Basel III capital buffer requirements and
credit union prudential regulation: Canadian evidence∗
Helyoth Hessou
Department of Finance, Insurance and Real Estate
Faculty of Business Administration
Laval University, Quebec, Canada
Email: [email protected]
Van Son Lai
Laboratory for Financial Engineering of Laval University
Department of Finance, Insurance and Real Estate
Faculty of Business Administration
Laval University, Quebec, Canada
Email: [email protected]
and
IPAG Business School, Paris, France
July 2016
∗
We acknowledge the financial support from the Autorité des Marchés Financiers of Quebec (Canada), the Conrad Leblanc
Fund, and the Social Sciences and Humanities Research Council of Canada. The authors thank Marc-André Pigeon and
Les Czarnota from Credit Union Central of Canada for their helpful advices and appreciate the excellent research assistance
provided by Amine Arfaoui, Jean-Philippe Allard-Desrochers, Houa Balit and Olivier Côté-Lapierre.
1
Basel III capital buffer requirements and
credit union prudential regulation: Canadian evidence
Abstract
Some Canadian provinces have already adopted Basel III rules for the oversight of their
administrated credit unions. We analyze the importance of the Basel III additional capital
buffer requirements for credit union prudential regulation. Based on a sample of the 100
largest credit unions in Canada from 1996 to 2014, we find that Canadian credit union
capital buffers behave countercyclically over the business cycle. Further, credit unions
hold a capital buffer bigger than the maximum buffer advocated under Basel III which is
5% of risk-weighted assets (RWA). These results suggest that, unlike commercial banks
worldwide, credit unions, by and large, are already in compliance with the new Basel III
buffer requirements. However, there is evidence that the capital buffers of low-capitalized
credit unions are procyclical. These credit unions increased their RWA during booms but
failed to build up additional capital accordingly. Hence, weakly capitalized credit unions
are more likely to adjust their capital buffers if they are subject to Basel III capital buffer
regulation.
Keywords: Capital regulation, Credit union capital, Business cycle fluctuations,
Countercyclical capital buffer, Conservative capital buffer, Basel III.
2
Introduction
In North America, Canada has the highest proportion of members of credit unions —
currently some 10 million, about one-third of Canada’s total population. Credit unions
being non-profit institutions, operate on the principle of one-member-one-vote and are
motivated by self-help and solidarity ideals (e.g., Brizland and Pigeon (2013), Goddard,
McKillop and Wilson (2015)).
As of September 2014, of the 696 credit unions that operated in Canada, 352 operate out
of the province of Quebec. The five (5) largest credit unions in Canada are Desjardins
Group (Quebec), Vancity (British Columbia), Coast Capital Savings (British Columbia),
Servus Credit Union (Alberta), Meridian Credit Union (Ontario), and First West Credit
Union (British Columbia). Desjardins Group is by far the largest credit union federation in
North America. Founded in 1900 by Alphonse Desjardins, it federates nearly half (344
local credit unions, or Caisses Populaires Desjardins) of credit unions in Canada. This
federation has over 6 million members and manages over 210 billion Canadian dollars. In
terms of assets, Desjardins Group achieves nearly the size of all other Canadian credit
unions outside of Quebec combined (Moore, 2014).
Compared to banks, credit unions hold less assets. For example, Royal Bank of Canada
(RBC), the largest Canadian bank, accounted for 940 billion $CAD in assets in 2014,
almost 4.5 times the size of the entire Desjardins Group, the largest federation of unions
in Canada, and roughly 25 times the size of the Caisse Centrale Desjardins. However,
credit unions have specific characteristics that make them highly complementary to
banking services. For example, credit unions are more efficient than banks in the
assessment of the borrowers’ creditworthiness, because the members know each other
fairly well, due to "common bond"1 and can impose sanctions on delinquent payers (see
Banerjee, Besley and Guinnane, 1994). Furthermore, unlike banks credit unions fund a
large share of their lending activities with stable "guaranteed" deposits. Vital to the national
economy, they provide alternative non-profit-oriented financing to small and medium
enterprises (SMEs), households and other economic agents that otherwise would have
difficulty accessing traditional bank financing (CIBP, 2014). Given this importance and the
fact that members' deposits are guaranteed by the regulatory bodies in Canada, credit
unions are subject to provincial oversight.
In Quebec, for example, the Autorité des Marchés Financiers (AMF) ensures micro and
macro prudential regulation of the credit union system. In a recent document (first issued
in 2014 and revised in January 2016), it released new guidelines on regulatory standards
(AMF (2016)) based on the recommendations of Basel III (BCBS (2011)). New
requirements for credit unions are: (a) a leverage ratio (including off-balance sheet
activities) as a supplementary measure to risk-based capital requirement of Basel II; (b) a
countercyclical capital buffer, to promote the build-up of capital buffers in periods of
expansion, that can be used during periods of stress; and (c) short-term and long-term
liquidity standards (liquidity coverage ratio (LCR), net stable funding ratio (NSFR)).
The conservation and countercyclical capital buffers are introduced by regulators in
response to the 2007-2008 banking crisis in which credit unions, known to perform better
than banks through the business cycle (Smith and Woodbury, 2010), were definitely not
1
The common bond (usually, community bond, occupational bond, associational bond) is the factor which unites all the
members of a credit union. Members know and trust each other, the savings of members are available to fellow members
as loans. Credit judgements are made on character and personal records on top of commercial risk factors, see for
instance, Emmons and Schmid (1999), McKillop and Wilson (2011, 2015).
3
the culprit. Unlike banks, due to their non-profit and cooperative status, credit unions face
particular challenges to comply with capital regulations (Moore, 2014). This suggests that
they have followed a prudent capital buffer management practice which has enabled them
to weather well the 2008 financial crisis.
Meanwhile, most papers on credit unions focus on the credit union efficiency or modelling
their objective function. Very few work has been done to analyze how their special
business fits with various regulations governing them. This paper builds upon the capital
structure adjustment theory to analyze the importance of Basel III countercyclical and
conservation capital buffer for credit union prudential regulation.
By studying the cyclical behavior of Canadian credit unions’ capital buffers, we assess the
desirability of the countercyclical capital buffer. We examine the importance of the
conservation capital buffer based on the adjustment behavior of credit unions which are
capital-constrained.
Closest to our paper is the one by Goddard, McKillop and Wilson (2015), which addresses
the issue of capital adjustment for US credit unions. Since credit unions are subject to
capital regulation based on their level of risk (asset risk), the existing literature highlights
the importance of studying capital adjustment behavior jointly with risk posture. Unlike
Goddard, McKillop and Wilson (2015), we account for the endogenous risk adjustment
behavior in our capital adjustment framework. To suit our study for credit unions, we
simultaneously take into consideration their non-for profit nature by jointly investigating
capital and risk posture as well as decisions about benefits or profits to members
(thereafter called as “benefit” to members).
Against this background, after building a unique dataset of 100 biggest Canadian credit
unions which account for more than 95 per cent of total credit union system assets in
Canada for the period of 1996 to 2014, we are the first to seek answers to two main
research questions 1- Given that the desirability of the conservation buffer is an unsettled
issue, especially for credit unions, are the new Basel III buffers requirements suitable and
necessary for Canadian credit union prudential regulation? and 2- Are Canadian credit
union capital buffer adjustments sensitive to the credit union capitalization?
We find that Canadian credit union capital buffers is significantly greater than 5% of riskweighted assets (RWA) and behave countercyclically over the business cycle. However,
there is evidence that low-capitalized credit unions capital buffers are procyclical. Well
capitalized credit unions build up their buffers during booms so that they are not obliged
to curtail credit allocation during busts, suggesting no need to impose Basel III
countercyclical capital buffer requirements to these credit unions as they already hold
sufficient countercyclical capital buffers. Interestingly, credit unions with higher buffers
also hold more liquid assets. Meanwhile, low-capitalized credit unions reduce capital and
raise risk-weighted assets over the business cycle. Credit unions with low net worth
(measured by retained earnings per dollar of member shares) prudently manage their
capital buffers. Furthermore, less profitable credit unions hold smaller buffers compared
to those with higher performance and fail to prudently manage their capital buffers through
the business cycle. By and large, weakly capitalized credit unions are those upon which
the regulators should impose Basel III countercyclical capital buffers.
To the best of our knowledge, no studies like ours clearly address the merits of applying
the conservation and countercyclical capital buffer requirements to credit unions prudential
regulation. The remaining sections are organized as follows. Section 1 presents the credit
union regulation followed by a brief literature in section 2. Section 3 discusses the
4
empirical model. After the data description in section 4, we discuss the results in section
5 and provide some additional analysis in section 6. The robustness check analysis is
done in section 7 and section 8 concludes.
1. Credit unions regulation in Canada
Deposit insurance provided by the government is one of the main motives for credit union
regulation. The incentive of moral hazard from managers is weaker in credit unions due
to the shareholders status of the credit union members (depositors or borrowers). As
owners of the credit union, they contribute to its management through their voting rights.
That said, it is not necessary to protect depositors from excessive risk-taking behavior as
is the case with banks. Unfortunately, evidence shows that members’ (depositors and
borrowers) capacity to control and discipline credit unions is limited. In fact, due to the
one-member-one vote situation, the incentive and ability of members to generate a
concentration of voting power is limited and attendance of members at votes are low (Hiller
et al., 2008). This reduction of the member supervisory power increases the information
asymmetry that can trigger credit union runs in the event of doubt about its financial
condition. Consequently, the regulation of credit unions is designed to protect depositors
and deposit insurers against excessive risk taking from executives and to ensure the
solvency of credit unions in difficult times2 (e.g., Goddard, McKillop and Wilson, 2015).
Therefore, a capital requirement is desirable for the stability of credit unions, mainly during
bad economic times. However, the exact capital ratio to apply to credit unions is to date
an unsettled question.
Unlike banks, which are federally regulated, various provincial jurisdictions are responsible
for the prudential regulation of Canadian credit unions. In each province, there are
organizations which provide deposit insurance and regulate credit unions. Subject to
provincial authorities, these entities ensure that Canadian credit unions maintain adequate
capital to support their business and to contribute to financial stability. Regulations of
capital take the form of minimum capital ratio requirements. Historically, credit unions must
maintain adequate amounts of capital to support their risk and growth. Two capital
requirements are implemented prior to the Basel III adoption. The risk-based capital ratio
(total capital / total risk-weighted assets) is being implemented worldwide, and the nonrisk-based capital ratio or leverage (total capital / total assets) was newly introduced under
Basel III but has already been in effect in Canada for quite a while. Different limits have
been applied by provinces. The 8% limits on the risk-based capital ratio is adopted by all
provinces except British Columbia (10%) and Alberta (10.5%). Regarding leverage limits,
all provinces have adopted a 5% leverage limit except Ontario (4%).
Following the 2007-2008 financial crisis, to upgrade the existing standards (Basel II), the
Basel Committee has released its latest international regulatory standards called Basel III.
The Basel III regulation strengthens capital adequacy by adding new capital buffer
requirements and liquidity standards. Most of Basel III standards are primarily designed
for banks and should be reviewed by provincial authorities when applied to credit unions.
As pointed out by Wilcox (2011) and others, these standards sometimes fail to account
for credit unions’ specificities. We focus in this paper on the newly introduced
countercyclical and conservation Tier 1 capital. Credit unions actual capital ratios (risk2
According to the Australian Financial Institutions Commission (AFIC) among others similar organizations, capital
regulation aims to protect the interests of depositors and promote integrity and financial efficiency.
5
based capital ratio and leverage ratio) are higher than the minimum required. This means
that they already hold a sufficient buffer. However, this buffer is not mandatory prior to the
new Basel III. The regulated buffer will impose new constraints and exert stress on credit
unions. Firstly, they were allowed to lower their buffer to expand activities (assets or risky
assets) without provisioning for capital. Such behavior is still possible under the
conservation capital buffer but the capital buffer must quickly be rebuilt to meet the
minimum capital buffer requirement. Secondly, the countercyclical capital buffer will
impose another burden on credit unions during credit expansion. They will be required to
rapidly come up with an additional core or Tier 1 capital buffer. Despite the desirability of
such Tier 1 capital buffer, a sudden increase in its requirement could challenge credit
unions due to their not-for-profit and private business status. For example, Hiller et al.
(2008) show that under the pressure to satisfy the newly introduced risk-based capital
ratio minimum of 8% in 1992, Australian credit unions resorted to regulatory arbitrage.
Instead of raising new capital or reducing risk, credit unions reshuffled the components of
their assets to make them seem artificially less risky. This behavior is a clear evidence of
the difficulties credit unions face in having to rapidly raise their capital level. To meet Tier
1 capital requirements, financial institutions mostly rely on common equity or retained
earnings (disclosed reserves). Unfortunately, credit unions are limited in raising either of
these. Firstly, credit unions are private entities and cannot issue common equity on
financial markets (Andrews, 2014; Hoel, 2007). They instead issue membership capital
shares, which are not eligible as capital instruments (under IFRS standards) because they
lack permanency. Members can withdraw their shares in the event of resignation. Some
credit unions issue investment shares, but this is limited by the one-member-one-vote
principle (Goddard, McKillop and Wilson, 2015). Hence, the only and least costly
alternative for credit unions to meet the Tier 1 under Basel III Tier 1 conservation capital
requirement is earnings retention (Andrews, 2014).
However, retention of earnings conflicts with the non-profit status of credit unions. It limits
their ability to charge higher spreads to their customers-members-owners and so to
generate enough profit to be retained to build up capital. Further, members have a
preference for distributed profit (as dividends) over earning retention for the purpose of
capital adequacy. Furthermore, credit unions have lost the tax advantage that allowed
them to retain more profit as capital (Moore, 2014) and are currently experiencing
competition with banks even in rural areas where they were major players. Due to this
reliance on profit, sudden capital requirements may not be desirable for credit unions.
Brown and Davis (2009) found that Australian credit unions set performance levels on
short-term assets to progressively bridge the gap in capital (the difference between the
target capital ratio and the actual one). In addition, Goddard, McKillop and Wilson (2015)
find evidence of a gradual capital adjustment by credit unions in anticipation of the United
States Prompt Corrective Action (PCA) in 2000.
Like US credit unions, Canadian credit unions set their desired level of capital ratio well
above the regulatory target and divulge it in their annual reports. They adopt a
conservation capital building behavior essentially based on their disclosed reserves. For
example, to meet their capital requirements, credit unions of Manitoba and Newfoundland
& Labrador maintain a minimum of 3% of their assets in the form of retained earnings. In
British Columbia, credit unions are required to hold at least 35% of retained earnings as
capital. In Quebec, the Desjardins Group, to strengthen its capital position, regularly
retains one-third (1/3) of its revenues for capital accumulation.
6
Since credit unions strive to build buffers despite their limited capital source alternatives,
it appears important then to challenge the usefulness of the newly introduced additional
buffer requirements for credit unions. We assess the desirability of the countercyclical
capital buffer by studying the cyclical behavior of Canadian credit unions’ capital buffer. A
positive co-movement with the cycle means that the capital buffer is high during hard times
and low during recessions. This is undesirable, since during recessions revenues are low
and it is difficult for credit unions to adjust for capital without cutting new loans or liquidating
existing assets. This makes the countercyclical capital buffer appealing. By examining the
adjustment behavior of the capital-constrained credit unions, we gauge the importance of
a conservation capital buffer for credit union financial stability. Specifically, we study the
adjustment behavior of credit unions when they are near to the regulatory limit. If the lowcapitalized credit unions (below median capital) capital adjustment speed is faster than
the one well-capitalized credit unions take, from a regulatory standpoint, it is desirable to
impose a conservation capital buffer so that credit unions are not obliged to make sudden
adjustments of capital.
We review next briefly the credit union capital adjustment literature. We also provide an
overview of studies on risk taking and benefits for members, although these address
research questions different than ours.
2. Brief literature review
According to Goddard, McKillop and Wilson (2015), US credit union capital buffers are
positively related to the business cycle (countercyclical). This means that credit unions
manage to build their buffers during booms in order to avoid undesirable and socially costly
adjustment during busts (by reducing assets or loans), and there is no need to require
them to hold countercyclical capital buffers. In contrast, the study of Stolz and Wedow
(2011) contradicts the non-desirability of countercyclical capital buffer documented in
Goddard, McKillop and Wilson (2015). They found that Germany’s local banks’ capital
buffers are instead procyclical during the 1993-2004 period. The main difference between
the two studies is that they are conducted in two different countries using different ratios.
There is also a difference in the capital ratios used in both studies. Stolz and Wedow
(2011) use the risk-based capital buffer whereas Goddard, McKillop and Wilson (2015)
use the capital-to-assets ratio. Interestingly, the fact that both ratios are monitored by
Canadian credit union regulators gives a better view of the desirability of the additional
buffer for credit unions. Note that there is evidence in the literature (e.g., Ayuso, Perez
and Saurina, 2004; Guidara, Lai and Soumaré, 2013; among others) that banks’ capital
buffers are procyclical, which makes the countercyclical capital buffer requirement very
desirable for banks.
The desirability of the conservation capital buffer requirements is also an unsettled issue.
Our contention is that the conservation capital buffer holding is only desirable if less
capitalized credit unions adjust faster than well capitalized credit unions in reducing assets
or cutting loans. As stated earlier, it is hard for credit unions to rapidly raise their capital
ratio without cutting in loans or reducing assets. Some studies (Goddard, McKillop and
Wilson, 2015; Stolz, Heid and Porath, 2003) confirm the asymmetry in adjustments based
on the buffer levels held by credit unions. Goddard, McKillop and Wilson (2015) find that
credit unions with low buffers adjust faster than well-capitalized credit unions. Stolz, Heid
and Porath (2003) studied the behavior of capital-constrained credit unions prior to the
crisis. They also investigated the impact of regulations on the adjustment of capital and
7
risk for financial institutions in Germany from 1994 to 2002. They find that adjustment to
capital and risk depends on the amount of capital buffers. Banks and credit unions with
lower capital buffers try to rebuild their buffers by increasing capital and reducing risk.
However, banks and credit unions with high buffers try to maintain their buffers by
increasing the risk when the capital stock increases. This result shows the importance to
impose conservation capital buffers on credit unions to avoid costly capital raising during
hard times and risk reduction through asset liquidation or credit crunch. Contrary to those
studies, the results in Stolz and Wedow (2011) support that low-capitalized banks do not
catch up with their well-capitalized peers over the observation period and they do not
decrease risk-weighted assets during a recession. This finding suggests that their low
capitalization does not force them to retreat from lending, which supports the desirability
of conservation capital buffers for credit unions under Basel III.
Note that all of these studies do not internalize the specific not-for-profit and cooperative
features of credit unions in their analysis. We complement their approach by estimating
how the credit union adjustment process affects the benefits to members. In the next
section, we describe the data and methodology for our study.
3. The empirical model
Let us recall that the aim of this paper is threefold. Firstly, we estimate the effect of
business cycle fluctuations on Canadian credit union capital buffers (risk-based and
leverage capital buffers). Secondly, we decompose the adjustment process by studying
adjustment in the numerator of the ratio and its denominator. Thirdly, we investigate how
capital, risk and the business cycle affect the credit union main objective, which is to offer
benefit to members. This section describes our empirical model estimation strategy. We
first present the partial adjustment model, state the hypotheses to be tested, and describe
the estimation methodology. Then, we define the measures of the variables of interest,
the credit union capital buffers and the business cycle. Finally, we define the measures
and the potential impact of the credit union-specific control variables.
A. The partial adjustment model and hypotheses
The incentive to hold buffers derives from two assumptions: first, credit unions cannot
adjust capital and risk instantaneously, otherwise they would not need to hold capital
buffers; second, a violation of the regulatory minimum capital requirements triggers costly
supervisory actions, possibly even leading to credit union closure (e.g., Stolz and Wedow
(2011) among others). Existing literature shows that credit unions hold large capital
buffers. Jackson (2007) describes the evolution of capital ratios for US credit unions over
the 1990-2006 period and finds that they are over-capitalized (compared to banks). They
hold on average 11.6% of capital compared to the minimum requirement of 8%. This result
is also confirmed by Goddard, McKillop and Wilson (2015). Most of the credit unions in
Canada declare in their annual reports that they target higher risk-based capital ratio
(around 12%, compared to the minimum of 8%). This additional capital buffer held by
credit unions allows them to guard against the risk of falling below the regulatory value.
Marcus (1984), Milne and Whalley (2001), Milne (2004) and others have shown that
financial institutions have an incentive to hold a capital buffer as insurance against breach
of compliance with minimum regulatory capital requirements. We assume that they do so
because of the high cost of capital adjustment.
8
In this paper, we consider two capital buffers, the risk-based capital buffer and the
exposure-based capital ratio. The buffer on the risk-based capital (leverage ratio) is the
additional capital that credit unions hold above the minimum of 8% of risk-weighted assets
(RWA) (respectively 5% of total assets) required. To adjust for the risk-based capital ratio
or leverage ratio, credit unions can increase their Tier 1 capital position (the numerator of
the ratio) or decrease their risky asset holding or their total assets. This substitution effect
between the numerator and the denominator of the ratio makes the adjustments in capital
and risk interrelated. Further, credit unions are highly dependent on their profit to build
capital, which contrasts with their non-profit nature. Therefore, we complement the joint
capital and risk adjustment by also taking into consideration benefits for members.
The theoretical model that supports gradual adjustment to target is the partial adjustment
model. It assumes that each year the credit union increases its capital to reduce the
shortfall between the target and realized capital. This is similar to the approach used by
Goddard, McKillop and Wilson (2015) for US credit unions and Stolz and Wedow (2011)
for German credit unions. In choosing their targeted buffer, credit unions may find a
tradeoff between the adjustment costs and the costs of operating with sub-optimal buffers.
The proposed model enables partial adjustments of the credit unions’ risk-based capital
and leverage buffers towards their desirable level. A quick adjustment towards a targeted
buffer may have two conflicting explanations. Either the adjustment is less costly or the
ratio is the binding one, in which case the credit union may quickly adjust in order to avoid
non-compliance costs. The typical partial adjustment model is the following:
∆‫ܨܨܷܤ‬௜௧ = ߙሺ‫ܨܨܷܤ‬௜௧∗ − ‫ܨܨܷܤ‬௜௧ିଵ ሻ + ߝ௜௧ , (1)
where ‫ܨܨܷܤ‬௜௧ is either the risk-based capital or the leverage buffer of the credit union i at
time t, and ߝ௜௧ is the error term. Equation (1) assumes that at every time t, the typical credit
union i closes a proportion ߙ of the gap between its actual buffer ‫ܨܨܷܤ‬௜௧ିଵ (the buffer
realized the previous period) and its desired or targeted buffer ‫ܨܨܷܤ‬௜௧∗ in the current period.
ߙ is also interpreted as the speed of adjustment. The targeted buffer, being unobservable,
is assumed to depend on the business cycle interacting with credit risk and credit unionspecific variables (e.g., Berger et al., 2008; Goddard, McKillop and Wilson, 2015; Stolz
and Wedow, 2011). Hence, the unobservable target buffer is expressed as follows:
‫ܨܨܷܤ‬௜௧∗ = ߚ଴ ‫ ܮܥܻܥ‬+ ߚଵ ‫ܹܱܮݕ݀ ∗ ܮܥܻܥ‬௜,௧ + ߜܺ௜௧ିଵ + ߤ௜௧ , (2)
where ܺ௜௧ିଵ is a vector of credit unions’ predetermined characteristics, ‫ ܮܥܻܥ‬is a proxy
for business, ݀‫ܹܱܮݕ‬௜,௧ is a proxy for the credit union capitalization and ߤ௜௧ is the error term.
Assuming that ܺ௜௧ିଵ is properly determined, if the tradeoff theory of capital structure choice
holds, then we should have ሺߚ଴ , ߚଵ , ߜሻ ≠ 0. We replace expression (2) in equation (1) and
obtain:
∆‫ܨܨܷܤ‬௜௧ = ߙሺߚ଴ ‫ ܮܥܻܥ‬+ ߚଵ ‫ ܩܧܴ ∗ ܮܥܻܥ‬+ ߜܺ௜௧ିଵ + ߤ௜௧ , −‫ܨܨܷܤ‬௜௧ିଵ ሻ + ߝ௜௧ (3)
= −ߙ‫ܨܨܷܤ‬௜௧ିଵ + ߙߚ଴ ‫ ܮܥܻܥ‬+ ߙߚଵ ‫ܹܱܮݕ݀ ∗ ܮܥܻܥ‬௜,௧ + ߙߜܺ௜௧ିଵ + ߝ௜௧ + ߙߤ௜௧ .
to obtain the standard form of an endogenous lag model. We then add ‫ܨܨܷܤ‬௜ି௧ to both
sides of Eq. (3) and get the following expression:
‫ܨܨܷܤ‬௜௧ = ሺ1 − ߙሻ‫ܨܨܷܤ‬௜௧ିଵ + ߙߚ଴ ‫ܮܥܻܥ‬௝,௧ + ߙߚଵ ‫ܮܥܻܥ‬௝,௧ ∗ ݀‫ܹܱܮݕ‬௜,௧ + ߙߜܺ௜௧ିଵ + ߝ௜௧ + ߙߤ௜௧ . (4)
We assume that the error term ߝ௜௧ + ߙߤ௜௧ can be decomposed into two independent
components: the credit union-specific unobservable variable and the white noise. These
9
are assumed to follow a normal distribution with 0 means and different constant variances
respectively.
Assuming the null hypothesis that business cycle fluctuations do not have an impact on
the change in credit unions’ capital buffers, we can state our hypotheses in terms of the
coefficient ߙߚ଴ as follows: H1a: ߙߚ଴ = 0. Significant values of ߙߚ଴ imply that the business
cycles affect the build-up of Canadian capital buffers. A positive sign of this coefficient
means that capital buffer moves countercyclically.
We also test for the effect of capitalization on the cyclical behavior of buffers. Under the
null hypothesis that capitalization does not affect cyclical behavior of buffers, we state our
hypotheses in terms of the coefficient ߙߚଵ as follows: H2a: ߙߚଵ = 0.
A significant ߙߚଵ means that the impact of the business cycle on the capital buffer
adjustment depends on the credit union buffer level. To dig further into how credit unions
adjust their buffer, we study separately the behavior of the numerator and the denominator
of the two capital ratios (risk-based capital ratio and leverage ratio). The two ratios share
the same numerator which is the total regulatory capital but have different denominators
(risk-weighted assets and total assets). We also evaluate how the business cycle and the
capitalization impact the total capital (CAP), total risk proxied by RWA and total benefits
to their members (BENEF). We estimate the following equation:
ܻ௜௧ = ߚ଴ + ߚଵ ∆‫ܮܥܻܥ‬௜௧ + ߚଶ ‫ܮܥܻܥ‬௝,௧ ∗ ݀‫ܹܱܮݕ‬௜,௧ + ߚଷ ܻ௜௧ିଵ + ߛଵ ܺ௜,௧ିଵ + ߴ௜,௧ (5)
where ܻ௜௧ can be replaced by the total capital (CAP), the risk-weighted assets (RWA), the
total asset (ASSETS) and the benefits for members (BENEF). In other words, business
cycle fluctuations determines the build-up of capital buffers.
For each value of ܻ௜௧ (CAP, RWA, BENEF), by mean of the coefficient ߚ1 we test the
cyclical behavior as in Eq. (4). We also test for the significance of ߚଷ . The idea behind this
test is to quantify how the components of the risk-based and leverage ratios or benefit to
members vary with the optimal or targeted buffers.
B. Estimation methodology
The models in Eqs. (4)-(5) have a dynamic panel structure, so we employ the Blundell and
Bond (1998) system generalized method of moments (Syst-GMM) estimator. Its controls
for the credit union-specific unobservable component of the error term. At the same time,
it also gets around the weak instrument problem that the Arellano and Bond (1991)
estimator would encounter given the quasi unit root characteristic of our data. For models
with endogenous regressors, as pointed out by Stolz and Wedow (2005), using too many
instruments could result in seriously biased estimates. Hence, we only use a subsample
of the entire series as instruments.
C. Dependent variables
a. Capital buffers, regulatory capital, risk
Capital buffers (BUFF_R, BUFF_A): A credit union’s capital buffer is the capital the credit
union holds in excess of the regulatory minimum capital requirement. Hence, we define
two measures of credit unions’ capital buffers (BUFF). The first one, BUFF_R is defined
as the risk-based capital ratio (total capital / total risk-weighted assets) minus the 8%
regulatory minimum. The second BUFF_A is defined as the leverage ratio (total capital /
total assets) minus 5%.
10
Regulatory capital (CAP)
Total regulatory capital is computed as the sum of Tier 1 capital or core capital and Tier 2
capital or additional capital instruments. Main components of Tier 1 capital are the retained
earnings and the capital injections by the credit union public owners and members. Tier 2
capital is made of subordinated debt and mostly issued by large credit unions to boost
their capital buffer (Stolz and Wedow, 2005).
Risk weighted assets (RISK)
By financing members’ long-term risky projects (loans) with short-term deposits, credit
unions take on credit and liquidity risk. They also bear market risk through their investment
portfolios. This risk-taking, although socially beneficial, is also a source of instability for
the credit unions in case of systematic defaults from borrowers. Therefore, regulators
require credit unions to hold capital and reserves to cushion and cover unexpected losses.
Credit unions should maximize their risk-adjusted profitability when allocating their
portfolios. We are unaware of papers that document credit union risk-taking behavior
following capital regulation except the one by Hillier et al. (2008), which finds evidence of
regulatory arbitrage by credit unions to artificially decrease their risk level. Most existing
literature on credit unions’ risk addresses how credit union revenue volatility is affected by
membership or income diversification (see Frame, Karels and McClatchey, 2002; Ely,
2014; Esho, Kofman and Sharpe, 2005; Goddard, McKillop and Wilson, 2008). However,
we are more interested in portfolio or asset risk rather than returns volatility or returnsbased risk. Our focus is on how credit unions adjust their risk-taking behavior to meet
regulatory requirements. As mentioned earlier, regulators measure banks risk taking by
their total risk-weighted assets (RWA). Jacques and Nigro (1997) argue that the RWA
captures both the allocation of risks and the quality of the portfolio risk. This risk measure
incorporates credit risk, operational risk and market risks.
b. Benefits to members
Following Bauer (2008), the performance of credit unions in this study is measured by how
much benefit members derived from their credit unions through loans and deposit
certificate rates. This measure of benefit to members is drawn from the objective function
of credit unions theoretically modelled by Taylor (1971, 1977), Smith, Cargill and Meyer
(1981) and Smith (1984). According to these authors, credit unions maximize benefits to
members by setting better rates on loans and deposits compared to the market.
Specifically, Smith (1986) performs an empirical test to show that credit unions are neutral
between the interests of their borrowing and saving members. Following the formulation
of the objective function in these models, we compute the benefits to members as follows:
௜
௜
‫ = ܨܧܰܧܤ‬0.5 ∗ ൫‫ݎ‬௅ெ − ‫ݎ‬௅,௧
൯ + 0.5 ∗ ሺ‫ݎ‬ௌ௧
−‫ݎ‬ௌெ ሻ
௜
where ‫ݎ‬௅,௧
=
௅௢௔௡ ௜௡௧௘௥௘௦௧ ௜௡௖௢௠௘
்௢௧௔௟ ௟௢௔௡
௜
and ‫ݎ‬ௌ,௧
=
ூ௡௧௘௥௘௦௧ ௘௫௣௘௡௦௘ ௢௡ ௗ௘௣௢௦௜௧
்௢௧௔௟ ௗ௘௣௢௦௜௧
are respectively the
average loan and deposit rates levied by cooperative i in year t. So, weighted equally
௜
‫ݎ‬௅ெ − ‫ݎ‬௅,௧
may be interpreted as the benefit to a borrowing member and ሺ‫ݎ‬ௌ௧௜ −‫ݎ‬ௌெ ሻ the
benefit to a lending member.
There is evidence in the literature that credit unions set their rates competitively with other
financial intermediaries. Feinberg and Rahman (2001) finds that credit unions actively
11
participate in market discipline, forcing banks to provide competitive lending rates to their
customers. Tokle and Tokle (2000) show that bank deposit rates are affected by the rates
offered by credit unions. There is clear evidence that credit unions seek to maximize
benefits to members.
Our variable benefit to members (BENEF) thus captures the average earnings available
to lenders and borrowers, assuming that both categories of members (borrowers and
lenders) have equal weight in the credit cooperative.
c. Credit unions specific control variables
To estimate the effect of business cycle fluctuations on changes in credit unions’ capital
buffers, we control for the effect of credit union-specific variables on optimum capital
buffers. In the following, we present the proxy variables suggested by the literature and
their expected impact on optimum capital buffers. The variable definitions are also given
in Table B.1 in Appendix B.
(Insert Table B.1 here)
The economic health of the province (GDPG): We use two different measures of GDPG
(Gross Domestic Product Growth) to proxy the business cycle. The first measure is GDP
growth by province and the second is GDP growth for all of Canada. Most credit unions
operate locally within their provinces so they are more affected by the provincial economic
condition. Indeed, economic conditions affect capital since the losses are more frequent
in a recession. Also, as for banks (see Albertazzi and Gambacorta (2009) among many
others), the cycle affects the risk because risk taking by credit unions depends on
economic activities. We also expect that during hard times, credit unions may make
tradeoffs between support to members or capital raising if they are less capitalized. We
investigate the credit union adjustment of capital behavior during a short time interval
(1996-2014), so the term of business cycle should be interpreted with caution. Instead, we
expect that our proxy, the GDP growth captures the recent economic shock caused by the
subprime crisis and delivers a “through-the-economic-activity” insight about credit union
capital buffer adjustments.
(Insert Graph 2-5)
We plot on graphs 2 to 5 the patterns of the credit union capital buffer and provincial and
Canadian GDP growth. Credit unions increase their buffers during booms and have not
experienced falls in their buffers during a huge drop in GDP growth in 2009. Instead they
have continuously increased their buffer following this period.
The volume of loans (LOAN): Goddard, McKillop and Wilson (2015) found a negative
relation between their capital ratio and their loans granted. Indeed, by granting loans to
members, credit unions increase their assets and risk measured by RWA (all else equal).
This justifies why the LOAN variable, proxied by the ratio of total loans to total assets, is
included in the equations of capital and risk.
Age of credit unions (AGE): In general, the longer the credit union existence, the more it
gains experience in risk management and accumulates a sufficiently adequate capital
12
buffer. It can therefore offer services with lower interest rates than those of the
competitors. The variable (AGE), as measured by the natural logarithm of the credit union
age, is positively related to members’ benefits.
The credit union size (SIZE): Size (log of total assets) may influence target capital due to
its relationship with risk diversification, investment opportunities and access to market
funding. We use SIZE to account for diversification and economy of scale enjoyed by large
credit unions (e.g., Goddard, McKillop and Wilson, 2015) and banks (Aggarwal and
Jacques, 1998; Ayuso, Perez and Saurina, 2004; Flannery and Rangan, 2006 and 2008;
Guidara et al., 2013).
Provision for loan losses (LOSS): Credit unions’ provision for anticipated loan losses
reduces the nominal value of total assets and could potentially affect the leverage (Total
capital/Total Assets), ceteris paribus. This variable is used in Shrieves and Dahl (1992)
and Rime (2001).
Regulatory pressure (dyLow): Capital buffer theory predicts that an institution approaching
the regulatory minimum capital ratio may have incentive to boost capital and reduce risk
to avoid the regulatory cost triggered by a violation of the capital requirement. We follow
the approach used by Aggarwal and Jacques (1998) and refined by Rime (2001) based
on the bank’s buffer distance to its peers. We build a dichotomous variable which takes a
value of 1 if the credit union risk-based capital ratio is among the 50% least capitalized.
As most credit unions hold capital above the minimum requirements, this measure
captures credit unions with a buffer below or above the median buffer value.
Liquidity (LIQUID): A higher ratio of liquid assets (such as Cash and Treasury bill to total
assets) may reduce credit union incentive to target higher capital buffer. Credit unions can
quickly sell their liquid position to cover their book capital needs (Stolz and Wedow, 2011).
Return on assets (ROA): The only and costless alternative for credit unions to adjust the
capital buffer is earnings retention (Andrews, 2014). So we expect a positive effect of the
return on assets (ROA) on capital buffers. However, a negative impact may also be
plausible. Credit unions may choose to pay dividends when they are well capitalized.
Holding less buffer when high returns on assets (ROA) are experienced may also be
justified by the ability of the credit union to permanently generate high profits and to
increase capital buffers by way of retained earnings when needed. Hence, we include
credit union return on assets (ROA) with an ambiguous expected sign.
Mergers (dyMERG): We also include a dummy variable to capture mergers. The reason
for including this variable is the ongoing merger wave within the Canadian credit union
system. The dummy variable takes a value of 1 for the acquirer in the year of the merger
and zero otherwise. The expected sign of the variable is negative given that acquiring
credit unions are typically better capitalized before a merger.
4. Data and descriptive analysis
We collected financial data on the 100 major credit unions in Canada based on the list of
the 100 largest credit unions published quarterly by Credit Union Central Canada.
According to System Results published by the Canadian Credit Union Association (March
2016), as of fourth quarter 2015, the top 100 credit unions account for 88.6 per cent of
total credit union system assets in Canada (excluding Quebec). If one includes Quebec,
this statistic jumps to over 95 per cent. Specifically, we downloaded balance sheets,
13
income statements and capital requirements data from annual reports published by credit
cooperatives on their websites, with 690 annual reports analyzed. This corresponds to an
average of 7 years of data per credit union. Some credit unions post few annual reports
on their websites. The collected data was then cleared to detect any inconsistencies that
may affect the quality of the analysis. The most represented provinces are: Quebec (with
a federation of 344 local credit unions), British Columbia (27 credit unions), Ontario (25
credit unions), Manitoba (20 credit unions) and Alberta (11 credit unions). Data on
macroeconomic variables comes from Statistics Canada and the Quebec Statistics
Institute. Our unique sample consists of an unbalanced panel of 100 major credit unions
(by total assets) in Canada from 1996 to 2014.
A. Credit union asset and liability structure
a. Credit union asset structure
In this section, we evaluate major assets and liabilities components. We provide
descriptive statistics for the whole sample period (1996-2014) and three sub-periods: The
pre-crisis period (1996 to 2007), the crisis period (2007-2009) and the post-crisis period
(2009-2014). Descriptive statistics on assets and liabilities components are summarized
in Tables A.4 and A.5.
(Insert Tables A.4 and A.5 here)
The average credit union has 9.192 billion $CAD in total assets. The largest total assets
among credit unions is 44 billion, attained in 2014 by Caisse Centrale Desjardins. This
can be compared to Canadian financial institutions, where the largest bank in Canada
(Royal Bank) is 4.5 times larger than the whole Desjardins group, the largest credit union
in asset volume. The smallest credit union by asset size is Pierceland Credit Union located
in the province of Saskatchewan, recorded in 2012 as having 35.441 million $CAD in
assets.
(Insert Table A.4, A.5 and A.6 here)
(Insert Graph 1 here)
The distribution of assets by year can be found in Table A.6. We also compute the
distribution of assets by year for a homogenous set of credit unions with available annual
reports for the 1996-2014 period (see Table A.6, panel A). There is evidence that credit
unions have continuously increased their total assets except during the 2009 crisis during
which the average level of total assets fell below its 2008 level (see Graph 1).
Concerning the structure of total assets, we find that 79% of credit unions’ total assets
consist of loans to members (commercial and agricultural (30%), residential mortgages
(54%) and personal loans (16%)). This loan-dominated business model was increasingly
emphasized over time in our sample, with a 75% loans-to-assets ratio before the crisis
(1996-2006), 78% during the crisis (2007-2009) and 81% in the post crisis period. This
highlights the credit unions’ traditional focus on providing credit to economy.
Investment is the second important component of credit union assets. Credit unions invest
some of their funds in secured assets or derivative products for hedging purposes. In term
of assets held they invest in government secured assets and credit union central shares.
On average, 12% of credit union assets are made of investments in such assets. Asset
14
holdings for investment purposes have decreased over time, from 14% in the pre-crisis
period to 11% in the post-crisis period up to 2014.
Credit unions also hold on average of 4% of their funds in cash-like assets. This proportion
is 3% today, down from a 5% in the pre-crisis period.
b. Credit union liabilities and members’ equity structure
Credit unions’ major source of fund is deposits. On average, 86% of their funds come from
deposits (personal and commercial members). This is quite stable through our whole
period. Asset growth, in particular member loan growth that cannot be funded by member
deposits, is funded through securitization, loan syndication, corporate borrowings or
issuance of investments or saving accounts shares. Credit union borrowings average 3%
of total funds. Members’ equity also provides an additional source of financing and is
comprised of retained earnings and members’ shares. On average, member equity
contributes to 6% of total funds.
B. Descriptive statistics of variables used in the regression analysis
(Insert Table B.2, B.3a, B.3b, B.3c and B4 here)
Descriptive statistics of the variables used in the regression analysis are computed for two
subsamples of credit unions (high and low capital buffers). We identify significant control
variables for our capital buffer analyses. In tables B.2 and B.3a we provide descriptive
statistics for the business cycle indicators and the credit union-specific variables. Table
B.3a provides the descriptive statistics for subsamples of credit unions with high and low
capital buffers. We include the median value in the descriptive statistics for robustness
motives. We also report the Wilcoxon rank sum test, which tests whether the subsamples
come from the same population. Table B.3a reveals that, on average, credit unions with
low capital buffers take higher risks, as given by higher risk-weighted assets per dollar of
assets (RISK) but are not commensurately rewarded on average by higher returns on
assets (ROA). These findings with respect to risk and return point to a possible inefficiency
of credit unions with low capital buffers. We also observe that, on average, large credit
unions are less capitalized. We test in Table B.3b whether credit unions’ actual risk-based
capital buffer exceed the minimum capital buffer requirements under Basel III. Credit
unions are required to hold at least 2.5% of their RWA for conservative buffer purpose and
a proportion which varies between 0% to 2.5% of their RWA for countercyclical buffer
purpose. In total, credit unions have to hold a capital buffer that comprises between 2.5%
and 5% of their RWA. Results suggest that the credit union average buffer value is greater
than 5% of RWA (at the 5 % statistical level). In Table B.3c we analyze the capital buffers
dynamics by computing the average and median value for three subperiods: the pre-crisis
period (1996 to 2007), the crisis period (2007-2009) and the post-crisis period (20092014). We find that credit union have continuously increased their buffer even during the
crisis period. Interestingly, they increased their assets-based buffer which was small in the
pre-crisis period.
Table B.4 gives the correlation matrix. It shows that most variables are significantly
correlated with each other. For example, the correlation matrix suggests that large credit
15
unions (SIZE) hold less capital buffer3 (BUFF_A and BUFF_R), liquid assets (LIQUID) and
provision for losses (LOSS) but take much more risk (RISK). The correlation between
buffers and the business cycle is not significant but becomes negatively significant when
we control for the credit union capitalization. This means that the effect of the business
cycle on the buffer depends on the credit union capitalization. This correlation might stem
from credit union-specific fixed effects, not accounted by the simple correlations analysis.
To tackle these possible shortcomings, we employ multivariate regression techniques
which allow for fixed effects.
5. Regression analysis
In the following subsections, we present the results of estimating Eqs. (4)-(5). We first
show the results for Eq. (4) where we account for asymmetries in the behavior of capital
buffers (Basel risk-based capital and leverage buffer) with respect to the business cycle
and the capitalization of the credit union. We then decompose the two capital buffers and
separately study their common numerator (CAP) and their denominators which include
the Basel risk measure (RWA) and total assets (ASSETS), corresponding to estimating
Eq. (5). Finally, we show how the business cycle and capitalization affect benefits to
members (BENEF).
(Insert Table B.5, Panel A here)
Table B.5 (Panel A) presents the result of estimating Eq. (4), the Hansen test and the tests
of serial correlation in the residuals. With respect to GDPG_P, provincial GDP growth, we
find a positive and significant coefficient. Hence, business cycles do affect the buffers’
behavior (H1a: ߙߚ଴ ≠ 0). This is consistent with the positive relation found by Goddard,
McKillop and Wilson (2015) for US credit unions. This result suggests that there is no need
to require credit unions to build their buffers during booms (as suggested by the Basel III
countercyclical capital buffer requirement), as they already do so. However, we find an
opposite result for low-capitalized credit unions (below the median capital), by interacting
the dummy variable of low capitalization (dyLOW) and the business cycle variable
(GDPG_P). Hence, low-capitalized credit unions are those who will be directly impacted
by the new Basel III countercyclical and conservative capital buffers. Results also suggest
that credit unions with higher buffers also hold more liquid assets. To explore further the
cyclical behavior depicted by Eq. (4), we run regressions relating to Eq. (5) on both
components of the Basel risk-based capital buffer, i.e., capital (CAP) and risk-weighted
assets (RWA). We only focus on the Basel risk-based buffer (BUFF_R) because new
buffer requirements in Basel III are risk-based.
(Insert Table B.6 here)
Based on results of Table B.6, we find evidence that credit unions increase their capital
position during booms. The coefficient of the CYLC_P in the first column is positive and
significant, meaning that credit unions build up their buffers during booms so that they are
not forced to curtail credit allocation during busts. However, the negative cyclical behavior
found for low-capitalized credit unions is due to the reduction in their capital position during
booms. We get this result by interacting the dummy variable of low capitalization (dyLOW)
3
Recall that the capital buffer is the additional capital banks hold above the minimum required. BUFF_A is the additional
capital banks hold above the minimum capital-to-asset or leverage ratio. BUFF_R is the additional capital credit unions
hold above the minimum capital-to-risk weighted assets ratio.
16
and the business cycle variable (GDPG_P). We obtain a negative and significant
coefficient in column 1. In addition to their low capital in booms, low-capitalized credit
unions increase their risk taking (RWA) during booms. We find a positive and significant
relation between credit unions’ risk proxied by RWA and GDPG_P*dyLOW, the interaction
variable term in column 2. This result is consistent with Stolz and Wedow (2011) for
German banks. While low-capitalized banks effectively increase their exposure to credit
risk by increasing risk-weighted assets in a boom, they fail to increase capital accordingly.
This is also consistent with Ayuso, Perez and Saurina (2004), who point out that banks
that do not build up capital sufficiently in booms to cover for the higher exposure to credit
risk will be forced to increase buffers during busts. This stance from low-capitalized credit
unions may be explained by the challenge they face to adjust quickly.
We finally address the cyclical behavior of the credit union benefits to members (BENEF).
Let us recall that the main objective of credit unions is to grant competitive rates on loans
and deposits to their members. Our analysis (column 3, Table B.6) suggests that there is
no cyclical behavior in the way credit unions provide benefit to members. However, there
is a negative relation between the size (SIZE) and age (AGE) of the credit union. This
result may be explained by the fact that large credit unions tend to behave like banks and
as such can diversify and expand the ways they reward their members. This may also be
valid for the variable AGE as major large credit unions in Canada are likely the old ones.
6. Other extensions
Section 5 suggests that low-capitalized credit unions (below median capital) may be
subject to a countercyclical capital buffer. In this section, we investigate additional credit
union characteristics which may suggest implementation of a countercyclical capital
buffer. We look at credit union profitability, credit union members’ shares value and credit
union diversification.
A. Capital buffer cyclical behavior and credit union profitability ratio
We assess whether credit unions’ profitability has any explanatory power on their capital
buffer management. We proxy the profitability ratio as the interest income divided by
interest expense, a measure close to the earning coverage ratio. It measures the return
on each $1 invested in interest activity. On average, credit unions’ interest activity
profitability ratio averages 2.80. So on average each $1 invested returned $2.08. This
profitability ratio has increased from 1.59 in the crisis period to 2.45 in the post-crisis period
(see Table A.5). We conduct this analysis by replacing the capitalization variable (dyLOW)
in Eq.(4) by a the dichotomous variable dyProfLOW which takes a value of 1 if the median
credit union profitability ratio is below the whole sample median profitability ratio. Results
show that credit unions with a higher profitability ratio (dyProfLOW=0) have a significant
higher capital buffer compared to less profitable ones. They grant smaller benefit to their
members compared to less profitable credit unions (see Table B.8, panel 2). The
correlation matrix reported in Table B.7 attests that profitability ratio affects the cyclical
behavior of Canadian credit union risk-based capital buffers (BUFF_R). The negative
relation between the risk-based capital buffer BUFF_R and dyProfLOW*GDPG_P
suggests that less profitable credit unions failed to prudently manage their capital buffer.
17
B. Capital buffer cyclical behavior and credit union members’ value
There is evidence in the banking literature that banks with a higher charter value may
adopt prudential capital management to protect their wealth against credit losses. In this
section, we evaluate whether any credit union proxy for charter value has any explanatory
power on the cyclical behavior of capital buffer. We proxy the credit union charter value
by the members’ share value calculated as the total retained earnings divided by total
member shares. The value of member shares increased significantly during the sample
period due to an accumulation of retained earnings. From an average of 4.05 it is now
33.59 (see Table A.5). We conduct this analysis by replacing the capitalization variable
(dyLOW) in Eq.(4) by a dichotomous variable dyValLOW which takes a value of 1 if the
median credit union member share value is below the whole sample members’ shares
value median. Results indicate that credit unions with high members’ shares value
(dyValLOW=0) have a significant lower assets-based buffer (BUFF_A) compared to less
valued ones. They, however, hold significantly higher liquid assets, take less risk and are
more profitable (see Table B.8, panel 2). The correlation matrix presented in Table B.7
shows that the members’ share value affects the cyclical behavior of Canadian credit
union capital buffer measured as the excess of capital above the minimum capital-toassets or leverage ratio (BUFF_A). The positive relation between the leverage ratio buffer
BUFF_A and dyValLOW*GDPG_P mean that credit unions with low net worth (measured
by retained earnings per dollar of member shares) prudently managed their capital buffer
through the business cycle.
C. Capital buffer cyclical behavior and credit union diversification
Credit unions diversifying by way of non-interest income may offset shocks to their interest
income during stress periods. Köhler (2015) shows that savings and cooperative banks in
the European Union will be significantly more stable and profitable if they increase their
share of non-interest income, benefitting greatly from income diversification. This revenue
diversification may strengthen their ability to build a capital buffer through earnings
retention and by supporting asset growth. In this section, we evaluate whether credit union
diversification via non-interest income has any explanatory power on their capital buffer
cyclical behavior. We proxy diversification as non-interest income divided by interest
income. This ratio aims to compare non-interest to interest income. Non-interest activities
can be used as a hedge for interest revenue volatility if they can provide additional revenue
to a credit union during a stress period. This ratio averages 0.46 through the whole sample,
i.e., non-interest activities earned $0.46 for each dollar obtained from interest activities,
on average. Up from $0.13 in non-interest earnings for each dollar from interest activities
in the pre-crisis period, this ratio was as high as $1.48 during the crisis. Its highest value
was recorded in 2009, the crisis year in Canada. This means that Canadian credit unions
have made $1.48 in non-interest income for each dollar returned by interest activities.
Interestingly, the diversification ratio drops to nearly its pre-crisis value today and
averages $0.15 (see Table A.5). We account for diversification effects to study how it
affects the cyclical behavior of credit union capital buffers. To achieve this goal, we replace
the capitalization dyLOW variable by a new dichotomous variable dyDivLOW which takes
a value of 1 if the credit union median diversification proxy is below the whole sample
diversification median. A correlation analysis shows that the diversification effect on the
cyclical behavior of capital buffers are non-significant (see Table B.7). Existing literature
18
on credit union diversification shows that there is a relation between credit union
capitalization and diversification. According to Goddard, McKillop and Wilson (2008), the
effect of diversification is positive for well-capitalized credit unions (found in Jackson,
2011) and negative for under-capitalized ones.
7. Robustness checks
Previously, we use the regulatory measure of risk (RISK) (risk-weighted assets per unit of
assets) and the provision per unit of asset (LOSS) to proxy for credit union backward and
forward looking risk. One may criticize that risk-weighted assets are easy to game (for
example, via securitization). In this section, we use an alternative risk measure that is
closer to the credit union default risk measure: the ZSCORE measure. According to Esho
et al. (2005), ZSCORE is an accounting-based measure of the probability of bankruptcy
and is defined as the likelihood of incurring a loss greater than the credit union’s total
capital.
ܼܵ‫= ܧܴܱܥ‬
‫ ܲܣܥ‬− ߤோை஺
ߪோை஺
where CAP is the capital-to-assets ratio, ߤோை஺ is the average ROA and ߪோை஺ is the ROA
volatility.
(Insert Table B.10 here)
Our results suggest that the relation of the capital buffer to GDP growth and the cross
variable CYCL*dyLOW remains unchanged.
(Insert Table B.9 here)
We also check whether results are driven by major credit unions such as Caisse Centrale
Desjardins and Vancity credit union, or smaller ones, by excluding them from the analysis.
We mainly compute deciles based on credit union size and run our regressions on the 2nd
to 9th decile. This allows us to exclude largest and smallest credit unions from our sample
without drastically reducing our sample. The results are similar to findings in section 5. We
conclude that our results are not driven by observations in the distribution tails.
(Insert Table B.5, Panel B here)
We also test whether our results on the cyclical behavior of the capital buffers are driven
by the major shock in GDP experienced in 2009 following the subprime crisis. We perform
the Blundell-Bond analysis ignoring the year 2009 and obtain similar results to those
reported previously (see Panel B of Table B.5).
19
8. Conclusion
This paper examines how the capital buffers (Basel risk-based and leverage ratio) of
Canadian credit unions fluctuate over the business cycle. We find strong evidence that
capital buffers (risk-based and assets-based) behave countercyclically. However, credit
unions with low capital buffers react differently to the business cycle than credit unions
with relatively higher capital buffers. Low-capitalized credit unions (below median capital)
decrease capital buffers during busts. This decrease is jointly driven by capital and riskweighted assets. Low-capitalized banks reduce capital and raise risk-weighted assets
over the business cycle. One plausible explanation, suggested by Stolz and Wedow
(2011), may be credit unions’ differing risk attitudes. A low capital buffer would then simply
reflect lower credit union risk aversion. Furthermore, Blum (1999) shows that constrained
banks may take higher risk to target higher profits in order to meet capital requirements.
This behavior of low-capitalized credit unions may also reflect their poor risk management.
Based on these results, we recommend regulators to implement the conservation buffer
requirement to force low-capitalized credit unions to hold additional buffers. In effect, new
buffer requirements would not impact well-capitalized credit unions as they already hold
sufficient countercyclical capital buffers. It is worth noting that credit unions do not
decrease their risk-taking during bad times, a feature that underscores the importance
they give to sustaining vigorous credit allocation during recessions to revive the economy.
Additional analyses suggest that credit unions with low net worth prudently build up their
capital buffers. Less profitable credit unions hold smaller buffers compared to those of
more profitable credit unions and fail to prudently manage their capital buffers through the
business cycle. Finally, we find that credit union benefits to members are not prone to the
business cycle but rather decrease with the credit union’s age and size.
20
Appendix A
Regulation of credit unions in Canada: A synthesis by province
Information on the credit union regulation comes from provincial Credit Union Act and Credit Unions’ annual reports.
1. British Columbia (BC)
Regulatory capital requirements are governed by the "Financial Institution Commission of British Columbia" (FICOM). They
use the risk-weighted assets approach developed by the Bank of International Settlements 'BIS'. FICOM establishes a
minimum risk-weighted capital ratio of 8%. In addition, FICOM recently required banks to hold a capital ratio of 10% and
asked the credit unions to set their own capital ratios or target a capital ratio limit above the 10% limit. This capital ratio must
include a minimum of 50% of core capital or Tier 1 (share capital "equity shares" and retained earnings). In addition, at least
35% of the regulatory capital must be retained earnings. Additional capital or Tier 2 capital, includes subordinated bonds,
other investment shares and the share capital that does not meet the requirements of the capital base.
2. Ontario (ON)
Credit unions operating in the Province of Ontario are required to have a sufficient level of capital to protect themselves
against unexpected losses and to align with the rules dictated by the "Credit Unions and Caisses Populaires Act Ontario."
Under the "Act" all cooperatives are required to respect the 02 following ratios: (a) regulatory capital / total assets ≥ 4%, (b)
regulatory capital / risk-weighted assets ≥ 8%. According to the "Act", the regulatory capital includes investment share of
investment operations (investment shares), the share capital (member shares), retained earnings, the job gains and losses
not yet realized (accumulated other comprehensive income) and the posted profits to be distributed to members (owner
shares). The calculation of the risk-weighted assets is based on the guidelines dictated by the "Act". In addition, no payment
will be made from regulatory capital that lowers it below regulatory requirements.
3. Saskatchewan (SK)
Credit Union Deposit Guarantee Corporation (CUDGC) is the regulator of the province of Saskatchewan credit unions and
dictates the capital adequacy measures. These measures are based on the work of the Basel III committee and were
adopted by all financial institutions in the world including Canadian credit unions. Currently the CUDGC implemented four
essential rules to assess the level of capital of cooperatives: A minimum capital ratio of 8%, a minimum Tier1 risk-based
ratio of 4.5% and a minimum leverage ratio of 5%. The threshold of the risk-weighted assets are calculated in accordance
with CUDGC guidelines. To this end, credit risk, derivatives, off-balance sheet commitments and operational risk must be
kept into account when calculating the risk-weighted assets.
4. Manitoba (MB)
Cooperatives operating in the Province of Manitoba are regulated by the "Deposit Guarantee Corporation of Manitoba". This
regulatory body requires all cooperatives to maintain a level of regulatory capital satisfying the following conditions: (a) a
leverage ratio limit of 5%, (b) a minimum "retained earnings" of 3% of the book value of assets and (c) a minimum riskbased capital ratio 8%.
5. Alberta (AB)
All cooperatives in the province of Alberta are subject to the guidelines and rules imposed by the "Credit Union Act of the
Province of Alberta" under the supervision of the "Credit Union Deposit Guarantee Corporation". The "Act" ensures that the
Province of Alberta honors its commitments to depositors. In addition, the "Act" requires all cooperatives to hold sufficient
capital to protect themselves against unexpected losses. To this end, retained earnings constitute the form of the most
stable and least expensive capital. Consequently, the "Act" encourages cooperatives to favor this category of capital. The
capital requirements are established by the "Act" and regulated by "The Corporation". This regulatory proceeding requires
that cooperatives must have a regulatory capital that meets the following two conditions: a leverage ratio limit of 5% and a
risk-based capital ratio of 10.5%.
6. Prince Edward Island (PEI)
The regulatory authority "Credit Union Insurance Corporation" (CUDIC) was incorporated under the laws of Prince Edward
Island to protect the deposits of the members of this province’s credit unions. This institution ensures that the Government
of Prince Edward Island provide protection to member deposits. All cooperatives are required under the guidelines of the
CUDIC to maintain a minimum capital-to-total assets ratio of 5%.
7. Newfoundland and Labrador (NL)
The organization "Credit Union Deposit Guarantee Corporation" is a provincial Crown corporation created to insure deposits
of cooperative members in Newfoundland and Labrador. The company is responsible for the administration of cooperative
activities and ensures the application of the regulations of the law on the part of cooperatives.
The province requires credit unions to hold a capital reserve made up of member shares and retained earnings equal to 5%
of total assets, and retained earnings to be minimally 3% of total assets. In addition, these financial institutions must maintain
a minimum capital ratio (calculated on risk-weighted assets) of 8%.
8. Nova Scotia (NS)
21
Credit unions in Nova Scotia must comply with the regulatory requirements for regulatory capital imposed by the "Credit
Union Act of Nova Scotia". The equity of the cooperative must at all times exceed 5% of total assets and the capital ratio
must exceed 8%.
9. New Brunswick (NB)
Credit unions in this province are required to hold a 5% minimum capital-to-assets ratio. In addition, the risk-based capitalto-total assets ratio must meet the regulatory requirement of 8%.
Table A.1: Breakdown by provinces of the 100 largest cooperatives
This table shows the distribution of the 100 largest cooperatives we use in this study by province. Among the 100 largest
cooperatives, we have not identified cooperatives in federal territories such as: NW Territories, Nunavut and Yukon.
Province
Number of credit unions
Alberta (AB)
11
British Colombia (BC)
27
Manitoba (MB)
20
Quebec (Qc)
1
New Brunswick (NB)
1
Newfoundland and Labrador (NL)
1
Nova Scotia (NS)
2
Ontario (ON)
25
Prince Edward Island (PEI)
1
Saskatchewan (SK)
11
Total
100
Table A.2: List of regulators by province
This table shows the various regulatory institutions in charge of regulation in the various provinces of Canada.
These regulatory institutions are also responsible to insure deposits in different provinces.
Provinces
Regulators
Abbreviations
BC
The Credit Union Deposit Insurance Corporation
« CUDIC »
AB
The Credit Union Deposit Guarantee Corporation
« CUDGC »
SK
The Credit Union Deposit Guarantee Corporation
« CUDGC »
MB
The Deposit Guarantee Corporation of Manitoba
« DGCM »
ON
The Deposit Insurance Corporation of Ontario
« DICO »
QC
Autorité des Marchés Financiers
« AMF »
NB
The New Brunswick Credit Union Deposit Insurance Corporation
« CUDIC »
NS
The Nova Scotia Credit Union Deposit Insurance Corporation
« NSCUDIC »
PEI
The Credit Union Deposit Insurance Corporation
« CUDIC »
NL
The Credit Union Deposit Guarantee Corporation
« CUDGC »
22
Table A.3: List of the 100 largest credit unions in alphabetical order
This table lists alphabetically the 100 largest Canadian credit unions. The table also provides information
about the periods covered and the province in which each credit union operates.
No
Credit unions
Years of
observation
Province
No
Years of
observation
Provinces
1
1st Choice Savings
2010 - 2014
AB
26
Atlantic
2010 - 2012
NS
2
Access
2009 - 2014
MB
27
Desjardins Group
1998 - 2014
QC
3
Affinity
2007 - 2014
SK
28
Diamond North
2011 - 2014
SK
4
Aldergrove
2008 - 2014
BC
29
Duca Financial
2011 - 2014
ON
5
Alterna
2004 - 2014
ON
30
East Coast
2010 - 2014
NS
6
Assiniboine
2010 - 2014
MB
31
Entegra
2009 - 2014
MB
7
Auto Worker Community
2012 - 2014
ON
32
First Calgary
2008 - 2014
AB
8
Bayview
2006 - 2014
NB
33
First Credit Union
2013 - 2014
BC
9
Beaumont
2009 - 2014
AB
34
First Ontario
2004 - 2014
ON
10 Bow Valley
2010 - 2014
AB
35
First West
2007 - 2014
BC
11 Buduchnist
2003 - 2014
ON
36
Grand Fork
2010 - 2014
BC
12 Caisse Financial Group
2010 - 2014
MB
37
Gulf and Fraser Fishermen
2005 - 2014
BC
13 Cambrian
1999 - 2014
MB
38
Innovation
2006 - 2014
SK
14 Carpathia
2006 - 2014
MB
39
Integris
2010 - 2014
BC
15 Casera
2009 - 2014
MB
40
Interior Savings
2003 - 2014
BC
16 Catalyst
2012 - 2014
MB
41
Island Savings
2003 - 2014
BC
17 Chinook
2009 - 2014
AB
42
Italian Canadian Savings
2011 - 2014
ON
18 Coast Capital Savings
1999 - 2014
BC
43
Kawartha
2008 - 2014
ON
19 Coastal Community
2001 - 2014
BC
44
Khalsa
2011 - 2014
BC
20 Community First
2010 - 2014
ON
45
Kootey Savings
2013 - 2014
BC
21 Conexus
2007 - 2014
SK
46
Ladysmith & District
2010 - 2014
BC
22 Copperfin
2009 - 2014
ON
47
Lake View
2009 - 2014
BC
23 Cornerstone
2012 - 2014
SK
48
Lakeland
2012 - 2014
AB
24 Creston and District
2010 - 2014
BC
49
Libro
2006 - 2014
ON
25 Cross Town Civic
2010 - 2014
MB
50
Luminus Financial Services 2008 - 2014
ON
Credit unions
23
Table A.3: List of the 100 largest credit unions in alphabetical order (continuation and end)
No
Credit unions
Years of
observation
Province
No
Credit unions
Years of
observation
Province
51
Mainstreet
2012 - 2014
ON
76
Servus
2007 - 2014
AB
52
Mennonite Savings
2010 - 2014
ON
77
Sharons
2009 - 2014
BC
53
Meridian
2004 - 2014
ON
78
Shell
2012 - 2014
AB
54
Mountain View
2007 - 2014
AB
79
Starbuck
2010 - 2014
MB
55
Newfoundland & Labrador
2005 - 2014
NL
80
Steinbach
2008 - 2014
MB
56
Niverville
2009 - 2014
MB
81
Sudbury
2006 - 2014
ON
57
North Peace Savings
2009 - 2014
BC
82
Sunova
2011 - 2014
MB
58
North Shore
2011 - 2014
BC
83
Sunrise
2008 - 2013
MB
59
Northern
2009 - 2014
ON
84
Sunshine Coast
2001 - 2014
BC
60
Northern Light
2011 - 2013
ON
85
Swan Valley
2013 - 2014
MB
61
Northshore Financial
2008 - 2014
BC
86
Synergie
2009 - 2014
SK
62
Northern Savings
2008 - 2014
BC
87
Tandia
2013 - 2014
ON
63
Noventis
2010 - 2014
MB
88
TCU
2009 - 2014
SK
64
Pace
2006 - 2013
ON
89
The Police
2011 - 2014
ON
65
Penfincial
2009 - 2014
ON
90
Ukrainian
2005 - 2014
ON
66
Pierceland
2012 - 2014
SK
91
Vancity
1996 - 2014
BC
67
Portage
2008 - 2014
MB
92
Vanguard
2007 - 2014
MB
68
Prairie Centre Union
2011 - 2014
SK
93
Vantage One Credit
Union
2012 - 2014
BC
69
Prospera
2001 - 2014
BC
94
Vision Battle River
1998 - 2014
AB
70
Provincial
2009 - 2013
PE
95
Westminster Savings
2009 - 2014
BC
71
Radius
2009 - 2014
SK
96
Westoba
2002 - 2014
MB
72
Revelstoke
2013 - 2014
BC
97
Weyburn
2012 - 2014
SK
73
Rocky
2006 - 2014
AB
98
WFCU
2006 - 2014
ON
74
Rosenort
2008 - 2014
MB
99
Your CU
2008 - 2014
ON
75
Salmon Arm Savings
2012 - 2014
BC
100
Your Neighbourhood
2007 - 2014
ON
24
Table A.4: Average assets and liabilities, major components in 1000s of $CAD
This table presents the average of major asset and liability components in 1000s of $CAD. We provide
descriptive statistics for the whole sample period (1996-2014) and three sub-periods: the pre-crisis period
(1996 to 2006), the crisis period (2007-2009) and the post-crisis period (2010-2014). We account for
heterogeneity in size by computing weighted statistics. Weights are computed for a given credit union as the
ratio of its average total assets and the sum of all credit union average assets. Weights must sum to 1. On the
assets side we compute statistics for cash, loans to members and investments. On the liabilities and members’
equity side, we look at the member deposits, borrowings and members’ equity. Data covers the 100 largest
credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the 19962014 period.
Assets and liabilities (in 1000s
1996-2014
1996-2007
2007-2009
2009-2014
$CAD)
Assets (major components) in 1000s $CAD
Cash
280,399.5
359,614
418,212.9
158,318.3
Loans to members 6,649,570.9
5,780,384.3
6,272,912.9
7,458,044.8
Investments 1,428,026.9
1,477,173.5
1,145,947.0
1,524,266.5
Total assets
9,192,280.4
8,263,917.8
8,852,468.9
10,000,000
Liabilities and member’s equity (major components) in 1000s $CAD
Member deposits 7,422,235.5
6,715,729.2
6,869,167.6
8,194,868.8
Borrowings
216,344
187,789.6
275,742.1
203,853.4
Total liabilities
8,221,307
7,880,625
7,423,582
8,843,668
Members’ equity
515,619.9
377,240
465,368.2
640,249.9
Table A.5: Assets and liabilities, major components in proportions, profitability, diversification and value measures
This table presents major components of assets and liabilities in relative proportions. We also add some
additional measures for profitability, membership value and diversification. We provide descriptive statistics
for the whole sample period (1996-2014) and three sub-periods: the pre-crisis period (1996 to 2006), the crisis
period (2007-2009) and the post-crisis period (2010-2014). We account for heterogeneity in size by computing
weighted statistics. Weights are computed for a given credit union as the ratio of its average total assets and
the sum of all credit union average assets. Weights must sum to 1. On the assets side we compute statistics
for cash, loans to members and investments. On the liabilities and members’ equity side, we look at the
member deposits, borrowings and members’ equity. Data covers the 100 largest credit unions in Canada
according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014 period.
1996199620072014
2006
2009
Asset structure (major components) in %
Cash
0.04
0.05
0.05
Loans to members
0.79
0.75
0.78
Investments
0.12
0.14
0.10
Total liabilities and member’s equity structure (major components) in %
Member deposits
0.86
0.85
0.87
Borrowings
0.03
0.03
0.03
Members’ equity
0.06
0.05
0.07
Others statistics
Members’ value = retained earnings / members’ shares
23.30
4.05
16.45
Interest profitability = interest income/ interest expenses
2.08
1.59
1.87
Diversification = non-interest income/ interest income
0.46
0.13
1.48
Shares of assets and liabilities components in %
20102014
0.03
0.81
0.11
0.87
0.02
0.06
33.59
2.45
0.15
25
Table A.6: Total assets, distribution by year (1000s $CAD)
This table presents the descriptive statistics (by year) of the total assets held by credit unions. Data covers
the 100 largest credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA)
during the 1996-2014 period.
Panel A: Asset distribution by year for the whole credit union system
Year
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
N. obs
93
99
95
85
76
61
44
33
26
18
Mean
2,112,724
1,810,147
1,709,835
1,757,447
1,713,599
1,846,629
2,223,224
2,426,342
2,265,204
2,828,980
Median
620,258
568,682
533,044
527,686
514,932
535,624
733,864
927,035
815,763
1,002,046
Std. dev
5,300,005
4,298,811
3,893,035
4,031,550
3,817,718
3,767,254
4,464,992
4,340,368
4,188,155
4,440,743
Min
41,042
37,083
35,441
69,077
51,118
44,550
43,992
157,468
141,691
255,238
Max
44,300,000
34,800,000
29,300,000
30,000,000
26,900,000
22,600,000
25,300,000
20,400,000
17,600,000
15,800,000
Skew (kurt.)
6.07 (45.73)
5.21 (38.53)
4.95 (31.00)
5.01 (31.81)
4.73 (28.33)
3.84 (18.75)
3.87 (18.17)
2.98 (11.53)
2.66 (9.12)
2.00 (5.67)
Panel B: Asset distribution by year for credit unions which continuously provide annuals report from 2005 to
2014
Year
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
N. obs
18
18
18
18
18
18
18
18
18
18
Mean
5,970,327
5,260,232
4,835,383
4,639,409
4,139,512
3,839,135
3,872,400
3,452,425
3,080,926
2,828,980
Median
1,961,590
1,925,562
1,752,696
1,516,758
1,494,454
1,471,968
1,383,817
1,284,265
1,180,277
1,002,046
Std. dev
10,800,000
8,748,023
7,660,676
7,675,352
6,866,562
6,067,704
6,566,588
5,599,230
4,843,571
4,440,743
Min
447,638
391,389
369,683
362,301
355,221
312,348
340,291
291,441
278,433
255,238
Max
44,300,000
34,800,000
29,300,000
30,000,000
26,900,000
25,300,000
22,600,000
20,400,000
17,600,000
15,800,000
Skew (kurt.)
2.793 (10.22)
2.46 (8.39)
4.95 (31.00)
5.01 (31.81)
2.35 (7.81)
2.35 (7.63)
2.12 (6.43)
2.98 (11.53)
2.66 (9.115)
2.00 (5.67)
26
Appendix B
Table B.1: Definition of the variables
This table presents the dependent variables, the business cycle indicators and the credit union-specific (control) variables.
BUFF_R (risk-based)
BUFF_A (assets-based)
CAP
RWA
CYCL(Provincial)
CYCL (Canada)
LIQUID
RISK
SIZE
ROA
dyMERGER
dyLOW
LOSS
AGE
BENEF
Dependent variables
Basel capital to risk-weighted assets ratio minus 8%
The leverage ratio minus 5%
Total regulatory capital scaled by the whole sample average regulatory capital
Risk-weighted assets scaled by the whole sample average risk-weighted assets
Business cycle variables
GDP growth by province
Canadian GDP growth
Credit unions specific (control) variables
Cash over total assets
Risk-weighted assets scaled by total assets
Natural log of total assets
Annual net profit before taxes over total assets
1 for the acquirer in the year of the merger and 0 otherwise
1 if the credit union is among the 50% least capitalized in a respective year
Loan loss provision over total loans
Age of the credit union
Benefit to members
Table B.2: Descriptive statistics for the business cycle indicator
This table presents descriptive statistics on the proxies for the business cycle. We use the provincial and Canadian GDP
(gross domestic product) growth, respectively GDPG_P and GDPG_C. Data covers the 100 largest credit unions in Canada
according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014 period.
GDPG_P
GDPG_C
Mean
0.038
0.040
Std. dev
0.053
0.030
Median
0.041
0.042
Minimum
-0.20
-0.04
Maximum
0.19
0.09
27
Table B.3a: Descriptive statistics for the dependent and credit union-specific (control) variables by capitalization
This table presents descriptive statistics on the dependent and credit union-specific (control) variables by capitalization. We
use the Basel risk-based capital ratio median value to build the capitalization variable dyLOW. The descriptive statistics are
provided for the credit unions below the median (dyLOW=1) and the ones above the median capital ratio value (dyLOW=0).
Our results are built on non-balanced panel data of the 100 largest credit unions in Canada according to the ranking of the
Credit Union Central Canada (CUCA) during the 1996-2014 period. Dependent variables are: BUFF_R (Basel risk-based
capital ratio minus 8%), BUFF_A (the leverage ratio minus 5%), CAP (total regulatory capital scaled by the whole sample
average regulatory capital) and RWA (risk-weighted assets scaled by the average whole sample risk-weighted assets).
Business cycle variables are: GDPG_P (GDP growth by province) and GDPG_C (Canada-wide GDP growth). Credit
union-specific (control) variables are: LIQUID (cash over total assets), RISK (risk-weighted assets scaled by the total
assets), SIZE (natural log of total assets), dyMERGER (1 for the acquirer in the year of the merger and 0 otherwise), dyLOW
(1 if the credit union capital is below the median value of the whole sample risk-based capital ratio for a respective year),
LOSS (loan loss provision over total loans), AGE (age of the credit union) and BENEF (the benefit to members). Values in
parentheses are median values. The definition of variables can be found in Table B.1. We provide a Wilcoxon test to
evaluate the statistical difference between the two capitalization groups.
Variables
BUFF_R (risk-based)
BUFF_A (assets-based)
CAP
RWA
ASSETS
BENEF
LOSS
LIQUID
RISK
SIZE
ROA
dyMERGER
AGE
Credit unions with risk-based
capital ratio above the whole
sample median capital
(dyLOW = 0)
Mean (Median)
Std. dev
0.08 (0.07)
0.05
0.02 (0.02)
0.02
1.16 (0.26)
2.35
1.11 (0.20)
2.14
1.01 (0.25)
2.35
<-0.001(0.001)
0.005
0.001 (0.001)
0.002
0.05 (0.03)
0.05
0.48 (0.47)
0.14
13.45 (13.15)
1.27
0.005 (0.004)
0.006
0.056 (-------)
0.23
59.49 (63)
18.67
Credit unions with risk-based
capital ratio below the whole
sample median capital
(dyLOW = 1)
Mean (median)
Std. dev
0.03 (0.03)
0.01
0.01 (0.01)
0.01
0.78 (0.40)
1.06
0.87 (0.46)
1.10
0.99 (0.50)
1.39
0.001(0.001)
0.007
0.001(0.001)
0.001
0.04 (0.03)
<0.001
0.58 (0.58)
0.11
13.80 (13.85)
1.19
0.004 (0.004)
0.002
0.081 (-------)
0.274
58.21 (64)
20.10
Wilcoxon test
(z-value)
19.05***
5.81***
-1.06
-3.40***
-4.10***
-2.464**
0.63
1.20
-10.06***
-4.10***
1.18
-1.31
-0.295
Notes: H0 (Wilcoxon test): Samples are from an identical population versus two-sided alternatives.
*** Indicates statistical significance at 1% level in a two-tailed t-test.
** Indicates statistical significance at 5% level in a two-tailed t-test.
Table B.3b: Comparing credit unions actual buffers to the minimum Basel III buffer requirement
In this table, we perform a t-test to test if the average value of the risk-based capital buffer is below or above the minimum
regulatory buffer of 2.5% and the maximum of 5% when we add the countercyclical buffer. This table summarizes our
findings.
Value test = 2.5%
Value test = 5%
p-value (= value test)
1.00
0.00
p-value (<value test)
1.00
0.99
p-value (>value test)
0.00
0.00
A p-value lower than 0.05 implies the alternative is more likely at 95%.
Table B.3c: Descriptive statistics on the dynamic of capital buffers
This table presents statistics on capital buffers. We provide descriptive statistics for three sub-periods: the
pre-crisis period (1996 to 2006), the crisis period (2007-2009) and the post-crisis period (2010-2014). Data
covers the 100 largest credit unions in Canada according to the ranking of the Credit Union Central Canada
(CUCA) during the 1996-2014 period.
BUFF_R (risk-based)
BUFF_A (assets-based)
1996-2006
2007-2009
2010-2014
0.043 (0.036)
0.007(0.001)
0.049 (0.043)
0.022 (0.016)
0.062 (0.052)
0.021 (0.019)
28
Table B.4: Correlation matrix
This table presents the Pearson correlation statistics on the dependent variables, business cycle variables and credit unionspecific (control) variables. Our results are built on non-balanced panel data of the 100 largest credit unions in Canada
according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014 period. Dependent variables
are: BUFF_R (Basel risk-based capital ratio minus 8%), BUFF_A (the leverage ratio minus 5%), CAP (total regulatory capital
scaled by the whole sample average regulatory capital) and RWA (risk-weighted assets scaled by the average whole sample
risk-weighted assets). Business cycle variables are: GDPG_P (GDP growth by province) and GDPG_C (Canada-wide
GDP growth). Credit unions specific (control) variables are: LIQUID (cash over total assets), RISK (risk-weighted assets
scaled by the total assets), SIZE (natural log of total assets), dyMERGER (1 for the acquirer in the year of the merger and
0 otherwise), dyLOW (1 if the credit union capital is below the median value of the whole sample risk-based capital ratio for
a respective year), LOSS (loan loss provision over total loans), AGE (age of the credit union) and BENEF (the benefit to
members).
CAP
RWA
RISK
LOSS
LIQUID
SIZE
GDPG_
P
dyLOW*GDPG_
P
BUFF_R
BUFF_R BUFF_A
1
ROA
BUFF_A
0.44
1.00
CAP
-0.01
0.01
1.00
RWA
-0.07
-0.02
0.99
1.00
RISK
-0.36
0.41
-0.13
-0.08
1.00
LOSS
0.02
0.06
-0.06
-0.03
-0.01
1.00
LIQUID
0.34
0.19
-0.13
-0.14
-0.23
0.07
1.00
SIZE
-0.25
-0.13
0.75
0.80
0.04
-0.11
-0.23
1.00
GDPG_P
-0.03
0.06
-0.04
-0.04
0.06
-0.01
-0.01
0.02
1.00
dyLOW*GDPG_P -0.29
-0.08
-0.03
-0.01
0.28
-0.04
-0.04
0.12
0.615
1.00
ROA
0.07
0.05
-0.03
-0.03
-0.05
-0.02
-0.03
-0.04
0.09
-0.03
1.00
AGE
0.09
-0.13
0.21
0.16
-0.25
-0.08
-0.05
0.09
-0.07
-0.09
-0.07
Note: We use bold for correlation values significant at the 1% level.
29
AGE
1.00
Table B.5: Blundell-Bond GMM, OLS and fixed effect (FE) estimates for the Basel risk-based (BUFF_R) and leverage
capital buffer (BUFF_A) (1996-2014)
These tables present the Blundell-Bond GMM, OLS and fixed effect (FE) estimates of the Basel risk-based capital buffer
(BUFF_R) and the leverage-based capital buffer (BUFF_A). Our results are built on non-balanced panel data of the 100
largest credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014
period. In panel B we compute our analysis without the crisis year 2009. Dependent variables are: BUFF_R (Basel riskbased capital ratio minus 8%) and BUFF_A (the leverage ratio minus 5%). Business cycle variables are: GDPG_P (GDP
growth by province) and GDPG_C (Canada-wide GDP growth). Credit unions specific (control) variables are: LIQUID
(cash over total assets), RISK (risk-weighted assets scaled by the total assets), SIZE (natural log of total assets),
dyMERGER (1 for the acquirer in the year of the merger and 0 otherwise), dyLOW (1 if the credit union capital is below the
median value of the whole sample risk-based capital ratio for a respective year), LOSS (loan loss provision over total loans),
AGE (age of the credit union) and BENEF (the benefit to members). Blundell-Bond AR (1) and AR (2) tests are provided at
the end of the table.
Panel A: Analysis performed on the whole sample period
Blundell-Bond GMM
OLS
FE
BUFF_R
0.081
(0.093)
-0.544
(0.668)
0.175*
(0.087)
-0.003
(0.002)
-0.079
(0.054)
0.206***
(0.019)
-0.335***
(0.070)
0.001
(0.001)
-7.8E-05
(0.002)
0.317
(0.307)
0.120**
(0.044)
BUFF_A
0.560***
(0.140)
-0.200
(0.256)
0.088***
(0.029)
-0.0001
(0.001)
0.165
(0.030)
0.117**
(0.029)
-0.176***
(0.045)
0.001
(0.000)
0.263
(0.186)
0.0003**
(0.002)
-0.092
(0.025)
BUFF_R
0.419**
(0.205)
-0.034
(0.682)
0.119*
(0.062)
-0.0003
(0.0008)
-0.061**
(0.021)
0.133*
(0.068)
-0.211**
(0.085)
7.5e-06
(0.00)
-0.002
(0.002)
0.187
(0.242)
0.062**
(0.025)
BUFF_A
0.713***
(0.101)
-0.236
(0.210)
0.036
(0.013)
-0.0005
(0.0004)
0.035
(0.013)
0.059
(0.044)
-0.081
(0.038)
5.1e-04
(0.044)
-2.66 e-04
(0.002)
0.171
(0.121)
-0.011
(2.58 e-04)
BUFF_R
0.069
(0.076)
-0.434
(0.535)
0.023
(0.024)
-0.051***
(0.016)
-0.132***
(0.033)
0.054
(0.034)
-0.06**
(0.041)
0.005
(0.001)
0.003
(0.002)
0.140
(0.293)
0.120***
(0.045)
BUFF_A
0.387***
(0.040)
-0.248
(0.248)
0.013
(0.017)
-0.029**
(0.011)
0.063**
(0.024)
0.031
(0.024)
-0.041**
(0.018)
0.003***
(0.000)
0.003
(0.003)
0.167
(0.179)
0.203
(0.109)
Obs.
327
320
327
320
327
320
No. credit unions
76
74
76
74
76
74
AR(1) test
0.034
0.026
AR(2) test
0.619
0.603
0.65
0.82
0.42
0.66
Dep. variablet-1
LOSS
LIQUID
SIZE
RISK
GDPG_P
GDPG_P*dyLOW
AGE
dyMERGER
ROA
CONST
R2
30
Panel B: Blundell-Bond GMM Analysis on the whole sample period discarding the crisis year 2009
Blundell-Bond GMM
BUFF_R
0.115
(0.123)
0.011
(1.029)
0.209*
(0.088)
-0.002
(0.001)
-0.001
(0.046)
0.249***
(0.079)
-0.495***
(0.122)
0.001
(0.001)
-7.8E-05
(0.002)
0.507
(0.529)
0.067**
(0.037)
BUFF_A
0.362***
(0.150)
-0.095
(0.240)
0.087***
(0.031)
0.0001
(0.001)
0.137***
(0.019)
0.124**
(0.029)
-0.245***
(0.045)
0.001
(0.000)
5.45 E-06
(0.002)
0.262**
(0.132)
-0.069
(0.017)
Obs.
236
295
No. credit unions
61
73
AR(1) test
0.058
0.004
AR(2) test
0.125
0.292
Dep. variablet-1
LOSS
LIQUID
SIZE
RISK
GDPG_P
GDPG_P*dyLOW
AGE
dyMERGER
ROA
CONST
31
Table B.6: Blundell-Bond GMM estimates for the regulatory capital (CAP) and risk-weighted assets (RWA) (19962014)
This table presents the Blundell-Bond GMM estimates for the regulatory capital (CAP) and risk-weighted assets (RWA).
Our results are built on non-balanced panel data of the 100 largest credit unions in Canada according to the ranking of the
Credit Union Central Canada (CUCA) during the 1996-2014 period. Dependent variables are: CAP (total regulatory capital
scaled by the whole sample average regulatory capital), RWA (risk-weighted assets scaled by the average whole sample
risk-weighted assets) and BENEF (the benefit to members). Business cycle variables are: GDPG_P (GDP growth by
province) and GDPG_C (Canada-wide GDP growth). Credit unions specific (control) variables are: LIQUID (cash over
total assets), RISK (risk-weighted assets scaled by total assets), SIZE (natural log of total assets), dyMERGER (1 for the
acquirer in the year of the merger and 0 otherwise), dyLOW (1 if the credit union capital is below the median value of the
whole sample risk-based capital ratio for a respective year), LOSS (loan loss provision over total loans), AGE (age of the
credit union) and BENEF (the benefit to members).
Blundell-Bond GMM
0.921*
(0.532)
-1.493*
(0.759)
0.001
(0.001)
0.064
(0.042)
2.943
(5.520)
-0.635**
(0.591)
RWA
0.791***
(0.202)
1.136
(4.734)
-0.332
(0.212)
-0.118
(0.074)
0.165
(0.030)
0.364**
0.137
-0.425
(0.279)
1.035***
(0.418)
-0.001
(0.001)
0.103**
(0.043)
-1.803
(2.084)
1.550
(1.008)
BENEF
0.176
0.252
-0.023
0.242
0.018
0.021
-0.038***
0.013
0.042
0.031
-0.0002
0.001
-0.002
0.015
0.025
0.023
0.003***
0.001
0.005**
0.003
-0.06
0.08
---
Obs.
320
320
194
No. credit unions
74
74
54
Dep. variablet-1
LOSS
LIQUID
SIZE
RISK
CAP
1.172***
(0.033)
2.132
(5.296)
0.631
(0.477)
-0.033
(0.037)
1.758*
(0.814)
CAP
GDPG_P
GDPG_P*dyLOW
AGE
dyMERGER
ROA
CONST
32
Table B.7: Correlation matrix (additional analysis)
This table presents the Pearson correlation statistics on the dependent variables, business cycle variables and credit unionspecific (control) variables for additional regression analysis. Our results are built on non-balanced panel data of the 100
largest credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014
period. Dependent variables are: BUFF_R (Basel risk-based capital ratio minus 8%), BUFF_A (the leverage ratio minus
5%), Business cycle variables are: GDPG_P (GDP growth by province), Credit unions specific (control) variables are:
dyProfLOW*GDPG_P where dyProfLOW takes a value of 1 if the credit union profitability ratio is below the median value
of the whole sample profitability ratio for a respective year), dyValLOW*GDPG_P where dyValLOW takes a value of 1 if the
credit union members’ share value is below the median value of the whole sample member shares value for a respective
year) and dyDivLOW*GDPG_P where dyDivLOW takes a value of 1 if the credit union level of diversification is below the
median value of the whole sample diversification measure for a respective year).
BUFF_R
1
BUFF_A
BUFF_R
GDPG_P
BUFF_A
0.44
1.00
GDPG_P
-0.03
0.06
1.00
dyProfLOW*GDPG_P
-0.13
-0.11
0.34
dyValLOW*GDPG_P
0.003
0.17
0.53
dyDivLOW*GDPG_P
0.03
0.08
0.46
Note: We use bold for correlation values significant at the 1% level.
33
Table B.8: Descriptive statistics for the dependent and credit union-specific (control) variables by profitability and
members’ share value
This table presents descriptive statistics on the dependent variables and credit union-specific (control) variables by profitability
in panel 1 and by members’ share values in panel 2. Our results are built on non-balanced panel data of the 100 largest credit
unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014 period. Dependent
variables are: BUFF_R (Basel risk-based capital ratio minus 8%), BUFF_A (the leverage ratio minus 5%), CAP (total regulatory
capital scaled by the whole sample average regulatory capital) and RWA (risk-weighted assets scaled by the average whole
sample risk-weighted assets). Business cycle variables are: GDPG_P (GDP growth by province) and GDPG_C (Canadawide GDP growth). Credit unions specific (control) variables are: LIQUID (cash over total assets), RISK (risk-weighted
assets scaled by the total assets), SIZE (natural log of total assets), dyMERGER (1 for the acquirer in the year of the merger
and 0 otherwise), dyLOW (1 if the credit union capital is below the median value of the whole sample risk-based capital ratio for
a respective year), LOSS (loan loss provision over total loans), AGE (age of the credit union) and BENEF (the benefit to
members). Values in parentheses are median values. The definition of variables can be found in Table B.1. We provide a
Wilcoxon test to evaluate the statistical difference between the two capitalization groups.
Panel 1: Statistics and rank-sum test of variables on two subgroups of credit unions based on the dyProfLOW variable (takes
a value of 1 if the credit union profitability ratio is below the median value of the whole sample profitability ratio for a respective
year
Variables
BUFF_R (risk-based)
BUFF_A (assets-based)
CAP
RWA
ASSETS
BENEF
LOSS
LIQUID
RISK
SIZE
ROA
dyMERGER
AGE
Credit unions with profitability
measure above the whole
sample median profitability
variable (dyProfLOW = 0)
Mean (Median)
Std. dev
0.06 (0.05)
0.05
0.02 (0.02)
0.02
0.79 (0.30)
1.30
0.79 (0.26)
1.25
0.80 (0.29)
1.44
<-0.001(0.001)
0.005
0.001 (0.001)
0.002
0.05 (0.03)
0.05
0.531 (0.53)
0.11
13.52 (13.31)
1.15
0.005 (0.004)
0.005
0.07 (-------)
0.25
58.85 (63)
16.97
Credit unions with profitability
below the whole sample
median capital
(dyProfLOW = 1)
Mean (Median)
Std. dev
0.04 (0.04)
0.02
0.01 (0.01)
0.02
1.52 (0.36)
2.82
1.51 (0.39)
2.47
1.44 (0.30)
2.97
0.001(0.001)
0.008
0.001(0.001)
0.002
0.05 (0.03)
0.044
0.51 (0.53)
0.11
13.70 (13.32)
1.45
0.004 (0.004)
0.003
0.06 (-------)
0.237
54.44 (63)
23.31
Wilcoxon test
(z-value)
3.78***
6.32***
-0.68
-1.11
-0.371
-1.956**
0.908
1.167
1.968
-0.371
0.245
0.41
-0.968
Panel 2: Statistics and rank-sum test of variables on two subgroups of credit unions based on the dyValLOW variable. It
takes a value of 1 if the credit union members’ shares value is below the whole sample median value for a respective year
Variables
BUF_R (risk-based)
BUF_A (assets-based)
CAP
RWA
ASSETS
BENEF
LOSS
LIQUID
RISK
SIZE
ROA
dyMERGER
AGE
Credit union with credit union
members’ value above the
whole sample median
members’ value
(dyValLOW = 1)
Mean (Median) Std. dev
0.06 (0.04)
0.05
0.02 (0.01)
0.02
0.94(0.37)
1.55
0.95 (0.37)
1.39
1.00 (0.37)
1.77
<-0.001(0.002)
0.006
0.001 (0.001)
0.001
0.05 (0.03)
0.05
0.53 (0.53)
0.11
13.69 (13.56)
1.23
0.005 (0.006)
0.004
0.06 (-------)
0.24
60.21 (63)
17.98
Credit union with credit union
members’ value below the
whole sample median
members’ value
(dyValLOW = 0)
Mean (Median)
Std. dev
0.06 (0.05)
0.03
0.03 (0.02)
0.03
1.09 (0.27)
2.35
1.07 (0.26)
2.15
0.99 (0.24)
2.55
0.001(0.001)
0.006
0.001(0.001)
0.002
0.04 (0.03)
0.04
0.54 (0.55)
0.09
13.70 (13.32)
1.45
0.004 (0.004)
0.003
0.07 (-------)
0.257
56.84 (63)
21.13
Wilcoxon test
(z-value)
-1.58
-5.46***
1.62
1.06
4.24***
-1.2
-1.3
2.36***
-2.85***
4.24***
2.33**
-0.45
1.83***
34
Table B.9: OLS and Fixed Effect (FE) estimates for the Basel risk-based (BUFF_R) and leverage capital buffer
(BUFF_A) (1996-2014) for credit union size between the 2nd and 9th decile.
This table presents the Blundell-Bond GMM, OLS and fixed effect (FE) estimates for the Basel risk-based capital buffer
(BUFF_R) and the leverage-based capital buffer (BUFF_A). Our results are built on non-balanced panel data of the 100
largest credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014
period. Dependent variables are: BUFF_R (Basel risk-based capital ratio minus 8%) and BUFF_A (the leverage ratio
minus 5%). Business cycle variables are: GDPG_P (GDP growth by province) and GDPG_C (Canada-wide GDP growth).
Credit unions specific (control) variables are: LIQUID (cash over total assets), RISK (risk-weighted assets scaled by
the total assets), SIZE (natural log of total assets), dyMERGER (1 for the acquirer in the year of the merger and 0 otherwise),
dyLOW (1 if the credit union capital is below the median value of the whole sample risk-based capital ratio for a respective
year), LOSS (loan loss provision over total loans), AGE (Age of the credit union) and ZSCORE (standard deviation of ROA
between the credit union leverage ratio and its return on assets).
.
OLS
FE
BUFF_R
0.791***
(0.047)
0.312
(0.623)
0.029*
(0.016)
0.002**
(0.0009)
-0.048**
(0.021)
0.028
(0.028)
-0.056*
(0.029)
<0.001
(<0.001)
-0.002
(0.003)
0.411
(0.302)
0.735
(0.720)
BUFF_A
0.810***
(0.054)
0.046
(0.254)
-0.008
(0.010)
-0.0005
(0.0005)
0.039
(0.017)
0.009
(0.017)
-0.034*
(0.019)
<0.001
(<0.001)
-0.002
(<0.001)
0.391
(0.216)
-0.15
(0.466)
BUFF_R
0.336***
(0.081)
0.675
(0.963)
0.046
(0.024)
-0.033***
(0.009)
-0.103***
(0.034)
0.024
(0.034)
-0.046**
(0.026)
-0.005
(0.006)
<0.001
(<0.001)
0.174
(0.268)
-18.00
(13.28)
BUFF_A
0.351***
(0.064)
0.156
(0.384)
0.009
(0.013)
-0.011***
(0.004)
0.071**
(0.027)
0.012
(0.010)
-0.031**
(0.018)
0.003***
(<0.001)
-0.001
(0.55)
0.179
(0.171)
-12.66
(9.79)
Obs.
231
227
231
227
No credit unions
76
74
76
59
R2
0.87
0.87
0.50
0.32
Province dummies
Yes
Yes
No
No
Dep. variablet-1
LOSS
LIQUID
SIZE
RISK
GDPG_P
GDPG_P*dyLOW
AGE
dyMERGER
ROA
CONST
35
Table B.10: OLS and fixed effect (FE) estimates for the Basel risk-based (BUFF_R) (1996-2014) with ZSCORE as
bank risk measure
This table presents the OLS and fixed effect (FE) estimates for the Basel risk-based capital buffer (BUFF_R) where we
replace the regulatory risk measure (RWA) by the ZSCORE measure. Our results are built on non-balanced panel data of
the 100 largest credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the
1996-2014 period. Dependent variables are: BUFF_R (Basel risk-based capital ratio minus 8%) and BUFF_A (the leverage
ratio minus 5%). Business cycle variables are: GDPG_P (GDP growth by province) and GDPG_C (the Canada GDP
growth). Credit unions specific (control) variables are: LIQUID (cash over total assets), RISK (risk-weighted assets
scaled by the total assets), SIZE (natural log of total assets), dyMERGER (1 for the acquirer in the year of the merger and
0 otherwise), dyLOW (1 if the credit union capital is below the median value of the whole sample risk-based capital ratio for
a respective year), LOSS (loan loss provision over total loans), AGE (age of the credit union) and ZSCORE (standard
deviation of ROA between the credit union leverage ratio and its return on assets).
OLS
FE
BUFF_R
0.462***
(0.206)
-0.03
(0.653)
0.124*
(0.062)
0.001
(0.001)
0.001*
(0.001)
0.157
(0.072)
-0.266***
(0.097)
<0.001
(<0.001)
-0.002
(0.002)
0.216
(0.208)
-1.35
(1.04)
BUFF_R
0.133*
(0.077)
-0.201
(0.534)
0.061
(0.023)
-0.036***
(0.014)
0.001
(0.001)
0.052
(0.026)
-0.106***
(0.028)
-0.033
(0.021)
0.001
(0.001)
0.031
(0.286)
-74.00
(42.90)
Obs.
388
388
No credit unions
Dep. variablet-1
LOSS
LIQUID
SIZE
ZSCORE
GDPG_P
GDPG_P*dyLOW
AGE
dyMERGER
ROA
CONST
87
87
R2
0.65
0.28
Province dummies
Yes
No
36
Graph 1: Asset distribution by year for credit unions with available annual reports from 2005 to 2014
This graph plots the average and median value of credit union total assets by years. For the sake of homogeneity, we only
use a set of 18 of the 100 largest credit unions which have continuous reported annual data for the 1996-2014 period.
7000000
1000s $CAD
6000000
5000000
4000000
3000000
Mean
2000000
Median
1000000
0
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Year
Graph 2: Basel risk-based capital buffer and GDP growth, smallest credit unions
This graph plots the risk-based capital buffer (BUFF_R), the minimum regulatory buffer (RegBUFF) of 2.5% and Canadian
GDP growth (GDPG_C) for two credit unions among the smallest in term of size: Sunshine Coast Credit Union and
Sudbury Credit Union. In panel A, we plot the buffer and Canadian GDP growth. The comparison with the provincial GDP
growth (GDPG_P) is made in panel B. We use non-balanced panel data of the 100 largest credit unions in Canada according
to the ranking of the Credit Union Central Canada (CUCA) during the 1996-2014 period.
Panel A: Risk-based capital buffer and Canadian GDP growth
Sudbury Credit Union
82
-.05
-.05
0
0
.05
.05
.1
.1
.15
.15
Sunshine Coast Credit Union
2006
2008
2010
Year
BUFF_R
RegBUFF
2012
2014
2000
2005
2010
2015
Year
GDPgrowth
BUFF_R
RegBUFF
GDPgrowth
37
Panel B: Risk-based capital buffer and provincial GDP growth
Sudbury Credit Union
-.05
0
0
.05
.05
.1
.1
.15
.15
Sunshine Coast Credit Union
2000
2005
2010
2006
2015
2008
2010
Year
Year
BUFF_R
RegBUFF
BUFF_R
RegBUFF
ProvGDP_growth
2012
2014
ProvGDP_growth
Graph 3: Largest credit unions’ risk-based capital buffer and GDP growth,
This graph plots the risk-based capital buffer (BUFF_R), the minimum regulatory buffer (RegBUFF) of 2.5% and Canadian
GDP growth (GDPG_C) for the two largest credit unions in term of size: Vancity and Caisse Centrale Desjardins. In panel
A, we plot the buffer and Canadian GDP growth on the left side and the provincial GDP growth (GDPG_P) on the right side
for Vancity, the second largest credit union in Canada. Panel B addresses the same issue for Desjardins, the largest credit
union.
-.05
-.05
0
0
.05
.05
.1
.1
Panel A: Risk-based capital buffer and Canadian GDP growth for the second largest credit union by size: Vancity
1995
2000
2005
Year
BUFF_R
RegBUFF
2010
1995
2015
GDPgrowth
2000
2005
Year
BUFF_R
RegBUFF
2010
2015
ProvGDP_growth
0
-.05
0
.05
.05
.1
.1
.15
Panel B: Risk-based capital buffer and provincial GDP growth for the largest credit union by size:
Caisse Centrale Desjardins
2000
2005
Year
BUFF_R
RegBUFF
2010
GDPgrowth
2015
2000
2005
Year
BUFF_R
RegBUFF
2010
2015
ProvGDP_growth
38
Graph 4: Basel risk-based capital buffer and GDP growth
-.05
0
.05
.1
This graph plots Canadian GDP growth (GDPG_C), the minimum regulatory buffer (RegBUFF) of 2.5% and the median of
the risk-based capital buffer, MdBUFFR.
2000
2005
Year
MdBUFFR
RegBUFF
2010
2015
GDPgrowth
Graph 5: Basel risk-based capital buffer and GDP growth for low- and well-capitalized credit unions
-.05
-.05
0
0
.05
.05
.1
.1
.15
This graph plots Canadian GDP growth (GDPG_C), the minimum regulatory buffer (RegBUFF) of 2.5% and the average of
the risk-based capital buffer BUFFLOW for low-capitalized (left) and BUFFHIGH for well-capitalized (right) credit unions.
Low-capitalized credit unions have a capital buffer below the median capital buffer. We use non-balanced panel data of the
100 largest credit unions in Canada according to the ranking of the Credit Union Central Canada (CUCA) during the 19962014 period.
2006
2008
2010
Year
BUFFLOW
RegBUFF
2012
GDPgrowth
2014
2005
2010
Year
BUFFHIGH
RegBUFF
2015
GDPgrowth
39
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