We investigate the shrinking community banking sector and the impact on local small business lending (SBL) in the context of mergers and acquisitions. From all mergers that involved community banks, we examine the varying impact on SBL depending on the local presence of the acquirers’ and the targets’ operations prior to acquisitions. Our results indicate that, relative to counties where the acquirer had operations before the merger, local SBL declined significantly more in counties where only the target had operations before the merger. This result holds even after controlling for the general local SBL market or local economic trends. These findings are consistent with an argument that SBL funding has been directed (after the mergers) toward the acquirers’ counties. We find even stronger evidence during and after the financial crisis. Overall, we find evidence that local community banks have continued to play an important role in providing funding to local small businesses. The absence of local community banks that became a target of a merger or acquisition by non-local acquirers has, on average, led to local SBL credit gaps that were not filled by the rest of the banking sector (but may have been filled by non-bank lenders).
Small businesses are believed to have a significant role to play in local communities as they contribute to the local economic growth. They account for roughly half of private employment and more than half of total economic output—see Reference [
Community banks traditionally served as the primary source for small businesses’ funding needs. Lending to small businesses often faces unique challenges since small firms and local start-ups tend to be opaque due to their short credit histories. Thus, SBL originations rely heavily on relationship-based lending and “soft” information, as discussed in Reference [
In contrast to the traditional paradigm, recent SBL trends suggest a new banking landscape in which large banks have become more active in providing funding to small businesses. Following this trend, large banks increasingly used credit scoring methods for small business borrowers (Reference [
In this paper, we explore the interplay among community banks’ comparative advantages in SBL, their local presence, and their involvement in mergers and acquisitions (M&As). Our previous study in Reference [
We recognize that the changes in SBL origination by banking firms that are involved in M&A may be offset by other banks so that there might be no net impact on the community. In addition, some new de novo banks could pop up in the local community to fill the SBL credit gap which was created by the mergers. In this study, we account for these possibilities in the analysis. We explore the net impact (allowing for other banks to fill the credit gap) by measuring the overall SBL activities in each local community and examine how the various type of community bank mergers that took place in the community may be related to the net change in local SBL funding. We find that the impact on local SCL varies—in particular, there is a net decline in SBL in Target-Only counties even after accounting for the possibility that other lenders could come in to fill the SBL credit gap. Overall, unlike in the Acquirer-Only counties, de novo banks and other banks do not fully compensate for the decline in SBL funding in the Target-Only counties.
Specifically, our results show that the merged (combined) banking firms are about 11 percentage points more likely to decrease their SBL activity in counties where only the target was operating prior to the merger, compared with counties where only the acquirer was operating before the merger. Furthermore, we find that reactions by other banking firms do not compensate for this SBL decrease. This evidence is consistent with an argument that local community banks still play an important role in providing SBL funding to local small businesses and that losing local community banks through a merger could potentially slow down SBL funding availability in the local community.
The rest of the paper is organized as follows: Section II reviews the existing literature on community bank mergers and our unique contributions to the literature. Section III describes our data sources. Section IV describes our identification strategy. The empirical results and our conclusions and policy discussion are presented in Section V and Section VI, respectively.
Existing research on community banks and SBL covers the various advantages and disadvantages of relationship-based lending at community banks with the transaction-based lending technologies that are more common at larger banks. There also exists a rich literature on community bank M&A activity and its impact on SBL markets.
The first strand of research argues that small community banks have a comparative advantage in certain types of lending that enable them to compete with large banks. For example, Reference [
The second strand of the literature suggests that large banks are better than community banks at certain important facets of SBL. Reference [
M&As are often used as a source of exogenous variation in the literature. Reference [
There is a rich literature examining mergers involving community banks and the impact on SBL. Reference [
Reference [
It is important to note that previous studies that find community bank mergers increase overall SBL volume are subject to significant geographic and time period limitations. Reference [
We find that there are significant negative responses to declines in community banking. As mentioned earlier, the merged firms are more likely to decrease their SBL activity in counties where only the target was operating before the merger (compared with counties where only the acquirer was operating before the merger). We also find that acquirer size is important in determining the decline in the Target-Only counties. Our results overall suggest that local community banks still play an important role in lending to small businesses and that M&A that involve community bank targets could result in long-term declines in lending to local small businesses (which are not offset by other bank lenders).
We use data from multiple sources and appropriately merge them for various analytical purposes. First, our bank merger sample consists of all bank mergers that took place during 2002-2014 that involved U.S. community bank targets, using two different definitions of community banks. Second, our data on SBL originations (by each bank, in each county, and in each year) cover the period from 2001 to 2015 (allowing us to analyze SBL activities for the period 12 months before and after the merger that took place in 2002-2014). It is important to note that we are able to identify the amount and the geographic location of each bank’s SBL activities (i.e. the amount of SBL that each bank originated and purchased in each county each year)2. Third, we collect information on the firms’ structures premerger and postmerger. We use the Federal Reserve’s bank structure database, along with the SBL allocation data, to evaluate changes in SBL allocation (at the county level and firm-level) across different merger types and county types. Merger types are classified by combinations of whether the target and acquirer have existing operations in the county: Target-Only, Acquirer-Only, or Both. Lastly, we use other data sources for local economic factors and general banking activities in each county. We describe each of the data sources in more detail below.
We collect transaction-level data on bank M&As from SNL Financial M&A Database. The database contains information about M&A targets, acquirers, and announcement/completion dates. The dataset also has financial information about the targets and the acquirers around the merger dates. We restrict the sample to M&A transactions during 2002-2014 that involved a community bank target. We also perform a separate analysis for M&A transactions that took place before and after the financial crisis to allow for the possibility that the nature of SBL activities may be significantly affected by the financial crisis3.
We define community banks in two ways: 1) banks with assets less than $1 billion (in the main results), and 2) banks with assets less than $10 billion4. While the definition of community banks was traditionally used for banks with assets less than $1 billion for decades, the new definition with the threshold of $10 billion became more common after the passage of the Dodd-Frank Wall Street Reform and Consumer Protection Act (DFA) of 2010. See Reference [
Our sample of community bank mergers changes according to the definition of community banks and the sample period. Our final samples for the entire period 2002-2014 include 1280 and 1366 community bank mergers when the target community banks are defined as having less than $1 billion and less than $10 billion, respectively. Of the 1280 community bank mergers (for the $1 billion definitions), 477 mergers were completed during what we define as the post-crisis period of 2010-2014. Similarly, of the 1366 community bank mergers (for the $10 billion definitions), 511 mergers took place during the 2010-2014 post-crisis period.
We collect each bank’s SBL activities in each county and each year from the Community Reinvestment Act (CRA) Disclosure Reports, which contain highly disaggregated information on SBL activities (newly originated and purchased SBL) at each banking institution in each county each year. These data are available for all banks that exceed a size threshold that varies over time5. Using the CRA data, we calculate, for each county in each year, a proportion of a bank’s overall SBL activities that occur in a given year in each specific county. This calculated firm-county-year level of SBL ratio is applied to the outstanding SBL volume that a bank reports in the following June Call Report. In other words, the overall SBL outstanding as of June in each year is allocated to firm-county-level SBL activities, based on the banking firm’s SBL origination and purchase activities reported by the bank in the CRA reports in the previous year.
For some small community banks that fall below the CRA reporting threshold, we use their deposit-taking location to allocate the overall SBL activities as reported in the Call Reports6, which are submitted to the Federal Reserve and the Federal Deposit Insurance Corporation (FDIC) by all banks7. We then geographically allocate total outstanding SBL volume into firm-county-level SBL following the allocation process used in Reference [
We gather information on the banks’ organizational structure from the Federal Reserve Structure Data, which allows us to identify the top and intermediate holders of a banking firm with details about the nature of the relationships. We use these data to classify target and acquirer size based on the top holder of each of the counterparties prior to the merger.
We gather information on general county-level macroeconomic indicators from the Haver Analytics database to use as control variables. The imputed SBL market concentration variables are derived from several data sources—the CRA reports, the Call Reports, and the FDIC Summary of Deposits database. Our specific set of economic factors includes 1) Population, which we expect to be positively correlated with SBL, as larger (highly populated) counties with higher population density can support more small businesses, and 2) Unemployment, which allows us to observe the general macroeconomic health of the county and to capture variation across the business cycle, 3) Per-Capita Personal Income as another proxy for how relatively well off the county is, and 4) Per-Capita Business and Personal Bankruptcy to capture the credit risk exposure in each county on the aggregate level.
A brief summary of data sources for all the variables and the data period covered in our analysis is presented in Appendix 2.
Our objective is to explore the impact of mergers that involved a community bank target on local small businesses’ ability to access credit. We measure the overall impact in two ways. First, we examine SBL activities by the banking firms that were involved in the mergers—so-called the firm-level impact. Second, we examine the county-level impact by allowing for the possibility that other local banking firms (that were not involved in the mergers) might come in to offer additional SBL funding to close the local SBL credit gaps that were created by the mergers. We describe the firm-level analysis and the county-level analysis in detail in Sections 4.1 and 4.2 below.
First, in examining the firm-level responses, we ask whether the community bank M&A resulted in changes in SBL activity compared with the rest of the acquirers’ operations. For example, we examine whether the acquirers would direct SBL funding from the Target-Only counties (counties where the target was operating before the merger) to its Acquirer-Only counties (counties where only the acquirer was operating before the merger).
We use a difference-in-differences approach—comparing the differences across different acquirer sizes and different types of counties (Target-Only, Acquirer-Only, Both) as classified by whether the target and/or acquirer (for each merger) was operating as of June the year prior to the merger year9,10. The three-county categories are:
· (1,0) county—Acquirer-Only counties (only the acquirer is operating in);
· (1,1) county—Both counties (both the acquirer and the target are operating in);
· (0,1) county—Target-Only counties (only the target is operating in).
Our identification assumption is that firm-level effects on SBL would differ systematically based on whether acquiring firms had premerger operations in the county11. Under this assumption, we can use Acquirer-Only counties as a quasi-experimental control group to identify the impacts of premerger operations in Target-Only counties. Since we are comparing firms with themselves, we avoid some typical identification challenges that arise in merger studies12.
We measure SBL activities here in terms of dollar change (Equation (1)) as well as the ratio change (defined as SBL commitment as shown in Equation (2)). The increased SBL are represented in Equations (3) and (4) for dollar change and ratio change, respectively.
C h a n g e _ S B L i , j , t = S B L i , j , t + 1 − S B L i , j , t − 1 (1)
C h a n g e _ S B L C o m m i t m e n t i , j , t = S B L i , j , t + 1 S B L i , t + 1 − S B L i , j , t − 1 S B L i , t − 1 (2)
D _ I n c r e a s e d _ S B L i , j , t = { 1 if C h a n g e _ S B L i , j , t > 0 0 otherwise (3)
D _ I n c r e a s e d _ S B L C o m m i t m e n t i , j , t = { 1 if C h a n g e _ S B L C o m m i t m e n t i , j , t > 0 0 otherwise (4)
where:
· S B L i , j , t represents our SBL measure for a the merger counterparties (target, acquirer) in merger i in county j at time t;
· I n c r e a s e d _ S B L i , j represents changes in SBL in inflation-adjusted dollar amounts;
· I n c r e a s e d _ S B L C o m m i t m e n t i , j , t represents shifts the relative importance of county j to the counterparties in merger i at time t.
The Change_SBL variable captures actual dollar amount changes in small business lending, whereas the Change_SBLCommitment variable captures strategic shifts (the change in the ratio of SBL in each county to the bank’s total SBL in all counties). We compare the combined firm’s SBL activities with the pre-merger SBL activities of the merger counterparties. If combined firms are systematically decreasing funding in the Target-Only counties, we should observe that trend in all four dependent variables in Equations (1) to (4).We use the continuous measures of SBL activities in the linear regression analysis and use the SBL increased dummy indicators in the probabilistic regression analysis as described next.
Equations (5) and (6) are the model specifications for D _ I n c r e a s e d _ S B L i , j and D _ I n c r e a s e d _ S B L C o m m i t m e n t i , j , respectively. We estimate the coefficients using logistic regression analysis, with a standard log-odds link function and estimated via maximum likelihood. We then calculate and report the marginal effects on the probability of increased SBL ratio, using the marginal effects at representative values method.
ln ( Pr ( D _ I n c r e a s e d S B L i , j = 1 ) 1 + Pr ( D _ I n c r e a s e d S B L i , j = 1 ) ) = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + ϵ i , j (5)
ln ( Pr ( D _ I n c r e a s e d S B L C o m m i t m e n t i , j = 1 ) 1 + Pr ( D _ I n c r e a s e d S B L C o m m i t m e n t i , j = 1 ) ) = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + ϵ i , j (6)
where the independent variables:
C i , j represents a vector of indicator variables for the county classifications interacted with indicator variables for whether the acquirer is a community bank or not. The base case is for a (1,1) county and the acquirer being a community bank;
Δ M j represents a vector of changes in macroeconomic conditions in county j between one year prior to the merger and one year after the merger;
Y i represents a vector of merger completion year dummies. The base case is for mergers that were completed in 2002.
Similarly, the linear regressions are estimated using OLS and the model specifications are presented in Equations (7) and (8). All covariates have the same definitions as in Equations (5) and (6) noted previously.
C h a n g e _ S B L i , j = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + ϵ i , j (7)
C h a n g e _ S B L C o m m i t m e n t i , j = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + ϵ i , j (8)
We apply two minor econometric modifications to the previous specifications. First, we cluster all standard errors at the merger level to account for the possibility of correlations in residuals within mergers. Second, to ensure that we give all mergers equal weight regardless of the number of counties that the targets and/or the acquirers were operating in before the merger, we weight our regressions such that for each merger transaction, the total weight of the acquirer’s counties adds to 1, the total weight of a target’s counties adds to 1, and, for a given county classification and merger, each county is weighted equally13,14. The estimated coefficient vector β ^ 1 contains our primary coefficients of interest as it captures the merger impact for each county type. Our hypothesis is that SBL activities are more likely to increase after the mergers in counties where the acquirer has an operation before the merger. The sample and basic relationship are demonstrated in
In
Post-crisis Mergers: We also perform a separate analysis using a sample of community bank mergers that took place in our 2010-2014 post-crisis period. During this period, there were very few mergers with a non-community bank acquirer. Thus, we make a couple of small modifications to Equations (5) to (8) for the post-crisis regressions to try to capture size effects15:
ln ( Pr ( D _ I n c r e a s e d _ S B L i , j = 1 ) 1 + Pr ( D _ I n c r e a s e d _ S B L i , j = 1 ) ) = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + β ^ 4 A i + ϵ i , j (9)
ln ( Pr ( D _ I n c r e a s e d _ S B L C o m m i t m e n t i , j = 1 ) 1 + Pr ( D _ I n c r e a s e d _ S B L C o m m i t m e n t i , j = 1 ) ) = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + β ^ 4 A i + ϵ i , j (10)
C h a n g e S _ B L i , j = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + β ^ 4 A i + ϵ i , j (11)
C h a n g e _ S B L C o m m i t m e n t i , j = β ^ 0 + β ^ 1 C i , j + β ^ 2 Δ M j + β ^ 3 Y i + β ^ 4 A i + ϵ i , j (12)
where:
Ci,jrepresents county classifications only (that is, it does not interact with the community bank acquirer dummy), with a (1,1) county as the base case;
Ai represents the ratio of the premerger acquirer’s total assets to the premerger target total assets.
In this section, we examine the overall county-level responses, rather than the SBL activities of the merger counterparties. Our goal is to estimate the overall impact of community bank mergers at the county level (allowing for SBL activities by local non-merged banking firms to also be included in the analysis), controlling for the level and type of community bank mergers that took place in the county. The analysis accounts for the possibility that local de novo or other banks (large or small and local or non-local) could potentially have filled the credit gap created by the merged firm’s reduction in SBL in the Target-Only counties. We investigate whether there is a relationship between the overall change in SBL activities in each county and the type of mergers that involved targets and/or acquirers in the county (in the prior period). Wherever possible, we also examine whether the size of the acquirer plays a role—i.e., whether the county-level impact is affected by whether the acquirer is a community bank or a large bank. Our hypothesis is that a county is likely to lose SBL funding if many local small community banks have been targets of bank mergers that involved large out-of-state acquirers.
To accomplish our goal in this section, we construct a balanced panel of all counties in the United States from 2002 to 2014 and calculate total SBL activities by all banks in each county in each year16. We then construct a measure of county-level merger activity each year. For each merger that involves a target and/or acquirer that have operations in the county, we classify the merger based on the county type that the county represents for the merger. For example, a merger is classified as Acquirer-Only (1,0) county if only the acquirer has a presence in the county, classified as Target-Only (0,1) county if only the target has a presence in the county, and classified as a (1,1) County if both the acquirer and the target have a presence in the county17. If acquirers divert SBL funding towards their own community, we would expect to observe that counties that had high frequency (and volume) of Target-Only mergers would tend to lose SBL funding, unless other banks increase the SBL activities to fill the credit gap.
For each type of merger in each county, we calculate the total amount of SBL in the county before and after the merger. We then calculate the share of SBL in the county that is associated with each type of merger that took place the county in each year.
M k t S h a r e { x , y } , A c q S i z e , j , t = ∑ i ∈ C { x , y } , A c q S i z e S B L i , j , t − 1 S B L j , t − 1 (13)
where:
{x,y} represents one of {1,0}, {0,1}, {1,1};
AcqSize represents an indicator of being a community bank or a large bank;
C{x,y},AcqSize represents the set of all firm-county observations that have county
classification {x,y} with an acquirer of AcqSize.
As an illustration, in year t, the total SBL funded by all banks in county j is $50 million. That year, three mergers involved banks that operated in county j in year t − 1. Two of the mergers classify county j as an Acquirer-Only (1,0) county (because only the acquirers had operations in county j before the merger); one of the mergers classifies county j as a Target-Only (0,1) county (because only the target had operations in county j before the merger). All three acquirers are community banks. If the sum of all the premerger SBL made by the targets and the acquirers in county j was $10 million for the two (1,0) type mergers and $6.5 million for the only (0,1) type merger in county j, the calculated market share measures for county j in year t for community bank acquirers would be 0.20 for (1,0) type, 0.00 for (1,1) type, and 0.13 for (0,1) type; for noncommunity bank acquirers, they would all be zero.
We then estimate the model as specified in Equation (14) with the market share measures described previously included on the right-hand side to determine how the change in county-level SBL activities may be influenced by the various types of mergers that took place in the county18. We cluster standard errors at the county level.
S B L j , t + 1 − S B L j , t − 1 = β 0 + β ^ 1 M k t S h a r e A c q S i z e , j , t + β ^ 2 M j , t + β ^ 3 Δ M j , t + β ^ 4 Y j , t + ϵ j , t (14)
where:
MktShareAcqSize,j,t represents a vector of (1,1), (1,0), and (0,1) merger market share measures as described previously for acquirers of size classification AcqSize in county j at time t;
Mj,t represents a vector of macroeconomic conditions in county j at time t;
All other variables are as defined earlier.
The basic summary statistics of the sample are presented in
First, we present the results based on Equations (5)-(6) and Equations (9)-(10) in
The results are presented for the entire sample period (for all mergers that took place in 2002-2014) and for the post-crisis sample (for mergers that took
Panel A: Panel Data (Firm-Level Analysis)
Observation represents SBL activities of a merger counterparty bank in each county. Means are weighted as explained in the text of the paper.
Panel B: Panel Data (County-Level Analysis)
Observation represents SBL activities in each county. Note that the county classifications listed here represent the total market share of firms that merged with that county classification for that county.
Data Sources: SNL Financial, Haver Analytics, CRA database, Call reports, Y-9C reports, and Summary of Deposits database.
| Variable | Dollar Amount of SBL | Ratio of Firm-County-SBL to total Firm-SBL | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| $ 1 Billion Definition | $10 Billion Definition | $ 1 Billion Definition | $10 Billion Definition | |||||||
| Full Sample (1) | Post-Crisis (2) | Full Sample (3) | Post-Crisis (4) | Full Sample (5) | Post-Crisis (6) | Full Sample (7) | Post-Crisis (8) | |||
| (0,1) County (1,0) County Ratio of Acq. to Tgt. Assets CB Acquirer, (1,0) county CB Acquirer, (0,1) county Non-CB Acq., (1,1) county Non-CB Acq., (1,0) county | − − − 0.422*** (0.121) −0.0481 (0.137) −0.434*** (0.146) −0.0479 (0.113) | −0.377** (0.182) 0.514*** (0.144) −0.00875** (0.00381) − − − − | − − − 0.386*** (0.0831) −0.0746 (0.104) −0.00177 (0.176) 0.200** (0.0993) | −0.409** (0.171) 0.474*** (0.134) −0.00837** (0.00365) − − − − | − − − 0.882*** (0.133) 0.249* (0.144) −0.353** (0.150) 0.402*** (0.115) | 0.161 (0.186) 1.098*** (0.157) −0.00960*** (0.00365) − − − − | − − − 0.802*** (0.0908) 0.179 (0.110) 0.256 (0.167) 0.650*** (0.0912) | 0.0630 (0.172) 0.997*** (0.144) 0.00950*** (0.00352) − − − − | ||
| Non-CB Acq., (0,1) county County Population Change Unemployment Rate Chg. Per-Capita Income Chg. Business BK rate Chg. Personal BK rate Chg. Intercept Year Dummies? | −0.778*** (0.181) 0.00000411*** (0.00000138) 0.0110 (0.0339) 0.0000 (0.0000) −76.76 (210.5) 28.50 (29.41) −0.158 (0.187) Yes | − 0.00000523** (0.0000) 0.0669 (0.0620) 0.0000 (0.0000) −724.8 (556.6) 53.11 (74.60) −0.887*** (0.261) Yes | −0.645*** (0.233) 0.00000330** (0.00000129) 0.0192 (0.0321) 0.0000 (0.0000) −64.24 (209.7) 22.28 (27.84) −0.269 (0.170) Yes | − 0.00000505** (0.0000) 0.0777 (0.0580) 0.0000 (0.0000) −641.8 (532.3) 46.35 (70.84) −0.848*** (0.248) Yes | −0.535*** (0.189) 0.0000 (0.0000) −0.0106 (0.0283) 0.0000 (0.0000) 92.07 (228.0) 11.55 (25.37) −0.835*** (0.154) Yes | − 0.0000 (0.0000) 0.126** (0.0521) 0.0000 (0.0000) −1026.9* (526.2) 89.60 (71.56) −0.920*** (0.192) Yes | −0.205 (0.232) 0.0000 (0.0000) −0.00229 (0.0271) 0.0000 (0.0000) 94.88 (226.7) 8.381 (23.99) −0.926*** (0.132) Yes | − 0.0000 (0.0000) 0.122** (0.0485) 0.0000 (0.0000) −951.1* (505.2) 87.04 (67.32) −0.852*** (0.180) Yes | ||
| Probability of Increase for elements of Ci,j vs. base case (1,1) County (1,0) County (0,1) County CB Acquirer, (1,1) county CB Acquirer, (1,0) county CB Acquirer, (0,1) county Non-CB Acq., (1,1) county Non-CB Acq., (1,0) county Non-CB Acq., (0,1) county | − − − 0.410*** (0.0258) 0.513*** (0.014) 0.398*** (0.021) 0.311*** (0.021) 0.398*** (0.007) 0.243*** (0.0264) | 0.353*** (0.0294) 0.475*** (0.0151) 0.274*** (0.0251) − − − − − − | − − − 0.372*** (0.0176) 0.464*** (0.009) 0.355*** (0.0166) 0.371*** (0.0367) 0.419*** (0.0155) 0.238*** (0.0397) | 0.355*** (0.0275) 0.468*** (0.0144) 0.269*** (0.0237) − − − − − − | − − − 0.281*** (0.023) 0.486*** (0.136) 0.334*** (0.020) 0.216*** (0.017) 0.370*** (0.006) 0.187*** (0.023) | 0.240*** (0.024) 0.483*** (0.015) 0.270*** (0.025) − − − − − − | − − − 0.258*** (0.0150) 0.437*** (0.009) 0.294*** (0.0156) 0.310*** (0.0314) 0.400*** (0.0110) 0.221*** (0.0374) | 0.253*** (0.0230) 0.476*** (0.0142) 0.265*** (0.0236) − − − − − − | ||
| Number of Obs. Cluster Level No. of Clusters | 66,882 Merger 1280 | 12,336 Merger 477 | 89,504 Merger 1366 | 15,734 Merger 511 | 66,882 Merger 1280 | 12,336 Merger 477 | 89,504 Merger 1366 | 15,734 Merger 511 | ||
Data Sources: SNL Financial, Haver Analytics, CRA database, Call reports, Y-9C reports, and Summary of Deposits database.
| $$ Amount of SBL | Ratio of County SBL to total SBL | |||||||
|---|---|---|---|---|---|---|---|---|
| $ 1 Billion Definition | $10 Billion Definition | $ 1 Billion Definition | $10 Billion Definition | |||||
| Variable | Full Sample | Postcrisis | Full Sample | Postcrisis | Full Sample | Postcrisis | Full Sample | Postcrisis |
| (0,1) County (1,0) County Ratio of Acq. to Tgt. Assets CB Acquirer, (1,0) county CB Acquirer, (0,1) county Non-CB Acq., (1,1) county Non-CB Acq., (1,0) county Non-CB Acq., (0,1) county County Population Change Unemployment Rate Chg. Per-Capita Income Chg. Business BK rate Chg. Personal BK rate Chg. Intercept Year Dummies? | − − − 4621.3*** (1112.5) 98.22 (1150.8) −2853.9 (2737.3) 3545.8*** (1095.9) −4451.4** (2049.7) −0.00699 (0.0456) 102.3 (346.6) 0.182 (0.127) 517058.6 (1348200.4) 306798.0 (310806.5) −1702.1 (1759.1) Yes | 726.0 (1500.9) 6557.8*** (1390.4) −13.34 (30.10) − − − − − 0.0794** (0.0330) 843.0* (498.5) 0.123 (0.148) 249062.0 (2912766.7) 957763.0 (738378.2) −9751.6*** (2209.5) Yes | − − − 5253.7*** (1251.6) 401.7 (1301.5) 348.8 (4141.7) 4804.5*** (1456.0) −7599.4 (7659.3) −0.00720 (0.0425) 217.6 (333.2) 0.146 (0.121) 540449.0 (1280569.5) 217816.2 (302807.5) −2464.1 (1950.6) Yes | 604.4 (1408.5) 6295.7*** (1313.8) −19.57 (31.04) − − − − − 0.0716** (0.0321) 894.7* (477.0) 0.0825 (0.145) 281690.4 (2794759.7) 767370.0 (707347.1) −9086.0*** (2174.9) Yes | − − − 0.0485*** (0.00798) 0.00618 (0.00822) 0.0121 (0.00820) 0.0461*** (0.00734) 0.0185** (0.00819) −2.04e−08 (0.0000) −0.000453 (0.00141) 0.0000 (0.0000) 4.750 (8.366) 0.604 (1.523) −0.0378*** (0.0126) Yes | −0.00192 (0.00867) 0.0432*** (0.00752) 0.0000712 (0.0000719) − − − − − 6.27e−08 (0.0000) 0.00405 (0.00255) 0.0000 (0.0000) 6.871 (19.03) 9.828* (5.339) −0.0286*** (0.00790) Yes | − − − 0.0426*** (0.00519) 0.00514 (0.00563) 0.0332*** (0.00544) 0.0410*** (0.00473) 0.0341*** (0.00591) −1.45e−08 (9.71e−08) −0.000532 (0.00132) 0.0000 (0.0000) 3.892 (8.070) 0.561 (1.439) −0.0338*** (0.0117) Yes | −0.00262 (0.00808) 0.0401*** (0.00706) 0.0000453 (0.0000679) − − − − − 6.28e−08 (0.0000) 0.00371 (0.00237) 0.0000 (0.0000) 6.590 (18.30) 9.481* (5.082) −0.0269*** (0.00751) Yes |
| Observation Level Number of Obs. Cluster Level No. of Clusters R2 | Merger- County 66,882 Merger 1280 3.0% | Merger- County 12,336 Merger 477 5.6% | Merger- County 89,504 Merger 1366 2.4% | Merger- County 15,734 Merger 511 4.7% | Merger- County 66,882 Merger 1280 4.9% | Merger- County 12,336 Merger 477 7.0% | Merger- County 89,504 Merger 1366 4.8% | Merger- County 15,734 Merger 511 6.5% |
Data Sources: SNL Financial, Haver Analytics, CRA database, Call reports, Y-9C reports, and Summary of Deposits database.
| Variable | Full Sample $1 Billion Definition | Full Sample $10 Billion Definition | Post-Crisis Sample $1 Billion Definition | Post-Crisis Sample $10 Billion Definition |
|---|---|---|---|---|
| Market Share of (1,0) mergers CB Acquirer Market Share of (1,1) mergers CB Acquirer Market Share of (0,1) mergers CB Acquirer Market Share of (1,0) mergers Non-CB Acquirer Market Share of (1,1) mergers Non-CB Acquirer Market Share of (0,1) mergers Non-CB Acquirer County Population Unemployment Rate Per-Capita Income Business Bankruptcy Rate Personal Bankruptcy Rate County Population Change Unemployment Rate Chg. Per-Capita Income Chg. Business BK rate Chg. Personal BK rate Chg. Intercept Year Dummies? | −2863.8 (2615.5) −19,929.1*** (6376.9) −9846.9*** (3482.8) 5309.5** (2144.0) 24,946.0* (13130.0) −18,060.8** (8094.8) 0.0103*** (0.00399) −380.4*** (105.7) 0.241*** (0.0636) −17,011,135.8*** (1,420,053.1) −744,304.3*** (116,317.3) 0.738*** (0.103) −5.618 (172.0) 0.721*** (0.0870) 1,990,139.8** (828,183.3) −1,961,648.6*** (249,914.8) 2658.5 (2106.7) Yes | 2565.1 (1872.5) −8954.9* (5110.6) −10,280.8*** (3468.3) 9502.2*** (2550.7) 87,294.8*** (22,041.7) −33,558.4*** (8986.7) 0.00999** (0.00396) −393.4*** (105.7) 0.232*** (0.0627) −17,023,316.8*** (1,421,329.9) −737,766.7*** (116,517.8) 0.725*** (0.103) 2.290 (172.1) 0.729*** (0.0871) 1,897,188.9** (824,805.7) −1,964,419.5*** (250,623.1) 2599.0 (2073.7) Yes | 4145.3 (2603.9) −20,049.3** (8866.4) −12037.1** (5102.5) −4881.5 (3978.1) −32,534.7*** (11851.1) −47,385.9*** (9175.8) −0.0144*** (0.00538) 126.6 (137.7) −0.0609 (0.0628) −16,578,400.6*** (2,387,683.0) −2,585,440.3*** (257,862.7) 0.812*** (0.187) −336.2 (274.7) 0.430*** (0.0857) 1,576,841.1 (1,320,356.1) −2,405,421.4*** (403,686.2) −14,973.4*** (3268.2) Yes | −1244.4 (2796.3) −20,868.9*** (6406.0) −20,967.3*** (5392.4) −6842.0 (7152.0) −41,381.5* (22,512.5) −53,095.3** (22,228.7) −0.0143*** (0.00537) 139.8 (137.3) −0.0614 (0.0630) −16,603,532.7*** (2,387,449.4) −2,596,084.3*** (258,820.1) 0.812*** (0.187) −337.7 (274.8) 0.437*** (0.0862) 1,513,277.8 (1,319,985.3) −2,390,974.6*** (403,988.8) −15,093.2*** (3264.2) Yes |
| Observation Level Number of Obs Cluster Level Number of Clusters R2 | County-Year 40,019 County 3079 15.1% | County-Year 40,019 County 3079 15.2% | County-Year 15,387 County 3078 9.7% | County-Year 15,387 County 3078 9.7% |
Data Sources: SNL Financial, Haver Analytics, CRA database, Call reports, Y-9C reports, and Summary of Deposits database.
place during 2010-2014). In
The dependent variable here is the probability that the combined (merged) firm would increase SBL origination in a specific county after the merger, compared with the SBL activities of the acquirer and the target in that county before the merger.
The results in
While the Acquirer-Only (1,0) counties tend to do better than Target-Only (0,1) counties, the relative change between Both (1,1) counties and Acquirer-Only (1,0) counties is less clear—probably because acquirers have a presence in both (1,1) and (1,0) counties. For mergers that involved community bank acquirers, the Acquirer-Only (1,0) counties are statistically and economically more likely to increase SBL, regardless of our definition of a community bank or how we measure the change in SBL (dollar volume or ratio as proxy for SBL commitment). For mergers that involved large acquirers, the results are less robust to different model specifications.
The dependent variable is the increase in SBL activities by the combined firm compared to the acquirer’s and the target’s SBL activities in the county prior to the merger. Consistent with previous results on the probability of increasing SBL, the results presented, based on Equations (7) and (8) for the full sample and Equations (11) and (12) for the subsample, in
The magnitude of the effect varies, depending on whether the acquirer is a community bank and when the acquisitions took place. For the full sample, community bank acquirers tend to increase their SBL lending by about $5 million more in Acquirer-Only (1,0) counties than in Target-Only (0,1) counties. For large bank acquirers, they would increase SBL lending by $8 million to $13 million more in the (1,0) counties than in the (0,1) counties. This translates to a relative commitment change of about 4 percent for community bank acquirers and about 0.7 percent to 2.5 percent for large acquirers, depending on the definition of the community bank threshold. These effects continue in the post-crisis period, with an average magnitude of about $6 million or a 4 percent change in the commitment for Acquirer-Only (1,0) counties relative to the Target-Only (0,1) counties19.
Overall, across all the 16 specifications reported in
One interesting trend that emerges in the results is that the definition of a community bank (whether $1 billion or $10 billion thresholds) tends not to matter all that much in most cases. It is, however, important whether the acquirer is a community bank or a large bank—especially for the non-community bank Acquirer-Only (1,0) counties. This is broadly consistent with Reference [
The dependent variable here is the overall change (net increase) in SBL activities in each county from the pre-merger to post-merger period. The results, based on equation (14), are presented in
In terms of economic significance, the coefficients of the variable Market Share of (1,0)mergers * Non-CB Acquirer indicate that counties with large-acquirers mergers have statistically significant positive effects of about $530,000 (and $950,000) increase in SBL for each 10 percent increase in its market share of SBL in the county for the $1 billion (and $10 billion) size thresholds of the community bank, respectively.
For counties with significant exposure to Target-Only (0,1) merger classifications, we find strong and significant negative impacts on the SBL volume in the county after the merger, regardless of the size of the acquirers or the sample period. Our results on the decline in SBL activities in the Target-Only counties, which we presented earlier in the firm-level analysis, remain strong even after allowing for other banks to substitute and fill the credit gap.
During the full sample period 2002-2014, the marginal negative effect is about a $1 million decline in SBL for each 10 percent increase in market share of community bank target in the overall SBL lending in the county before the merger. The coefficients of the variables Market Share of (0,1)mergers * CB Acquirer and the variable Market Share of (0,1)mergers * Non-CB Acquirer are both strongly negative across all specifications. This effect is even larger when the acquirers are large banks—with a marginal negative effect of about $1,800,000 (and $3,350,000) decline in SBL volume for each 10 percent increase in market share of SBL by large acquirers in the county—for the $1 billion (and $10 billion) size thresholds, respectively.
Our results overall are consistent with the conventional belief that there would be an adverse impact on credit availability to the local small businesses in the counties where small community banks are acquired by large banks (especially those that operate mostly outside the target’s local community).
The decline in the number of community banks in the past two decades along with the significant decline in their market share of SBL have spurred concerns about potential unintended consequences of community bank mergers. As more community banks have disappeared through mergers and failures, there might be a shortage of funding supply available to local small businesses. Previous studies have produced mixed results. In this paper, we fill the literature gap by focusing on the local community impacts and accounting for the merger types and characteristics of the acquirers.
Reference [
The overall impact of community bank mergers depends significantly on where the acquirers and the targets had operations before the mergers. We find a statistically significant increase in the SBL activities in counties where only the acquirers operated, which is met with a corresponding SBL decline in counties where only the target operated (before the merger). These changes in SBL activities are significant even after controlling for the general market trends or changes in the local macroeconomic variables. Funding availability to small businesses seems to be directed away from the target’s community especially if the acquirers did not also have operations in the same county before the merger. We observe an even stronger result (with a larger decline in SBL in the target’s local community) when the acquiring bank is a large bank.
Although the magnitude of this decline in SBL in the target community varies from specification to specification, it is always statistically and economically significant regardless of whether the acquirers are a community bank or large banks. Our results are also robust to different estimation techniques, different measures of changes in SBL activities, different definitions of the community bank, and different sample periods.
Overall, our results are consistent with the hypothesis that community banks have continued to play an important role in providing funding to local small businesses. The absence of local community banks that became a target of a merger or acquisition by non-local acquirers has, on average, led to local SBL credit gaps that were not filled by other banking firms (including local de novo banks). This SBL credit gap could portend significant negative economic consequences in the absence of new interventions to ensure the continuing functionality of SBL markets and community banks’ niches within these markets20.
Finally, we note that our findings are based on the specific data period and economic environment. Due to the data limitation associated with those small banks that chose not to submit CRA reports annually during our study period, we had to resort to our own calculation of the bank’s SBL share in each county each year. This calculation assumes that the bank’s SBL origination share in each county would resemble its deposit-taking share in the county. This is a reasonable assumption because these banks are mostly very small and focus their activities primarily on the local geographic footprint only. The plot in Appendix 1 provides support for our assumption during the study period. In addition, it is also possible that the local credit gap may be filled by nonbank or fintech lenders which are not included in our analysis in this paper.
The authors declare no conflicts of interest regarding the publication of this paper.
Jagtiani, J., Maingi, R.Q. and Dolson, E. (2022) How Important Are Local Community Banks to Small Business Lending? Evidence from Mergers and Acquisitions. Journal of Mathematical Finan- ce, 12, 382-410. https://doi.org/10.4236/jmf.2022.122022
The plot below presents the relationship between deposit taking and SBL origination ratio at the county level. The sample includes small banks with total assets less than $10 billion that filed the CRA Reports. The vertical axis is the ratio of SBL originated (and purchased) by Bank i in County j to total SBL originated (and purchased) by Bank i in all counties. The horizontal axis is the ratio of deposit taking by Bank i in County j to total deposit taking by Bank i in all counties in the same period.