Risk Migration from the Banking Industry to the Real Economy:

An Examination of Spillover from Basel III

Jing Wen* Graduate School of Business Columbia University [email protected]

December 4, 2020

Abstract This study investigates whether bank regulations pertaining to capital and liquidity, which are designed to promote a resilient banking system, cause risk migration from the banking industry to the real economy. Specifically, I examine whether borrowers increase their risk-taking after incurring higher borrowing costs due to Basel III. Using a difference-in-differences research design to compare borrowers of banks that are more affected by Basel III (i.e., banks with $250 billion or more in total consolidated assets) with borrowers of banks that are less affected, I find that the borrowers more affected by Basel III (a) experienced a relative increase in loan costs, (b) displayed a relative increase in accounting- and market-based volatility, and (c) incurred a relative increase in investments in risky activities with uncertain benefits. These findings suggest that borrowers are exposed to moral hazard: to compensate for the increased borrowing costs, they are incentivized to take on more risk in pursuit of higher expected returns. Such results are not driven by adverse selection, time trend, or bank size. This study highlights a potential unintended consequence of bank regulations on borrower risk-taking.

Keywords: Banking Regulation, Basel III, Real Economy, Risk Migration, Risk-Taking JEL Classifications: G21, G28

*I am very grateful to the members of my dissertation committee for their guidance, support, and many helpful insights: Urooj Khan (advisor), Fabrizio Ferri, Doron Nissim, and Stephen Penman. I thank Cyrus Aghamolla, Emily Breza, Thomas Bourveau, Matthias Breuer, Jonathan Glover, Matthieu Gomez, Émilien Gouin-Bonenfant, Moritz Hiemann, Alon Kalay, Sehwa Kim, Lisa Yao Liu, Giorgia Piacentino, Shiva Rajgopal, Stephen Ryan, Stephanie Schmitt-Grohé, Robert Stoumbos, and Anjan Thakor. I also thank seminar participants at Columbia Bernstein Center Research Lightning Talks, Columbia Business School, Columbia Deming Center Doctoral Fellowships Seminar, Columbia Financial Economic Colloquium, Columbia Economic Fluctuation Colloquium, University of Texas at Austin PhD Symposium, and fellow PhD students. I am also grateful for the helpful insights shared by Evan Picoult, as well as anonymous bank analysts and regulators. Finally, I thank the Deming Center at Columbia Business School, the Chazen Institute for Global Business at Columbia Business School, as well as the Sanford C. Bernstein & Co. Center for Leadership and Ethics at Columbia Business School for their generous financial support. All errors are my own. The most recent version of the paper is available here. The Online Appendix is available here. 1. Introduction

Following the 2008 financial crisis, the Basel Committee on Bank Supervision (the “Basel

Committee”) proposed Basel III, a new set of regulations pertaining to bank capital and liquidity, to address perceived weaknesses in the banking system revealed by the crisis. One of the prime objectives of the Basel Committee was to strengthen the resilience of the banking sector and reduce the risk of economic failure due to the “spillover from the financial sector to the real economy”

(Basel Committee, 2010). In this paper, I investigate whether bank regulations, such as Basel III, themselves set the stage for an additional kind of spillover, namely risk migration from the banking industry to the real economy even in the absence of a total collapse. Specifically, I examine whether the regulatory burden imposed on banks under Basel III increased the risk-taking of corporate borrowers in the United States (the “borrowers”). In this paper, I measure risk-taking using eight alternative metrics, including everything from accounting- and market-based volatility to different types of investments that have uncertain benefits and are hard for banks to monitor.

A few theorists predict that bank regulations—aimed at promoting a more resilient banking system—may increase subsequent borrower risk-taking (Hakenes and Schnabel, 2011). They suggest that when borrowers’ loan costs are exogenously increased due to the introduction of bank regulations, borrowers, regardless of their fundamentals, may undertake riskier projects with higher expected returns to compensate for the increase in their borrowing costs. This moral hazard is analogous to the effect of the increased cost of capital: the higher the cost of capital, the higher the required return, and the higher the required return, the higher the risk-taking.

The prediction, however, may not be certain, depending on a myriad of factors, including the severity of burden imposed by a bank regulation, the extent of information asymmetry between banks and borrowers, and the extent of borrowers’ dependency on banks. For example, a bank

1 regulation may be designed in a lenient way and, thus, may not necessarily impose much burden on banks. In such a case, the regulation may not increase the cost of lending of banks and risk- taking of borrowers. Moreover, if there is little information asymmetry between banks and borrowers, borrowers faced with greater loan costs due to bank regulations may be incentivized to reduce their risk-taking in the hope of being able to renegotiate better terms in the future.

Furthermore, when borrowers are not dependent on bank loans, they may be able to obtain low cost funding from nonbank sources (Glancy and Kurtzman, 2018). In such circumstances, these borrowers may not need to adjust their risk-taking. Whether bank regulations increase borrower risk-taking is, therefore, theoretically ambiguous and, thus, an open empirical question.

Examining this question is important. Typically, to ensure the stability of the financial system, bank regulators focus their attention on maintaining banks’ strong capital and liquidity positions. However, the stability of borrowers is also a key to the stability of the financial system

(Boyd and De Nicoló, 2005; Hakenes and Schnabel, 2011). The risk of borrowers contributes greatly to the stability of banks, as loans compose the majority of bank assets. 1 The risk of borrowers feed back to the banking system through loan delinquency, and such feedback may increase the systemic risk in the banking system. In fact, it was the massive default by household borrowers that was often blamed for the failure of banks during the 2008 financial crisis (Baily,

Litan, and Johnson, 2011). Moreover, the syndication of corporate loans may pose another threat to the systemic risk in the banking system as “syndication increases the overlap of bank loan portfolios and makes them more vulnerable to contagious effects” (Cai et al., 2018).

This issue has concerned at least one regulator. In 2018, Pablo D’Erasmo at the Research

Department of the Federal Reserve Bank of Philadelphia expressed concerns about the potential

1 https://www.federalreserve.gov/releases/h8/current/default.htm

2 increase in borrower risk-taking resulting from the higher rates that accompany bank regulations and called for more research “to measure the economic effects of higher capital requirements to gain a firmer understanding of what amount of bank capital is optimal” (D’Erasmo, 2018).

To the best of my knowledge, there has been no empirical research regarding the unintended consequences of bank regulations on borrowers risk-taking. Prior research on the effect of bank regulations on financial stability has focused exclusively on banks’ risk-taking choices

(e.g., Ongena, Popov, and Udell, 2013) rather than borrowers’ risk-taking decisions. This paper, therefore, investigates the occurrence and magnitude of the increase in borrower risk-taking resulting from bank regulations, in this case from Basel III, and serves as a cornerstone for future research and regulators to understand the full effects of bank regulations.

To investigate the effects of bank regulations on borrower risk-taking, I compare banks and borrowers in the syndicate market that are “more affected” by Basel III with banks and borrowers that are “less affected.” I focus on banks and borrowers participating in the syndicated loan market, because the risk of syndicate borrowers is more likely to systemically affect banks in those syndicates. In this paper, banks and their borrowers are deemed “more affected” by Basel III if the banks have $250 billion or more in total consolidated assets. Banks and their borrowers are deemed “less affected” if not meeting that criterion. Under the regulations, banks with $250 billion or more in total consolidated assets are subject to additional Basel III provisions and, thus, greater regulatory burdens. The most common and costly of these additional Basel III provisions are: the advanced approaches rule (AA), the supplementary leverage ratio (SLR), the liquidity coverage ratio (LCR), the enhanced SLR, and the enhanced rule for Global Systemically Important Banks

(GSIBs), although not all of these provisions are applicable to every bank with $250 billion or more in total consolidated assets. Despite the wide variability and disparate applicability of the

3 regulations, I use the difference-in-differences (DD) research design to estimate the average treatment effect of greater exposure to regulatory burdens under Basel III.

Since Basel III is not likely to have resulted from changes in any individual bank’s or borrower’s fundamentals, I use Basel III as an exogenous source of variation in banks’ regulatory burdens to examine the impact of additional regulatory burdens on borrowers’ loan costs and risk- taking. To circumvent the issue of banks sorting borrowers with different characteristics, I limit the sample to borrowers that have borrowed in both pre-Basel III and post-Basel III periods to make the borrowers comparable before and after Basel III. Furthermore, I use the research design developed by Khwaja and Mian (2008), which controls for the borrower-specific demand change and, thus, separates loan demand change from loan supply change resulting from Basel III.

In the first set of tests, I examine the effects of additional Basel III provisions on borrowers’ loan costs. While I largely follow the research design in Khwaja and Mian (2008), I replace the first-differencing models with fixed effects to analyze granular loan-level data. Using the DD framework, I compare the loan costs of borrowers that obtain loans from the two groups of banks

(i.e., banks that are more affected by Basel III and banks that are less affected) around the time when the first set of additional Basel III provisions was proposed (“the Basel III proposal year”) in 2012. I find that borrowers more affected by Basel III, compared to borrowers less affected, had, on average, an 8.88% significant relative increase in interest spreads and fees from the “pre-Basel period” (i.e., the period before 2012) to the “post-Basel III period” (i.e., the period beginning with

2012).2 Moreover, I find that borrowers more affected by Basel III experienced a 7.95% significant

2 To be specific, although both groups experienced decreases in interest spreads and fees during the period, borrowers more affected by Basel III experienced an 10.89% smaller decrease in interest spreads and fees.

4 relative decrease in loan size from the pre- to post-Basel III period.3 It is worth noting that the change in loan price has both greater economic and statistical significance than the change in loan size. Collectively, my findings suggest that bank regulations make bank credit more expensive for borrowers, consistent with the prior literature (see Section 3.1).

However, I do not find that exposure to Basel III changed the likelihood that banks would impose loan terms regarding: collateral, covenants, and performance-pricing provisions. This suggests that, when providing new loans, banks did not impose extra protections against the potential increase in borrower riskiness.

I then examine the effects of additional regulatory burdens on borrower risk-taking, using the following eight alternative metrics as a proxy for borrower risk-taking: implied option volatility, asset volatility, distance to default, downside EBIT margin volatility, sales concentration, mean growth of capital expenditures, mean growth of investments in intangible assets (including goodwill), and mean growth of R&D expenditures. Although each of these metrics has its own strengths and weaknesses (discussed in Section 4.2), taken together, these metrics complement each other. While largely following the spirit of Khwaja and Mian (2008), I slightly modify the method by using granular borrower-time level data. I compare the risk-taking for borrowers of banks more affected by Basel III and borrowers of banks less affected in both the pre-Basel III and post-Basel III periods. More precisely, I subtract each borrower’s pre-loan risk-taking from its post-loan risk-taking and then examine how the change in borrower risk-taking differed between borrowers more affected by Basel III and borrowers less affected and how these changes shifted

“after the proposal year” (i.e., beginning with 2012). This approach links changes in borrower risk-

3 To be specific, although both groups experienced increases in loan size during the period, borrowers more affected by Basel III experienced an 10.59% smaller increase in loan size.

5 taking to loan originations and thus mitigates the endogeneity concern that a change in borrower risk-taking is due to reasons other than loan originations. I control for the bank × borrower fixed effect, as well as the industry × year × quarter (or industry × year) fixed effect and a comprehensive set of controls, to preclude the alternative explanations that banks more affected by Basel III and high-risk borrowers self-selected each other and that firm-level demand shocks coincide with the proposals of bank regulations.

Using this DD research design, I find that borrowers more affected by Basel III, compared to borrowers less affected, displayed the following: a 9.20% significant relative increase in implied option volatility, an 11.63% significant relative increase in asset volatility, an 18.45% significant relative decrease in distance to default, a 67.74% significant relative increase in downside EBIT margin volatility, a 2.46% significant relative increase in segment sales-based Herfindahl-

Hirschman Index, a 14.31% significant relative mean growth in capital expenditures, a 472.15% significant relative mean growth in investments in intangible assets, and an 8.11% significant relative mean growth in R&D expenditures. This collective evidence supports the hypothesis that borrowers increase their risk-taking as a result of bank regulations.

In addition, I examine the cross-sectional differences in the effects of Basel III on loan costs and risk-taking using the triple-difference research design. Specifically, I explore the role of financial frictions in driving the overall results by testing the heterogeneous effects of Basel III by borrower size, which inversely serves as a proxy for credit constraints, following Misra (2019). I find that Basel III increased loan price and risk-taking for small borrowers significantly more than for large borrowers. However, I do not find that Basel III decreased loan size for small borrowers significantly more than for large borrowers. This triple-difference research design further enables me to attribute the economic responses to Basel III rather than other biasing forces.

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Moreover, I conduct additional analyses to support the assumption that borrowers cannot perfectly substitute bank loans with other sources of financing, such as public debts and equity securities. First, I find that, after the proposal year, borrowers with more exposure to Basel III, compared to borrowers with less exposure, tended to incur significantly higher interest expenses on long-term debts although the amounts of long-term debts that borrowers received did not change much. Second, I do not find that borrowers with more exposure to Basel III issued more shares in response to Basel III than borrowers with less exposure. These results collectively support the validity of the assumption that borrowers have difficulty in perfectly replacing bank loans with other sources of external financing at cheap price.

Furthermore, I conduct two sets of univariate analyses to support the explanation that the increase in borrower riskiness is due to borrowers themselves increasing their risk-taking (i.e., moral hazard) rather than banks having a riskier composition of borrowers (i.e., adverse selection).

First, I find that banks with greater exposure to Basel III were not more likely to lend to borrowers with higher pre-loan risk-taking. Second, I find that banks with greater exposure to Basel III recognized smaller provisions for loans and leases losses (PLLLs) even after borrowers increase their risk-taking. These results undermine the alternative explanation that banks intentionally increase the riskiness of their loan portfolios.

Finally, I conduct two sets of falsification tests in order to further check that my results are indeed attributable to Basel III. First, I replicate my main results by randomizing the proposal dates.

Specifically, I examine the dates of January 1, 2007 and January 1, 2014; and the results do not hold. Second, I replicate my main results by randomizing the asset-size thresholds to redefine banks with greater exposure to Basel III. In particular, I use $150 billion or $450 billion as the asset-size cutoffs. Here too, I do not observe similar results as my original findings. Assuming that

7 other economic forces than Basel III cannot explain the combination of these results, the placebo analyses help establish the plausibility of Basel III’s impact on borrowers’ loan costs and subsequent change in risk-taking.

This paper contributes to the literature in several ways. First, I expand the literature on the role that bank regulations play in financial stability. Prior research in this area has focused exclusively on banks’ choices (e.g., Ongena, Popov, and Udell, 2013; Buchak et al., 2018; Gete and Reher, 2017; Cortés et al., 2020; Slovik and Cournède, 2011; Perotti, Ratnovski, and Vlahu,

2011; Plantin, 2014) rather than borrowers’ decisions. For example, Ongena, Popov, and Udell

(2013) show that bank regulations in domestic markets affect multinational banks’ lending standards in foreign markets. To the best of my knowledge, my paper provides the first empirical evidence of a new mechanism by which bank regulations affect the stability of the financial system: namely, that borrowers themselves increase risk-taking due to bank regulations.

Second, I provide new evidence of the impact of bank regulations on borrowers’ investments. I find that, among other things, bank regulations adopted in the U.S. during the recovery period after the 2008 financial crisis increased borrowers’ investments in fixed assets.

This finding appears to contradict prior studies which show that bank regulations adopted in

Europe (i.e., Basel II4 and EBA5) during crisis periods reduced borrowers’ investments in fixed assets (i.e., Fraisse, Lé, and Thesmar, 2020; Gropp et al., 2019). This seemingly “opposite” finding could be the result of the fact that we focus on regulations adopted in different geographic locations and at different time periods. To be specific, while I focus on regulations adopted during the recovery period after the financial crisis in the U.S., prior literature focuses on regulations adopted

4 Fraisse, Lé, and Thesmar (2020). 5 Gropp et al. (2019).

8 during crisis periods in Europe (i.e. the 2008 financial crisis6 and the European sovereign debt crisis7). While both European and U.S. banks that were affected by bank regulations tended to reduce the relative loan size, affected U.S. banks on average did not reduce the relative loan size as much as their European counterparts. In addition, and perhaps more important, affected U.S. banks significantly raised the relative loan price. In other words, affected European borrowers may not have had sufficient financing to purchase assets during the crisis periods, whereas affected U.S. borrowers may have had sufficient financing during the recovery period, albeit at a relatively higher rate than in the past.

Third, my paper provides additional evidence for the degree to which bank regulations increase borrowers’ loan costs. Prior studies have examined how bank regulations, such as capital requirements, make various types of loans more costly to borrowers (see Dagher et al. (2016) for a review). My research provides additional evidence, in this case from Basel III, that bank regulations increase syndicated loan costs to corporate borrowers. Yet I find the scale of the increase in borrowers’ loan cost is about two times the estimated effect of Basel III put forth by

King in 2010,8 two years before Basel III was proposed in the United States.

The remainder of this paper proceeds as follows: Section 2 summarizes the institutional background for Basel III; Section 3 explores the related literature and develops hypotheses; Section

4 illustrates the DD approach; and Section 5 describes the sample and data. In Section 6, I discuss the results, and, in Section 7, I conduct additional analyses. Section 8 concludes.

2. Institutional background

6 Fraisse, Lé, and Thesmar (2020). 7 Gropp et al. (2019). 8 This paper is relevant because it is published by Bank for International Settlements, which hosts and supports Basel Committee on Banking Supervision.

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In December 2010, the Basel Committee released the framework of Basel III, which focused on two main areas: bank capital and bank liquidity. In response, U.S. banking regulators proposed several rules. These proposals included modifications to the original Basel III framework, such as the ranges of applicability and reconciliation with the Dodd-Frank Wall Street Reform and

Consumer Protection Act (“Dodd-Frank Act”). Following public comment, the Federal Reserve made a few slight adjustments. The rules were finalized on a rolling basis, generally within one year after each proposal, and each rule became effective three months following its finalization.

(See Appendix B for the implementation details of these additional Basel III provisions.) Because the final rules were largely identical to the proposed rules, I focus on the proposal dates, rather than the finalization dates, in my research design.

Although most bank holding companies (BHCs) are subject to Basel III’s risk-weighted capital (RWC) requirements,9 some banks are subject to greater regulatory burdens than others because they are larger in size. For example, BHCs with $250 billion or more in total consolidated assets are subject to additional provisions under Basel III, such as the supplementary leverage ratio

(SLR), the advanced approaches rule (the AA rule), and the liquidity coverage ratio (LCR).10

BHCs with $700 billion or more in total consolidated assets must additionally comply with the enhanced supplementary leverage ratio (the “enhanced SLR”). Moreover, a few very large banks, i.e., Global Systemically Important Banks (GSIBs), must also comply with certain enhanced capital rules. My decision to use $250 billion of total consolidated assets as the cutoff to define banks more affected by Basel III and banks less affected is bolstered by Figure 1, which suggests

9 BHCs with $500 million or more in total consolidated assets are subject to Basel III’s RWC. But it does not allow for a DD analysis, because it consists of the majority of banks in the syndicated loan market. 10 Although Basel Committee also introduced Countercyclical Capital Buffer, it has never been implemented in the U.S.

10 that the additional Basel III provisions (in the case of Figure 3, LCR) are the binding constraints for applicable banks (Yankov, 2020).11

Once bank regulations are implemented, banks subject to the additional Basel III provisions have even stronger incentives to comply, because failing to meet such requirements can be extremely costly: it is likely to result in fines, trigger restrictive supervisory actions, cause serious reputational loss, and create adverse market reactions. Nowadays, dropping below the required capital or liquidity minimums “would, for all intents and purposes, be regarded as the kiss of death”

(Borio and Zhu, 2012). In order to meet the minimum requirements, banks are likely to issue more equity and hire more experts, which in turn increase banks’ costs of operations; and banks may be tempted to pass these costs on to borrowers by increasing loan prices.

3. Literature Review and Hypothesis Development

3.1 Related literature

Many prior studies have found that bank regulations affect bank lending (for a review, see

Dagher et al. (2016)). Specifically, studies have consistently found that Basel III increases loan rates and reduces loan amounts (e.g., Roulet, 2018; Gavalas, 2015). Moreover, the prior literature suggests that the impacts of bank regulations on bank lending can also affect certain borrower decisions. For example, Hancock and Wilcox (1997) find that capital requirements reduce commercial real estate investments in the economy. A more recent study by Fraisse, Lé, and

Thesmar (2020) finds that Basel II, a regulation with weaker enforcement and requirements than

Basel III, reduced borrowers’ fixed assets, capital expenditures, and number of employees. A

11 Yankov (2020) states that maintaining large amounts of HQLA is at the opportunity cost of forgone income from higher yielding and riskier investments, such as equity investments. The effect of the opportunity cost can be inferred from the behaviors of modified LCR banks and non-LCR banks—not only did both of them maintain their HQLA-to- assets ratios at lower levels, but the latter also reduced their HQLA-to-assets ratios.

11 thorough search of the literature, however, reveals no empirical evidence as to whether bank regulations increase borrower risk-taking.

Two empirical studies explore the effects of Basel III generally on borrowers’ performance.

First, Baloria, Cheng, and Gallimberti (2018) analyze the effects of the Dodd-Frank Act and find that borrowers experienced an increase in performance as measured by ROA, net profit margin, and Z-Score (all unadjusted for risk) as a result of the Dodd-Frank Act. The authors interpret those effects as having resulted from closer bank monitoring. My results offer an alternative explanation: that borrowers more affected by regulations improve their performance because they are willing to take on riskier projects, which generate higher expected returns, to offset their higher loan costs.

In the second study, Ohlrogge (2017) finds that Basel III reduced home and small-business mortgage default rates, results which would seem to contradict mine. However, my results speak to a question that cannot be answered by Ohlrogge (2017), namely whether large public corporate borrowers vary their risk-taking after receiving loans from banks burdened by Basel III. Ohlrogge

(2017) cannot address this question, because his sample is comprised of small borrowers who have more limited choices in terms of risk-taking.

3.2 Hypothesis development

Before establishing how bank regulations may increase borrower risk-taking, it is important to look at whether bank regulations affect banks’ cost of lending and loan terms offered to borrowers. A few researchers have posited that increased capital and liquidity requirements imposed by bank regulations may decrease the cost of lending for banks due to many reasons, including: (i) uninformed investors may perceive the banks as safer and thus be more willing to invest (Holmstrom and Tirole, 1997), and (ii) borrowers may perceive banks as more efficient monitors when banks have more equity and, thus, greater bankruptcy costs (Allen, Carletti, and

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Marquez, 2011). However, the vast majority of researchers have discovered that higher capital and liquidity requirements increase the cost of lending for banks (e.g. Baker and Wurgler, 2015). The rationale is that, by being forced to maintain more equity to meet the requirements of capital regulations, banks are at a disadvantage because: (i) equity has less favorable tax treatment than debt (De Mooij, 2011), (ii) the issuance of equity carries significant underwriting fees (Dagher et al., 2016), and (iii) the issuance of equity incurs costs due to information asymmetry (Myers and

Majluf, 1984). Moreover, when banks are forced to maintain more liquid assets to meet the requirements of liquidity regulations, banks freeze liquid assets into immobility and this reduces their profitability (Thakor, 2018). When banks’ cost of lending increases, many studies have also found that banks pass on that cost to borrowers by reducing loan amounts and raising loan rates

(for a review, see Jackson et al. (1999)).12 Following this line of reasoning, I propose my first hypothesis, stated in alternative form:

H1: Bank regulations result in greater bank loan costs for corporate borrowers.

Assuming that bank regulations are costly for banks and that banks are able to pass on that cost to borrowers, I move on to the second hypothesis. Hakenes and Schnabel (2011) have developed a model to study the effect of bank regulations on borrower risk-taking. First, they assume that banks control the funding for borrowers’ projects, while borrowers determine the risk of projects. Since borrowers have only limited liability and there is information asymmetry between banks and borrowers, Hakenes and Schnabel (2011) posit that borrowers, in order to maximize expected profits, are faced with a risk-shifting problem (i.e., moral hazard)13 when bank

12 Although one may argue that banks could avoid incurring this increased cost of lending by shifting their business focus to relatively higher-yielding activities, such as retail loans, there is likely a limit on the scale of such attempts due to the growing competition from small banks and nonbanks (Gete and Reher, 2017). 13 However, according to Hakenes and Schnabel (2011), the increase in borrower riskiness does not necessarily increase banks’ probability of default, because banks can choose correlation of their loans. Specifically, when banks

13 regulations are introduced. In other words, Hakenes and Schnabel (2011) posit that, when bank regulations are introduced, borrowers’ loan costs are exogenously increased and, as a result, borrowers have an incentive to undertake riskier projects with higher expected returns to compensate for the higher borrowing costs. Since borrowers’ choices deviate from the first best,

Hakenes and Schnabel (2011) argue that this increased borrower risk-taking is an unintended consequence of bank regulations and may pose a potential threat to the stability of the financial system.

The increase in borrower risk-taking proposed by Hakenes and Schnabel (2011) relies on two key assumptions. First, that there is information asymmetry between banks and borrowers.

The extant literature provides numerous arguments and evidence to support this assumption

(DeFond and Jiambalvo, 1994; Graham, Li, and Qiu, 2008; Guttman and Marinovic, 2018;

Sweeney, 1994; Billett et al., 2016; Billett, King, and Mauer, 2007). Second, Hakenes and

Schnabel (2011) assumes that borrowers are bank-dependent and have difficulty in accessing financing from other sources. A number of studies support this assumption. For example, some researchers have found that the cost and time involved in establishing new lending relationships may prohibit borrowers from quickly obtaining funding elsewhere14 (Khan and Lo, 2018; Santos and Winton, 2008; Jackson et al., 1999). Another researcher argues that large borrowers may have obstacles in switching banks due to the fact that not all banks have the financial wherewithal to make large loans (Lo, 2014). Other researchers in the relationship lending literature point out that

expect borrowers’ excess risk-taking, they can decrease the correlation between their loans and, thus, decrease their own risk-taking and default probability. 14 Later in the paper, Table 10 provides evidence in support of this notion.

14 other types of financing, including public debt,15 equity financing,16 and trade credit,17 are much more costly than bank loans. Given the strong support for the assumption behind the suggested increase in borrower risk-taking, I propose my second hypothesis, stated in alternative form:

H2: Bank regulations lead to borrowers increasing their risk-taking.

4. Research design

I use a difference-in-differences (DD) research design to compare borrowers of the banks with more exposure to Basel III (i.e., banks with $250 billion or more in total consolidated assets) to borrowers with less exposure in both the pre- and post-Basel III periods. The DD method estimates the average treatment effect of greater regulatory burdens resulting from Basel III. I define the event year as when the SLR was proposed (i.e., 2012), because the SLR was the first among the additional Basel III provisions to be proposed in the U.S.18 The selection of the proposal year as the event year is consistent with Bindal et al. (2020) and Bouwman, Hu, and Johnson

(2018) as well as the trends of HQLA-to-assets ratio (i.e., a crude LCR metric) (see Figure 1).

Specifically, starting at the end of 2012, immediately before the LCR rule was proposed in the

U.S., banks subject to standard LCR began to accumulate liquid assets. However, beginning at end of 2014, immediately before the rule became effective in the U.S., these banks stopped acquiring liquid assets. Consistent with Bindal et al. (2020) and Bouwman, Hu, and Johnson (2018), Figure

1 indicates that banks started to take action when the rule was first proposed, long before its finalization and implementation. I believe that this phenomenon likely happened because banks

15 See Adrian, Colla, and Song Shin (2013), Bolton and Freixas (2000), Diamond (1991), Diamond (1984), Petersen and Rajan (1994), Becker and Ivashina (2014), Chava and Purnanandam (2011), Fama (1985), Chemmanur and Fulghieri (1994), Denis and Mihov (2003), and Gertner and Scharfstein (1991). 16 See Lo (2014), Dagher et al. (2016), and Myers and Majluf (1984). 17 See Danielson and Scott (2004), Beck, Demirgüç-Kunt, and Maksimovic (2008), and Danielson and Scott (2004). 18 I do not define the Basel III proposal date as December 10, 2009 because the Basel Committee released merely the general framework of the additional Basel III provisions without details on implementation.

15 realized, from past experience, that the regulations would probably be adopted as proposed and within a very short time frame and that compliance might be costly if they were forced to buy liquid assets (or sell illiquid assets) in large amounts and too quickly.

4.1 Borrower loan costs

I use the following DD model to estimate the effects of additional Basel III provisions

(hereafter “the effects of Basel III” for brevity) on borrowers’ loan costs (see Figure 2 Panel A for a visual presentation of the research design):

퐿표푎푛퐶표푠푡푠푖푗푡 = 훽푇푟푒푎푡푖푗푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 휑퐶표푛푡푟표푙푠푖푗푡−1 +

훼푖푗 + 훼푠푡 + 휀푖푗푡 [1] where i represents the i-th borrower; j represents the j-th bank; t represents the t-th year-quarter; s represents the s-th industry division; and LoanCostsijt represents interest spreads and fees in basis points over LIBOR (SpreadFee) that bank j charged its borrower i in year-quarter t upon a loan origination. Alternatively, LoanCostsijt can also be: loan size (Loan/Asset), the number of covenants (NumCov), an indicator for collateral requirements (IndCol), or an indicator for performance pricing provisions (IndPricing). In this model, Treatijt indicates whether or not the dominant lead bank j of the borrower i’s syndicated loan (see the definition of “dominant lead bank of a borrower’s loan” at the end of Section 4.1) has total consolidated assets of $250 billion or more at the beginning of year-quarter t.19 Since a borrower’s dominant lead bank on a loan may switch from loan to loan, Treatijt is a time-varying variable. In this model, Postt indicates whether

19 In untabulated tests, I also define Treat as whether or not the dominant lead bank j of the borrower i’s syndicated loan in year-quarter t had total consolidated assets of $250 billion or more as of the second quarter of 2012. This definition prevents the potential endogeneity concern that banks manipulated their assets in order to avoid being subject to regulations (Bouwman, Hu, and Johnson, 2018). Results are robust to this alternative definition of Treat. This is likely because, with this alternative definition, there are only two (out of 26) banks that have different Treat status before 2009. After 2009, all banks have the same Treat status between the two definitions due to later growth of the banks.

16 or not the loan was originated after January 1, 2012, and LogBankAssetijt-1 represents a BHC’s total consolidated assets at the beginning of year-quarter t. In other words, LogBankAssetijt-1 is the running variable that determines the Treatijt status of a dominant lead bank j on a loan. In this

20 model, Controlsijt-1 represents control variables including one-quarter lagged borrower characteristics (i.e., assets, ROA, and leverage) and contemporaneous loan characteristics (i.e.,

21 maturity, loan type indicator, bank-borrower relationship). Finally, εijt represents the standard errors clustered at the bank level.22 More detailed definitions for all variables are in Appendix A.

In order to mitigate the omitted variable bias, I explicitly control for lagged borrower characteristics and contemporaneous loan terms that may affect borrowers’ loan costs. To be specific, I control for borrower characteristics because borrower financial health (Lo, 2014) is an important consideration when banks determine loan terms. Specifically, I control for borrowers’ assets, ROA, and leverage, since they may affect a bank’s loan terms. Moreover, I control for loan characteristics because they too may affect the loan terms (Dou, 2019). Consequently, I control for loan maturity, loan type, and whether there exists a prior relationship between banks and borrowers.

Following the spirit of Khwaja and Mian (2008), I control for αij and αst, which are the bank

× borrower and industry × year × quarter fixed effects, respectively. The bank × borrower fixed effect controls for the time-invariant bank characteristics (i.e., the bank fixed effect), time-

20 Following Angrist and Pischke (2008), I lag the control variables by one quarter to ensure that the control variables are fixed at the time the regressor of interest is determined. 21 In untabulated tests, results are robust to the inclusion of additional bank characteristics as controls, including bank ROE, bank leverage, and bank deposit-to-total-liabilities ratio (which are proxies for banks’ profitability, capital structure, and funding sources, respectively). Results are also robust when I exclude all control variables. 22 One potential concern with this level of clustering is that the standard errors can be biased when the number of clusters are small—there are solely 26 clusters (i.e., 26 banks) in my sample. To mitigate this potential concern, I verify that my results are also robust to clustering the standard errors by borrower.

17 invariant borrower characteristics (i.e., the borrower fixed effect), 23 and time-invariant characteristics of the bank-borrower pairs. The bank × borrower fixed effect essentially establishes a within-borrower estimator, as in Khwaja and Mian (2008), which exploits cross-bank variation in compliance with Basel III. To be specific, this allows me to compare the loans that are made to the same borrower but are issued by banks more affected by Basel III and by banks less affected by Basel III. Since a borrower experiences the same borrower-specific demand change for loans, the different degrees of loan costs experienced by that borrower could be attributed to the differences in its banks’ exposure to Basel III. In other words, the method separates loan demand change from loan supply change resulting from Basel III.24

My research design improves Khwaja and Mian (2008)’s design in three ways. First, I substitute Khwaja and Mian’s (2008) first-differencing method with the bank × borrower fixed effect. This enables me to analyze loan-level observations, rather than collapsed pre-Basel III and post-Basel III observations for each bank-borrower pair. Second, my method allows me to use a larger sample, as I am not limited to borrowers with multiple bank relationships. My method allows me to analyze any borrower that has borrowed from a bank more than once. Third, similar to

Degryse et al. (2019), I also control for the industry × year × quarter fixed effect, which allows me to absorb unobservable time-varying industry trends. This mitigates the concern that industry-level demand shocks, which might affect borrowers’ loan costs and risk-taking, might have coincided with the proposal of Basel III. In sum, my estimator uses richer information and eliminates more alternative explanations than that of Khwaja and Mian (2008).

23 The bank × borrower fixed effect perfectly absorbs the stand-alone bank fixed effect and borrower fixed effect. 24 Of course, this paper suffers from the same limitation as Khwaja and Mian (2008). For example, this method does not take into account that different banks may specialize in different types of loans.

18

However, I do not include the bank × quarter fixed effect or borrower × quarter fixed effect for two reasons: First, not enough borrowers obtained several syndicated loans from different syndicate groups within one year-quarter. Second, there were also not enough banks that originated multiple syndicated loans within one year-quarter.25 Therefore, there was not enough variation in the data to include the bank × quarter or borrower × quarter fixed effect.

I identify the “dominant lead bank of a borrower’s loan” following the majority of the extant research (e.g., Khan and Lo, 2018; Lo, 2014; Bharath et al., 2007; Adam and Streitz, 2016;

Chen and Martin, 2011). I focus on only the lead banks because, as Sufi (2007) and Lo (2014) note, the loan terms of each syndicated loan are generally negotiated only between the lead bank and borrower. After the loan terms are set, the lead bank turns to participant banks who typically must accept the negotiated loan terms before joining the syndicate. Moreover, a lead bank usually holds a significantly larger share of the syndicate than participant banks. Thus, participant banks do not have bargaining power over loan terms. As a result, it is the lead banks and the lead banks’ exposure to Basel III that determine the characteristics of syndicated loan terms.

If a syndicated loan has only one lead bank, I treat the lead bank as the dominant lead bank.

However, if a syndicated loan has zero or more than one lead banks explicitly identified, following the majority of the extant literature (e.g., Khan and Lo, 2018; Lo, 2014; Bharath et al., 2007), I identify the dominant lead bank of a borrower’s loan by using the following four steps. First, I identify the lead banks for a loan if the banks are identified as “lead arranger.” If no banks are explicitly identified as “lead arranger,” I deem all banks on the loan as lead banks. Second, to capture the borrower’s active banking relationships, I identify all loans obtained by that borrower

25 These observations make intuitive sense because syndicated loans tend to be large loans.

19 in the six years preceding each loan origination. Third, I calculate the following ratio Kij for the pair between borrower i and its lead bank j on each loan:26

푇표푡푎푙 푙표푎푛 푎푚표푢푛푡 푙푒푛푡 푏푦 푙푒푎푑 푏푎푛푘 푗 푡표 푏표푟푟표푤푒푟 푖 푖푛 푡ℎ푒 푝푎푠푡 푠푖푥 푦푒푎푟푠 퐾 = 푖푗 푇표푡푎푙 푙표푎푛 푎푚표푢푛푡 푏표푟푟표푤푒푑 푏푦 푏표푟푟표푤푒푟 푖 푖푛 푡ℎ푒 푝푎푠푡 푠푖푥 푦푒푎푟푠

Fourth, I define the dominant lead bank of the borrower’s loan as the borrower’s largest loan

27 supplier (i.e., the bank with the largest Kij ratio) among the loan’s lead banks, because the largest loan supplier is likely to have the greatest bargaining power over loan terms (Chen, Gong, and

Muckley, 2020; Khan and Lo, 2018; Lo, 2014; Bharath et al., 2007).

4.2 Borrower risk-taking

Similarly, in order to estimate the effects of Basel III on borrower risk-taking, I examine the borrower-year-quarter (or borrower-year) level dataset, rather than the loan level dataset. This is because borrower risk-taking is at the borrower level, rather than at the loan level. Specifically,

I use the following DD model (see Figure 2 Panel B for a visual exhibit of the research design)

∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 +

휑퐶표푛푡푟표푙푠푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푡+12 [2] where ΔRiskTaking(it-12,it+12) can represent: change in implied option volatility (∆OptV), change in asset volatility (∆AssetV), change in distance to default (∆DTD), change in downside EBIT margin volatility (∆DEBITM), change in Herfindahl-Hirschman Index (∆HHI), change in mean growth of capital expenditures (∆CAPEX), change in mean growth of investments in intangible assets

(∆IntanInv), and change in mean growth of R&D expenditures (∆R&D) of borrower 푖 between year-quarter t-12 (or t-8) prior to a loan origination and year-quarter t+12 (or t+8) following the

26 In estimating 퐾푖푗, I assume the lead bank receives full credit for the total amount of a loan even though a syndicate could have funded some of the loan, following Khan and Lo (2018) and Lo (2014). 27 If a borrower has multiple dominant lead banks on a loan, I drop the observation. This reduces the observations by less than 5%.

20 loan origination.28 This metric subtracts the borrower’s pre-loan risk-taking from the borrower’s post-loan risk-taking. This subtraction takes out the time-varying borrower-specific risk-taking trends and serves a function similar to the borrower × year × quarter (or borrower × year) fixed

29 30 effect. Treati(j)t indicates whether or not borrower i’s dominant lead bank j in year-quarter (or year) t (see the definition of the “borrower’s dominant lead bank in a year-quarter (or year)” in the next paragraph) has total consolidated assets of $250 billion or more at the beginning of year-

31 32 quarter (or year) t. αst represents the industry × year × quarter (or industry × year) fixed effect.

33 Note that Controlsi(j)t-1 in this equation does not contain loan characteristics, which is different from Equation [1]. This is because the regression here is at the borrower-year-quarter (or borrower-

28 The first five variables evaluating a borrower’s overall riskiness are measured over the window of eight quarters before and after loan originations. The rest four variables evaluating a borrower’s risky activities with uncertain benefits are measured over the window of 12 quarters before and after loan originations. I use a longer period to capture changes in borrowers’ risky activities with uncertain benefits because: (i) it may take time for firms to adjust their real activities, and (ii) firms may not conduct certain types of activities each year. 29 However, I cannot include borrower × quarter fixed effect because the dependent variable is at the borrower × quarter level, and, thus, the borrower × quarter fixed effect would subsume it. Moreover, I also cannot include bank × quarter fixed effect, because it would subsume the key independent variable, Treati(j)t × Postt, which is at the bank × quarter level.

30 It was supposed to be denoted as Treatijt. However, since a borrower i in quarter t has only one dominant lead bank j, Treatijt is the same as Treatit. 31 In untabulated tests, I also define Treat as whether or not borrower i’s dominant lead bank j in quarter t had total consolidated assets of $250 billion or more as of the second quarter of 2012. This definition prevents the potential endogeneity concern that banks manipulated their assets in order to avoid being subject to the additional provisions of Basel III. Results are robust to this alternative definition of Treat. This is likely because, with this alternative definition, there are only two (out of 26) banks that have different Treat status before 2009. After 2009, all banks have the same Treat status between the two definitions due to later growth of the banks. 32 When the dependent variable is implied option volatility (ΔOptV), I control for the year × quarter fixed effect because ΔOptV is measured at the quarterly level. When dependent variable is one of other eight alternative metrics, I control for the year fixed effect, because those metrics are calculated at the yearly level. 33 In untabulated tests, results are robust to the inclusion of additional bank characteristics as controls, including bank ROE, bank leverage, and bank deposit-to-total-liabilities ratio (which are proxies for banks’ profitability, capital structure, and funding sources, respectively). Results are also robust when I exclude all control variables. Similar to Treati(j)t, Controlsi(j)t-1 was supposed to be denoted as Controlsijt-1. However, since a borrower i in quarter t has only one dominant lead bank j, Controlsijt-1 is the same as Controlsit-1.

21 year) level, whereas Equation [1] is at the loan level. All other variables are defined as in Equation

34 [1]. 휀푖푡+12 represents the standard error clustered at the bank level.

Because a borrower may have multiple loans originated in a year-quarter (or year), I identify the “borrower’s dominant lead bank in a year-quarter (or year)” as the borrower’s largest loan supplier (for the past six years) among the dominant lead banks for the borrower’s loans originated in the year-quarter (or year), following the majority of the existing literature (e.g., Khan and Lo, 2018; Lo, 2014; Bharath et al., 2007).35 This is because the largest loan supplier is likely to have the greatest influence over borrower behavior. It is worth noting that the definition of the

“borrower’s dominant lead bank” here is slightly different from the “dominant lead bank of a borrower’s loan” in Section 4.1—the “dominant lead bank of a borrower’s loan” defined in Section

4.1 is at the loan level, while the “borrower’s dominant lead bank” defined in this section is at the borrower-year-quarter (or borrower-year) level.

I use two groups of alternative variables to measure borrower risk-taking in Equation [2].

Although each of these variables has its own strengths and weaknesses, taken together, these metrics complement each other. In the first group, I evaluate the overall riskiness of borrowers using four alternative metrics. First, I use implied option volatility (OptV) to estimate the market expectation of firm risk (Fleming, 1998). Second, I employ asset volatility (AssetV) estimated from the KMV model to examine a firm’s unlevered volatility (Acharya, Almeida, and Campello, 2013).

Third, I also use distance to default (DTD) to measure borrowers’ credit quality (Dou, 2019).

Fourth, I employ downside EBIT margin volatility (DEBITM) to measure the downside risk of earnings (Faccio, Marchica, and Mura, 2011).

34 I also verify that my results are robust to clustering the standard errors by borrower. 35 If a borrower has multiple dominant lead banks in a quarter, I drop the observation. This reduces the borrower-time observations by 4.50%.

22

In the second group, I examine the risky activities with uncertain benefits conducted by borrowers using four alternative metrics. First, I use the segment sales-based Herfindahl-

Hirschman Index (HHI) to capture a firm’s customer concentration in particular business segments. This metric evaluates the riskiness of a firm’s strategy and earnings, since a more concentrated customer base translates into higher risk (Dhaliwal et al., 2016; Kini and Williams,

2012). Second, I use the mean growth of capital expenditures (CAPEX) to capture a firm’s risk- shifting behavior, following Nini, Smith, and Sufi (2009) and Kini and Williams (2012). Third, I use the mean growth of investments in intangible assets (including goodwill) (IntanInv) to capture a firm’s initiatives in risky projects that are hard to monitor (Bargeron, Lehn, and Zutter, 2010).

Fourth, I examine the mean growth of R&D expenditures (R&D), which evaluates a firm’s risky investments with uncertain benefits and are difficult for banks to monitor (Bischof, 2014; Faccio,

Marchica, and Mura, 2011; Bargeron, Lehn, and Zutter, 2010; Kini and Williams, 2012).

In Section 7.1, I provide evidence in support of the parallel-trend assumption of the DD research design.

5. Sample and data

5.1 Sample construction

I use the following data in the paper: data regarding the syndicated loans from DealScan, data regarding characteristics of public corporate borrowers from CRSP/Compustat, and data regarding characteristics of public BHCs from Compustat and Y9 reports. I link the three datasets together using link tables provided by Chava and Roberts (2008) and Schwert (2018).36 This linked

36 I am only able to identify public borrowers and public BHCs in DealScan because the link tables provided by Chava and Roberts (2008) and Schwert (2018) only link DealScan to borrowers’ and banks’ Compustat, respectively. However, I further link banks’ Compustat with Y9 reports using CRSP-FRB (2017) link table in order to replace banks' total consolidated assets in Compustat with those in Y9 reports. This step allows me to obtain more accurate numbers for banks’ total consolidated assets because the FR Y9 reports have smaller rounding errors in bank total consolidated assets than Compustat does.

23 dataset contains information on syndicated loans and participating BHCs and borrowers, and it forms my initial sample.

Starting with this initial sample, I then exclude borrowers that are financial institutions, such as banks and credit institutions, since they can also be affected by Basel III (Plantin, 2014). I also exclude borrowers in the utility industry, since their risk-taking decisions can be a byproduct of regulation (Fama and French, 2001). Moreover, I exclude BHCs with less than $50 billion in total consolidated assets, since these BHCs are subject to a different set of regulations.37 In order to compare changes in borrower riskiness around Basel III, I further limit the sample to borrowers that have borrowed in both pre- and post-Basel III periods. Finally, in order to define the Treat variable, I remove banks with missing quarterly and annual information regarding their total consolidated assets.

I implement Equation [1] in a loan-level dataset and carry out Equation [2] in a borrower- year-quarter-level (or borrower-year-level) dataset. I examine the effects of Basel III on borrowers’ loan costs using the loan-level dataset, while I investigate the effects of Basel III on borrower risk- taking using borrower-year-quarter-level (or borrower-year-level) dataset. The number of observations in the loan-level dataset is greater than that in the borrower-year-quarter-level (and borrower-year-level) dataset, because a borrower can occasionally obtain more than one loan from more than one bank in a year-quarter (and a year). Nonetheless, the two datasets are similar in the sense that both contain information on banks and borrowers so that I can control for their characteristics in regression analyses. My final main sample is comprised of 9,730 loan initiations

(what DealScan defines as “facilities”) in the loan-level dataset and 6,621 borrower-year-quarter

37 See the Dodd-Frank Act implemented by the Federal Reserve (https://www.govinfo.gov/content/pkg/FR-2012-10- 12/pdf/2012-24987.pdf).

24 observations (or 5,919 borrower-year observations) in the borrower-year-quarter-level (or borrower-year-level) dataset.

5.2 Sample period

My main sample period is from 2002 to 2016, excluding the financial crisis period from the first quarter of 2007 to the second quarter of 2010. I exclude that period, because borrower risk-taking may have been abnormal during this period. I start the sample ten years before the proposal year to include as many borrowers that borrowed both before and after the proposal year

(i.e., beginning with 2012) as possible.38 I end the sample period with 2016 to calculate borrower subsequent risk-taking through 2018.39 For a robustness check and to ensure the integrity of the sample period and maintain the semi-symmetry of periods around the proposal year, I also shorten the sample period to a subperiod, beginning with the third quarter of 2010 and ending with the fourth quarter of 2014. See Figure 3 for a visual presentation of the sample periods.

5.3 Sample description

Table 1 summarizes the number of unique banks and borrowers in both the loan-level and borrower-year-quarter-level datasets of the main sample. Panel A focuses on the loan-level dataset, showing how the number of banks and borrowers changed after the Basel III proposal year, while

Panel B focuses on the borrower-year-quarter-level dataset, also describing how the number of banks and borrowers changed after the proposal year. In Panel A, before the Basel III proposal year, there are 19 banks more affected by Basel III and 23 banks less affected; after the proposal year, these numbers drop to 13 and 14, respectively. Before the Basel III proposal year, there are

38 The longer the pre-Basel III period is, the more borrowers are identified as having borrowed before the Basel III proposal year. In such a case, I am likely to identify more borrowers that borrowed again after the Basel III proposal year, and, thus, to be able to include more borrowers in my sample. 39 Implied option volatility is calculated only up to 2017, because option suite by WRDS is discontinued in 2017.

25 also 1,069 borrowers more affected by Basel III and 316 borrowers less affected. After the proposal year, the number of those more affected remains almost the same (i.e., 1,072), while the number of those less affected dramatically decreases (i.e., 121).

Although, in sample construction, I require that the borrowers must exist in both the pre- and post-Basel III periods, the total number of borrowers in the pre-Basel period is slightly different from that in the post-Basel period. This is because a borrower can obtain several loans from several dominant lead banks; while some of the dominant lead banks are more affected by

Basel III, some of the dominant lead banks are less affected. In other words, a borrower can be more affected by Basel III in a year-quarter but less affected in different year-quarter in each of the pre- and post-Basel III periods. Therefore, such borrowers would be presented as both borrowers more affected by Basel III and borrowers less affected in each of the pre- and post-Basel

III periods in Table 1.

Similarly, in Panel B, regarding the borrower-year-quarter-level dataset, before the Basel

III proposal year, there are 12 banks that are more affected by Basel III and 16 banks that are less affected. After the proposal year, these numbers drop to 9 and 11, respectively. Regarding the borrower-year-quarter-level dataset, before the Basel III proposal year, there are 1,068 borrowers more affected by Basel III and 293 borrowers less affected. After the proposal year, the number of those more affected remained almost the same (i.e., 1,071), while the number of those less affected dramatically decreased (i.e., 115).

When comparing Panel A to Panel B, one may notice that, although there are fewer banks with greater exposure to Basel III than banks with less exposure to Basel III. Conversely, there are more borrowers that are more exposed to Basel III than borrowers that are less exposed to Basel

III. This suggests that larger banks tend to make more syndicated loans. Moreover, one may also

26 notice that the number of unique banks and borrowers in the loan-level dataset is greater than the number in the borrower-year-quarter-level dataset. This is because I exclude observations in the borrower-year-quarter-level dataset, when I cannot identify a borrower’s dominant lead bank in a year-quarter.

In order to mitigate the concern that the potential increase in borrower riskiness is due to banks having a riskier composition of borrowers (i.e., adverse selection), rather than borrowers themselves increasing their risk-taking (i.e., moral hazard), I use a univariate DD method in the borrower-year-quarter-level dataset. Specifically, I compare, in both the pre- and post-Basel periods, before-loan-origination characteristics of borrowers more affected by Basel III to those of borrowers less affected. As is shown in Table 2, borrowers more affected by Basel III, compared to borrowers less affected, do not seem to have had a relative increase in their pre-loan riskiness from the pre- to post-Basel period. To be specific, although borrowers more affected by Basel III, compared to borrowers less affected, had a relative increase in downside EBIT margin volatility, borrowers more affected also had a relative decrease in asset volatility, a relative increase in distance to default, and a relative decrease in mean growth of capital expenditures. This descriptive evidence indicates that, before obtaining loans, borrowers of banks more affected by Basel III were not riskier than borrowers of banks less affected. Therefore, the potential increase in borrower riskiness is not likely due to banks with greater exposure to Basel III and high-risk borrowers selecting each other.

One may be also concerned that there are systematic differences in the pre-Basel period between borrowers more affected by Basel III and borrowers less affected that could lead to differences in their subsequent risk-taking. Although, as is presented in Table 2, borrowers with more exposure to Basel III indeed seem to be very different from borrowers with less exposure in

27 a number of pre-loan characteristics in the pre-Basel period,40 this does not invalidate the DD research design. Theoretically, as long as the dependent variables between the two groups of borrowers change parallelly, they are allowed to be at different levels for DD research design to be valid. In other words, the two parallel trends can have different intercepts. Therefore, I provide evidence in support of the parallel-trend assumption in Section 7.1.

Using a univariate DD method, I compare borrowers’ loan costs for borrowers with more exposure to Basel III and borrowers with less exposure between the pre- and post-Basel periods.

Table 3 Panel A shows that borrowers more affected by Basel III, compared to borrowers less affected, seem to have experienced a relative decrease in interest spreads and fees. Moreover, borrowers more affected by Basel III do not seem to have experienced a relative change in loan size. However, these are because I do not compare changes in loan price and loan size within the same bank-borrower pairs in the same year-quarter and do not cluster the standard errors.

Therefore, I conduct regression analyses in later parts.41 At the same time, it appears that borrowers more affected by Basel III, compared to borrowers less affected, encountered a relative increase in the number of covenants, a relative decrease in maturity, and a relative decrease in the likelihood to borrow from their relationship banks.

In the univariate DD analysis, I also compare changes in risk-taking after loan origination for both borrowers with more exposure to Basel III and borrowers with less exposure between the pre- and post-Basel periods. Table 3 Panel B shows that borrowers more affected by Basel III became riskier after the proposal year. Specifically, I find that borrowers more affected by Basel

III, compared to borrowers less affected, experienced a relative increase in asset volatility

40 Those characteristics include bank assets, borrower assets, ROA, leverage, and option volatility. 41 Once I control for the bank × borrower and industry × year × quarter fixed effects and cluster the standard errors by bank (or by borrower), I find that borrowers more affected by Basel III, compared to borrowers less affected, actually experienced a relative increase in interest spreads and fees and a relative decrease in loan size.

28

(∆AssetV), a relative decrease in distance to default (∆DTD), a relative increase in Herfindahl-

Hirschman Index (∆HHI), and a relative increase in mean growth of capital expenditures

(∆CAPEX) from the pre- to post-Basel period. This univariate evidence is suggestive of borrowers increasing their risk-taking following the Basel III proposal year. Nevertheless, this evidence is not yet conclusive since the univariate DD analysis does not take controls into account and does not adjust the standard errors for cross-sectional dependence. Therefore, I next conduct regression analyses.

6. Results

Table 4 reports changes in borrowers’ loan costs following the Basel III proposal year using

Equation [1]. In Column (1), the coefficient of interest on the interaction term Treat × Post is positive and statistically significant [coefficient = 17.049, t statistics = 2.85]. The coefficient implies that interest spreads and fees borne by borrowers more affected by Basel III, compared to those by borrowers less affected, increased by at least 8.88% (= 17.049 / 192.096 42 ) as a consequence of Basel III. This magnitude is economically significant, as it is equivalent to an increase in the loan cost of $103.66 million (= 17.049 / 100 × $608 million43). Column (2) reports the estimated effects of Basel III on loan size. The result implies that loan size decreased by 7.95%

(= -0.012 / 0.151 44 ) in the post-Basel period. The decrease is statistically and economically significant, equivalent to a decrease in the loan size of $48.34 million (= 7.95% × $608 million).

Columns (3) through (5) represent the effects of Basel III on the number of covenants, the occurrence of collateral requirements, and the occurrence of performance pricing provisions. I do not find that Basel III had a significant impact on these loan terms. Results in Table 4 collectively

42 In untabulated tables, the sample mean of interest spreads and fees is 192.096 in basis points. 43 In untabulated tables, the sample mean of loan size in dollar amount is $608 million. 44 In untabulated tables, the sample mean of loan size is 0.151.

29 suggest that, as borrowers became more exposed to Basel III, their loan costs increased, although they were not faced with more contractual terms than before. One can notice that the change in interest spreads and fees has both greater economic and statistical significance than the change in loan amounts. The results in Table 4 overall supports H1 that borrowers experienced greater loan costs due to Basel III.

Table 5 reports changes in borrower risk-taking resulting from Basel III using Equation

[2]. Columns (1) through (8) present the estimated effects of Basel III on eight alternative metrics of borrower risk-taking using the borrower-year-quarter (or borrower-year) level dataset. All of the coefficients on the interaction term Treat × Post reported in Columns (1) through (8) have expected signs and the effects are statistically and economically significant. Specifically, I find that borrowers more affected by Basel III, compared to borrowers less affected, experienced a

9.20% ( = 0.037 / 0.40245) relative increase in implied option volatility (∆OptV) and an 11.63% ( =

0.040 / 0.34446) relative increase in asset volatility (∆AssetV). These suggest that borrowers became overall riskier following the proposal of additional Basel III provisions. Moreover, borrowers more affected by Basel III, compared to borrowers less affected also experienced an 18.45% ( = -1.353

/ 7.33347) relative decrease in distance to default (∆DTD), which implies borrowers have greater default probability after additional Basel III provisions were proposed. Furthermore, I observe a

67.74% ( = 0.063 / 0.09348) relative increase in downside EBIT margin volatility (∆DEBITM) for borrowers more affected by Basel III, compared to borrowers less affected. This suggest that, in addition to the increase in overall riskiness following the proposal of Basel III, borrowers also

45 This is the sample mean of implied option volatility (untabulated). 46 This is the sample mean of asset volatility (untabulated). 47 This is the sample mean of distance to default (untabulated). 48 This is the sample mean of downside EBIT margin volatility (untabulated).

30 experienced, in particular, an increase in downside risk, which is likely concerning for debtholders.

In addition, I find that borrowers more affected by Basel III, compared to borrowers less affected, had a 2.46% ( = 0.049 / 1.98849) relative increase in segment sales-based Herfindahl-Hirschman

Index (∆HHI). This suggests that borrowers had a more focused customer base as a result of Basel

III. Lastly, I find that borrowers more affected by Basel III, in comparison to borrowers less affected, incurred a 14.31% ( = 0.171 / 1.19550) relative growth in capital expenditures (∆CAPEX), a 472.15% ( = 14.476 / -3.06651) relative growth in investments in intangible assets (∆IntanInv), and an 8.11% ( = 0.089 / 1.09852) relative growth in R&D expenditures (∆R&D)53 from the pre- to post-Basel period. Since capital expenditures, intangible assets, and R&D expenditures are difficult for banks to monitor and are associated with uncertain outcomes, these are likely the ways that borrowers increase their risk-taking. Overall, the results in Table 5 are in support of H2, that is, Basel III leads to greater borrower risk-taking.

7. Additional analyses

7.1 Dynamic Analyses

The identifying assumption for the DD approach is that there would have been no marked post-trends in dependent variables between borrowers with more than $250 billion in total consolidated assets and borrowers with less if Basel III had not been proposed. To provide comfort on the validity of the identifying assumption, I examine the dynamic effects of Basel III on

49 This is the sample mean of Herfindahl-Hirschman Index (untabulated). 50 This is the sample mean of capital expenditures (untabulated). 51 This is the sample mean of investments in intangible assets (untabulated). 52 This is the sample mean of R&D expenditures (untabulated). 53 I replace the bank × borrower fixed effect with stand-alone bank and borrower fixed effects when estimating the effects of Basel III on R&D expenditures, because many firms do not have R&D expenditures and this substitution increases the effective number of observations that can enter the regression analysis.

31 borrowers’ loan costs and subsequent change in risk-taking, following Bertrand and Mullainathan

(2003). To be specific, I replace the Treat dummy with six dummy variables: Treat(-1) is an indicator variable that equals one if a loan was initiated by banks with greater exposure to Basel

III in 2011, and zero otherwise. Treat(0) is an indicator variable that equals one if a loan was initiated by banks with greater exposure to Basel III in 2012, and zero otherwise. Treat(1) is an indicator variable that equals one if a loan was initiated by banks with greater exposure to Basel

III in 2013, and zero otherwise. Treat(2) is a dummy variable that equals one if a loan was initiated by banks with greater exposure to Basel III in 2014, and zero otherwise. Treat(3) is an indicator variable that equals one if a loan was initiated by banks with greater exposure to Basel III in 2015, and zero otherwise. Treat(4) is a dummy variable that equals one if a loan was initiated by banks with greater exposure to Basel III in 2016, and zero otherwise. According to Bertrand and

Mullainathan (2003), the dummy variable Treat(-1) allows me to assess whether any effects on borrowers’ loan costs and subsequent change in risk-taking can be found prior to the Basel III proposal year. If the estimated coefficient on Treat(-1) is neither statistically nor economically significant, it suggests that the two groups of borrowers had similar trends before the additional

Basel III provisions were proposed and provides comfort that the identifying assumption of the

DD research design is not violated.

I use the following DD models to estimate the dynamic effects of greater exposure to Basel

III on borrowers’ loan costs and subsequent change in risk-taking, respectively:

퐿표푎푛퐶표푠푡푠푖푗푡 = 훽−1푇푟푒푎푡(−1)푖푗,−1 + 훽0푇푟푒푎푡(0)푖푗,0 + 훽1푇푟푒푎푡(1)푖푗,1 + 훽2푇푟푒푎푡(2)푖푗,2 +

훽3푇푟푒푎푡(3)푖푗,3 + 훽4푇푟푒푎푡(4)푖푗,4 + 훿푇푟푒푎푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡 [3] and

32

∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽−1푇푟푒푎푡(−1)푖(푗),−1 + 훽0푇푟푒푎푡(0)푖(푗),0 + 훽1푇푟푒푎푡(1)푖(푗),1 +

훽2푇푟푒푎푡(2)푖(푗),2 + 훽3푇푟푒푎푡(3)푖(푗),3 + 훽4푇푟푒푎푡(4)푖(푗),4 + 훿푇푟푒푎푡푖(푗)푡 +

훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡+12 [4] where all variables are defined as in Equations [1] and [2].

Table 6 reports the estimated dynamic effects using Equations [3] an [4]. Columns (1) and

(2) report the dynamic effects of Basel III on loan costs using the loan-level dataset, and Columns

(3) through (10) report the dynamic effects of Basel III on eight alternative metrics of borrower risk-taking using the borrower-year-quarter-level (or borrower-year-level) dataset. None of the coefficients on Treat(-1) throughout these columns are either statistically or economically significant. Moreover, in each of the Columns (1), (3) through (8), and (10), at least one of the estimated coefficients on Treat(0), Treat(1), Treat(2), Treat(3), or Treat(4) are economically and statistically significant. The comprehensive results are in favor of that the parallel-trend assumption holds and are consistent with the causal interpretation that Basel III leads to the increase in borrower risk-taking. In untabulated tests, these results are robust to the inclusion of

Controlsi(j)t-1, which is defined in Section 4.

Figure 4 presents a visual presentation of the dynamic effects reported in Table 6.

7.2 Shorter sample period

In this subsection, I shorten the sample period to a shorter subsample period, which is more symmetric around the proposal year, and examine the robustness of the main results. Specifically,

I shorten the sample period to a subperiod, beginning with the third quarter of 2010 and ending with the fourth quarter of 2014, in order to maintain the integrity and semi-symmetry of the sample period. See Figure 3 Panel B for a visual presentation of the sample period.

33

Table 7 reports the effects of Basel III using Equations [1] an [2]. Columns (1) and (2) report the effects of Basel III on loan costs using the loan-level dataset, and Columns (3) through

(10) report the effects of Basel III on eight alternative metrics of borrower risk-taking using the borrower-year-quarter-level (or borrower-year-level) dataset. The coefficients on the interaction term Treat × Post in most columns are statistically and economically significant with expected signs, indicating the Basel III increases borrowers’ loan costs and risk-taking in the subsample as well. The coefficient in Column (2) is negative (as expected) but not significant, which indicates that the effect of Basel III on loan size is not as significant as that on loan price, consistent with the results in Tables 4 and 6. Moreover, altough the coefficients on the interaction term Treat ×

Post in Column (3), (7), and (10) are not statistically significant, the direction and magnitude of the coefficient on the interaction term are still consistent with the main results. Therefore, the results are in general robust when I examine a subsample period.

7.3 Cross-sectional analyses

In this subsection, I examine the cross-sectional differences in the effects of Basel III on loan costs and risk-taking using the triple-difference research design. Specifically, I explore the role of financial frictions in driving the overall results by testing the heterogeneous effects of Basel

III by borrower size, which inversely serves as a proxy for credit constraints, following Misra

(2019). In other words, I compare the effects of Basel III on loan costs and risk-taking for small borrowers to the effects of Basel III for large borrowers. This triple-difference research design enables me to further attribute the economic responses to Basel III rather than other biasing forces, such as time trend or bank size.

I use the following triple-difference models to examine the heterogeneous effects of Basel

III on borrowers’ loan costs and subsequent risk-taking, respectively:

34

퐿표푎푛퐶표푠푡푠푖푗푡 = 훽1푇푟푒푎푡푖푗푡 × 푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽2푇푟푒푎푡푖푗푡 × 푃표푠푡푡 + 훽3푇푟푒푎푡푖푗푡 ×

퐿표𝑔퐴푠푠푒푡푖푡 + 훽4푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽5푇푟푒푎푡푖푗푡 + 훽6퐿표𝑔퐴푠푠푒푡푖푗푡 +

훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 휇퐿표𝑔푀푎푡푢푟푖푡푦푖푗푡−1 + 훼푖푗 + 훼푡 + 휀푖푗푡 [5] and

∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽1푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽2푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 +

훽3푇푟푒푎푡푖(푗)푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽4푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽5푇푟푒푎푡푖(푗)푡 + 훽6퐿표𝑔퐴푠푠푒푡푖푗푡 +

훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 훼푖푗 + 훼푡 + 휀푖푗푡+12 [6] where all variables are defined as in Equations [1] and [2]. I include only the essential controls

(i.e., bank assets, borrower assets, and loan maturity) and replace the industry × year × quarter (or industry × year) fixed effect with the year × quarter (or year) fixed effect in the triple difference research design. This is because there is a limited number of observations for borrowers less affected by Basel III, and thus there may not be enough degrees of freedom for estimating too many coefficients.

Table 8 reports the effects of Basel III using Equations [5] an [6]. Columns (1) and (2) report the heterogeneous effects of Basel III on loan costs using the loan-level dataset, and

Columns (3) through (10) report the heterogeneous effects of Basel III on eight alternative metrics of borrower risk-taking using the borrower-year-quarter-level (or borrower-year-level) dataset.

The heterogeneous effects of Basel III estimated in Columns (1), (3), (4), (6) and (7), as captured by the coefficients on the interaction term Treat × Post × LogAsset, are negative as well as statistically and economically significant. This suggests that the effects of Basel III on borrowers’ loan price and risk-taking were more profound for borrowers with credit constraints than for borrowers without such constraints. However, I do not find that the effect of Basel III on loan size was more significant for borrowers with credit constraints than that for borrowers without the

35 constraints. This is consistent with the results in Tables 4, 6, and 7, which suggest that the main effect of Basel III was to increase loan price, rather than to reduce loan size. Although the coefficients on the interaction term Treat × Post × LogAsset in Columns (5), (8), (9), and (10) are not statistically significant, I conjecture that this is due to the limited number of observations for borrowers less affected by Basel III that have valid ∆DTD and non-zero ∆CAPEX, ∆IntanInv, and

∆R&D. The results in general suggest that the effects of Basel III were stronger for borrowers with greater credit constraints and that my main findings in Table 4 and 5 are attributable to Basel III.

7.4 Alternative funding sources

In this subsection, I conduct additional analyses to support the assumption that borrowers cannot perfectly substitute bank loans with other sources of financing. Specifically, I examine whether borrowers incur higher interest expenses on long-term debts, receive smaller amounts of long-term debts, and issue more equity securities follows Basel III. To do so, I estimate the following DD model:

퐹푢푛푑푖푛𝑔푖푡 = 훽푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 +

휑퐶표푛푡푟표푙푠푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푡+12 [7] where Funding represents borrowers’ interest expenses on long-term debts, the amounts of long- term debts, and equity issuance. All other variables are defined as in Equations [1] and [2].

As is presented in Table 9, I find that, after the proposal year, borrowers with more exposure to Basel III, compared to borrowers with less exposure, tended to incur significantly higher interest expenses on long-term debts although the amounts of long-term debts that borrowers received did not change much. Moreover, I do not find that borrowers with more exposure to Basel III issued more shares in response to Basel III than borrowers with less exposure.

These results are collectively in support of my assumption that borrowers have difficulty in

36 perfectly replacing bank loans with debts and equity at cheap price. The results also explain why borrowers more affected by Basel III are willing to accept the relative unfavorable loan terms.

7.5 Adverse selection

In this subsection, I conduct an additional set of univariate analyses to support the explanation that the increase in borrower riskiness is due to borrowers themselves increasing their risk-taking (i.e., moral hazard) rather than banks having a riskier composition of borrowers (i.e., adverse selection). As is shown in Table 10, I find that the banks with greater exposure to Basel

III, compared to banks with less exposure, recognized smaller PLLLs in three consecutive years after Basel III. The univariate results do not support the alternative explanation that banks intentionally increased the riskiness of their loan portfolios and are consist with my univariate findings in Section 5.3.

7.6 Falsification tests

In this subsection, I conduct two sets of falsification tests in the Online Appendix to further check that my results are indeed attributable to Basel III. First, I replicate my main results by randomizing the proposal dates of the additional Basel III provisions. In particular, I examine the dates of January 1, 2010 and January 1, 2014 and find that the results do not hold. Second, I replicate my main results by randomizing the asset-size thresholds to redefine banks with greater exposure to Basel III. Specifically, I use $150 billion or $450 billion as the asset-size cutoffs, and

I do not observe similar results. Assuming that other economic forces than Basel III cannot not explain the combination of these results, the placebo analyses help establish the plausibility of

Basel III’s impact on borrowers’ loan costs and subsequent change in risk-taking.

8. Conclusion

37

This paper provides evidence suggesting that bank regulations cause risk to migrate from the banking industry to the real economy and highlights the negative spillovers of such regulations.

Specifically, my findings suggest that borrowers increase their risk-taking in order to compensate for the higher borrowing costs resulting from bank regulations. This paper provides novel and hitherto unexamined evidence that bank regulations could potentially increase borrower risk- taking. Future work can further evaluate precisely how much the increased risk of borrowers can offset the regulations’ intended effect.

There have been lively debates central on the appropriate amount and consequences of bank regulations since the 2008 financial crisis among policymakers, business leaders, and researchers (Adams and Gramlich, 2014; Hoskins and Labonte, 2015). Although it is not the intention of this paper to draw definitive policy implications from this documented increase in borrower risk-taking, the findings in this paper call for more comprehensive considerations in setting the regulations for the banking system. Many have argued that it was the excess risk that built up in the economy, i.e., the housing bubble and imprudent lending, that provoked the outburst of the financial crisis (Baily, Litan, and Johnson, 2011). In order to prevent or, at least, delay the onset of the next financial crisis by all means, it is important for the policy makers to be, at a minimum, fully aware of the occurrence and magnitude of the excess increase in borrower risk- taking resulting from Basel III. Moreover, regulators should also consider pairing bank regulations with banks more carefully monitoring borrower risk-taking to prevent unwanted risk taken on by borrowers and feeding back to the banking system.

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42

Appendix

A: Variable definitions

Variable Name Variable Definition Key independent variables LogBankAsset = Natural log of BHCs’ total consolidated assets (measured in thousands); Post = 1if the loan was originated after January 1, 2012, and 0 otherwise; 1 if the borrower’s dominant lead bank has $250 billion or more in total consolidated assets when the loan was originated, and 0 otherwise;

In untabulated robustness tests, the variable is also defined as 1 if the Treat = borrower’s dominant lead bank had total consolidated assets of $250 billion or more as of the second quarter of 2012, and 0 otherwise. This definition prevents the potential endogeneity concern that banks manipulated their assets in order to avoid being subject to the additional provisions of Basel III; Loan characteristics IndCol = 1 if the loan is secured with collateral, and 0 otherwise, following Ball, Bushman, and Vasvari (2008); IndPricing = 1 if the loan contains a performance pricing provision, and 0 otherwise, following Ball, Bushman, and Vasvari (2008); LoanType = 0 if the facility is a revolver, 1 if others; LogMaturity = Natural log of loan maturity (measured in months); LSize = The total amount of the facility scaled by the borrower’s total consolidated assets; NumCov The total number of covenants, including (i) income statement-based (performance) covenants, such as debt-to-earnings ratio, fixed charge coverage, interest coverage, earnings, senior debt-to-earnings ratio, other coverage, and debt service coverage, and (ii) balance sheet-based (capital) covenants, such as net worth, tangible net worth, debt to capitalization, debt to net worth, current ratio, quick ratio, working capital, shareholder’s equity, other liquidity, and other balance sheet ratios; PriorRel = 1 if a borrower has borrowed from a bank in the past six years, and 0 otherwise; SprdFee = The tranche amount the borrower pays in basis points over LIBOR for each dollar drawn down a loan deal. It adds the spread of the loan to any annual (or facility) fee paid to the bank group; Borrower characteristics Leverage = Total liabilities scaled by total shareholder equity; LogAsset = Natural log of total assets (measured in thousands); ROA = Net income scaled by total assets; Borrowers’ overall riskiness

43

∆AssetV = Change in asset volatility, by comparing asset volatility (AssetV) measured at the beginning of the second year before the loan origination (y-2) to that measured at the beginning of the second year after the loan origination (y+2). Asset volatility is calculated using KMV model following Hillegeist et al. (2004); ∆DEBITM = Change in standard deviation of downside EBIT margin from the period of two years before the loan origination (y-2 to y-1) to the period of two years after the loan origination (y+1 to y+2). Downside EBIT margin is defined as the ratio of EBIT over sales below industry division median in each year-quarter; ∆DTD = Change in distance to default, by comparing distance to default (DTD) measured at the beginning of the second year before the loan origination (y-2) to that measured at the beginning of the second year after the loan origination (y+2). Distance to default is calculated using KMV model following Hillegeist et al. (2004); ∆OptV = Change in mean implied option volatility, by comparing mean implied option volatility during the eighth quarter before the loan origination (q-8) to that during the eighth quarter after the loan origination (q+8); Borrowers’ risky activities with uncertain benefits ∆CAPEX = Change in mean yearly growth rate of capital expenditures from the period of three years before the loan origination (y-3 to y-1) to the period of three years after the loan origination (y+1 to y+3); ∆HHI = Customer sales-based Herfindahl-Hirschman Index, which is calculated by summing the squares of customer sales in each business segment to the total sales of the borrower. This metric ranges between zero and one, with higher values corresponding to a more concentrated customer base; ∆IntanInv = Change in mean yearly growth rate of investments in intangible assets from the period of three years before the loan origination (y-3 to y-1) to the period of three years after the loan origination (y+1 to y+3); ∆R&D = Change in mean yearly growth rate of R&D expenditures from the period of three years before the loan origination (y-3 to y-1) to the period of three years after the loan origination (y+1 to y+3); Additional variables EquityIssuance = Common shares issued during year t divided by common shares outstanding at the beginning of year t; Industry = Industry divisions are defined based on ranges of SIC codes. SIC Codes 0100-0999: Agriculture, Forestry, and Fishing; SIC Codes 1000-1499: Mining; SIC Codes 1500-1799: Construction; SIC Codes 1800-1999: not used; SIC Codes 2000-3999: Manufacturing; SIC Codes 4000-4999: Transportation, Communications, Electric, Gas, and Sanitary service; SIC Codes 5000-5199: Wholesale Trade; SIC Codes 5200-5999: Retail Trade;

44

SIC Codes 6000-6799: Finance, Insurance, and Real Estate; SIC Codes 7000-8999: Services; SIC Codes 9100-9729: Public Administration; SIC Codes 9900-9999: Non-classifiable; LTDebt/Asset = Long-term debt during year t divided by total assets at the beginning of year t; LTIntExp/LTLoan = Interest expense on long-term debt during year t divided by long-term debt during year t; PLLL/Loan = The ratio of provision for loan and lease losses divided by total loans and leases (net of unearned income) minus loans and leases held for sale in the first, second, or third year following the loan origination (y+1, y+2, or y+3).

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B: Implementation details on the additional Basel III provisions mentioned in the paper

The U.S. SLR is a non-risk-based metric54 to supplement risk-based capital requirements in order to mitigate risks of excessive leverage by banks. It is defined as Tier 1 capital over total leverage exposure. Total leverage exposure is equal to (1) on-balance sheet assets less amounts deducted from Tier 1 capital, plus (2) off-balance sheet items, including derivative exposures, repo-style transaction exposures, and other off-balance sheet exposures (such as commitments and guarantees).55 Banks are required to maintain a minimum SLR of 3%. In the United States, it is applied to BHCs with $250 billion or more in total consolidated assets or $10 billion or more in on-balance sheet foreign exposure. 56 It was proposed in the United States on June 12, 2012, finalized on September 3, 2014, and became effective on January 1, 2015 with transition periods for full compliance with the requirements until January 1, 2018. The LCR requires that banks maintain high quality liquid assets (HQLAs) in an amount equal to or greater than its projected net cash outflows in 30 days. Some examples of HQLA include central bank reserves, government debts, and corporate debts that can be converted easily and quickly into cash. Likewise, it is also applied to BHCs with $250 billion or more in total consolidated assets or $10 billion or more in on-balance sheet foreign exposure. The LCR was proposed by the Federal Reserve on October 24, 2013, finalized on September 3, 2014, and became effective on January 1, 2015 with transition periods for full compliance with the requirements until January 1, 2017. The AA rule under Basel III requires certain BHCs to use internal ratings-based approaches to evaluate credit risk and advanced measurement approaches to assess operational risk. Similarly, the AA rule also applies to BHCs with $250 billion or more in total consolidated assets or $10 billion or more in on-balance sheet foreign exposure. Although the AA rules under Basel II and Basel III are applied to the same set of banks, the U.S. version of Basel III additionally requires that the banks subject to the AA rule should also meet the capital requirements under the standardized approaches. This additional requirement is particularly costly for applicable banks because it may affect banks lending strategies (Mariathasan and Merrouche, 2014). The AA rule was proposed to be revised on August 30, 2012 in response to the released framework of Basel III. The revision was then finalized in October 11, 2013 and required mandatory compliance starting from January 1, 2014.57 The enhanced SLR is a SLR buffer for covered BHCs of 2 percent above the minimum SLR requirement of 3 percent. The enhanced SLR applies to banks with $700 billion or more in

54 A non-risk-based metric does not have risk weights in calculation. 55 The values of the off-balance sheet items are converted into credit exposure equivalents through the use of credit conversion factors (https://www.bis.org/publ/bcbs270.pdf). 56 Individual BHCs’ on-balance sheet foreign exposures are not publicly available (https://www.federalregister.gov/documents/2012/08/30/2012-16761/regulatory-capital-rules-advanced-approaches- risk-based-capital-rule-market-risk-capital-rule; https://www.ffiec.gov/PDF/FFIEC_forms/FFIEC009_201903_i.pdf), so I do not explore this criteria in my paper. 57 The AA rule was first proposed in the U.S. on September 25, 2006, was finalized on December 7, 2007, and became effective on April 1, 2008. It was then proposed to be amended to be consistent with the Dodd-Frank Act on December 30, 2010. The amendment was finalized on June 28, 2011 and came into effect on July 28, 2011. The Federal Reserve later also proposed technical corrections and clarifications of the rule on November 18, 2014. The changes were finalized on July 15, 2015 and became effective on October 1, 2015.

46 total consolidated assets. These banks are the largest in the U.S. The rule was proposed on August 20, 2013, was finalized on May 1, 2014, and became effective starting from January 1, 2018. The enhanced rule for GSIBs is intended to strengthen the capital positions for the largest and most systemically significant BHCs. Different from the enhanced SLR, it employs a model to identify GSIBs and the identified GSIBs are subject to a risk-based capital surcharge, which is calibrated to each firm’s overall systemic risk. Eight U.S. banks was identified as GSIBs when the enhanced rule was introduced: Bank of America Corporation; The Bank of New York Mellon Corporation; Citigroup, Inc.; The Goldman Sachs Group, Inc.; JPMorgan Chase & Co.; Morgan Stanley; State Street Corporation; and Wells Fargo & Company. The enhanced rule was proposed on December 9, 2014, was finalized on July 20, 2015, was phased in beginning on January 1, 2016, and became fully effective on January 1, 2019.

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Figure 1: HQLA-to-assets ratio

These figures depict how the HQLA-to-assets ratio changed around the LCR proposal date between banks more affected by Basel III (i.e., “standard LCR”) and banks less affected (i.e., “modified LCR”). The source of the figures is Vladimir Yankov (2020).58 Vertical lines indicate different stages of the LCR implementation. Standard LCR banks are BHCs with total consolidated assets above $250 billion. Modified LCR banks are BHCs with total consolidated assets between $50 and $250 billion. Non-LCR banks are BHCs with total consolidated assets below $50 billion.

58 I cannot estimate SLR and LCR myself because they require access to confidential data.

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Figure 2: Research design

These figures summarize my research designs. Borrowers of banks with $250 billion or more in total consolidated assets are defined as the borrowers more affected by Basel III. Borrowers of banks with between $50 billion and $250 billion in total consolidated assets are defined as the borrowers less affected by Basel III. The proposal year of additional Basel III provisions is defined as 2012: the pre-Basel period is defined as the period before January 1, 2012, while the post-Basel period is defined as the period after January 1, 2012. LoanCosts are measured when loans were originated. ∆RiskTaking variables are measured between 12 quarters (or three years) prior to and the 12 quarters (or three years) following each loan initiation.

Panel A: Borrower loan costs

Borrowers more affected by Basel III

Borrowers less affected by Basel III

(continued)

49

Figure 2 (continued)

Panel B: Borrower risk-taking

Borrowers more affected by Basel III

Borrowers less affected by Basel III

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Figure 3: Sample periods

These figures summarize my sample periods. The proposal year of additional Basel III provisions is defined as 2012. The pre-Basel period is defined as the period between January 1, 2002 and January 1, 2012, excluding the financial crisis period between the first quarter of 2007 and the second quarter of 2010, whereas the post-Basel period is defined as the period between January 1, 2012 and December 31, 2016. The pre-Basel period for subsample robustness tests is defined as the period between July 1, 2010 and January 1, 2012, whereas the post-Basel period for subsample robustness tests is defined as the period between January 1, 2012 and December 31, 2014. Panel A presents the main sample’s timeline, while Panel B presents the subsample’s timeline.

Panel A: Main sample’s period

Panel B: Subsample’s period for robustness tests

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Figure 4: Trends of independent variables between borrowers more affected by Basel III and borrowers less affected

These graphs visualize how loan costs and subsequent change in risk-taking changed for borrowers with more exposure to Basel III, compared to those for borrowers with less exposure. Specifically, they present trends from estimating Equations [3] and [4]: 퐿표푎푛퐶표푠푡푠푖푗푡 = 훽−1푇푟푒푎푡(−1)푖푗,−1 + 훽0푇푟푒푎푡(0)푖푗,0 + 훽1푇푟푒푎푡(1)푖푗,1 + 훽2푇푟푒푎푡(2)푖푗,2 + 훽3푇푟푒푎푡(3)푖푗,3 + 훽4푇푟푒푎푡(4)푖푗,4 + 훿푇푟푒푎푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡 [3] and ∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽−1푇푟푒푎푡(−1)푖(푗),−1 + 훽0푇푟푒푎푡(0)푖(푗),0 + 훽1푇푟푒푎푡(1)푖(푗),1 + 훽2푇푟푒푎푡(2)푖(푗),2 + 훽3푇푟푒푎푡(3)푖(푗),3 + 훽4푇푟푒푎푡(4)푖(푗),4 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡+12 [4] for borrower i, bank j, and year t. I measure LoanCosts as interest spreads and fees in basis points over LIBOR (SprdFee) in Panel A and loan size (LSize) in Panel B. I measure ∆RiskTaking as change in implied option volatility (∆OptV) in Panel C, change in asset volatility (∆AssetV) in Panel D, change in distance to default (∆DTD) in Panel E, change in downside EBIT margin volatility (∆DEBITM) in Panel F, change in Herfindahl-Hirschman Index (∆HHI) in Panel G, change in mean growth of investments in intangible assets (∆IntanInv) in Panel H, change in mean growth of capital expenditures (∆CAPEX) in Panel I, and change in mean growth of R&D expenditures (∆R&D) in Panel J. Treat(1)—Treat(4) indicate whether a loan was initiated by banks with greater exposure to Basel III in 2011—2016, respectively. Treat indicates whether a loan was initiated by banks with greater exposure to Basel III. 훼푖푗 and 훼푠푡 are the bank × borrower and industry × year × quarter (or industry × year) fixed effects, respectively. All continuous variables are winsorized at the top and bottom 1%. Variables in Panels A, B, and C are computed at the quarterly level, whereas variables in Panels D through K are computed at the yearly level. All variables are defined in Appendix A. The solid dots indicate the average difference for the dependent variables of interest while the solid vertical lines present two-sided 90% confidence intervals based on standard errors clustered at the bank level. Values close to zero (i.e., the red line) indicate no difference in the trends of independent variables between the treatment and control samples. The two groups of borrowers do not show significant statistical differences in the pre-Basel trends of dependent variables of interest.

Panel A: SprdFee Trends

(continued)

52

Figures 4 (continued) Panel B: LSize Trends

Panel C: OptV Trends

Panel D: AssetV Trends

(continued)

53

Figures 4 (continued) Panel E: DTD Trends

Figure F: DEBITM Trends

Figure G: HHI Trends

(continued)

54

Figures 4 (continued) Figure H: CAPEX Trends

Figure I: IntanInv Trends

Figure J: R&D Trends

55

Table 1: Numbers of unique banks and borrowers

This table presents the number of unique banks and their borrowers in the loan-level and borrower- year quarter-level datasets. Panel A describes the number of unique banks and borrowers in the loan-level dataset. Panel B describes the number of unique banks and borrowers in the borrower- year-quarter-level dataset.

Panel A: Number of unique banks and borrowers in the loan-level dataset (1) (2) Pre-period Post-period Banks more affected by Basel III 19 13 Banks less affected by Basel III 23 14 Borrowers more affected by Basel III 1069 1072 Borrowers less affected by Basel III 316 121

Panel B: Number of unique banks and borrowers in the borrower-year-quarter-level dataset (1) (2) Pre-period Post-period Banks more affected by Basel III 12 9 Banks less affected by Basel III 16 11 Borrowers more affected by Basel III 1068 1071 Borrowers less affected by Basel III 293 115

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Table 2: Descriptive statistics for borrower characteristics

This table compares the pre-loan characteristics of borrowers more affected by Basel III and those of borrowers less affected by Basel III between the pre- and post-Basel III periods in the borrower-year-quarter-level (or borrower-year-level) dataset— the first five variables are at the borrower-year-quarter level, while the rest sight variables are at the borrower-year level. All continuous variables are winsorized at the top and bottom 1%. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels.

(1) (2) (3) Differences between Post-Period Pre-Period periods Mean N Mean N Mean t-stat BankAsset(in trillions) 1.948 2,495 1.208 3,343 0.740*** (47.210) Asset(in billions) 12.071 2,458 9.023 3,265 3.048*** (5.045) ROA 0.010 2,457 0.014 3,262 -0.004*** (-6.495) Leverage 0.624 2,458 0.590 3,265 0.033*** (6.037) Borrowers OptV 0.345 1,877 0.448 2,044 -0.102*** (-20.811) more AssetV 0.304 1,997 0.393 1,486 -0.089*** (-15.540) affected DTD 8.097 1,914 6.085 1,033 2.011*** (11.679) by Basel DEBITM 0.099 1,138 0.084 1,345 0.015 (1.316) III HHI 1.965 1,832 2.002 2,525 -0.036 (-1.377) CAPEX 1.228 2,209 1.158 2,791 0.070*** (5.975) IntanInv -4.723 1,918 -2.397 2,020 -2.326* (-1.685) R&D 1.099 908 1.097 1,169 0.002 (0.162) BankAsset(in trillions) 0.146 223 0.128 560 0.018*** (5.082) Asset(in billions) 3.952 221 3.378 550 0.574 (0.852) ROA 0.010 221 0.011 549 -0.001 (-0.321) Leverage 0.574 221 0.534 550 0.039** (2.348) Borrowers OptV 0.359 145 0.477 258 -0.117*** (-8.593) less AssetV 0.337 167 0.394 179 -0.056*** (-3.339) affected DTD 6.933 160 6.416 118 0.517 (1.080) by Basel DEBITM 0.066 124 0.121 258 -0.055* (-1.746) III HHI 1.923 169 2.029 451 -0.106 (-1.522) CAPEX 1.370 193 1.195 466 0.175*** (3.861) IntanInv 1.445 164 0.264 326 1.181 (0.340) R&D 1.093 68 1.099 172 -0.006 (-0.163) (1) (2) (3) Post-period Pre-period Diff-in-diff Mean t-stat Mean t-stat Mean t-stat BankAsset(in trillions) 1.802*** (152.202) 1.080*** (99.544) 0.722*** (15.376) Asset(in billions) 8.119*** (12.412) 5.645*** (9.030) 2.474 (1.394) Difference ROA -0.000 (-0.134) 0.003*** (2.886) -0.003* (-1.756) between Leverage 0.050*** (3.270) 0.056*** (6.571) -0.006 (-0.331) borrowers OptV -0.014 (-1.477) -0.029*** (-2.660) 0.015 (0.901) more AssetV -0.033*** (-2.781) -0.001 (-0.060) -0.033* (-1.803) affected DTD 1.163*** (3.423) -0.331 (-0.872) 1.494** (2.554) and less affected DEBITM 0.033 (1.408) -0.036 (-1.543) 0.070** (2.098) by Basel HHI 0.042 (0.679) -0.027 (-0.666) 0.069 (0.856) III CAPEX -0.142*** (-3.497) -0.037 (-1.580) -0.105*** (-2.751) IntanInv -6.169* (-1.940) -2.661 (-1.357) -3.507 (-0.823) R&D 0.005 (0.178) -0.002 (-0.094) 0.007 (0.218)

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Table 3: Univariate analyses on borrowers’ loan terms and change in risk-taking

This table compares loan terms and change in risk-taking for borrowers more affected by Basel III and borrowers less affected between the pre- and post-Basel periods. Panel A compares loan terms offered by banks more affected by Basel III to those offered by banks less affected in both pre- and post-Basel periods in the loan-level dataset—observations are all at the facility level. Panel B compares the mean changes in borrower risk-taking around loan originations between borrowers more affected by Basel III and borrowers less affected in both pre- and post-Basel periods in the borrower-year-quarter-level (or borrower-year-level) dataset—the first variable is calculated at the borrower-year-quarter level, while the rest eight variables are at the borrower-year level. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively. All variables are defined in Appendix A.

Panel A: Mean comparison of loan terms offered by banks more affected by Basel III and those by banks less affected by Basel III in both pre- and post-Basel periods in the loan-level dataset (1) (2) (3) Differences between Post-Period Pre-Period periods Mean N Mean N Mean t-stat SprdFee 209.785 3,833 172.067 4,722 37.718*** (14.592) LSize 0.151 3,833 0.146 4,722 0.005* (1.684) Borrowers NumCov 1.056 3,833 1.552 4,722 -0.496*** (-18.197) more IndCol 0.474 3,833 0.454 4,722 0.019* (1.781) affected by IndPricing 0.350 3,833 0.498 4,722 -0.148*** (-13.956) Basel III Maturity 55.184 3,820 49.346 4,679 5.838*** (14.543) LoanType 0.385 3,833 0.280 4,722 0.105*** (10.258) PriorRel 0.992 3,833 0.992 4,722 0.000 (0.062) SprdFee 252.122 347 199.276 828 52.846*** (6.271) LSize 0.180 347 0.164 828 0.016 (1.505) Borrowers NumCov 1.055 347 1.909 828 -0.855*** (-10.383) less IndCol 0.527 347 0.543 828 -0.016 (-0.504) affected by IndPricing 0.326 347 0.527 828 -0.201*** (-6.567) Basel III Maturity 57.682 343 48.429 813 9.253*** (8.530) LoanType 0.464 347 0.313 828 0.151*** (4.832) PriorRel 0.986 347 0.966 828 0.019** (2.162) (1) (2) (3)

Post-period Pre-period Difference-in-difference Mean t-stat Mean t-stat Mean t-stat Differences SprdFee -42.338*** (-5.705) -27.209*** (-5.722) -15.129* (-1.862) between LSize -0.028*** (-3.025) -0.018*** (-3.191) -0.011 (-1.115) borrowers NumCov 0.001 (0.022) -0.357*** (-5.847) 0.359*** (4.025) more IndCol -0.054* (-1.912) -0.089*** (-4.741) 0.035 (1.051) affected IndPricing 0.025 (0.939) -0.028 (-1.501) 0.053 (1.601) and less Maturity -2.498*** (-2.895) 0.917 (1.191) -3.415*** (-2.673) affected by LoanType -0.079*** (-2.833) -0.033* (-1.888) -0.046 (-1.468) Basel III PriorRel 0.006 (0.922) 0.025*** (3.945) -0.019*** (-2.769) (continued)

58

Table 3 (continued)

Panel B: Mean comparison of changes in borrower risk-taking around loan originations between borrowers more affected by Basel III and borrowers less affected by Basel III in both pre- and post-Basel periods in the borrower-year-quarter-level (or borrower-year-level) dataset (1) (2) (3) Differences between Post-Period Pre-Period periods Mean N Mean N Mean t-stat ∆OptV -0.031 1,430 -0.119 1,963 0.088*** (14.611) ∆AssetV -0.061 1,850 -0.131 1,453 0.070*** (12.065) Borrowers ∆DTD 1.446 1,753 2.504 997 -1.058*** (-5.329) more ∆DEBITM 0.051 877 -0.005 1,065 0.056*** (3.888) affected by ∆HHI 0.000 1,485 -0.011 2,291 0.011** (2.186) Basel III ∆CAPEX -0.130 1,648 0.036 2,742 -0.166*** (-10.523) ∆IntanInv 3.137 1,375 2.927 1,888 0.210 (0.094) ∆R&D -0.035 662 0.014 1,136 -0.049*** (-3.953) ∆OptV -0.055 109 -0.129 243 0.074*** (4.398) ∆AssetV -0.096 156 -0.100 174 0.003 (0.189) Borrowers ∆DTD 2.293 149 1.988 115 0.305 (0.516) less ∆DEBITM 0.042 90 -0.039 213 0.081** (2.516) affected by ∆HHI -0.016 136 0.004 418 -0.020 (-1.258) Basel III ∆CAPEX -0.234 146 0.064 461 -0.298*** (-5.376) ∆IntanInv -6.198 121 0.991 300 -7.189 (-1.122) ∆R&D -0.053 54 0.020 164 -0.073 (-1.452) (1) (2) (3) Post-period Pre-period Difference-in-difference Mean t-stat Mean t-stat Mean t-stat Differences ∆OptV 0.024** (2.047) 0.009 (0.680) 0.014 (0.630) between ∆AssetV 0.035*** (3.085) -0.031** (-2.299) 0.067*** (3.643) borrowers ∆DTD -0.847** (-2.093) 0.516 (1.086) -1.363** (-2.117) more ∆DEBITM 0.010 (0.324) 0.034* (1.743) -0.025 (-0.611) affected ∆HHI 0.016 (1.128) -0.015* (-1.750) 0.031** (1.984) and less ∆CAPEX 0.104** (2.085) -0.028 (-0.957) 0.132** (2.543) affected by ∆IntanInv 9.335 (1.639) 1.936 (0.524) 7.399 (1.039) Basel III ∆R&D 0.018 (0.408) -0.006 (-0.233) 0.024 (0.541)

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Table 4：Changes in borrowers’ loan costs resulting from Basel III

This table presents whether loan costs for borrowers with more exposure to Basel III increased, compared to those for borrowers with less exposure, after the Basel III proposal year. Specifically, it presents results from estimating Equation [1]: 퐿표푎푛퐶표푠푡푠푖푗푡 = 훽푇푟푒푎푡푖푗푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 휑퐶표푛푡푟표푙푠푖푗푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡 for borrower i, bank j, year t, and industry s. I measure LoanCosts as interest spreads and fees in basis points over LIBOR (SpreadFee) in Column (1), loan size (Loan/Asset) in Column (2), the number of covenants (NumCov) in Column (3), an indicator for collateral requirements (IndCol) in Column (4), and an indicator for performance pricing provisions (IndPricing) in Column (5). Treat indicates whether or not the dominant lead bank 푗 of borrower 푖’s loan has $250 billion or more in total consolidated assets at the beginning of year-quarter 푡. Post indicates whether or not the loan was originated after January 1, 2012. All variables are computed at the quarterly level. 훼푠푡 represents the industry × year × quarter fixed effect. 훼푖푗 represents the bank × borrower fixed effect, which perfectly subsumes the stand-alone bank fixed effect and borrower fixed effect. Standard errors are clustered at the bank level. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively.

(1) (2) (3) (4) (5) SprdFee LSize NumCov IndCol IndPricing Treat × Post 17.049*** -0.012* 0.065 0.030 0.066 (2.85) (-1.73) (0.53) (0.57) (1.22) Treat 6.951 -0.001 -0.178 -0.019 -0.048 (0.78) (-0.13) (-1.12) (-0.35) (-1.01) LogBankAsset -39.452*** -0.007 -0.275 -0.054 -0.046 (-4.21) (-0.49) (-1.60) (-1.00) (-0.68) LogAsset -24.927*** -0.054*** -0.162*** -0.099*** -0.016 (-6.09) (-8.93) (-3.28) (-7.97) (-0.87) ROA -513.631*** 0.219*** 0.752 -1.109*** 1.075** (-4.27) (3.53) (0.84) (-3.73) (2.40) Leverage 92.275*** 0.011 0.047 0.221*** -0.146** (6.25) (0.68) (0.33) (3.42) (-2.67) LogMaturity -2.424 0.019*** 0.275*** 0.094*** 0.085*** (-0.93) (8.04) (6.78) (8.10) (3.58) LoanType 47.022*** -0.006 -0.059*** 0.019 -0.124*** (15.96) (-1.00) (-4.15) (1.58) (-10.96) PriorRel 18.665 -0.051 1.326*** 0.145 0.177* (0.43) (-0.99) (4.67) (1.04) (1.81) Bank × Borrower FE Yes Yes Yes Yes Yes Industry × Year × Quarter FE Yes Yes Yes Yes Yes Observations 8,906 8,906 8,906 8,906 8,906 Adj. R-Square 0.682 0.460 0.567 0.636 0.321

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Table 5: Changes in borrower risk-taking resulting from Basel III

This table presents whether the risk-taking by borrowers with more exposure to Basel III increased, compared to that by borrowers with less exposure, after the Basel III proposal year. Specifically, it presents results from estimating Equation [2]: ∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 + 휑퐶표푛푡푟표푙푠푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푡+12 for borrower i, bank j, year t, and industry s. ∆푅푖푠푘푇푎푘푖푛𝑔 is the change in borrower risk-taking between the eight quarters (or two years) prior to and the eight quarters (or two years) following the loan initiation. I measure ∆RiskTaking as change in implied option volatility (∆OptV) in Column (1), change in asset volatility (∆AssetV) in Column (2), change in distance to default (∆DTD) in Column (3), change in downside EBIT margin (∆DEBITM) in Column (4), change in Herfindahl-Hirschman Index (∆HHI) in Column (5), change in mean growth of capital expenditures (∆CAPEX) in Column (6), change in mean growth of investments in intangible assets (∆IntanInv) in Column (7), and change in mean growth of R&D expenditures (∆R&D) in Column (8). Treat indicates whether or not borrower 푖’s dominant lead bank 푗 in year-quarter t has $250 billion or more in total consolidated assets at the beginning of year-quarter 푡. 훼푖푗 represents the bank × borrower fixed effect, which perfectly subsumes the stand-alone bank fixed effect and borrower fixed effect. 훼푠푡 represents the industry × year × quarter (or industry × year) fixed effect. Variables in Column (1) are computed at the quarterly level, whereas variables in Columns (2) through (8) are at the yearly level. It is worth noting that Column (8) controls for the stand-alone borrower and bank fixed effects (rather than bank × borrower fixed effect) due to limited number of observations for banks with R&D expenditures. Standard errors are clustered by bank. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels.

(1) (2) (3) (4) (5) (6) (7) (8) ∆OptV ∆AssetV ∆DTD ∆DEBITM ∆HHI ∆CAPEX ∆IntanInv ∆R&D Treat × Post 0.037*** 0.040*** -1.353*** 0.063** 0.049** 0.171*** 14.476** 0.089** (3.21) (5.45) (-5.06) (2.76) (2.60) (4.79) (2.67) (2.29) Treat -0.014 -0.056** -0.896 0.044*** 0.008 -0.025 18.341 0.000 (-0.85) (-2.64) (-1.47) (3.32) (0.14) (-0.61) (1.68) (0.01) LogBankAsset -0.001 0.006 -2.157** 0.049** -0.013 -0.076* -7.139 -0.013 (-0.06) (0.29) (-2.34) (2.79) (-0.84) (-1.98) (-0.62) (-0.84) LogAsset 0.042*** -0.000 -1.750** 0.027 0.001 -0.282*** 15.517*** -0.170*** (10.80) (-0.03) (-2.76) (0.77) (0.24) (-9.25) (5.91) (-10.22) ROA 0.083 -0.078 -1.806 0.318 -0.079** -0.732*** -1.489 0.067 (0.71) (-1.68) (-0.88) (1.41) (-2.18) (-5.05) (-0.03) (0.61) Leverage -0.017 0.106*** -2.169** 0.202 0.015 0.198* 4.188 -0.038 (-1.28) (3.32) (-2.91) (1.69) (0.41) (2.07) (0.19) (-0.53) Borrower FE / / / / / / / Yes Bank × Borrower FE Yes Yes Yes Yes Yes Yes Yes / Industry × Year × Quarter FE Yes / / / / / / / Industry × Year FE / Yes Yes Yes Yes Yes Yes Yes Observations 3,144 3,099 2,528 1,725 3,641 4,293 3,097 1,931 Adj. R-Square 0.748 0.581 0.445 0.245 0.286 0.264 0.148 0.245

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Table 6: Dynamic changes in borrowers’ loan costs and subsequent change in risk-taking resulting from Basel III

This table presents how loan costs and subsequent risk-taking for borrowers with more exposure to Basel III, compared to those for borrowers with less exposure, evolved over time. Specifically, it presents results from estimating Equations [3] and [4]: 퐿표푎푛퐶표푠푡푠푖푗푡 = 훽−1푇푟푒푎푡(−1)푖푗,−1 + 훽0푇푟푒푎푡(0)푖푗,0 + 훽1푇푟푒푎푡(1)푖푗,1 + 훽2푇푟푒푎푡(2)푖푗,2 + 훽3푇푟푒푎푡(3)푖푗,3 + 훽4푇푟푒푎푡(4)푖푗,4 + 훿푇푟푒푎푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡 [3] and ∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽−1푇푟푒푎푡(−1)푖(푗),−1 + 훽0푇푟푒푎푡(0)푖(푗),0 + 훽1푇푟푒푎푡(1)푖(푗),1 + 훽2푇푟푒푎푡(2)푖(푗),2 + 훽3푇푟푒푎푡(3)푖(푗),3 + 훽4푇푟푒푎푡(4)푖(푗),4 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡+12 [4] for borrower i, bank j, year t, and industry s. I examine the effects of Base III on 퐿표푎푛퐶표푠푡푠 in Columns (1) and (2) and examine the effects of ∆푅푖푠푘T푎푘푖푛𝑔 in Columns (3) through (10). All variables are defined in Appendix A. 훼푖푗 represents the bank × borrower fixed effect, which perfectly subsumes the stand-alone bank fixed effect and borrower fixed effect. 훼푠푡 represents the industry × year × quarter (or industry × year) fixed effect. Variables in Columns (1) and (3) are computed at the quarterly level, whereas variables in Columns (4) through (10) are at the yearly level. It is worth noting that Column (10) controls for the stand-alone borrower and bank fixed effects (rather than bank × borrower fixed effect) due to limited number of observations for banks with R&D expenditures. Standard errors are clustered at the bank level. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) SprdFee LSize ∆OptV ∆AssetV ∆DTD ∆DEBITM ∆HHI ∆CAPEX ∆IntanInv ∆R&D Treat(-1) -1.781 -0.019 -0.028 0.029 -0.645 0.007 -0.002 0.046 -2.258 0.137 (-0.08) (-0.53) (-1.13) (1.09) (-1.01) (0.16) (-0.09) (0.44) (-0.10) (1.52) Treat(0) 20.744 -0.008 0.031 0.095*** -2.626*** 0.025 0.073*** 0.253** 4.524 0.103 (1.08) (-0.31) (0.92) (3.29) (-3.65) (1.02) (3.81) (2.26) (0.27) (1.03) Treat(1) 35.537 -0.007 -0.014 0.043 -0.787 0.111*** 0.037 0.336*** 13.476 0.175** (1.50) (-0.49) (-0.65) (1.18) (-1.52) (3.31) (1.35) (4.51) (0.51) (2.49) Treat(2) 28.042* -0.036 0.042** 0.049*** -1.809** 0.073*** 0.087*** 0.295*** 6.993 0.183** (1.76) (-0.98) (2.56) (3.87) (-2.26) (3.73) (3.67) (3.07) (1.20) (2.70) Treat(3) -14.777 -0.011 0.015 0.042** -0.276 0.030 0.063 0.103 23.996 0.079 (-0.99) (-0.58) (0.62) (2.31) (-0.25) (1.12) (1.69) (1.62) (1.54) (1.13) Treat(4) 16.518 0.001 0.000 0.062* -2.709* 0.043** -0.073** -0.088 -6.350 0.269*** (1.09) (0.02) (.) (2.02) (-2.11) (2.22) (-2.56) (-0.49) (-0.16) (9.17) Treat 5.373 -0.018 -0.018 -0.056** -1.652** 0.043*** 0.009 -0.087 24.749** -0.014 (0.43) (-1.46) (-1.40) (-2.30) (-2.55) (3.65) (0.17) (-1.63) (2.62) (-0.40) LogBankAsset -45.870*** -0.005 0.008 -0.010 -1.750* 0.044* -0.015 -0.117 -5.260 -0.016 (-3.07) (-0.62) (0.57) (-0.47) (-2.10) (1.91) (-0.76) (-1.41) (-0.89) (-1.17) Borrower FE / / / / / / / / / Yes Bank × Borrower FE Yes Yes Yes Yes Yes Yes Yes Yes Yes / Industry × Year × Quarter FE Yes Yes Yes / / / / / / / Industry × Year FE / / / Yes Yes Yes Yes Yes Yes Yes Observations 8,906 8,906 3,144 3,099 2,528 1,725 3,641 4,293 3,097 1,931 Adj. R-Square 0.639 0.437 0.744 0.577 0.436 0.238 0.288 0.222 0.142 0.193

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Table 7: Changes in loan costs and subsequent risk-taking during a shorter sample period for robustness

This table presents the results for robustness tests on changes in loan costs and subsequent risk-taking during a shorter sample period. Specifically, it presents results from estimating Equations [1] and [2] between 2010Q3 and 2014: 퐿표푎푛퐶표푠푡푠푖푗푡 = 훽푇푟푒푎푡푖푗푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푗푡 [1] and ∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푡+12 [2] for borrower i, bank j, year t, and industry s. I examine the effects of Base III on 퐿표푎푛퐶표푠푡푠 in Columns (1) and (2) and examine the effects of ∆푅푖푠푘T푎푘푖푛𝑔 in Columns (3) through (10). All variables are defined in Appendix A. 훼푖푗 represents the bank × borrower fixed effect, which perfectly subsumes the stand-alone bank fixed effect and borrower fixed effect. 훼푠푡 represents the industry × year × quarter (or industry × year) fixed effect. Variables in Columns (1) and (3) are computed at the quarterly level, whereas variables in Columns (4) through (10) are at the yearly level. It is worth noting that Column (10) controls for the stand-alone borrower and bank fixed effects (rather than bank × borrower fixed effect) due to limited number of observations for banks with R&D expenditures. Standard errors are clustered at the bank level. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) SprdFee LSize ∆OptV ∆AssetV ∆DTD ∆DEBITM ∆HHI ∆CAPEX ∆IntanInv ∆R&D Treat × Post 26.300*** -0.004 0.064 0.047*** -1.287*** 0.154*** 0.033 0.175*** 13.066* 0.028 (4.00) (-0.35) (1.76) (5.39) (-5.75) (3.41) (1.11) (3.10) (2.02) (0.82) Treat 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.131 (.) (.) (.) (.) (.) (.) (.) (.) (.) (-0.59) LogBankAsset 20.674 -0.017 -0.140** -0.053 -0.824 0.535** -0.025 -0.159 -0.555 0.042 (1.20) (-0.41) (-2.21) (-0.89) (-0.44) (2.29) (-0.48) (-0.81) (-0.04) (0.74) LogAsset -27.149*** -0.045*** 0.042** 0.038** -1.787** 0.127 -0.007 -0.555*** 20.349** -0.184*** (-3.05) (-3.52) (2.71) (2.48) (-2.15) (1.69) (-0.89) (-4.82) (2.85) (-3.67) ROA -323.235 0.187 0.313 -0.149** 0.365 0.727** 0.016 -0.449 -5.324 -0.713*** (-1.69) (0.97) (1.63) (-2.39) (0.28) (2.22) (0.13) (-1.49) (-0.09) (-3.26) Leverage 61.404* 0.009 -0.084 0.141** -2.716* 0.590** 0.098** -0.097 -59.771* -0.066 (2.09) (0.17) (-1.11) (2.51) (-1.96) (2.67) (2.38) (-0.39) (-1.91) (-0.20) LogMaturity -17.468* 0.020*** (-1.96) (3.52) LoanType 47.983*** 0.004 (9.94) (0.34) PriorRel 47.983*** -0.206*** (9.94) (-17.87) Borrower FE / / / / / / / / / Yes Bank × Borrower FE Yes Yes Yes Yes Yes Yes Yes Yes Yes / Industry × Year × Quarter FE Yes Yes Yes / / / / / / / Industry × Year FE / / / Yes Yes Yes Yes Yes Yes Yes Observations 3,061 3,061 1,214 1,422 1,350 682 1,198 1,506 1,207 634 Adj. R-Square 0.662 0.484 0.724 0.627 0.530 0.332 0.526 0.346 0.318 0.262

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Table 8: Cross-sectional differences in changes in loan costs and risk-taking using the triple-difference research design This table presents the cross-sectional differences in changes in loan costs and risk-taking using the triple-difference research design. Specifically, it presents results from estimating Equations [5] and [6]: 퐿표푎푛퐶표푠푡푠푖푗푡 = 훽1푇푟푒푎푡푖푗푡 × 푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽2푇푟푒푎푡푖푗푡 × 푃표푠푡푡 + 훽3푇푟푒푎푡푖푗푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽4푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽5푇푟푒푎푡푖푗푡 + 훽6퐿표𝑔퐴푠푠푒푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 휇퐿표𝑔푀푎푡푢푟푖푡푦푖푗푡−1 + 훼푖푗 + 훼푡 + 휀푖푗푡 [5] and ∆푅푖푠푘푇푎푘푖푛𝑔(푖푡−12,푖푡+12) = 훽1푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽2푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 + 훽3푇푟푒푎푡푖(푗)푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽4푃표푠푡푡 × 퐿표𝑔퐴푠푠푒푡푖푡 + 훽5푇푟푒푎푡푖(푗)푡 + 훽6퐿표𝑔퐴푠푠푒푡푖푗푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖푗푡−1 + 훼푖푗 + 훼푡 + 휀푖푗푡+12 [6] for borrower i, bank j, year t, and industry s. I examine the effects of Base III on 퐿표푎푛퐶표푠푡푠 in Columns (1) and (2) and examine the effects of ∆푅푖푠푘T푎푘푖푛𝑔 in Columns (3) through (10). All variables are defined in Appendix A. 훼푖푗 represents the bank × borrower fixed effect, which perfectly subsumes the stand-alone bank fixed effect and borrower fixed effect. 훼푡 represents the year × quarter (or year) fixed effect. Variables in Columns (1) and (3) are computed at the quarterly level, whereas variables in Columns (4) through (10) are at the yearly level. It is worth noting that Column (10) controls for the stand-alone borrower and bank fixed effects (rather than bank × borrower fixed effect) due to limited number of observations for banks with R&D expenditures. Standard errors are clustered at the bank level. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) SprdFee LSize ∆OptV ∆AssetV ∆DTD ∆DEBITM ∆HHI ∆CAPEX ∆IntanInv ∆R&D Treat × Post × LogAsset -18.688* 0.003 -0.041* -0.025** 0.005 -0.017* -0.041*** -0.022 9.236 0.020 (-1.71) (0.27) (-1.82) (-2.14) (0.01) (-1.88) (-3.08) (-0.59) (1.26) (0.49) Treat × Post 149.696* -0.024 0.366* 0.239** -0.546 0.149 0.376*** 0.339 -61.741 -0.058 (1.86) (-0.25) (1.96) (2.57) (-0.22) (1.66) (3.91) (1.13) (-1.02) (-0.18) Treat × LogAsset 19.436 -0.011 -0.002 -0.030 -1.073 0.089* -0.032 -0.065 0.236 -0.041 (1.36) (-1.07) (-0.13) (-1.32) (-1.68) (1.77) (-1.43) (-1.52) (0.02) (-1.32) Post × LogAsset 21.575* -0.012 0.043* 0.034*** -0.408 0.003 0.035** 0.043 -8.517 -0.014 (1.98) (-1.07) (1.99) (2.94) (-1.37) (0.29) (2.72) (1.27) (-1.22) (-0.40) Treat -116.337 0.077 0.008 0.144 6.526 -0.548 0.225 0.375 15.571 0.284 (-1.29) (1.04) (0.07) (1.07) (1.53) (-1.62) (1.41) (1.18) (0.21) (1.21) LogAsset -41.905*** -0.043*** 0.050*** 0.016 -0.611 -0.037 0.028 -0.214*** 12.917 -0.131*** (-2.85) (-3.93) (3.32) (0.72) (-0.88) (-0.91) (1.29) (-4.93) (1.19) (-4.08) LogBankAsset -56.497*** -0.006 0.005 -0.001 -1.971** 0.044 -0.014 -0.054 -5.854 -0.005 (-3.67) (-0.53) (0.27) (-0.07) (-2.71) (1.05) (-0.71) (-1.45) (-0.69) (-0.30) LogMaturity -3.557 0.019*** (-1.18) (7.92) Constant 1,602.319*** 0.606** -0.768 -0.277 53.210*** -0.750 -0.039 2.757*** 27.913 1.232*** (4.60) (2.74) (-1.62) (-0.53) (3.22) (-0.85) (-0.08) (3.22) (0.22) (2.96) Borrower FE / / / / / / / / / Yes Year FE / / / Yes Yes Yes Yes Yes Yes Yes Bank × Borrower FE Yes Yes Yes Yes Yes Yes Yes Yes Yes / Year × Quarter FE Yes Yes Yes / / / / / / / Observations 8,941 8,941 3,183 3,108 2,531 1,752 3,652 4,302 3,110 1,956 Adj. R-Square 0.632 0.463 0.705 0.552 0.439 0.144 0.292 0.243 0.152 0.247

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Table 9: Borrowers’ access to alternative funding sources other than bank loans

This table presents whether borrowers are able to access alternative funding sources other than bank loans in response to Basel III. Specifically, it presents results from estimating Equation [7]: 퐹푢푛푑푖푛𝑔푖푡 = 훽푇푟푒푎푡푖(푗)푡 × 푃표푠푡푡 + 훿푇푟푒푎푡푖(푗)푡 + 훾퐿표𝑔퐵푎푛푘퐴푠푠푒푡푖(푗)푡−1 + 휑퐶표푛푡푟표푙푠푖(푗)푡−1 + 훼푖푗 + 훼푠푡 + 휀푖푡+12 [7] for borrower i, bank j, year t, and industry s. I examine how borrowers’ interest expenses on long-term debts, the amounts of long-term debts, and equity issuance changed after the proposal of Basel III. All variables are defined in Appendix A. 훼푖푗 represents the bank × borrower fixed effect, which perfectly subsumes the stand-alone bank fixed effect and borrower fixed effect. 훼푠푡 represents the industry × year × quarter (or industry × year) fixed effect. Variables in all columns are computed at the yearly level. Standard errors are clustered at the bank level. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively.

(1) (2) (3) LTIntExp/LTLoan LTDebt/Asset EquityIssuance Treat × Post 0.037*** 0.005 0.008 (4.67) (0.27) (0.31) Treat 0.000 0.059*** 0.043 (.) (4.56) (0.79) LogBankAsset -0.023* -0.001 -0.022 (-2.17) (-0.04) (-0.83) LogAsset 0.011 -0.083*** -0.134*** (1.83) (-6.84) (-9.89) ROA 0.076*** 0.120* 0.338*** (4.28) (1.84) (4.40) Leverage 0.020 0.359*** 0.341*** (0.52) (11.53) (9.79) Bank × Borrower FE Yes Yes Yes Industry × Year × Quarter FE Yes Yes Yes Observations 160 5,154 5,087 Adj. R-Square 0.476 0.603 0.268

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Table 10: Univariate analyses on banks’ provisions for loan and lease losses

This table presents how provisions for loan and lease losses (PLLL/Loan) changed from the pre- to the post-Basel period in the borrower-year-level dataset. All continuous variables are winsorized at the top and bottom 1%. T-statistics are in parentheses. *, **, *** indicate statistical significance (two-sided) at the 0.1, 0.05, and 0.01 levels, respectively.

(1) (2) (3)

Post-period Pre-period Differences between periods Mean N Mean N Mean t-stat 1st Year PLLL/Loan 0.001 2,234 0.002 2,937 -0.001*** (-34.041) Borrowers more affected by Basel III 2st Year PLLL/Loan 0.001 2,234 0.003 2,900 -0.002*** (-32.701) 3st Year PLLL/Loan 0.001 1,862 0.005 2,823 -0.003*** (-36.117) 1st Year PLLL/Loan 0.001 195 0.001 496 -0.000*** (-3.381) Borrowers less affected by Basel III 2st Year PLLL/Loan 0.001 195 0.001 461 -0.001*** (-6.353) 3st Year PLLL/Loan 0.001 167 0.002 410 -0.002*** (-9.144) (1) (2) (3) Post-period Pre-period Diff-in-diff Mean t-stat Mean t-stat Mean t-stat 1st Year PLLL/Loan 0.000*** (10.591) 0.001*** (21.094) -0.001*** (-7.865) Difference between borrowers more 2st Year PLLL/Loan 0.001*** (17.795) 0.002*** (17.840) -0.001*** (-6.383) affected and less affected by Basel III 3st Year PLLL/Loan 0.001*** (17.490) 0.002*** (12.585) -0.002*** (-5.116)

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