Government Support of Banks and Bank Lending

ARTICLE IN PRESS JID: JBF [m5G; August 7, 2017;7:25 ]

Journal of Banking and Finance 0 0 0 (2017) 1–16

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Journal of Banking and Finance

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R Government support of banks and bank lending

∗ William Bassett a, Selva Demiralp b, , Nathan Lloyd a a Board of Governors of the Federal Reserve System, 20th Street and Constitution Avenue N.W., Washington, D.C. 20551 USA b Koc University, Rumeli Feneri Yolu, Sariyer, Istanbul, 34450 Turkey

a r t i c l e i n f o a b s t r a c t

Article history: The extraordinary steps taken by governments during the 20 07–20 09 ﬁnancial crisis to prevent the fail- Received 30 September 2016 ure of large ﬁnancial institutions and support credit availability have invited heated debate. This paper

Accepted 28 July 2017 comprehensively reviews empirical assessments of the beneﬁts of those programs—such as their effec- Available online xxx tiveness in reducing bank failures or supporting new lending—introduces a combined dataset of ﬁve key

JEL Codes: programs that provided term debt or equity to banks in the U.S., and assesses the effects of such support G11 on lending by U.S. banks. The results, using an instrumental variable approach, suggest that bank loans G21 did not increase at institutions receiving government support. E58 ©2017 Elsevier B.V. All rights reserved.

Keywords: Bank loans TAF TARP Capital purchase program

1. Introduction of concerns about their liquidity and capital positions ( Bassett and Covas, 2013 ). Indeed, banks tightened their lending policies over The worldwide financial crisis of 2007 to 2009 had many stages the crisis period to an even greater extent than would have been and wide ranging effects on both financial markets and global eco- expected given the developments in financial markets and the nomic output. Many ramifications of those disruptions continue economy ( Bassett et al., 2014 ). Predictably, bank lending declined to vex consumers, businesses, and policymakers almost a decade steeply starting in October 2008 and did not resume growing un- later. When securitization and interbank funding markets began til mid-2011, as many factors restrained the supply of credit and to seize during the second half of 2007, loans held by commer- depressed loan demand over that period. cial banks (the solid line in Fig. 1 ) initially expanded even as the Central banks recognized early in the crisis period the danger economy weakened and financial strains mounted. This growth re- that an adverse feedback loop could develop if troubles in the fi- flected in part loans that banks had intended to sell or securi- nancial sector spilled over to the real economy. To limit that dam- tize but instead were forced to hold on their balance sheet, and age, they took extraordinary steps to inject liquidity into the finan- drawdowns of pre-existing loan commitments by businesses and cial system. 1 When those measures proved insufficient to staunch households (the dashed line in Fig. 1 ). However, that expansion the crisis, some national governments took steps to recapitalize put additional pressure on banks, whose balance sheets were al- their banking sectors. Such measures were designed not only to ready strained by mounting losses. prevent the disorderly failure of multiple large financial institu- As illustrated in Fig. 2 , greater than usual numbers of banks in tions, but also to support the continued extension of credit to busi- the Federal Reserve’s Senior Loan Officer Opinion Survey reported nesses and households. having tightened lending policies between 2007 and 2009 because Because of both the enormous size and the unconventional nature of the policies enacted to mitigate the damage done by the

R Disclaimer: The views expressed are those of the authors and do not necessarily reflect the views of the Board of Governors or anyone else associated with the 1 For instance, the press release following the Federal Open Market Committee Federal Reserve System. meeting on September 18, 2007, stated, “Today’s action is intended to help forestall ∗ Corresponding author. some of the adverse effects on the broader economy that might otherwise arise E-mail addresses: [email protected] (W. Bassett), [email protected] (S. from the disruptions in financial markets and to promote moderate growth over Demiralp), [email protected] (N. Lloyd). time.” http://dx.doi.org/10.1016/j.jbankfin.2017.07.010 0378-4266/© 2017 Elsevier B.V. All rights reserved.

Please cite this article as: W. Bassett et al., Government support of banks and bank lending, Journal of Banking and Finance (2017), http://dx.doi.org/10.1016/j.jbankﬁn.2017.07.010 ARTICLE IN PRESS JID: JBF [m5G; August 7, 2017;7:25 ]

2 W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16

whether the benefits of the programs exceeded their costs. Those costs include a potential increase in moral hazard, as some argued that financial firms may in the future take similar risks in antici- pation of another “bailout.” This paper makes three contributions. The next section is a comprehensive and critical review of the existing literature on crisis-era support programs. The third section describes our new dataset combining five of the largest crisis-era programs. The fourth section uses an instrumental variable approach to study the joint effect of those programs on bank lending. We hope that combining five crisis-era support programs into a single dataset will spur future research. The dataset includes two equity-purchase initiatives—the Capital Purchase Program (CPP) and the Small Business Lending Facility (SBLF)—which were funded by the U.S. Treasury’s Troubled Asset Relief Program (TARP). The long-term debt issuance under the Debt Guarantee Program (DGP), which was part of the Federal Deposit Insurance Corporation’s (FDIC) Term Liquidity Guarantee Program (TLGP), is also included. Finally, we merge data on shorter-term borrowing from the Federal Reserve’s Term Auction Facility (TAF) and the term borrowing from the Discount Window (TDW) that was introduced by the Federal Reserve during the crisis to supplement the usual overnight lending associated with the lender-of-last-resort function. This exercise highlights several facts that are often overlooked. For instance, after the CPP and DGP were made operational, usage of the Federal Reserve’s TAF and TDW programs quickly abated,

Fig. 1. Loans and unused commitments. keeping the overall size of government support to banks roughly constant. In addition, the DGP provided more peak funding than the much more controversial and more frequently studied CPP, and so may have been an equally or even more important source of support for bank stability and lending. To date, empirical research has focused primarily on the CPP and the TAF, but no consensus as to their effect on lending has been reached. However, little research has been done on other programs, such as the FDIC’s DGP. The lack of consensus in the existing literature and lack of research on some programs reflects, in part, the difficulty of devising an empirical strategy to disen- tangle potential endogeneity between the demand for bank loans, the supply of alternative private funding sources, and banks’ will- ingness to access the external funds offered by the government. In order to control for endogeneity, we note that participation in these programs was controversial, and, at times, the public disap- proval of banks that used the programs broke down according to customers’ political affiliations. Therefore, following Li (2013) , we use variations in the intensity of support for the two major U.S. political parties in the states where the bank operates its branches as an instrument for participation in the government funding programs. As an alternative instrument, we also consider the government support received by similarly positioned banks. Using those variables as instruments for the sum of the five term-funding programs in two-stage least squares procedures, we find that loan growth at banks that used those programs was not significantly related to participation in these programs. Statistical Fig. 2. Capital and liquidity concerns. tests suggest that the instruments are valid for total government support received. Two important caveats apply to these results. Governments provided extraordinary support not only to stimulate new lending, but financial crisis, the wisdom and efficacy of those programs has also to prevent the widespread failure of otherwise solvent insti- been the subject of heated debate. 2 That debate intensified in light tutions, which has been shown to lead to a potentially deeper and of the protracted decline in bank lending and the uneven eco- more sustained crisis ( Bernanke, 1983 ). Gaby and Walker (2011) ar- nomic recovery after the crisis began to ebb, with some wondering gue that the TARP injections for the largest bank holding companies had such material benefits. Berger, Imbierowicz, and Rauch (2016) and Aubuchon and Wheelock (2010) also show that banks 2 For instance, see the debate between the Federal Reserve and Bloomberg that received TARP injections were less likely to fail than those News over the amount of funding provided through discount window loans, here: http://www.federalreserve.gov/generalinfo/foia/emergency- lending- financial- that did not, though they do not attribute causality. Jordan et al. crisis-20111206.pdf (2011) show that a low market-to-book ratio was a strong pre-

W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16 3 dictor of banks that received TARP funds. The analysis in this pa- ply. Li (2013) instruments for TARP participation using four vari- per does not account for that stabilizing effect, in that it looks ables based on political and regulatory influence and finds that only at the growth of loans conditional on a bank being in opera- the program increased credit supply by a large amount, $404 bil- tion rather than the counterfactual of how many loans would not lion. Material additional lending is also consistent with Berger have been extended had numerous otherwise healthy banks failed and Raluca (2016) , who find substantial real effects for “main ( Veronesi and Zingales, 2010 ). Moreover, measuring the effect of street” —increased employment and decreased bankruptcies—by TAF on total U.S. lending is complicated by the significant fraction comparing trends in each state before and after the TARP pro- of TAF funding that was extended to U.S. branches and agencies gram and controlling for the proportion of banks operating in of foreign banks, which are important lenders to U.S.-based busi- the state that received funds from the CPP. Using firms that bor- nesses. However, data limitations restrict the analysis of the TAF rowed from multiple banks at the same time to control for credit program to its effect on lending by domestic commercial banks. 3 demand, Berger, Bouwman, Kick, and Schaeck (2016) and Chu et al. (2016) also find evidence that banks increased credit sup-

2. Literature review ply to businesses as a result of TARP-related capital injections. Puddu and Wachli (2013) show that banks receiving TARP funds

Authors who have studied the effects of crisis lending programs increased their small business lending by exploiting differences have largely used four strategies to identify their effects. Studies between well-capitalized and under-capitalized banks within the relying on macroeconomic and ﬁnancial time series have largely same holding company. implemented vector autoregression (VAR) models with identiﬁca- But, other authors have found little increase in lending as a re- tion assumptions implicit in the ordering of the variables in the sult of government support. Taliaferro (2009) observed that banks

VAR. Some studies using time series as well as some using mi- that received TARP funds through the CPP used the bulk of the croeconomic data have argued that the onset of the financial cri- investment to shore up their regulatory capital ratios and subse- sis itself can be used as an event study, with conditions prior to quently expanded lending only $1.04 for every dollar of CPP in- the crisis predetermined and therefore a valid control mechanism. vestment. Duchin and Sosyura (2012) find no significant impact of

Other microeconomic studies have used bank structure, such as TARP on lending despite using one of the same instruments for differences across banks within the same holding company. And TARP—whether the bank was in a district represented by a mem-

ﬁnally, researchers have tried to identify instrumental variables for ber of congress on a banking oversight committee—that was used the take-up of government assistance, with most such papers to by Li (2013). Berger et al. (forthcoming) use whether a bank had date using variables related to political preferences. This section pledged collateral to the Federal Reserve’s discount window prior reviews the results obtained using those strategies in more detail. to the crisis as an instrumental variable for the bank’s use of TAF

A number of papers investigate which characteristics of banks and ﬁnd that banks receiving TAF had slower growth of some types most affected their lending during the crisis, without speciﬁ- of loans. cally addressing their use of government programs. Cornett et al. Another strand of literature focuses on how the non-standard

(2011) found that banks entering the crisis with more liquid bal- support measures affected risk taking by banks. Puddu and Wächli ance sheets and higher capital ratios sustained lending better than (2012) use an instrumental variables approach relying on balance- other banks, and also point to off-balance sheet risks as important sheet composition prior to the ﬁnancial crisis to conclude that constraints on lending during the crisis period. Ivashina and Sharf- banks that received TAF reduced their liquidity risk going forward. stein (2010) found that banks with better access to deposit funding Asking a similar question but this time for a different facility, Black cut their lending less. and Hazelwood (2013) ﬁnd that the self-reported riskiness of new

The majority of the literature in the early stages of the cri- loan originations increased at large banks that received CPP injec- sis focused on the impacts of non-standard policy measures on tions while the same measure decreased at small banks that re- bank funding markets rather than on bank loans, and were incon- ceived funds from the CPP. Duchin and Sosyusa (2014), using the clusive. 4 Carpenter et al. (2014) carry the previous literature one same instrumental variable as in their 2012 paper, also find that step further by providing a link between non-standard measures, banks receiving TARP funds increased the riskiness of their loans, bank funding markets, and bank loans. They find that non-standard and did so in ways that did not affect measured risk-weighted as- measures reduced liquidity risk in the US and the Euro Area and sets, thus boosting their capital ratios despite taking more risk. that the reduction in this stress stimulated bank loans. However, Harris et al. (2013) document lesser gains in measures of bank ef- the direct impact of capital injections on bank loans was only sig- ficiency at banks that received TARP funds than in banks that did nificant in the Euro Area. Berrospide and Edge (2010) find, in a VAR not, attributing the cause to TARP funds lessening a sense of ur- framework, that adding capital equal to the size of CPP injections gency to cut costs and strengthen balance sheets. would be associated with a material increase in bank loans. Three papers make clear that controlling for the timing of re-

A growing number of papers look speciﬁcally at the effects payment of TARP funds can be important inﬂuence on the results, of TARP and TAF on bank lending, using a number of different something that the panel framework used in our paper can address strategies to identify the effects of the program on loan sup- and that a cross section approach like in Li (2013) would abstract from. Bayazitova and Shivdasani (2012) investigated the decisions of banks to participate in CPP, the Treasury’s approval decision of

3 U.S. branches and agencies of foreign banks lend hundreds of billions of dollars CPP applications, the banks’ decisions to reject CPP injections, exit to firms in the United States, but generally do not make loans to U.S. households from CPP, and the impact of CPP on banks’ valuation gains, cap- and hold a tiny fraction of commercial real estate loans. ital ratios and asset quality. Cornett et al. (2013) document the 4 Taylor and Williams (2009) do not find robust evidence that the TAF had a sig- initial characteristics of banks that received TARP and their ef- nificant effect on term spreads in bank funding markets in late 2007 and early

2008, instead pointing to elevated levels of counterparty risk and expectations of fect on subsequent ﬁnancial performance and timing of repayment future policy action. Thornton (2009) suggests that the TAF may not have affected of the TARP investment. Under the assumption that a difference- spreads prior to September 2008, because the Fed had been offsetting extensions in-difference framework for TARP recipients compared before and of credit under the TAF to keep the monetary base relatively constant. Thornton after the bailout is an identiﬁcation strategy, Berger and Raluca

(2011) ﬁnds that the announcement of TAF signaled that conditions were worse (2015) ﬁnd that banks who received TARP funds and repaid them than previously thought and so increased risk premiums in bank funding markets.

In contrast, McAndrews et al. (2008), Christensen et al. (2009) , and Wu (2008) ﬁnd early gained market share over other banks, possibly due to the that TAF is associated with signiﬁcant reductions in the Libor-OIS spread. increased safety associated with the higher capital levels.

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Table 1 Summary statistics of alternative programs.

TDW TAF DGP CPP SBLF (Millions of $, rounded)

Dates of program operation 8/20/2007– 12/17/2007– 10/14/2008– 10/28/2008– 6/21/2011– 3/1/2010 12/14/2009 10/31/2009 12/29/2009 9/27/2011 Participants by total assets ∗ Less than $1B 279 157 37 537 247 Between $1B and $20B 66 85 32 195 37 Greater than $20B 7 31 31 31 1 Sum of maximums ﬁnanced ∗∗ 167,385 451,282 397,221 245,465 3923 Cumulative repaid through 12/31/2012 All All All 234,373 120 Cumulative written-off (where applicable) 0 0 0 0 0 Debt outstanding as of 12/31/2012 0 0 4 7204 3808 Individual Observations ∗∗∗ (Millions of $, rounded) Max 37,0 0 0 78,0 0 0 75,289 45,0 0 0 141 Mean 264 1038 4490 134 14 Median 10 50 75 10 9

∗Participants refers to the number of unique top holders with a positive balance in at least one quarter. ∗∗ Sum of maximums ﬁnanced for the programs represents the sum of each unique top holder’s maximum daily balance. ∗∗∗Includes only non-zero quarterly observations of variables as of the last day in each quarter.

3. Comprehensive government support database As a result of the apparent substitution of CPP and DGP for TAF and TDW funds, the total amount of extraordinary government As described briefly above and in more detail in the appendix, support for banks peaked in the first half of 2009 before falling one of the purposes of this paper is to introduce a new compre- quickly to about half the peak in 2010 and 2011 ( Fig. 4 ). Likewise, hensive database that merges publicly available information about banks often had both the DGP and CPP outstanding and some may the usage of five key government support programs introduced be- have repaid CPP injections more quickly because they had access tween 2007 and 2010. Two programs, the TDW facility and the to long-term funding through the DGP. Later, most of the small TAF, provided liquidity support to sound institutions for relatively and mid-size banks that participated in the SBLF took advantage short periods of time, usually less than 90 days. The CPP and the of the opportunity to repay their outstanding CPP balances with SBLF provided preferred stock at subsidized dividend rates for up SBLF funds. Thus, a focus solely on the effects of any single pro- to three years (and the option to extend the investment at closer gram will suffer from omitted variable bias or measurement error, to market rates) and the DGP guaranteed new long- and short- as the many programs in operation simultaneously from late 2008 term debt issuance for up to three years. Both the CPP and DGP through 2012 were designed for similar purposes. provided subsidized long-term funding in an effort to calm the As shown by the top panel of Fig. 5 , the correlation between us- widespread concerns about the viability of individual institutions age of the CPP and usage of the DGP was between 0.5 percent and and reduce the need for them to shrink or restructure their bal- 0.8 percent in 2009 and 2010, reflecting the outstanding balances ance sheets to become more liquid. The primary goal of the SBLF, of both at large banks. But, those large banks did not refinance introduced in the fourth quarter of 2010 as an expansion of, and their DGP issuance into nonguaranteed debt the way they aggres- potentially a substitute for, the CPP, was to boost lending to small sively paid down CPP, and the correlation fell to zero in 2011 and businesses. 2012. Similarly, the correlation between the TAF and the DGP tells Combining these five programs into one database allows for the story of the substitution in the usage of those two programs. the establishment, or at least reinforcement, of some stylized facts The high correlation in the first half of 2009 shows that banks about the size of the programs and the way in which they inter- were using both initially, but then dropped TAF as the longer-term acted. Perusing the five panels of Fig. 3 and the columns of Table funding was locked in. The negative correlation between SBLF and 1 , one notices that even after controlling for the rollover of some DGP and CPP reflects the actions of many banks in taking out SBLF debt issuance, the peak amount guaranteed by the more-under- funds specifically to repay those other programs. the-radar DGP was 33 percent larger than the amount that was The substitution between government support programs illus- injected through the CPP. Another feature of the DGP was that over trates one aspect of the endogeneity between loan demand, loan the entire period of its operation, almost all of its beneficiaries supply, and alternative funding sources. Fig. 6 shows the difficulty were large banks, those with more than $20 billion in assets. In of discerning the effect of the programs using the unconditional contrast, the largest banks aggressively repaid their CPP funds be- rates of loan growth, by participation and bank size. The vertical ginning in the second half of 2009, and by the end of 2010, the lines correspond to the periods during which the particular facil- majority of the remaining beneficiaries of the CPP program were ity was open for new originations. For instance, large banks that mid-size and smaller institutions. were participants in the TAF and DGP (middle and bottom panels, TAF financing of U.S. chartered BHCs, also adjusted to account dotted black line) had consistently slower growth of loans over the for repeated rollovers of short-term financing, was about the same 2009 to 2012 period than large banks that were not participants in order of magnitude as the CPP at their respective peaks. Demand those programs (dotted blue line). for this program also seemed to dry up quickly once the CPP and DGP were made available, suggesting that most banks preferred 4. Empirical strategy the longer-term funding to the TAF, even though CPP and DGP were somewhat more expensive. The market-calming effect of the When bank funding markets are functioning normally, the releases of the results of the Supervisory Capital Assessment Pro- growth rate of bank loans should depend primarily on loan de- gram, or SCAP, in May 2009 also likely reduced demand for TAF mand and the appetite for risk of the bank management. For a funds. Though much smaller in size than the TAF and demanded well-capitalized bank, growth likely would not be constrained in more by smaller banks, the TDW option also shrank precipitously the near-term by the need to issue new equity, and funding for as soon as other sources of funds were offered. a new loan at prevailing interest rates would usually be available.

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Fig. 3. Government assistance program outstandings, by ﬁnancial institution size. Note: Large entities consist of those whose assets exceed $20 billion. Mid-size entities have assets ranging between $1 billion and $20 billion. Small entities are those with asssets less than $1 billion.

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Fig. 4. Government assistance outstandings, by program type. Note: TDW represents Term Discount Window lending, TAF represents the Term Auction Facility, CPP represents the sum of capital injected under the Capital Purchase Program, Targeted Investment Program, and Community Development Capital Initiative, DGP represents the Debt Guarantee Program of the Temporary Liquidity Guarantee Program, and SBLF represents the Small Business Lending Fund.

As argued in Carpenter and Demiralp (2012) , a generally accepted soundness, measuring the impact of government support programs tenet of bank liability management is that the marginal sources solely through their impact on bank loans may underestimate their of funding are short- and intermediate-term managed liabilities, true effects in the economy. which allow banks to lock in funding for a speciﬁed term in con- In order to study the relationship between the growth of bank sideration of the maturity of their assets and the stability of their loans and usage of those external funding opportunities, we treat other sources of funding. 5 the sum of CPP, SBLF, DGP, TAF, and TDW, which we call “Total Gov- During the crisis, however, borrowing costs skyrocketed—even ernment Support (TGS)”, as an endogenous variable in a two-stage for healthy institutions. As ﬁnancial institutions hoarded liquid- least squares framework: ity, the supply of managed liabilities, particularly at tenors of log (Loans ) = T + B + β × (T GS) , /T A , − longer than a few days, dried up. In large part, that panic was t t i 1 i t i t 1 the result of lenders in interbank markets being unable to discern + β2 × (Managed Liabilities ) i,t−1 /T A i,t−1 whether their potential counterparty remained solvent, and even- + β3 × (Core Deposits ) i,t /T A i,t−1 tually the dysfunction proved too endemic to be solved by short- + β × ( Tan gible common equity ) , − term liquidity programs, such as the TDW and TAF. That situation 4 i t 1 + β × ( ) / ( + ) prompted additional government intervention to alleviate solvency 5 REcommit i,t−1 Commit TA i,t−1 concerns through programs like the CPP and the DGP. The SBLF, + β6 × (Othcommit) i,t−1 / (Commit + T A ) i,t−1 introduced subsequently to those programs partly in response to + β7 × (Il l iquid Assets ) i,t−1 /T A i,t−1 criticism that not enough had been done to help “main street,” + β × (Delinquencies/ tier 1 capital) , − also was described by policymakers as an attempt to increase loan 8 i t 1 supply. + β9 × (net ch arg eof f s/deliquencies ) i,t−1

The programs should have been supportive of loan growth by + β10 × (Employment Growth ) i,t−1 reducing the cost of funding (move along the demand curve) and + β × (House Pr ice Index ) , − + ε (1) by easing risk aversion (shift the supply curve out). However, in 11 i t 1 it the aftermath of the financial crisis, demand for loans from the In this equation, i indexes banks, t indexes quarters, Bi are bank- nonfinancial sector had dropped precipitously, and so the equilib- specific fixed effects, and T t are quarterly time fixed effects. The ɛ rium quantity of loans may have declined even in the presence of errors, i, t , are clustered at the bank level. The dependent variable that support. Furthermore, to the extent that the dominant mo- is the growth rate of total loans, or C&I loans or Residential Real tivation of banks using the programs was to enhance safety and Estate (RRE) loans. 6 Unless otherwise noted, balance sheet vari-

5 6 Managed liabilities are deﬁned in this paper as large time deposits, federal For speciﬁc loan categories, an additional term, β13 × (Delinquency rate ) i,t−1 is funds purchased, repurchase agreements, and federal home loan bank advances. added to the equation.

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Fig. 5. Correlations of financial institutions’ participation in government assistance programs. Note: TDW represents Term Discount Window lending, TAF represents the Term Auction Facility, CPP represents the sum of capital injected under the Capital Purchase Program, Targeted Investment Program, and Community Development Capital Initiative, DGP represents the Debt Guarantee Program of the Temporary Liquidity Guarantee Program, and SBLF represents the Small Business Lending Fund. ables are normalized by the merger-adjusted total assets at the but also its access to other funding markets. Therefore, we expect 7 beginning of each quarterTAi,t−1 . We use the sum of the aver- CPP funding to have an impact over the duration of the period for age daily outstanding for each of the five government support pro- which the bank benefits from a higher level of capital rather than grams in a given quarter rather than the quarter-end values (which just in the quarters in which there was a change in CPP invest- is the convention of the Call Report data) because even if a bank ments. To reflect this belief, the CPP variable enters as a step func- pays back its funding in the last month or last day of a quarter, tion, which changes when there is a change in the value of CPP any amount of funding in the previous months may have provided and stays at that level until there is another change, usually from support at the time the loan was extended. a bank’s decision to repay the Treasury. This way, the cumulative Going over the components of TGS, TAF and TDW borrowing is borrowing from the CPP has an impact on bank loans for as long expected to be a close substitute for managed liabilities (ML) and as the bank holds the investment rather than expecting only a rel- affect bank loans in the same way as ML. That is, we expect an in- atively immediate impact of CPP investments and redemptions on crease in TAF in a particular quarter to fund an expansion of bank bank loans. The same rationale and process is used to construct loans in the same quarter. Capital typically is viewed as a long- the variables for the DGP and SBLF. term and stable source of funding that does not vary significantly However, funding through the government support programs with short-term changes in loan-demand, and increases in capi- could also be used to purchase securities or to pay down more ex- tal generally improve not only a bank’s lending capacity directly pensive liabilities and support profits while keeping the size of the balance sheet roughly constant. In that case, those programs may

not necessarily be a signiﬁcant factor in the loan equation. In fact, 7 The data are adjusted for mergers within a given period. For banks not involved if banks that took government support were driven by risk aver- in a merger, beginning-of-period balance sheet quantities are deﬁned as the prior period quarter-end value. For banks involved in a merger, beginning-of-period val- sion or “animal spirits”, a spurious negative relationship between ues equal the sum of the prior period quarter-end values of the balance sheet item government support and bank loans might arise. across the merging banks.

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Fig. 6. Comparing loan growth rates.

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4.1. Instruments for the TGS ing sector likely was more unpopular in less populous areas, which tend also to lean Republican in national elections. 10 4.1.1. Local market political support for democratic party candidates Within each state, the number of seats in Congress held by (Dseats) members of the Democratic Party ranges from 0 to 35, with a The main problem in any instrumental variables (IV) estimation mean of 10 and standard deviation of 10. As is widely understood, is the difficulty of finding valid instruments. As described above, the numbers tend to be larger in the northeast and west coast than several authors have found that variables controlling for political in the central plains and southeast. relationships or party affiliation affect demand for the CCP. These relationships resonate because TARP legislation was highly contro- 4.1.2. Government support received by comparable banks (Other GS) versial and unpopular across the political spectrum in America. We use the government support received by similarly posi- In fact, many bankers believe that they benefitted by advertising tioned competitor banks as an alternative instrument. The amount that they were not participating. 8 Evidence also suggests that the of government support received by competitors would be corre- “bank bailout” was one of the key factors in the organization of lated with the amount received by an individual bank due to the the “TEA (Taxed Enough Already) Party” movement, a loose col- mechanics of the programs and the systemic nature of the finan- lection of groups across the country that began to organize in the cial crisis affecting most banks at least to some extent. However, spring of 2009 and whose self-identified members have tended to using the government support received by other banks allows us vote disproportionately for Republican Party candidates ( Madestam to break the balance sheet identity and would be orthogonal to et al., 2013 ). Many sources also report that the TEA Party move- idiosyncratic shocks to demand or supply at particular bank. This ment was further strengthened by the passage of the package of instrument also has the advantage of being able to capture both health insurance changes known as the Affordable Care Act later the period in which government support was increasing—2007: Q2 that year, a development unrelated to supply of, or demand for, to 2009: Q1—and the subsequent period during which banks were bank loans. The Republican Party gained control of the U.S. House paying back CPP and the Federal Reserve was winding down the of Representatives in the November 2010 elections, more than re- TAF. versing gains made by the Democratic Party in 2008. The Repub- To identify competitor banks, we segment the sample by six lican Party also took control of several state legislatures and gov- size categories for domestic banks (less than $1 b, $1 b - $3 billion, ernorships that year, which in many cases were able to redefine $3 b - $10 b, $10 b - $50 b, $50 b - $250 b, and more than $250 b) congressional districts that helped the party retain control of the and a separate category for foreign-owned (who were eligible for House of Representatives in the 2012 elections despite the reelec- TAF but not other programs). For domestic size categories of banks tion of a Democratic president, Barack Obama. below $10 billion, we further segment by whether the bank is pub- Thus, banks whose business is concentrated in areas where the licly traded, and two variables that identify major business models: Republican Party has strong support may have been less likely to the share of assets funded by core deposits (less than 30 percent, accept government support than banks in areas where the Demo- between 30 and 45 percent, more than 45 percent) and the share cratic Party was dominant, regardless of their financial condition of assets held as loans (less than 60 percent, between 60 and 75 or prevailing loan demand. Moreover, banks that witnessed the percent, more than 75 percent). Domestic banks with $10 billion success of the TEA Party movement in their local markets dur- - $50 billion were divided by only two loan categories (greater or ing the 2010 election cycle may have repaid government support less than 66 percent of assets) and two deposit categories (greater more quickly than planned and more quickly than banks in other or less than 35 percent of assets funded by core deposits). Banks of areas, again irrespective of existing trends in loan demand. Thus, $50 billion or more were segregated only by size. Foreign-owned we propose that the intensity of support for political parties in banks were subdivided by whether they had more, or less, than banks’ local markets, can be used as an instrumental variable for 60 percent of assets held as loans. Restrictions on the number of banks’ likelihood of applying for government support. Moreover, segments for the largest domestic banks and foreign banks were the change in support for Republicans between 2008 and 2010 and necessary to limit the number of year-competitor buckets that had 2012 may be correlated with the length of time that such invest- only one bank and thus were not usable. ments were outstanding.

An observable measure of intensity of support for the Demo- 4.2. Variables treated as predetermined cratic Party in the banks’ local markets is the number Democrats in each state’s delegation to the House of Representatives from 2007 Control variables that act as proxies for overall and bank- 9 to 2012. The average number of democratic representatives across speciﬁc loan demand are included directly in the estimation. The states in which the bank has operations is heretofore referred to as quarterly time ﬁxed effects, T t , absorb market-wide funding condi-

Dseats. This is the branch-weighted average of the number of seats tions and economy-wide demand conditions during each quarter. in the House of Representative held by Democrats in the states in The time ﬁxed effects are more likely to be effective controls for which the bank operates branches. The total number of seats held these key inﬂuences on loan growth for large banks operating in by Democrats was chosen rather than a fraction of the total dele- multiple markets. To better control for economic conditions in in- gation in order to better account for diversity of political opinion dividual markets populated by smaller banks, we match state-level in larger states, and because government intervention in the bank- employment growth data from the Bureau of Labor Statistics and the state-level house price index from CoreLogic with the locations of the bank’s branches. An increase in the employment growth in a bank’s local markets is expected to decrease both loan supply β > 8 For instance, see LaMonica Paul R, “The bankers who said ’hell no’ to bailouts” and loan demand such that 10 0. Similarly, improvements in the at http://money.cnn.com/2010/09/15/news/companies/thebuzz/index.htm .

9 Given the two-party system in the United States, mapping the seats held by the Democratic Party is a sufficient statistic and without loss of generality. The data 10 The relationship between the number of Democratic Party representatives and on party affiliation of congressional delegations was sourced from Wikipedia, De- receipt of government support could also reflect a relationship between population cember 12, 2013. http://en.wikipedia.org/wiki/Members _of _the _110th _ United _ and loan demand. However, a control for average population in markets served by States _ Congress , http://en.wikipedia.org/wiki/Members _of _the _111th _ United _ a BHC, defined analogously to dseats, is insignificant in both the first and second States _ Congress , http://en.wikipedia.org/wiki/Members _of _the _ 112th _ United _ stage regressions if it were included. For parsimony, it is excluded from the specifi- States _ Congress . cations that follow.

10 W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16 housing price index (weighted by branch locations) are expected Poor loan performance may reflect strains among the bank’s β > to promote loan growth: 11 0. customer base that would reduce loan demand and prompt the Most of the remaining right-hand-side variables in Eq. (1) are bank to substitute away from loans and toward safer and more liq- common controls for loan supply from the works cited above. uid assets, in part by shrinking their balance sheet to improve cap- We use the lagged values of the explanatory variables in part to ital adequacy. The controls for these factors are delinquent loans break the explicit balance sheet identity and argue that the prede- divided by previous period tangible common equity ( Delinquency termined quantity also aides identification, because balance sheet ratio ), the net loss rate —gross charge-offs of loans during the period quantities are often difficult or costly for a bank to adjust in the divided by delinquent loans at the beginning of the period. Both short-term. variables are needed because loss rates on delinquent loans can We control for the extent to which the bank relies on fund- be quite different across loan categories, so either one in isolation ing by core deposits , which are defined as transactions and savings could be misleading. For specific loan categories, controls include deposits. A large fraction of core deposits are typically “sticky” be- the delinquency rate for those loans, defined as the beginning of cause of customers’ switching costs, and so banks use them to fund period delinquent loans in that category divided by tangible com- long-term investments, such as loans, even though they can techni- mon equity, because that ratio will have important effects on sup- β < β < cally be withdrawn immediately. A higher fraction of core deposits ply of that type of loans. We expect 8 0 and 9 0 as well. β > should be positively related to bank loans ( 3 0) and negatively related to demand for other sources of funding. 4.3. Description of sample and data The TCE ratio is defined as the ratio of a bank’s tangible common equity capital—common stock and surplus plus retained The sample used in the paper includes bank holding compa- earnings—to its total risk-weighted assets at the beginning of the nies (BHCs) with assets greater than $1 billion that are included 11 quarter. Thus, the TCE ratio is not affected by the increase in pre- in the Y-9C Report and the FDIC Summary of Deposits data. 13 The ferred stock at banks that received CPP or SBLF funds. The TCE ra- sample period extends from 2007: Q2, when TDW loans first be- tio was also reportedly the most commonly referenced indicator of came available, until 2012: Q4, two years after the implementation capital strength by market participants during the crisis. of the SBLF. No new CPP funds were disbursed after 2009: Q3, but The theoretical literature is divided on the impact of bank capi- CPP funds were still being held by some banks and repaid by oth- tal on bank loans. Some theories argue that bank capital might im- ers through 2012: Q4. The sample ends in 2012: Q4 because it was pede liquidity creation because it makes the bank’s capital struc- the end of the debt guarantee under the DGP. The sample for the ture less fragile (see e.g. Diamond and Rajan, 20 0 0 ). Others argue regression for growth of total loans has 8356 observations and 472 that higher capital improves the bank’s ability to absorb risk and distinct BHCs. Table 2 displays summary statistics for the variables hence higher capital ratios allow banks to create more liquidity used in the analysis. (see e.g. Coval and Thakor, 2005 ). Hence, we do not have priors about the sign of β in the loan growth equation. 4 4.4. Panel estimation and results Off-balance-sheet exposures also importantly affect lending flows, but the effects are sometimes hard to determine ex ante If the chosen instruments are relatively weak, then IV estima- ( Cornett et al., 2011 ). Because the collapse of the real estate market tion becomes less precise and the standard errors can become in the U.S. was a primary cause of the crisis, unused commitments many times larger compared with those from inconsistent OLS. Be- have been split into REcommit , commitments to fund residential or cause of this potential complication and the moderate sample size, commercial real estate loans, and Othcommit, which refers to un- we use the GMM continuously updated estimator (CUE), which is used commitments to fund other types of loans, excluding credit reported to have superior finite sample performance (Hahn et al., cards. 12 On the one hand, banks with greater unused commitments 2004 ). 14 As a result, our findings regarding the significance of a are exposed to liquidity risk, which may prompt an increase in particular funding facility are expected to err on the conservative their liquid assets and reduce loan supply going forward ( β < 0, 5 side. 15 β < 0). On the other hand, banks are unable to cancel some types 6 Table 3a shows the first-stage regression where the five gov- of unused commitments, and the heightened demand for liquid- ernment support programs are aggregated together and the sum ity by customers at the beginning of a period of economic stress can generate loan growth against those previously existing com- β > β > 13 mitments ( 5 0, 6 0). The data excludes BHCs whose primary subsidiaries were mutual savings banks, The composition of assets also influences prospective lending because these institutions were not initially eligible for TARP funding and therefore capacity. Illiquid Assets are defined as structured financial products, would not be well identified by our instrumental variables, and certain BHCs that are headquartered in US territories. The $1 billion size threshold was chosen be- asset-backed securities (including mortgages) that were not issued cause it is the current threshold for reporting on the Y-9C, and as shown above, or guaranteed by federal government agencies, fixed assets, intan- banks below this threshold had very little participation in government support pro- gible assets, investments in subsidiaries, and total other assets de- grams other than TARP and SBLF.

fined on schedule RC-F of the Call Reports. Banks holding a large 14 We use user-developed xtivreg2 module in STATA (see Schaffer, 2010 ). share of illiquid assets are expected to have slower loan growth 15 Our panel has 22 quarters from the first distribution of government support in 2007:Q2 through the two-year anniversary of SBLF in 2012:Q4. On the one hand, ( β < 0). 7 that time series length raises the question of whether the dynamic panel regression will be sufficiently unbiased, as the bias converges to 0 at the rate 1/T. On the other hand, that bias may be smaller than the loss of efficiency from use of an Arellano and Bond (1991) type of estimator with so many potential instruments.

11 The key results of the analysis are not sensitive to the choice of capital ratio. The Arellano and Bond estimator is designed for short panels with a wide cross The alternatives considered were total capital/assets, deviations from a bank-specific section. In long panels, a shock to the fixed effect declines with time and the cor- target defined as a long-run moving average, or the minimum of deviations from relation of the lagged dependent variable with the error term becomes insignificant the regulatory well-capitalized thresholds. ( Roodman, 2006 ). Judson and Owen (1999) use Monte-Carlo simulations and show

12 Credit card commitments were excluded because unlike other commitments, that the so-called “Nickell bias” is no longer significant for panels where the time they are very granular exposures that are unconditionally cancellable by the finan- dimension is larger than 30. Indeed, we attempted to employ the Arellano-Bond cial institution issuing the card. They also tend to have very low average usage rates estimator, trying several different lag lengths and combinations of groupings of ex- relative to other types of commitments in this category. Thus, they are not likely to ogenous and endogenous variables. All of those attempts failed at least one of the result in the type of liquidity stress that can arise from a large, unexpected draw standard specification tests included in the STATA package for the procedure. Thus, on a business line of credit. the results are not included in the paper.

W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16 11

Table 2 Descriptive statistics of the variables used in the empirical analysis.

Description Number of obs. Mean Std. Dev. P10 P90

1. ( TGS ) i,t /( Assets ) i.t 8369 0.72 1.37 0.00 2.48 2. Total Loan Growth i 8369 3.10 21.09 −14.26 19.60 3. C&I Loan Growth i,t 8367 106.11 6839.45 −30.79 37.49 4. RRE Loan Growth i,t 8328 14.32 355.29 −16.95 29.94 5. ( Managed Liabilities i.t )/( Assets ) i, t 8369 16.77 9.15 7.31 27.62 6. ( Core Deposits i, t )/( Assets ) i, t 8369 38.75 12.94 23.45 55.46 7. TCE Ratio i,t 8369 10.28 5.45 6.02 14.86 8. REcommit Ratio i,t 8369 5.30 3.53 1.69 9.85 9. Other Commit Ratio i,t 8369 7.70 4.95 2.37 13.92 10. ( Illiquid Assets ) i, t /( Assets ) i, t 8369 8.07 3.98 4.22 12.55 11. ( Delinquencies ) i, t /( Tier 1 Common ) i, t 8369 46.40 67.95 8.16 93.58 12. Net Chargeoff Rate i, t 8369 7.52 8.77 0.73 16.01 13. Employment i, t 8369 −0.02 1.64 −1.08 0.65 14. House Pr ice Index i,t 8369 −0.85 1.65 −2.86 0.87 15. C&I Delinquencies/(Tier 1 common) i,t 8369 4.71 7.47 0.43 10.23 16. RRE Delinquencies/(Tier 1 common) i,t 8369 9.20 15.35 0.77 19.20 17. Dseats i,t 8369 8.88 8.31 2.00 21.56

The abbreviation RE stands for real estate.

Table 3a First stage regression for all banks.

Dependent variable: (TGS)i,t /Assetsi.t-1 .

Total Loans C&I Loans RRE Loans

∗∗ ∗∗ ∗∗ 1. Dseats i (Instrument) 0.0594 0.0582 0.0541 (0.0259) (0.0260) (0.0261) ∗∗∗ ∗∗∗ 2. Loan Growt h i,t−1 −0.00164 −0.0 0 0947 −0.0 0 0242 (0.0 0 0594) (0.0 0 0276) (0.0 0 0304) ∗∗∗ ∗∗∗ ∗∗∗ 3. (Managed Liabilitie s i.t−1 ) / (Assets ) i,t−1 −0.0246 −0.0244 −0.0250 (0.00595) (0.00593) (0.00599) ∗∗∗ ∗∗∗ ∗∗∗ 4. (Core Deposit s i,t−1 ) / (Assets ) i,t−1 −0.0146 −0.0147 −0.0155 (0.0 050 0) (0.0 050 0) (0.00514) ∗ 5. (Il l iquid Assets ) i,t−1 / (Assets ) i,t−1 −0.0241 −0.0238 −0.0279 (0.0151) (0.0151) (0.0162)

6. (Delinquencies ) i,t−1 / (T ier 1 Common ) i,t−1 0.0 0 0441 8.34 E −05 −0.0 0 0141 (0.0 0 0405) (0.0 0 0426) (0.0 0 0619) ∗∗∗ ∗∗∗ ∗∗∗ 7. Net loss rat e i,t−1 0.00557 0.00585 0.00562 (0.00172) (0.00171) (0.00174)

8. Employment Growt h i,t−1 −0.00196 −0.00161 −0.00288 (0.0162) (0.0162) (0.0165) ∗∗ ∗∗ ∗∗ 9. House Pr ice Inde x i,t−1 −0.0470 −0.0463 −0.0455 (0.0205) (0.0201) (0.0203) ∗∗∗ ∗∗∗ ∗∗∗ 10. TCE Ratio i,t-1 −0.0561 −0.0550 −0.0545 (0.0123) (0.0120) (0.0121) ∗∗ ∗∗∗ ∗∗∗ 11. REcommit Ratio i,t-1 −0.0438 −0.0455 −0.0474 (0.0173) (0.0173) (0.0174) ∗∗∗ ∗∗∗ ∗∗∗ 12. Other Commit Ratioi, t-1 −0.0398 −0.0365 −0.0373 (0.0151) (0.0142) (0.0141) ∗∗ 13. C&I Delinquency Raioe i,t-1 0.00766 0.00486 – (0.00339) (0.00362)

14. RRE Delinquency Ratio i,t-1

15. Bank dummies Yes Yes Yes 16. Quarterly time dummies Yes Yes Yes 17. F test of excluded instruments F(1471) = 5.26 F(1469) = 5.03 F(1469) = 4.30 Porb > F = 0.0223 Porb > F = 0.0253 Porb > F = 0.0387 18. Number of banks 472 470 470 19. Number of observations 8356 8325 8270

Robust standard errors are reported in parenthesis under coefficient estimates. ∗∗∗/ ∗∗/ ∗ indicates significance at 99/95/90 percent level of confidence respectively. FE Model, errors clustered around banks. is treated endogenously. 16 The dependent variable is either total seats in the U.S. House of Representatives held by Democrats in loans, C&I loans, or RRE loans as shown in columns I-III respec- states in which the bank operates is both statistically and econom- tively. Starting with the performance of the instrument for the ically significant in the first stage regression for all three types of total amount accessed from TGS (row 1), the average number of loans. A difference of 5 seats held by Democrats leads to a TGS investment that is about 0.30 percent of assets larger, compared with the average TGS of 0.70 percent of assets. This is the ex-

16 Even though it seems plausible to use an indicator variable for borrowing from pected relationship if banks operating in less populous states and the TAF or receipt of TARP funds along with probit or logit estimation to generate Republican-leaning areas viewed participation in the TGS as a po- first-stage predicted values, this is not necessary and can even cause more mis- specification problems ( Angrist and Krueger, 2001 ). Moreover, consistency of the tential negative signal to their most likely customers. The F-test of second-stage estimates does not rely on using the right functional form for the first; excluded instruments is rejected, indicating that TGS is identified estimates from a first-stage linear regression generate consistent second-stage estimates even if the endogenous variable is discrete or censored ( Kelejian, 1971 ).

12 W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16

Table 3b Second stage and OLS regression results for all banks.

Total Loans C&I Loans RRE Loans OLS CUE OLS CUE OLS CUE ∗ ∗ 1. ( TGS ) i,t /( Assets ) i.t-1 0.395 14.94 −0.370 21.07 0.764 19.18 (0.253) (8.082) (0.372) (13.42) (0.450) (13.77) ∗∗∗ ∗∗∗ 2. Loan Growt h i,t−1 0.0560 0.0783 −0.00473 0.0147 −0.0198 −0.0137 (0.0181) (0.0242) (0.0213) (0.0273) (0.0346) (0.0342) ∗∗ ∗∗ ∗ 3. (Managed Liabilitie s i.t−1 ) / (Assets ) i,t−1 0.149 0.500 0.134 0.645 0.127 0.586 (0.0724) (0.229) (0.127) (0.361) (0.163) (0.399) ∗ ∗∗ ∗ 4. (Core Deposit s i,t−1 ) / (Assets ) i,t−1 0.117 0.322 0.0557 0.359 0.216 0.504 (0.0654) (0.148) (0.0988) (0.236) (0.142) (0.290)

5. (Il l iquid Assets ) i,t−1 / (Assets ) i,t−1 −0.285 0.0656 0.233 0.740 −0.129 0.413 (0.221) (0.343) (0.409) (0.586) (0.274) (0.542) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ ∗∗∗ 6. (Delinquencies ) i,t−1 / (T ier 1 Common ) i,t−1 −0.0417 −0.0483 −0.0335 −0.0353 −0.0269 −0.00336 (0.00827) (0.0110) (0.0112) (0.0148) (0.0100) (0.0184) ∗ ∗∗ ∗ 7. Net loss rat e i,t−1 −0.0797 −0.160 −0.0753 −0.199 0.0234 −0.0719 (0.0450) (0.0646) (0.0721) (0.112) (0.0571) (0.0918)

8. Employment change i,t−1 0.468 0.484 0.195 0.208 0.660 0.712 (0.309) (0.395) (0.288) (0.478) (0.425) (0.584) ∗∗ ∗ ∗ 9. House price change i,t−1 0.543 1.204 0.676 1.631 0.967 1.805 (0.347) (0.569) (0.495) (0.928) (0.641) (0.938) ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗∗ 10. TCE Ratio i,t-1 0.713 1.531 0.453 1.632 0.728 1.746 (0.190) (0.580) (0.343) (0.952) (0.225) (0.810) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗ 11. REcommit Ratio i,t-1 1.685 2.313 1.487 2.451 1.037 1.971 (0.210) (0.487) (0.343) (0.796) (0.371) (0.841) ∗∗∗ ∗∗∗ ∗ ∗ ∗∗ ∗∗ 12. Other Commit Ratio i,t-1 0.664 1.252 0.700 1.500 0.649 1.374 (0.209) (0.459) (0.414) (0.769) (0.257) 0.586 ∗∗∗ ∗∗∗ 13. C&I Delinquency Ratio i,t-1 −0.356 −0.523 (0.115) (0.177) − ∗∗∗ − ∗∗ 14. RRE Delinquency Ratioi, t-1 0.121 0.313 (0.0438) (0.127) 15. Bank dummies Yes Yes Yes Yes Yes Yes 16. Quarterly time dummies Yes Yes Yes Yes Yes Yes 17. Underidentification test – 5.258 (p-val. = 0.02) –5.035 (p-val. = 0.03) –4.297 (p-val. = 0.04) (Kleibergen-Paap rk LM statistic) 18. Overidentification test of all – Just-identified – Just-identified – Just-identified instruments (Hansen J statistic) 19. Number of banks 485 472 484 470 482 470 20. Number of observations 8369 8356 8339 8325 8282 8270

Robust standard errors are reported in parenthesis under coefficient estimates. ∗∗∗/ ∗∗/ ∗ indicates significance at 99/95/90 percent level of confidence respectively. FE Model, errors clustered around banks. by the proposed instrument after partialing out the linear projec- Table 3c tion of other regressors. Tests of Endogeneity based on CUE. Most of the control variables have the expected sign in the first Equation Test Statistic P-value stage and many are statistically significant. Banks with higher lev- 1. Total Loans Chi-sq(1) = 6.140 0.0132 els of core deposits or TCE ratio are less likely to have accessed 2. C&I Loans Chi-sq(1) = 4.410 0.0357 the long-term funding programs at any point in the sample period, 3. RRE Loans Chi-sq(1) = 3.067 0.0799 consistent with those banks with stable funding sources and strong capital positions declining participation in an unpopular program, or ending that participation earlier. Banks that had higher loss rates on delinquent loans were more likely to obtain TGS and re- exogenous variable. Looking first at the OLS results, the coefficient tain the funding through the end of the sample period. It is also on the government support variable (row 1) is not statistically sig- important to note that house prices are very significant and neg- nificant for total loans, suggesting that government funding did not ative in the first stage regression – banks that got TGS were in provide material support to growth in overall bank loans. areas that were hit worst by the house price collapse. This adds We also produced results for specific loan categories, two of credibility to the use of that variable as a demand indicator. Banks which are shown in the table. For residential real estate loans, that have more loan commitments are less likely to obtain fund- which were at the heart of the crisis, government support shows ing, which is somewhat difficult to explain unless banks that had a weakly significant, positive relationship with loan growth. The more unused commitments were better situated going into the cri- point estimate suggests that one percentage point of government sis. Banks with more managed liabilities also were less likely to support provided about ¾ percentage point of additional residen- use government support programs, even though such funding be- tial real estate loan growth at an annual rate, which amounts to came strained during the crisis. This result is also somewhat sur- about $[15] billion of additional loans held on banks’ books over prising, but consistent with the directive that such programs be the course of a year. However, for C&I loans, the support of which made available only to banks that were fundamentally sound and was a key factor in arguments in favor of providing government as- thus more likely to have retained access to those funding markets. sistance to banks, no relationship between prior government sup- Table 3b shows the results from the second stage where the port and lending growth is apparent. Results for other loan cat- growth rate of bank loans are regressed on the predicted values of egories were also statistically insignificant and are available upon the TGS variable along with the control variables. For comparison request. purposes, we also show the OLS estimation, which treats TGS as an Turning to other control variables, prior-period balance sheet strength is strongly correlated with loan growth. A 1-percentage-

W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16 13

Table 4a First stage regressions for large banks with alternative instrumental variable.

Total Loans C&I Loans RRE Loans

∗∗∗ ∗∗∗ ∗∗∗ 1. Other GS i (Instrument) 0.365 0.362 0.369 (0.0928) (0.0924) (0.0954) ∗ 2. Loan Growt h i,t−1 −0.0 0 0587 −0.00131 −7.25e −05 (0.00109) (0.0 0 0685) (0.0 0 0702)

3. (Managed Liabilitie s i.t−1 ) / (Assets ) i,t−1 −0.0201 −0.0193 −0.0220 (0.0170) (0.0174) (0.0175) ∗∗∗ ∗∗∗ ∗∗∗ 4. (Core Deposit s i,t−1 ) / (Assets ) i,t−1 −0.0268 −0.0270 −0.0269 (0.00922) (0.00925) (0.00910)

5. (Il l iquid Assets ) i,t−1 / (Assets ) i,t−1 0.0131 0.0199 0.00803 (0.0309) (0.0305) (0.0322)

6. (Delinquencies ) i,t−1 / (T ier 1 Common ) i,t−1 0.00121 0.0 0 0255 −0.0 0 0337 (0.00131) (0.00199) (0.00230)

7. Net loss rat e i,t−1 0.00708 0.00729 0.00782 (0.00688) (0.00674) (0.00714) ∗∗∗ ∗∗∗ ∗∗∗ 8. Employment Growt h i,t−1 −0.0824 −0.0801 −0.0820 (0.0220) (0.0221) (0.0220)

9. Housin g i,t−1 −0.0528 −0.0437 −0.0524 (0.0658) (0.0668) (0.0685)

10. TCE Ratio i,t-1 −0.0362 −0.0404 −0.0396 (0.0296) (0.0311) (0.0295)

11. REcommit Ratio i,t-1 −0.0217 −0.0192 −0.0260 (0.0581) (0.0593) (0.0612) ∗∗∗ ∗∗∗ ∗∗∗ 12. Other Commit Ratio i,t-1 −0.0897 −0.0877 −0.0957 (0.0257) (0.0283) (0.0296)

13. C&I Delinquency Rate i,t-1 0.00290 (0.00933)

14. RRE Delinquency Rate i,t-1 0.00418 (0.00901) 15. Bank dummies Yes Yes Yes 16. Quarterly time dummies Yes Yes Yes 17. F test of excluded instruments F(1,90) = 15.47 F(1,90) = 15.29 F(1,88) = 14.97 Prob > F = 0.002 Prob > F = 0.0 0 0 Prob > F = 0.0 0 0 18. Number of banks 91 91 89 19. Number of observations 1457 1447 1413

Robust standard errors are reported in parenthesis under coefficient estimates. ∗∗∗/ ∗∗/ ∗ indicates significance at 99/95/90 percent level of confidence respectively. FE Model, errors clustered around banks. point increase in the beginning of period TCE ratio is associated Although the coefficient is statistically significant at the 10 per- with about ¾-percentage-point higher annualized growth rate of cent level for total loans, the magnitude is called into question by bank loans, suggesting that banks with high book value of equity the imprecision of the estimate. Moreover, the coefficient estimates were more willing to absorb risk and generate more liquidity, as in each of the individual loan categories, while similarly large and in Berger et al., (2016 ). A higher ratio of core deposits or managed uniformly positive, are not significant at standard confidence levels. liabilities are associated with faster growth in bank loans. Mean- Combining these results with the OLS results, our findings suggest while, higher ratios of delinquencies to TCE (row 6) are negatively that an increase in government funding is only weakly associated related to bank loan growth as are category-specific delinquency with an increase in bank loans. The imprecision of the IV estimates ratios (rows 13 and 14), which is expected given the likelihood that is symptomatic of the instrument failing the test for weak instru- banks experiencing losses might reduce loan supply and the prob- ments, which reflects the collinearity of the instrument with the ability that creditworthy borrowers in distressed areas would de- bank fixed effect over the short time frame in which the instru- mand fewer loans. Unused commitments had a positive effect on ment only changes values on two occasions, with the swearing in bank loan growth, signifying that the extent of draws on commit- of a new Congress in 2009 and 2011. ted credit lines exceeded banks’ ability to offset that growth with Among the other control variables, the most important change tighter standards in spot loan markets. from the OLS to the IV approach occurs in the coefficient on the The Kleibergen-Paap rk LM statistic (row 17) suggests that we change in house prices in markets where the bank is active. This reject the null hypothesis that the equation is under-identified for coefficient on this variable remains positive but becomes larger the CUE estimation shown in the second column. This indicates and highly statistically significant, as banks operating in areas less that the excluded regressors are relevant and correlated with the affected by the housing bust should be expected to make more endogenous regressor; i.e., the Dseats variable is a relevant instru- loans. The result is consistent with our use of this variable to proxy ment. Nevertheless, the test statistic is not strong enough to pass for loan demand faced by banks. Likewise, the net loss rate on Stock-Yogo weak identification test, reinforcing our decision to use delinquent loans also becomes statistically significant in the total the CUE estimator in order to account for this potential bias. 17 loans equation, and exhibits negative correlation with loan growth, After instrumenting for TGS in the loan growth equation, the as expected given the mechanical reduction in outstanding loans magnitude and significance of the coefficients on a number of key when they are charged off as well as the likely reduction in loan variables change in important ways from the OLS results. Most im- supply and demand when conditions are such that large loan loss portantly, the coefficient estimate associated with TGS becomes rates are materializing. fairly large and positive when we address the endogeneity bias. Table 3c shows the results of a statistical test of the null hypothesis that bank’s allocations under the TGS programs was exogenous. The tests suggest that TGS is endogenous in all three loan

17 The critical value for 10 percent maximal LIML size is larger than 16.

14 W. Bassett et al. / Journal of Banking and Finance 0 0 0 (2017) 1–16

Table 4b Second Stage and OLS regression results for Large Banks.

Total Loans C&I Loans RRE Loans OLS CUE OLS CUE OLS CUE

1. ( TGS ) i ,t /( Assets ) i.t-1 0.440 0.768 −0.649 0.476 0.593 −4.012 (0.463) (1.628) (0.739) (2.993) (0.921) (4.505)

2. Loan Growt h i,t−1 0.0251 0.0247 0.0831 0.111 0.100 0.0992 (0.0508) (0.0496) (0.0805) (0.0821) (0.0922) (0.0915) ∗ ∗ 3. (Managed Liabilitie s i.t−1 ) / (Assets ) i,t−1 0.455 0.456 0.499 0.438 0.623 0.476 (0.265) (0.277) (0.346) (0.325) (0.537) (0.571) ∗∗ ∗ 4. (Core Deposit s i,t−1 ) / (Assets ) i,t−1 0.0962 0.0988 −0.157 −0.0965 0.494 0.376 (0.117) (0.126) (0.205) (0.223) (0.202) (0.201) ∗ ∗ 5. (Il l iquid Assets ) i,t−1 / (Assets ) i,t−1 0.621 0.579 1.028 0.713 0.334 0.148 (0.361) (0.367) (0.573) (0.524) (0.571) (0.616) ∗∗ ∗∗ 6. (Delinquencies ) i,t−1 / (T ier 1 Common ) i,t−1 −0.0733 −0.0817 −0.0476 −0.0629 −0.0559 −0.0419 (0.0280) (0.0350) (0.0384) (0.0559) (0.0367) (0.0644)

7. Net loss rat e i,t−1 0.154 0.158 0.276 0.259 0.495 0.540 (0.280) (0.274) (0.261) (0.254) (0.364) (0.384)

8. Employment change i,t−1 −0.636 −0.609 0.339 0.434 0.891 0.515 (0.434) (0.441) (0.494) (0.515) (0.791) (0.785) ∗ 9. House price change i,t−1 −0.111 −0.0972 2.452 2.922 1.839 1.451 (1.201) (1.236) (1.695) (1.694) (1.500) (1.496) ∗∗ ∗∗ 10. TCE Ratio i,t-1 −0.151 −0.182 −1.027 −1.024 −0.436 −0.657 (0.285) (0.298) (0.471) (0.486) (0.615) (0.717)

11. REcommit Ratio i,t-1 0.815 0.784 −0.517 −0.364 −0.208 −0.252 (0.593) (0.600) (0.835) (0.815) (1.237) (1.317)

12. Other Commit Ratio i,t-1 0.0737 0.0818 1.575 1.839 0.0119 −0.483 (0.384) (0.389) 0.499 (1.570) (0.668) (0.872) ∗ ∗∗ 13. C&I Delinquency Rate i,t-1 −0.249 −0.311 (0.144) (0.152)

14. RRE Delinquency Rate i, t-1 −0.0641 −0.152 (0.0461) (0.166) 15. Bank dummies Yes Yes Yes Yes Yes Yes 16. Quarterly time dummies Yes Yes Yes Yes Yes Yes 17. Underidentification test –15.47 (p-val. = 0.00) – 15.29 (p-val. = 0.00) –14.97 (p-val. = 0.00) (Kleibergen-Paap rk LM statistic) 18. Overidentification test of all – Just-identified – Just-identified – Just-identified instruments (Hansen J statistic) 19. Number of banks 96 91 96 91 94 89 20. Number of observations 1482 1457 1472 1447 1438 1413

Robust standard errors are reported in parenthesis under coefficient estimates. ∗∗∗/ ∗∗/ ∗ indicates significance at 99/95/90 percent level of confidence respectively. FE Model, errors clustered around banks. equations, justifying our treatment of TGS as an endogenous vari- port if similar banks obtain such funding, which likely reduces the able throughout the analysis. stigma associated with it. The F-test of excluded instruments is re- As a robustness check, we estimated the baseline regressions jected, indicating that Other GS is a valid instrument. The magni- for different bank sizes–those with asssets greater than/smaller tude of the coefficient is economically large, with a 1 percentage than $10 billion—and for different levels of the leverage ratio— point increase in TGS/assets at competitor banks associated with banks above/below the median leverage ratio. While the qualitative about a 1/3 percentage point increase in TGS at the target bank. results resemble the ones reported for the full sample, the statisti- The signs and the significance of other control variables are cal significance of the Dseats instrument evaporates and the coef- consistent with those reported in Table 3a . Core deposits is sig- ficients on TGS in the second stage regressions are even more im- nificant and negatively related to demand for government support precisely estimated. This is likely because the collinearity between among the large bank sample, as was the case in the overall sam- the instrument and the fixed effect is exacerbated by the smaller ple. Employment growth in states where the bank operates ap- sample sizes. In the interest of space, these results are not shown. pears to be an additional control variable that is highly significant Next, we check the performance of the alternative instru- for the large bank sample. Banks that experienced higher employment discussed above, government support received by compara- ment growth in the states in which they operated tended to get ble banks (Other GS). As described above, comparable banks were less government support. Stronger employment growth might help determined by size, funding, and asset allocation categories. While banks forestall financial troubles and hence have less need for gov- we estimated this instrument for all banks as well as different ernment support. bank sizes, we only show the results for the large bank cate- Table 4b shows the second stage and OLS results. These results gory, which is defined as banks with assets greater than $10 bil- are consistent with Table 3b as well, but do not elicit the same lion. Other GS is not a good instrument for small banks because degree of concern about multicollinearity. Furthermore, the rela- our stratification does not identify any pockets of banks that were tively sizable value of the Kleibergen-Paap rk LM Statistic (row 17) more or less likely to receive it. Because small banks dominate the allows us to reject both underidentification and weak instrument sample, that problem makes this instrument ineffective as an in- tests in this sample. The CUE results again show a positive coef- strument for all banks. ficient on TGS for total loans and for C&I loans, but the estimate Table 4a shows the results for first stage regressions for the is statistically insignificant, as was the case with the dseats instru- large bank category using this alternative instrumental variable. ment. Results for RRE loans show an insignificant negative coeffi- We note that Other GS is highly significant in the first stage regres- cient, which somewhat tempers the marginally significant positive sions for all three loan categories. The positive sign of the coeffi- result from the OLS regression in the full sample. cient suggests that banks are more likely to use government sup-

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5. Conclusion small banks, thrifts, and credit unions. Many CDFIs were CPP participants in good standing and were allowed to exchange CPP se- We have constructed a comprehensive dataset of banks’ partic- curities for CDCI securities, which had more favorable financing ipation in five different crisis-era programs designed to support fi- terms. Financing under the CPP, CDCI, and TIP programs totaled nancial stability and increase loan supply. We show that at various $245.5 billion. times banks used these programs as substitutes or complements, We obtained raw public CPP, CDCI, and TIP data from the U.S. so that results based on a study of the effect of one program in iso- Treasury and aggregated data from all three programs into a single lation may be incomplete or misleading. In addition, we show that data set. Using National Information Center (NIC) data and Trea- usage of the DGP was most skewed toward the largest banks, while sury’s raw data fields for participant name, city, and state, we iden- the CPP was used extensively by institutions of all sizes, with us- tified unique RSSD identifiers of the participants and their ultimate age shifting to midsize and smaller BHCs after the middle of 2009. top holders. In many cases, identification of correct RSSD numbers In an application of this dataset, we study the combined effect required a manual selection process. We created a time series of of participation in the CPP, the DGP, the SBLF, TAF, and TDW on each unique top holder’s holdings of the sum of CPP, CDCI and loan growth. This research identifies two valid instrumental vari- TIP securities, adjusted for transformative events such as mergers ables for the take-up of government support by banks during the and acquisitions. As of December 31, 2012, $234.4 billion had been financial crisis. The first is a variable based on the dominant po- repaid by participating entities and write-offs totaled $3.9 billion, litical affiliation in areas where the bank operates branches, an ap- with $7.2 billion outstanding. proach similar to those used by other authors in this literature, because take up of government support was stronger in areas where SBLF data description political opposition to the programs was more diffuse. The second, which is shown to be valid for banks with more than $10 The Small Business Lending Fund (SBLF) was established as a billion in total assets, is the use of government support by com- result of the Small Business Jobs Act of 2010, passed in December petitor banks, on the argument that idiosyncratic shocks to sup- 2010, to provide capital to certain community banks and commu- ply or demand would not affect competitors’ decisions to use gov- nity development loan funds (CDLFs). The fund was administered ernment support. We also expand the existing literature by apply- by the U.S. Treasury, which provides their raw SBLF data to the ing these instrumental variables to the more comprehensive mea- public. We excluded CDLF participants from our data. A total of sure of government support across the 5 programs, and by using a $3.9 billion was disbursed to 281 unique community banks. Using panel dataset spanning 2007: Q3 to 2012: Q4, rather than a cross- NIC data and Treasury’s raw data fields for participant name, city, sectional framework. and state, we identified unique RSSD identifiers of the participants Our findings suggest that banks that participated in at least one and their ultimate top holders. In many cases, identification of cor- of those programs had similar loan growth to banks that did not. rect RSSD numbers was a manual process. We created a time se- Although most of the estimated coefficients using the two differ- ries of each unique top holder’s holdings of SBLF debt, adjusted for ent instruments are positive and some are quite large, their sta- transformative events such as mergers and acquisitions. Treasury tistical significance is questionable or nonexistent. The results are had received $120 million in cumulative repayments as of Decem- qualitatively similar whether the dependent variable is total loans ber 31st, 2012, leaving $3.8 billion aggregate debt outstanding as or disaggregated loan categories, regardless of which instrumen- of that date. tal variable is used, or whether we split the sample by bank size. Those results are also consistent with OLS estimates using the one- quarter lag of TGS as a control variable, which showed a small DGP data description and marginally statistically significant correlation between TGS and loan growth in the following quarter. The key caveat to those re- The FDIC initiated the Temporary Liquidity Guarantee Program sults is that the programs almost surely had an additional benefit (TLGP) on October 14, 2008 in an effort to bring financial stabil- of reducing the number of bank failures, and that benefit cannot ity to the economy. This program consisted of two components: be quantified separately within this analytical framework. The Transaction Account Guarantee Program (TAGP) and the Debt Guarantee Program (DGP). Under the DGP, the FDIC fully guaran- Appendix teed some senior unsecured debt instruments issued by financial institutions for up to three years from the date of issuance. All el- CPP data description igible debt matured on or before December 31, 2012. Debt instruments were originated between October 14, 2008 and October 31, The Capital Purchase Program (CPP) was part of the Troubled 2009. Asset Relief Program (TARP), which became law in October 2008. The FDIC avails DGP data to the public. In addition, the FDIC Under the CPP, the U.S. Treasury provided capital ($204.9 billion) provided us with RSSD identifiers for many of the participants to certain financial institutions in exchange for preferred stock or upon a FOIA request in order to identity participating institutions. debt securities, beginning October 28th, 2008. This program was We manually matched remaining participating institutions to cor- designed to secure financial stability and maintain confidence in rect RSSD IDs by comparing entity names and locations in FDIC’s participating financial institutions among counterparties to such data with corresponding data managed by the NIC data. We also institutions. The final disbursement from the CPP facility originated used NIC data to determine ultimate top holders for each partici- on December 29th, 2009. Another TARP program, the Targeted pating entity. We created a time series of each unique top holder’s Investment Program (TIP), was established in December 2008 to debt secured by the FDIC under the DGP, adjusted for transfor- stabilize two firms considered systemically important: Citigroup mative events such as mergers and acquisitions. Short-term debt and Bank of America. Each firm received $20 billion in exchange instruments, those with maturities less than one year, accounted for preferred stock. Treasury created the Community Development for about 96% of the DGP-participating instruments and about 51% Capital Initiative (CDCI) in February 2010, which was also a compo- of the volume of those debt instruments. A total of $618 billion nent of TARP. This program was much smaller in size ($570 million of debt received an FDIC guaranty, but that figure double counts disbursed) than the CPP or TIP and it provided capital specifically individual issuance amounts each time an entity rolled over its to Community Development Financial Institutions (CDFIs), such as FDIC-guaranteed debt upon maturity. The sum of each unique top

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Please cite this article as: W. Bassett et al., Government support of banks and bank lending, Journal of Banking and Finance (2017), http://dx.doi.org/10.1016/j.jbankﬁn.2017.07.010