1. society
2. work
3. unions

Competition between Organizational Forms:
Banks vs. Credit Unions after the Financial Crisis *
Aaron K. Chatterji
Fuqua School of Business
Duke University
100 Fuqua Drive, Box 90120
Durham, NC 27708
Tel: +1-919-660-7903
e-mail: [email protected]
Jiao Luo
Carlson School of Management
University of Minnesota
321 19th Avenue South
Minneapolis, MN 55455
Tel: +1-612-626-1907
e-mail: [email protected]
Robert C. Seamans
Leonard N. Stern School of Business
Kaufman Management Center
44 West Fourth Street, 7-58
New York, NY 10012
Tel: +1-212-998-0417
e-mail: [email protected]
April 20 2015
Keywords: organizational forms; cooperatives; credit unions; financial crisis; banking
*Acknowledgement: We are grateful to Mary Benner, Caroline Flammer, Olga Hawn, Paul Ingram,
Mukti Khaire, Mae McDonnell, Mark Mizruchi, Christopher Rider, Marc Schneiberg, Zur Shapira, and
seminar and conference participants at the 2013 HBS Institutional Analysis Conference, the 2014 SASE
Annual Conference, the NYU Stern Research Brownbag, the 2014 BPP I&I Conference and the
University of Minnesota Carlson SME Seminar Series, for providing excellent comments and feedback.
We benefited tremendously from discussions with Rodney Ramcharan, John Worth and the team at
NCUA. We are thankful for funding from the Dean’s grant at the Carlson School of Management, and the
Grant-in-Aid from Office of the Vice President for Research, University of Minnesota. All errors are our
own.
1
Competition between Organizational Forms:
Banks vs. Credit Unions after the Financial Crisis
Abstract: We explore competition between organizational forms and explain how imitation and
differentiation influences whether alternative forms gain wide acceptance. Our empirical context
is corporate and cooperative organizational forms. Prior work suggests that cooperatives are able
to gain market share when the corporate form experiences a threat to legitimacy. However, we
argue that cooperatives’ ability to gain market share will be determined not only by the severity
of the legitimacy threat to the corporate form, but also by the strategic tactics that corporates and
cooperatives undertake. We investigate these issues by exploring how the recent financial crisis
influenced market share for American banks and credit unions from 2004-2012. We find that
credit unions gain market share from banks following the financial crisis, on average. In
accordance with our predictions, these gains primarily accrue to credit unions that embody
traditional identities that are distinct from banks. We also find that the effects are larger in
regions where cooperative organizations, with missions analogous to credit unions, are already
more prevalent. However, the effects are mitigated in regions where banks co-opt specific
features of credit unions, in this case through local philanthropic giving.
1. Introduction
A key open question in organizational theory is how organizational identity impacts strategy and
vice versa (Hsu and Hannan, 2005; Ingram and McEvily, 2007). Organizational identity is the
“social codes, or sets of rules, specifying the features an organization is expected to possess”
(Hsu and Hannan, 2005: 475). Prior work has demonstrated how the strength and coherence of
organizational identities is influenced by competitive pressures (Ingram and McEvily, 2007). We
adopt a different tack, arguing that organizational forms often intentionally blur categorical
distinctions between themselves and their competitors to gain competitive advantage. However,
this imitation strategy has potential costs. If the legitimacy of the dominant organizational form
is threatened, we theorize that alternate forms are less likely to gain wider acceptance if they are
not sufficiently differentiated. This theoretical tension yields practical implications for
competition between organizational forms, particularly for alternate forms challenging
2
entrenched dominant forms. We explore this idea in the context of cooperative organizations
(“co-ops”) challenging the corporate form in the financial services industry.
Ranging from agricultural producers to artists and kibbutzim, scholars have long been
interested in studying various types of cooperatives, which are defined as membership-based
associations of consumers or producers that are organized around the mission of creating mutual
benefits (Carroll, Goodstein, and Gyenes, 1988; Boone and Özcan, 2014; Ashforth and Reingen,
2015). Prior research has typically characterized co-ops as a distinctive organizational form and
explored their organizing principles and social and institutional foundations. There has been
particular emphasis on understanding when and under what conditions the cooperative form
becomes viable. The viability and proliferation of cooperatives are typically considered in
relation to the organizational forms that they emerge against and compete with: corporate forms.
With the historical rise of mass markets and giant corporations as the dominant organizing logics
in the U.S. (Davis, 2009; Mizruchi, 2010), cooperatives and corporate forms in the same market
space have oscillated in popularity: when the corporate is ascendant, the cooperative declines and
vice versa (Simons and Ingram, 2003). As a result, cooperatives are expected to benefit, often
with the assistance of successful social movements (Schneiberg, King, and Smith, 2008;
Schneiberg, 2013), at the expense of corporates during periods where the legitimacy of the
dominant form is threatened.
In this study, we extend prior work by showing that cooperatives’ ability to gain market
share from the incumbent corporate form rests not just on the declining popularity of the latter
during disruptive events, but also on the competitive tactics that corporates and cooperatives
undertake in the period preceding the legitimacy shock. While we concur with the prior literature
that cooperative forms can provide appealing alternatives to corporate forms, our point of
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departure is that, over time, cooperatives often move strategically closer to the corporate forms in
terms of the products they offer and their organizational attributes (Simons and Ingram, 1997).
The features of products and attributes of organizations contribute to how external audiences
create the categories that contain organizations with a collective identity (Hannan, László. and
Carroll, 2007). This shift in identity of cooperatives yields increasing similarity, which is
potentially attractive in normal market conditions. However, this strategy can limit the gains by
cooperatives during disruptive events for the corporate form. Cooperatives are therefore pulled
by two oppositional forces: on one hand they seek to differentiate their identity from the
corporate organizational form, while maintaining enough similarity so as not to alienate or
confuse potential consumers (Hotelling, 1929).
We draw from organizational theory and business strategy literatures to develop several
hypotheses regarding the interaction between cooperative and corporate organizational forms.
Our baseline hypothesis predicts that cooperatives will benefit at the expense of corporates when
the latter experiences a threat to legitimacy, on average. We then provide three additional
hypotheses that are designed to uncover conditions under which cooperatives will benefit more
or less from such a threat to legitimacy of the corporate form. We expect that cooperatives will
benefit less when the categorical boundaries between the cooperative and corporate forms are
blurred. As such, when the corporate form is challenged, cooperatives that have identities distinct
from corporates should benefit more than cooperatives that have identities less distinct from
corporates. We also expect that cooperatives will benefit more when based in areas where the
traditional cooperative identity is already well-understood and accepted. Finally, we expect that
corporates will suffer less damage when they undertake strategies to mimic cooperatives, thereby
intentionally blurring the categorical boundaries between these two organizational forms.
4
To test our theoretical predictions, we study how the recent US financial crisis affects
market share for banks and credit unions from 2004-2012. The financial crisis that began in late
2007 (hereafter referred to as “the financial crisis” or “the crisis”) was a pivotal event in modern
economic history. This empirical setting offers at least three advantages. First, it was a salient
episode during which commercial banks and the corporate forms that they represent were
severely challenged. In the search for culprits for the crisis, the world’s leading banks, many of
them headquartered in the U.S., were an easy target. Public trust in America’s financial system
decreased markedly during and following the crisis (Greve and Kim, 2013; Guiso, Sapienza, and
Zingales, 2013) and it became commonplace for politicians, consumer groups, and even other
business people to condemn banks and question their outsized role in the global economy.
Second, credit unions offer a ready cooperative alternative to commercial banks that
existed before and after the crisis (Emmons and Schmidd, 2000). This is evidenced by the
observation that as public outrage at banks escalated, nationwide campaigns such as the Move
Your Money Project emerged which encouraged individuals and institutions to withdraw
deposits from Wall Street banks and move their assets to local financial institutions such as
credit unions.
Third, it is not immediately clear that the financial crisis had a significant long-term
impact on the power of large financial institutions, a matter of considerable public debate. While
banks faced bad publicity and the passage of new laws such as the Dodd-Frank legislation in the
U.S., there is no empirical evidence to suggest that alternative financial institutions such as credit
unions benefitted as a result. Our study offers a notable opportunity to empirically document
market dynamics during a pivotal period in the financial services industry and the nation’s
economic history more broadly.
5
We offer several theoretical contributions to the extant literature. First, we contribute to
the literature on cooperatives by highlighting that certain competitive tactics by cooperatives
may actually limit their growth potential. While most prior studies on cooperatives have
attributed their rise or decline to the current popularity of the incumbent forms, be it the
corporate or state (Simons and Ingram, 2003), or related social movements (Schneiberg, King
and Smith, 2008; Schneiberg, 2013), we stress the role of the tactics these organizations adopt
themselves, so as to strike a balance between adopting salient features of the corporate forms to
pursue growth, while also maintaining an authentic identity that is distinct from the dominant
form. Second, while the prior literature has shown how exogenous legitimacy threats create
opportunities for challengers in the market (Weber, Heinze, and DeSoucey, 2008; Hiatt, Sine,
and Tolbert, 2009; Pacheco, York, and Hargrave, 2014), we document that incumbents can
strategically address these challenges (McDonnell and King, 2013), by co-opting features of the
challengers. Third, we add to a prolific body of research in organization theory that considers the
competition between different organizing forms and logics (e.g., Haveman and Rao, 1997;
Marquis and Lounsbury, 2007; Marquis, Huang, and Almandoz, 2011; Yue, Luo, and Ingram,
2013) by explicating the strategic tactics used by competing forms and the trade-offs implicit in
these activities. Specifically, we find that competing organizational forms might both employ
tactics to mimic one another and compete for market share and acceptance in the short term, but
forego longer-term opportunities for differentiation, which suggest that managing these tactics is
often a dynamic process (King, Clemans, and Fry, 2011).
2. Theoretical Development
2.1. Banks vs. Credit Unions: Different Forms, Similar Services
6
Credit unions, which first appeared in the U.S. in the early part of the 20th century, represent “a
distinctive organizational form within the class of deposit institutions” (Moody and Fite, 1984;
Barron, West, and Hannan, 1994: 392). Credit unions became increasingly popular between 1935,
when 1% of the US population belonged to one, and 1996, when over 11,000 credit unions were
in operation serving 34% of the population (Hannan, 2003). However, deposits in credit unions
continue to be dwarfed by bank deposits and the average credit union member typically holds a
small amount of money in their account (Hannan, 2003).
There are several important institutional features of credit unions that differentiate them
from banks. First, as opposed to for-profit commercial banks, credit unions are non-profit entities
which operate on the democratic concept of “one member, one vote” (Barron, West, and Hannan,
1994: 392). Second, in contrast to banks, credit unions generally offer their services to members
only. Individuals traditionally could only become credit union members if they shared a
“common bond” with other members, for example through employment, neighborhood or faith
based organizations (Barron, West, and Hannan, 1994: 392). Third, while banks are owned by
shareholders, credit unions are owned and operated by members (Fried, Knox Lovell, and
Eeckaut, 1993). Finally, while banks seek to maximize profits, the mission of credit unions is to
maximize the benefits of its members (Fried, Knox Lovell, and Eeckaut, 1993) and the broader
community (Bergengren, 1952).
Despite these key differences, changes in regulation have allowed credit unions to now
offer many of the same services as banks, including checking and savings accounts and loans
(Barron, West, and Hannan, 1994). Negro, Visentin, and Swaminathan (2014) describe banks as
occupying the “center” of the market for financial services while credit unions operate at the
“near center”, suggesting increasing overlap between these two populations of firms. These
7
authors empirically document an inverse relationship between the concentration of banks and the
survival of credit unions (p. 701). Similarly, limited empirical work in economics and finance
indicates that credit unions and banks offer similar services in many cases (e.g., Emmons and
Schmid, 2000; Feinberg, 2001; Feinberg and Rahman, 2001). In sum, while the salient features
differentiating banks from credit unions are still present, regulatory changes have allowed credit
union to undertake strategies and offer products that are closer to that of commercial banks.
2.2. The Financial Crisis as a Threat to the Legitimacy of Banks
Public debate over the organizing forms of banks, and a consolidated financial sector in
particular, has been lively ever since the founding of the Republic. The debate spawned the
formation of two early political parties, with Alexander Hamilton’s Federalists being more
sanguine about the efficiency of centralized banking than Thomas Jefferson’s DemocraticRepublicans, who were greatly skeptical about the prospect of concentrated power among
banking elites (Elkins and McKitrick, 1994). This divide continued through the 19th century,
punctuated by Andrew Jackson’s invalidation of the charter of the 2nd Bank of the U.S. (See
Marquis and Lounsbury (2007) for an excellent summary of this period). Through the populist
social movements of the late 19th century and the public outcry over the role of failing banks in
the run-up to the Great Depression, commercial banks were frequently under scrutiny but also
continued to wield significant political power (for a summary, see Mizruchi, 2010). Later in the
20th century, there was a broad deregulation of the American financial sector, including changes
in bankruptcy law (e.g., White, 2007) , interstate branching (e.g., Kroszner and Strahan, 1998)
and the deregulation of credit card interest rates (e.g., Chatterji and Seamans, 2012). In particular,
the 1999 Financial Services Modernization Act (Barth, Brumbaugh, and Wilcox, 2000; Mizruchi,
8
2010) , which repealed key provisions of the Glass-Steagall Act, is frequently identified by
critics as a key enabler of a consolidated banking sector and a contributing factor to the financial
crisis.1
Even in light of this history, the backlash against banks after the most recent financial
crisis was particularly severe. The crisis gripped the U.S. and much of the global economy and
gave way to the biggest economic downturn in American history since the Great Depression. In
addition to millions of jobs lost, trillions of dollars in retirement accounts, housing wealth and
national economic output also vanished. Large U.S. banks were the subject of numerous negative
opinion articles, congressional scrutiny, and even legal investigations. Opinion polling data
indicated a dramatic decrease in the favorability of banks and sweeping legislation was
introduced to regulate banks.2 Phrases like “too big to fail”3 and movements such as Occupy
Wall Street4 sprung up as general critiques of the negative impact of banks on society. In
addition, initiatives like the Move Your Money Project urged consumers and institutions to move
their deposits away from large banks5.
These efforts directly threatened the legitimacy of banks in a specific way. Legitimacy,
defined as the extent to which the population is recognized as socially desirable, proper, or
1
As one illustrative example see “Glass-Steagall Now: Because the Banks Own Washington”, August 5th, 2013.
(http://www.cepr.net/index.php/op-eds-&-columns/op-eds-&-columns/glass-steagall-now-because-the-banks-ownwashington) Last accessed August 13th, 2014
2
For example see: Erman, Michael. Reuters “Five years after Lehman, Americans still angry at Wall Street:
Reuters/Ipsos poll” September 15th, 2013 (http://www.reuters.com/article/2013/09/15/us-wallstreet-crisisidUSBRE98E06Q20130915) Last accessed August 13th, 2014
3
Dash, Eric. The New York Times “If It’s Too Big to Fail, Is It Too Big to Exist?” June 20th, 2009
(http://www.nytimes.com/2009/06/21/weekinreview/21dash.html?partner=rss&emc=rss) Last accessed August 13th,
2014
4
Watson, Tom. Forbes “Occupy Wall Street's Year: Three Outcomes for the History Books” September 17th, 2012
(http://www.forbes.com/sites/tomwatson/2012/09/17/occupy-wall-streets-year/) Last accessed August 13th, 2014
5
Huffington, A. and R. Johnson “Move Your Money: A New Year's Resolution”, December 29th, 2009
(http://www.huffingtonpost.com/arianna-huffington/move-your-money-a-new-yea_b_406022.html), Last accessed
August 13th, 2014
9
appropriate (Suchman, 1995). As public opinion turned against banks and regulators devised the
appropriate response, there was growing misalignment between the perceived values of the
banks and the general public, in particular on the question of the relative importance of value
creation for the broader community. In our study, we define a threat to legitimacy as disruptive
events that cause audiences to lose trust in the dominant organizational form.
As discussed above, credit unions appeared to be in a particularly advantageous position
to benefit from the threat to legitimacy of banks posed by the financial crisis. Importantly,
consumers who perceived banks to be destroying social value did not appear to question the
appropriateness and legitimacy of financial services itself. Instead, since credit unions had
evolved in recent years to provide many of the same financial services as banks, while trying to
retain their commitment to value creation for the community, they would seem to be especially
well-suited to appeal to dissatisfied consumers. This dynamic created a battlefield for competing
organizational populations (Carroll and Harrison, 1994) and competing institutional logics
(Battilana and Dorado, 2010) that was especially active during the aftermath of the financial
crisis.
We propose that the most salient feature of a credit union’s identity, especially after the
financial crisis, was the mission of creating shared value for its members. The “one member one
vote” and common bond rules serve as reinforcements of a mission that values community
members equally, no matter how powerful they might be. Credit unions’ focus on shared value
rather than profit maximization, despite offering many of the same financial services, provided a
key distinction from banks. As the legitimacy of banks was threatened during the financial crisis,
we predict that a significant fraction of consumers found credit unions more appealing and
shifted their money accordingly. Thus, we propose:
10
H1: After the financial crisis, credit unions gained market share at the expense of banks.
2.3. Heterogeneous Taste for Cooperative Organizations
Despite our prediction that credit unions gained market share from banks on average, we expect
significant variation depending on local conditions. It is not enough for a rival organizational
population to exist and offer the same goods or services; this population must already be
accepted, or rapidly gain acceptance from the relevant audience if it expects to grow when the
legitimacy of the corporate form is threatened. Indeed, Schneiberg (2013) shows that cooperative
movements in the early 20th century only thrived in areas which enjoyed large support from the
National Grange. In some geographic regions across the U.S., we expect that cooperative forms
of organization, like credit unions, are more prevalent and well-accepted. These local conditions
should positively impact credit unions.
We argue that there are several mechanisms by which the presence of existing
cooperative organizations will positively impact credit union market share. First, the presence of
existing cooperatives may reflect an underlying community preference for alternative modes of
economic organization and the distinct institutional logic governing coops. For example, Greve
and Rao (2012) find that communities which, through accidents of history, were early adopters
of one type of cooperative organizational structure were in turn more likely to adopt other forms
of cooperatives. They argue that these early events imprinted the communities, such that they
had a greater proclivity for cooperatives in the future. Second, existing producer and consumer
cooperatives raise awareness for alternative organizational forms among local consumers and the
stakeholders of existing cooperatives can also marshal support for organizations that share a
11
common institutional logic (Molotch, Freudenburg, and Paulsen, 2000). Again, there is historical
precedent to support this argument. Moody and Fite (1984) recount that the three major farm
organizations, the Farmers’ Union, the National Grange, and the American Farm Bureau
Federation, appear to have thrown political support behind credit unions in the early 1930s,
leading to passage of state laws authorizing credit unions in a number of states.6
Based on these mechanisms, we expect that the wide adoption of cooperative forms of
organizing in various economic sectors in a particular geographic region should be positively
related to gains in credit union market share after the financial crisis. We propose:
H2: After the financial crisis, credit unions gained more market share from banks in
locations where cooperative organizations were more prevalent.
2.4. Credit Union Membership Type
The foundation of our first hypothesis is that banks and credit unions have distinct identities, as
perceived by current and potential customers. However, since their invention, there has been a
proliferation in the varieties of credit unions and increasing overlap between certain types of
credit unions and banks. A key concept in understanding these changes in credit unions is the
field of membership, which sets the scope of a credit union’s members. Credit unions are
chartered under different fields of membership, and may only accept as members those
individuals identified in these charters. Specifically, a single-bond federal credit union consists
of one group having a common bond of occupation or association. A community federal credit
union consists of persons or organizations within a well-defined local community, neighborhood
6
See particularly Moody and Fite (1984), pp. 98, 105-106.
12
or rural district. A multi-bond federal credit union consists of more than one group, each of
which has a common bond of occupation or association.
Broadly speaking, single- and community-bond credit unions are narrowly constituted
and composed of members belonging to a well-defined group (e.g., firefighters in a particular
city). These credit unions are especially differentiated from banks, which offer their financial
services to anyone who walks in the door. In contrast, multiple-bond credit unions appeal to
multiple groups simultaneously (e.g., firefighters, police officers and federal employees), and are
by design less distinct from banks and frequently compete with them more directly.
The common bond has been a central organizational attribute in the evolution of credit
unions. Early credit unions were built around tight-knit social groups, and to some extent had the
social elements of “clubs” where the members were all “extensively acquainted with each other”
(Barron, West, and Hannan, 1994). Single-bond and in particular the occupational common bond
credit unions were once dominant, especially during periods of economic prosperity (Burger and
Dacin, 1992).
Changing social and economic conditions altered what was competitively necessary and
organizationally feasible for credit unions. The narrowly defined field of membership limited the
pool of potential new customers that credit unions were able to attract (Barron, West, and
Hannan, 1994). At the same time, as the technology for delivering financial services improved, it
became economically possible to serve larger geographic areas and to offer membership to a
broader group of individuals (Burger and Dacin, 1992). To maintain and enhance their
competitive positions, credit unions overcame strenuous resistance by banks and convinced
policymakers to change regulations to allow for multi-bond status, achieve multi-bond
designations, and loosen the definition of common bonds.
13
While these regulatory changes allowed for greater flexibility in terms of which
individuals credit unions could attract, we argue that the potential benefits in terms of increased
membership must be balanced against the potential costs, particularly those related to the dilution
of a strong identity. The broader the field of membership for a particular credit union, the less
distinct the membership identity. Thus, credit unions with broad fields of membership seem
more similar to banks, which allow anyone to be a customer. Moreover, the broader the field of
membership becomes, it is increasingly difficult to defend a mission of creating value for all of
the members, diluting a key identity claim for credit unions.
We argue that, in the aftermath of the financial crisis, it was precisely the organizational
attributes that made credit unions different from banks that made them most appealing to existing
and potential customers. To the extent that single-bond credit unions are the most distinct subpopulation vis-a-vis banks, we expect that these institutions had particular success in gaining
market share after the crisis. Thus, we propose:
H3: After the financial crisis, single-bond credit unions gained market share at the
expense of banks at a higher rate in comparison to multi-bond credit unions.
2.5. Co-opting Attributes of Credit Unions
While the extant literature focuses on the opportunities for alternative organizational forms that
arise from legitimacy threats to incumbent organizational forms (Hiatt, Sine, and Tolbert, 2009),
it seldom considers the strategic responses by incumbents to insulate themselves. One possible ex
ante strategic tactic is to co-opt attractive attributes of the alternative organizational form. Oliver
(1991) discusses co-option as an organizational response to institutional pressure in an effort to
14
maintain legitimacy. Examples in the spirit might include an oil and gas company hiring an
environmental advocate or a bank adding a board member to focus on community development.
We have in mind a different kind of co-option by which the dominant form adopts key
organizational attributes of the alternative form in an effort to blur categorical distinctions in
identity. We suggest organizational mission, posited by Hannan et al. (2006) as a key part of
organizational identity, would be one attribute that could be amenable to co-option. In our case,
the dominant form, represented by banks, will try to co-opt the mission of credit unions under
some conditions, aping the most distinguishing attribute of the alternate form. These co-opting
tactics, if successful, could blur categorical distinctions between banks and credit unions. If this
effect was persistent, alternative forms would then be less able to gain an advantage during
periods of threats to the legitimacy of the dominant form.
One particularly visible approach banks took in the run-up to the financial crisis to
persuade audiences of their commitment to mutual value creation was allocating funds to local
philanthropy, often viewed as one version of corporate social responsibility. While scholars have
posited several motivations for corporate philanthropy, including gaining tax benefits (Guthrie et
al., 2008), publicity (Burt, 1983), political resources (Wang and Qian, 2011; Jia, Shi, and Wang,
2013), one candidate explanation is that donations are an effort to shape and manage the
perceptions of external audiences (Godfrey, 2005; Muller and Kraussl, 2011), particularly those
in the recipient’s community (Galaskiewicz, 1985, 1991, 1997; Marquis, Glynn, and Davis,
2007; Marquis and Lee, 2012; Tilcsik and Marquis, 2013). By donating to local charities, banks
can demonstrate their own efforts to create value for their customers in the local area, co-opting
the mission of credit unions. These activities make credit unions less distinct and reduce their
attractiveness to potential customers.
15
To the extent these efforts are successful in changing audience perceptions, we expect
that banks would be more likely to preserve their market share during the financial crisis in
regions where they allocated more funds towards local philanthropy. Thus, we propose:
H4: After the financial crisis, credit unions gained less market share from banks in
locations where banks historically allocated more funds towards local philanthropy.
3. Data, Variables and Methods
3.1. Data on Credit Unions and Banks
We collect data on credit unions from the National Credit Union Administration (NCUA) Call
Reports from the second quarters of each year during 2004-2012. The call reports record deposit
data at the headquarters level for each credit union, and we calculate the deposit amounts by
summing up total number of shares and deposits for both CU members and nonmembers.
Given that credit unions often serve communities through branches, the use of aggregated
institutional-level deposit data can lead to erroneous conclusions about presence in the local
market. While branch deposit data is not available, we assume that the amount of deposits is
highly correlated with loan originations,7 and make use of the FFIEC Home Mortgage Disclosure
Act (HMDA) data that are available for credit unions at the market level. Specifically, for each
multi-branch credit union, we disaggregate deposits into the markets where it has branches,
proportional to the volumes of home loans for which the credit union receives applications. We
define markets at the metropolitan statistical areas (MSA) level8, and start the panel from 2004 as
that year marked a major overhaul of the HMDA reporting regulations. Appendix 1 explains the
7
Appendix 1 provides tests of this assumption.
We follow the definition of the metropolitan statistical areas in version of November 2004 by the Office of
Management and Budget (OMB), available at http://www.census.gov/population/estimates/metro-city/List1.txt
8
16
disaggregation procedures in detail.
We collect data on commercial banks from the FDIC Summary of Deposits Database
during 2004-2012.9 As banks are required to submit branch office annual deposit data as of June
30 every year, the depository data of credit unions and commercial banks are drawn from the
same time period throughout the sample. Branches for saving banks, savings associations as well
as foreign-chartered institutions are excluded from our commercial bank panel.10 We further
restrict the sample to banks that are located within the fifty states and Washington D.C. Unlike
credit unions, for commercial banks we are able to directly obtain deposit data at the branch level
and aggregate to the market level.
3.2. Dependent and Independent Variables
Consistent with the economic literature on the competition between credit unions and
commercial banks (e.g., Feinberg, 2001), our main dependent variable is credit unions’ deposit
market share for a given year and market, which is the share of credit union deposits as a
fraction of total deposits, which is measured as the credit union deposits plus commercial bank
deposits. Post Crisis is a dummy variable that equals one for all years after 2007 (inclusive), and
zero otherwise.11
To examine the mechanisms through which deposit market share shifted after the crisis,
we also calculate four other dependent variables, all at the market-year level, including the total
9
We use deposit data from yearly Summary of Deposits Database rather than quarterly Reports of Condition and
Income (e.g., Acharya and Mora, 2015) because the former contains comprehensive data on the branch level.
10
We perform robustness checks that include saving banks and savings associations in the calculation of total
deposits, and the results are comparable to the main results below. We have omitted these results due to space
constraints.
11
Note that a “year” in our setting begins June 30 of the year, rather than January 1, so the post crisis dummy is for
June 30, 2007 and after. We believe this is appropriate given (i) the increase in bank failures that began in early
2007 and (ii) the increase in foreclosures that began in late 2006. Results are robust to using a 2008 post crisis
dummy, as reported in Table 2, Model 6 and Appendix 4.
17
deposits of commercial banks, total deposits of credit unions, total number of members of credit
unions as well as total number of deposit accounts of credit unions.12
To test H2, we gauge the prevalence of cooperative organizations using a diversity
measure that accounts for how widely adopted the cooperative form of organizing is in a given
market. We source data from a 2006 sampling survey conducted by the University of Wisconsin
Center for Cooperatives, which is to our knowledge the most comprehensive source on the
economic and social activities by U.S. cooperatives. We collect data on the number of
cooperatives in different sectors for each market, including commercial sales & marketing, social
& public services, financial services, and utilities. We then calculate the diversity measure of
how widely used the cooperative forms are in the market’s various economic and social activities
using the Blau index (1 − Σ𝑞! , where 𝑞 is the sector category proportion). We split the sample
by median so that markets with high Blau index are coded as having a high prevalence of
cooperative organizations.
To test H3, we draw on the NCUA data to determine the field of membership for credit
unions. For reporting simplicity, we group single-bond and community federal credit unions
together as S&C. For each type of credit unions (S&C or multi-bond), we calculate their deposit
market share as a fraction of total deposits measured by the credit union deposits (of all types)
plus commercial bank deposits.
To test H4, we measure the banks’ community value creating funds using the
philanthropic giving of the nation’s largest banks in each market, normalized by population. We
use philanthropic giving as it is an important means for firms to solidify their identity with the
local audience (Galaskiewicz, 1997; Marquis, Glynn, and Davis, 2013), and firms’ level of
12
Total number of members/depository accounts of credit unions are obtained from the NCUA at the headquarters
level and are also disaggregated using the methods described in Appendix 1.
18
generosity typically responds closely to local demands (Tilcsik and Marquis, 2013). We define
large banks as the top 10 U.S. commercial banks in terms of consolidated assets on the holding
company level in 2006, a year preceding the financial crisis.13 We collect corporate
philanthropic giving data for these large banks from the Foundation Directory Online (FDO)
database during 2003-2006. FDO documents data on corporate cash donations to foundations
that are required to report under federal regulations; as well as corporate direct giving programs
that reports data for tax deduction purposes. FDO records both the amount of each donation as
well as the recipient organization, so that we are able to match the receipt organization to market
and obtain the market-year level data on bank philanthropic giving. We split the sample by
median so that markets with high levels of philanthropic giving during 2003-2006 are coded as
markets with high abundance of banks’ community value creating funds.14
3.3. Control Variables
We include a number of control variables. First, we control for market fixed effects across all
models, which deal with the time-invariant unobservable market level characteristics that are
correlated with both the post-crisis time period and deposit ratios. Second, we control for a
13
As the rank orders for assets are calculated quarterly and vary, our final list consists of 15 banks including, Bank
of America, JP Morgan Chase, Citibank, Wachovia, Wells Fargo, US Bank, Suntrust Bank, State Street Corp,
KeyBank, Bank of New York, PNC, National City Bank, Regions Bank, Branch Bank, Fifth Third Bank. We also
create two alternative lists of large banks. One list takes the top five commercial banks by consolidated assets in
2006, i.e., Bank of America, JP Morgan Chase, Citibank, Wachovia, and Wells Fargo. Another list consists of eight
U.S. banks that are designed as systematically important financial institutions by Financial Stability Board, i.e., the
so-called too-big-to-fail banks, that include Bank of America, Bank of New York Mellon, Citigroup, Golden Sachs,
JP Morgan Stanley, Morgan Stanley, State Street, and Wells Fargo. The results are robust to alternative definitions
of large banks.
14
We perform a number of robustness checks. First, we use the donation data in 2003, 2004, 2005, 2006
respectively to conduct the split-sample analysis. Second, allowing that corporate donations are most concentrated in
headquarter location, we drop the markets that the largest banks are headquartered in, and run the split sample
analysis on the mean donations across years. In both cases, we obtain results comparable to the main results. Results
are omitted due to space constraints but available upon request.
19
number of time-variant market level characteristics. We control for the number of bank and
credit union branches in each market to account for classic market structure explanations for
competitive outcomes. We control for how financially sound banks and credit unions are by
using loan performance measures such as the net charge off ratio and delinquency rate.15 These
controls help us to address an alternative explanation that customers choose to bank more with
credit unions after the crisis because they think their money will be safer there. Another set of
alternative explanations has to do with market-level characteristics that correlate with the crisis
as well as change in deposit ratios. For example, it could be that in areas where people lose jobs
due to the crisis, these same individuals also withdrew more deposits from banks. Thus, an
income effect could explain lower bank market share post-crisis. To alleviate these concerns, we
control for market level demographic factors including the median household income,
unemployment rate, as well as population by age group buckets. Table 1 reports the descriptive
statistics of the main variables and controls.
***Insert Table 1 Here***
3.4. Methods
To test our hypotheses, the main specification we use is:
𝑦!" = 𝛼 + 𝛽! 𝑃𝑜𝑠𝑡 𝐶𝑟𝑖𝑠𝑖𝑠! + 𝛿! + 𝑋!" 𝛽 + 𝜀!" ,
(1)
where 𝑦!" is the deposit share of credit unions of market i in year 𝑡. 𝑃𝑜𝑠𝑡 𝐶𝑟𝑖𝑠𝑖𝑠! is set equal to 1
during 2007-2012, the years during and after the crisis. As mentioned above, we include market
fixed effects 𝛿! to control for the time-invariant differences in geographic characteristics, local
regulations on cooperatives, as well as demographic factors that influence the choice of financial
15
The charge-off ratio is calculated as the total amount of loans charged-off during the year less all recoveries on
charged-off loans during the year divided by average loans. The delinquency rate is the amount of loans past due
thirty days or more divided by the amount of total loans outstanding. Both measures are calculated at the
institutional level, with bank data drawn from the Bank Regulatory Database on Compustat, and CU data from the
NCUA call reports.
20
institutions and vary across states. 𝑋!" 𝛽 is a vector of market characteristics. Throughout all of
our specifications the error terms are clustered at the market level. We are particularly interested
in the coefficient 𝛽! , which is hypothesized to be positive in H1. We assess H2 – H4 using splitsample analyses in which we compare the relative magnitude of the coefficient 𝛽! across
samples. We estimate equation (1) using OLS models.16
4. Results
The baseline results are presented in Table 2. Models 1-4 in Table 2 run OLS regressions
of various deposits measures on a crisis dummy, while controlling for alternative geographic,
regulatory and demographic factors that also affect consumer choices. Model 1 indicates that the
banks’ deposit level is on average significantly lower post crisis compared to 2004-2006. Models
2-4 shows that credit unions had stable total deposits but in contrast to banks expanded
significantly in terms of the absolute number of deposit accounts and members. Models 5-7 run
linear probability regressions of the proportion of deposit share on a crisis dummy with the same
set of controls. Model 5 shows that post crisis, the credit union deposit ratio is significantly
higher than that of 2004-2006. Model 6 uses a 2008 post crisis dummy, and finds a comparable
result to Model 5 both in terms of significance and magnitude. Model 7 splits the post-crisis era
into two time periods of 2007-2008, and 2009-2012. The results indicate that the impact of the
financial crisis on credit union deposits deepened over time. Overall, Table 2 provides evidence
in support of Hypothesis 1, namely that credit unions gained market share at the expense of
banks after the financial crisis. The result is robust to the inclusion of time-invariant and various
time-varying market characteristics as well as an alternative definition of the crisis period. The
16
As described in Appendix 2, our results are robust to alternative functional forms, such as arcsin and log-odds
transformations.
21
result is economically meaningful as well: the estimated coefficient in Model 5 indicates that in
2007-2012, the credit unions’ deposit ratio is on average 2.4% proportionally higher than that of
2004-2006, which is equivalent to an increase of $124.8 billion dollars at 2006 deposit levels.
Table 3 investigates H2, H3, and H4, by running different sub-sample analyses on 𝑦!" , the
deposit share ratio of credit unions. Model 8 (L), (H) run a split sample analysis based on the
prevalence of cooperatives in the market. The results indicate that both high- and lowprevalence markets experienced a statistically significant increase in credit unions’ deposit ratio.
The tests of coefficient differences across the models suggest that we can reject coefficient
equality at the 1% level of confidence, in support for Hypothesis 2. Model 9 (A), (B) examines
the ratio by the two types of credit union. We see that the S&C credit unions gained the most
market share in terms of deposits, which provides support to H3. M10 (L), (H) runs a split
sample analysis based on the low and high mean levels of bank philanthropic donations in the
market. We find evidence that the markets with lower philanthropic giving experienced a larger
post-crisis increase in credit unions’ deposit market share. The credit unions’ deposit ratio in
2007-2012 was on average 1.57% higher than that of 2004-2006 in high donation markets, and
3.28% in low donation markets. Tests of coefficient differences across the models suggest that
we can reject coefficient equality at the 5% level of confidence. Overall, we find support for
Hypothesis 4.
***Insert Tables 2-3 Here***
5. Alternative Explanations
Our regressions include market fixed-effects and numerous control variables that rule out a large
number of alternative explanations, as discussed previously. In this section, we consider four
additional explanations that might explain the patterns observed in the data. First, it is plausible
22
that credit unions attract deposits not because of their organizational form, but because their
“smallness” or “localness” appeals to certain consumer segments. By this logic, small banks
would also benefit from the crisis. Table 4 examines this possibility by splitting commercial
banks into small and large categories, and finds that the 2.4% drop in banks’ deposit ratio is
shared equally by small and large banks, providing evidence that is not consistent with this
alternative explanation.
Second, it could be that depositors are attracted to credit unions by better interest rates.
Credit unions are known to provide higher rates than banks due to their non-profit tax exempt
status. To assess this possibility, we collect interest rate data for both credit unions and banks,
calculate average rate differences in each market, and spit the sample into markets with high and
low rate differences. Specifically, we obtain branch-level data on three-month checking interest
rates for both banks and credit unions from January to June in 2007 from the SNL Financials
database. We then aggregate the data to obtain market-level rate differentials between banks and
credit unions. On average, credit unions do offer better rates than commercial banks. However,
the difference is minimal with a mean of 0.31%. Table 5 reports results on the split sample
analysis; we find no significant differences between markets with high and low rate differences.
While Table 5 suggests that differences in the three-month checking rates did not attract
depositors, credit unions offer a wide range of financial products that might otherwise have
appealed to potential customers. It is therefore possible that credit unions actively used rates on
other products as a competitive device to attract customers post-crisis. With these considerations
in mind, we collect data on rates at credit unions and banks across other financial products at the
national level. As listed in Appendix 3, while credit unions on average offer better rates on
products such as CDs, money markets and fixed rate mortgages, banks offer better rates on credit
23
cards, auto loans and to some extent, floating rate mortgages. More importantly, there were
minimal changes in the rate differentials across banks and credit unions before and after the
crisis. If anything, the gap in rates narrows after the crisis. The combination of evidence
presented in Table 5 and Appendix 3 suggests that interest rates were unlikely to be the reason
that credit unions gained competitive advantage in the deposit market after the financial crisis.
A third alternative explanation is that credit unions gain market share over banks during
recessions more generally, and our results are unrelated to a specific threat to bank legitimacy. If
this were the case, then we would expect credit unions to gain market share during other
recessions. We test this possibility by collecting historical data on credit unions’ deposits and
accounts from 1934-1990 at the national level and combining this with historical data on the
timing of recessions from the National Bureau of Economic Research (NBER).17 Table 6 reports
results from OLS regressions of various credit unions’ deposits and accounts variables on a
dummy for recessions. The coefficient on the recession dummy is negative but not significant.
This suggests that there is no relationship between recessions and changes in credit union market
share, and helps us rule out this alternative explanation (if anything, the relationship appears to
be negative).
***Insert Table 4-6 here***
Fourth, aside from economic considerations, there was at least one high profile social
movement aimed at explicitly encouraging Americans to shift their money away from large
banks during our sample period. As mentioned above, the “Move Your Money” (MYM)
campaign was launched by Arianna Huffington and Rob Johnson at the end of 2009 as a
17
NBER recession dates available online here: http://www.nber.org/cycles.html. Last accessed April 15, 2015.
24
challenge to large banks involved in the financial crisis.18 After initial discussions in late
December 2009, the campaign was quickly launched by the end of the year, complete with a
website, introductory video, and post on Huffington Post. The campaign specifically identified
six banks as targets, JP Morgan Chase, Citibank, Bank of America, Wells Fargo, Goldman Sachs
and Morgan Stanley, and urged consumers to move their money to local community banks, even
providing a “bank finder” tool on the website. Huffington and Johnson updated their original
post to mention credit unions after receiving comments from readers. This campaign spurred
several other actions, including Bank Transfer Day in 2011, which specifically urged consumers
to shift money from banks to credit unions in response to increases in debit card fees. According
to the Credit Union National Association, this event led to large increases in account openings
and total savings19, though they later revised their estimates significantly downward20.
Since the most prominent social movement campaign and related events happened after
the initial move to credit unions had already begun, likely coinciding with other important
unobserved factors, we are unable to precisely identify the causal effect the MYM campaign or
Bank Transfer Day. However, model 2 in Table 2 provides some suggestive evidence of their
impact. There is statistically significant difference between the coefficient on “Year 2007-2008”
and “Year 2009-2012”, suggesting the shift towards credit unions was more pronounced in this
later period. Whether the MYM campaign and Bank Transfer campaign were simply indicators
or an underlying trend or contributing factors remains an intriguing topic for future research.
18
Huffington, A. and R. Johnson “Move Your Money: A New Year's Resolution”, December 29th, 2009
(http://www.huffingtonpost.com/arianna-huffington/move-your-money-a-new-yea_b_406022.html), Last accessed
August 13th, 2014
19
NY Post Staff Reports, “Credit Unions Boom”, October 16th, 2011 (http://nypost.com/2011/10/16/credit-unionsboom/), Last accessed August 13th 2014
20
Stewart, Jackie. The American Banker, “Credit Unions Eat Crow on Customer Numbers” December 5, 2011
(http://www.americanbanker.com/issues/176_234/credit-unions-cuna-membership-bank-transfer-day-10445991.html) Last accessed August 13th, 2014
25
6. Conclusion and Discussion
The most recent financial crisis had a significant impact on a wide range of economic and social
indicators. We argue that one important result of the crisis was the shock to legitimacy of
commercial banks and subsequent gains for certain credit unions. Drawing on organizational
theories of competition between organizational populations, we examine the conditions under
which credit unions, which offer similar services to banks but abide by a different mission,
benefitted from negative public sentiment towards banks. We theorize that credit unions gained
share at the expense of banks after the crisis, and that this effect was driven by those credit
unions that had the most distinct identity vis-à-vis banks, namely single-bond and community
federal credit unions. Moreover, we predict that this main effect is moderated by local factors,
namely the acceptance of alternative organizational forms such as co-ops and bank charitable
giving.
We find broad evidence in support of our propositions. From 2007-2012, bank deposit
share dropped from 78.8% to 76%. This 2.8% drop is equivalent to $168B in deposits (at 2006
deposit levels). Single-bond and community credit unions were the primary beneficiaries of the
drop in bank market share; multi-bond credit unions did not appear to gain market share. The
drop of the commercial banks’ deposit market share is more pronounced in markets where
cooperative forms are more prevalent. In addition, the drop in commercial banks’ deposit market
share is mitigated in markets where the nation’s largest commercial banks had higher levels of
pre-crisis philanthropic giving. Finally, the results are robust to a number of alternative
explanations.
We make several contributions to the extant literature. First, we provide a theoretical
26
explanation for the promise and peril of alternate organizational forms imitating the dominant
form on product features and organizational attributes. It may be tempting for alternate
organizational forms to become more similar to the dominant form in an effort to attract greater
acceptance. However, when the legitimacy of the dominant form is threatened, it will be more
challenging for the alternate form to seize the opportunity. Within credit unions and most
organizational populations, there is considerable variation across organizations. It seems likely
that some organizations will choose imitation strategies and others will choose differentiation
strategies, creating an interesting tension within populations.
Further, we provide an explanation for how co-opting tactics by the dominant form can
successfully mitigate threats to legitimacy. In our specific case, corporate social responsibility
(CSR) initiatives, such as philanthropic giving, helps corporations mimic the behavior of
cooperative organizations and blur categorical distinctions.21 Prior research (e.g., Waddock and
Graves, 1997; Barnett and Salomon, 2006; Flammer, 2014) has indicated a positive correlation
between CSR and firm performance. Our research suggests that one mechanism that might
underlie this relationship is that, by mimicking cooperative organizations, for–profit corporations
are able to shield themselves from threats to legitimacy and other downside risks. Third, we
contribute to recent strategy and OT literatures on competition between organizational forms
(e.g., Mahoney, McGahan, and Pitelis, 2009; Seamans, 2012), and highlight some of the
challenges faced by alternative organizational forms when competing against incumbent
organizational forms.
In addition, we add to the literature on legitimacy of organizational populations by
21
We focus on ex ante competitive tactics used by the corporate form. This is not meant to preclude the existence of
ex post competitive tactics. Moreover, to the extent that banks also relied on ex post competitive tactics, this implies
that our results are conservative. An examination of the full array of ex ante and ex post competitive tactics is
beyond the scope of our current study but would be a fruitful research avenue.
27
demonstrating the role of competition in influencing the loss of critical resources. When two
organizational populations are competing in the same product categories, a shock to the
legitimacy of one population can have a pronounced effect, particularly if this shock is related to
a distinct institutional logic (e.g., profit maximization) and not the appropriateness of the product
itself (e.g., financial services). We view our work as complementary to Xu et al. (2014), who
document how new forms can gain legitimacy from dominant forms while simultaneously
attacking them. In our case, we use variation within the population of credit unions to explain
how differences in positioning impact audience perceptions during times of crisis. Future work
might consider cases where the entire product category is discredited in one population and
explore the spillovers on the competing populations. Finally, as mentioned by Dobrev et al.
(2006), competitive dynamics between populations dependent heavily on whether the alternate
form is well-established or new. Future theoretical and empirical work should explore how the
stage of development of the alternate form influences competitive dynamics.
Our work offers some key practical implications. Despite efforts to regulate banks in the
U.S., many analysts worry that new regulations will not be implemented or that they will be
inadequate to cope with the changing landscape of finance (Jacobides, Drexler, and Rico, 2014).
Others have suggested that banks are even more powerful today, and that the threat to legitimacy
in the aftermath of the financial crisis was only a small perturbation. We offer an explanation for
why this may be the case. Well-established and economically potent populations that provide an
essential product, such as financial services, are very difficult to de-legitimize. Our results
indicate that, for fundamental change to occur, there must be a competing organizational
population with an obviously distinct identity that offers the same good or service. Importantly,
this population must already be well-accepted by the relevant audience, or quickly become so as
28
a result of a successful social movement (Hiatt, Sine and Tolbert, 2009). Even in these rare cases,
the focal population, banks in our study, may still mitigate negative effects by blurring
distinctions between the competing forms. Taken together, we suggest that there will be very few
cases where an entrenched incumbent population of organizations will be dethroned. Using the
factors above as a “checklist” might help organizational scholars gain insight into other battles
between organizations, whether it be non-profit vs. for-profit higher education or venture capital
firms vs. crowdfunding platforms. However, further theoretical work would be required to
generalize our theoretical propositions.
Specific to credit unions, our results imply that to the extent credit unions provide similar
services to banks but can remain distinct in terms of their identities and logic, they will continue
to be in a position to benefit from legitimacy threats to banks. However, if credit unions continue
to migrate towards banks so much so that their identities are indistinguishable, they may suffer
from the next crisis as well. This logic suggests that advocates for credit unions are wise to adopt
a “same, but different” approach to providing similar financial services with a different
institutional logic. However, this strategy will also allow banks to mimic credit unions more
successfully, potentially mitigating the benefits.
Finally, our results highlight a more general paradox faced by alternative organizational
forms. There appears to be a natural limit to the growth and acceptance of alternative
organizational forms under most conditions. These forms can benefit during periods when
incumbent organizational forms experience threats to legitimacy. But it is challenging for these
alternative organizations to both gain market share during normal times through imitation and
maintain a consistent and strong identity during times of crisis in the dominant form. Ironically,
the very attributes that makes them so appealing to external audiences also limits their growth.
29
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34
Table 1: Summary Statistics
Variables
Credit Unions’ Deposit Market Share
Post Crisis
Year 2007-8
Year 2009-12
Total Deposits of Commercial Banks (in billions of dollars)
Total Deposits of Credit Unions (in billions of dollars)
Total Number of Members of Credit Unions (in thousands)
Total Number of Deposit Accounts of Credit Unions (in thousands)
S&C Federal Credit Unions’ Deposit Market Share
Multi-bond Federal Credit Unions’ Deposit Market Share
No. of Bank Branches
Bank Net Charge offs (%)
Bank Delinquency Ratio (30 days, %)
No. of CU Branches
CU Net Charge offs (%)
CU Delinquency Ratio (30 days, %)
Median HH Income
Unemployment Rate
Pop: yr==0
Pop: 1<=yr<=4
Pop: 5<=yr<=9
Pop: 10<=yr<=14
Pop: 15<=yr<=19
Pop: 20<=yr<=24
Pop: 25<=yr<=29
Pop: 30<=yr<=34
Pop: 35<=yr<=39
Pop: 40<=yr<=44
Pop: 45<=yr<=49
Pop: 50<=yr<=54
Pop: 55<=yr<=59
Pop: 60<=yr<=64
Pop: 65<=yr<=69
Pop: 70<=yr<=74
Pop: 75<=yr<=79
Pop: 80<=yr<=84
Pop: yr>=85
N
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
3411
35
Min
0.000
0.000
0.000
0.000
0.293
0.000
0.000
0.000
0.000
0.000
10.000
-0.061
0.461
1.000
-4.530
0.320
24.808
0.021
0.605
2.495
3.126
3.229
3.189
3.257
3.183
3.123
2.939
3.547
3.780
3.374
2.584
1.919
1.396
0.911
0.648
0.404
0.323
Max
0.922
1.000
1.000
1.000
840.159
82.547
8264.764
15626.020
0.980
0.910
2559.000
79.776
14.622
245.000
14.231
155.741
89.974
0.290
162.663
596.905
701.821
785.806
769.140
861.453
988.839
931.008
882.121
887.961
834.147
804.537
729.870
625.347
475.921
349.908
283.454
212.305
224.412
Mean
0.218
0.667
0.222
0.444
13.753
2.244
276.335
507.258
0.067
0.093
166.200
0.818
3.374
22.205
0.701
3.530
47.580
0.068
8.959
35.723
44.217
46.192
47.874
47.822
46.636
44.821
45.961
48.328
49.522
46.650
40.734
32.661
24.403
18.873
15.516
12.048
11.030
Median
0.179
1.000
0.000
0.000
3.418
0.964
141.441
254.470
0.021
0.040
74.000
0.582
2.758
13.000
0.566
2.906
45.836
0.061
3.371
13.452
17.021
17.635
19.532
22.133
17.112
16.209
16.422
17.484
18.909
18.684
16.736
13.460
10.113
7.899
6.372
4.832
4.352
SD
0.153
0.471
0.416
0.497
40.694
4.004
407.396
766.169
0.134
0.132
258.544
1.525
2.173
28.929
0.592
3.668
9.557
0.029
16.184
63.458
77.178
81.247
81.785
81.444
89.027
85.290
85.475
86.902
85.808
79.125
68.057
54.142
39.947
30.726
25.226
19.771
18.594
Table 2: Effects of Financial Crisis on the Banks’ and Credit Unions’ Deposits, Members and Market Share (H1)
Post Crisis
DV 1: total
deposits of
commercial
banks
DV2: total
deposits of
credit unions
DV3: total
number of credit
union members
DV4: total
number of credit
union accounts
M4
48.3938**
(22.1670)
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
M5
0.0242***
(0.0044)
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
M6
0.0213***
(0.0046)
M1
-1.0741**
(0.5214)
M2
0.1690
(0.1251)
M3
24.2890**
(11.8762)
0.0471
(0.0310)
0.0294
(0.0527)
-0.0588
0.0024
(0.0045)
0.0097
(0.0138)
0.0301
-0.0223
(0.4307)
0.8053
(1.4159)
2.2404
-0.0782
(0.8205)
1.3138
(2.6723)
4.4516
-0.0001
(0.0001)
-0.0005**
(0.0003)
0.0016
-0.0001
(0.0001)
-0.0006**
(0.0003)
0.0017
Year 2007-8
Year 2009-12
No. of Bank Branches
Bank Net Charge offs (%)
Bank Delinquency Ratio (30
days, %)
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
M7
0.0226***
(0.0044)
0.0491***
(0.0077)
-0.0001
(0.0001)
-0.0004*
(0.0002)
-0.0001
(0.2759)
(0.0475)
(4.0638)
(7.7878)
(0.0018)
(0.0018)
(0.0019)
-0.5674
-0.0035
0.9506
1.2769
0.0050***
0.0052***
0.0053***
(0.4362)
(0.0439)
(4.2420)
(8.0794)
(0.0010)
(0.0010)
(0.0010)
CU Net Charge offs (%)
0.3749
-0.1189*
-9.4252
-17.3345
-0.0045**
-0.0043**
-0.0034*
(0.5284)
(0.0653)
(5.8538)
(11.2985)
(0.0022)
(0.0021)
(0.0020)
CU Delinquency Ratio (30 days, %)
-0.0641
-0.0112**
-0.4710
-1.5069*
-0.0003
-0.0003
-0.0002
(0.0690)
(0.0045)
(0.5012)
(0.7991)
(0.0005)
(0.0005)
(0.0005)
Median HH Income
0.1955
0.0341**
2.2340*
3.6151
0.0025***
0.0032***
0.0020**
(0.1670)
(0.0155)
(1.3119)
(2.5941)
(0.0008)
(0.0008)
(0.0008)
Unemployment Rate
-12.7170
-1.5879
-284.5475
-547.6394
-0.0074
-0.1183
-0.3577**
(17.4780)
(2.4591)
(214.9492)
(413.7519)
(0.1386)
(0.1415)
(0.1605)
Constant
-2.1059
-2.8511***
-220.8422***
-429.4925***
-0.0279
-0.0470
-0.0027
(5.7501)
(0.8726)
(78.3160)
(155.5516)
(0.0322)
(0.0334)
(0.0330)
R2
0.9571
0.8898
0.9067
0.9041
0.8879
0.8874
0.8891
Population by Age Group
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MarketFE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
3411
3411
3411
3411
3411
3411
3411
Notes: Dependent Variables are measured at a given market in a given year. Coefficients of OLS regressions (M1-M4) and linear probability regressions (M5-M7). Post crisis is a
dummy variable, that equals one for all years after 2007 (except for M6, 2008) inclusive, and zero otherwise. Robust standard errors, clustered at the market level, are included in
brackets. Significance Level: * p<0.10; ** p<0.05; *** p<0.01
No. of CU Branches
36
Table 3: Split Sample Analysis of the Effects of Financial Crisis on the Credit Unions’ Deposits Market Share (H2,H3, H4)
Post Crisis
No. of Bank Branches
Bank Net Charge offs (%)
Bank Delinquency Ratio (30 days, %)
No. of CU Branches
CU Net Charge offs (%)
CU Delinquency Ratio (30 days, %)
Median HH Income
Unemployment Rate
Constant
DV5: fraction of the
credit union
deposits over the
total deposits at
commercial banks
and credit unions
DV5: fraction of the
credit union
deposits over the
total deposits at
commercial banks
and credit unions
DV6: fraction of
multi-bond federal
credit union
deposits with over
the total deposits at
commercial banks
and credit unions
M8L
0.0084**
(0.0042)
-0.0002
(0.0001)
-0.0104***
(0.0038)
0.0042***
(0.0015)
0.0046***
(0.0006)
0.0001
(0.0023)
-0.0009*
(0.0005)
0.0023***
(0.0006)
0.1997*
(0.1145)
-0.6268**
(0.2460)
14.34
(P value: 0.00)
M8H
0.0396***
(0.0068)
-0.0001
(0.0001)
-0.0002
(0.0009)
0.0015
(0.0022)
0.0061***
(0.0013)
-0.0062*
(0.0034)
-0.0001
(0.0004)
0.0027***
(0.0009)
-0.0800
(0.1862)
-0.0371
(0.0439)
M9A
-0.0025
(0.0057)
-0.0001
(0.0001)
-0.0010
(0.0006)
0.0034
(0.0024)
0.0023***
(0.0008)
-0.0058
(0.0038)
0.0001
(0.0005)
0.0000**
(0.0000)
-0.8676***
(0.2475)
0.0176
(0.0412)
DV7: fraction of
single-bond and
community federal
credit union deposits
with over the total
deposits at
commercial banks
and credit unions
M9B
0.0168***
(0.0055)
0.0000
(0.0001)
0.0008
(0.0010)
0.0041
(0.0033)
0.0022**
(0.0010)
0.0030
(0.0062)
-0.0004*
(0.0002)
0.0000
(0.0000)
-0.2221
(0.2486)
0.0284
(0.0359)
4.83
(P value: 0.03)
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
M10L
0.0328***
(0.0062)
-0.0010***
(0.0003)
0.0013
(0.0054)
0.0023
(0.0022)
0.0113***
(0.0013)
-0.0029
(0.0036)
-0.0012*
(0.0007)
0.0033***
(0.0010)
-0.0397
(0.1547)
-0.2276***
(0.0581)
M10H
0.0157***
(0.0050)
-0.0001
(0.0001)
-0.0003
(0.0006)
-0.0019
(0.0017)
0.0028***
(0.0006)
-0.0041*
(0.0023)
-0.0000
(0.0003)
0.0021***
(0.0007)
0.2544*
(0.1527)
-0.5828***
(0.1609)
Chi-square
Test of Differences
Across Split Samples
R2
0.9373
0.8425
0.7206
0.6602
0.8923
0.8853
Population by Age Group
Yes
Yes
Yes
Yes
Yes
Yes
MarketFE
Yes
Yes
Yes
Yes
Yes
Yes
Observations
1701
1710
3411
3411
1710
1701
Notes: Dependent Variables are measured at a given market in a given year: DV5, fraction of the credit union deposits over the total deposits at commercial banks and credit
unions; DV6, fraction of multi-bond federal credit union deposits with over the total deposits at commercial banks and credit unions; DV7, fraction of single-bond and
community federal credit union deposits with over the total deposits at commercial banks and credit unions. Coefficients of linear probability regressions. Post crisis is a
dummy variable, that equals one for all years after 2007 inclusive, and zero otherwise. Robust standard errors, clustered at the market level, are included in brackets.
Significance Level: * p<0.10; ** p<0.05; *** p<0.01
37
Table 4: Alternative Explanation on Smallness – Small/Large Bank Deposit Ratio
DV8: fraction of the
DV9: fraction of small
DV10: fraction of large
commercial bank deposits
bank deposits over the
bank deposits over the total
over the total deposits at
total deposits at
deposits at commercial
commercial banks and
commercial banks and
banks and credit unions
credit unions
credit unions
M11
M11A
M11B
Post Crisis
-0.0240***
-0.0124**
-0.0116*
(0.0045)
(0.0055)
(0.0066)
No. of Bank Branches
0.0001
-0.0000
0.0001
(0.0001)
(0.0001)
(0.0001)
Bank Net Charge offs (%)
0.0005**
0.0007**
-0.0001
(0.0003)
(0.0003)
(0.0003)
Bank Delinquency Ratio (30 days, %)
-0.0017
-0.0019
0.0003
(0.0018)
(0.0017)
(0.0022)
No. of CU Branches
-0.0050***
-0.0013*
-0.0037***
(0.0010)
(0.0007)
(0.0011)
CU Net Charge offs (%)
0.0046**
-0.0006
0.0051
(0.0022)
(0.0019)
(0.0032)
CU Delinquency Ratio (30 days, %)
0.0003
0.0001
0.0002
(0.0005)
(0.0003)
(0.0004)
Median HH Income
-0.0026***
-0.0010
-0.0016
(0.0008)
(0.0008)
(0.0010)
Unemployment Rate
0.0072
0.0714
-0.0643
(0.1382)
(0.1522)
(0.1874)
Constant
1.0283***
0.6910***
0.3373***
(0.0321)
(0.0298)
(0.0382)
R2
0.8876
0.9246
0.9029
Population by Age Group
Yes
Yes
Yes
MarketFE
Yes
Yes
Yes
Observations
3411
3411
3411
Notes: Dependent Variables are measured at a given market in a given year: DV8, fraction of the commercial
bank deposits over the total deposits at commercial banks and credit unions; DV9, fraction of small bank deposits
over the total deposits at commercial banks and credit unions; DV10, fraction of large bank deposits over the
total deposits at commercial banks and credit unions. Coefficients of linear probability regressions. Post crisis is
a dummy variable, that equals one for all years after 2007 inclusive, and zero otherwise. Robust standard errors,
clustered at the market level, are included in brackets. Significance Level: * p<0.10; ** p<0.05; *** p<0.01
38
Table 5: Alternative Explanation on Rate Differentials between Credit Unions vs. Banks
DV5: fraction of the credit
DV5: fraction of the credit
Post Crisis
No. of Bank Branches
Bank Net Charge offs (%)
Bank Delinquency Ratio (30 days, %)
No. of CU Branches
CU Net Charge offs (%)
CU Delinquency Ratio (30 days, %)
Median HH Income
Unemployment Rate
Constant
Chi-square
Test of Differences
Across Split Samples
R2
Population by Age Group
MarketFE
Observations
union deposits over the total
deposits at commercial banks
and credit unions
union deposits over the total
deposits at commercial banks
and credit unions
M12L
0.0265***
(0.0064)
-0.0004**
(0.0002)
-0.0033
(0.0058)
0.0003
(0.0026)
0.0075***
(0.0010)
-0.0071*
(0.0040)
-0.0025***
(0.0008)
0.0015*
(0.0009)
0.2527
(0.1833)
0.3161
(0.5168)
0.23
(P value: 0.6325)
M12H
0.0219***
(0.0060)
-0.0001
(0.0001)
-0.0001
(0.0006)
0.0021
(0.0018)
0.0019**
(0.0009)
-0.0072**
(0.0035)
-0.0001
(0.0003)
0.0028***
(0.0009)
-0.0609
(0.1644)
-1.4779*
(0.7821)
0.9034
Yes
Yes
1161
0.8950
Yes
Yes
1161
Notes: Dependent Variables: DV5, fraction of the credit union deposits over the total deposits at
commercial banks and credit unions, at a given market in a given year. Coefficients of linear
probability regressions. Post crisis is a dummy variable, that equals one for all years after 2007
inclusive, and zero otherwise. Robust standard errors, clustered at the market level, are included in
brackets. Significance Level: * p<0.10; ** p<0.05; *** p<0.01
39
Table 6: Alternative Explanation on Recession – Credit Union vs. Banks and Historical
Recession, 1932-1990
DV11: Number of
credit union
members
DV14: ratio of credit
union deposits
divided by the sum
of deposits from
banks and credit
unions
Mt6a
Mt6b
Mt6c
Mt6d
Recession
-1.5059
-8.7668
-8.64e+04
-0.0025
(1.6961)
(9.5189)
(9.65e+04)
(0.0018)
Trend
1.0876***
2.5062***
3.83e+04***
0.0012***
(0.0486)
(0.2727)
(2766.0707)
(0.0001)
Constant
-9.3768***
-34.6855***
-3.86e+05***
-0.0094***
(1.6744)
(9.3972)
(9.53e+04)
(0.0018)
R2
0.9028
0.6121
0.7809
0.9148
Observations
57
57
57
57
Notes: Dependent Variables are measured as yearly national aggregates: DV11, Number of credit
union members; DV12, total of credit union shares and deposits; DV13, total of credit union
deposits; DV14, ratio of credit union deposits divided by the sum of deposits from banks and
credit unions. Historical data on credit unions are drawn from Berger and Dacin (1991). Historical
data on banks are drawn from FDIC, recession data drawn from NBER. Coefficients of OLS
regressions (Mt6a-Mt6c), and linear probability regressions (Mt6d). Standard errors are included
in brackets. Significance Level: * p<0.10; ** p<0.05; *** p<0.01
DV12: total of credit
union shares and
deposits
40
DV13: total of credit
union deposits
Appendix 1: Credit Union Branches22
This appendix describes how we allocate credit union deposits to the credit union branch level.
From the National Credit Union Administration (NCUA) 5300 Call Report Quarterly we obtain
the total amount of deposits at each credit union. However, many credit unions are multi-branch,
where the branches span several MSAs. The NCUA does not provide a breakdown of the
deposits at each branch until 2010Q3 (and no data prior to 2004Q2, to the best of our
knowledge).We therefore use annual data from the FFIEC Home Mortgage Disclosure Act
(HMDA) to determine the amount of deposit for branch of a credit unions.
HMDA, enacted by Congress in 1975, requires financial institutions to disclose
information to their regulatory agency about every home loan received and originated.
Specifically, two bits of information are available on a yearly basis: (1) for each reported credit
union, the metropolitan areas in which credit unions have branches, and (2) each loan application
including location of the property, loan amount, and originating credit union. HMDA coverage is
quite complete for metropolitan areas, but reporting exceptions lead to significant distortions in
the coverage of non-metropolitan areas. We therefore work only with metropolitan areas and
drop non-metropolitan areas.
We assume that multi-branch credit union receive (and grant) home loan applications
through the branch that is in the same metropolitan areas as the home property. Under this
assumption, we are able calculate the proportion of total loans originated by a credit union at
each of its branches. 23 Using this proportion, we disaggregated the credit unions total deposits to
22
We thank John Worth from NCUA for his helpful suggestions that lead us to use HMDA data for branching.
Following Igan, Mishra and Tressel (2012), and to make sure that the HMDA data are clear of outliers and
erroneous values, we eliminate the edited applications from HMDA. In unreported analysis, we also evenly
distributed the deposits to all markets that CUs have branches in according to HMDA and obtained comparable
results.
23
41
each of its branches from 2004-2012. 24
Although HMDA provides us with a relatively consistent dataset, there are some
limitations that need to be addressed. Specifically, federal regulation exempt certain credit
unions from reporting to HMDA, including (1) the ones below minimal asset threshold; (2) the
ones with offices only in non-metropolitan areas; (3) the ones that didn’t originate at least one
home purchase or refinancing loan. Our analysis shows that the exempted credit unions tend not
to operate across multiple markets, and we therefore believe that the bulk of branching activities
for multi-market credit unions are captured under the HMDA. As shown in Table A2.1, 14%
(1048 out of 7580) of the credit unions operate branches in more than one metropolitan market in
2010, and 81% of them are qualified for HMDA reporting (852 out of 1048). Combining these
two data sources gives us accurate branching information that covers 97% of the sample. The
coverage for 2011 and 2012 is respectively 97% and 98%.
We use 2010 – 2012 data to test our identifying assumption that a multi-branch credit
union receives (and grants) home loan applications through the branch that is in the same
metropolitan areas as the home property. First, HMDA data identifies the majority of the markets
that credit unions operate in. In 2010, 84% of the CU-markets that the 852 credit unions are
operated in are matched using the HMDA data. Second, HMDA tends to over-identify markets
that credit unions operate in, but those markets overall claim a small allocation of loan
applications (6.0% in 2010) and thus receive a small proportion of deposits. Third, when we
assign headquarter to the markets that originate the highest volume of home loan applications
(and thus the highest proportional weight) in a particular year, 94.2% of the time we are able to
match HMDA and NCUA data on headquarter locations. Table A2.2 reports the statistics for all
24
We start collecting HMDA data from 2004, as HMDA regulation had a major overhaul in that year which resulted
in changes of reporting format so that the data is not comparable with the preceding years.
42
three years. Overall, HMDA loan application data provides a robust way of dividing credit union
deposits into markets that is reflective of the actual patterns.
Table A1.1 - Comparison of HMDA and NCUA Branch Data: Full Sample CUs
No of Unique No of Unique
Pct. of
Multi-branch Multi-market
MultiCUs
CUs market CUs
Year Source
No of
Unique CUs
2010 NCUA
7580
2806
1048
HMDA
1995
1759
852
HMDA/NCUA (%)
26%
61%
81%
2011 NCUA
7367
3072
1061
HMDA
1976
1778
866
HMDA/NCUA (%)
27%
61%
82%
2012 NCUA
7086
3079
1083
HMDA
1995
1814
929
HMDA/NCUA (%)
28%
53%
86%
Coverage
Rate
14%
97%
14%
97%
18%
98%
Note: HMDA data is sourced from the Federal Financial Institutions Examination Council (FFIEC) and the National Archive.
NCUA data is sourced from the National Credit Union Administration Call Reports, 2010Q3 (2010Q2 data is not available),
2011Q2 and 2012Q2. We restrict the sample to credit unions that are located within the fifty states and DC. Market is the
metropolitan area defined by Office of Management and Budget, Nov 2004.
Table A1.2 - Comparison of HMDA and NCUA Branch Data: HMDA Reported CUs
Year
2010
2011
2012
No of
Unique
CUMarkets in
NCUA
3758
3691
3866
No of Unique
CU-Markets
with NCUA
and HMDA
matches
3137
3066
3158
No of
Unique CUMarkets in
NCUA but
not HMDA
621
625
708
Pct. of
Matches
for CUMarkets
83.5%
83.1%
81.7%
No of
Unique
CUMarkets in
HMDA
but not
NCUA
701
673
601
Deposit
Share of
CuMarkets
in
HMDA
but not
NCUA
6.0%
4.6%
1.6%
Pct.
Of
Match
es for
CU
main
offices
94.2%
92.7%
91.5%
Note: HMDA data is sourced from the Federal Financial Institutions Examination Council (FFIEC) and the National Archive.
NCUA data is sourced from the National Credit Union Administration Call Reports, 2010Q3 (2010Q2 data is not available),
2011Q2 and 2012Q2. We restrict the sample to credit unions that are located within the fifty states and DC. Market is the
metropolitan area defined by Office of Management and Budget, Nov 2004.
43
Appendix 2: Robustness to Alternative Functional Forms
Our dependent variable is commercial bank deposit market share, which by construction is
bounded between zero and one. We choose to run linear probability models on this variable. This
approach is relatively standard in the literature, especially given the panel structure of our dataset
(for a broader discussion, see Angrist and Pischke (2009), especially pp. 105-107). Other papers
that take a similar approach when using a financial ratio as a dependent variable include Vicente
(2001) and Obloj and Sengul (2012). While linear probability models have many benefits, one
drawback is that predicted values may fall outside of zero and one; this happens for about 0.03%
of our observations. We therefore test the robustness of our results to alternative functional forms,
the results of which are depicted in Appendix Table A2.
For reference, Column 1 of Table A2 replicates our main results from Column 4 of Table
2 (M4), where the dependent variable is log of commercial bank deposit market share. Column 2
presents results without first taking the log of commercial bank deposit market share. Column 3
presents results after taking the log-odds ratio of commercial bank deposit market share, which
uses the following transformation: log (commercial bank deposit market share/ (1- commercial
bank deposit market share)). Before the transformation, observations with values of 0 are
replaced with 0.001 and values of 1 are replaced with 0.999, an approach taken in David,
O’Brien and Yoshikawa (2008). Column 4 presents results after taking the arcsin transformation
of commercial bank deposit market share, which uses the following transformation: arcsin
(squareroot (commercial bank deposit market share)). The results are similar across these
multiple transformations, suggesting that our results are robust to alternative functional forms.
44
References:
Angrist JD, Pischke J-S. 2009. Mostly harmless econometrics: An empiricist’s companion.
Princeton University Press: Princeton, NJ.
David P, O’Brien JP, Yoshikawa T. 2008. The implications of debt heterogeneity for R&D
investment and firm performance. Academy of Management Journal 51: 165-181.
Obloj, Tomasz and Metin Sengul. 2012. Incentive life cycles: Learning and the division of value
in firms,” Administrative Science Quarterly, 57(2): 305-347.
Vicente-Lorente JD. 2001. Specificity and opacity as resource-based determinants of capital
structure: evidence for Spanish manufacturing firms. Strategic Management Journal 22: 157-177.
Table A2. Alternative Specifications
Post Crisis
No. of Bank Branches
Bank Net Charge offs (%)
Bank Delinquency Ratio (30
days, %)
No. of CU Branches
CU Net Charge offs (%)
CU Delinquency Ratio (30 days, %)
Median HH Income
Unemployment Rate
Constant
R2
Population by Age Group
MarketFE
Observations
MtA31
0.0242***
(0.0044)
-0.0001
(0.0001)
-0.0005**
(0.0003)
0.0016
MtA32
0.0949***
(0.0237)
-0.0006
(0.0007)
-0.0037**
(0.0015)
0.0095
MtA33
0.0179***
(0.0033)
-0.0001
(0.0001)
-0.0004**
(0.0002)
0.0012
MtA34
0.0288***
(0.0054)
-0.0002
(0.0001)
-0.0007**
(0.0003)
0.0020
(0.0018)
0.0050***
(0.0010)
-0.0045**
(0.0022)
-0.0003
(0.0005)
0.0025***
(0.0008)
-0.0074
(0.1386)
-0.0279
(0.0322)
0.8879
Yes
Yes
3411
(0.0083)
0.0305***
(0.0066)
-0.0166
(0.0164)
-0.0008
(0.0028)
0.0152***
(0.0052)
0.6557
(0.6709)
-3.0500***
(0.2415)
0.8593
Yes
Yes
3411
(0.0013)
0.0040***
(0.0008)
-0.0032*
(0.0017)
-0.0002
(0.0004)
0.0019***
(0.0006)
0.0106
(0.1005)
-0.7957***
(0.0249)
0.8864
Yes
Yes
3411
(0.0021)
0.0066***
(0.0013)
-0.0054*
(0.0029)
-0.0004
(0.0006)
0.0031***
(0.0009)
0.0336
(0.1607)
0.1667***
(0.0400)
0.8900
Yes
Yes
3411
Significance Level: * p<0.10; ** p<0.05; *** p<0.01
45
Appendix 3: Comparison of Interest Rates Across Credit Union and Bank Products Over Time
2003
2004
2005
2008
2009
2010
2011
2012
2013
2014
2003
2004
2005
2008
2009
2010
2011
2012
2013
2014
CU
3.43
4.07
4.58
3.61
2.86
2.58
2.09
1.53
1.28
1.34
CU
4.72
4.78
5.44
5.40
5.25
4.64
3.80
3.03
2.77
2.64
5 Year CD
Bank
Diff
3.08
0.35
3.61
0.46
4.13
0.45
3.34
0.27
2.50
0.36
2.30
0.28
1.78
0.31
1.04
0.49
1.11
0.17
1.15
0.19
% Diff
0.11
0.13
0.11
0.08
0.14
0.12
0.17
0.47
0.15
0.17
48 mo New Auto Loan
Bank
Diff
% Diff
6.49
-1.77
-0.27
6.43
-1.65
-0.26
7.12
-1.68
-0.24
6.81
-1.41
-0.21
6.62
-1.37
-0.21
5.88
-1.24
-0.21
5.12
-1.32
-0.26
4.26
-1.23
-0.29
5.08
-2.31
-0.45
4.78
-2.14
-0.45
CU
0.97
1.01
1.44
1.33
0.74
0.47
0.31
0.21
0.17
0.16
Money Market
Bank
Diff
% Diff
0.51
0.46
0.90
0.57
0.44
0.77
0.85
0.59
0.69
0.72
0.61
0.85
0.42
0.32
0.76
0.29
0.18
0.62
0.21
0.10
0.48
0.10
0.11
1.10
0.13
0.04
0.31
0.12
0.04
0.33
30 Year Fix Rate Mortgage
CU
Bank
Diff
% Diff
5.92
5.95
-0.03
-0.01
5.82
5.79
0.03
0.01
6.38
6.39
-0.01
0.00
6.55
6.56
-0.01
0.00
5.66
5.65
0.01
0.00
4.89
4.85
0.04
0.01
4.78
4.64
0.14
0.03
3.83
3.71
0.12
0.03
4.11
3.64
0.47
0.13
4.29
4.28
0.01
0.00
46
CU
0.48
0.48
0.51
0.52
0.34
0.25
0.18
0.11
0.11
0.10
Interest Checking
Bank
Diff
% Diff
0.32
0.16
0.50
0.34
0.14
0.41
0.45
0.06
0.13
0.42
0.10
0.24
0.25
0.09
0.36
0.19
0.06
0.32
0.14
0.04
0.29
0.06
0.05
0.83
0.10
0.01
0.10
0.09
0.01
0.11
5 Year Adj Rate Mortgage
CU
Bank
Diff
% Diff
4.96
5.04
-0.08
-0.02
5.06
5.19
-0.13
-0.03
5.82
6.09
-0.27
-0.04
5.87
6.24
-0.37
-0.06
5.37
5.30
0.07
0.01
4.31
4.38
-0.07
-0.02
3.76
3.58
0.18
0.05
3.21
2.91
0.30
0.10
3.21
3.33
-0.12
-0.04
3.19
3.52
-0.33
-0.09
CU
12.18
11.97
12.06
11.75
11.68
11.63
11.64
11.42
11.56
11.55
Credit Card
Bank
Diff
12.33
-0.15
12.39
-0.42
14.13
-2.07
13.17
-1.42
12.50
-0.82
12.44
-0.81
13.17
-1.53
11.69
-0.27
12.85
-1.29
12.89
-1.34
% Diff
-0.01
-0.03
-0.15
-0.11
-0.07
-0.07
-0.12
-0.02
-0.10
-0.10
Appendix 4: Replication of Table 3 with 2008 crisis Dummy
Post Crisis
No. of Bank Branches
Bank Net Charge offs (%)
Bank Delinquency Ratio (30 days, %)
No. of CU Branches
CU Net Charge offs (%)
CU Delinquency Ratio (30 days, %)
Median HH Income
Unemployment Rate
Constant
Chi-square Test of Differences
Across Split Samples
R2
Population by Age Group
MarketFE
Observations
DV5: fraction of the
credit union
deposits over the
total deposits at
commercial banks
and credit unions
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial banks
and credit unions
MA4aL
0.0140***
(0.0046)
-0.0002
(0.0001)
-0.0114***
(0.0038)
0.0041***
(0.0015)
0.0047***
(0.0006)
0.0005
(0.0023)
-0.0010**
(0.0005)
0.0021***
(0.0006)
0.0980
(0.1213)
-0.6637***
(0.2458)
2.60
(P value: 0.10)
0.9375
Yes
Yes
1701
MA4aH
0.0277***
(0.0073)
-0.0001
(0.0001)
-0.0003
(0.0009)
0.0021
(0.0022)
0.0062***
(0.0013)
-0.0067**
(0.0034)
-0.0001
(0.0004)
0.0043***
(0.0009)
-0.1965
(0.1948)
-0.0883**
(0.0431)
DV6: fraction of
multi-bond federal
credit union
deposits with over
the total deposits at
commercial banks
and credit unions
MtA4b1
-0.0153**
(0.0071)
-0.0001
(0.0001)
-0.0010*
(0.0006)
0.0044*
(0.0024)
0.0023***
(0.0008)
-0.0063*
(0.0038)
0.0001
(0.0006)
0.0000***
(0.0000)
-0.7415***
(0.2622)
-0.0143
(0.0399)
0.8404
Yes
Yes
1710
0.7212
Yes
Yes
3411
DV7: fraction of singlebond and community
federal credit union
deposits with over the
total deposits at
commercial banks and
credit unions
MtA4b2
0.0182***
(0.0065)
0.0000
(0.0001)
0.0007
(0.0009)
0.0039
(0.0034)
0.0023**
(0.0010)
0.0033
(0.0061)
-0.0004*
(0.0002)
0.0000
(0.0000)
-0.3290
(0.2321)
0.0238
(0.0363)
3.22
(P Value: 0.0729)
0.6602
Yes
Yes
3411
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
MtA4cL
0.0287***
(0.0068)
-0.0010***
(0.0003)
-0.0016
(0.0055)
0.0033
(0.0022)
0.0114***
(0.0013)
-0.0022
(0.0036)
-0.0012*
(0.0007)
0.0042***
(0.0009)
-0.1869
(0.1624)
-0.2601***
(0.0574)
DV5: fraction of
the credit union
deposits over the
total deposits at
commercial
banks and credit
unions
MtA4cH
0.0132**
(0.0054)
-0.0001
(0.0001)
-0.0003
(0.0006)
-0.0019
(0.0017)
0.0028***
(0.0006)
-0.0041*
(0.0023)
-0.0000
(0.0003)
0.0026***
(0.0006)
0.1933
(0.1593)
-0.6212***
(0.1605)
0.8916
Yes
Yes
1710
0.8850
Yes
Yes
1701
Notes: Dependent Variables are measured at a given market in a given year: DV5, fraction of the credit union deposits over the total deposits at commercial banks
and credit unions; DV6, fraction of multi-bond federal credit union deposits with over the total deposits at commercial banks and credit unions; DV7, fraction of
single-bond and community federal credit union deposits with over the total deposits at commercial banks and credit unions. Coefficients of linear probability
regressions. Post crisis is a dummy variable, that equals one for all years after 2008 inclusive, and zero otherwise. Robust standard errors, clustered at the market
level, are included in brackets. Significance Level: * p<0.10; ** p<0.05; *** p<0.01
47