the effect of tarp funding on recipient credit unions · consumer lending credit unions began to...

The Effect of TARP Funding on Recipient Credit Unions

Keldon Bauer

Tarleton State University

Abstract: Credit unions have become an important component of the American financial

system, with more than 10% of all savings deposits and non-revolving consumer loans;

as well as some 12% of all depository institution employees. With nearly any measure of

depository institution market share, credit unions have been growing.

Credit unions fared better than banks in the past two big financial crises (1980s

and 2008-2010). But there were some institutions whose common bond forced them to

take on more risk during that time period. In some of those cases, their members were

underserved by other financial institutions, making their credit union a systemically

important financial institution for them. Therefore, in 2010, a TARP program was

offered to shore up community development financial institutions (CDFIs). The

Community Development Capital Initiative (CDCI) was the only direct TARP loan

program offered to credit unions. Although nearly 200 credit unions qualified, only 48

received funding (all in late September 2010). The recipient credit unions borrowed less

than $70 million total.

This paper assesses the effectiveness of the TARP program for the recipient credit

unions. We find that TARP recipient credit unions were those credit unions with lower

capital, stronger loan portfolios, and/or those whose headquarters were in the districts

of states of congressional committee members who supervised financial institutions.

We further find that TARP funding had no significant impact on whether the credit

union failed, but find that of those credit unions surviving, TARP funded institutions

tended to have stronger performance.

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Introduction: Credit unions have been slowly but surely increasing market share in the

depository institutions industry. Wheelock and Wilson (2010) estimate that credit

unions hold 10% of all savings deposits, and over 13% of non-revolving consumer loans.

They further describe credit unions’ share of depository institution assets as nearly

doubling over a 25 year period.

Industry Background:

Credit unions have remained very close to the needs of their customers/owners

which may be the biggest reason for their growing market share. Cooperative lenders

began in Germany in the mid 19th century as a remedy to information production

problems among lenders to small businesses and small farmers. The initial cooperative

lenders (including Raifeissen’s Kreditvereine literally translated as credit unions), made

no consumer loans, and had no retail clients. Their focus was exclusively on small

businesses and farms which were credit constrained1.

The first successful transplant to North America began in 1900 in the province of

Quebec, Canada. Alphonse Desjardin tailored the European cooperative bank model to

the needs of its intended Québécois customers/owners. In this instance, some

consumer lending was allowed, although the focus was still on either commercial or

agricultural lending. The target market was a set of underserved farmers who could not

find reasonably priced loan products due to information asymmetry, and the cost to

incumbent financial institutions to produce the information to overcome it.

1 For more information on the early history of American credit unions the reader is referred to Clark (1943). For a good analysis of the early German credit union system, the reader is referred to Bonus and Schmidt (1990). A good source of early history of Canadian Caisses populaires is Croteau (1950).

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In the United States, it took a couple of decades for credit unions to find traction

because they were forced to operate in industries without the information production

problems which had led their predecessors to success in Europe and Canada. Finally,

consumer lending credit unions began to grow. Again the only competitors were “loan

sharks”2. In the late 1920s and early 1930s, most consumer loans were made by either

small loan companies, or “industrial banking companies” – not commercial banks (see

Clark, 1943). Over time, commercial banks and credit unions have taken a larger

portion of this market (see Dauten, 1960). In the case of credit unions, they originally

had a unique way of overcoming information asymmetry – the common bond.

Their common bond allowed those with little access to traditional markets to gain

access through a credit union. The credit union, through the common bond, could gain

information about the credit worthiness of the applicant in ways a traditional financial

institution could not – through those who knew them best. Because credit unions were

offering financial services to those with few other options, the US Attorney General in

1917 issued an opinion that credit unions were “organized and operated for mutual

purposes and without profit”3. With the Federal Credit Union Act of 1934, federally

chartered credit unions were seen as federal instrumentalities, exempt from taxation4.

Although this designation conferred tax-exempt status upon credit unions, it cames with

its disadvantages. The greatest of these is that credit unions cannot raise equity capital

in the equity market. Instead, equity must be retained (or donated).

2 This is a term used by Bergengren (1937) where he gives an example of a borrower during the early years of credit unions having to pay 3400% interest on a $30 loan to one of these “Simon-pure loan sharks”. 3 See the National Credit Union Administration (NCUA), Member Service Assessment Pilot Program: A Study of Federal Credit Union Service (2006), page 49-50 for a brief, but well footnoted history of credit union tax treatment. 4 The federal instrumentality designation classifies federal credit unions as 501(c)(1) tax-exempt entities (see NCUA, 2006, page 41 – footnote).

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Since its infancy, the credit union industry has changed – but to be fair, so have

most financial institutions. To maintain a closer tie to members, and thus help in

overcoming information asymmetry, Raifeisen in Germany, and Bergengren in the

United States expected credit unions to remain small5. Any needed economies of scale

could hopefully (in their minds) be achieved through binding these retail credit unions

together with a wholesale credit cooperative – the corporate credit union – and then

passed onto the individual credit unions and their owners and managers. However,

more recent researchers have found that economies of scale are needed in the individual

institutions, and for that, they need to be larger (see Wilcox, 2005b; and Wheelock and

Wilson, 2010). The need for credit unions to grow larger was made even more poignant

with the failure of most of the corporate credit unions during the global financial crisis

of 2009 (see Bauer, 2015).

As the credit union grows larger to stay competitive, its ties to its members have

become less pronounced and important in its business model. Indeed the common

bond in many credit unions has become meaningless (see Emmons and Schmid, 1999).

Over the past few decades, most credit unions have developed into very different

institutions than they once were. In the services they provide, they are looking more like

banks (see Walter, 2006). Yet even though the services and cost structures of credit

unions are beginning to resemble those of banks, their objective function still differs,

leading to a change in organizational behavior6.

5 On the part of Raiffeisen, this statement can be justified by others such a Bonus and Schmidt (1990), but also by his own writings, some of which have been translated into English, such as Raiffeisen (1970). Bergengren (1937) has implied similar feelings when he expects that the nation would eventually support 100,000 credit unions. It never has. The number of credit unions maxed out at just under 23,000 in 1974, and is now under 8,000 with a growing market share. 6 The differing objective function leading to a different behavior was proposed by Bauer, Miles and Nishikawa (2009) to explain merger behavior. They point out that members only benefit financially by

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That difference in behavior has been observable during periods of economic

distress (see Wilcox 2005a). During the period of high inflation, during the late 1970s

and early 1980s, many credit unions failed. However, the impact of those failures on the

viability of the NCUSIF (National Credit Union Share Insurance Fund) was far less than

that experienced by either FSLIC or FDIC (see Kane and Hendershott, 1996).

Again during the most recent financial crisis, credit unions fared better than their

FDIC insured counterparts (see Bauer, 2015). But not all components of the market

faired equally well. Those credit unions who focused on underbanked populations were

harder hit during the GFC. These were institutions which were closer to the original

credit unions, whose members had few other options for financial services.

TARP Background

The Emergency Economic Stabilization Act of 2008 authorized the establishment

of the Troubled Asset Relief Program (TARP). The stated goals of TARP were to (1)

ensure the overall stability and liquidity of the financial system, (2) prevent avoidable

foreclosures and help preserve homeownership, (3) prevent avoidable foreclosures and

help preserve homeownership, and (4) promote transparency7.

There were four programs that were used by the Treasury to invest directly in

financial institutions in order to “ensure the overall stability and liquidity of the

financial system.” Within days of its passage, TARP was used to initiate the Capital

higher deposit rates and/or lower loan rates. Therefore, owners of credit unions are interested only in the debt market, and should therefore be more risk averse. There is no residual claimant in credit unions. Even the value of the firm is meaningless, because owners can never receive that value – not even if the credit union demutualized. 7 These goals come from those stated in the Citizen’s Report (2011) from the US Treasury Department’s Office of financial Stability – Troubled Asset Relief Program, page 4.

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Purchase Program (CPP). Under the CPP, TARP could buy debt, or equity in a financial

institution. Under the CPP, nearly $209 billion was invested. The program was open

only to banks and thrifts. Funding under the CPP program began in October of 2008.

As it became clear that additional funds would be needed for systemically

important institutions, the Treasury Department established the Targeted Investment

Program (TIP) to invest in both equity and warrants of Bank of America and Citigroup.

Through TIP, the Treasury invested about $40 billion. This program was only used for a

select group of banks.

Then to further stabilize systemically critical institutions, the Asset Guarantee

Program (AGP) was established. Under this program, the US Treasury only agreed to

absorb losses on troubled assets if they occurred. Again this program was only open to

FDIC insured institutions.

The fourth and final direct investment TARP program was the Community

Development Capital Initiative (CDCI). It was announced in February of 2010. The

program’s goals were to stabilize the financial market “providing financial services to

communities underserved by traditional banks and financial services, such as low- and

moderate- income, minority, and other underserved communities”8. To qualify for the

CDCI program a financial institution had to certify with the Treasury Department as a

Community Development Financial Institution (CDFI). Treasury invested $570 million

in the CDCI program. The largest portion of that, $363.3 million, was used to pay off

debts under the CPP. This was the only TARP program that allowed for credit union

participation.

8 Ibid. page 5.

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The CDFI program of the US Treasury Department was established in 1994,

under the Riegle-Neal Interstate Banking and Branching Efficiency Act. The program

offers several funding programs for institutions targeting underserved populations. The

CDCI program very briefly through TARP in 2010 offered subordinated debentures with

an interest rate of 2%, and a maturity of eight years. The Treasury’s authorization to

issue new debt under TARP expired on October 3, 2010. All credit unions receiving

funds under the CDCI program received them within three weeks of the end of that

authorization.

At the end of September 2010, there were 205 certified CDFI credit unions, only

181 of them filed call reports with NCUA for September 30, 20109. All 181 CDFI credit

unions filing call reports were listed as limited income credit unions. Of the 181 CDFIs

that qualified for TARP, 48 were given TARP funding. Table 1 lists the credit union

TARP recipients and the amount they borrowed. Also listed is how that amount

borrowed compared to the amount of balance sheet assets based on their call report at

the end of June 2010 (the prior call report). These figures are listed in dollars (not

thousands).

The reason that TARP was applied to CDFIs might be found in the statistics for

credit unions qualifying for the program. Of the 181 qualifying credit unions, four failed

in the twelve months ending September 30, 2011, and one more was merged due to its

“poor financial condition”. Table 2 presents descriptive statistics and a non-parametric

test that CDFI credit unions failed at a similar rate to other credit unions during that

9 Many of those without corresponding filings with NCUA were listed as “federal credit unions”. If that were true they would be required to file a call report with the NCUA. The fact that none could be found, either meant that they were failed credit unions whose name had not been removed from the CDFI list, merged institutions, or were no longer insured by the NCUSIF, and therefore no longer federal credit unions. Searches were made on CEO name and address to verify that the name was not the issue.

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same time period. As can be seen from panel c of table 2, the failure rate is significantly

higher for CDFIs than for general credit unions.

A second reason that has been implied for TARP funding is for political gain.

Pana and Wilson (2012) report that credit union funding through this CDFI program

was highly influenced by politics. 2010 was a congressional election year, and they

found that credit unions receiving TARP funds was dependent on the credit union being

headquartered in a district where the House member served on the House Financial

Services Committee.

Organization of our Study:

The purpose of our study is empirically to test the effectiveness of TARP funding

to the participating credit unions. However, depending on the extent of political

influence, that impact could become contaminated by the politics. So first we will

further explore the effect that politics may have played in granting TARP funding to

credit unions.

But the focus of this paper is on whether or not TARP had a positive impact on

the recipient credit unions. That impact is measured in two ways. First, did TARP

funding reduce the likelihood of failure? And second, did TARP funding improve the

viability of the institution without expropriating wealth from taxpayers to the recipient

credit union?

Which Institutions were Awarded TARP Funds?:

TARP funding should be based not just on need, but also on repayment ability.

We use a logistic regression to model the probability that a credit union is given TARP

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funding. To model repayment ability, we use the CAMEL ratios used by Bauer et al.

(2009). As the capital, earnings and liquidity ratios increase, the credit union’s

condition improves. The asset quality ratio is delinquent loans to total loans. Therefore,

as it increases, the credit union’s condition worsens. A similar argument could be made

for the management ratio (which is loans to shares). As the credit union’s condition

worsens, they would be more likely to demand TARP funding, but the borrower would

be less willing to give them the loan. We use two sets of CAMEL variables. First, we use

the ratios for each credit union in the study. But since some of these ratios can produce

extreme outliers, we also replaced the CAMEL ratios with their percentile within the

dataset. We assume that the demand will be what is picked up in the logistic regression.

The “C” in CAMEL stands for capital, the capital component is probably the most

critical. For regulatory purposes, the capital ratio (or “net worth ratio” as it is formally

called in NCUA) triggers specific treatment which can include conservatorship and/or

liquidation. Those institutions that are “well capitalized” receive no special regulatory

attention. But as their capital ratio moves from “well capitalized” to “adequately

capitalized” (or in the case of new credit unions to “moderately” or “minimally

capitalized”), or to “undercapitalized”, the regulatory scrutiny increases, and the

likelihood of failure increases. The category is partly determined by the capital ratio,

and partly by whether the institution can be considered a “new” credit union10. Because

the capitalization category is so critical, we do consider the category separately from the

ratio itself. And because the capital ratio is so important, we also consider how quickly

that ratio has changed in the last year.

10 A “new” credit union is one that is both younger than 10 years and has fewer than $10 million in assets.

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Finally, we control for institution size, and whether or not there was political

influence (Pana and Wilson, 2012). Pana and Wilson only looked at the House district

where the credit union was headquartered. The NCUA call report lists the

“congressional district”, but for some credit unions it reports multiple districts (not

specifying which ones). In the instance where “multiple districts” were reported, we

looked up the district where headquarters were located. However, in many instances

the credit union’s headquarters were located so close to a boundary, we considered two

measures. First, we considered just the district where headquarters were located. A

second measure considered the next closest district as well11. These were indicator

variables which were one if the credit union headquarters was in a district whose

representative served on the House Financial Services Committee, and a zero if not.

There are 435 members of the House of Representatives, and there were 71 members of

the House Financial Services Committee.

Because there are fewer senator, their influence should be greater. However,

their interest in something as small as this program may not be as intense. We therefore

only considered those senators who were up for reelection in 2010. We looked at two

measures. First, all members of the Senate Banking, Housing and Urban Affairs

Committee up for reelection in 2010. Then we looked at just the members of the

Financial Institutions Subcommittee who were up for reelection in 2010. For each

credit union, if their headquarters was in a state with a member on that subcommittee

the indicator variable was a one (and a zero if not). Out of 100 senators, 23 served on

the Senate Banking Committee (only seven of whom were running for re-election in

11 In some instances that second district was literally just across the street.

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2010). The Financial Institutions Subcommittee had 16 member (only five of whom

were running for reelection).

Which Institutions Failed?:

In modeling which institutions failed we were interested in a longer term than

simply one year. Instead we looked at five years after the issuance of TARP funding.

The purpose of this model was to see whether TARP funding reduced the likelihood of

failing. Because we were interested to know whether TARP funding was an effective

means of avoiding failure, we used pre-event variables. In fact, we used all of the same

variables as described earlier for the TARP model. The only variable added here was an

indicator variable showing whether or not the credit union had received TARP funding.

If TARP funding made a difference in the survival of the credit union, then that variable

would be statistically significant.

It should be noted that the response variable was whether a credit union was

actually liquidated, forced to merge by NCUA, or merged with the justification “poor

financial condition” listed. The first two categories can be found in reports on failed

credit unions at the NCUA. The third category is drawn from the monthly Insurance

Activity Reports of the NCUA.

Teasing out Political Influence versus Information:

Even if a credit union is more likely to receive TARP funding if their

congressional representatives serve in the right committees, there are at least two good

reasons for that empirical result. First, the credit union may not have qualified, and the

political influence may have been exerted to gain favors with the government. But it is

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equally likely that credit union managers were simply unaware of this temporary

funding option and that congressional representatives simply were more aware and let

their constituents know this funding source.

The model considering whether or not the credit union failed cannot answer that

question directly, since the TARP funding may have a different marginal benefit on

modifying the likelihood of failing if it were granted due to political influence rather

than on merit. To tease out the difference, we use all of the same variables as described

in the TARP funding model, but use it in a multinomial logistic regression. The outcome

of that regression is used to predict the joint probability described in the probability

table below:

TARP No TARP

Not Fail P11 P12

Fail P21 P22

In this model, each Pij are estimated separately. We then use the coefficient

estimates to estimate the conditional probability of failing given they were given TARP.

We use the methodology proposed by Bauer and Hein (2006). If the congressional

variable is significant and positive, then inappropriate political influence was exerted.

Was Wealth Expropriated from Taxpayers to Credit Union Members?:

Credit unions are cooperative lending institutions. Taylor (1971) saw them as

non-profits, where members are both lending and borrowing funds. If one group of

members seeks low loan rates, and the other group seeks high deposit rates, each group

might try to expropriate wealth from the other. He showed that a balanced approach

was more efficient, one set up to benefit both groups. Taylor (1977) later showed that if

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borrowers and lenders are seeking to optimize their respective rates, the credit union

would experience a rate squeeze, implying that their operating expenses should be kept

to a minimum.

Therefore, some empiricists have focused on technical efficiency. Data

envelopment analysis (DEA) has been employed to test whether an institution’s

efficiency has improved due to an event12. There has been some work aimed at finding a

better measure of improved efficiency for the credit union industry13; but these methods

are only refinements or standardizations of the DEA methodology for credit unions.

However the credit union’s goal is not to minimize costs, but to maximize utility.

Smith, Cargill and Meyer (1981) model the credit union owner/customer receiving

benefit from credit union participation in one of two ways: by receiving higher than

market interest on deposits or by paying below market interest on loans. Smith (1984)

assumes that the fundamental motivation for members is to access savings and loans at

rates better than the next best alternative. He proposes an abnormal net gain that

would measure how much better (or worse) off a customer is for being the customer of a

specific credit union.

Bauer (2008) formalized Smith’s abnormal net gain. While efficiency

methodologies tend to test outputs given inputs, the Bauer methodology looks only at

financial remuneration to owners, and seems to us to be better suited for testing

whether wealth expropriation existed due to TARP funding. We will use the Bauer

methodology as a base, and modify it to measure whether or not the credit union gave

12 See Fried, Lovell and Yaisawarng (1999), or Ralston, Wright and Garden (2001) to cite just two examples. 13 See Fried, and Lovell (1994), or Fried, Lovell and Eeckaut (1993) for proposals. Even the method used by Wheelock and Wilson (2010) is a refinement of a simple DEA approach.

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more money to their owners/members due to TARP funding. Bauer proposes

estimating the abnormal net gain vector as follows:

OEEO SSLLEmpirical rrrrANG ,ia

[1]

Where : 𝑟𝐿𝑂 = Observed lending rate,

𝑟𝐿𝐸 = Expected lending rate,

𝑟𝑆𝐸 = Expected savings rate,

𝑟𝑆𝑂 = Observed savings rate.

We estimated the expected savings and lending rates using a cross-sectional

bivariate regression of credit unions not receiving TARP funding, using the annual pre-

event savings and lending rates, the equity to assets ratio (post-event), the percent

change in assets (over the post-event year), and the natural log of assets (post-event).

Because we are interested in performance over the life of the TARP funding, we adjust

these inputs so that all post-event measures are the five post-event year average of those

measures14. The pre-event savings and lending rates we calculated using just the one

pre-event year.

The Bauer method uses NCUA call report data. Because the event we are

studying, receiving TARP funding, happened in the last two weeks of September 2010

for all institutions in our study, the pre-event window is defined as the end of second

quarter 2010. All of our post-event variables are calculated using data beginning just

days after the event, and running through third quarter 2015.

14 In every case the equations are the same as in the annual ratios, but all inputs use the average of five years instead of the last full year of data.

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We split our control and experimental groups by asset size. Wilcox (2005b)

found that credit unions don’t begin to demonstrate scale economies until they have at

least $100 million in assets. We therefore split our sample at that point, to further

control for a different size dynamic.

We used both the proposed parametric and non-parametric tests. The

parametric test uses the mean of all ais in the sample, testing it against the alternative

hypothesis that the mean a̅ is not 𝟎. We tested using the one quadrant test proposed by

Bauer:

aSa0aS0a11

nn [2]

Where

n

in 1

1iaa

ai = the abnormal return vector from equation [1].

0 = a 1×2 zero vector (which is the expected value of the ai vector under

H0).

S-1 = the inverse of the sample variance-covariance matrix of abnormal

returns.

Under the null hypothesis, would be approximately 2 distributed with 2

degrees of freedom. As long as the sample size is large, the test should be well specified.

The non-parametric test focuses on the number (and proportions) of 𝐚𝐢 vectors

that fall in either the first or third quartiles – where both the savings and lending

abnormal net gains are either positive (1st quartile) or negative (3rd quartile). The test

uses the binomial probability that we could observe such an extreme numbers, given the

relative frequency of that occurring with the much larger control group.

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Empirical Results

We begin by modeling the likelihood that a credit union receive TARP funding

(see Table 3). We use different definitions of House and Senate, and in the end find that

the second definition of House committee (with the broader definition of house

representation) is a slightly better fit. We also verify that having a House member on

the Financial Services Committee improved your chance of receiving TARP funding. It

should be noted that if the Senate Banking Committee members running for re-election

are included in the model, then the House members nearly become irrelevant15. The

capital adequacy categories were also not as relevant as the actual capital ratio.

There are two big takeaways from this table. First, the credit unions most likely

to get TARP had low capital, but high quality assets (fewer delinquent loans as a rule).

Second, those credit unions with a member of congress (House or Senate) on the right

committee were significantly more likely to be funded, even controlling for CAMEL

ratios.

Table 4 presents the model results of the logistic regression model that modeled

failure based on a priori variables that would have been known at the time TARP

funding was issued. It is interesting that in this instance the capital ratio variables that

are most powerful at predicting failure are the NCUA’s categories. The higher the

proportion of delinquent loans, the more likely the credit union will not survive five

years into the future. However, the most interesting takeaway is that TARP funding did

15 Note included in this table are several other regressions that we ran, considering different combinations of House Financial Services, and Banking Committee members. The combination that worked the best is reported in the far left column of table 3. In addition, we also tried the CAMEL ratios as percentile, but that was found to be inferior in this model to the actual ratio results.

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not seem to have much effect on whether or not a credit union failed. Its only

redeeming quality was that the coefficient was negative (meaning that it would lead

away from failure), just not significantly negative in two of the three models.

Table 5 reports the conditional logistic regression models, modeling the

probability of not failing given the credit received TARP funding. In the interest of

space, the multinomial logistic regression models from which these models were derived

are not presented in greater detail. Panel A models the P(Not Failing|TARP) using

actual CAMEL ratios. Panels B and C use the percentile restatement. Panel C also

controls for a capital ratio category.

Since this model is looking at the probability a credit union will fall in a specific

cell of a probability table, we are constrained by the sample size and the number of

observations we actually observe in each of these categories. Therefore, we collapsed

the congressional variable into House only, Senate only, and an indicator variable of

either House Financial Services Committee or Senate Financial Institutions

Subcommittee. We also only used the Adequately/Moderately capital ratio category

variable16.

The primary reason for these models is to test whether congressional connections

systemically tended to influence the award of TARP funding. If it did, we would expect

the congressional coefficient should be negative. All estimates of congressional

variables were in fact negative, meaning that the probability that the credit union will

not fail within the five years is negatively affected by members of oversight committees.

However, only in one instance is that coefficient marginally statistically significant. In

16 Again we ran far more regressions than are reported here. The Undercapitalized capital ratio category was not significant in these conditional logistic regression models.

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general if a credit union wants to avoid failure, it should maintain higher capital ratios,

and have a higher five year average earnings ratio (which is how equity and capital

ratios are built).

Table 6 reports the results of testing whether TARP recipient credit unions

experienced higher performance due to receipt of TARP funds. This was a bit difficult

because the large credit unions were such a small sample (only 23 credit unions were in

the sample – and only four of them received TARP funds). This sample was so small

that nothing could be inferred. The parametric test was not well specified (with a

sample of only 4), and the non-parametric test may have been able to conclude

something if all observations would have landed in the first quartile (but they didn’t).

The small credit union sample size was not that much better, but we did get some

interesting and statistically significant results from it. The number of observations

falling in the 1st quartile was almost the same as the control group. Therefore, credit

union members/owners received no excess returns from TARP funding. But by the

same token, there were significantly fewer observations of institutions falling in the 3rd

quartile. Therefore, there were fewer credit unions distressed enough to feel the need to

widen their net margins. These results are consistent with institutions that are

strengthening their financial condition rather than enriching their owners over these

five years.

Summary & Conclusions

Credit unions have become an important component of the financial services

industry. In general, credit unions survived the effects of the Global Financial Crisis

better than most financial institutions. However, credit unions focusing on underserved

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populations were an exception to that general rule. Their failure rate began to surpass

that of commercial banks. During the last round of funding through TARP, community

development financial institutions were able to apply for funding – including credit

unions for the first time.

This paper focuses on the effect of that funding on the recipient credit union. We

began by considering what factors led to a credit union receiving funding through TARP.

We found that credit unions with lower capital, but an otherwise strong balance sheet

was more likely to get funding. Unfortunately, we also found that having a

congressional representative (or senator facing re-election) was also an important factor

in receiving TARP funding.

Next we considered what made credit unions fail over the five year following the

disbursement of TARP funding to credit unions. If TARP funding was meant to lead to

fewer failures, then an indicator variable indicating which credit unions received TARP

funding should be significant and be negative. Although the coefficient for this variable

was negative, it was rarely statistically significant.

One possible reason for TARP not being as effective is it may been could be that

more funding went to the politically connected institutions rather than to the credit

unions that needed it. If political influence overcame basic due diligence, credit union

failure rates could be partly explained by which institutions had used that influence to

get TARP funding in the first place. We used a conditional logistic regression model to

see if the probability of not failing given TARP funding would have statistically

significant negative coefficients for congressional indicator variables. And again, the

coefficients were negative, but only one of four models had a statistically significant

coefficient.

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In both hypotheses regarding inappropriate political influence, the hypothesis

had only the weakest of evidence (and none in most models). That result could either be

because there was no inappropriate influence, or because we are dealing with a smaller

sample size. Either way, the fact that TARP funding was not effective in changing the

likelihood of failure is somewhat troubling.

However, the fact that surviving TARP recipient credit unions appear to be more

vibrant than non-recipient credit unions is encouraging. It is further encouraging that

recipient credit unions did not simply enrich their owners, but employed capital in

strengthening their balance sheets.

In short, the results of this study have been somewhat mixed. In large measure,

those credit unions with strong fundamentals but temporarily low capital were most

likely to receive funding (and those with the right legislators). The TARP injection had

no effect on whether they survived over the five years since the investment. However, if

they did survive, those credit unions that received funding have stronger balance sheets

than those who did not receive TARP funding.

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References: Bauer, Keldon, 2015, The corporate credit union crisis: Does it call for reform or re-

engineering? Journal of Banking Regulation, 16, 89-105.

Bauer, Keldon, 2008, Detecting abnormal credit union performance, Journal of Banking and Finance, 32, 573-586.

Bauer, Keldon, Scott E. Hein, 2006, The effect of heterogeneous risk on the early adoption of Internet banking technologies, Journal of Banking and Finance, 30, 1713-1725.

Bauer, Keldon J., Linda L. Miles, and Takeshi Nishikawa, 2009, The effect of mergers on credit union performance, Journal of Banking and Finance, 33, 2267–2274.

Bergengren, Roy F., 1937, Cooperative credit, Annals of American Political and Social Science, 191, 144-148.

Bonus, Holger and Georg Schmidt, 1990, The cooperative banking group in the Federal Republic of Germany: Aspects of institutional change, Journal of Institutional and Theoretical Economics, 146, 180-207.

Clark, Lincoln, 1943, Credit unions in the United States, Journal of Business, 16, 235-246.

Croteau, John T., 1950, Caisses populaires Desjardins of Quebec: A modern system of people’s banks, Agricultural History, 24, 227-238.

Dauten, Carl A., 1960, Recent institutional developments in the field of consumer credit, Journal of Finance, 15, 206-220.

Emmons, William R. and Frank A. Schmid, 1999, Credit unions and the common bond, The Federal Reserve Bank of St. Louis Review, 81 (5), 41-64.

Kane, Edward J., and Robert Hendershott, 1996, The federal deposit insurance fund that didn't put a bite on U.S. taxpayers, Journal of Banking and Finance, 20, 1305-1327.

National Credit Union Administration, 2006, Member Service Assessment Pilot Program: A Study of Federal Credit Union Service.

Pana, Elsabeta, and Linus Wilson, Political Influence and TARP investments in credit unions, Quarterly Journal of Finance, 2, 4 (December), 1-26.

Raiffeisen, F. W., 1970, The Credit Unions, Neuwied on the Rhine, Ger. : Raiffeisen Print. And Pub. Co.

Ralston, Deborah, April Wright, and Kaylee Garden, 2001, Can mergers ensure the survival of credit unions in the third millennium? Journal of Banking and Finance 25, 2277-2304.

US Department of Treasury, Office of Financial Stability – Troubled Asset Relief Program, 2011, Citizen’s Report.

Walter, John A., 2006, Not your father’s credit union, Federal Reserve Bank of Richmond Economic Quarterly, 92, 353-377.

21

Wheelock, David C., and Paul W. Wilson, 2010, Are credit unions too small?, Working paper, Federal Reserve Bank of St. Louis.

Wilcox, James A., 2005a, Credit union failures and insurance fund losses: 1971–2004, FRBSF Economic Letter, 2005- 20,August 19, 2005.

Wilcox, James A., 2005b, Economies of scale and continuing consolidation of credit unions, FRBSF Economic Letter, 2005-29, November 4, 2005.

22

Table 1 Credit Union TARP Recipients

The table below lists all credit union TARP recipients. The TARP funds listed is the amount of investment received through the CDCI program. The amount listed as total assets is the amount listed on their call report for June 2010, the quarter before the funding was issued. Both the TARP funds and total assets are listed in dollars.

*Note: NCUA reports the exact same figures, but the sum of all TARP funds is listed as $67,114,799.

CU Number CU Name City State TARP Funds % of Assets Total Assets

15523 Tongass Federal Credit Union Ketchikan AK 1,600,000 3.09% 51,743,782

13852 Phenix Pride Federal Credit Union Phenix City AL 153,000 3.33% 4,593,899

24826 Pyramid Federal Credit Union Tucson AZ 2,500,000 3.24% 77,200,501

643 Butte Federal Credit Union Biggs CA 1,000,000 2.91% 34,327,327

4900 Cooperative Center Federal Credit Union Berkeley CA 2,799,000 3.26% 85,793,543

24506 Episcopal Community Federal Credit Union Los Angeles CA 100,000 2.07% 4,825,839

24687 Faith Based Federal Credit Union Oceanside CA 30,000 3.45% 868,689

23780 Northeast Community Federal Credit Union San Francisco CA 350,000 3.21% 10,909,385

64029 Santa Cruz Community Credit Union Santa Cruz CA 2,828,000 3.28% 86,095,980

23896 East End Baptist Tabernacle Federal Credit Union Bridgeport CT 7,000 2.76% 253,280

16411 D.C. Federal Credit Union Washington DC 1,522,000 3.34% 45,502,538

15051 Community First Guam Federal Credit Union Hagatna GU 2,650,000 3.31% 80,087,717

5628 Independent Employers Group Federal Credit Union Hilo HI 698,000 3.23% 21,589,706

20187 Prince Kuhio Federal Credit Union Honolulu HI 273,000 3.27% 8,355,087

24751 Community Plus Federal Credit Union Rantoul IL 450,000 3.16% 14,260,766

21550 North Side Community Federal Credit Union Chicago IL 325,000 3.09% 10,515,711

24781 Union Baptist Church Federal Credit Union Fort Wayne IN 10,000 3.14% 318,788

138 Vigo County Federal Credit Union Terre Haute IN 1,229,000 3.34% 36,794,163

9164 Carter Federal Credit Union Springhill LA 6,300,000 3.27% 192,865,323

11263 Shreveport Federal Credit Union Shreveport LA 2,646,000 2.99% 88,624,789

23540 Tulane-Loyola Federal Credit Union New Orleans LA 424,000 3.24% 13,092,321

20842 UNO Federal Credit Union New Orleans LA 743,000 3.40% 21,845,123

24829 Hope Federal Credit Union Jackson MS 4,520,000 3.38% 133,734,271

18983 Gateway Community Federal Credit Union Missoula MT 1,657,000 3.02% 54,796,168

68593 First Legacy Community Credit Union Charlotte NC 1,000,000 2.46% 40,592,915

64034 Greater Kinston Credit Union Kinston NC 350,000 2.40% 14,583,344

68195 Renaissance Community Development Credit Union Somerset NJ 31,000 3.41% 908,679

23283 Alternatives Federal Credit Union Ithaca NY 2,234,000 3.23% 69,085,087

19907 Bethex Federal Credit Union Bronx NY 502,000 3.15% 15,932,333

24642 Brooklyn Cooperative Federal Credit Union Brooklyn NY 300,000 2.93% 10,251,452

23495 Buffalo Cooperative Federal Credit Union Buffalo NY 145,000 3.22% 4,508,459

11380 Fidelis Federal Credit Union New York NY 14,000 3.27% 428,169

23848 Genesee Co-op Federal Credit Union Rochester NY 300,000 2.90% 10,353,127

24232 Lower East Side People's Federal Credit Union New York NY 898,000 3.43% 26,167,815

24589 Neighborhood Trust Federal Credit Union New York NY 283,000 3.47% 8,160,017

9107 Southern Chautauqua Federal Credit Union Lakewood NY 1,709,000 3.21% 53,246,159

11702 Union Settlement Federal Credit Union New York NY 295,000 3.35% 8,795,599

24772 Workers United Federal Credit Union New York NY 57,000 2.84% 2,006,059

20354 Hill District Federal Credit Union Pittsburgh PA 100,000 2.78% 3,599,194

24304 Border Federal Credit Union Del Rio TX 3,260,000 3.28% 99,439,970

14052 Liberty County Teachers Federal Credit Union Liberty TX 435,000 3.31% 13,141,331

68193 Southside Credit Union San Antonio TX 1,100,000 3.41% 32,215,942

24658 Fairfax County Federal Credit Union Fairfax VA 8,044,000 3.37% 238,343,943

11111 Freedom First Federal Credit Union Roanoke VA 9,278,000 3.54% 261,941,458

67251 Opportunities Credit Union Burlington VT 1,091,000 3.57% 30,539,596

68528 Thurston Union of Low-Income People (TULIP) Cooperative Credit UnionOlympia WA 75,000 3.38% 2,221,825

66637 Brewery Credit Union Milwaukee WI 1,096,000 3.28% 33,392,904

16009 Atlantic City Federal Credit Union Lander WY 2,500,000 2.95% 84,753,699

69,911,000

23

Table 2 Comparing Failure Rates for CDFI Credit Unions

The table below compares number of failed institutions between TARP recipient credit unions, certified CDFI credit unions (which qualified for TARP funding), and all NCUSIF credit unions. The comparison is made during the one year following the issuance of TARP money. The column listing failed credit unions were those that NCUA listed as failed during that time period. Those listed as merged were those listed as merged by NCUA for “poor financial condition”. Panel A contains the number of institutions in each category. Panel B restates that information as proportions. And Panel C calculates the binomial probability that the CDFI institutions’ failure numbers could be that high or higher given the observed relative frequency of failure for all NCUSIF credit unions. Panel A – Number per category

Number of CUs in each Category

Failed Merged Total

TARP recipient credit unions 0 0 48

CDFI credit unions 4 1 181

All NCUSIF insured credit unions 26 39 7,402

Panel B – Proportional restatement

Proportion of CUs in each Category

Failed Failed or Merged

TARP recipient credit unions 0.0000 0.0000

CDFI credit unions 0.0221 0.0276

All NCUSIF insured credit unions 0.0035 0.0088 Panel C – Non-parametric tests

P(CDFI CU Failure>3|p=0.0035) = 0.00402 ***

P(CDFI CU Failure>4|p=0.0088) = 0.02253 **

***, **, * signify significance at the 1%, 5%, and 10% level respectively

24

Table 3 Logistic Regression Model – TARP Recipient

The table below contains the coefficient and standard error estimates for a binary

logistic regression model predicting the likelihood that a credit union was a TARP recipient. All credit unions received TARP funding in September of 2009. The predictor variables include indicator variables of whether or not the House member representing the district including the credit union’s headquarters was on the Financial Services Committee (House Fin Services 1). We alternatively tested a similar variable, where the nearest two districts to their headquarters were used in cases where the credit union reported multiple congressional districts (House Fin Services 2).

Similar indicator variables were used to test whether senators up for re-election seemed to affect the receiving of TARP funds. In this instance we used one variable to indicate if the credit union was in a state where a member of the Senate Banking, Housing and Urban Affairs Committee was facing re-election (Senate Bank Comm). The other variable indicated if the credit union was in a state where a member of the Senate Financial Institutions Subcommittee (Senate Fin Inst Sub).

In addition, we used the calculated Capital, Asset Quality, Management, Earnings and Liquidity Ratios described by Bauer et al. (2009) to measure the CAMEL ratios of the credit union. Note that the Capital Ratio is really net worth to total assets (the higher the ratio, the more likely they are to take on TARP funding). The Asset Quality Ratio is the dollar value of delinquent loans to total loans (the higher this ratio, the less credit worthy the institution). The Management Ratio is simply loans to shares. The Earnings Ratio is the annual percent change in net worth (which shows how quickly the credit union can rebuild equity).

To control for other risk and size variables, we also looked at how much the capital ratio changed over the year ending June 2009 (the last full year prior to TARP funding). And we used the natural log of reported assets June 2009 [Ln(Assets)] to measure and control for any size effect.

Because the way the NCUA uses capital ratio to regulate credit unions is by category, and because the way they interpret capital adequacy depends not just on level, but on whether the institution is “new”, we also used two categories which mean that credit union is in increasing trouble. Adequately/Moderately indicates that the capital of the credit union is deemed “adequate” through “minimal”. Undercapitalized indicates that the credit union is undercapitalized.

25

Table 3 – Continued


Variable

Constant 1.1183 1.2010 0.9701 0.8901

(3.1710) (2.9800) (3.0588) (3.0450)

House Fin Services 1 0.8921 * 0.8549 * 0.5839

(0.6435) (0.6436) (0.6725)

House Fin Services 2 0.8025 *

(0.5832)

Senate Bank Comm 0.8551 ** 0.8487 **

(0.4228) (0.4218)

Senate Fin Inst Sub 1.4325 *** 1.2842 **

(0.5633) (0.5797)

Capital Ratio -18.9148 *** -19.8239 *** -18.6947 *** -18.9294 ***

(7.1442) (6.7045) (6.5079) (6.5671)

Asset Quality Ratio -9.3172 ** -9.1593 ** -9.8724 ** -10.4411 **

(4.8830) (4.8632) (4.9641) (5.1195)

Management Ratio -2.7429 ** -2.7664 ** -2.8763 ** -2.8427 **

(1.6066) (1.5965) (1.6160) (1.6144)

Earnings Ratio -0.5758 -0.5517 -0.6115 -0.5609

(0.8348) (0.8079) (0.8291) (0.8186)

Liquidity Ratio 0.1191 0.1757 0.0804 0.1716

(1.9913) (1.9646) (2.0054) (2.0048)

D Capital Ratio 2.6699 *** 2.7427 *** 2.7282 *** 2.7089 ***

(0.9596) (0.9547) (0.9588) (0.9556)

Ln(Assets) 0.1091 0.1113 0.1297 0.1318

(0.1344) (0.1295) (0.1328) (0.1327)

Adequately/Moderately 0.3214

(0.6099)

Undercapitalized -0.3088

(0.9493)

n 181 181 181 181

2 28.38 *** 27.88 *** 30.43 *** 31.54 ***

R20.1383 0.1359 0.1483 0.1537

Coefficient/

Std. Error

Coefficient/

Std. Error

Coefficient/ Coefficient/

Std. Error Std. Error

26

Table 4 Logistic Regression Model – Failure within Five Years of TARP

In the table below we present the coefficient and standard error estimates for the logistic regression model predicting the likelihood of the credit union failing within five years of the disbursement of TARP funds. All CDFI credit unions were used in the sample. All the same variables were used in this model as were used in the model predicting receipt of TARP funds. The response variable was whether the credit union was liquidated, forced to merger or merged with a justification of “poor financial condition” during the five years following TARP disbursement. In these models we compare the calculated CAMEL ratios (the first column – CAMEL Interval) with the CAMEL ratios remapped as percentiles (the second and third columns – CAMEL Prop). We also add an indicator variable to test the effect of receiving TARP funds, controlling for all other variables.

27

Table 4 -Continued


Variable

Constant -5.9275 -2.8983 -3.2858

(5.6305) (4.4587) (4.5226)

House Fin Services 2 0.4330

(0.8490)

Senate Fin Inst Sub -0.3586

(0.8826)

Capital Ratio -6.3479 1.2447 1.2761

(6.4195) (1.4188) (1.4177)

Asset Quality Ratio 2.2419 1.5462 ** 1.5433 *

(2.6069) (0.9143) (0.9394)

Management Ratio 5.3491 * 1.7004 * 1.7132

(3.2957) (1.6198) (1.6064)

Earnings Ratio 0.6842 0.4031 0.4140

(0.7537) (1.0116) (1.0220)

Liquidity Ratio 4.7153 0.2182 0.3491

(4.0645) (1.8033) (1.8090)

TARP (Indicator) -0.9622 * -0.8266 -0.8201

(0.7174) (0.7304) (0.7740)

D Capital Ratio 0.7199 0.4816 0.3603

(1.0931) (1.0293) (1.0343)

Ln(Assets) -0.0622 -0.1374 -0.1208

(0.1982) (0.1849) (0.1873)

Adequately/Moderately 2.2132 *** 2.8442 *** 2.8592 ***

(0.7051) (0.9208) (0.9309)

Undercapitalized 1.7680 ** 2.2454 ** 2.2437 **

(0.9936) (1.1490) (1.1487)

n 181 181 181

2 22.66 ** 23.43 *** 23.79 ***

R2 0.1692 0.1749 0.1776

Std. Error Std. Error Std. Error

CAMEL (Interval) CAMEL (Prop) CAMEL (Prop)

Coefficient/ Coefficient/ Coefficient/

28

Table 5 Conditional Logistic Regression Models

We estimate the conditional probability of a credit union not failing, given that the credit union received TARP funding. These regressions are based on a multinomial regression model estimating joint probabilities of failing (or not) and receiving TARP funding (or not). The coefficients and standard errors are reported in the tables below. Each column comes from a different multinomial regression model. Panel A considers CAMEL ratios as calculated. Panels B and C consider CAMEL ratios as percentiles. Multinomial logistic regression models require larger sample sizes, and more observations in each response option (probability table cell in our application) to estimate properly if you want to use many predictor variables. In our instance we could not easily use both House and Senate indicator variables, so we created a third variable that indicated the credit union had at least one of those two in the corresponding committee and was up for re-election in 2010 (Congressional). For a similar reason, we only used one of the capital ratio categories in panel C. All other variables were the same as used in earlier models. All r-squared and chi-squared figures at the bottom of each column represents the underlying multinomial logistic regression model upon which these conditional logistic regressions are based.

29

Table 5 - Continued Panel A – Actual CAMEL Ratios:


Variable

Constant 12.6602 15.8734 14.4866

(12.6207) (16.8262) (14.6064)

House Fin Services 2 -1.9498 *

(1.4450)


(1.5067)

Congressional -1.0405

(1.3718)

Capital Ratio -18.6742 -12.3617 -15.2091

(18.9613) (21.3751) (17.8946)

Asset Quality Ratio -7.9189 -9.7593 -10.1195

(9.7653) (7.8743) (8.8037)

Management Ratio -6.5719 -10.2133 -8.5865

(7.5970) (10.7868) (8.9385)

Earnings Ratio 4.8940 * 5.8433 * 4.6832

(3.7209) (4.1963) (3.8297)

Liquidity Ratio -5.6069 -8.5045 -7.3586

(9.6148) (12.8200) (11.2642)

D Capital Ratio 0.2414 -1.5520 -0.1700

(2.1111) (2.7378) (2.1293)

Ln(Assets) -0.0351 -0.0469 -0.0605

(0.4960) (0.5488) (0.5293)

n 181 181 181

2 48.43 *** 48.85 *** 49.56 ***

R2 0.1422 0.1434 0.1455

P(Not Fail|TARP)



30

Table 5 - Continued Panel B –CAMEL Ratios Remapped as Percentiles:


Variable

Constant 1.5403 1.7353 1.2124

(9.4403) (9.5900) (9.9441)

House Fin Services 2 -1.8185

(1.4931)


(1.6440)


(1.4606)

Capital Ratio -4.9196 * -3.9545 -4.8769 *

(3.1368) (3.4519) (3.2643)

Asset Quality Ratio -0.7787 -1.2299 -1.1104

(2.6917) (2.8785) (2.7628)


(3.9379) (4.1758) (3.9744)

Earnings Ratio 9.3425 * 9.7898 * 9.4724 *

(6.6880) (6.1829) (6.3283)

Liquidity Ratio 1.3003 2.3154 2.0490

(4.8792) (4.9227) (4.9989)

D Capital Ratio -1.9599 -3.8519 -2.5774

(3.1322) (3.4009) (2.9545)

Ln(Assets) 0.0839 -0.0303 0.0441

(0.4735) (0.4755) (0.4965)

n 181 181 181

2 52.36 *** 54.80 *** 53.88 ***

R2 0.1537 0.1609 0.1582

P(Not Fail|TARP)



31

Table 5 - Continued Panel C –CAMEL Ratios Remapped as Percentiles and Adding a Capital Ratio Category:


Variable

Constant 19.7642 10.3743 20.9893

(21.8029) (13.7944) (24.7514)

House Fin Services 2 -11.9194

(10.0718)


(2.4821)


(4.3395)

Capital Ratio -18.5154 ** -9.0326 * -16.8590 *

(11.0087) (5.7658) (10.9589)

Asset Quality Ratio 3.3665 -0.3870 1.0210

(4.1647) (3.0778) (3.0886)


(5.4424) (4.8130) (4.9130)

Earnings Ratio 24.9430 * 12.9526 * 21.4501 *

(18.5573) (8.8055) (15.8463)

Liquidity Ratio -3.4264 0.9062 -2.9631

(6.6460) (5.8536) (7.6998)

D Capital Ratio -5.3699 -4.8292 -4.5900

(6.3510) (4.8209) (5.0968)

Ln(Assets) -0.1614 -0.3052 -0.5155

(0.8346) (0.6246) (1.0298)

Adequately/Moderately -17.5242 * -5.3916 * -11.2353 *

(13.4065) (3.7820) (8.3293)

n 181 181 181

2 65.35 *** 63.54 *** 64.87 ***

R2 0.1918 0.1865 0.1904

P(Not Fail|TARP)



32

Table 6 Test of Abnormal Net Gain

In the tables below we apply a modified Bauer (2008) methodology to the

surviving credit union recipients of TARP funding. The methodology was modified because we were interested in longer-term abnormal net gains (over the five years since the disbursement of TARP funds). Because economies of scale are so different for smaller credit unions than for larger credit unions (Wilcox, 2005b, suggests that economies of scale don’t really start until the credit union is about $100 million in assets), we separated our sample into those which had more (larger) than $100 million and those that had less than that (smaller). The regression models reported below is based on the non-TARP credit unions.

The response variables were the five year average savings and lending rates (the original methodology just considered one year). The pre-event savings and lending rates still just considered the one pre-event year. The other three variables (Equity to Assets, Percent Change in Assets, and Log of Assets) all considered the five years following the disbursement of TARP funding.

The Equity to Assets ratio was calculated using average equity (over the five years) to average assets. The Percent Change in Assets was calculated using 3rd quarter 2010 assets as the beginning assets, and 3rd quarter 2015 as the ending assets. And the Log of Assets was calculated using average annual assets over that same time period.

Panel B reports the estimated abnormal net gain using a parametric test. The expected savings and lending rates were calculated using the regression models from the regression in Panel A. Abnormal net gain in savings rate to the owner/member is calculated as [𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝑂𝑏𝑒𝑠𝑟𝑣𝑒𝑑 − 𝑆𝑎𝑣𝑖𝑛𝑔𝑠𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑]. The abnormal net gain in lending

rate is calculated just backwards [𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 − 𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑]. The response

space is bivariate (savings and lending). The only quadrant where inferences regarding improved (worsened) performance can be made are the first and third quadrants where both the savings and lending abnormal returns have the same sign.

The parametric test results in panel B require a sample size of 25 to 30 because it assumes normality. Therefore only the parametric test on the smaller credit unions can be done.

Panel C uses the non-parametric methodology proposed by Bauer (2008). No distributional assumption is made. The test is used using a binomial probability distribution. Again the first and third quartiles are the only quartiles from which inferences can be drawn. The p-values reported in panel C represent the p-value associated with improved performance (even in the case of third quartile).

33

Panel A – Regressions to Calculate Expected Vectors:


Panel B – Parametric Tests of Abnormal Net Gains:

Panel C – Non-Parametric Test of Abnormal Net Gains:

Variable

Constant -0.0036 0.0228 -0.0076 0.0322

(0.0032) (0.0159) (0.0122) (0.0362)

Pre-Event Savings 0.3554 *** 0.1087 0.7010 *** 0.2721

(0.0372) (0.1867) (0.0854) (0.2522)

Pre-Event Lending 0.0252 ** 0.8512 *** 0.0111 0.9897 ***

(0.0129) (0.0647) (0.0444) (0.1312)

Equity/Assets 0.0101 ** 0.0190 -0.0363 * -0.0211

(0.0043) (0.0218) (0.0214) (0.0632)

D% Assets 0.0007 *** 0.0028 *** -0.0012 -0.0046

(0.0002) (0.0012) (0.0013) (0.0039)

Ln(Assets) 0.0001 -0.0013 * 0.0004 -0.0021

(0.0002) (0.0008) (0.0006) (0.0017)

n 83 83 19 19

F 23.17 *** 61.74 *** 20.08 *** 15.42 ***

R20.6007 0.8004 0.8854 0.8557

Post Lending

Coefficient/

Std. Error

Post Lending

Coefficient/

Std. Error

Small Credit Unions Large Credit Unions

Post Saving

Coefficient/

Std. Error

Post Saving

Coefficient/

Std. Error

Savings Lending

Sample

Size Chi-Square

One

Quadrant

p-value

Small Credit Unions 0.0114% 0.0496% 37 0.5489 0.1558

Large Credit Unions -0.4005% 0.1804% 4 NM NM

1st Quartile 3rd Quartile 1st Quartile 3rd Quartile 1st Quartile 3rd Quartile

Small Credit Unions 0.2771 0.3133 0.2432 0.1622 0.4012 0.0305

Large Credit Unions 0.2105 0.2105 0.5000 0.2500 0.3082 0.8028

Non-TARP TARP P-Value

Restated as Proportions

the effect of tarp funding on recipient credit unions · consumer lending credit unions began to...

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