Does Diversification
Improve or Worsen
US Credit Union Performance?
William E. Jackson III, PhD
Professor of Finance
Professor of Management
Smith Foundation Endowed Chair of Business Integrity
Culverhouse College of Commerce
University of Alabama
ideas grow here
PO Box 2998
Madison, WI 53701-2998
Phone (608) 231-8550
www.filene.org
PUBLICATION #241 (5/11)
Does Diversification
Improve or Worsen
US Credit Union Performance?
William E. Jackson III, PhD
Professor of Finance
Professor of Management
Smith Foundation Endowed Chair of Business Integrity
Culverhouse College of Commerce
University of Alabama
Copyright © 2011 by Filene Research Institute. All rights reserved.
Printed in U.S.A.
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iii
Acknowledgments
I offer my sincere appreciation to Ben Rogers, George Hofheimer,
and members of the Filene Research Council for providing continued encouragement as well as very valuable comments and suggestions. I also offer earnest gratitude to Luis Dopico of Macrometrix,
who provided wise counsel on several empirical and theoretical issues
related to this research, and who generously shared several important
databases. The input of these individuals greatly improved the quality of this report. Nevertheless, any errors or omissions in this report
remain the sole responsibility of the author.
iv
Table of Contents
List of Figures
Executive Summary and Commentary
vi
viii
About the Author
xi
Chapter 1
Introduction
2
Chapter 2
Research Strategy
6
Chapter 3
The Results
14
Chapter 4
Conclusion
24
Appendix 1
Definition of Study Variables
26
Appendix 2
The Model and Methods
28
Appendix 3
Regression Analysis
30
Appendix 4
Future Research
36
Endnote
37
References
38
v
List of Figures
1. Summary Relationships between Diversification Variables and
Credit Union Performance Variables
2. Diversification Measured by Higher-than-Median Offerings
3. Summary Statistics (N = 1,393)
4. Correlation Matrix for Diversification Variables and Size
(N = 1,393)
5. Correlation Matrix for Performance Variables and Size
(N = 1,393)
6. Correlation Coefficients for Performance Variables, High
Diversification Variables, and Size (N = 1,393)
7. Estimated Impact of High Diversification on ROA
(in Basis Points)
8. Estimated Impact of High Diversification on OE
(in Basis Points)
9. Estimated Impact of High Diversification on VOE
(in Basis Points)
10. Estimated Impact of High Diversification on Asset Growth
(in Basis Points)
11. Summary Relationships between Diversification Variables and
Credit Union Performance Variables
12. Impact of Size on Credit Union Performance Variables
13. The Relationship between Diversification and ROA at
US Credit Unions, 2000–2009 (N = 1,393)
14. The Relationship between Diversification and VROA at
US Credit Unions, 2000–2009 (N = 1,393)
15. The Relationship between Diversification and OE at US Credit
Unions, 2000–2009 (N = 1,393)
16. The Relationship between Diversification and VOE at
US Credit Unions, 2000–2009 (N = 1,393)
17. The Relationship between Diversification and Asset Growth at
US Credit Unions, 2000–2009 (N = 1,393)
18. The Relationship between Diversification and Member Growth
at US Credit Unions, 2000–2009 (N = 1,393)
19. The Relationship between FOM, Other Diversification, and
ROA at US Federally Chartered Credit Unions, 2000–2009
(N = 639)
20. The Relationship between FOM, Other Diversification, and
VROA at US Federally Chartered Credit Unions, 2000–2009
(N = 639)
vi
21. The Relationship between FOM, Other Diversification, and
OE at US Federally Chartered Credit Unions, 2000–2009
(N = 639)
22. The Relationship between FOM, Other Diversification, and
VOE at US Federally Chartered Credit Unions, 2000–2009
(N = 639)
23. The Relationship between FOM, Other Diversification,
and Asset Growth at US Federally Chartered Credit Unions,
2000–2009 (N = 639)
24. The Relationship between FOM, Other Diversification, and
Member Growth at US Federally Chartered Credit Unions,
2000–2009 (N = 639)
vii
Executive Summary and Commentary
by George Hofheimer,
Chief Research Officer
“Bad advice tends to be simplistic. It tends to be definite, universal,
and certain. But, of course, that’s the advice we love to hear. The
best advice tends to be less certain—those researchers who say, ‘I
think this is true in certain situations for some people.’ We should
avoid the kind of advice that tends to resonate the most—it’s exciting, it’s a breakthrough, it’s going to solve your problems—and
instead look at the advice that embraces complexity and uncertainty.”
David H. Freedman made this statement in support of his recent
book Wrong: Why Experts* Keep Failing Us—And How to Know When
Not to Trust Them (Little, Brown and Co. 2010), which criticizes the
research/expert community for publishing scientific findings that are
extremely sexy but not necessarily true. Finding the one definitive silver bullet to solve a complex problem is a compelling proposition for
researchers and consumers of research alike. We, too, would love to
see definitive, simple answers to the important questions facing the
credit union system, but frequently the results we present are filled
with provisos, important considerations, and uncertainty.
What Is the Research About?
William Jackson, a professor of management and finance at the University of Alabama, asks one of the most fundamental and important
strategic questions for credit unions: whether to diversify across a
range of products, services, and markets or to specialize in particular
products, services, and markets. To accomplish this task, Jackson
methodically tests whether diversification impacts credit union
performance over a 10-year period (2000–2009). Jackson constructs
diversification measurements around loan products, deposit products, revenue sources, and markets served (i.e., field of membership).
He then conducts sophisticated statistical analysis to determine if, for
instance, a credit union with a high level of deposit product diversification is correlated with better-than-average organizational performance. Jackson provides a number of control variables (asset size, net
worth ratio, average state unemployment measures, etc.) to ensure
that the relationships between the diversification and performance
variables are the main source of comparisons.
What Did the Research Reveal?
Jackson reports that diversification is sometimes linked with better
performance and sometimes linked with worse performance. Despite
this wishy-washy pronouncement, important (and actionable) summary relationships were discovered during the research (see Figure 1).
viii
Figure 1: Summary Relationships between Diversification
Variables and Credit Union Performance Variables
ROA
High revenue
diversification
High loan
diversification
High deposit
diversification
Better
No impact
Worse
No impact
No impact
No impact
Operating Efficiency
Worse
Worse
Worse
Variability of Operating Efficiency
Worse
No impact
No impact
Asset Growth
Better
No impact
Worse
No impact
No impact
Worse
Variability of ROA
Member Growth
Note: Field-of-membership diversification is not presented in this chart. The results are inconclusive due to data availability issues.
Jackson quantifies the impacts of specific diversification strategies on
credit union performance with the following two key findings, which
should excite the credit union executive reader:
• High revenue diversification (i.e., higher than credit union
median revenue from noninterest income) is associated with
higher return on assets (ROA) at 10.7 basis points over the
10-year study period.
• High deposit diversification (i.e., higher than credit union
median number of deposit products) is associated with lower
ROA at 7.0 basis points over the 10-year study period.
What Are the Implications for Credit
Unions?
For the first time, we are able to empirically illustrate the impact of
diversification strategies on a host of credit union performance metrics over a long time frame. The key, actionable findings appear to be
that credit unions could benefit by diversifying their noninterest revenue
streams, decreasing the variety of deposit products offered, and questioning the effectiveness of a diversified loan product strategy. When discussing how this research study impacts your credit union’s strategy, ask
the following questions:
• Is less more? Economists are beginning to explain a unique
phenomenon called “choice overload,” which contends that in
certain situations, less choice is actually preferable to consumers
(Iyengar and Kamenica 2007). Perhaps a less diversified deposit
and loan product set would encourage more members to take
up your credit union’s services. ING Direct has very few deposit
and loan products yet has managed to become a financial services
juggernaut.
• Low cost and differentiation? In 2006, Filene applied the value
innovation concept to credit unions and proposed a novel
ix
strategic approach that urged credit unions to search for uncontested market opportunities through the simultaneous pursuit
of lower cost and differentiation (Parayre 2006). To accomplish
these paradoxical goals, credit unions would need to eliminate
or reduce certain elements of what is considered “standard” in
financial services. So, if more is considered better in the me-too
financial services sector, perhaps rethinking this strategic assumption may bear fruit for your credit union.
• Should we temper these findings? Despite Professor Jackson’s careful analysis, you need to remember this study presents aggregate
credit union data. Individual results may (and do) vary. Jackson
concludes that the biggest takeaway from this study is that “diversification is sometimes directly linked with better (or much better) performance, and sometimes it is directly linked with worse
(or much worse) performance.”
Not exactly the most exciting, sexy, or breakthrough finding, which
Freedman would likely say is exactly the right reason to pay very close
attention to Jackson’s study.
x
About the Author
William E. Jackson III, PhD
Dr. William E. Jackson III holds the appointments of professor
of finance, professor of management, and the Smith Foundation
Endowed Chair of Business Integrity in the Culverhouse College of
Commerce at the University of Alabama. Before joining the faculty
at the University of Alabama, Dr. Jackson was a financial economist
and associate policy advisor in the research department at the Federal
Reserve Bank of Atlanta. At the Atlanta Fed, Dr. Jackson conducted
original research on financial markets and financial institutions. He was
also an advisor to the bank on the making of monetary policy in the
United States. Previous to his position at the Federal Reserve Bank of
Atlanta, Dr. Jackson was an associate professor of finance at the KenanFlagler Business School of the University of North Carolina at Chapel
Hill. His academic areas of expertise are financial intermediation and
industrial economics. Dr. Jackson’s research centers on the role financial
markets and financial institutions play in making the modern economy
more efficient and productive. Specific areas of research include corporate governance, entrepreneurial finance, monetary policy and macroeconomics, industrial economics, financial markets and institutions,
corporate finance, financial literacy, and public policy.
Dr. Jackson earned his BA in economics and applied mathematics
at Centre College, his MBA in finance at Stanford University, and
his PhD in economics at the University of Chicago. Dr. Jackson’s
research has been published in some of the leading academic journals in the areas of empirical economics, management, and financial
institutions and markets. His articles have appeared in such journals
as the Review of Economics and Statistics, the Journal of Money, Credit
and Banking, the Review of Industrial Organization, the Journal of
Banking and Finance, Management Science, the Journal of Small Business Management, and the Small Business Economics Journal. Dr. Jackson is currently an associate editor of one of the premier small-firm
research journals, the Journal of Small Business Management. His
monograph The Future of Credit Unions: Public Policy Issues was published by the Filene Research Institute in 2004.
In July 2004, Dr. Jackson provided expert testimony before the US
House of Representatives on the deregulation of credit unions. In
2005 and 2006 he served as founding special issue editor for the Journal of Small Business Management. The special issue was entitled “Small
Firm Finance, Governance, and Imperfect Capital Markets.” Recently,
Dr. Jackson was the principal investigator for a major research project
sponsored by the Filene Research Institute called “Capital Levels and
Systemic Risk Trends in the U.S. Credit Union Industry.” He was also
an inaugural member of the prestigious Filene Fellows program.
xi
CHAPTER 1
Introduction
This is the first study of credit unions to consider the impact on performance of loan and
deposit portfolio diversification (along with
revenue and field-of-membership diversification). The results are exploratory but profound.
Credit union diversification is important. It
matters. It is a major determinant, or at least
a correlate, of better performance. The correlation between diversification and performance is
negative as often as it is positive.
One of the most fundamental strategic questions that credit union
CEOs must face is whether to diversify across a range of different
products and markets or to specialize in particular markets, products, or services. This diversification versus focus question has been
extensively addressed in strategic management and corporate finance
literature. However, this literature does not provide a general consensus as to whether diversified firms tend to outperform focused firms.
Additionally, the findings in this literature may not apply directly to
credit unions, as credit unions face conflicting supervision and regulation that create their own incentives to follow a focus or diversification strategy.
Proponents of diversification for credit unions would argue that
there are substantial gains in leveraging managerial skills and abilities
across different product and geographic markets (or across field-ofmembership categories). These gains may come from the spreading
of fixed costs over additional products and markets through economies of scope. On the other hand, proponents of a focus strategy for
credit unions may argue that diversification will dilute the current
competitive advantage of management by pushing managers beyond
their existing expertise and
capabilities.
Statistics 101: Dependent, Independent, and Control
Thus, the question of whether
Variables
diversification enhances the
The dependent variable is what we study and what we expect
performance of credit unions
to change when an independent variable is altered. Control
can only be answered through
variables are used to regulate and monitor factors that may
rigorous empirical analysis. Very
influence the effect of an independent variable on the depenfew studies have addressed this
dent variable.
important issue. Yet, overall,
credit unions are becoming
more diversified. For example, since 1999 there has been a steady
rise in the share of noninterest income to total operating income for
US credit unions. In 1999 the share was about 11%, but by 2009
the share had increased to almost 22%. This increase in the share of
noninterest income represents a fundamental shift by credit unions
3
toward more diversification of their income streams. But is this
increase in diversification associated with better performance? This is
one of the central research questions that this study seeks to answer.
My research questions are addressed by collecting the necessary data
and by developing and implementing the appropriate econometric
models to test the research question (or related hypotheses) using
these data. I use a generalized linear regression model1 to test the
hypotheses. The dependent variables are measures of credit union
performance. The main independent variables are measures of credit
union revenue, loans, deposits, and field-of-membership diversification. Of course, I also include numerous control variables in my
model depending on the nature of the relationships being estimated.
Although many studies have addressed the question of the value of
diversification as a strategy to improve performance in the nonfinancial sector, very few studies have investigated this question for
financial firms. And, to my knowledge, only one study has investigated this question for credit unions. Since 1999, credit unions
have systematically increased their operating income diversification
by relying more on noninterest income. Has this strategy helped
performance? How far should credit unions pursue this diversification strategy? Should they attempt to be more like banks, where
noninterest income represents about 50% of operating income? This
study begins to provide a reasonable framework for addressing these
questions by providing results from an exploration of some of these
basic relationships. And, in doing so, this study begins the process of
helping credit union executives to make better-informed decisions
about these important strategic issues.
This is the first study of credit unions to consider the impact on
performance of loan and deposit portfolio diversification (along with
revenue and field-of-membership diversification). The results are
exploratory but profound. Credit union diversification is important.
It matters. It is a major determinant, or at least a correlate, of better
performance. The correlation between diversification and performance is negative as often as it is positive. For example, I find evidence that a higher revenue diversification strategy is associated with
credit unions that exhibit a higher average return on assets (ROA).
My regression analysis implies that this group of credit unions exhibits an ROA that is 10.7 basis points (bps) higher than the sample
average. A higher revenue diversification strategy is also associated
with credit unions that exhibit an average annual growth rate that
is about 89 bps higher than the sample average. But a higher deposit
diversification strategy is associated with credit unions that exhibit
an average annual growth rate that is about 98 bps lower than the
sample average. For my sample of credit unions, then, revenue diversification appears to be a superior strategy to revenue focus; however,
4
deposit portfolio focus appears to be a superior strategy relative to
deposit diversification.
The basic conclusion or takeaway from this study is that diversification is sometimes directly linked with better (or much better)
performance and sometimes it is directly linked with worse (or much
worse) performance. And the value of this takeaway may result in a
tremendous strategic advantage for credit unions whose executives
understand and appreciate the details associated with this conclusion.
LITERATURE REVIEW
I will not burden the reader with a full
Small credit unions have neither sufficient
review of the academic literature as part
scale nor the requisite expertise to diver-
of the background for this study. I shall
sify away from their core product of loan
discuss only three academic articles here.
provision to members. Therefore smaller
These three articles offer a fairly compre-
credit unions should limit diversification and
hensive review of the relevant literature.
continue to operate as simple savings and
Although there is not a clear consensus in
loans vehicles. In contrast, the larger credit
the literature on the relationship between
unions, many of which now have shares of
diversification and performance, these
non-interest income in total income of 25%
three articles provide three major empirical
or more, should be encouraged to further
results that inform and motivate this study.
exploit diversification opportunities around
The three articles are Stiroh and Rumble
their core expertise in retail financial ser-
(2006), Goddard, McKillop, and Wilson
vices” (1847).
(2008), and Palich, Cardinal, and Miller
(2000).
Lastly, Palich, Cardinal, and Miller (2000)
review the results of 55 previously pub-
Using a large sample of financial hold-
lished academic articles to develop their
ing companies, Stiroh and Rumble (2006)
conclusions. They report that some diver-
demonstrate that diversification by financial
sification may improve performance, but
institutions may increase risk by adding
too much may actually make performance
relatively more volatile streams of revenue
worse. They state that the results of their
and costs. Further, this increase in risk may
tests “indicate that moderate levels of
offset some (or most) of the benefits of
diversification yield higher levels of per-
diversification.
formance than either limited or extensive
Goddard, McKillop, and Wilson (2008)
diversification” (155).
report that large credit unions are likely to
The three main ideas from these three
benefit from diversification, but small credit
articles —(1) that risk matters, (2) that size
unions may not. They state that “similar
matters, and (3) that diversification may
diversification strategies are not appropri-
help or hinder performance—provide the
ate for large and small US credit unions.
basic tenets for this research study.
5
CHAPTER 2
Research Strategy
As with all major empirical studies, the research
strategy for this study is to: (1) establish the
main research question or specific hypotheses,
(2) select a reasonable model, (3) determine
the relevant variables, (4) collect an appropriate sample, and (5) estimate the model using a
theoretically valid estimation method.
Data
My data are taken from National Credit Union Administration
(NCUA) call reports for the 1,393 credit unions that maintained
over $100 million (M) in total assets at year-end for each of the years
2000–2009. I choose credit unions with over $100M in total assets
because it is well recognized that larger credit unions may operate
differently than smaller credit unions (Hoel 2007). Additionally, the
only empirical study of credit union diversification that has appeared
in a major academic journal to date reports that more diversification is associated with worse performance for small credit unions
but generally better performance for large credit unions (Goddard,
McKillop, and Wilson 2008).
For each of the credit unions in my sample, data are collected for the
relevant variables about performance, the relative degree of diversification, and certain credit union–specific characteristics. Data are also
collected from the Census Bureau, the Bureau of Labor Statistics,
and the Bureau of Economic Analysis on certain characteristics of the
market in which the credit union is headquartered. I will discuss each
of these three categories of variables in the next section.
The Performance Variables
Measuring the performance of credit unions can be a very controversial undertaking. There are many different opinions about what
it means to be a high-performing credit union (Sollenberger 2008).
Nonetheless, most credit union CEOs would likely include measures
of financial return, risk, operating efficiency, and growth on their
list of top measures. I attempt to include a similar list of performance variables in this study. The performance variables I include
are: (1) ROA, (2) the Variability of ROA, (3) Operating Efficiency,
(4) the Variability of Operating Efficiency, (5) Asset Growth, and
(6) Member Growth.
7
ROA
My ROA variable is calculated as the average annual ROA over the
2000–2009 sample period for each of the 1,393 credit unions in my
sample. Each annual ROA is calculated as net income divided by
year-end total assets times 100. Even though ROA is not a perfect
measure of credit union performance, it does provide some evidence
of earnings quality and the credit union’s ability to generate retained
earnings to meet its capital requirements.
Variability of ROA (VROA)
The value of a stream of earnings is related to its magnitude and its
riskiness. By the same token, a credit union with a very volatile ROA
may have a less valuable and dependable source of earnings relative to
a credit union with a more stable ROA. I measure the variability of
a credit union’s ROA as the standard deviation of the annual ROAs
over the sample period 2000–2009.
Operating Efficiency (OE)
The ability of credit unions to manage and maintain lower levels of
expenses is usually a sign of better operating efficiency (Hoel 2007).
My measure of operating efficiency (OE) is calculated as the average
annual total noninterest expenses divided by total assets at year-end
for each of the 10 years over the sample period. These fractions are
multiplied by 100 to convert them into percentage points. A lower
percentage point value is associated with better OE.
Variability of OE (VOE)
Maintaining low levels of expenses is valuable. Consistently maintaining low levels of expenses reduces operational risk and is therefore
even more valuable. My measure of OE consistency (or variability) is
the standard deviation of the annual OE variable for the credit union
over the sample period 2000–2009.
Asset Growth
Most credit union CEOs would agree that a healthy credit union
tends to grow. Of course, this growth may be enhanced or tempered
by the environmental conditions of the markets where the credit
union operates. For this study I define Asset Growth as the average annual percentage change in total assets over the sample period
2000–2009. My Asset Growth measure is adjusted for mergers and
acquisitions. Increases in total assets of credit unions because of
acquisitions are not treated as asset growth. The Asset Growth measure I use in this study is defined to consider only organic growth.
8
Member Growth
My calculation for member growth is adjusted for mergers and
acquisitions in the same manner as asset growth. Member Growth is
the average annual organic percentage growth in membership over
the sample period 2000–2009.
The Diversification Variables
Revenue, Loans, and Deposits
I use this method to construct diversification variables for credit
union revenue streams, loan portfolio composition, and deposit
portfolio composition. For example, consider the variable that
represents high loan diversification, which I refer to as HLD. If
the average loan portfolio HHI measure (see sidebar below) is less
than the median value for the sample (2,559.9), then the indicator
HOW DOES ONE GO ABOUT MEASURING DIVERSIFICATION?
To develop my diversification constructs,
category. Also, assume that this credit
I start with a standard measure of con-
union has 20% shares in both auto and
centration (or focus) that is widely used in
credit card loans. The HHI for this credit
the economics, finance, and management
union would be:
literatures. That measure is the HerfindahlHirschman Index (HHI). HHI is calculated
(60)² + (20)² + (20)² = 4,400
as the sum of the squared percentage
This latter credit union has a much higher
shares in different products, services, or
HHI (32% higher). This means the latter
geographic market categories.
credit union has a much more focused
For example, consider a credit union that
has three types of loans: (1) mortgages,
(2) auto loans, and (3) credit card loans.
loan portfolio or, stated differently, a much
less diversified loan portfolio relative to the
former credit union.
Further assume that this credit union has
I operationalize my diversification mea-
a percentage share of 33.3% in each loan
sures by using an indicator variable. The
category (i.e., equal shares). The loan
indicator variable is equal to one (zero
portfolio HHI for this credit union is calcu-
otherwise) when a credit union exhibits
lated as:
less than the median level of concentra-
HHI = sum of squared percentage shares =
(33.3)² + (33.3)² + (33.3)² = 3,326.67
tion in my entire sample of 1,393 credit
unions over the sample period. Recall that
a lower-than-median level of concentration
Now, consider another credit union that
is equivalent to a higher-than-median level
focuses more on mortgage loans and has
of diversification.
a loan percentage share of 60% in this
9
variable for HLD is set equal to one. The corresponding variable for
deposits is high deposit diversification (HDD). HDD is an indicator
variable set equal to one (zero otherwise) when a credit union has an
average deposit-based concentration index (HHI) that is less than the
median HHI for the entire sample (i.e., 2,656).
For revenue, the diversification variable is high revenue diversification (HRD). If a credit union has a concentration index for revenue
that is below the median value of 6,954, then the indicator variable
HRD is set equal to one, or zero otherwise. Notice that the median
concentration index for revenue is much higher than those for loans
or deposits. This has to do with the fact that my revenue concentration index is based on only two categories: interest revenue and noninterest revenue. But the loan concentration index is based on seven
loan types (new car, used car, first mortgage, other real estate, credit
card, other unsecured consumer, and other loans) and the deposit
concentration index is based on six deposit categories (share draft,
regular savings, money market,
Figure 2: Diversification Measured by Higher-than-Median
CDs, IRAs, and other). Of
Offerings
course, part of the explanation
for the index for revenue is that
Deposit diversification
Loan diversification
Revenue diversification
credit unions have historically
New car
Share draft
focused on interest revenue as
Used car
Regular savings
Higher-than-median
First mortgage
opposed to noninterest revenue
Money market
revenue (18.7%) from
Other real estate
CDs
noninterest income
as their main source of income.
Credit card
IRAs
(fees and other charges)
Other unsecured consumer
For example, the median
Other
Other loans
revenue concentration index of
6,954 suggests a split between
interest and noninterest revenue of about 81.3% (interest revenue)
and 18.7% (noninterest revenue).
Field of Membership (FOM)
In addition to my diversification variables for revenue (HRD), loans
(HLD), and deposits (HDD), I also use field-of-membership (FOM)
measures of diversification in this study.
My measures of FOM diversification are also developed using indicator variables. The first indicator variable is equal to one (zero otherwise) if the credit union has a community-based FOM. This variable
is called FOMC. The second indicator variable is equal to one (zero
otherwise) if the credit union has multiple FOMs. I refer to this variable as FOMM.
FOM data for state-chartered credit unions have not been readily
available from NCUA since about 2002. Because of this limitation, I
only use federally chartered credit unions when analyzing the impact
of FOM diversification. This necessitates a separate set of regression
10
output and results for this analysis, which are presented later in this
report.
The Control Variables
Several individual credit union characteristics besides diversification
traits may be important in explaining credit union performance. In
addition, certain market-related variables may be important predictors of credit union performance. I include four variables from
each of these categories in my model to help control for differentials in individual credit union characteristics, as well as differences
in the geographic markets in which the credit unions operate. I
define geographic market as the state in which the credit union is
headquartered.
Credit Union Characteristics
My control variables for credit union characteristics are: (1) Size,
(2) Net Worth, (3) Loans/Assets, and (4) Delinquent.
Size
My measure of size is the natural log of average year-end total assets
over the 2000–2009 sample period. Even though my sample consists
of only credit unions with over $100M in total assets for each year in
the sample period, it still contains a very wide variation in the total
assets of sample credit unions. For example, there are a handful of
credit unions in the sample that are larger than $10 billion (B) in
average total assets. These credit unions are more than 100 times as
large as the $100M credit unions. Such variations in size may lead
to very different strategic decisions about operations because of scale
and scope economics. This is why I include the Size variable in my
model and why I choose to use the natural log transformation of
total assets as my measure of size.
Net Worth
The Net Worth variable is defined as the average annual year-end net
worth divided by total assets times 100 over the 2000–2009 sample
period. This is the traditional net worth ratio. Net Worth provides
information on the capital adequacy management policies at the
credit union.
Loans/Assets
Some credit unions are much more aggressive than others in investing larger fractions of total assets into loans to members as opposed
to certain lower-risk investments. My Loans/Assets variable helps to
control for these strategic differences. Loans/Assets is calculated as
11
average annual total loans divided by total assets over the 2000–2009
sample period.
Delinquent
Credit unions may have substantially different underwriting standards for granting member loans. This results in diverse risk profiles
for loan portfolios at different credit unions. To allow for this difference in loan portfolio quality, I include a measure of loan delinquencies (Delinquent). The variable Delinquent is calculated as average
annual year-end total loan delinquencies divided by total loans over
the 2000–2009 sample period.
Geographic Market Conditions
My control variables for geographic market conditions are:
(1) Population Growth, (2) Income Growth, (3) Income Level, and
(4) Unemployment.
Population Growth
Credit unions in states with higher population growth rates may
have different opportunities and challenges to consider. Because of
this, I include the variable Population Growth in my model. Population Growth is defined as the average annual percentage growth in
population in the state where the credit union is headquartered over
the 2000–2009 sample period.
Income Growth
States with similar rates of growth in population may present very
different opportunities for credit unions when per-capita income
growth differs. Higher income growth usually is associated with
higher incentives for more aggressive competitive strategies by financial institutions. My measure of Income Growth is defined as the
average annual percentage growth in per-capita income in the credit
union’s home state. This variable is measured in real terms using
2009 prices.
Income Level
Higher levels of income usually are associated with higher demand
for financial services. The inherent significance that Income Level
may have on credit union market conditions leads me to include this
variable in my model. Income Level is defined as the average annual
per-capita income in thousands of dollars in the credit union’s home
state over the 2000–2009 sample period. This variable is measured in
2009 prices.
12
Unemployment
Because of the recent financial crisis, several states have experienced
extremely high unemployment rates in the last few years. These
major changes in state-level unemployment rates had significant
impacts on credit union performance. I recognize the importance
of unemployment on credit union performance by including the
Unemployment variable in my model. My measure of unemployment is defined as the average annual unemployment rate in the
credit union’s home state over the 2000–2009 sample period.
13
CHAPTER 3
The Results
As credit union leaders decide on whether to
diversify or focus their strategic product and
service offerings to better meet the demands of
their membership, research findings on how
diversification will likely impact traditional
measures of credit union performance become
critically important. The findings of this study,
presented below, provide the most current and
comprehensive analysis of this strategic issue.
Summary Statistics and Correlations
Summary Statistics
Figure 3 provides summary statistics for the performance measures
and control variables. Notice that there is a reasonably high level of
variation in the performance measures (ROA, VROA, OE, VOE,
Asset Growth, and Member Growth) as measured by their standard
deviations.
This is an important feature as it suggests there is a consequential
amount of variation for the regression models to explain. And, my
purpose is to explore the amount of variation in credit union performance that can be explained by our diversification variables. Figure 3
does not include summary statistics for the diversification variables
because of their manner of construction. Recall that the diversification variables (HRD, HLD, and HDD) for the entire sample are
constructed as indicator variables equal to one (zero otherwise) when
a credit union exhibits higher-than-median diversification.
Figure 3: Summary Statistics (N = 1,393)
Mean
Standard
deviation
Minimum
ROA
0.69
0.35
–1.17
3.56
VROA
0.56
0.37
0.07
3.10
OE
3.55
1.03
0.13
8.63
VOE
0.37
0.25
0.02
5.87
Asset Growth
8.65
5.33
–2.55
97.39
Member Growth
1.85
5.07
–81.82
85.23
Size
19.29
0.81
18.42
23.94
Net Worth
Maximum
11.00
3.66
0.00
100.00
Loans/Assets
0.66
0.14
0.07
0.98
Delinquent
0.01
0.01
0.00
0.10
Population Growth
0.91
0.68
–0.41
3.41
Income Growth
Income Level
Unemployment
15
0.42
1.09
–4.55
5.32
39.21
5.34
28.88
66.04
5.73
1.38
2.90
14.03
Correlations
Figure 4 provides information on the correlations between the
diversification measures themselves and between the diversification
measures and Size. Size is measured as the natural
Figure 4: Correlation Matrix for
log of average annual total assets at year-end over
Diversification Variables and Size
the 2000–2009 sample period.
(N = 1,393)
Two trends stand out in Figure 4. First, all three
diversification measures are significantly correlated
HRD
HLD
HDD
Size
HRD
1.00
with each other. This means that a credit union
HLD
0.22*
1.00
that follows a high diversification strategy for revHDD
0.18*
0.13*
1.00
enue is also likely to exhibit a high diversification
Size
–0.10*
0.02
0.05
1.00
measure for its loan portfolio and deposit portfolio.
* indicates a correlation coefficient that is significant at the 5% level or better.
Second, notice that Size is not significantly correlated with either the loan or deposit diversification measures. But Size is negatively significantly correlated with
revenue diversification. The finding that larger credit unions tend to
have lower amounts of revenue diversification is somewhat surprising. However, the correlation
coefficient is rather small, so
This means that a credit union that follows a high diversificathis relationship should not be
tion strategy for revenue is also likely to exhibit a high diversifioverstated.
cation measure for its loan portfolio and deposit portfolio.
I present the correlation matrix
for the performance measures
and Size in Figure 5. This is offered to provide a quick glimpse at the
general associations between different types of performance for credit
unions. For example, notice the correlation coefficient for ROA and
VROA. The coefficient is large and statistically significant (–0.45).
The coefficient strongly suggests that credit unions with a relatively
high average ROA also have a relatively low VROA. In a risk/return
world this result is very impressive, as it suggests that credit unions
with higher returns (ROA) also exhibit lower risk profiles (VROA).
Further, it appears from Figure 5 that credit unions with higher
ROAs also exhibit higher asset growth and higher membership
growth. This suggests that four of our six measurements of credit
union performance are correlated in such a fashion that a
In a risk/return world this result is very impressive, as it sugrelatively high ROA indicates
gests that credit unions with higher returns (ROA) also exhibit
the likelihood of better perforlower risk profiles (VROA).
mance on the other measures as
well. This trend holds for OE
also. The correlation coefficient
between ROA and OE is moderately sized, negative, and statistically significant. Not surprisingly, this suggests that credit unions
with higher ROAs also exhibit better OE. Thus, even with its many
16
Figure 5: Correlation Matrix for Performance Variables and Size (N = 1,393)
ROA
VROA
OE
VOE
Asset
Growth
Member
Growth
ROA
1.00
VROA
–0.45*
1.00
OE
–0.28*
0.22*
1.00
VOE
–0.20*
0.37*
0.41*
Asset Growth
0.36*
–0.20*
–0.05
–0.002
1.00
Member Growth
0.21*
–0.06*
–0.09*
0.01
0.22*
1.00
Size
0.14*
0.02
–0.30*
–0.12*
0.03
0.17*
Size
1.00
1.00
* indicates a correlation coefficient that is significant at the 5% level or better.
deficiencies as a measure of credit union performance, ROA is consistently correlated with my other measures of performance.
In Figure 6, I present evidence on the simple correlations of the performance measures and the high diversification indicator variables.
For example, HRD is positively
significantly correlated with
Thus, even with its many deficiencies as a measure of credit
OE, VOE, and Asset Growth.
union performance, ROA is consistently correlated with my
This can be interpreted as
other measures of performance.
relatively high revenue diversification being associated with
higher noninterest expense
to total asset ratios (less efficiency) and higher variability in OE
(more risk) but also higher growth in total assets. The correlations
between the other two high diversification indicator variables (HLD
and HDD) and credit union performance measures exhibit similar
mixed or negative results. However, simple correlations do not tell
the complete story. As we explore the relationships between credit
union performance and diversification strategies, we desire to present
a more compelling analysis of the “marginal” impact of higher levels
of diversification. This more compelling analysis using regression
models is offered in the next section.
Figure 6: Correlation Coefficients for Performance Variables, High
Diversification Variables, and Size (N = 1,393)
ROA
VROA
OE
VOE
Asset
Growth
Member
Growth
Size
0.09*
HRD
0.03
0.05
0.62*
0.21*
–0.002
–0.10*
HLD
–0.04
–0.01
0.26*
0.06*
–0.04
0.04
0.02
HDD
–0.15*
0.04
0.25*
0.02
–0.06*
–0.07*
0.05
* indicates a correlation coefficient that is significant at the 5% level or better.
17
Regression Results for Full Sample
ROA
As credit union leaders decide on whether to diversify or focus their
strategic product and service offerings to better meet the demands of
their membership, research findings on how diversification will likely
impact traditional measures of credit union performance become
critically important. The findings of this study, presented below,
provide the most current and
comprehensive analysis of this
strategic issue.
Figure 7 suggests that credit unions with revenue diversification
indices above the median for the sample (HRD) have ROAs
that are about 10.7 bps higher than the sample average ROA
ROA Regression Results
over the sample period 2000–2009.
The results for ROA are quite
profound. The results in Figure 7 suggest that credit unions
with revenue diversification indices above the median for the sample
(HRD) have ROAs that are about 10.7 bps higher than the sample
average ROA over the sample period 2000–2009. This relationship is statistically significant and economically
important. On the other hand, credit unions that
Figure 7: Estimated Impact of High
exhibit above-median deposit diversification indices
Diversification on ROA (in Basis Points)
(HDD) tend to have ROAs that are about 7.0 bps
below the sample average. Again, this relationship is
HRD, 10.7
economically important and statistically significant.
HLD, 0
So, it appears that revenue diversification is associated with better performance, or higher ROA. This
HDD, –7.0
finding is consistent with Goddard, McKillop, and
Wilson (2008).
Note: The estimates in this chart are the coefficients on the independent variables HRD,
HLD, and HDD shown in Figure 13. The coefficient for HLD in Figure 13 is not significant,
so its estimate in this chart is assigned a value of zero. This is because it is not statistically
different from zero (at the 5% level of significance).
No other study, as far as I am aware, has addressed
the issue of the impact of deposit portfolio diversification on credit union performance. So, the
negative and significant coefficient on the variable
HDD is new to the credit union, strategic management, and economics and finance literatures. The result of higher deposit portfolio
diversification being associated with lower average ROAs may be
explained by a lack of economies of scope in deposit gathering, or
significantly more economies of scale when credit unions focus their
deposit-gathering resources.
What about loan portfolio diversification? The results shown in Figure 7 suggest that higher-than-median loan portfolio diversification
(HLD) is not statistically significantly associated with levels of credit
union ROA. So, the diversification results for ROA are mixed.
The credit union control variables in Figure 13 in Appendix 3
have statistically significant coefficients. But only two of the four
18
coefficients have the signs that I expected. For example, I did not
expect Size to be positively associated with ROA. Larger credit
unions exhibiting higher ROAs in this sample is somewhat of a
surprise. The same is true with Net Worth. The negative and significant coefficient on Delinquent
was expected, as was the positive
The negative and significant coefficient on the variable HDD is
and significant coefficient on
new to the credit union, strategic management, and economics
Loans/Assets. The market-based
and finance literatures.
control variables in Figure 13
provide some rather mysterious
results. Two variables, Population Growth and Income Growth, have
statistically significant and negative coefficient estimates. However,
neither coefficient is extremely large. Nonetheless, a deeper appreciation for those relationships may well prove to be valuable to credit
unions’ long-range strategic planning.
VROA Regression Results
Higher diversification of revenue streams is associated with higher
average ROA for credit unions. However, this apparent increase in
the level of performance may be offset by the costs of increases in the
volatility of performance. This is the risk/return trade-off, or the dark
side of diversification, discussed in Stiroh and Rumble (2006). In
Figure 14 in Appendix 3, I explicitly examine this issue by analyzing the impact of diversification on VROA. Notice that none of the
coefficients for the diversification variables are statistically significant.
This suggests that the increases in average ROA levels associated with
higher-than-median revenue
diversification are not offset
by increases in ROA volatility.
This suggests that the increases in average ROA levels associBoth Loans/Assets and Delinated with higher-than-median revenue diversification are not
quent have large positive coefoffset by increases in ROA volatility.
ficients, which is statistically
significant and economically
important. The idea that a larger loans-to-assets ratio or a larger
percentage of delinquent loans would increase the volatility of ROA
seems reasonable. All of the market-based control variables have positive and significant coefficients. Additionally, the Population Growth
coefficient is rather large and important.
OE Regression Results
We’ve established that higher revenue diversification is associated
with higher average annual ROAs. Are these higher returns the result
of revenue diversification increasing overall revenue or reducing
overall expenses? I address this important question in Figure 15 in
19
Appendix 3, where I report the association between an expense control measure, OE, and my high diversification variables. This analysis
leads to one of the major results contributed by this study: whether
diversification significantly impacts OE or expense control.
Diversification does not come cheap. Better efficiency is traditionally associated with a lower expense ratio. Therefore, the variables
in Figure 15 with significant and negative coefficients suggest better
performance, because as the magnitudes of those variables increase,
they are associated with lower expense ratios. Accordingly, those variables with significant and positive coefficients correspond to higher
expense ratios and worse performance. Notice that the coefficients on
all three diversification variables are large, positive, and statistically
significant. This demonstrates that revenue, loan, and deposit diversification are associated with higher expense ratios and lower OE.
For example, as illustrated in Figure 8, the HRD coefficient is 0.936.
This suggests that credit unions with revenue diversification indices
above the sample median have
noninterest expense ratios that
are almost one percentage point
High diversification may be a very expensive strategy for credit
higher than the sample averunions.
age (of about 3.55 percentage
points). This is extraordinary.
And by adding to this the positive coefficients of HLD and HDD, it
suggests that high diversification may be a very expensive strategy for
credit unions.
Figure 8: Estimated Impact of High
Diversification on OE (in Basis Points)
HRD, 93.6
The results in Figure 15 in Appendix 3 for the
credit union–specific variables are not very surprising, including the notions that larger asset size is
associated with better OE or lower expense ratios
and that higher loans-to-assets ratios and higher
loan delinquency ratios are associated with lower
OE or higher expense ratios.
OE
Some might consider the significant relationship
between higher net worth and better OE to be
somewhat surprising.
HLD, 31.8
HDD, 20.3
Notes: The estimates in this chart are the coefficients on the independent variables HRD,
HLD, and HDD shown in Figure 15.
Recall that OE is calculated as noninterest expense divided by total assets times 100.
Thus, increases in this variable are associated with higher levels of expenses per dollar
of assets. Positive coefficients may be viewed as worse expense management.
20
Of the four control variables related to market or
environment, three have positive and significant
coefficients: Population Growth, Income Growth,
and Unemployment. To the extent that these variables represent higher levels of market uncertainty,
it’s reasonable to posit that higher levels of market
uncertainty are associated with higher expense ratios
and less efficient operations.
VOE Regression Results
VOE
Revenue diversification is a double-edged sword. While it may
contribute to more revenue, the results in Figure 16 in Appendix 3
provide evidence of the impact of diversification on
Figure 9: Estimated Impact of High
credit unions’ ability to maintain a stable level of
Diversification on VOE (in Basis Points)
OE. The evidence is not positive. While two of the
high diversification variables are insignificant, the
statistically significant variable (HRD) has a positive coefficient, meaning that revenue diversification increases VOE, as evidenced in Figure 9.
HRD, 8.8
Of the credit union–specific variables, Net Worth
and Loans/Assets are both insignificant. However,
Size has a negative and significant coefficient and
Note: The estimates in this chart are the coefficients on the independent variables HRD,
HLD, and HDD shown in Figure 16. The coefficients for HLD and HDD in Figure 16 are not
Delinquent has a positive and significant coefsignificant, so their estimates in this chart are assigned a value of zero. This is because
they are not statistically different from zero (at the 5% level of significance).
ficient. This evidence suggests that larger credit
unions are better able to maintain a more stable
level of OE and that credit unions with relatively
larger loan delinquency ratios
have much more difficulty
maintaining a stable expense
Larger credit unions are better able to maintain a more stable
ratio.
level of OE, and credit unions with relatively larger loan delinquency ratios have much more difficulty maintaining a stable
The results for the marketexpense ratio.
specific variables are similar for
HLD, 0
HDD, 0
VOE to the results for OE.
Asset Growth and Member Growth
Perhaps the most amazing results of this study are contained in the
Asset Growth and Member Growth variables. Looking at Figure 17
in Appendix 3, consider that the significant and positive coefficient
on HRD (0.885) means that credit unions with higher-than-median
revenue diversification are associated with average annual growth
in assets that is almost one percentage point higher over the sample
period. Additionally, consider
that the significant and negative
coefficient of HDD (–0.978)
A credit union that follows both high revenue diversification
means that credit unions with
and low deposit diversification strategies is associated with a
higher-than-median deposit
relative average annual asset growth rate that is almost two
diversification are associated
percentage points higher than the sample average.
with average annual growth in
assets that is about one percentage point lower over the sample period (see Figure 10). This suggests
that a credit union that follows both high revenue diversification
and low deposit diversification strategies is associated with a relative
average annual asset growth rate that is almost two percentage points
higher than the sample average. This is indeed remarkable! However,
21
association does not mean causation. It will take
additional research to uncover the strategic value of
these relationships.
Figure 10: Estimated Impact of High
Diversification on Asset Growth
(in Basis Points)
The results for Member Growth in Figure 18 in
Appendix 3 are similar for HDD but insignificant
for HRD. Note that Size has a large positive and
significant coefficient in both Figures 17 and 18.
This implies that the average annual asset and
member growth rates are higher for larger credit
unions. The other credit union–specific variables
are not consistent across Figures 17 and 18.
Asset Growth
HRD, 88.5
As for the market-specific variables in Figures 17
and 18, it appears that Unemployment is associated
with higher Asset Growth and Income Growth is
correlated with lower Asset Growth in Figure 17.
The other market-specific variables have insignificant coefficients.
HLD, 0
Summary of Performance and
Diversification Relationship
The regression results in Figures 13–18 in Appendix 3 lead to the summary relationships shown in
Figure 11.
HDD, –97.8
Note: The estimates in this chart are the coefficients on the independent variables HRD,
HLD, and HDD shown in Figure 17. The coefficient for HLD in Figure 17 is not significant, so
its estimate in this chart is assigned a value of zero. This is because it is not statistically
different from zero (at the 5% level of significance).
Just for comparison, also consider the impact of
Size on these performance variables, shown in
Figure 12. These summaries highlight the fact that
both diversification and size have a major impact on
credit union performance.
Figure 11: Summary Relationships between
Diversification Variables and Credit Union
Performance Variables
Figure 12: Impact of
Size on Credit Union
Performance Variables
HRD
HLD
HDD
ROA
Better
No impact
Worse
ROA
Larger Size
Better
VROA
VROA
No impact
No impact
No impact
No impact
OE
Worse
Worse
Worse
OE
Better
VOE
Worse
No impact
No impact
VOE
Better
Asset Growth
Better
No impact
Worse
Asset Growth
Better
No impact
No impact
Worse
Member Growth
Better
Member Growth
22
REGRESSION RESULTS FOR FOM DIVERSIFICATION
The results in Figures 19–24 in Appen-
a community-based FOM. However, this
dix 3 provide tests of significance for all
result must be interpreted with care for at
of the independent and control variables
least two reasons. First, the sample I use
from Figures 13–18, plus the independent
to estimate the econometric model that
variables Field of Membership Community
includes the independent variables FOMC
(FOMC) and Field of Membership Multiple
and FOMM is limited to federally chartered
(FOMM). Recall that FOMC is an indica-
credit unions. This sample (N = 639) is
tor variable equal to one (zero otherwise)
less than half the size of the full sample
if the credit union reported a Community
(N = 1,393). Second, the relationship
Field of Membership classification to the
between credit union performance and
NCUA over the entire 2000–2009 sample
FOM may be too complex to estimate with
period, while FOMM is an indicator variable
a simple linear regression model. Because
equal to one (zero otherwise) if the credit
of this, I would suggest further investigation
union reported a Multiple Field of Member-
of these relationships.
ship classification to the NCUA over the
entire 2000–2009 sample period.
For FOMM, the results in Figures 19–24
suggest that credit unions with multiple
Summarizing the results for FOMC in Fig-
FOMs perform worse (or much worse) on
ures 19–24 is very simple. There are no sig-
ROA, VROA, Asset Growth, and Member
nificant coefficients on the FOMC variables
Growth. There is no significant relationship
in Figures 19–24. This suggests that ROA,
between FOMM and OE or VOE. Again, I
OE, Asset Growth, and the other three
would advise that the results for FOMM be
performance variables are not likely influ-
interpreted with care for the same reasons
enced by whether or not a credit union has
I stated for FOMC.
23
CHAPTER 4
Conclusion
This is the first study of credit unions to consider the impact on performance of loan and
deposit portfolio diversification along with
revenue and FOM diversification. The results
are exploratory but profound. Credit union
diversification is important. It matters. It is a
major determinant, or at least correlate, of better performance.
This report is the first study of credit unions to investigate the
impact of loan and deposit portfolio diversification, along with
revenue and FOM diversification, on well-established measures
of credit union performance. The results are exploratory but profound. Credit union diversification is important. It matters. It is a
major determinant, or at least a correlate, of better performance. For
example, I find evidence that a higher revenue diversification strategy
is associated with credit unions that also exhibit higher average ROA.
A higher revenue diversification
strategy is also associated with
credit unions that exhibit higher
I find evidence that a higher revenue diversification strategy is
average annual growth. On the
associated with credit unions that also exhibit higher average
other hand, a higher deposit
ROA. A higher revenue diversification strategy is also associated
diversification strategy is associwith credit unions that exhibit higher average annual growth.
ated with credit unions that
exhibit a lower average annual
growth rate, as is field-of-membership diversification. For my sample
of credit unions, revenue diversification appears to be a superior
strategy to revenue focus. However, deposit portfolio focus and fieldof-membership focus both appear to be superior strategies relative to
deposit or field-of-membership diversification.
In summary, the correlation between diversification and performance
is negative as often as it is positive. Thus, diversification is sometimes
linked with better performance and sometimes linked with worse
performance. That is the basic conclusion or takeaway from this
study. The value of this takeaway may result in a tremendous strategic advantage for credit union management that understands and
appreciates the details associated with this conclusion.
25
Appendix 1
Definition of Study
Variables
• ROA is the average of the 10 annual ROAs for the credit union
over the sample period 2000–2009. Each of the 10 annual ROAs
is calculated as net income divided by year-end total assets times
100.
• VROA is variability of ROA. It is the standard deviation of the
annual ROAs over the 10-year sample period.
• OE is the average of the 10 annual Operating Efficiency (OE)
measures for the credit union over the sample period 2000–2009.
Each of the 10 annual OE measures is calculated as noninterest
expense divided by total assets times 100.
• VOE is Variability of Operating Efficiency. It is calculated as the
standard deviation of the annual OEs over the 10-year sample
period.
• Asset Growth is the average annual percentage growth in total
assets over the 2000–2009 sample period, adjusted for mergers.
• Member Growth is the average annual percentage growth in total
membership over the 2000–2009 sample period, adjusted for
mergers.
• HRD is High Revenue Diversification. It is an indicator variable
equal to one (zero otherwise) if the credit union has an average
revenue diversification index that is above the median for the
sample.
• HLD is High Loan Diversification. It is an indicator variable
equal to one (zero otherwise) if the credit union has an average
loan portfolio diversification index that is above the median for
the sample.
• HDD is High Deposit Diversification. It is an indicator variable
equal to one (zero otherwise) if the credit union has an average
deposit portfolio diversification index that is above the median
for the sample.
• FOMC is Field of Membership Community. It is an indicator
variable equal to one (zero otherwise) if the credit union reported
a Community Field of Membership classification to the NCUA
over the entire 2000–2009 sample period.
• FOMM is Field of Membership Multiple. It is an indicator variable equal to one (zero otherwise) if the credit union reported a
26
Multiple Field of Membership classification to the NCUA over
the entire 2000–2009 sample period.
• Size is the natural log of average year-end total assets over the
2000–2009 sample period.
• Net Worth is the average annual year-end net worth ratio over the
2000–2009 sample period. The net worth ratio is calculated as
net worth divided by total assets times 100.
• Loans/Assets is calculated as average annual total loans divided by
total assets over the 2000–2009 sample period.
• Delinquent is calculated as average annual year-end total loan
delinquencies divided by total loans over the 2000–2009 sample
period.
• Population Growth is defined as the average annual percentage growth in population in the state where the credit union is
headquartered.
• Income Growth is defined as the average annual percentage
growth in per-capita income in the credit union’s home state.
This variable is measured in real terms using 2009 prices.
• Income Level is defined as the average annual per-capita income
in thousands of dollars in the credit union’s home state over the
2000–2009 sample period. To allow for inflation, this variable is
measured in constant 2009 dollars.
• Unemployment is defined as the average annual unemployment
rate in the credit union’s home state over the 2000–2009 sample
period.
27
Appendix 2
The Model and Methods
The model used to estimate the relationship between credit union
performance and diversification is based on simple linear regression
methods.
The model takes this general form:
Y = α + β × IV + γ × CV + ε
(1.0)
Y represents the set of credit union performance measures. These
are my so-called dependent variables. Alpha (α) is an intercept term
that assists in the normalization of the model. IV represents the
set of independent variables. These are the diversification variables
that I investigate, and they are the primary variables of interest
in this study. Recall that the main purpose of the study is to test
whether these diversification variables are statistically significant and
economically important. Beta (β) is the set of coefficients for the
corresponding independent variables. The magnitude and associated
significance measure (the T-ratio) of the βs provide the test of economic importance and statistical significance that this study requires.
CV represents my set of control variables. Control variables are used
to refine the model by incorporating important relationships that
also impact the dependent variable. Gamma (γ) represents the set of
coefficients for the control variables. Like the set of βs, the size and
T-ratios associated with this set of γs provide information about the
statistical significance and economic impact of the individual control
variables. Lastly, epsilon (ε) is the set of error terms that are minimized to operationalize the simple linear regression model using the
ordinary least squares technique.
Expanding equation 1.0 to include the specific variables that I use in
the regression model leads to equation 2.0:
Performance measure = α + β1 HRD + β2 HLD +
β3 HDD + γ1 Size + γ2 Net Worth +
γ3 Loans/Assets + γ4 Delinquent +
(2.0)
γ5 Population Growth +
γ6 Income Growth + γ7 Income Level +
γ8 Unemployment + ε
HRD is my high revenue diversification variable, HLD is my high
loan diversification variable, and HDD is my high deposit diversification variable. Each of these three variables is constructed as an
indicator variable equal to one (zero otherwise) when the diversification index for that credit union is higher than the median for
28
the entire sample of credit unions for that particular category (i.e.,
revenues, loans, or deposits). I estimate equation 2.0 for each of the
credit union performance measures. These performance measures
include ROA (average return on assets), VROA (variability of annual
ROA over the sample period measured by the standard deviation),
OE (average operating efficiency), VOE (variability of annual OE
over the sample period measured by the standard deviation), Asset
Growth (average annual percentage growth in total assets over the
sample period), and Member Growth (average annual percentage
growth in membership over the sample period).
29
Appendix 3
Regression Analysis
Figure 13: The Relationship between Diversification and ROA
at US Credit Unions, 2000–2009 (N = 1,393)
Intercept
Parameter
estimate
T-ratio
Significant?
–1.204
–5.07
Yes
HRD
0.107
5.93
Yes
HLD
–0.032
–1.86
No
HDD
–0.070
–4.08
Yes
Size
0.074
6.85
Yes
Net Worth
0.057
17.50
Yes
Loans/Assets
0.189
2.87
Yes
Delinquent
–6.603
–6.25
Yes
Population Growth
–0.037
–2.76
Yes
Income Growth
–0.014
–1.41
No
Income Level
–0.003
–2.10
Yes
Unemployment
–0.008
–1.08
No
Adjusted R-square
F-value
0.252
43.64
Yes
Figure 14: The Relationship between Diversification and
VROA at US Credit Unions, 2000–2009 (N = 1,393)
Parameter
estimate
T-ratio
Significant?
Intercept
–0.602
–2.32
No
HRD
–0.015
–0.74
No
HLD
–0.008
–0.41
No
HDD
0.026
1.39
No
Size
0.002
0.13
No
Net Worth
0.003
0.84
No
Loans/Assets
0.395
5.50
Yes
15.869
13.79
Yes
Population Growth
0.161
11.06
Yes
Income Growth
0.050
4.59
Yes
Income Level
0.009
4.97
Yes
Unemployment
0.028
3.32
Yes
Delinquent
Adjusted R-square
F-value
30
0.207
33.96
Yes
Figure 15: The Relationship between Diversification and OE
at US Credit Unions, 2000–2009 (N = 1,393)
Parameter
estimate
T-ratio
Significant?
Intercept
7.976
15.30
Yes
HRD
0.936
23.56
Yes
HLD
0.318
8.50
Yes
HDD
0.203
5.40
Yes
Size
–0.349
–14.71
Yes
Net Worth
–0.037
–5.15
Yes
Loans/Assets
2.051
14.18
Yes
Delinquent
6.597
2.85
Yes
Population Growth
0.068
2.31
Yes
Income Growth
0.067
3.04
Yes
Income Level
0.002
0.58
No
Unemployment
0.067
3.98
Yes
Adjusted R-square
F-value
0.586
172.75
Yes
Figure 16: The Relationship between Diversification and VOE
at US Credit Unions, 2000–2009 (N = 1,393)
Parameter
estimate
T-ratio
Significant?
Intercept
0.720
3.85
Yes
HRD
0.088
6.14
Yes
HLD
0.012
0.91
No
HDD
–0.010
–0.77
No
Size
–0.033
–3.84
Yes
Net Worth
–0.003
–1.29
No
Loans/Assets
0.069
1.34
No
Delinquent
3.727
4.49
Yes
Population Growth
0.023
2.17
Yes
Income Growth
0.033
4.14
Yes
Income Level
0.000
0.14
No
Unemployment
0.026
4.21
Yes
Adjusted R-square
0.083
F-value
31
12.45
Yes
Figure 17: The Relationship between Diversification and Asset
Growth at US Credit Unions, 2000–2009 (N = 1,393)
Intercept
Parameter
estimate
T-ratio
Significant?
–3.836
–1.09
No
HRD
0.885
3.29
Yes
HLD
–0.331
–1.31
No
HDD
–0.978
–3.85
Yes
Size
0.767
4.77
Yes
Net Worth
–0.267
–5.51
Yes
Loans/Assets
–2.487
–2.54
Yes
Delinquent
–2.388
–0.15
No
Population Growth
–0.140
–0.71
No
Income Growth
–0.716
–4.80
Yes
Income Level
0.002
0.07
No
Unemployment
0.495
4.32
Yes
Adjusted R-square
F-value
0.104
15.69
Yes
Figure 18: The Relationship between Diversification and
Member Growth at US Credit Unions, 2000–2009
(N = 1,393)
Parameter
estimate
T-ratio
Significant?
–16.249
–4.69
Yes
HRD
–0.064
–0.24
No
HLD
0.265
1.06
No
HDD
–0.926
–3.71
Yes
Size
0.887
5.62
Yes
–0.040
–0.84
No
4.625
4.81
Yes
10.137
0.66
No
Intercept
Net Worth
Loans/Assets
Delinquent
Population Growth
0.192
0.98
No
Income Growth
–0.006
–0.04
No
Income Level
–0.030
–1.29
No
Unemployment
–0.040
–0.36
No
Adjusted R-square
0.051
F-value
7.85
32
Yes
Figure 19: The Relationship between FOM, Other
Diversification, and ROA at US Federally Chartered Credit
Unions, 2000–2009 (N = 639)
Parameter
estimate
T-ratio
Significant?
–1.629
–4.31
Yes
HRD
0.155
5.58
Yes
HLD
–0.043
–1.66
No
HDD
–0.092
–3.53
Yes
FOMC
–0.072
–0.37
No
FOMM
–0.442
–3.05
Yes
Intercept
Size
0.112
6.54
Yes
Net Worth
0.064
13.68
Yes
Loans/Assets
0.328
3.12
Yes
Delinquent
–8.766
–5.25
Yes
Population Growth
–0.066
–3.33
Yes
Income Growth
–0.051
–2.92
Yes
Income Level
–0.010
–3.43
Yes
Unemployment
–0.042
–3.45
Yes
Adjusted R-square
F-value
0.325
24.63
Yes
Figure 20: The Relationship between FOM, Other
Diversification, and VROA at US Federally Chartered Credit
Unions, 2000–2009 (N = 639)
Parameter
estimate
T-ratio
Significant?
Intercept
–0.318
–0.75
No
HRD
–0.041
–1.30
No
HLD
–0.010
–0.33
No
HDD
0.016
0.54
No
FOMC
–0.198
–0.91
No
FOMM
0.446
2.73
Yes
Size
–0.040
–2.06
Yes
Net Worth
–0.003
–0.63
No
0.421
3.56
Yes
Loans/Assets
Delinquent
18.823
10.02
Yes
Population Growth
0.201
8.97
Yes
Income Growth
0.110
5.63
Yes
Income Level
0.015
4.40
Yes
Unemployment
0.077
5.62
Yes
Adjusted R-square
0.266
F-value
33
18.79
Yes
Figure 21: The Relationship between FOM, Other
Diversification, and OE at US Federally Chartered Credit
Unions, 2000–2009 (N = 639)
Parameter
estimate
T-ratio
Significant?
Intercept
8.212
10.10
Yes
HRD
0.872
14.61
Yes
HLD
0.348
6.23
Yes
HDD
0.220
3.92
Yes
FOMC
–0.182
–0.43
No
FOMM
0.314
1.01
No
Size
–0.377
–10.18
Yes
Net Worth
–0.039
–3.84
Yes
2.003
8.88
Yes
Loans/Assets
Delinquent
11.338
3.16
Yes
Population Growth
0.086
2.01
Yes
Income Growth
0.146
3.91
Yes
Income Level
0.001
0.18
No
Unemployment
0.123
4.73
Yes
Adjusted R-square
0.570
F-value
66.17
Yes
Figure 22: The Relationship between FOM, Other
Diversification, and VOE at US Federally Chartered Credit
Unions, 2000–2009 (N = 639)
Parameter
estimate
T-ratio
Intercept
0.428
1.25
No
HRD
0.088
3.50
Yes
Significant?
HLD
0.013
0.57
No
HDD
–0.025
–1.05
No
FOMC
–0.082
–0.47
No
FOMM
–0.119
–0.91
No
Size
–0.024
–1.53
No
Net Worth
0.000
0.00
No
Loans/Assets
0.122
1.29
No
Delinquent
5.968
3.95
Yes
Population Growth
0.032
1.79
No
Income Growth
0.050
3.17
Yes
–0.002
–0.83
No
Unemployment
0.047
4.27
Yes
Adjusted R-square
0.091
F-value
5.90
Income Level
34
Yes
Figure 23: The Relationship between FOM, Other
Diversification, and Asset Growth at US Federally Chartered
Credit Unions, 2000–2009 (N = 639)
Parameter
estimate
T-ratio
–8.596
–1.83
No
HRD
1.164
3.38
Yes
HLD
–0.793
–2.46
Yes
HDD
–1.307
–4.04
Yes
FOMC
–1.100
–0.46
No
FOMM
–7.238
–4.02
Yes
Intercept
Size
Net Worth
Loans/Assets
Delinquent
Significant?
1.179
5.53
Yes
–0.057
–0.97
No
0.778
0.60
No
–10.901
–0.53
No
Population Growth
–0.402
–1.63
No
Income Growth
–0.839
–3.89
Yes
Income Level
–0.114
–3.09
Yes
Unemployment
–0.005
–0.03
No
Adjusted R-square
0.119
F-value
7.60
Yes
Figure 24: The Relationship between FOM, Other
Diversification, and Member Growth at US Federally
Chartered Credit Unions, 2000–2009 (N = 639)
Intercept
HRD
Parameter
estimate
T-ratio
Significant?
–18.092
–4.57
Yes
–0.038
–0.13
No
HLD
0.231
0.85
No
HDD
–0.717
–2.62
Yes
FOMC
–0.998
–0.49
No
FOMM
–5.100
–3.36
Yes
Yes
Size
Net Worth
Loans/Assets
Delinquent
1.091
6.06
–0.007
–0.14
No
4.226
3.84
Yes
–36.925
–2.11
Yes
Population Growth
–0.032
–0.15
No
Income Growth
–0.389
–2.14
Yes
Income Level
–0.038
–1.22
No
Unemployment
–0.262
–2.07
Yes
Adjusted R-square
0.097
F-value
6.24
35
Yes
Appendix 4
Future Research
I consider this to be an exploratory study. This means the study
was conducted for a problem or question that has not been fully
defined. By its nature, exploratory research should not attempt to
draw detailed, definitive conclusions. Rather, it should provide some
insight into the fundamental nature of the relationships, and it
should provide some guidance about future research that may add to
our understanding of these relationships. That is what I attempt to
do here.
This particular study provides an abundance of possible future
research topics. However, for sake of brevity I will only discuss four
topics. The first topic addresses the dynamics of the diversification
and performance relationship at the individual credit union level.
This study was designed to estimate cross-sectional differences in
credit union performance—that is, the average performance of one
group of credit unions relative to another group over some defined
time period. It would be informative to investigate how changes in
diversification over time are associated with changes in performance
over time. This would be an effective method to develop a better
understanding of the dynamics of credit union performance.
The second topic relates to the ability of diversification to mitigate
the impact of major upheavals in the economic environment. In
particular, research on whether diversified credit unions fared better
during the financial crisis of 2007–2008 and the Great Recession of
2008 would be very informative and strategically valuable.
The third topic recognizes the limits of the current set of performance measures used in this study. Some might argue that there are
better, more comprehensive measures of credit union performance
than those used in this study. Such an argument has some merit. For
example, a performance measure that incorporated the full economic
value created by the credit union for its members would be more
appropriate than a simple accounting measure like ROA. However,
Jackson (2008) points out that these types of measures are very difficult to construct. Nonetheless, developing more advanced performance measures that are directly linked to value creation by credit
union management for their members is worthy of future research.
And, lastly, the question of what determines US credit union growth
is a natural extension of the current study. Given the mysterious
findings reported in this study about credit union asset growth and
certain market conditions, it would be wise to explore those research
results in much greater detail.
36
Endnote
1. The linear regression model is a statistical procedure for predicting the value of a dependent variable using one or more
independent variables. It is used when the relationship between
the dependent and independent variables can be described as
constant, or linear.
37
References
Goddard, John, Donal McKillop, and John O.S. Wilson. 2008.
“The Diversification and Financial Performance of US Credit
Unions.” Journal of Banking & Finance 32:1836–49.
Hoel, Robert F. 2007. Thriving Midsize and Small Credit Unions.
Madison, WI: Filene Research Institute.
Iyengar, Sheena S., and Emir Kamenica. 2007. “Choice Overload
and Simplicity Seeking.” Center for Behavioral Decision Research.
cbdr.cmu.edu/seminar/Emir2.pdf.
Jackson, William E. III. 2008. “The Counter-Factual Approach to
Measuring the Financial Value Created by Credit Unions.” Working paper: Culverhouse College of Commerce, The University of
Alabama.
Palich, Leslie, Laura B. Cardinal, and C. Chet Miller. 2000. “Curvilinearity in the Diversification–Performance Linkage: An Examination of Over Three Decades of Research.” Strategic Management
Journal 21:155–74.
Parayre, Roch. 2006. How Blue Is Your Ocean? Value Innovation and
Credit Union Strategy Development. Madison, WI: Filene Research
Institute.
Sollenberger, Harold M. 2008. Financially “High Performing” Credit
Unions: Evaluating Performance within a Strategic Financial Vision.
Madison, WI: Filene Research Institute.
Stiroh, Kevin J., and Adrienne Rumble. 2006. “The Dark Side of
Diversification: The Case of US Financial Holding Companies.”
Journal of Banking & Finance 30:2131–61.
38
Does Diversification
Improve or Worsen
US Credit Union Performance?
William E. Jackson III, PhD
Professor of Finance
Professor of Management
Smith Foundation Endowed Chair of Business Integrity
Culverhouse College of Commerce
University of Alabama
ideas grow here
PO Box 2998
Madison, WI 53701-2998
Phone (608) 231-8550
www.filene.org
PUBLICATION #241 (5/11)