Liquidity creation and funding ability during the interbank lending crunch

International Review of Financial Analysis xxx (xxxx) xxxx

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International Review of Financial Analysis journal homepage: www.elsevier.com/locate/irfa

Liquidity creation and funding ability during the interbank lending crunch Hamid Beladia, , May Hub, Jason Parkc, Janice Howd ⁎

a

The University of Texas at San Antonio, USA School of Economics and Finance, RMIT University, Australia School of Economics and Finance, Monash University, Australia d School of Economics and Finance, Queensland University of Technology, Australia b c

ARTICLE INFO

ABSTRACT

JEL classifications: G01 G21 G28

This study examines the portfolio response of US banks to the interbank lending collapse during the global financial crisis. The paper documents that a bank's response to the collapse of interbank markets is related to whether or not the bank was a net borrower or lender of funds. In particular, we find that typical borrowers had lower loan growth than typical lenders, but that the crisis did not differentially affect borrowers and lenders with respect to loan growth. However, borrowing and lending banks were differentially affected by the crisis in terms of their liquid asset growth. The typical borrowers reduced their liquid asset growth relative to lending banks during the crisis. We interpret this finding as saying that borrowing banks had to reduce their risky asset holdings because access to interbank funds had been reduced. The paper presents analogous analyses of the possible differential response of borrowers and lenders to changes in counterparty risk and lending through the Fed's TAF facility.

Keywords: Counterparty risk Interbank market Lending Liquidity Term auction facility

1. Introduction A key and unique function of banks is liquidity creation. Banks issue short-term deposits and stand ready to provide liquidity on demand to depositors while transforming them into loans to businesses and households to fund long-term illiquid assets. However, this transformation process inevitably leads to a fragile capital structure with maturities mismatched between the asset and liability sides, exposing banks to liquidity risk, i.e., the risk of loss resulting from inability to meet unexpected liquidity needs (Diamond & Dybvig, 1983; Diamond & Rajan, 2001a, 2001b; Lin, Chen, Lv, Zhou, & Jin, 2019; Rozite, Bezemer, & Jacobs, 2019). Through the interbank market, banks can coinsure against idiosyncratic liquidity risk by reallocating funds from those with an excess to others with a deficit (Allen, Carletti, & Gale, 2009; Castiglionesi, Feriozzi, Lóránth, & Pelizzon, 2014). However, in the absence of a well-functioning interbank market, idiosyncratic liquidity risk may be hard to coinsure against (Castiglionesi et al., 2014), leading to credit rationing, liquidity hoarding for self-insurance, and higher funding costs.

In this paper, we study the impact of the disruption of the interbank market on banks' liquidity creation and funding ability. In 2008, shortly after the collapse of Lehman Brothers, an increasing number of financial institutions declared insolvency and were acquired by other institutions.1 This created an indiscriminate distrust of counterparties to any financial transactions. As a result, a large number of financial institutions found it increasingly difficult to access interbank funding and manage their liquidity risk.2 The disruption of the interbank market bears on banks the following three testable predictions. First, faced with a strained interbank market, liquidity-poor banks will ration lending and hoard liquidity for selfinsurance. This prediction resonates with Iyer, Peydró, da-Rocha-Lopez, and Schoar (2013). Using loan-level data and controlling for loan demand effects, they find that the freeze of the interbank market caused banks with heavier reliance on interbank borrowing to cut lending and hoard liquidity. Second, liquidity-poor banks will try to attract external funding by offering higher rates. If the interbank market was fully functional as a channel for efficient allocation of funds, banks in need of liquidity

Corresponding author. E-mail address: [email protected] (H. Beladi). 1 The most remarkable examples include Merrill Lynch acquired by Bank of America; Washington Mutual by JP Morgan Chase; Wachovia by Wells Fargo; and Sovereign Bank and National City Bank by PNC Financial Services in 2008. 2 Afonso et al. (2011) show that the number of lenders in the federal funds market fell from approximately 250–300 in the summer of 2008 to 225 after Lehman Brothers' default, and the fed funds rate in 2008. experienced a one-day jump by > 60 basis points on September 15, 2008, the date on which Lehman Brothers filed for Chapter 11 bankruptcy. ⁎

https://doi.org/10.1016/j.irfa.2019.101433 Received 10 June 2019; Received in revised form 26 November 2019; Accepted 26 November 2019 1057-5219/ © 2019 Published by Elsevier Inc.

Please cite this article as: Hamid Beladi, et al., International Review of Financial Analysis, https://doi.org/10.1016/j.irfa.2019.101433

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would not have to seek costly funding outside the interbank market. A similar manifestation of high costs of self-insurance in funding rates can be found in Acharya and Mora (2014).3 Third, liquidity-rich banks will ration lending and hoard liquidity as well due to increased counterparty risk.4 Freixas and Jorge (2008) show counterparty risk can lead liquidity-rich banks to ration lending to liquidity-poor banks, which in turn cut back on their lending. Heider, Hoerova, and Holthausen (2010) show counterparty risk causes liquidity hoarding by liquidity-rich banks and can lead to a breakdown of the interbank market.5 Afonso, Kovner, and Schoar (2011) find that counterparty risk played a major role in interbank lending disruptions during the banking crisis of 2008. Similarly, Paolo, Nobili, and Picillo (2011) find that during the crisis, interbank rates became more sensitive to borrowers' creditworthiness. An array of unconventional government interventions yields two further predictions. First, liquidity-poor banks will charge higher rates on loans. In the wake of the collapse of Lehman Brothers in September 2008, the Federal Reserve (herein, the Fed) started pouring unprecedentedly large amounts of liquidity into the banking system. The size of the Fed's balance sheet grew rapidly from below $1 trillion in August 2008 to $2 trillion in October of the same year and, by the end of 2012, reached $3 trillion. This high level of liquidity created by the Fed effectively pushed down funding costs for banks, and at the same time placed downward pressure on the policy rate. To effectively control the policy rate, the Fed started paying interests on reserves at a near-market rate. However, possibly as an unintended consequence, this interest-on-reserve policy disincentivized bank lending (Ennis & Wolman, 2012),6 and such lending rationing could accompany higher rates on loans (Arnold & Riley, 2009). The second empirical prediction concerns the Fed's Term Auction Facility (TAF). Introduced in December 2007, TAF was a new approach taken by the Fed to address concerns of stigma attached to the discount window (Armantier, Ghysels, Sarkar, & Shrader, 2014). TAF delivered term funds through auctions to banks that were in need. It expanded immediately following Lehman Brothers' default. Previous studies on the effectiveness of TAF in mitigating liquidity problems in the interbank market offer mixed results (McAndrews, Sarkar, & Wang, 2008; Taylor & Williams, 2009), motivating us to re-examine the effectiveness of TAF. If TAF had effectively helped restore the interbank market and alleviated self-insurance motives for liquidity hoarding, recipient banks, especially net borrowers, would have unlocked their liquid asset holdings, increased lending, and not have needed to offer high rates for external funding. Using a large dataset of all U.S. banks over the period from 2003 to

2013, we find results largely in support of our predictions. First, during the interbank lending crunch, we find higher lending rates and lower loan growth for net interbank borrowers than for net interbank lenders. Second, we find evidence that net borrowers hoarded liquidity to insure against anticipated liquidity shortfalls. Third, counterparty risk prompted net lenders to hoard liquidity. Fourth, while there is no significant difference in deposit rates between net borrowers and net lenders, net borrowers offered higher rates for external financing. Lastly, while TAF helped net borrowers shed liquid asset holdings and lowered external funding costs, as expected, lending rates and nontransaction deposit rates were still higher for TAF net borrowers than for TAF net lenders. Assuming high lending rates are a sign of lending rationing (Arnold & Riley, 2009) and banks raised rates to attract (noncore) deposits (Acharya & Mora, 2014), our finding suggests that TAF managed to mitigate self-insurance motives for liquidity hoarding but did not effectively remove lending rationing and the funding pressure. An important policy implication that we can draw from our findings is that the central bank has to focus more on the efficient allocation of funds in the interbank market. In the absence of a well-functioning interbank market, banks will always hold high levels of liquidity—net lenders will hoard due to information asymmetry and net borrowers will hoard for self-insurance. This dysfunctional behavior would render liquidity injection policy ineffective in encouraging bank lending. Moreover, the Fed's practice of paying a near-market rate on reserves, especially in the face of excess supply of reserves, will induce banks to hold liquidity and curb lending, further contributing to the ineffectiveness of liquidity injection policy. However, we do not refute liquidity injection policy in general. Whenever liquidity injection is deemed inevitable, we suggest that the central bank should subsidize interbank lending. For example, if the central bank adds a faction of a percentage to the rates at which banks lend to each other, net lenders are more likely to unlock their liquidity holdings and efficient allocation of funds in the interbank market can be achieved. However, implementing this policy requires continuous revision and adjustment of the subsidy rate by the central bank. As such, the subsidy rate has to widen as the interbank market deviates from efficient allocation and fall to zero when efficient allocation is reached. Unlike the interest-on-reserve policy, the interbank lending subsidy is unlikely to decrease banks' incentives to lend regardless of whether the interbank market is close to or distant from full efficiency. When distant from full efficiency, the interbank lending subsidy induces liquidity-rich banks to lend to liquidity-poor banks thereby easing rationed lending. When close to full efficiency, the subsidy rate is zero and therefore will not disincentivize bank lending. Further, as interbank lending subsidy encourages more lending activities in the interbank market, it will keep the policy rate from falling too low. Our main contribution is first to challenge the theory and practice of bank bailouts and market interventions amidst banking crises like the one we observed in 2007–2008. Our policy recommendation for severe banking crises at the turn of the last decade is to reduce counterparty risk in the interbank market. Second, our paper complements Keister and McAndrews (2009)'s theory on the reserves. Further to their theory, we should how the reserves were distributed among individual banks and why they were hoarded rather than lent out, leading to a lending freeze. We also suggest how the liquidity supply could better have been executed in this paper. The rest of the paper is organized as follows. Sections 2 and 3 discuss the research methodology and data respectively. Our results are presented in Section 4, followed by a discussion on policy design in Section 5. A brief summary and concluding remarks are delivered in Section 6.

3 While Acharya and Mora's (2014) paper does not concern the interbank market, they find that banks under greater pressure of loan drawdowns attracted deposits by increasing their rates. However, despite the scrambling for deposits by raising rates, banks with greater exposure to drawdown demand failed to cover liquidity shortfalls and had to cut lending. 4 As noted by Keynes (1936), if the return at maturity is uncertain, the only way to resolve fear over the creditworthiness of counterparty is to hold cash rather than lending it at interest. This so-called absolute liquidity preference occurs even at a high level of interest rates because no interest rate is high enough to offset the fear. 5 Heider et al. (2010) also argue that counterparty risk accounts for the prolonged nature of interbank market tensions despite unprecedented interventions by the monetary authorities during 2007–2009. 6 It is equally possible that banks piled up liquidity instead of lending because of lacking lending opportunities (or lacking loan demand). However, Ennis and Wolman (2012) find that the effect of a lack of lending opportunities is only marginal for 100 largest banks and nearly non-existent overall. Using a general banking economy model, Martin, McAndrews, and Skeie (2013) show banks would expand their lending portfolios as long as the marginal opportunity cost, i.e., the marginal return on reserves, is less than the marginal return on bank lending.

2. Methodology Our first prediction is that net borrowers curbed lending during the 2

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where Interbank Lending Crunch takes the value of one for the period from the 3rd quarter of 2008, in which Lehman Brothers bankrupted and the interbank market started shrinking, onwards and zero otherwise. Net Lenders takes the value of one when Net Interbank Loans exceeds the 70th percentile value in the previous quarter and zero otherwise; Net Borrowers takes the value of one when Net Interbank Loans is less than the 30th percentile value in the previous quarter and zero otherwise. Therefore, because no bank can be a net borrower and a net lender at the same time for any one quarter, (Net Borrowers − Net Lenders) takes the value of minus one for net borrowers, one for net lenders, and zero for all other cases. It captures the marginal difference in Y between net borrowers and net lenders. Tt (t = 1, …, T) is a set of quarter dummies which control for unobserved time-specific effects at the macro level; and fi (i = 1, …, N) is a set of bank fixed effects which control for time-invariant unobserved bank-specific heterogeneity.8 We define Net interbank loans as the sum of loans to depository institutions, federal funds sold, and securities purchased under agreements to resell, less the sum of federal funds purchased and securities sold under agreements to repurchase, scaled by Total Assets. We also include a set of control variables Xj (j = 1, …, J) to account for other bank-specific characteristics. We employ CAEL,9 Loan Loss Allowance,10 Non-performing Loans, and Charge-offs as proxies for banks' financial conditions; the logarithm of Total Assets as a proxy for bank size11; the senior loan officer opinion survey (SLOOS) and Federal Funds

Rate as proxies for aggregate loan demand12,13 the interaction between SLOOS and CAEL, and the interaction between SLOOS and Large Bank Dummy to control for firm-specific heterogeneity in loan demand14; Unused Commitments and Market-to-book Value of Security Holdings as proxies for self-insurance motives; and the spread between the 3-month London interbank offer rate (LIBOR) and the overnight index swap (OIS) rate as our proxy for counterparty risk. We estimate the same model for Liquid Asset Growth. If net borrowers curbed lending because of failure to borrow from the interbank market, they would have to run down their liquid asset holdings. Similarly, if net lenders held liquidity surpluses because they were not willing to lend to other banks, they would have plenty of liquidity left even after accommodating all loan demand. We also test whether lending rates were higher for net borrowers. According to Arnold and Riley (2009), lending rationing by net borrowers could occur at a lower rate, accompanied by a higher rate at which the loan market clears. To test this proposition, we estimate Eq. (1) for Total Interest Income Rate, computed as total interest income divided by the quarterly average balance of total interest-earning assets. We include Transaction Deposit Rate, Non-transaction Deposit Rate, and Total Interest Expense Rate as additional controls because banks could pass on any increase in funding costs to borrowers, which cannot be attributed to lending rationing. Further, to draw meaningful conclusions on lending rates, we add Treasury Rate, Mortgage Rate, and Aaa Corporate Bond Rate as controls for returns on security holdings. Transaction Deposit Rate is interest expenses paid on transaction deposits divided by the quarterly average balance of transaction deposits; Non-transaction Deposit Rate is interest expenses paid on nontransaction deposits divided by the quarterly average balance of nontransaction deposits; Total Interest Expense Rate is total interest expenses divided by the quarterly average balance of total interestbearing liabilities. Our second prediction poses that net borrowers amassed liquidity to insure against potential liquidity shortfalls. In testing this proposition, we make a notable distinction between liquidity leftovers and liquidity hoarding.15 To illustrate the difference, we assume two hypothetical

7 All our growth variables (i.e., Loan Growth, Liquid Asset Growth, and Deposit Growth) are scaled by the beginning-of-quarter values of Total Assets. 8 Our choice of the fixed-effects model is justified for the following reasons. First, in a pooled GLS estimation with panel-corrected standard errors, we have found generally similar patterns of results. However, since the standard error correction is mainly to account for unobservable (or latent) heteroscedasticity in the standard errors in testing for statistical significance, we have deemed it more adequate to control for cross-sectional heteroscedasticity in the regression equation directly. Second, as our panel data includes the entire population of the U.S. banks during the given time period of 2003–2013, we have opted for the fixed effect model over the random-effect model. Third, the Arellano-Bond method could be an alternative, but since some of our control variables could be not completely free from serial correlation, such as those expressed in percentage of total assets and interest rates, the deeper lags of the dependent variables are to be used as instruments. This, unfortunately, would limit our sample size substantially and, given that some of the time dummies are turned on only for one quarter, it is simply not feasible. We appreciate the reviewer's insightful comments on these technical issues. 9 CAEL represents capital adequacy (C), asset quality (A), earnings (E), and liquidity (L), and was developed by the FDIC as an off-site monitoring system in mid 1980s. See Collier et al. (2003) for further details. In our sample, CAEL is most closely correlated with the ratio of equity capital to total assets and the ratio of liquid assets to total assets. We estimate CAEL using call report data. 10 Loan loss allowance is the sum of all estimated (unrealized) credit losses, which is a contra-asset account that reduces the book value of loans to the amount deemed collectible. Loan loss allowance increases with loan loss provision and recoveries, and decreases with charge-offs. Therefore, failing to control for loan loss allowance and charge-offs could lead to wrong conclusions. 11 Afonso, Santos, and Traina (2014) show large banks engage in riskier lending activities and produce a larger volume of impaired loans due to implicit government guarantees (i.e., too-big-to-fail subsidy).

12 A decrease in loans and an increase in liquid asset holdings do not necessarily imply that banks cut lending and hoard liquidity instead unless the effect of loan demand is controlled for. SLOOS data are available from the Fed (http://www.federalreserve.gov/boarddocs/snloansurvey/). We select the data on commercial and industrial loan demand. 13 Bernanke and Gertler (1995) consider direct and indirect effects of the central bank's interest rate policy. The central bank controls the short-term interest rate to directly influence the cost of borrowing and spending by households and corporations. Indirectly, interest rate changes affect loan demand. For example, a rise in the interest rate increases the cost of external financing more for borrowers with weak financial standings and thus restricts their demand for credit relative to those with strong financial conditions. 14 Noting potential differences in loan demand across banks conditional on size, leverage, and affiliation with bank holding companies, Ashcraft (2006) interacts various bank characteristics with macroeconomic variables, such as aggregate output growth, as proxies for loan demand. In the same spirit, we interact our loan demand proxy, SLOOS, with two main bank characteristics, CAEL and Large Bank Dummy. Large Bank Dummy takes the value of one for observations with total assets greater than its 70th percentile value in the previous quarter. We argue that SLOOS is a better proxy for loan demand than macroeconomic variables as it reflects direct observation by loan officers at their desks. 15 For simplicity, let us assume a bank with liquid assets and loans on the debit side of the balance sheet, financed by deposits and equity capital only. Holding constant equity capital, growth in liquid assets (∆Q) should equal the sum of changes in deposits (∆D) and loans (∆L). If ∆D > ∆L, the residual deposit inflow will transform into liquid assets. If ∆D < ∆L, the bank will have to run down its liquid asset holdings. In this sense, Eq. (1) estimated for Liquid Asset Growth is meant to test residual liquidity because it examines the change in liquid assets after funding and lending decisions are made.

interbank lending crunch, when the interbank borrowing channel was broken. To test this prediction, we specify the following regression model for Loan Growth7:

Loan Growthit =

0

+

1 Interbank

(Net Borrowers

Lending Cruncht + Net Lenders )it +

{(Net Borrowers J j=1

j Xjit

+

2

3

Net Lenders )it × Interbank Lending Cruncht } + T t=1

t Tt

+ fi +

it

(1)

3

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H. Beladi, et al.

banks A and B with identical characteristics, except that bank A has precautionary needs (i.e., expected liquidity shortfalls) but bank B does not. Irrespective of whether or not the interbank market is performing efficiently, bank B has little incentives to hoard. On the other hand, to cover liquidity shortfalls, bank A borrows from the interbank market, but if that channel is disrupted, bank A would have to attract additional deposits. To capture such self-insurance motives of liquidity hoarding, we match Liquid Asset Growth with Deposit Growth, and specify the following model:

Liquid Asset Growthit =

0

+

1 Interbank

Lending Cruncht +

Deposit Growthit + 6 {Deposit

J j=1

2 Liquidity

5 {Deposit

+

Net Borrowers )it +

LIBOR–OIS Spreadt +

2

3

Net Borrowers )it } +

4

5 {LIBOR

–OIS Spreadt × Interbank Lending Cruncht } + {LIBOR–OIS Spreadt × (Net Lenders

6

Net Borrowers )it } +

7

{LIBOR

Shortageit +

× (Net Lenders

3

Tt + fi +

4

Growthit × Interbank Lending Cruncht }

Growthit × Liquidity Shortageit } +

j Xjit

Lending Cruncht +

{Interbank Lending Cruncht × (Net Lenders

T t=1

t Tt

+ fi +

it

Net Borrowers )it } +

J j =1

j Xjit

+

T t=1

t

(3)

it ,

where LIBOR-OIS Spread is the spread between the 3-month LIBOR and OIS rate, and serves as our proxy for counterparty risk. The remaining variables are as specified above. Fourth, to test whether net borrowers attempted to attract external financing by raising their rates, we estimate Eq. (1) for Transaction Deposit Rate, Non-transaction Deposit Rate, and (Non-transaction − Transaction) Deposit Rate (the rate differential between nontransaction and transaction deposits), and Total Interest Expense Rate respectively. In estimating Eq. (1) for Total Interest Expense Rate, Transaction Deposit Rate and Non-transaction Deposit Rate are included as additional controls to allow us to draw a conclusion on the costs of external funding. These external funding sources include uninsured brokered deposits, deposits in foreign offices, other borrowed money, subordinate debt, and debentures. Lastly, we examine the impacts of TAF on bank lending and funding costs using the two-step Heckman procedure to address potential inference biases arising from non-random sample selection. Such biases may emerge from the possibility that the distribution of TAF money was not random; banks that needed TAF the most were not necessarily the ones that were granted money. This is because TAF money was granted through auctions and banks had to first apply to be considered for funding. Duchin and Sosyura (2012) note the political rationale behind auctions under the Troubled Asset Relief Program (TARP), and find politically connected firms were more likely to have their funding applications approved. In the first-stage, we estimate the probit model of TAF selection:

7

{Deposit Growthit × Interbank Lending Cruncht × Liquidity Shortageit } +

1 Interbank

–OIS Spreadt × Interbank Lending Cruncht

{Interbank Lending Cruncht × Liquidity Shortageit } + +

+

(Net Lenders

Liquid Asset Growthit =

0

(2)

where Liquidity Shortage is our liquidity shortage indicator, for which we employ UC90 and MV < AC. UC90 takes the value of one for observations where Unused Commitments is greater than its 90th percentile value in the previous quarter and zero otherwise. MV < AC takes the value of one for observations with negative Market-to-book Value of Security Holdings in the previous quarter and zero otherwise. In estimating Eq. (2), when UC90 is used as a liquidity shortage proxy, we leave the unused commitment ratio out of the set of controls. Similarly, when MV < AC is used, we leave out the percentage deviation between the market and book values of security holdings. We estimate Eq. (2) separately for net lenders and net borrowers. Our choice of unused commitments and value depreciation in security holdings as proxies for expected liquidity shortage is well justified in the literature. Loan commitments are off-balance sheet bilateral contracts, and are issued by banks for fees. When the funding market toughens, the holders of loan commitments can borrow up to a certain limit at a certain rate as specified in the bilateral contracts. While previous studies contend that banks exposed more to drawdowns serve as a liquidity backstop (Gatev, Schuermann, & Strahan, 2009; Gatev & Strahan, 2006; Kashyap, Rajan, & Stein, 2002), Acharya and Mora (2014) find in the context of the recent financial crisis that more exposed banks received less deposit inflows and consequently scrambled to attract deposits by offering higher rates. Security holdings are used as collateral for secured funding. Defining the percentage deviation of the market value of security holdings from the book value as haircuts (or margins), Brunnermeier (2009) argues that haircuts capture funding risk.16 In analogy to traditional banking, Gorton and Metrick (2012) show that rising haircuts in the secured funding market are tantamount to deposit withdrawals, forcing sales of securities on a large scale, which in turn further increases haircuts and exacerbates funding problems. To test our third prediction that net lenders piled up liquidity due to counterparty risk, we estimate the following regression model for Liquid Asset Growth:

Prob (TAF Dummyi = 1 | Xit , Zit ) =

(

0

+

J j=1

j Xjit

+

K k=1

J + k Zkit

)

(4) where Φ denotes the standard normal cumulative probability function; Xj (j = 1, …, J) is a set of control variables; and Zk (k = 1, …, K) is a set of instrumental variables. We employ the same set of instrumental variables as in Li (2013) for TARP.17 Our first instrumental variable (Local FIRE Donation) is the campaign contribution from local finance, insurance, and real estate industries as a percentage of total contribution received by a local Representative in the 2007–2008 election cycle.18 A larger donation implies a Representative relied more on the local FIRE's support in the campaign and was obliged to represent the FIRE industries' interests.19 Our second variable (Subcommittee on FICC) 17

TARP funds were distributed through the Treasury Department while TAF funds were distributed by the Fed through auctions. We are grateful to Lei Li for providing this data. 18 Since geographic ties with politicians are valuable to firms, local financial institutions could ask their Representatives for help when they wanted the federal government aid. 19 “Companies that received bailout money giving generously to candidates”, Washington Post, 24 October 2010 (See https://www.washingtonpost.com/wpdyn/content/article/2010/10/24/AR2010102401561.html?hpid=top %20news)

16 Brunnermeier (2009) specifies a margin spiral whereby funding problems force leveraged investors to unwind their positions increasing margins further, which in turn feeds into the funding problems.

4

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H. Beladi, et al.

takes the value of one if a Representative sat on the subcommittee on financial institutions and consumer credit, which supervises all federal banking regulators, and zero otherwise. A Representative that sat on this subcommittee is likely to be more successful in pushing federal banking regulators and the Treasury. Our third variable (Democrat) takes the value of one if a Representative was a member of the Democratic Party and zero otherwise. Republicans' free-market ideology does not agree with government bailouts of private firms since it is synonymous with a big government. Since TAF applications must be approved by the Fed, our final instrumental variable (Fed Director) takes the value of one if an executive of the bank served as a director of a branch of the Fed and zero otherwise. Banks that were connected to the Fed are expected to have been treated more favorably when applying for TAF funds. Using the estimated coefficients from (4), we obtain the inverse Mills ratio (λi) which is expressed as follows:

( (

=

i

i i

provided by the Federal Deposit Insurance Corporation (FDIC) (http:// www.fdic.gov/bank/individual/failed/banklist.html), we identify the acquiring institutions and remove them from the quarters in which acquisitions took place.21 Our final data set consists of an unbalanced panel of 282,435 observations for 9879 banks from the first quarter of 2003 to the fourth quarter of 2013. TAF Transaction data are obtained from the Fed (http://www. federalreserve.gov/newsevents/reform_taf.htm). The LIBOR and OIS rate are obtained from Bloomberg Data Service, and the SLOOS data are available from the Fed website (http://www.federalreserve.gov/ boarddocs/snloansurvey/). Federal Funds Rate, the 10-year Treasury Rate, the 30-year conventional Mortgage Rate, and Moody's Aaa Corporate Bond Rate are sourced from the Fed's statistical release H.15 (http://www.federalreserve.gov/releases/h15/). We provide definitions and sources of all variables in Appendix A. 4. Empirical results

) )

4.1. Descriptive statistics

where φ and Φ respectively denote the standard normal probability density function and the standard normal cumulative density function conditional on all independent variables ωi (=1, Xi1, …, XiJ, Zi1, …, ZiK) as specified in Eq. (4). The inverse Mills ratio is then included along with other explanatory variables in the second-stage regression:

Table 1 contains the summary statistics of the variables used in our empirical analyses. Panel A describes the dependent variables. The median value of Liquid Asset Growth is 0.13%, ranging from −4.7% at the 5th percentile to 6.71% at the 95th percentile. Loan Growth has a median value of 0.68%, ranging from −3.77% to 7.39%. Total Interest Income Rate varies from 1.18% to 6.45%, with a standard deviation of 1.79%. Non-transaction deposits are a more expensive source of funding than transaction deposits, as indicated by their higher mean and median rates. Panel B shows the main test variable of our interest, Net Interbank Loans, has the highest variation among all the bank-level independent variables, with a mean (median) value of 1.79% (0.61%), and ranges from −6.56% at the 5th percentile to 12.55% at the 95th percentile. Deposit Growth is also highly volatile during our sample period, with a mean (median) value of 1.39% (0.89%), and ranges between −5.04% and 9.73%. Total Assets for the sample ranges from $21 million at the 5th percentile to $1.44 billion at the 95th percentile. On average, 3.5% of our sample banks were aided by TAF. The average (median) amount of TAF funds granted was 26.54% (5.59%) of Total Assets.22 CAEL has a mean (median) value of 2.84 (2.82), ranging from 1.91 to 3.82. The average (median) bank in our sample has total assets of $1.39 billion ($125.8 million). Our liquidity shortage measures, Unused Commitments and Market-to-book Value of Security Holdings, range from 0% to 18.65% and from −3.69% to 2.59%, respectively. Our control variables for bank risk (Loan Loss Allowance, Non-performing Loans, and Charge-offs) are highly skewed to the right, suggesting a large proportion of banks in our sample period were in deep trouble. Panel C displays the summary statistics of our macro-level variables. SLOOS, our loan demand proxy, represents the percentage of senior loan officers reporting stronger demand for loans. SLOOS is negatively skewed and ranges from −60.4% at the 5th percentile (2009Q1&2) to 37% at the 95th percentile (2005Q2). This suggests weaker aggregate loan demand during the interbank lending crunch. Fed Funds Rate ranges between 0.09% at the 5th percentile (2006Q3) and 5.25% at the 95th percentile (2011Q2). LIBOR-OIS Spread ranges from 0.07% at the

Yit =

0

+

1 TAF

Dummyi +

2 Post 2007Q 4t

{TAF Dummyi × Post 2007Q 4t } + (Net Borrowers

Net Lenders )it +

+

3

4 5

{(Net Borrowers

Net Lenders )it × TAF Dummyi } +

{(Net Borrowers

Net Lenders )it × Post 2007Q 4t } +

{(Net Borrowers

Net Lenders )it × TAF Dummyi × Post 2007Q 4t }

+

8 i

+

J j=1

j Xjit

+

it .

6 7

(5)

In Eq. (5), the dependent variable (Y) is either Loan Growth, Liquid Asset Growth, Total Interest Income Rate, Transaction Deposit Rate, Non-transaction Deposit Rate, (Non-transaction − Transaction) Deposit Rate, or Total Interest Expense Rate. TAF Dummy takes the value of one if the bank received TAF funds at least once and zero otherwise; and Post2007Q4 takes the value of one after the 4th quarter of 2007 (inclusive) in which the first TAF auction took place and zero thereafter. The use of Post2007Q4 is to avoid the need to determine the precise timing of TAF fund distribution for individual banks, and assesses whether there are statistical significant differences across TAF recipients between the two sub-periods. The effect of TAF on the recipients is measured by the interaction between TAF Dummy and Post2007Q4 (Belke, Gros, & Osowski, 2017). The same control variables as for Eq. (1) are used in all regressions. Our theoretical underpinnings come from the theories of bank capital and liquidity (Acharya & Mora, 2014; Allen et al., 2009; Castiglionesi et al., 2014; Diamond & Rajan, 2001a, 2001b; Freixas & Jorge, 2008; MacKinnon & White, 1985; White, 1980). 3. Data

(footnote continued) by banks over the study period. The 99th percentile value of quarterly liquidity growth is 16.47% whereas the aggregate growth of banks' reserve balances with the Fed in the third and fourth quarters of 2008 is 37.8% and 87.59%, respectively. 21 For example, JP Morgan Chase purchased Washington Mutual Bank in the third quarter of 2008. We thus remove JP Morgan Chase from our sample in that quarter. 22 Instead of dollar amounts, we scale TAF money distributed by Total Assets to control for the possibility that larger amounts were given to larger banks.

Our sample consists of quarterly data on all U.S. banks constructed from the Reports of Income and Condition (the Call Reports) for the period 2003 to 2013. We remove all observations that lie outside the 99th percentile of the distribution of quarterly growth variables, except for liquid asset growth.20 By matching with the list of failed banks 20

We make an exception for liquidity growth because of liquidity hoarding 5

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Table 1 Descriptive statistics. Mean Panel A: dependent variables Liquid assets growth (%) Loan growth (%) Total Interest income rate (%) Transaction deposit rate (%) Non-transaction deposit rate (%) Total Interest expense rate (%)

Median

SD

p(5)

p(95)

0.480 1.084 3.461

0.131 0.681 3.255

4.023 3.847 1.792

−4.700 −3.767 1.176

6.714 7.393 6.454

0.246

0.128

0.726

0.009

0.805

1.441

1.217

0.973

0.303

3.327

1.199

1.005

0.816

0.243

2.813

7.093 4.955 26,417.83

−6.555 −5.039 21.06

12.551 9.732 1441.21

0.184 136.649 0.572 7.908 4.525

0.000 0.739 1.909 0.000 −3.694

0.000 51.766 3.818 18.650 2.589

1.294 3.551 0.616

0.680 0.010 0.000

3.197 8.657 0.918

30.345 −60.400 1.846 0.093 0.351 0.070 1.030 1.780 1.000 3.570 0.845 3.650

37.000 5.251 0.942 5.150 7.010 6.810

Panel B: bank-level covariates Net interbank loans (%) 1.794 0.605 Deposit growth (%) 1.399 0.896 Total assets (in $ 1390.87 125.83 millions) TAF dummy 0.035 0.000 TAF money (%) 26.540 5.595 CAEL 2.844 2.818 Unused commitments (%) 6.160 4.066 Market-to-book value of −0.433 −0.374 security holdings (%) Loan loss allowance (%) 1.569 1.315 Non-performing loans (%) 2.633 1.564 Charge-offs (%) 0.213 0.052 Panel C: macro-level covariates SLOOS (%) −9.917 −7.000 Fed funds rate (%) 1.981 1.444 LIBOR-OIS spread (%) 0.274 0.149 Treasury rate (%) 3.751 3.860 Mortgage rate (%) 5.551 5.770 Aaa corporate bond rate 5.366 5.460 (%)

Table 2 Univariate tests of mean and median differences between net lenders and net borrowers.

Panel A: dependent variables Liquid assets growth (%) Loan growth (%) Total interest income rate (%) Transaction deposit rate (%) Non-transaction deposit rate (%) Total interest expense rate (%) Panel B: bank-level covariates Net interbank loans Deposit growth (%) Total assets (in $ millions) TAF dummy TAF Money (%) CAEL Unused commitments (%) Market-to-book value of security holdings (%) Loan loss allowance (%)

Note: This table reports the summary statistics of our dependent variables (Panel A), bank-level covariates (Panel B), and macro-level covariates (Panel C). All data are quarterly from 2003 to 2013. Our bank-level data are a panel of all U.S. banks, constructed from the Reports of Income and Conditions (the Call Reports). All acquiring banks are removed from the quarters in which the acquisition occurred. To reduce the adverse impact of outliers, observations outside the 99th percentile values of quarterly growth variables are removed (except liquid asset growth). See Appendix A for detailed variable definitions.

Non-performing loans (%) Charge-offs (%)

Net borrowers

Net lenders

p-Values

(1)

(2)

(3)

0.310 (−0.035) 1.299 (0.730) 3.550 (3.428) 0.466 (0.134) 1.488 (1.220) 1.270 (1.074)

1.448⁎ (0.479) 3.261 (0.856) 3.321 (3.056) 0.709 (0.125) 1.470 (1.240) 1.197 (0.963)

0.000⁎⁎ (0.000)⁎⁎ 0.799⁎⁎ (0.000)⁎⁎ 0.000⁎⁎ (0.000)⁎⁎ 62.856 (0.000)⁎⁎ 29.959 (0.224)⁎⁎ 0.271⁎⁎ (0.000)⁎⁎

−2.917 (−1.753) 1.882 (1.302) 1257.5351 (1240.67) 0.074 (0.000) 16.092 (5.468) 2.727 (2.727) 7.720 (5.687) −0.354 (−0.348) 1.509 (1.302) 2.437 (1.465) 0.223 (0.065)

7.084 (5.455) 1.042 (0.467) 1137.7398 (1130.90) 0.016 (0.000) 145.579 (8.651) 2.960 (3.000) 4.729 (2.227) −0.420 (−0.256) 1.634 (1.334) 2.467 (1.412) 0.186 (0.037)

0.000⁎⁎ (0.000)⁎⁎ 0.000⁎⁎ (0.000)⁎⁎ 0.000⁎⁎ (0.00)⁎⁎ 0.000⁎⁎ (0.000)⁎⁎ 0.000⁎⁎ (8.143) 0.000⁎⁎ (0.000)⁎⁎ 0.000⁎⁎ (0.000)⁎⁎ 0.004⁎⁎ (0.000)⁎⁎ 0.000⁎⁎ (0.000)⁎⁎ 6.962 (0.000)⁎⁎ 0.000⁎⁎ (0.000)⁎⁎

Note: This table reports the mean in the first row and the median in the second row (in brackets) for each variable. Column (1) is for Net Borrowers and column (2) is for Net Lenders. Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. In column (3), the first (second) row shows p-values of the significant tests for the mean (median) differences. Panel A displays results for our dependent variables, and Panel B for all bank-level covariates. ⁎⁎ Indicates the 1% significance level. ⁎ Indicates the 5% significance level.

5th percentile (2008Q3) to 0.94% at the 95th percentile (2008Q4). This is consistent with the observed heightened level of counterparty risk subsequent to the failure of Lehman Brothers in September 2008. Table 2 presents the mean and median differences in bank-level variables between net borrowers and net lenders. Panel A shows, as expected, net lenders have substantially higher Liquid Asset Growth and Loan Growth than net borrowers. Total Interest Income Rate and Total Interest Expense Rate are higher for net borrowers, while the average Transaction Deposit Rate and Non-transaction Deposit Rate are similar for net borrowers and net lenders. These results provide preliminary support for our prediction that net borrowers were subject to lending rationing and strived to attract funds by offering higher rates. Panel B shows Deposit Growth is higher for net borrowers than net lenders, confirming our view that net borrowers raised rates to draw additional funding. Net borrowers are substantially larger in size, implying that larger banks are more dependent on interbank borrowings perhaps because they have higher liquidity risk. Consistent with our characterization that net borrowers are liquidity-poor and financially weak, net borrowers generally have lower CAEL but higher Unused Commitments and Charge-offs. More net borrowers received TAF funds than net lenders, but the average amount of funds received (relative to total assets) by net lenders was substantially higher (by a factor of nearly 10). Such considerably larger amounts of TAF funds distributed to net lenders seem to suggest that TAF had a weak economic rationale.

4.2. Lending rationing by net borrowers Table 3 presents the results from estimating eq. (1) for Loan Growth, Liquid Asset Growth, and Total Interest Income Rate in columns (1), (2), and (3) respectively. For brevity, we do not show the coefficients for the control variables. Both bank- and time-fixed effects are included in all regressions with the residuals clustered at the bank level. In line with the theory of lending rationing by Arnold and Riley (2009), we find evidence of lending rationing by net borrowers. Column (1) shows that Loan Growth between net borrowers and net lenders is insignificant before the interbank lending crunch. However, during the crunch, when the interbank market was unable to perform efficient allocation of funds, Loan Growth was 1.55 percentage points lower for net borrowers and net lenders.23 Notably, this result holds after controlling for loan demand effects.

23 We cannot calculate the total effect because Interbank Lending Crunch is dropped due to its collinearity with time fixed effects.

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interbank lending crunch, they kept 13.3 cents more. Before the crunch, net lenders with unused commitments did not need high levels of liquidity, perhaps because funds were readily available in the interbank market, and managed to keep 7.8 cents less. This is consistent with Gatev and Strahan (2006) and Gatev et al. (2009) who find that banks with more unused commitments enjoy greater deposit inflows and lower funding costs especially in times of market stress. However, in line with Acharya and Mora (2014), during the crunch, unused commitments posed a great deal of pressure even on net lenders; net lenders with unused commitments kept 10.2 cents more. Overall, during the interbank lending crunch, net lenders with unused commitments retained 38.7 cents in the form of liquid assets for every dollar of deposit. In column (2), a similar pattern is found for net borrowers, except that net borrowers with unused commitments transformed 17.1 cents more into liquid assets during the interbank lending crunch (6.9 cents more than net lenders with unused commitments). For every dollar of deposit, they hoarded 56.7 cents in liquid assets, 18.1 cents more than net lenders with unused commitments. Column (3) shows the results for net lenders with low-quality security holdings. Unconditionally, net lenders keep 20.3 cents for every dollar deposited. During the interbank lending crunch, they saved 13.9 cents more. Before the crunch, net lenders with low-quality security holdings set aside 5.2 cents more. However, during the crunch, net lenders with low-quality security holdings shed 1.8 cents (albeit statistically insignificant). This suggests net lenders already had enough liquidity to manage any potential liquidity shortfalls. In total, they hoarded 37.5 cents in liquid assets for every dollar of deposit inflow. In contrast, net borrowers with low-quality security holdings kept 6.6 cents more in liquid assets during the interbank lending crunch, 8.4 cents more than net lenders with low-quality security holdings. The total amount of liquid assets they funded for every dollar of deposit was 53.6 cents, 16.3 cents more than net lenders with low-quality security holdings.

Table 3 Lending rationing by net borrowers.

Test variables (Net borrowers − Net Lenders) (Net borrowers − Net Lenders) × Interbank Lending Crunch Control variables included Bank Fixed Effects Time Fixed Effects R2 Within Between Overall Number of observations Number of banks

Loan growth

Liquid asset growth

Total interest income rate

(1)

(2)

(3)

0.0001 (0.0303) −0.0155⁎⁎ (−4.2427)

0.0004 (0.1295) 0.005 (1.1486)

−0.0063⁎ (−6.9274)⁎⁎ 0.0079⁎⁎ (7.1055)

Yes Yes Yes

Yes Yes Yes

Yes Yes Yes

0.1928 0.0005 0.0218 275,870 9191

0.0307 0.0028 0.0049 275,870 9191

0.9413 0.2143 0.8672 274,226 9147

Note: This table reports the regression results for Loan Growth, Liquid Asset Growth, and Total Interest Income Rate in columns (1) to (3) respectively. The main test variable is (Net Borrowers − Net Lenders) which takes the value of 1 for Net Borrowers, −1 for Net Lenders, and 0 for the rest. Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. Interbank Lending Crunch is a dummy equal to 1 for the period from the 3rd quarter of 2008 and 0 otherwise. We employ bank and time fixed effects as well as the full set of control variables, including CAEL; the natural logarithm of Total Assets; loan demand (SLOOS); the interaction between SLOOS and CAEL; the interaction between SLOOS and Large Bank Dummy; Unused Commitments; Market-to-book Value of Security Holdings; Loan Loss Allowance; Non-performing Loans; Charge-offs; Fed Funds Rate; and the 3-month LIBOR-OIS Spread. For Total Interest Income Rate, we add as additional controls Transaction Deposit Rate; Non-transaction Deposit Rate; Total Interest Expense Rate; the 10-year Treasury Rate; the 30-year conventional Mortgage Rate; and Moody's Aaa Corporate Bond Rate. T-statistics are in parentheses. ⁎⁎ Indicates the 1% significance level. The residuals are clustered at the bank level. ⁎ Indicates the 5% significance level. The residuals are clustered at the bank level.

4.4. Liquidity hoarding by net lenders due to counterparty risk Empirical results for our prediction that counterparty risk prompted net lenders to hoard liquidity are in Table 5. The same set of controls as for the Liquid Asset Growth regressions are included, and (Net Lenders − Net Borrowers) is used instead of (Net Borrowers − Net Lenders) so that we can draw direct inference about net lenders. Table 5 shows that a 1% increase in LIBOR-OIS Spread unconditionally leads to a 1.15% increase in Liquid Asset Growth.24 During the interbank lending crunch, a 1% rise in LIBOR- OIS Spread brought about a 0.64% drop in Liquid Asset Growth for all banks, regardless whether they are net lenders or net borrowers. Before the crunch, LIBOR-IS Spread had no differential impact on Liquid Asset Growth between net lenders and net borrowers. However, during the crunch, we find that a 1% rise in LIBOR-OIS spread prompted net lenders to hoard 0.24% more liquid assets than net borrowers. This result is consistent with Ashcraft, McAndrews, and Skeie (2011) who find evidence of precautionary liquidity hoarding by liquidity-rich banks due to counterparty risk.

In column (2), unlike Loan Growth, we find no significant different in Liquid Asset Growth between net borrowers and net lenders between the periods before and during the interbank lending crunch. Column (3) presents the result on lending rates. This specification includes proxies for funding costs and returns on security holdings. We find that lending rates are lower for net borrowers than net lenders by 0.63 percentage points before the interbank lending crunch. However, during the crunch, net borrowers made loans at 0.79 percentage points higher rates than net lenders. These findings are in line with Arnold and Riley (2009) who argue that lending rationing must manifest two lending rates—rationing (a drop in loan volume) occurs at the lower rate, and the loan market clears at the higher rate (a rise in the marginal/average lending rate).

4.5. Higher funding costs for net borrowers Table 6 describes the results for our proposition that net borrowers offered higher rates to attract deposits. As net borrowers scrambled for self-insurance when the interbank market was disrupted, they could have faced higher funding costs. However, subsequent to the failure of Lehman Brothers, the Fed started injecting unprecedentedly large

4.3. Liquidity hoarding by net borrowers for self-insurance In Table 4, we test our prediction on self-insurance motives by net borrowers. In the absence of a coinsurance channel (i.e., the interbank market), Castiglionesi et al. (2014) show liquidity-poor banks hoard liquidity for self-insurance against liquidity shortfalls. We fit eq. (2) to net borrowers and net lenders respectively. Column (1) shows net lenders transform 22.9 cents of every dollar of deposit inflows into liquid assets unconditionally. During the

24 LIBOR-OIS Spread is in percentage and liquid asset growth is in fraction. Therefore, we adjust the scale inconsistency by multiplying the coefficient estimates by 100.

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Table 4 Liquidity hoarding by net borrowers for self-insurance. Liquid asset growth Liquidity shortfall = UC90

Liquidity shortfall = MV < AC

Net lenders

Net borrowers

Net lenders

Net borrowers

(1)

(2)

(3)

(4)

Control variables included Bank fixed effects Time fixed effects

0.2292⁎⁎ (32.9167) 0.1328⁎⁎ (9.5201) −0.077⁎⁎ (−3.3148) 0.1022⁎ (2.4272) Yes Yes Yes

0.2609⁎⁎ (35.912) 0.2261⁎⁎ (17.175) −0.0907⁎⁎ (−3.5834) 0.1708⁎⁎ (5.1499) Yes Yes Yes

0.2026 (25.8738) 0.1391 (5.2555) 0.0517 (4.7269) −0.0184 (−0.6301) Yes Yes Yes

0.238⁎⁎ (27.7774) 0.1933⁎⁎ (9.5723) 0.0402⁎⁎ (3.5663) 0.066⁎⁎ (2.7399) Yes Yes Yes

R2 Within Between Overall Number of observations Number of banks

0.1296 0.1026 0.1145 83,521 7401

0.2365 0.0896 0.1699 84,512 6983

0.13 0.1083 0.1159 83,521 7401

0.2367 0.088 0.1684 84,512 6983

Test variables Deposit Growth Deposit Growth × Interbank Lending Crunch Deposit Growth × Liquidity Shortfall Deposit Growth × Interbank Lending Crunch × Liquidity Shortfall

Note: This table reports the regression results for Liquid Asset Growth using Unused Commitments greater than 90th percentile (UC90) as an anticipated liquidity shortfall proxy for Net Lenders in column (1) and for Net Borrowers in column (2), and using MV < AC as a liquidity shortfall proxy for Net Lenders in column (3) and for Net Borrowers in column (4). Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. Interbank Lending Crunch is a dummy equal to 1 for the period from the 3rd quarter of 2008 and 0 otherwise. We employ bank and time fixed effects as well as the full set of control variables, including CAEL; and the natural logarithm of Total Assets; loan demand (SLOOS); the interaction between SLOOS and CAEL; the interaction between SLOOS and Large Bank Dummy; Unused Commitments; Market-to-book Value of Security Holdings; Loan Loss Allowance; Non-performing Loans; Charge-offs; Fed Funds Rate; and the 3-month LIBOR-OIS Spread. T-statistics are in parentheses. ⁎⁎ Indicates the 1% significance level. The residuals are clustered at the bank level. ⁎ Indicates the 5% significance level. The residuals are clustered at the bank level.

amounts of liquidity into the banking sector. The Fed's liquidity injections created considerable amounts of deposit flows.25 In light of this, we examine Transaction Deposit Rate, Non-transaction Deposit Rate, and Total Interest Expense Rate. In estimating the regression for Total Interest Expense Rate, we include Transaction Deposit Rate and Non-transaction Deposit Rate as additional controls. This way, we can relate the result directly to external funding. Columns (1) to (2) show no difference for Transaction Deposit Rate and Non-transaction Deposit Rate between net borrowers and net lenders. Further, in column (3), we find no evidence that (Nontransaction–Transaction) Deposit Rate differs between net borrowers and lenders. However, Column (4) shows the cost of external funding was higher by 0.68 percentage points during the interbank lending crunch for net borrowers than net lenders. Our results complement the findings of Acharya and Mora (2014). They examine rates on large time deposits,26 while we use non-transaction deposits which include both large and small time deposits. Due to the large proportion of small time deposits,27 we cannot draw any implication about large time deposit

rates separately from Non-transaction Deposit Rate. Instead, we expand the scope of funding sources by focusing on more volatile sources of funding, and find evidence that banks reached for external funding beyond non-transaction deposits. 4.6. Effect of TAF Banks were in severe pressure for funds as economic conditions worsened in 2007. To address the funding issues, the Fed took the first step by expanding credit to banks over the discount window. However, due to stigma concerns, banks were reluctant to use the discount window despite acute funding stress (Armantier et al., 2014). To ameliorate such stigma concerns, the Fed established TAF in December 2007 with 28-day maturity and, beginning in August 2008, offered up to 84-day loans. The expansion of TAF in August 2008 was particularly timely given the breakdown of Lehman Brothers in the following month. From the first TAF auction on December 17, 2007, to the last on March 8, 2010, the Fed conducted 60 auctions and made 4214 individual loans to 416 financial institutions. If the interbank market played a pivotal role in coinsuring against liquidity risk, TAF, which is designed primarily to alleviate tension in the interbank market, should have helped recipient banks, especially net borrowers, drop liquid asset holdings amassed for self-insurance, increase lending, and lower funding costs. Results on the effect of TAF on lending and liquidity hoarding are in Table 7, and those on the effect of TAF on funding costs are in Table 8. In the selection equations across all columns in both tables, the regulatory and political variables are mostly significant and in the expected direction. The exception is Democrat, which is negatively related to the granting of TAF funds. This negative relation reflects the Democrats' stance on the Fed as an unaccountable power with improper relations with financial institutions; one-quarter of democrats did not support

25 In theory, unless every bank holds the injected liquidity with the Fed, the injected liquidity should create deposits as it changes hands. See Keister and McAndrews (2009). Ennis and Wolman (2012) empirically show the tight link between the Fed's liquidity injections and deposit creation. 26 Large (small) time deposits are defined as time deposits beyond (below) the FDIC deposit insurance limit. The deposit insurance limit of $100,000 was first raised to $250,000 on October 3, 2008 on a temporary basis, and on July 21, 2010, the Wall Street Reform and Consumer Protection Act was signed into law and made the limit of $250,000 permanent. 27 In our dataset constructed from the Call Reports, large time deposits are available as of 2010, and its average (median) proportion to non-transaction deposits is 26.5% (24.5%). Assuming the rest is comprised mostly of small time deposits, we believe their proportion to be sizeable.

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to just 0.2 percentage points higher Loan Growth for TAF net borrowers than net lenders in the post-2007Q4 period (albeit significance at a 10% level only). The corresponding result in the regression for 4Q Loan Growth (cumulative loan growth over the next 4 quarters) is 0.99 percentage points, although statistically insignificant (column (3)). These results suggest that TAF was not effective in increasing lending by TAF net borrowers relative to net lenders over both short and long terms. Column (2) shows that in the pre-2007Q4 period, that there was no gap in Liquid Asset Growth between non-TAF net borrowers and net lenders (=β4). However, for TAF banks, this gap was −0.1 percentage points (=β4 + β5), suggesting that TAF net borrowers dropped 0.11 percentage points more cash than net lenders in the pre-2007Q4 period. In the post-2007Q4 period, Liquid Asset Growth on average was lower for TAF banks than for non-TAF banks by 0.49 percentage points (=β3). However, we find that while TAF was not immediately effective in unlocking liquid asset holdings for TAF net borrowers relative to net lenders in the next quarter (column (2)), it helped TAF net borrowers to drop o.91 percentage points more liquid asset holdings than TAF net lenders over the course of a year (column (4)). This finding implies that TAF was at least effective in mitigating self-insurance motives for liquidity hoarding. So far, we find a significant decline in 4Q Liquid Asset Growth for TAF net borrowers relative to net lenders in the post-2007Q4 period, but no significant evidence of an increase in loan volume. In the spirit of Arnold and Riley (2009), if we find evidence that TAF narrowed the gap in lending rates between net borrowers and net lenders, we can interpret this as a sign of alleviation of lending rationing. We test for this in column (5). In the pre-2007Q4 period, TAF net borrowers had a 6 basis points (=β4 + β5) higher lending rate than net lenders. This gap widened to 12 basis points (=β4 + β5 + β6 + β7) in the post-2007Q4 period. For non-TAF banks, the gap in lending rates between net borrowers and net lenders is negligible both in the pre- and post-2007Q4 periods. Given these findings, we conclude that TAF managed to only partially address lending rationing by narrowing the gap in loan volume between net borrowers and net lenders, but failed to close the gap in lending rates. Column (1) of Table 8 displays the results for Transaction Deposit Rate. In the pre-2007Q4 period, there is statistically no difference in Transaction Deposit Rate between non-TAF net borrowers and net lenders. However, there is a gap of 10 basis points (=β4 + β5) in Transaction Deposit Rate between TAF net borrowers and net lenders, with net lenders offering higher rates. In the post-2007Q4 period, we find a 24 basis points increase (=β3) in Transaction Deposit Rate for TAF banks only. However, we find no evidence that the gap in Transaction Deposit Rate between TAF net borrowers and net lenders widened (=β7). This result is expected since banks with liquidity needs mostly drew noncore deposits by raising rates (Acharya & Mora, 2014).29 In column (2), we find that in the pre-2007Q4 period the gap in Non-transaction Deposit Rate between TAF net borrowers and net lenders was 19 basis points (=β4 + β5), 6 basis points wider than for non-TAF banks, with net borrowers offering lower rates. In the post-2007Q4 period, for TAF banks, this gap narrowed by 9 basis points (=β7), compared to an increase in the gap for non-TAF banks points (=β6). This finding suggests that in the post-2007Q4 period, TAF net borrowers offered 9 basis points higher rates relative to TAF net lenders so as to attract non-transaction deposits, suggesting that TAF was not

Table 5 Liquidity hoarding by net lenders due to counterparty risk. Liquid AssetGrowth Test variables LIBOR-OIS Spread

LIBOR-OIS Spread × Interbank Lending Crunch × (Net Lender − Net Borrowers) Control variables included Bank fixed effects Time fixed effects

0.0115⁎⁎ (20.115) −0.0064⁎⁎ (−9.5270) 0.001 (1.5864) 0.0024⁎⁎ (3.039) Yes Yes Yes

R2 Within Between Overall Number of observations Number of banks

0.0364⁎ 0.0034 0.0059 275,870 9191

LIBOR-OIS Spread × Interbank Lending Crunch LIBOR-OIS Spread × (Net Lender − Net Borrowers)

Note: This table reports the regression results for Liquid Asset Growth. The main test variable is the 3-month LIBOR-OIS Spread as a proxy for counterparty risk. (Net Borrowers − Net Lenders) takes the value of 1 for Net Borrowers, −1 for Net Lenders, and 0 for the rest. Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. Interbank Lending Crunch is a dummy equal to 1 for the period from the 3rd quarter of 2008 and 0 otherwise. We employ bank and time fixed effects as well as the full set of control variables, including CAEL; and the natural logarithm of Total Assets; loan demand (SLOOS); the interaction between SLOOS and CAEL; the interaction between SLOOS and Large Bank Dummy; Unused Commitments; Market-to-book Value of Security Holdings; Loan Loss Allowance; Non-performing Loans; Charge-offs; Fed Funds Rate; and the 3month LIBOR-OIS Spread. T-statistics are in parentheses. ⁎⁎ Indicates the 1% significance level. The residuals are clustered at the bank level. ⁎ Indicates the 5% significance level. The residuals are clustered at the bank level.

TAF (www.opencongress.org). The inverse Mills ratio (Lambda) estimated from the selection equation enters the outcome equation and corrects for potential selection bias. It takes a significant positive coefficient, confirming endogeneity between our selection and outcome equations. The outcome equation in column (1) of Table 7 shows that in the pre-2007Q4 period, Loan Growth was on average 0.49 percentage points lower (=β4) for non-TAF net borrowers than net lenders. For TAF banks, this gap was on average 0.07 percentage points wider (=β5) compared to non-TAF banks over the same period. In the post-2007Q4 period, somewhat contrary to the declared intention of TAF, we find a 3.86% (=β0 + β1 + β2 + β3) drop in Loan Growth for TAF banks, 2.05 percentage points larger than for non-TAF banks (=β0 + β2). Loan Growth was 0.46 percentage points lower for non-TAF net borrowers than net lenders (=β4 + β6), a gap that is 0.03 percentage points smaller than the pre-2007Q4 period, while for TAF banks, this gap closes to 0.33 percentage points. Of our particular interest is the marginal effect of TAF on the gap in Loan Growth between TAF net borrowers and net lenders across the time periods—a difference-in-differences test.28 This marginal effect, denoted by β7, amounts 28 To be more precise, we first estimate (i) the pre-2007Q4 difference between TAF and non-TAF banks in the difference between net borrowers and net lenders (i.e., TAF Dummy = 1 and Post2007Q4 = 0 for the difference in Loan Growth between net lenders and net borrowers); and (ii) the post-2007Q4 difference (i.e., TAF Dummy = 1 and Post2007Q4 = 1 for the difference in Loan Growth between net lenders and net borrowers). Then, test the difference between (ii) and (i), which equals the coefficient on the term, (Net Borrowers − Net Lenders) × TAF Dummy × Post2007Q4.

29 Acharya and Mora (2014) find commitment-exposed banks increased core deposit rates at the peak of the crisis when specified without bank fixed effects. This result could be due to the omission of unobserved cross-sectional differences which are correlated with the unused commitment ratio. They also find large banks generally paid lower rates, including core deposit rates, and point to the implicit benefit of too-big-to-fail subsidy. Therefore, it is not surprising that TAF, as an explicit form of subsidy, helped suppressing the gap in Transaction Deposit Rate between net borrowers and net lenders.

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Table 6 Funding costs for net borrowers. Transaction deposit rate

Non-transaction deposit rate

(Non-transaction − Transaction) deposit rate

Total interest expense rate

(1)

(2)

(3)

(4)

(Net Borrowers − Net Lenders) × Interbank Lending Crunch Control variables included Bank fixed effects Time fixed effects

−0.0186 (−0.8691) −0.0052 (−0.2446) Yes Yes Yes

−0.0021 (−0.5026) 0.0009 (0.2745) Yes Yes Yes

0.0166⁎ (0.7271) 0.0054 (0.2395) Yes Yes Yes

−0.0013 (−0.9568) 0.0068⁎⁎ (4.928) Yes Yes Yes

R2 Within Between Overall Number of observations Number of banks

0.0005 0.0008 0 274,500 9155

0.5048 0.0024 0.3542 275,574 9183

0.0004 0.0009 0 274,226 9147

0.9483 0.0892 0.8744 274,226 9147

Test variables (Net Borrowers − Net Lenders)

Note: This table reports the regression results for Transaction Deposit Rate, Non-transaction Deposit Rate, (Non-transaction − Transaction) Deposit Rate, and Total Interest Expense Rate in columns (1) to (4) respectively. The main test variable is (Net Borrowers − Net Lenders) which takes the value of 1 for Net Borrowers, −1 for Net Lenders, and 0 for the rest. Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. Interbank Lending Crunch is a dummy equal to 1 for the period from the 3rd quarter of 2008 and o otherwise. We employ bank and time fixed effects as well as the full set of control variables, including CAEL; and the natural logarithm of Total Assets; loan demand (SLOOS); the interaction between SLOOS and CAEL; the interaction between SLOOS and Large Bank Dummy; Unused Commitments; Market-to-book Value of Security Holdings; Loan Loss Allowance; Non-performing Loans; Charge-offs; Fed Funds Rate; and the 3-month LIBOR-OIS Spread. For Total Interest Expense Rate, we add as additional controls Transaction Deposit Rate and Non-transaction Deposit Rate. T-statistics are in parentheses. ⁎⁎ Indicates the 1% significance level. The residuals are clustered at the bank level. ⁎ Indicates the 5% significance level. The residuals are clustered at the bank level.

effective in alleviating the funding pressure. Column (3) shows that the difference between Transaction Deposit Rate and Non-transaction Deposit Rate manifests no meaningful difference between net borrowers and net lenders regardless of the time period or whether the bank was aided under TAF or not. Somewhat in contrast to column (2), column (4) provides evidence of a drop in the costs of external funding for TAF net borrowers relative to net lenders in the post-200Q4 period. The external funding costs in the pre-2007Q4 period were 4 basis points lower (=β4) for non-TAF net borrowers compared to net lenders. In the same time period, the external funding costs were on average 7 basis points lower (=β4 + β5) for TAF net borrowers than net lenders. The cost differential in external funding between non-TAF net borrowers and net lenders was 2 basis points lower (=β6) in the post-2007Q4 period compared to the pre2007Q4 period. Interestingly, the cost differential in external funding between TAF net borrowers and net lenders was 4 basis points lower (=β7) in the post-2007Q4 period compared to the pre-2007Q4 period. The above results raise the question why the marginal rate differential between TAF net borrowers and net lenders was wider for nontransaction deposits than for external funding in the post 2007Q4 period. We argue that while TAF helped reduce overall funding costs, especially for net borrowers, it was not effective enough to assuage the scramble of banks with greater liquidity needs for noncore deposits (Acharya & Mora, 2014). Of course, we do not discredit the effect of TAF as a whole; the gap in Non-transaction Deposit Rate between net borrowers and net lenders could have been considerably higher had it not been for TAF. The Fed operated TAF alongside the Term Securities Lending Facility (TSLF) and Primary Dealer Credit Facility (PDCF) with specific focus on the functioning of the interbank market. However, partly because of its limited effectiveness, due in part to a lack of an economic rationale and the substantial presence of a political rationale in TAF funds distribution (as shown in the selection equations in Tables 7 and 8), TAF was closed in the first quarter of 2010, and so were TSLF and PDCF. We have also conducted sub-sample tests for the normal periods (or non-crisis periods) and alternative specifications the pooled

GLS estimation with heteroscedasticity and autocorrelation corrected (or panel-corrected standard errors), we find generally similar results. 5. Discussion The Fed's policy actions in battling with the recent financial crisis can broadly be summarized in two parts: (i) liquidity injections; and (ii) interest-on-reserve policy. While each of these actions are said to be effective on its own, together they seem to be conflicting each other. Liquidity injections were delivered through credit and liquidity facilities at the onset of the subprime mortgage crisis in late 2007, and were gradually replaced by quantitative easing (QE) over the course of 2009. The injected liquidity pushed down the federal funds rate, the policy rate, below the target. To set a floor for the federal funds rate, the Fed started paying interests on reserves at 0.25% per annum. However, because banks base lending decisions on the spread between the return on loans and its opportunity cost (the return on reserves), low lending rates and interests on reserves discouraged bank lending and encouraged liquidity hoarding during the financial crisis.30 Further, unrestricted supply of liquidity by the Fed provided banks with all the more reason not to lend but to hoard instead. The findings of our paper lead us to conclude that the interbank market performs a crucial part in insuring effective transmission of monetary expansion. The Fed moved its focus almost entirely to QE from targeted supports through credit and liquidity facilities including those designed specifically for the interbank market (e.g., TAF, TSLF, and PDCF). While TAF was at least partially effective, its early tapering for QE is not well rationalized. QE is devised to directly credit liquidity to financial institutions and create deposit inflows. However, because QE is performed outside the interbank market, it appears that efficient fund allocation among banks was not the concern of the Fed. The 30 The average value of Total Interest Income Rate is 3.9% before the interbank lending crunch and 2.7% during the crunch.

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Table 7 Effects of TAF on lending.

Outcome equation TAF Dummy Post2007Q4 TAF Dummy × Post2007Q4 (Net Borrowers − Net Lenders) (Net Borrowers − Net Lenders) × TAF Dummy (Net Borrowers − Net Lenders) × Post2007Q4 (Net Borrowers − Net Lenders) × TAF Dummy × Post2007Q4 Constant Control variables included Bank fixed effects Time fixed effects Number of observations Selection Equation (Probit) Fed Director Subcommittee FIRE Democrat Control variables included Lambda

Loan growth

Liquid asset growth

4Q loan growth

4Q liquid asset growth

Total interest income rate

(1)

(2)

(3)

(4)

(5)

−0.0180⁎⁎ (−11.8373) −0.0091⁎⁎ (−40.1795) −0.0025⁎⁎ (−18.5077) −0.0049⁎⁎ (−5.3271) −0.0007⁎⁎ (−3.4592) 0.0003 (0.3929) 0.0020 (1.8449) −0.009 (−7.5655) Yes No No 244,338

−0.0024 (−1.4120) 0.0044⁎⁎ (17.3959) −0.0049⁎⁎ (−32.5632) 0.0001 (−0.0420) −0.0011⁎⁎s (−5.3580) 0.0016 (1.9507) −0.0011 (−0.9169) 0.0029⁎ (2.1477) Yes No No 244,338

0.0120 (1.3556) −0.0448⁎⁎ (−34.7893) −0.0057⁎⁎ (−7.5685) −0.0381⁎⁎ (−6.9152) −0.0016 (−1.4602) −0.0160⁎⁎ (−3.8024) 0.0099 (1.5505) 0.0183 (2.7019) Yes No No 241,428

0.0000 (0.0052) 0.0206⁎⁎ (29.3214) −0.0073⁎⁎ (−17.7225) −0.0016 (−0.5212) −0.0042⁎⁎ (−7.1825) 0.0041 (1.7834) −0.0091⁎⁎ (−2.6023) 0.0223⁎⁎ (6.033) Yes No No 241,428

0.0028⁎⁎ (6.9632) −0.0015⁎⁎ (−19.4395) −0.0003⁎⁎ (−9.3503) 0.0001 (−0.1686) 0.0005⁎⁎ (9.8245) 0.0001 (0.7005) 0.0006⁎ (2.0226) 0.0479⁎⁎ (109.7352) Yes No No 242,933

0.1973⁎⁎ (5.7663) 0.1294⁎⁎ (5.7164) 0.9774⁎⁎ (3.8096) −0.2098⁎⁎ (−7.9456) Yes 0.0125⁎⁎ (18.8701)

0.1973⁎⁎ (5.7663) 0.1294⁎⁎ (5.7164) 0.9774⁎⁎ (3.8096) −0.2098⁎⁎ (−7.9456) Yes 0.0015⁎ (2.0057)

0.1947⁎⁎ (5.6315) 0.1361⁎⁎ (5.9698) 0.9385⁎⁎ (3.6221) −0.2059⁎⁎ (−7.7402) Yes 0.0240⁎⁎ (6.2067)

0.1947⁎⁎ (5.6315) 0.1361⁎⁎ (5.9698) 0.9385⁎⁎ (3.6221) −0.2059⁎⁎ (−7.7402) Yes 0.0036 (1.7027)

0.1915⁎⁎ (5.5623) 0.1375⁎⁎ (6.0156) 0.5617⁎ (2.1414) −0.2228⁎⁎ (−8.3693) Yes −0.0015⁎⁎ (−8.4299)

Note: This table reports the regression results for the effect of TAF on Loan Growth, Liquid Asset Growth, 4Q Loan Growth (cumulative loan growth over the next 4 quarters), 4Q Liquid Asset Growth (cumulative liquid asset growth over the next 4 quarters), and Total Interest Income Rate in columns (1) to (5) respectively. TAF Dummy takes the value of 1 if the bank received financial support granted through the Term Auction Facility (TAF) at least once and 0 otherwise. (Net Borrowers − Net Lenders) takes the value of 1 for Net Borrowers, −1 for Net Lenders, and 0 for the rest. Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. To satisfy the over-identification condition, we employ the following instruments: Democrat, which takes the value of 1 if the bank's local Representative was a Democrat and 0 otherwise; Subcommittee on FICC, which takes the value of 1 if the bank's local Representative sat on the Subcommittee on Financial Institutions and Consumer Credit (FICC) in the Financial Services Committee and 0 otherwise; Local FIRE Donation, which is the percentage of campaign contribution from local FIRE industries in total contribution received by a Representative in the 2007–2008 election cycle; and Fed Director, which takes the value of 1 if an executive of the bank served as director of a (branch of the) Federal Reserve Bank and 0 otherwise. Lambda is the inverse Mills ratio. We employ the full set of control variables, including CAEL; and the natural logarithm of Total Assets; loan demand (SLOOS); the interaction between SLOOS and CAEL; the interaction between SLOOS and Large Bank Dummy; Unused Commitments; Market-to-book Value of Security Holdings; Loan Loss Allowance; Non-performing Loans; Charge-offs; Fed Funds Rate; and the 3-month LIBOR-OIS Spread. For Total Interest Income Rate, we add as additional controls Transaction Deposit Rate; Non-transaction Deposit Rate; Total Interest Expense Rate; the 10-year Treasury Rate; the 30-year conventional Mortgage Rate; and Moody's Aaa Corporate Bond Rate. T-statistics are in parentheses. The residuals are clustered at the bank level. ⁎⁎ Indicates the 1% significance level. The residuals are clustered at the bank level. ⁎ Indicates the 5% significance level. The residuals are clustered at the bank level.

absence of a well-functioning interbank market could cost the Fed unnecessarily large amounts of liquidity injections as banks seek self-insurance and curb lending. On this basis, we argue the Fed should focus more on insuring efficient allocation of funds in the interbank market. Nevertheless, whenever liquidity injection is inevitable, we suggest that the central bank should subsidize interbank lending. The subsidy rate can be a function of the gap between the current state of the interbank market and its desired full efficiency. As such, the central bank should add a faction of a percentage more to the rates at which banks lend to each other to encourage interbank lending, and taper off as the interbank market moves towards full efficiency. However, this requires careful and continuous revision as well as adjustment by the central bank. The interbank lending subsidy policy has a few important advantages. First, interbank lending subsidy is compatible with liquidity injection policy. In fact, a close monitoring of the subsidy rate and the

functioning of the interbank market will help the central bank analyse the optimal amount of liquidity to inject. Second, unlike the interest-onreserve policy, interbank lending subsidization does not interfere with bank lending. When distant from full efficiency, a higher interbank lending subsidy rate will induce liquidity-rich banks to lend to liquiditypoor banks, and thus reduce lending rationing. As the interbank market approaches full efficiency, the subsidy rate will subside so as not to deter bank lending. Further, as interbank lending subsidy will encourage more lending activities in the interbank market, it can keep the policy rate from falling below the target. 6. Conclusion In this paper, we examine the impact of the disruption of the interbank market on banks' liquidity creation and funding ability. Consistent with our predictions, we find evidence of self-insurance 11

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Table 8 Effects of TAF on funding costs.

Outcome equation TAF Dummy Post2007Q4 TAF Dummy × Post2007Q4 (Net Borrowers − Net Lenders) (Net Borrowers − Net Lenders) × TAF Dummy (Net Borrowers − Net Lenders) × Post2007Q4 (Net Borrowers − Net Lenders) × TAF Dummy × Post2007Q4 Constant Control variables included Bank fixed effects Time fixed effects Number of observations Selection Equation (Probit) Fed Director Subcommittee FIRE Democrat Control variables included Lambda

Transaction deposit rate

Non-transaction deposit rate

(Non-transaction − Transaction) deposit rate

Total interest expense rate

(1)

(2)

(3)

(4)

−0.0091 (−1.1233) −0.0019 (−1.5860) 0.0024⁎⁎ (3.4048) 0.0014 (0.2668) −0.0024⁎ (−2.4226) −0.0011 (−0.2829) 0.0018 (0.3108) −0.0079 (−1.2540) Yes No No 243,170

−0.0037⁎⁎ (−9.4806) 0.0003⁎⁎ (4.8536) 0.0006⁎⁎ (17.9772) −0.0013⁎⁎ (−5.4415) −0.0006⁎⁎ (−11.6431) −0.0009⁎⁎ (−4.5696) 0.0009⁎⁎ (3.2063) 0.0184⁎⁎ (60.0852) Yes No No 244,101

0.0056⁎ (0.6937) 0.0022 (1.8049) −0.0018⁎ (−2.5195) −0.0028 (−0.5402) 0.0018 (1.8453) 0.0003 (0.0672) −0.0008 (−0.1391) 0.0265⁎⁎ (4.2106) Yes No No 242,933

−0.0022⁎⁎ (−12.6983) 0.0001 (1.035) 0.0004⁎⁎ (29.0201) −0.0004⁎⁎ (−3.5457) −0.0003⁎⁎ (−14.7326) 0.0002⁎⁎ (2.8586) −0.0004⁎⁎ (−2.8378) −0.0005⁎⁎ (−3.6472) Yes No No 242,933

0.1959⁎⁎ (5.7044) 0.1338⁎⁎ (5.8824) 0.6788⁎⁎ (2.6118) −0.2151⁎⁎ (−8.1093) Yes 0.0029 (0.8272)

0.1942⁎⁎ (5.6706) 0.1317⁎⁎ (5.805) 0.841⁎⁎ (3.2596) −0.2103⁎⁎ (−7.9466) Yes 0.0019⁎⁎ (11.288)

0.1927⁎⁎ (5.6079) 0.1360⁎⁎ (5.9605) 0.5407⁎ (2.0683) −0.2155⁎⁎ (−8.1069) Yes −0.0011 (−0.3157)

0.1907⁎⁎ (5.5434) 0.1367⁎⁎ (5.9943) 0.5354⁎ (2.048) −0.216⁎⁎ (−8.1266) Yes 0.0012⁎⁎ (15.772)

Note: This table reports the regression results for the effect of TAF on Transaction Deposit Rate, Non-transaction Deposit Rate, (Non-transaction – Transaction) Deposit Rate, and Total Interest Expense Rate in columns (1) to (4) respectively. TAF Dummy takes the value of 1 if the bank received financial support granted through the Term Auction Facility (TAF) at least once and 0 otherwise. (Net Borrowers − Net Lenders) takes the value of 1 for Net Borrowers, −1 for Net Lenders, and 0 for the rest. Net Borrowers (Net Lenders) are those with Net Interbank Loans less (greater) than its 30th (70th) percentile value in the previous quarter. To satisfy the overidentification condition, we employ the following instruments: Democrat, which takes the value of 1 if the bank's local Representative was a Democrat and 0 otherwise; Subcommittee on FICC, which takes the value of 1 if the bank's local Representative sat on the Subcommittee on Financial Institutions and Consumer Credit (FICC) in the Financial Services Committee and 0 otherwise; Local FIRE Donation, which is the percentage of campaign contribution from local FIRE industries in total contribution received by a Representative in the 2007–2008 election cycle; and Fed Director, which takes the value of 1 if an executive of the bank served as director of a (branch of the) Federal Reserve Bank and 0 otherwise. Lambda is the inverse Mills ratio. We employ the full set of control variables, including CAEL; and the natural logarithm of Total Assets; loan demand (SLOOS); the interaction between SLOOS and CAEL; the interaction between SLOOS and Large Bank Dummy; Unused Commitments; Market-to-book Value of Security Holdings; Loan Loss Allowance; Non-performing Loans; Charge-offs; Fed Funds Rate; and the 3-month LIBOR-OIS Spread. For Total Interest Expense Rate, we add as additional controls Transaction Deposit Rate and Non-transaction Deposit Rate. T-statistics are in parentheses ⁎⁎ Indicates the 1% significance level. The residuals are clustered at the bank level. ⁎ Indicate the 5% significance level. The residuals are clustered at the bank level.

motives and lending rationing by net borrowers. We also find net borrowers offered higher rates to attract external funding, and net lenders hoarded liquidity due to heightened counterparty risk. On the policy frontier, we argue that the central bank has to ensure the well-functioning of the interbank market for its money expansion policy to be effective. Besides credit and securities lending programs targeted at the interbank market, such as TAF, TSLF, and PDCF, we suggest interbank lending subsidization as well. The central bank should consider paying a spread over the rate at which banks lend to

each other whenever the interbank market is not performing efficiently to incentivize interbank lending. In implementing this policy, the central bank has to constantly and closely monitor the interbank market so that, as full efficiency is reached, it can narrow the spread. In our belief, unlike the interest-on-reserve policy, interbank lending subsidization will work in perfect harmony with liquidity injections. Further research on the interbank market, especially a theoretical framework to formularize the interbank lending subsidy, is warranted.

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Appendix A. Variable definition Variable

Definition

Source

Total loans

Total loans and leases (net of unearned income), including real estate loans, commercial & industrial loans, loans to depository and non-depository financial institutions, loans to individuals, and loans to foreign governments and official institutions Loan growth Quarter-to-quarter change in Total Loans, scaled by the beginning-of-quarter value of Total Assets Liquid assets The sum of cash, balances due from depository institutions, and securities available for sale Liquid asset growth Quarter-to-quarter change in Liquid Assets, scaled by the beginning-of-quarter value of Total Assets Total interest income r- Total interest income divided by the quarterly average balance of interest-earning assets. Interest-earning ate assets is the sum of interest-bearing balances due from depository institutions (RC-A), securities (RC-B), federal funds sold and securities purchased under resale agreements (RC), total loans (RC-C), and fixedincome trading assets (RC-D). Transaction deposit ra- Interest expenses on transaction deposits divided by the quarterly average balance of transaction deposits te Non-transaction deposit Interest expenses on nontransaction deposits divided by the quarterly average balance of nontransaction rate deposits Total interest expense Total interest expenses on the quarterly average balance of interest-bearing liabilities. Interest-bearing rate liabilities are the sum of interest-bearing deposits (RC-E), federal funds purchased and securities sold under repurchase agreements (RC), trading liabilities (RC-D), other borrowed money (RC-M), subordinate notes and debentures (RC). Net interbank loans The sum of loans to depository institutions, federal funds sold, and securities purchased under agreements to resell, less the sum of federal funds purchased and securities sold under agreements to repurchase, scaled by Total Assets Net borrowers Takes the value of 1 for observations with Net Interbank Loans < 0 (30th-percentile) in the previous quarter and 0 otherwise Net lenders Takes the value of 1 for observations with Net Interbank Loans > 0.03203 (70th-percentile) in the previous quarter and 0 otherwise Deposits Total deposits, including both transaction and non-transaction deposits of individuals, partnerships, and corporations; U.S. government; state and political subdivisions in the U.S.; commercial banks and other depository institutions in the U.S.; banks in foreign countries; and foreign governments and official institutions Deposit growth Quarter-to-quarter change in Deposits, scaled by the beginning-of-quarter value of Total Assets TAF dummy Takes the value of 1 if the bank received financial support granted through the Term Auction Facility (TAF) at least once, and 0 otherwise TAF money The actual amount of financial support granted through the Term Auction Facility scaled by Total Assets

Schedule RC-C of the Call Reports Schedule RC-C of the Call Reports Schedule RC-A and RC-B of the Call Reports Schedule RC-C of the Call Reports Schedule RI, RC, RC-A, RC-B, RC-C, and RC-D of the Call Reports Schedule RI and RC-E of the Call Reports Schedule RI and RC-E of the Call Reports Schedule RI, RC, RC-D, RC-E, and RC-M of the Call Reports Schedule RC, and RC-A of the Call Reports

Schedule RC-E of the call reports

Schedule RC-C of the call reports http://www.federalreserve.gov/newsevents/ reform_transaction.htm http://www.federalreserve.gov/newsevents/ reform_transaction.htm CAEL A simplified version of the CAMELS score system. CAEL takes the range from 1 (weak) to 5 (strong). For Schedule RI, RI-B, RC, RC-A, RC-B, RC-N and more details about CAEL, see Collier, Forbush, Nuxoll, and Keefe (2003) RC-M of the call reports SLOOS Net percentage of domestic banks reporting stronger demand for commercial and industrial loans Senior loan officer opinion survey: http:// www.federalreserve.gov/boarddocs/ SnLoanSurvey/ Total assets Book value of total assets Schedule RC of the call reports Unused Commitments Unused commitments scaled by the sum of unused commitments and Total Loans. When included in the Schedule RC-L of the call reports regressions, the beginning-of-quarter values are used. Market value Market (fair) value of all security holdings Schedule RC-B of the call reports Amortization cost Amortization cost of all security holdings Schedule RC-B of the call reports Market-to-book value (Market Value − Amortization Cost)/Amortization Cost. When included in the regressions, the beginningof security holdings of-quarter values are used. Loan loss allowance Loan loss allowance is a contra-asset account and calculated as the sum of all accumulated loan loss Schedule RI-B of the call reports provisions, scaled by Total Loans. When included in the regressions, the beginning-of-quarter values are used. Non-performing loans The sum of loans past due 90 days or longer and nonaccrual loans, scaled by Total Loans. When included Schedule RC-N of the call reports in the regressions, the beginning-of-quarter values are used. Charge-offs Charge-offs scaled by Total Assets. Charge-offs refer to the part of debt unlikely to be collected, and hence Schedule RI-B of the call reports charged off the balance sheet. When included in the regressions, the beginning-of-quarter values are used. Federal funds rate The effective federal funds rate, the rate at which depository instututions trade federal funds (balances The Fed's statistical release H.15. held with the Fed, also known as reserves) on an overnight and uncollateralized basis. LIBOR The 3-month London interbank offer rate Bloomberg data service OIS The overnight index swap rate Bloomberg data service Treasury rate The 10-year rate on the daily yield curve interpolated from non-inflation-indexed Treasury securities The Fed's statistical release H.15. Mortgage rate Contract interest rates on commitments for 30-year fixed-rate first mortgages The Fed's statistical release H.15. Aaa corporate bond ra- Moody's seasoned Aaa corporate bond yield of approximately 30-year maturity The Fed's statistical release H.15. te UC90 Takes the value of 1 when Unused Commitments > 0.15009 (90th-percentile) in the previous quarter and 0 otherwise MV < AC Takes the value of 1 for observations with Market-to-book Value of Security Holdings < 0 in the previous quarter and 0 otherwise Large bank dummy Takes the value of 1 if the bank has Total Assets larger than $230 million (70th-percentile) in the previous quarter and 0 otherwise Interbank lending cru- Takes the value of 1 for the period from the 3rd quarter of 2008 onwards and 0 otherwise nch Post2007Q4 Takes the value of 1 from the fourth quarter of 2007 in which the first TAF auction was conducted and 0 otherwise

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Takes the value of 1 if the bank's local Representative was a Democrat and 0 otherwise Takes the value of 1 if the bank's local Representative sat on the Subcommittee on Financial Institutions and Consumer Credit (FICC) in the Financial Services Committee and 0 otherwise The percentage of campaign contribution from local FIRE industries in total contribution received by a Representative in the 2007–2008 election cycle Takes the value of 1 if an executive of the bank served as a director of a (branch of the) Federal Reserve Bank and 0 otherwise

Li (2013) Li (2013) Li (2013) Li (2013)

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