarily indicate bank market power.

One way of addressing these concerns is to use the CD rates, instead of transactional deposit

rates. Transactional deposits—such as checking and savings accounts—may incur more variable

costs associated with the services provided. On the other hand, CD accounts are time deposits

that have minimal, if any, service attached to the product. Outside of �xed costs, it is hard

to imagine there is a signi�cant marginal cost other than deposit rates for CD accounts. The

empirical analysis throughout this paper uses CD markups for this reason, but using savings

account markups provides similar results.

I complement the analysis by comparing the measures to the deposit rate pass-through, or

the sensitivity of deposit rates to the changes in the Fed funds rate. This is another proxy for

deposit market power. The Fed funds target rate changes over time according to the monetary

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policy. In a competitive market, deposit rates move one-to-one to the changes in the target rate.

For example, assume that the banking market is under perfect competition and all banks have

the deposit Lerner index of zero. The index is maintained at zero if and only if deposit rates

track the Fed funds rate perfectly over time. More importantly, this argument applies even when

the deposit Lerner index is positive due to the presence of marginal maintenance costs beyond

deposit rates or high �xed costs. The underlying assumption is that the operating costs—both

�xed and variable—for maintaining the deposit franchise are una�ected by the changes in the Fed

funds target rate. In other words, the deposit Lerner index may not capture bank market power

if the interest rate pass-through is close to zero and uncorrelated with the deposit Lerner index.

However, I show that the two measures line up well: Banks with a high Lerner index have low

deposit rate sensitivity to the changes in the Fed funds rate, con�rming that the index is a good

proxy for market power.

3 Data Sources

I create a panel data set of branch-level deposit rates for the empirical analysis. This section

describes the data sources and how the main variables are constructed. A detailed description of

the data merging process is provided in the Appendix.

3.1 RateWatch

The data on deposit rates comes from RateWatch. RateWatch surveys weekly �nancial institutions

on the deposit rates o�ered. The survey includes a large array of deposit products to retail and

business customers. The main deposit products I use are 12-month CD accounts and small savings

accounts as they are the most commonly o�ered deposit products widely available across banks.

The reports are at the bank branch level, allowing for a granular comparison both within and

across banks and regions.

Banks commonly have rate-setting branches that set deposit rates for other branches nearby.

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The coverage of rate-setting branches varies across banks and often spans multiple counties.

Since the variation in county characteristics such as HHI is crucial for the analysis, I include both

rate-setting and non-rate-setting branches, essentially treating each branch as if it sets its own

deposit rates. The deposit rate data start from 1997, although the �rst few years are scarcely

populated.

3.2 Report of Condition and Income (Call Reports)

Bank-level balance sheet data come from the Report of Condition and Income, commonly referred

to as the Call report.

3.3 FDIC Summary of Deposits

I collect branch-level deposit quantities from the FDIC Summary of Deposits (SOD) database. Each

FDIC insured banking institution reports deposit holdings as of June 30 at an annual frequency.

Branch deposit quantities are mainly used to calculate market concentration. All branches in SOD

are used in the calculation of market shares and HHI regardless of whether they are covered in

the RateWatch data to fully account for local market structures. Branch deposits are also used as a

weighting variable when aggregating branch-level data to bank- or county-level.

In addition to the calculation of HHI, I utilize the SOD data in two ways when constructing

bank M&A samples. First, I use the data to match the RateWatch branch-level data with bank Call

reports. RateWatch tracks changes in the ownership of branches, but the history goes back only

to the end of 2006. By using SOD as matching data, I can reliably extend the M&A sample to 1997,

the beginning of the RateWatch survey. Second, I use the data to determine whether acquiring

and target banks have an overlapping presence in each county pre-merger.

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3.4 National Information Center (NIC)

Bank mergers and acquisitions data are obtained from the National Information Center. The

dataset contains bank identi�ers of the acquirer and targets, completion dates, and types of

acquisition.

3.5 Federal Reserve Economic Data (FRED)

Lastly, I obtain the fed funds rate and T-bill rate from Federal Reserve Economic Data (FRED). I

collect the 12-month T-bill rates to match the maturity of the CD rates I use.

4 Comparison of Bank Market Power Measures: Empirical

Analysis

The direct theoretical foundation of the Lerner index as a measure of �rm market power, as

discussed in Section 2, does not necessarily guarantee better empirical performance. This section

critically analyzes the empirical validities of both the Lerner index and HHI as measures of bank

deposit market power. I present three key results. First, I �nd that deposit HHI is in fact worse

than merely a noisy proxy of bank market power. When taken at face value, HHI prediction

points to the opposite direction of bank market power. Secondly, the deposit Lerner index is a

more consistent measure of market power. The two results are shown by using interest rate

pass-through and bank size as additional benchmarks. Lastly, I show that bank market power is

explained almost entirely at the bank-level, while county-level characteristics including HHI have

minimal explanatory power. Without accounting for any county characteristics, bank and time

�xed e�ects account for roughly 90% of the cross-sectional deposit rate variation. In other words,

multi-market banks do not di�erentiate deposit rates much across their branches regardless of the

branch location. At the same time, branches of di�erent banks within the same region pay deposit

rates with a sizable variation. The last �nding further raises the empirical concern of using the

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degree of local market concentration as a proxy for bank market power.

4.1 Variable De�nitions

The Lerner index: The deposit Lerner index is de�ned as Lit = (Tbillt − rDit )/(1 + rD

it ), where

Tbillt is the 12-month T-bill rate at time t and rDit is the 12-month $10K CD rate of bank branch i

at time t. To match the maturity of the benchmark rates, when calculating the Lerner index for

savings products, I use the fed funds rate instead of the T-bill rate. A positive gap between the

FFR and deposit rate indicates a bank’s ability to pay deposit rates lower than the market rate.

Therefore, the Lerner index positively predicts the degree of bank market power.

HHI: HHI is de�ned as the sum of squared market shares: HHIct = ΣNb s

2bct, where sbct is

the market share of bank b in county c at time t, and N is the number of banks in the county.

Following the convention in banking literature, I de�ne county as the relevant banking market and

calculate the market shares of banks within each county. Banks in a more concentrated market

(high HHI) are predicted to have more market power than those in less concentrated markets (low

HHI). Therefore, HHI and market power are expected to have a positive relationship.

4.2 Alternative Benchmarks

I corroborate the comparison with two additional independent measures of market power: interest

rate pass-through and bank size.

Interest rate pass-through: Interest rate pass-through measures how sensitive deposit rates

move along with changes in the fed funds rate. Similar to the Lerner index, the pass-through has

a theoretical foundation directly associated with the pricing power of banks. In a competitive

market in which banks have minimal market power, changes in the FFR cause bank deposit rates

to respond close to one-to-one—such that the Lerner index remains at the same low level. Thus,

the (in)sensitivity of deposit rates gives us another valid estimate of bank deposit market power.

Unlike other measures, pass-through has a negative relation with market power: Lower sensitivity

corresponds to greater market power.

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The sensitivity βi for branch i is estimated at the branch level by running the following

regression at the end of each year using the past 5 years (20 quarters) of data:

∆rDit = αi + βi∆FFRit + εit, (1)

where ∆rDit is the changes in the deposit rate of branch i from quarters t− 1 to t and ∆FFRit is

the change in the fed funds rate from quarters t− 1 to t.

Using interest rate pass-through has one notable advantage that addresses the previously

discussed concern of the deposit Lerner index, which is the e�ect of potentially omitted marginal

revenues and costs. Suppose a bank with minimal market power has sizable marginal costs outside

of the deposit rate that I do not observe. The calculated deposit Lerner index for the bank will

be low even though the bank has minimal market power. This is because the large gap between

the FFR and deposit rate is not from the bank exerting market power but due to the unobserved

marginal costs. The Lerner index is biased in this case. However, the bank’s interest rate pass-

through will be close to one, correctly indicating that the bank has minimal market power. This is

assuming that the omitted marginal revenues and costs, such as fees and services, are una�ected

by the changes in the FFR. On that account, using interest rate pass-through further substantiates

the analysis.

Despite its conceptual advantage, it should also be noted that the pass-through measure has

empirical challenges. A sensitivity measure conveys more information when there is enough

variation in the time-series of the independent variable. However, interest rate pass-through

relies on infrequent changes in the target fed funds rate. This is a major problem for the measure

especially during 2009–2015 when the FFR remained constant at the zero lower bound. Also,

estimation—as opposed to calculations in cases of the Lerner index and HHI—induces noise and

can be sensitive to the speci�cation. These properties make pass-through di�cult to be used as

the primary measure of market power. For that reason, when used as a benchmark, I focus on

periods with su�cient variation in the FFR.4

4These concerns only apply to the interest rate pass-through measure. Other measures (the Lerner index, HHI,

16

Size: I use the logarithm of bank asset size as another benchmark of market power. Although

there is no direct theoretical association between size and market power, in the cross-section

bank size is likely to be positively correlated with market power for various reasons. First, bank

market shares are calculated with outstanding deposit amounts,5 which are highly correlated

with bank asset size with a correlation coe�cient greater than 95%. Bank size, therefore, is in

relative terms roughly equivalent to its market share in each quarter. Regardless of the direction

of causality—whether market share leads to market power or vice versa—a higher market share

would correspond positively to a greater �rm market power.

Secondly, previous research documents depositors’ preference for large banks. Dick (2008)

estimates a structural demand model for bank deposit markets and—although size is not the

primary focus—�nds that depositors strongly prefer large banks even after controlling for other

characteristics such as deposit rates, age, and branch network. We can infer from the �nding

that bank size would positively correlate with market power, absent perfect competition. Again,

whether it is a preference directly for bank size or for some omitted variables correlated with the

size is less of a concern; a strong correlation with market power is important as an additional

benchmark.

Lastly, bank size relates to �nancial frictions. Large banks on average are known to have better

access to alternative funding sources (Kashyap and Stein 1995; Kashyap and Stein 2000; Williams

2020), while small banks have to rely more heavily on core deposits. The �exibility of funding

sources adds to banks’ deposit market power. When core deposits become more expensive relative

to other funding sources, small banks have no choice but to take the increased costs, while large

banks can shift some of their deposits to others. It will be a negative shock to all banks, but large

banks have a better capacity to absorb the shock and maintain their deposit market power.

and size) are fairly stable over time.5In general, a more commonly used variable to calculate market shares is revenues.

17

4.3 Descriptive Statistics

Table 1 shows the descriptive statistics. The last column indicates the four main variable’s

predicted relation to market power; only FFR pass-through has a negative sign. FFR pass-through

is winsorized at the 1st and 99th percentiles to account for estimation noise.

4.4 Cross-sectional Comparison

I start the cross-sectional analysis by showing that the Lerner index and HHI exhibit a strongly

negative relationship against each other. That is, a bank with a large degree of market power

suggested by one measure is in fact more likely to have a small degree of market power according

to the other. The two measures are then compared to bank size and interest rate pass-through to

further validate the results. I �nd that all but HHI show consistent predictions with each other.

Comparisons are performed at two di�erent levels: county- and bank-level. This is to account

for the di�erence in the levels at which the Lerner index and HHI are calculated; the Lerner index

can be calculated at any level as granular as branch-level, while HHI is measured only at the

market-level such as counties.

4.4.1 County-level

For the county-level analysis, the Lerner index, pass-through, and bank size are �rst obtained at

the branch level then aggregated to the county level weighted by branch deposits. The measures

represent the average bank market power in each county. Figure 2 provides a visual comparison

of measures at the county-level as of 2017.6 Each panel of Figure 2 portrays the geographic

distribution of the market power measures, with a darker color corresponding to a greater market

power suggested by each measure.

In general, the plots display a strong, systematic divergence across regions, which can be

roughly summarized as a stark contrast between urban and rural areas. For example, concentration6As discussed in Section 4.2, using di�erent quarters gives similar results for all but the pass-through measure,

which requires variation in the FFR in previous quarters. Results are consistent over time except during zero lowerbound period for the same reason.

18

is higher in rural areas and lower in urban areas. This is not so surprising given that the urban

areas attract a greater number of banks competing for a larger pool of depositors. HHI suggests

that the banks in urban areas have lower degrees of market power than those in rural areas.

It is what other measures tell us about market power that is surprising. The cross-sectional

distributions of the Lerner index, pass-through, and bank size look surprisingly similar to each

other, and they point to the exact opposite of what HHI indicates. The Lerner index is on average

higher in the coastal urban areas. Depositors in the urban areas face on average lower deposit

rates. Bank size and interest rate pass-through further validate the Lerner index; in areas where

the Lerner index is high, banks are larger and interest rate pass-through is low. Table 2 shows

the correlation matrix of the measures. Correlation coe�cients across all measures are highly

signi�cant and con�rm the observation from Figure 2, with HHI having signi�cantly opposite

implications.

The results critically cast doubt on the reliability of using HHI as a proxy for bank market

power. If taken at face value, HHI will lead to an opposite assessment of market power, which is

much more concerning than merely being a noisy or weakly correlated proxy.

4.4.2 Bank-level

Next, I perform a similar analysis by aggregating measures at the bank-level. For HHI, I follow

Drechsler, Savov, and Schnabl (2017) and calculate bank-level HHI. Bank-HHI is calculated as

the deposit weighted average of the HHI of the county each branch is located in. The idea is to

capture the average concentration level each bank faces. A high bank-HHI indicates that the bank

operates on average in concentrated markets, and is expected to have a greater market power

than a bank with a low bank-HHI.

I �rst focus on the relationship between the Lerner index and HHI. Figure 3 shows a binned

scatterplot between the two measures. This is a cross-sectional comparison at the bank-level, with

quarter �xed e�ects included. Similar to the results in Section 4.4.1, the two measures show a

strong negative relationship. Banks that pay low deposit rates are more likely to operate in less

19

concentrated counties on average, diverging to the opposite direction in their assessment of bank

market power.

Figure 4 displays pairwise binned scatterplots using alternative benchmarks. Each analysis

again accounts for quarter �xed e�ects. The Lerner index again perfectly lines up with the two

benchmark measures, while HHI has a strongly negative relation with bank size and no clear

relation with the pass-through measure. HHI-based assessment will likely overestimate the market

power of small regional banks operating in rural areas that actually pay more competitive deposit

rates and underestimate the market power of large banks in urban areas that are more likely

o�ering lower deposit rates.

4.5 Determinants of Deposit Market Power: Bank Characteristics

vs. County Characteristics

What explains the strong divergence between HHI and the Lerner index? Does concentration

actually cause less market power? In this section, I further look into the cross-sectional distribution

of market power at the branch-level. I �nd that market power is almost entirely determined at

the bank-level, while di�erences in county-level characteristics have minimal explanatory power.

This suggests that HHI does not capture market power likely because 1) bank locations are chosen

endogenously, and 2) HHI is also endogenously determined, rather than having a direct negative

impact.

To test this, I compared the R2 of regressions using bank- and county-level variables and �xed

e�ects. Variables are time-demeaned in order to take out the explanatory power of simple time

e�ects. I run the following regression:

LDict = β0 + β1HHIct + β2log(assets)bt + γc + δb + εict, (2)

where LDict is the deposit Lerner index of branch i, HHIct is HHI of county c, log(assets)it is

logarithm of total assets of bank b, γc is county �xed e�ects, and δb is bank �xed e�ects.

20

The results are presented in Table 3. Column (1) is an univariate OLS regression with HHI as the

independent variable. The coe�cient is negative and strongly signi�cant. This is consistent with

the previous observations in Sections 4.4.1 and 4.4.2. Column (2) has bank size as the independent

variable and Column (3) includes both. It is worth noting that theR2 jumps from 0.5% to 21.5% with

the substitution of HHI with bank size, and adding back HHI does not seem to add explanatory

power shown by the same R2 as in Column (2). Columns (4)–(6) run similar analysis, but with

county and bank �xed e�ects. The pattern is identical. Lastly, Columns (7) and (8) include county-

quarter and bank-quarter �xed e�ects. This is analogous to running cross-sectional regressions

separately for each quarter. For a given quarter, 89% of the entire cross-sectional variation of

branch deposit rates is explained by across-bank variation.

5 M&A Impact on Bank Market Power

Mergers and acquisitions bring substantial shocks to bank market power, providing a great

laboratory to study its dynamics. The goal of this section is to utilize the Lerner index to study

how market power changes when two banks merge. The Lerner index allows us to directly

investigate the impact of bank M&As on market power.

The main approach is to examine how target bank branches respond after being acquired. There

are two reasons for focusing on targets. First, target banks’ responses are more pronounced and

easily observable as they are generally smaller in size than acquiring banks. Second, the responses

of acquiring banks are more di�cult to analyze because a bank can acquire multiple other banks

in a short period of time, which makes the identi�cation of the impact of a single merger event

challenging. Later, I look at the acquirers’ responses when utilizing branch divestitures [yet].

I present three main results. First, I show that the impact of M&As on target banks’ market

power is instant and large in magnitude. On average, target banks have lower degrees of market

power than acquiring banks. After being acquired, target banks’ deposit Lerner index signi�cantly

increase to the level of their acquirers’. Second, the increase in market power is signi�cant relative

21

to the other bank branches in the same counties where target bank branches are located in. Other

branches’ Lerner index remains �at after the events, which is surprising given that bank mergers

increase local market concentration. Lastly, I look into the cross-market di�erences within each

target bank and show that the increase in target bank market power is uniform across branches

regardless of their locations. In other words, county characteristics—including degrees of market

concentration—do not explain the impact of M&As on market power.

The �ndings throughout this paper cast doubt on the e�cacy of concentration as a measure

of bank market power, and the results from this section directly show that the M&A impact on

market power is mostly independent of the increase in market concentration. This raises concerns

against the rationale behind the current approach of bank antitrust regulation as it relies on

concentration measures as the main tool for assessing the impact of horizontal mergers. The

results in this section suggest that while mergers and acquisitions bring signi�cantly increased

market power to target banks, the e�ect is not from increased local market concentration but

from target banks’ market power converging to the levels of acquirers’ market power, which is

generally greater.

5.1 Sample Description

Figure 5 plots the number of acquisitions and average target bank asset size over time in the

sample. Data on merger events come from the National Information Center. I use M&A events

that arise from bank failures or result in target bank charter discontinuation, which are speci�ed

in the data.7 I exclude mergers of banks within the same bank holding company.

Table 4 shows descriptive statistics of the sample target branches pre-and post-merger. Branch-

level statistics show that the deposit rates of target bank branches decrease on average after being

acquired, signaling increases in deposit market power. However, changes in the Lerner index

seem to be smaller in degree for CDs than changes in deposit rates and even slightly negative for

savings deposits. This seemingly contradictory observation arises from the fact that the number7The remaining types of events are splits, sale of assets, and retained charter, which only account for less than

one percent of total events.

22

of M&A events is higher in periods with decreases in the FFR than periods with increases, shown

by the lower average levels of the FFR in the post-merger period.8 The di�erence-in-di�erences

speci�cation in this section identi�es the increase in target bank market power relative to the

changes in control banks’ market power over the same time window, and I show that both the CD

and savings Lerner index signi�cantly increase relative to the control samples.

5.2 Target vs. Acquirer

The �rst part of the analysis shows that the impact of M&A on deposit market power is instant

and persistent by looking at target bank deposit rates in relation to acquirer bank deposit rates

following the merger. To do this, I calculate for each target branch the contemporaneous di�erence

between its Lerner index and the average Lerner index of the acquirer,9 and see how the gap

evolves over time. To focus on the direct impact of M&As to target bank market power, the sample

for this speci�cation includes only the target bank branches. The event window is set to compare

target banks’ market power in the year before and three years after the merger event to account

for a long-term e�ect, following Focarelli and Panetta (2003). I run the following regression:

Gapit = β0 + β1Postit + γct + εit, (3)

where Gapit is the di�erence at quarter t between the average Lerner index of the acquirer and

the Lerner index of the target bank branch i, Postit is a dummy that indicates the quarters from

the completion date of the merger to three years afterwards, and γct accounts for the county by

quarter �xed e�ects. A positive value of Gapit indicates that the acquirer has a greater degree

of market power. Alternatively, I also use three separate dummies for each year following the

merger to examine the short- and long-term e�ects.8Because the denominator of the Lerner index (1 + r) is close to 1, changes in deposit rates and the Lerner index

should be similar in scale, but opposite in direction. However, this is only true if there is no change in the FFR. Whenthe FFR increases, for example, the Lerner index will increase even if deposit rates remain constant, so the changeswill deviate from each other.

9The calculation of the average Lerner index of the acquirer excludes target Lerner index (which becomes a partof the acquirer after the merger).

23

Table 5 presents the regression results. Columns (1)–(4) includes only the Post dummy and

(5)–(7) use three separate dummies for each year. The main di�erence across the columns is the in-

clusion of �xed e�ects at di�erent levels. The coe�cients are stable across all the speci�cations.On

average, acquiring banks had higher Lerner index pre-merger than target bank branches by around

0.1, as shown by the constant. After the target is acquired, the gap becomes negligeable. The

magnitude of the coe�cient on the Post dummy is almost equal to the pre-merger gap. Moreover,

columns (5)–(7) show that the impact is immediate from the �rst year and that it persists over

time.

Figure 6 provides a visual representation of the change in the gap. It plots the average gap of

all the target banks with time-demeaned observations. I �rst calculate the average e�ects for each

merger events and then aggregate the e�ects across all events. For both CD and savings deposits,

the e�ect is instant and persistent, consistent with the regression results in Table 5. Figure 7 also

includes similar results by plotting the Lerner index of acquirers and targets separately.

5.3 Target vs. Other Banks in the Same County

Next, I compare target bank deposit rates with control samples—other banks in the same county

where the merger occurs. Speci�cally, I test whether the target deposit Lerner index increases

relative to the control samples. If market power is determined at the bank-level, but less at the

county-level, it is possible that only the target banks’ market power signi�cantly increases. On

the other hand, if local market concentration is an important predictor of bank market power, all

banks in the a�ected counties will have similar changes in the degrees of market power due to the

increased concentration in the area. To test this, I run the following regression:

Lernerit = β0 + β1Treatit + β2Postit × Treatit + γct + εit, (4)

where Lernerit is the deposit Lerner index of branch i at time t, Treatit is an indicator variable

that is equal to one if branch i is acquired by another bank around time t [I will be more precise],

24

Postit is equal to one if t within three years after branch i acquisition, and γct accounts for the

county by quarter �xed e�ects.

The regression results are shown in Table 6. Coe�cients for the interaction between Post

and Treat dummies are always positive and signi�cant, meaning that the merger e�ect to the

target bank branches is signi�cantly greater compared to that to the other bank branches in the

same region. Similar to the results in Table 5, the e�ect is instant and persistent, as can be seen

in columns (4)–(5). This implies that not only the target banks have increased levels of market

power, but also that other banks do not see a direct impact even after three years from the event.

5.4 Target Branches with Large vs. Small Changes in Concentration

The last part explores whether the responses of target bank branches vary across regions. The

goal of this analysis is to test whether the observed increase in target bank market power is due

to a higher degree of concentration. I �nd that this is not the case.

To test the hypothesis, I compare among branches of the same target bank around M&A events.

I divide target bank branches in two di�erent ways. First, I calculate the increase in HHI following

the merger in the counties where target bank branches are in and divide target branches into

higher and lower than median changes in HHI. If concentration is the driving force, branches that

are in higher than median changes in HHI are expected to have a greater increase in the Lerner

index. For a balanced sample, I only use target banks with their branches in multiple counties and

only use strictly higher or lower than median branches. Second, I divide target branches by the

existence of acquiring bank branches in the same county. This is a well-identi�ed way of testing

the e�ects of concentration on market power because counties with no overlapping presence of

the target and the acquirer will not have any changes in the market structure after the merger.

For the second approach, I only use target banks that have both overlapping and non-overlapping

branches.

Figure 8 shows the results visually. Consistent with the previous results, the Lerner index of

the target bank branches converge to the acquirers’ Lerner index. However, the convergence is

25

equal for both overlapping and non-overlapping branches. Again, non-overlapping branches are

in counties where HHI is unchanged after the merger. The fact that their Lerner index increases

and that the increase is equal to that of the overlapping branches casts doubt on the argument

that concentration is the driving force of the increase in market power.

Table 7 presents the corresponding regression results. Columns (1)–(3) uses the �rst approach

and columns (4)–(6) uses the second approach.Columns (1) and (2) run the regressions separately

for each sample, and both the magnitude and signi�cance of the coe�cients look close to identical.

When regressed together with an interaction term, the result remains the same. The e�ect of

mergers on the Lerner index is the same as when run separately, and the coe�cients on the

interaction term is statistically not di�erent from zero and the magnitude is much smaller than

that of the coe�cient on Post.

6 Conclusion

This paper aims to �nd an empirically accurate method of assessing bank market power. Using

product-level interest rate data, I show that for commercial banks it is feasible to directly calculate

the Lerner index, a theoretical representation of market power. This intuitive approach is theoreti-

cally sound and easy to implement. Moreover, the resulting Lerner index is empirically consistent

with other proxies of bank market power such as interest rate passthrough and bank size. In turn,

I refute the validity of the widely used concentration approach using the Lerner index. HHI is an

empirically inaccurate proxy of bank market power; it has a strong negative relationship with the

Lerner index. Also, while bank mergers have signi�cant impact on market power—as shown by the

changes in the Lerner index—it is unlikely driven by the changing degree of market concentration.

All in all, commercial banks have unique properties that allow for measuring market power with

pricing information directly, and this approach is preferred to the concentration approach.

26

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30

Figure 1: Binned Scatterplot of Bank Asset Size and HHI

.16

.18

.2

.22

.24

Ba

nk−

HH

I

10 12 14 16Log(Assets)

Note: This �gure shows the relationship between logarithm of bank asset size and HHI usingbinned scatterplot. HHI is �rst calculated at the county level with branch deposits from SOD,then aggregated at the bank level weighted by branch deposit to create Bank-HHI, following thede�nition in Drechsler et al. (2017). Quarter �xed e�ects are included. Bank asset size is from theCall reports.

31

Figure 2: Distribution of Market Power Measures in 2017q4

Note: This �gure maps the geographical distribution of four bank market power measures as of2017 4th quarter. HHI is calculated at the county level directly. Other measures are �rst obtainedat the branch level, then aggregated to the county level weighted by branch deposits.

32

Figure 3: Binned Scatterplot of Lerner Index and HHI

.205

.21

.215

.22

.225h

hi

−1 −.5 0 .5 1

lerner_cd

Note: This �gure shows the relationship between the Lerner index and HHI using binned scatterplot.Variables are de�ned at the bank level. Bank-HHI is branch deposit weighted HHI, following thede�nition in Drechsler et al. (2017). The Lerner index is also averaged at the bank level weightedby branch deposits. Quarter �xed e�ects are included.

33

Figure 4: Pairwise Binned Scatterplots

ln(assets) (-)FFR passthrough

CD Lerner

−.1

0

.1

.2

10 12 14 16

−.2

−.1

0

.1

−1 −.8 −.6 −.4 −.2 0

HHI

.16

.18

.2

.22

.24

10 12 14 16

.2

.205

.21

.215

.22

.225

−1 −.8 −.6 −.4 −.2 0

Note: This �gure shows the pairwise relationships between the four measures (The Lerner index,HHI, bank size, and interest rate passthrough) using binned scatterplot. Variables are de�ned atthe bank level. Bank-HHI is branch deposit weighted HHI, following the de�nition in Drechsler etal. (2017). The Lerner index, size, and interest rate passthrough is also averaged at the bank levelweighted by branch deposits. Size is de�ned as natural logarithm of book assets. Quarter �xede�ects are included.

34

Figure 5: Merger Sample Distribution

0

20

40

60

Asse

ts (

in $

1B

)

0

40

80

120

160

200

Nu

mb

er

of

acq

uis

itio

ns

2000 2005 2010 2015Year

Number of acquisitions

Average asset size of target

Notes: This �gure plots the time trends of number of acquisitions and average asset size of targetbanks in each year from 1999 to 2017. Bank merger data come from National Information Center.Asset size is from the Call reports.

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Figure 6: Di�erences in Lerner Index between Target and Acquirer(a) CD Lerner Index

0

.02

.04

.06

.08

.1D

iffe

ren

ce

−4 0 4 8 12Event window (in quarters)

(Acquirer CD Lerner − Target CD Lerner)

(b) Savings Lerner Index

0

.01

.02

.03

.04

.05

.06

Diffe

ren

ce

−4 0 4 8 12Event window (in quarters)

(Acquirer Sav Lerner − Target Sav Lerner)

Notes: This �gure plots the evolution of the interest rate gap between target and acquiring bankbranches around the merger event. Variables are time-demeaned.

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Figure 7: Lerner Index Around Merger(a) CD Lerner Index

−.2

−.15

−.1

−.05

0C

D L

ern

er

−4 0 4 8 12Event window (in quarters)

Acquirer

Target

Control

(b) Savings Lerner Index

−.1

−.05

0

Sa

v L

ern

er

−4 0 4 8 12Event window (in quarters)

Acquirer

Target

Control

Notes: This �gure plots the evolution of the interest rate Lerner index of target, acquirer, andcontrol banks. Control samples are banks unrelated to the merger event but located in the samecounty in which the merger takes place. Variables are time-demeaned.

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Figure 8: Overlapping vs. Non-overlapping County(a) CD Lerner Index

−.15

−.1

−.05

0C

D L

ern

er

−4 0 4 8 12Event window (in quarters)

Acquirer

Target CD rate (overlap)

Target CD rate (no overlap)

(b) Savings Lerner Index

−.04

−.02

0

.02

.04

Sa

v L

ern

er

−4 0 4 8 12Event window (in quarters)

Acquirer

Target Sav rate (overlap)

Target Sav rate (no overlap)

Notes: This �gure plots the evolution of the interest rate Lerner index focusing on the comparisonbetween target branches with and without overlap pre-merger. A county is de�ned to have anoverlap when both target and acquirer branches are present before the merger. Variables aretime-demeaned.

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Table 1: Descriptive Statistics

count mean sd p10 p50 p90Branch-levelCD rate 8440077 1.65 1.64 0.10 1 4.35CD Lerner 8440077 0.21 0.82 -0.70 0.090 1.28Sav rate 8425277 0.50 0.70 0.0100 0.20 1.50Sav Lerner 8425277 1.31 1.60 -0.075 0.65 4.12FFR passthrough 7908349 0.43 0.35 0 0.51 0.79Branch deposits 5535468 81.3 1200.2 7.62 36.9 123.8HHI 8541279 0.18 0.11 0.089 0.15 0.31Bank-levelAssets 482371 1678.9 33010.5 36.0 138.0 811.3Deposits 482371 1047.8 18587.4 30.4 116.0 653.6

Notes: This table presents descriptive statistics of bank and branch characteristics. Interest ratesare from RateWatch. FFR passthrough is estimated using past �ve years data. HHI is calculatedat the county level with branch deposits from SOD. Bank balance sheet items are from the Callreports.

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Table 2: Correlation Matrix in 2017q4

CD Lerner (-)FFR passthrough log(assets) HHICD Lerner 1(-)FFR passthrough 0.57∗∗∗ 1log(assets) 0.58∗∗∗ 0.32∗∗∗ 1HHI -0.21∗∗∗ -0.035∗ -0.25∗∗∗ 1

Notes: This table presents a correlation matrix of four proxies of bank market power. The sign ofFFR pass-through is �ipped for an easier interpretation.

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Table 3: Bank vs. County

(1) (2) (3) (4) (5) (6) (7) (8)Lerner Lerner Lerner Lerner Lerner Lerner Lerner Lerner

HHI -24.6∗∗∗ 3.22∗∗∗

(1.94) (1.01)log(assets) 5.89∗∗∗ 5.91∗∗∗

(0.35) (0.35)County FE x xBank FE x xCnty#Time FE xBank#Time FE x

Observations 6058576 6058576 6058576 6058576 6058521 6058521 6046065 5961322Adjusted R2 0.005 0.215 0.215 0.111 0.433 0.436 0.233 0.891

Notes: This table estimates the predictive power of bank and county characteristics. The dependentvariable is the Lerner index using 12-month CDs. All speci�cations are run with time-demeanedvariables.

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Table 4: Merger Sample Description

pre-merger post-mergercount mean sd count mean sd

Branch-levelCD rate 88991 1.55 1.56 80403 1.30 1.46CD Lerner 88991 0.01 0.80 80403 0.16 0.73Sav rate 87676 0.39 0.55 79761 0.33 0.49Sav Lerner 87676 1.08 1.66 79761 1.06 1.52FFR 89497 1.53 2.03 80888 1.43 1.84HHI 89474 0.17 0.11 80854 0.17 0.11Bank-levelAssets 11160 1216.55 11934.49 9737 11206.55 64523.30Deposits 11160 861.20 7794.34 9737 6966.54 29087.43N branches 7580 14.89 84.59 9885 115.49 366.78N states 7580 1.20 0.88 9885 2.53 3.23N counties 7580 4.10 13.74 9885 26.26 64.41

Notes: This table presents merger sample descriptive statistics pre- and post-merger. Each mergersample covers one year (four quarters) before and after the event. Only target bank branches areincluded.

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Table 5: Gap between Target and Acquirer Lerner Index

(1) (2) (3) (4) (5) (6) (7)Gap Gap Gap Gap Gap Gap Gap

Post -0.07∗∗ -0.08∗∗ -0.12∗∗∗ -0.12∗∗∗

(0.03) (0.03) (0.04) (0.03)Post 1 Year -0.06∗∗ -0.12∗∗∗ -0.11∗∗∗

(0.03) (0.04) (0.03)Post 2 Year -0.08∗∗ -0.12∗∗∗ -0.12∗∗∗

(0.03) (0.04) (0.03)Post 3 Year -0.09∗∗∗ -0.14∗∗∗ -0.13∗∗∗

(0.03) (0.04) (0.03)Constant 0.09∗∗∗ 0.09∗∗∗ 0.13∗∗∗ 0.12∗∗∗ 0.09∗∗∗ 0.13∗∗∗ 0.12∗∗∗

(0.03) (0.02) (0.03) (0.02) (0.02) (0.03) (0.02)Quarter FE No Yes No No Yes No NoCnty × Qtr FE No No Yes Yes No Yes YesAcquirer FE No No No Yes No No YesObservations 252849 252849 228219 228203 252849 228219 228203Adjusted R2 0.014 0.078 0.516 0.615 0.079 0.517 0.615

Notes: This table estimates the changes in the deposit interest rate gap between the target andacquiring bank branches. The dependent variable gap is de�ned as the di�erence in deposit ratesbetween target branch and average acquiring bank branches. The event window covers one yearbefore and up to three years after the merger. Post in columns 1-4 is one if it is up to three yearsafter the merger. Columns 5-7 divide Post into three separate years.

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Table 6: Target vs. Control Samples

(1) (2) (3) (4) (5)CD Lerner CD Lerner CD Lerner CD Lerner CD Lerner

Treat -0.20∗∗∗ -0.08∗∗∗ -0.08∗∗∗ -0.08∗∗∗ -0.08∗∗∗

(0.06) (0.03) (0.02) (0.03) (0.02)Post × Treat 0.19∗∗∗ 0.10∗∗∗ 0.10∗∗∗

(0.07) (0.03) (0.03)Post 1 Year × Treat 0.09∗∗ 0.10∗∗∗

(0.04) (0.04)Post 2 Year × Treat 0.10∗∗∗ 0.11∗∗∗

(0.03) (0.03)Post 3 Year × Treat 0.09∗∗∗ 0.10∗∗∗

(0.02) (0.02)Constant 0.21∗∗∗ 0.21∗∗∗ 0.21∗∗∗ 0.21∗∗∗ 0.21∗∗∗

(0.08) (0.01) (0.00) (0.01) (0.00)Quarter FE No Yes No Yes NoCnty × Qtr FE No No Yes No YesObservations 5437198 5437198 5436773 5437198 5436773Adjusted R2 0.001 0.704 0.744 0.704 0.744

Notes: This table estimates the changes in the deposit Lerner index after being acquired. Treatedsamples are target branches and control samples are other bank branches unrelated to the mergerbut located in the same county. The event window covers one year before and up to three yearsafter the merger. Post in columns 1-3 is one if it is up to three years after the merger. Columns4-5 divide Post into three separate years.

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Table 7: High ∆HHI vs. Low ∆HHI and Overlap vs. Non-overlap

(1) (2) (3) (4) (5) (6)High ∆HHI Low ∆HHI Interaction Overlap Non-overlap Interaction

Post 0.18∗∗∗ 0.19∗∗∗ 0.19∗∗∗ 0.28∗∗∗ 0.25∗∗ 0.23∗∗∗

(0.05) (0.06) (0.05) (0.10) (0.10) (0.07)

High ∆HHI -0.02(0.02)

Post × High ∆HHI 0.02(0.03)

Overlap -0.05(0.03)

Post × Overlap 0.06(0.05)

Constant 0.07∗∗ 0.06 0.06∗ -0.07 0.02 0.01(0.03) (0.04) (0.04) (0.07) (0.07) (0.05)

Cnty × Qtr FE Yes Yes Yes Yes Yes Yes

Target FE Yes Yes Yes Yes Yes Yes

Observations 70037 72369 147229 52726 67693 122860Adjusted R2 0.972 0.964 0.956 0.965 0.973 0.955

Notes: This table estimates the changes in the deposit Lerner index after being acquired. It aims tocompare the e�ects relative to the changes in concentration levels following the merger. The eventwindow covers one year before and up to three years after the merger. Columns 1-3 divide themerger events based on changes in HHI. High counties are those with HHI changes greater thanthe median change. Similarly, columns 4-6 compare branches in counties that had overlappingbranches between acquirer and target pre-merger to those that did not have.

45