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Universal Banking and Performance of Industrial Firms – US Evidence

Lily Fang1 INSEAD

Wei-ling Song Drexel University

March 9th, 2004

Abstract

We study firm financing and performance when their banks can both lend and underwrite securities. We show that lenders’ underwriting capabilities significantly improve firms’ access to capital markets and contribute to the substitution of public debt for loans. We also find that firms employing previous lenders as underwriters invest more, pay more dividends, and experience positive abnormal stock returns. Thus universal banking is related to positive changes in industrial firms. We interpret the evidence as suggesting that, aware of conflict of interest concerns, firms and banks rationally select contracts so that only high quality issues are underwritten by previous lenders.

1 Corresponding author. Lily.Fang@insead.edu We thank seminar participants at Wharton for helpful comment and suggestions. Financial support from the Wharton Financial Institution Center is greatly appreciated.

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1. Introduction

The Glass-Steagall Act of 1933 erected a wall between commercial and investment banking by

prohibiting commercial banks from conducting security-related businesses and vise versa. For

more than half a century, this piece of legislation was one of the pillars of US banking law and

shaped a segmented financial system in the US that stands in stark contrast to the universal-

banking system in countries like Germany and Japan. At the time of enactment, the law was a

response to two wide-spread concerns about the banking sector: one that the commingling of

commercial banking and security dealing introduces conflict of interest, and two that it increases

the overall riskiness of banks and the financial system. Amidst the worst depression in US

history and massive bank failure2, lawmakers, lead by Senator Carter Glass, believed that the

combining of commercial and investment banking activities had contributed to the stock market

crash in the 1920s, and had been a main culprit for the loss of public trust in the banking sector.

The act was quickly signed into law.

Sixty-six years later, however, it seems that the legislation pendulum has swung back in

full circle. On Nov 12th 1999, President Clinton signed into law the Gramm-Leach-Bliley Act,

which, by legalizing the so-called Financial Holding Companies to conduct commercial banking

as well as security underwriting and insurance businesses, officially repealed the Glass-Steagall

Act. This time, top policy makers hailed the new law as the one that “modernized [the US]

financial system for the twenty-first century”.3

2 According to some account, by 1933, over 11,000 banks had failed or had to merge, reducing the number of banks by 40%, from 25,000 to 14,000. See BrainBank Special Report “Understanding How Glass-Steagall Act Impacts Investment Banking and the Role of Commercial Banks”, 1998. 3 See, for example, speech by the Federal Reserve Board Governor Laurence H. Meyer before the American Law Institute and the American Bar Association, Feburary, 15th, 2001, and Federal Reserve Chairman Alan Greenspan’s testimony on Banking and Financial Services before the House of Representatives, 106th Cong. 254, 1999.

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What are the pros and cons of the universal banking system (under the new Gramm-

Leach-Bliley Act) compared to a segmented system (under the old Glass-Steagall Act)? This is

the question at the heart of the policy debate regarding financial system reform, and is the focus

of this paper.

This topic is not new. Pros and cons of different financial systems have attracted debates

among policy makers and academics alike. On the theoretical front, Allen and Gale (1995),

among others, provides a thorough comparison of the two banking systems. Empirically,

researchers have also made attempts to answer the question. For example, Hoshi, Kashyap, and

Scharfstein (1991) is an earlier work on the role of Japanese main banks in firm growth; more

recently, Gorton and Schmid (2001) examines the role of German universal banks in firm

performance. Using multi-country data, Levine (1998), Demirguc-Kunt and Maksimovic (1998)

study the role of the banking sector in firm development.

Our paper contributes to this strand of literature in two ways. Since the existing

empirical work uses foreign data, the implication for the US market is not immediately clear and

remains an empirical question. To our best knowledge, this paper provides the first set of results

on the pros and cons of universal banking using US data. Secondly, cross-sectional studies such

as Levine (1998) and Demirguc-Kunt and Maksimovic (1998) encounter the inherent difficulty

that country level differences are difficult to control for in a direct comparison using data from

different countries. The repeal of the Glass-Steagall Act provides us a unique opportunity to

compare universal banking with separated intermediation within the US.

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The same regulatory change has in recent years inspired another body of literature that

examines the costs and benefits of commercial bank entry into investment banking. See, among

others, Gande et. al. (1997) and (1999), Puri (1996), and Song (2001). Our work is related to this

strand of literature, but with a major difference. This strand of existing literature focuses on the

regulatory impact on the financial intermediary, comparing and contrasting commercial banks’

underwriting performance to that of the traditional investment houses. Welfare implication of

the regulation change for industrial firms can be inferred only indirectly at the best. In this

paper, however, we directly study industrial firms’ financing and investment behavior before

and after the deregulation, thus provide direct evidence on the implications of the deregulation

for the US industrial sector. This is an important question because the ultimate goal of bank

reforms should be to enhance financial intermediation and benefit corporate America. And yet

this question has so far been overlooked in the literature.

To examine the impact of universal banking on US firms, we study two central questions.

First, how does universal banking affect firms’ financing activities and their access to the capital

market? Second, if universal banking changes firms’ financing behavior, how does this change

in turn affect firm investment and operating performance?

Regarding the first question, we find that banks’ combined lending and underwriting

capabilities significantly improve their clients’ access to the public debt market. Using cross-

sectional comparisons, we find that for firms that issue debt in both the 80s and the 90s

(henceforth seasoned issuers), the aggregate debt issuance for the latter decade is significantly

positively related to the usage of their lenders’ underwriting service. To further control for firm-

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specific factors, we examine the within-firm change in the 10-year aggregate debt issuance from

the pre-deregulation decade (1981–1990) to the post-deregulation decade (1991–2000). Again

we find that usage of lenders’ underwriting service significantly explains the increase in debt

issuance, ceteris paribus. Further more, we document that the reduction of bank loans and the

corresponding increase in public debt in the ‘90s can be significantly explained by the usage of

previous lenders’ underwriting service, even after other factors are controlled for. In other

words, universal banking has contributed to the general shift from bank loans to public debt

financing in the ‘90s. To the extent that the “graduation” from bank loans to the public debt

market is a sign of economic growth4, the evidence indicates that universal banking has

contributed to firm growth.

Thus the answer to our first empirical question is positive: lenders’ combined lending and

underwriting capabilities enhance clients’ access to the capital market. But is this change in

financing related to real changes in firm investment, dividend policy, and operating

performance? For this second question, we find that parallel to the changes in financing, firms

that use lenders’ underwriting service make more capital investments, pay more dividends and

repurchase more of their own shares compared to matching firms that do not employ previous

lenders as underwriters. There is some evidence for better operating performance and strong

evidence for positive abnormal stock returns among firms that employ previous lenders as

underwriters. Thus, the answer to the second main question is also positive and overall our

evidence in this paper suggests that universal banking is related to positive changes in US

industrial firms.

4 See Diamond (1991).

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These findings seem to stand in contrast to conflict of interest concerns, which is at heart

of the policy debate between universal banking and segmented intermediation. Does our

evidence suggest that conflict of interest is not a problem?

Our interpretation of the results is that, contrary to disproving conflict of interest, they are

precisely consistent with conflict of interest and rational behavior of economic agents. After

more than half a century under the Glass-Steagall regime, in which conflict of interest was the

reason for separating commercial banking and underwriting, banks as well as firms are certainly

aware of the conflict of interest concerns among investors. Thus as rational economic agents,

they will take this concern into account ex ante. As a result, firms and banks rationally select

the underwriting contract in such a way that only high quality issues are underwritten by

previous lenders. Ex post, the superior performance of the issues helps calm market fear for

conflict of interest, and keeps the door to the market open for both the issuers and the banks.

The rest of the paper is organized as follows. Section 2 reviews relevant history of

banking law reform and surveys the literature for arguments for and against universal banking.

Section 3 describes our data and methodology. Section 4 studies the impact of universal banking

on firm financing behavior. Section 5 analyzes firms’ investment and performance under

universal banking. Section 6 interprets our results and concludes.

2. History and Literature Review

The repeal of the Glass-Steagall Act was actually a gradual process that took over a

decade, culminating in the final signing of the Gramm-Leach-Bliley Act in 1999.

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In 1987, Federal Reserve Board approved a landmark order that allowed three

commercial banks, Citicorp, J.P. Morgan, and Bankers Trust, to engage in limited underwriting

of municipal bonds and commercial papers through the so-called “Section-20 subsidiaries”.5 In

1989, these banks obtained additional permission to underwrite corporate bond and equity issues

subject to a revenue restriction of not more than 5% of overall revenue. The revenue limitation

was raised to 10% in 1989, and to 25% by the end of 1996. Finally, with the signing of the

Gramm-Leach-Bliley Act in 1999, all revenue restrictions were removed, and the US banking

formally entered a new era, in which combined lending and underwriting (as well as insurance

business) within the same intermediary is legalized in the United States. Throughout the

decades, the number of banks with section-20 subsidiaries gradually increased. By May 1st

2000, there were fifty-five section-20 subsidiaries officially listed on the Federal Reserve web

site.

These regulatory changes in the late ‘80s and early ‘90s provide a clean environment to

study the pros and cons of separated versus combined intermediation from the perspective of

U.S. firms: prior to 1990, the system largely separated bank lending from underwriting; post

1990, the two functions became integrated.6 Our study exploits this regulation change and

examines the financing and performance of a large sample of US industrial firms for the decade

5 For the detailed order, see Citicorp, J.P. Morgan & Co Incorporated and Bankers Trust New York Corporation, Federal Reserve Bulletin 73 (1987), pages 473-508. Section-20 of Glass-Steagall Act mandates that a member bank of the Federal Reserve System may not be affiliated with a company that is “engaged principally” in underwriting and dealing of securities. The Federal Reserve Board reinterpreted this clause to allow bank affiliates to engage in security-related activities subject to a 5% revenue limitation. The limitation is subsequently raised to 10% in 1989, and to 25% at the end of 1996. For revenue limitation changes over time, see Federal Register 61 (1996), pages 68750-68756.

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before the deregulation (1981-1990) and the decade after (1991 – 2000). Our goal is to see

whether universal banking improves or hampers firm financing and performance, and hence shed

light on the pros and cons of universal banking versus segmented intermediation.

The main cost of universal banking, or combined intermediation, is the conflict of interest

it induces within the financial intermediary. It is argued (and used as the main argument behind

the Glass-Steagall Act) that, having private information about borrowers’ prospects and acting

out of self-interest, banks could misrepresent adverse information and market low quality

securities to public investors, sometimes with the express purpose of recouping their own

investments in the firms.7 This creates a Lemon’s problem for the capital market, and threatens

the functioning and existence of that market.

Universal banking however, at least theoretically, has several advantages over segmented

intermediation. First and most importantly, it achieves efficiency in information production.

Information generated from lending can be reused during the underwriting process, and since

information production is costly, reuse of information is socially efficient.8 Secondly, combined

intermediation reduces the number of claim-holders of firm cash-flows, and such an arrangement

can provide more flexibility during refinancing negotiations. This feature is particularly valuable

for firms in the brink of distress because it helps the borrower escape inefficient liquidation.

Finally, related to the re-use of information argument, bank monitoring through its lending

6 Although the landmark order came in 1987, it was not until after 1990 that major banks made significant inroads in the security underwriting business. For our study to capture economic, rather than legalistic significance, we define the decade before 1990 to be the period of separated intermediation, and the decade after to be the integrated era. 7 Pre Glass-Steagall, the abusive practices of two most prominent national banks, National City Bank, and Chase National Bank, in precisely this fashion, was cited as evidence of conflict of interest and helped win support for the Glass-Steagall Act. See Wigmore, 1985. 8 See, for example, Kanatas and Qi (1998), (2001).

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relationship with the client creates a bank certification role during security issuance, which

benefits the client in terms of a lower cost of capital. This bank certification effect has been

documented in the empirical literature, see, for instance, Gande et al (1997), (1999), Puri (1996),

Song (2001).

Since universal banking has its benefits and costs, the effect of the deregulation is an

empirical question. Does the newly enacted Gramm-Leach-Bliley Act improve financial

intermediation and improve firms’ access to the capital market? This depends on the trade-off

between the pros and cons of universal banking. If benefits of universal banking – information

efficiency, financing flexibility, and certification – outweigh the cost of conflict of interest, then

the answer is yes and we should observe that permitting banks to underwrite is a net positive for

their clients’ access to the capital market, ceteris paribus. Therefore comparing firms’ financing

before and after the deregulation (which is our first main research question), provides insights

into whether the benefits of universal banking outweigh the costs.

If the answer to the first question is positive, that is, lenders’ underwriting capability

indeed improves clients’ access to the capital market, a natural follow-up question is, is the

improved financing related to better investment and performance? This second question is

related to the literature on the role of the financial intermediary in firm growth and development.

The existing evidence is mixed. Using Japanese data, Hoshi, Kashyap and Scharfstein (1991)

document that firms with main bank relations are less liquidity- constrained for investments. But

this improved liquidity does not seem to translate into better performance as Weinstein and

Yafeh (1998) show that these same firms perform poorly in contrast to those without main bank

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relations. However, using a set of German firms, Gorton and Schmid (2001) conclude that bank

involvement improves corporate performance. Our second empirical question re-examines the

role of the financial intermediary in firm growth using US data, and shed light on whether

universal banking is associated with positive change in firm performance.

3. Data, Methodology, and Summary Statistics

Universal banking is a vast topic. It is therefore useful to clearly define the scope of our

empirical investigation.

In studying universal banking activities, we limit our attention to the corporate bond

underwriting practice of the newly created universal banks. This limited focus is based on the

observation that during our sample period, while the new universal banks made significant

inroads in the bond underwriting market, they did not develop a major role in other traditional

investment-banking businesses such as equity underwriting and M&A advisory.

As a second scope limitation, we focus our attention on eight large, money center banks

with section-20 subsidiaries. These eight banks (henceforth “the Big-8”) are: BankAmerica,

Bankers Trust, Citicorp, Chase Manhattan, Chemical Bank, First Chicago, J. P. Morgan, and

NationsBank.

Restricting our attention to these eight banks is based on considerations for economic

significance. First, these eight banks all established Section-20 Subsidiaries before 1990, which

is start of the deregulation period. Since our deregulation period is from 1990 to 2000, firms that

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established Section-20 subsidiaries after 1990 may not have developed material universal

banking capabilities in the sample period, and ignoring them would not produce economically

significant differences. Second, the eight banks already claim a lion’s share in the underwriting

as well as the lending done by all commercial banks. From 1991 to 2000, 89% of newly issued

corporate bonds underwritten by all section-20 banks are lead-underwritten by these eight banks.

In terms of loan initiation, for our sample period, the Big-8 combined lending is 16.5 trillion,

representing 75.2% of all bank loans in Loan Pricing Corporations’ Dealscan Database for the

same sample period. Therefore for both underwriting and lending activities, the Big-8 serves as

a good representation for the entire section-20 group. Furthermore, there were sixteen foreign

banks with section-20 subsidiaries. Since we focus on U.S. domestic banks and thus rule out

foreign banks, the number of actually “neglected” universal banks is reduced from forty-seven to

thirty-one.

Our data come from three sources. Information on bank loans is obtained from the

Dealscan database of Loan Pricing Corporation. Information on public debt issuance is obtained

from the SDC Platinum database. Borrowing firms’ stock return and accounting data are

collected from the CRSP/Compustat database. We use the ticker symbol, where available and

reliable, to match firms in Dealscan with those in CRSP/Compustat. In many cases however, the

ticker symbol is missing or incorrect. In such instances, borrower names are manually matched

between the databases. Only firms in all databases are included in the analysis. We are able to

match 8,058 firms between the three databases.

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Our goal is to study how different types of relationship with universal banks affect firm

financing and performance. Thus we need a scheme to classify various types of relationships

with banks. To this end, we assign each firm to one of the following four categories based on the

status of their lender.

(1) A firm is a “Big-8” client if the firm borrows from any of the Big-8 banks;

(2) A firm is a “Non-big-8 Sec20” client if the firm borrows from a non-big-8 bank with

Section-20 subsidiary but does not borrow from any of the Big-8 banks;

(3) A firm is a “Nonsec20” client if the firm borrows only from banks without a Section-

20 subsidiary;

(4) A firm is a “Nonbank” client if the firm that does not have loan information in the

Dealscan database.

Table I provides summary statistics on loan data from Dealscan. Panel A shows the

breakdown of lending by bank type and by calendar year. The most salient observation is that

Big-8 banks account for a dominating share of the overall corporate lending: 43.8% of all deal

counts and 75.2% of total dollar amount over the 10-year period. Panel B reports the same

statistics for only those firms that are also in the CRSP database, i.e., firms that are in our final

sample. This panel shows that lending to publicly listed firms is even more concentrated: Big-8

banks account for 73.7% of loan deals and 96.3% of total loan amount for these firms. The same

panel also shows that the concentration is even higher for firms listed on the New York Stock

Exchange. These observations lend support to our argument that the Big-8 banks as a group is a

reasonable representation of the universal bank sample in terms of economic significance.

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Table II provides summary statistics on firms’ financing behavior, classifying firms by

lender type. Panel A, B, and C tabulate bank borrowing, public debt, and equity financing

activities, respectively. Several interesting observations can be made. First, looking across the

panels, it is clear that bank borrowing is by far the most significant source of external financing.

This pattern is particularly strong for Big-8 clients, which consists of mostly large firms. For

these firms, average bank borrowing over the 10-year period was $2,185 million dollars, whereas

the amounts raised from public debt and equity are $902 millions and $198 millions respectively.

Secondly, more firms have access to bank loans than to either public debt or equity financing.

Out of the 8,058 firms with information in both DealScan and CRSP/Compustat, 6,876 borrowed

from banks during 1991-2000, 5,908 raised equity financing, and only 1,062 issued public debt.

These statistics offer casual evidence to the pecking order of corporate financing. They are also

consistent with the “life cycle” effect of firm financing in Diamond (1991) that public debt is

accessible for firms only after they have established a reputation of credit worthiness in the

private loan market.

It is worth noting that among the 1,062 firms that have access to public debt market,

82.1% have banking relationships with a Big-8 bank. This demonstrates that association with a

large bank alone, not necessarily a universal bank, may appear to facilitate the access to the

capital market. This can be the case if large-bank association is correlated with firm

characteristics that also affect the firm’s access to capital. To isolate the effect of universal

banking on capital market access, it is therefore important to control for relevant firm

characteristics as well as the association with large banks.

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4. Universal Banking and Firm Access to the Public Debt Market

Having established an overview of the firms’ financing activities, in this section we study

whether bank lenders’ underwriting capability enhances clients’ access to the public debt market.

We first present evidence on how combined intermediation affects the frequency and amount of

public debt financing, and then show how it has contributed to the shift from bank borrowing to

capital market financing over the last 10 years.

4.1. Frequency and Amount of Public Debt Issuance

Table III reports the growth rate of the 10-year aggregate debt financing for seasoned public debt

issuers. Panel A compares the ‘70s to the ‘80s, and panel B compares the ‘80s to the ‘90s.

During the 30-year period, there is an increase in public debt financing in all subgroups of bank

clients. For the Big-8 clients, debt issuance increased by 167% in the ‘80s compared to the ‘70s,

and the increase accelerated to 230% in the ‘90s compared to the ‘80s. Other groups

experienced large increases as well, although at different, mostly slower speed.

While these observations are in line with the conjecture that associations with large

universal banks improve clients’ access to capital markets, this univariate tabulation does not

isolate the effect due to combined intermediation. To separate out the effect due to combined

intermediation, we need to perform a multiple regression analysis of debt issuance on a variable

that indicates the firm’s usage of universal banking services, after controlling other factors that

also affect the firm’s access to capital. That is, we want to estimate the following regression

equation:

∑++=j

jijii XcUsageccD ,10 ** (1)

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where the dependent variable is debt issuance (amount or frequency); “Usage” is the key variable

of interest that indicates the firm’s usage of universal banking services; and the vector X is a list

of controls that affect the firm’s access to capital.

In deriving the list of controls, we are guided by the theoretical consideration that the

availability and quality of information about a firm are key determinants of its access to the

capital market. The better the information availability about a firm, the higher is the likelihood

that it has access to the capital market. Since intuitively information should be more readily

available for larger, publicly listed firms, and firms that have multiple banking and underwriting

relationships with financial institutions, we use firm size, exchange listing status, relationship

with large banks, the reputations of firms’ affiliated underwriters, and the multiplicity of

underwriting relationships as information proxies that control for firms’ access to capital.

Empirically, we use the market value of equity as a measure for firm size. Exchange

listing information comes from the CRSP/Compustat database; it is a dummy variable equaling

one if the firm is listed on NYSE, and zero otherwise. We use the average market share of all

underwriters used by a firm during the sample period to proxy for the reputation of affiliated

underwriters. For each issuer we compute the number of unique underwriters used as the

measure of multiplicity of underwriting relationships.

The key variable of interest in Equation (1) is the “Usage” variable, which reflects the

extent to which firms employ the new underwriting service that is available from its previous

lenders. After controlling for other information proxies, the coefficient on this variable indicates

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whether or not lenders’ universal banking services contribute to firms’ public debt financing.

Empirically, the Usage variable is measured as follows. For each firm, we first identify all debt

issues that are lead-underwritten by a bank that has served as a lender to the firm before the debt

issue. We compute the total frequency of these lender-lead deals, and divide this number by the

total number of public debt issues completed by the firm for the sample period to obtain the

percentage of deals lead-underwritten by previous lenders. A similar measure is constructed

using dollar amounts. The two measures of Usage will be employed in the regressions of debt

issue frequency and amount respectively.

Table IV provides summary statistics on select independent variables that are unique to

our study, such as the multiplicity of underwriting relationships, the reputations of associated

underwriters, and the usage of lenders’ underwriting service. The salient feature of this table is

that firms associated with Big-8 banks are also associated with more, and larger underwriters.

The mean (median) number of underwriters used in the sample period is 2.43 (2) for Big-8

clients, 1.47 (1) for Non-Big 8 Sec20 clients and 1.32 (1) for Nonsec20 clients. The mean

(median) lead underwriter market share is 10% (8%) for Big-8 clients, 8% (5%) for Non-Big-8

Sec20 clients, and 7% (5%) for Nonsec20 clients. This correlation again points out the

importance of controlling for these variables that can affect firms’ access to capital.

Table IV also reveals that, Big-8 clients on average hire previous lenders to underwrite

about 8.5% of their bond issues (8.48% of total amount, 8.38% of total frequency). As an

interesting side note, most of the instances of using lenders’ underwriting service occur after

1995, which is more than five years after the banks obtained legal permission to underwrite.

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This demonstrates that it takes time for banks to cultivate underwriting capabilities, and provides

additional justification for our focus on the Big-8 banks as representing the universal bank group.

Table V presents the regression results for Equation (1) for both seasoned issuers and

entrants to the bond market. To provide robustness, the dependent variable, 10-year aggregate

debt usage, is measured in both 1990-dollar amount and in frequency. Among the control

variables, average underwriter’s market share, number of underwriters used, the dummy variable

for Big-8 bank association, and firm size are all significantly positive. This is consistent with

the theory that information quality and availability on a firm increase the firm’s access to capital

markets.

The central result of this table, however, is that even after controlling for information-

related factors, the usage of previous lender’s underwriting service still significantly increases

public debt issuance. For the seasoned issuers, both the frequency and amount of debt issuance

is positively explained by the usage variable; for the entrant issuers, total amount is positively

explained by the usage variable. These results indicate that seasoned issuers borrow more

frequently and larger amounts from the public debt market as a result of employing previous

lenders’ underwriting service. For entrant firms, though they do not seem to borrow more

frequently, they can raise larger amounts when a previous lender is hired as a lead underwriter.

To further control for firm specific factors, we examine the changes in the 10-year

aggregate debt issuance from the pre-deregulation decade of 1980-1989 to the post-deregulation

decade of 1991-2000. The findings are reported in Table VI. The key variable of interest, usage

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of lender’s underwriting service, again significantly explains the increase in debt issuance, after

other information-related factors are controlled for.

Therefore, the evidence in both Tables V and VI suggests that the newly established

underwriting services from commercial banks enhance clients’ access to the public debt market.

This result is consistent with, and complements the existing evidence that commercial banks

obtain lower yields (hence higher bond prices) for their clients.9 Together, these two sets of

evidence make a strong case that combined intermediation is a net positive for the clients, as

both the price (yield) and quantity (amount and frequency) of debt issuance are improved with

commercial bank underwriting. This in turn implies that benefits associated with combined

intermediation outweigh the cost due to conflict of interest.

4.2. Substitution from Bank Loans to Public Debt

One trend indicated by the data in the previous sub-section is that there is a general increase in

the use of public debt during the past two decades (see Table III for instance). A natural

question is, is there a corresponding decline in banking borrowing? In other words, has there

been a shift from bank borrowing to public debt financing? If so, does combined intermediation

contribute to this process? These are the questions we attempt to shed lights on in this sub-

section.

Table VII tabulates the ratio between bank loan and public debt financing for 1990 and

2000, the beginning of the deregulation period and the end of the sample period respectively.

9 See, for example, Puri (1996) and Gande et. al. (1997).

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From this table, it is evident that for all subgroups of clients, there has been an increase in debt

financing and a contemporaneous decrease in bank borrowing, and that the trend is most

dramatic for clients of the Big-8 banks. To examine the role that combined intermediation has

played in this process, Table VIII presents regression results where the change in the loan/debt

ratio (1990 ratio – 2000 ratio) is regressed on information proxies as well as the usage of lenders’

underwriting service. The results show that the usage of lenders’ underwriting service

significantly explains the substitution from loans to capital market borrowing at the 10% level.

Summarizing results in sections 4.1 and 4.2, we find that combined intermediation

enhances firms’ access to capital markets; employing previous lender’s underwriting service

significantly contributed to the frequency and amount of firms’ public debt financing. At the

same time, firms also experience a reduction in bank borrowing, and the substitution from bank

loans to public debt financing can also be partially attributed to lenders’ combined

intermediation function. Thus, we conclude that universal banking (combined intermediation)

has contributed positively to firms’ access to capital markets. To the extent that the “graduation”

from bank loans to public market signals growth, universal banking has contributed to firm

growth.

5. Universal Banking and Firm Investment and Performance

Having established that combined intermediation improves firms’ access to the capital market,

we now address the second main research question of this paper: is firms’ enhanced access to

capital related to positive changes in firms’ investment and performance?

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5.1. Investment-Cash Flow Sensitivity

Intuitively, better access to the capital market creates an additional source of financing and

therefore should reduce the liquidity constraints on investment. Based on this reasoning, we

should expect, all else equal, that firms that use lenders’ underwriting service should see larger

improvements in investment and larger reductions of investment sensitivity to cash flow.

To investigate this hypothesis, we estimate regression equations relating firm investment

to cash flow.10 Specifically, we estimate one regression for the group of firms that employed a

previous lender as a lead underwriter (the “treatment” sample), and a second, identical regression

for a group of matching firms (the “control” sample) that do not retain any previous lender as a

lead underwriter. The goal is to see if the “treatment” firms experience a larger reduction of

investment-cash flow sensitivity after their lenders started also underwriting their securities.

To construct datasets needed for the regressions, we define, for the “treatment” group, the

first instance that a firm uses a previous lender as a lead underwriter for its bond issue as the

“event”. We then compile quarterly investment and cash flow data for the 2 years before and 2

years after the event for each firm in the “treatment” group.

For each firm in the “treatment” group, we choose a matching firm that satisfy the

following criteria: the firm must be in the same industry as indicated by the SIC code, and must

have a bond issue in the same year that the treatment firm does, but it must always use a non-

10 The idea behind this analysis is based on the literature on firm investment and liquidity, see, for example Hoshi, Kashyap, and Scharfstein (1991), and Ramirez (1995). More recently, Kaplan and Zingales (1997) argue that the traditional liquidity variables such as cash do not properly measure the degree of financial constraints that the firm

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lender as the lead underwriter. To provide the closest match, we further select from the potential

matches the firm that is most similar to the treatment firm in terms of size, book-to market-ratio

and past performance.11 This procedure yields one matching firm for each treatment firm, and

this group constitutes the control group.

The results on the investment -- cash flow regressions are reported in Table IX, and

support the conjecture that using lenders’ underwriting service improves liquidity and reduces

the dependence of investment on cash flow. For the “treatment” firms, the interaction term

between cash flow and the “after-event” indicator has a significant negative sign, indicating that

the cash flow sensitivity of investment is significantly reduced after the event. For the matching

firms, however, the interaction term is insignificant, i.e., this group does not experience

significant reduction in the cash-flow dependence of investment. Since we have constructed the

matching sample carefully to control for other firm effects, this set of results suggests that using

lenders’ underwriting service indeed relaxes financial constrains on investment, and improves

firms’ financing liquidity.

5.2. Capital Investment, Shareholder Distribution, and Firm Performance

To study other measures of firm investment and performance, we adopt a matching firm event

study methodology.12 A matching firm is found by the same procedure outlined in section 5.1,

and each pair of firms is followed over the two years before and two years after the event. The

faces. We adopt this methodology as a starting point in the analysis of the effect of universal banking on firm investment. 11 Past performance is measured by average operating returns to asset for the two quarters before the bond issue. We minimize the sum of absolute percentage differences of size, book-to-market and performance. We also use various combinations of the criteria: size matched, size and book to market matched, size and performance matched, etc. The results are robust to the matching criteria used.

22

quantities of interest are the differences in the investment, cash distribution, and performance

measures between the “treatment” firms and the “control” firms. Empirically, capital investment

is measured by the net purchase of plant and equipment, operating performance is measured by

earnings per share (EPS), and shareholder distribution is measured by dividend payments. The

event study results are tabulated in Table X.

On capital investment (the first vertical panel of Table X), we see that firms that employ

previous lenders as underwriters exhibit larger increases in investment than matching firms.

Notably, the two groups of firms have no difference in capital investment before the event, but

the treatment group invests significantly more during the year after the event. This could

indicate that the bonds issued by the “treatment” firms were more likely used for capital

investments, whereas the financing raised by the matching firms are more likely to be for general

purpose.

Similarly, on shareholder distribution, we see (from the second vertical panel of Table X)

that there is no difference in dividend payments between the two groups before the event, but the

treatment group consistently pays more dividends after the event and earns a higher return for

about two quarters after the event.

In terms of operating performance (the third vertical panel of Table X), again there are no

difference between the two groups before the event, but the treatment group exhibit superior

performance for about two quarters after the event.

12 This methodology is inspired by Barber and Lyon (1997), and used in Loughran and Ritter (1995) and Speiss and Affleck-Graves (1994) in their studies on the long-term performance of seasoned equity issues.

23

In Table XI, we compare the two groups’ stock repurchase activities during the five years

before and five years after the bond issue.13 There are two reasons that stock repurchase

activities are of interest to us. First, it has been documented that increasingly firms are using

stock repurchase rather than dividends as means of shareholder distribution, therefore looking at

share repurchase activities provides a check on our dividend payment result. Secondly, stock

repurchase is a signal of management belief of market undervaluation, and therefore looking at

this variable allows us to infer something about the private information on firm prospects.

From Table XI, we see that before the event, the treatment firms repurchase significantly

less than their matching peers, a pattern that is true for both the amount and the frequency of

repurchase activities. Post-event, however, there is no difference in stock repurchase pattern

between the two groups. This comparison shows that firms that employ previous lenders as

underwriters increased stock repurchase activities relative to the benchmark.

The significance of this result is two-fold. First, it is consistent with the dividend

payment results, and both suggest that firms using lenders’ universal banking services increase

cash distribution to investors. Second, to the extent that increased cash distribution and

increased repurchase activities signal better prospects of the firm, the results indicated that firms

that sought previous lenders as lead underwriters are the ones that have better prospects.

13 Stock repurchase data are collected from the SDC Platinum database. We chose a longer sample period for this variable because stock repurchase is a relatively infrequent event.

24

As the final measure of firm performance, we compare the buy-and-hold stock returns of

the treatment group and the control group for the five years before and one year after the event.

The result is presented in Table XII. The different panels tabulate the comparisons using

different matching criteria. Regardless of the criteria used however, a striking pattern emerges.

There is no difference in the stock performance during the five years before the event, but during

the year after the event, the treatment group experiences significantly higher stock return at the

5% to 10% level.

This evidence is consistent with previous results on firm investment, shareholder

distribution, operating performance. All together, in this section we find that firms’ enhanced

access to capital due to lender’s combined intermediation is accompanied by real, positive

changes in the firm. Firms that take advantage of lender’s combined intermediation face less

liquidity constraints in their investments; they make larger investments, distribute more cash to

their shareholders; and ultimately these improvements are reflected in their higher stock returns.

A striking common feature among all the variables we examine is that if the “treatment”

firms (the firms that employ previous lenders’ underwriting services) look no different from their

matching firms prior to the event, they certainly exhibit superior performance afterwards. Thus,

we conclude this section by observing that even though it is difficulty to draw causal inferences

between universal banking and firm performance, our evidence strongly indicates that universal

banking is at least related to ex post positive changes of firm performance.

25

How do we interpret these findings? What do we learn from the findings about conflict

of interest in universal banking? These important questions are discussed in the final session of

the paper.

6. Interpretations and Conclusions

With a goal to understand the pros and cons of universal banking, we study a large sample of US

industrial firms’ financing and performance before and after the deregulation that repealed the

Glass-Steagall Act and installed universal banking in the US. We ask two empirical questions.

First, does combined intermediation in lending and underwriting enhance firms’ access to the

capital markets? Second, if so, is the enhanced access to capital related to better investment and

performance?

The evidence we present in earlier sections suggests that the answer to both questions are

“yes”. In Session 4, we find that using previous lender’s underwriting service significantly

increases the frequency and amount of borrowing that firms complete in the public debt market.

This is true for seasoned as well as novice issuers in the public debt market. We also show that

employment of lender’s underwriting services also contributes to the graduation of firms from

bank loans to the public debt market. To the extent that access to the public market is a sign of

growth, we conclude that universal banking benefits clients in terms of improving their financing

and facilitating their growth.

In Session 5 we examine firm performance under universal banking. We find that the

investments of firms that use previous lenders’ underwriting service become much less

26

dependant on internal cash generation; these firms make significantly larger capital investments,

pay more dividends and conduct more stock repurchases than their matching comparables, and

ultimately these improvements seem to be priced by the stock market and reflected in the

positive abnormal returns that these firms earn over their matching firms.

Overall, the evidence in this paper suggests that universal banking is related to positive

changes in US industrial firms. Perhaps the most interesting question is how to interpret the

performance results. Do the results mean that universal banking causes the enhanced

investments and performance? Do the results disprove the conflict of interest concerns and

depict banks neutral intermediaries that look out for the interest of the investing public?

We take the view that while we document a positive relationship between universal

banking and performance, we do not interpret this relationship as a causal one, for a couple of

reasons. First, factors of first order importance in determining firm performance are the firms’

investment opportunity set and the capital budgeting decisions of their managers; financing and

the role of the intermediary is of secondary importance. Availability of financing and improved

liquidity make it easier for firms to pursue positive NPV projects, when there are positive NPV

projects around, but they do not cause the investment opportunity set or managerial decisions to

improve. Therefore it would be a far stretch to state that the findings suggest a causal

relationship between universal banking and performance.

Secondly, such a causal relationship is hard to support because in fact, it is likely that the

causal relationship goes the other way round. That is, since banks have superior information

27

about their client firms, they are able to cherry-pick the good-deals to underwrite, and therefore

we observe superior performance after the security issuance. Indeed, this explanation seems

highly plausible judging by the timing of events: we found that before the first bond issue

underwritten by a previous lender, firms in the “treatment” sample (firms that employ previous

lenders as lead underwriters) do not appear different from control sample along all dimensions,

and yet, after the bond issue, these firms exhibit significant improvements in investment,

distribution and performance. Therefore it is highly likely that firms and their previous lenders

entered into the underwriting contact with each other because they know the issue is a low risk,

high prospect one.

This provides a rationally consistent way of interpreting our results. Far from suggesting

that our findings disprove the conflict of interest in universal banking, on the contrary, we

interpret our findings as to be exactly consistent with its existence and its impact on the ex ante

decisions of rational economic agents. Conflict of interest and excess risk-taking were the two

main arguments behind Glass-Steagall Act, which bared commercial banks from investment

banking businesses for over half a century. Surely both the banks and their clients are keenly

aware of the perceived conflict of interest in a security issue underwritten by a previous lender.

Knowing this, as rational players, both banks as well as their clients would take into account ex

ante this perceived conflict of interest, and select contracts in such a way that this fear can be

mitigated. This results in an endogenous selection, in which only the deals of superior quality

will be underwritten by previous lenders. Ex post, the superior performance of the issues helps

calm investors’ fear for Lemons, which in turn keeps the door to the market open to both the

banks and their clients in the future.

28

This interpretation echoes that of Kroszner and Rajan (1994), which concludes that the

Glass-Steagall Act probably wasn’t justified in the first place, since the public seemed to have

rationally accounted for the potential conflict of interest, and constrained the banks to issue only

high quality securities. Here for essentially the same token, the enactment of Gramm-Leach-

Bliley Act probably will not unleash another era of abuse, because of the very same self-

selection on part of rational economic agents.

29

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32

Table I. Frequency and Amount of Bank Loan Deals in the Dealscan Database This table presents summary statistics on the bank loan data from the DealScan Database. Panel A tabulates the statistics by calendar year, and Panel B summaries the data by borrowers’ exchange listing status. The “Amount” is in billions of dollars. We use the Federal Reserve Bank’s list of Section-20 Securities Subsidiaries as of May 1, 2000 to classify bank types. “Big-8” banks are BankAmerica, Bankers Trust, Citicorp, Chase Manhattan, Chemical Bank, First Chicago, J. P. Morgan, and NationsBank. “Non-Big-8-Sec20” are banks with section-20 subsidiaries but are not one of the Big-8 banks. “Nonsec20” are banks without Section-20 subsidiaries. “%deal” (“%amt”) is the percentage of deals in number (dollar amount) borrowed from each type of bank. All Banks Big-8 Banks Non-Big-8

Sec20 Nonsec20

Number Amount Amount in 1990

Constant Dollars

% deal

% amt

% deal

% amt

% deal

% amt

Panel A: By Year All Deals 76376 21942.3 18622.1 43.8 75.2 31.8 16.9 24.4 8.0

1981 1 0.1 0.2 0.0 0.0 0.0 0.0 100.0 100.0 1982 6 5.1 7.0 83.3 86.6 16.7 13.4 0.0 0.0 1983 1 0.6 0.8 100.0 100.0 0.0 0.0 0.0 0.0 1984 8 29.6 37.3 87.5 91.6 0.0 0.0 12.5 8.4 1985 30 37.1 45.1 53.3 86.4 10.0 3.5 36.7 10.1 1986 203 115.5 137.7 57.1 87.8 17.2 4.8 25.6 7.3 1987 1305 332.5 382.3 32.8 74.6 29.1 15.0 38.1 10.4 1988 2742 704.1 777.8 33.9 73.9 27.6 15.5 38.5 10.6 1989 2766 801.4 844.8 34.2 79.4 24.4 8.5 41.4 12.1 1990 2719 464.1 464.1 34.4 70.1 24.9 17.3 40.7 12.6 1991 2651 369.6 354.5 34.9 69.1 25.7 16.7 39.4 14.2 1992 3560 562.7 524.0 42.3 70.5 29.3 16.8 28.5 12.7 1993 4583 927.8 839.2 40.8 74.9 30.2 14.3 29.0 10.8 1994 5611 1369.4 1207.2 46.8 78.4 29.5 13.5 23.7 8.1 1995 6202 1884.8 1616.0 49.4 78.8 29.1 13.6 21.6 7.6 1996 8022 2070.2 1724.4 45.1 76.3 29.7 15.1 25.2 8.5 1997 10221 2890.0 2352.6 44.5 74.6 32.1 17.6 23.4 7.8 1998 8523 2632.9 2110.4 45.8 75.6 35.8 18.5 18.4 5.8 1999 8547 3081.5 2417.1 45.6 74.0 38.0 20.4 16.4 5.6 2000 8675 3663.2 2779.7 48.0 75.6 36.0 17.8 16.0 6.6

Panel B: Public Firms Only: By Exchange Listing in 1995 Firms in CRSP 27580 9586.2 8237.2 73.7 96.3 18.0 3.2 8.3 0.5 NYSE 13748 7718.2 6664.2 89.8 98.4 8.5 1.5 1.7 0.1 AMEX 1879 262.5 230.2 58.0 87.7 28.3 9.71 13.7 2.5

NASDAQ 11826 1584.5 1324.9 57.6 88.0 27.6 10.1 14.8 1.9

33

Table II. Summary Statistics of Industrial Firms’ Financing Activities 1991 -- 2000 This table summarizes the 10-year aggregate loan, public debt, and equity financing of the industrial firms in our sample. We classify firms by their lender type. A firm belongs to the “Big-8” group if it borrows from one of the Big-8 banks. A firm belongs to the “Non-Big-8 Sec20” group if it borrows from a bank with a Section-20 subsidiary other than one of the Big-8 banks. A firm belongs to the “Nonsec20” group if it does not borrow from any of the banks with Section-20 subsidiaries. Finally a firm belongs to the “Nonbank” group if DealScan does not contain loan data on the firm. The Big-8 banks are BankAmerica, Bankers Trust, Citicorp, Chase Manhattan, Chemical Bank, First Chicago, J. P. Morgan, and NationsBank. Section20 status of each bank is determined from the Federal Reserve Bank’s list as of May 1st, 2000.

Amount (in Millions of 1990 Constant Dollars) and Frequency of Financing Client Type Mean Median Std. Dev. Min Max Obs.

Panel A: Bank Borrowing All Banks 1180.4 138.4 4349.9 0.1 116194.6 6876

4.4 3.0 4.2 1.0 67.0 100.0% Big-8 2185.8 551.8 5908.4 0.4 116194.6 3253

6.0 5.0 4.9 1.0 67.0 47.3% Non-Big-8 Sec20 267.4 68.6 1038.1 0.1 33588.5 1865

3.5 3.0 2.9 1.0 30.0 27.1% Nonsec20 109.3 11.8 1244.7 0.1 44761.0 1758

2.3 2.0 2.0 1.0 27.0 25.6% Panel B: Public Debt Financing

All Issuers 809.5 271.6 1648.2 1.9 18346.7 1062 5.6 2.0 12.8 1.0 253.0 100.0%

Big-8 Banks 902.7 313.7 1780.2 7.8 18346.7 872 6.2 2.0 13.8 1.0 253.0 82.1%

Non-Big-8 Sec20 265.0 135.7 463.6 4.5 4082.6 83 2.0 1.0 2.3 1.0 14.0 7.8%

Nonsec20 300.8 152.8 387.5 5.9 1874.1 38 1.6 1.0 1.3 1.0 8.0 3.6%

Nonbank 567.2 200.4 881.4 1.9 4410.6 69 4.4 1.0 8.1 1.0 54.0 6.5%

Panel C: Public Equity Financing All Issuers 101.8 39.6 243.8 0.4 6849.9 5908

1.4 1 0.8 1 8 100.0% Big-8 Banks 197.8 88.5 367.4 1.7 6849.9 1745

1.7 1 1.0 1 8 29.5% Non-Big-8 Sec20 65.6 35.3 107.1 1.3 1668.8 942

1.5 1 0.8 1 6 15.9% Nonsec20 54.6 27.5 121.4 1.2 2150.7 887

1.4 1 0.7 1 5 15.0% Nonbank 62.4 26.9 172.0 0.4 5341.6 2334

1.3 1 0.6 1 5 39.5%

34

Table III. Changes of Public Debt Activities This table reports the changes in the 10-year aggregate amount and frequency of public debt financing of bond issuers during the past three decades. We classify firms by their lender type. A firm belongs to the “Big-8” group if it borrows from one of the Big-8 banks. A firm belongs to the “Non-Big-8 Sec20” group if it borrows from a bank with a Section-20 subsidiary other than one of the Big-8 banks. A firm belongs to the “Nonsec20” group if it does not borrow from any of the banks with Section-20 subsidiaries. Finally a firm belongs to the “Nonbank” group if DealScan does not contain loan data on the firm. The Big-8 banks are BankAmerica, Bankers Trust, Citicorp, Chase Manhattan, Chemical Bank, First Chicago, J. P. Morgan, and NationsBank. Section20 status of each bank is determined from the Federal Reserve Bank’s list as of May 1st, 2000. All amounts are measured in 1990 constant dollars.

Change of Public Debt Financing Amount (in 100%) Change of Public Debt Financing Frequency (in 100%)

Client Type Mean Median Std. Dev. Min Max Obs. Panel A. Periods 1970-1979 versus 1980-1989

All Issuers 1.39 0.17 4.57 -0.94 42.09 279 0.92 0.00 2.18 -0.94 15.00 100.0%

Big-8 Banks 1.67 0.30 5.13 -0.92 42.09 183 1.25 1.00 2.40 -0.94 15.00 65.6%

Non-Big-8 Sec20 3.62 0.20 7.11 -0.80 24.73 13 0.88 0.20 1.54 -0.72 5.00 4.7%

Nonsec20 0.40 0.10 0.96 -0.71 1.65 8 0.33 0.00 1.08 -0.33 3.00 2.9%

Nonbank 0.44 -0.32 1.84 -0.94 8.12 75 0.16 -0.33 1.49 -0.90 9.00 26.9%

Panel B. Periods 1980-1989 versus 1991-2000 All Issuers 2.1 0.6 4.8 -0.9 47.9 378

2.9 1.0 8.4 -0.9 83.3 100.0% Big-8 Banks 2.3 0.7 5.1 -0.9 47.9 327

3.2 1.0 8.8 -0.9 83.3 86.5% Non-Big-8 Sec20 0.4 0.2 1.8 -0.8 7.3 19

0.4 0.0 1.1 -0.8 3.5 5.0% Nonsec20 1.0 0.1 1.8 -0.4 3.9 5

0.4 0.7 0.7 -0.5 1.0 1.3% Nonbank 1.6 0.2 3.5 -0.6 16.8 27

2.5 0.4 5.9 -0.5 27.0 7.1%

35

Table IV. Usage of Debt Underwriting Service 1991 -- 2000 This table provides summary data on bond issuers’ usage of debt underwriting service during 1991-2000. We classify issuers by their lender type. A firm belongs to the “Big-8” group if it borrows from one of the Big-8 banks. A firm belongs to the “Non-Big-8 Sec20” group if it borrows from a bank with a Section-20 subsidiary other than one of the Big-8 banks. A firm belongs to the “Nonsec20” group if it does not borrow from any of the banks with Section-20 subsidiaries. Finally a firm belongs to the “Nonbank” group if DealScan does not contain loan data on the firm. The Big-8 banks are BankAmerica, Bankers Trust, Citicorp, Chase Manhattan, Chemical Bank, First Chicago, J. P. Morgan, and NationsBank. Section20 status of each bank is determined from the Federal Reserve Bank’s list as of May 1st, 2000.

Panel A: Number of Different Lead Underwriters Used Client Type Mean Median Std. Dev. Min Max Obs All Issuer 2.29 1.00 2.26 1.00 19.00 1062

Big-8 2.43 2.00 2.38 1.00 19.00 872 Non-Big-8 Sec20 1.47 1.00 1.05 1.00 7.00 83

Nonsec20 1.32 1.00 0.57 1.00 3.00 38 Nonbank 2.07 1.00 1.92 1.00 10.00 69

Panel B: Average Lead Underwriter’s Market share Mean Median Std. Dev. Min Max Obs

All Issuer 0.10 0.08 0.07 0.00 0.22 1062 Big-8 0.10 0.08 0.07 0.00 0.22 872

Non-Big-8 Sec20 0.08 0.05 0.07 0.00 0.22 83 Nonsec20 0.07 0.05 0.07 0.00 0.22 38 Nonbank 0.09 0.08 0.06 0.00 0.22 69

Panel C: Usage of Lender’s Underwriting Service for Big-8 Clients Mean Median Std. Dev. Min Max Obs

Total Amount 95.19 0.00 417.20 0.00 7588.21 872 Total Frequency 0.53 0.00 1.84 0.00 30.00 872

% Amount 8.48 0.00 23.97 0.00 100 872 % Frequency 8.38 0.00 23.50 0.00 100 872

36

Table V. Regressions of Public Debt Issuance 1991-2000 This table reports regression results on public debt issuance for the post deregulation decade. “Entrant Issuers” are issuers that do not issue public debt in the 1980s. “Seasoned Issuers” are those that issue public debt in both the ‘80s and the ‘90s. The dependent variable, “Frequency of Issue” (“Total Amount of Issue”) is the number of bond issues (log of the total amount of bond issue) during the 10-year period. All amounts are in 1990 constant dollars. “Usage of Universal-Banking Service” is the fraction of total bond issues (frequency or amount) that is lead-underwritten by a previous bank lender. “Big-8 Client” is an indicator variable equaling 1 if the bond issuer has a loan relationship with one of the Big-8 banks, and 0 otherwise. “Firm Size” is measured by the market capitalization of equity. “Exchange Listing” is an indicator variable equaling 1 if the firm is listed on NYSE in 1995, and 0 otherwise. “Average Underwriter Market Share” is the average of market shares among all distinct underwriters ever employed by an issuer. “Number of Different Underwriters Used” measures the multiplicity of investment bank relationships that a firm has. See Section 4.1 for details on the construction of variables.

Dependent Variable Frequency of Issue Total Amount of Issue Issuer Type Entrant Issuers Seasoned Issuers Entrant Issuers Seasoned Issuers

Coef T-stat Coef T-stat Coef T-stat Coef T-stat Usage of Universal-Banking

Services 0.19 0.88 2.16*** 6.62 0.0005*** 5.11 0.0003** 2.56 Big-8 Client 0.08 0.15 -0.14 -0.05 0.33** 2.31 0.35** 1.98

Firm Size 0.02 0.19 -0.01 -0.03 0.19*** 5.97 0.23*** 7.46 Exchange Listing -0.02 -0.03 -12.34*** -4.09 0.14 1.10 -0.02 -0.13

Average Underwriter Market share 7.04** 2.48 41.66*** 2.79 1.39* 1.83 2.88*** 3.26

Number of Different Underwriters Used 2.51*** 19.07 3.96*** 12.65 0.34*** 9.98 0.20*** 11.42

Constant -2.41*** -3.28 2.55 0.59 3.15*** 15.91 3.52*** 13.65 N 308.00 298.00 308.00 298.00

R-squared 0.61 0.58 0.50 0.60

*, **, *** Indicates significance at the 10, 5, and 1 percent levels, respectively.

37

Table VI. Regressions of Changes in Debt Issuance from 1980-1989 to 1991-2000 This table reports the regressions of the changes in public debt issuance from 1980-1989 to 1991-2000. The dependent variable, “Change in Issue Frequency” (“Change in Issue Amount”) is the difference in 10-year aggregate frequency (log of total issue amount) between the earlier and latter periods. All amounts are in 1990 constant dollars. “Usage of Universal-Banking Service” is the fraction of total bond issues (frequency or amount) that is lead-underwritten by a previous bank lender. “Big-8 Client” is an indicator variable equaling 1 if the bond issuer has a loan relationship with one of the Big-8 banks, and 0 otherwise. “Firm Size” is measured by the market capitalization of equity. “Exchange Listing” is an indicator variable equaling 1 if the firm is listed on NYSE in 1995, and 0 otherwise. “Average Underwriter Market Share” is the average of market shares among all distinct underwriters ever employed by an issuer. “Number of Different Underwriters Used” measures the multiplicity of investment bank relationships that a firm has. See Section 4.1 for details on the construction of variables.

Change in Bond Issue

Frequency Change in Bond Issue

Amount Variables Coef. T-Stat Coef. T-Stat

Usage of Universal-Banking Service 2.28*** 6.66 0.0003* 1.78 Big-8 Bank Client (indicator variable) -0.42 -0.14 0.73** 2.35

Firm Size -0.48 -0.88 0.21*** 3.49 Exchange Listing -13.19*** -4.17 0.06 0.19

Average Underwriter Market Share 37.01** 2.36 0.40 0.24 Number of Different Underwriters Used 3.27*** 9.97 0.21*** 6.92

Constant 5.74 1.26 2.82*** 6.19 N 298.00 238.00

R-squared 0.49 0.40

*, **, *** Indicates significance at the 10, 5, and 1 percent levels, respectively.

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Table VII. Substitution Between Loan and Public Debt For Seasoned Issuers This table presents the average active loan-to-debt ratio for 1990 and 2000, classifying firms by their lender type. Active loan amount for a year is calculated as the sum of face values of those loans that are initiated before but mature after Jan. 1 of that year. Active debt amount is calculated in a similar fashion.

Client Type Mean Ratio in 1990 Mean Ratio in 2000 T statistic for Difference

Panel A: Loan / Public Debt Big-8 6.77 2.01 3.77

Non-Big-8 Sec 20 2.71 0.95 2.57 Non Sec20 2.52 0.33 2.25

Panel B: Loan / Total Asset Big-8 0.43 0.19 5.23

Non-Big-8 Sec 20 0.39 0.24 1.77 Non Sec20 0.24 0.13 1.81

Panel C: Public Debt / Total Asset Big-8 0.16 0.20 -2.10

Non-Big-8 Sec 20 0.27 0.61 -1.11 Non Sec20 0.29 0.65 -1.45

Table VIII. Regression Results For Loan/Debt Substitution For Seasoned Issuers This Table reports regression results for the changes in the loan/debt ratio. We find that “usage of universal-banking service” significantly explains the increase in debt relative to loan at the 10% level. All variables have the same definition as in Tables V and VI. See Section 4.1 for details on variable construction.

Dependent Variable: Change in Loan/Debt Ratio Usage of Universal-Banking Service 2.75 1.63*

Big-8 Client -4.64 -0.83 Firm Size -0.18 -0.40

Exchange Listing -1.11 -0.47 Average Underwriter Market share 9.30 0.67

Number of Different Underwriters Used -0.04 -0.14

Constant 1.78 0.29 N 132.00

R-squared 0.03

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Table IX. Investment – Cash Flow Sensitivity Analysis This table compares the investment-cash flow sensitivity of firms that take advantage of universal-banking service by hiring previous lenders as lead-bond underwriters (treatment firms), to that of the matching firms that do not use previous lenders’ underwriting service (control firms). The “event” of interest is the instance when the “treatment” firm uses a previous lender as a lead-underwriter for the first time. See Section 5.1. for the details of identifying matching firms. The dependent variable is firm investment, measured as capital expenditure (Compustat variable 128) deflated by total sales. The regressors are “Cash”, which is the ratio between cash and total assets; an interaction term of the cash ratio and an indicator variable for “after” the event; and the Book-to-Market ratio.

Users of Lender Underwriting Service

Matching Firms

Variable Coefficient T Statistic Coefficient T Statistic Cash 3.9915*** 3.8700 1.7525*** 2.4400

Cash * after event -3.9084** -2.0690 -0.1388 -0.0980 Book-to-Market 0.0558 1.1460 -0.0877 -1.5720

Constant 0.3914*** 10.7560 0.3976*** 11.1650

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Table X. Matching-Firm Event Study Comparison of Investment, Dividend and EPS This table traces the difference in investment, dividend payout, and EPS between the group of firms that take advantage of universal-banking services by hiring previous lenders as lead-bond underwriters (treatment firms), and their matching controls that do not use previous lenders’ underwriting service. The “event” of interest is the instance when the “treatment” firm uses a previous lender as a lead-underwriter for the first time. See Section 5.1. for the details of identifying matching firms. “Investment” is measured by net purchase of plant and equipment, scaled by sales. “Dividend” and “EPS” are per share measures, scaled by per share price.

Investment Dividend EPS Event

Quarter Difference T Stat Difference T Stat Difference T Stat

-8 -0.4364 -1.2941 0.0235 1.3155 0.0809 0.4350 -7 -0.2690 -0.7271 0.0163 1.0503 0.1184 0.4731 -6 -0.2305 -0.6874 0.0261 1.6291 0.1238 0.8992 -5 -0.1409 -0.4615 0.0143 0.9482 0.1270 1.1701 -4 0.0519 0.1494 0.0179 1.2137 0.2131 1.3893 -3 0.1917 0.5075 0.0126 0.8179 -0.3118 -0.8757 -2 0.0110 0.0312 0.0096 0.6316 0.2920 2.0665 -1 -0.0635 -0.1847 0.0106 0.6786 0.0295 0.2062 0 -0.2317 -0.4264 0.0183 1.1962 0.4181 2.1821** 1 -0.6479 -0.5114 0.0126 0.7577 0.4991 2.2253** 2 -0.6428 -0.5096 0.0290 1.8249* 0.1143 1.0309 3 -2.2681 -0.7678 0.0290 1.7839* 0.0064 0.0501 4 0.8296 2.1874** 0.0292 1.7481* 0.1254 1.0285 5 0.8324 1.9002* 0.0355 1.9920** 0.0789 0.7267 6 0.7255 2.0435** 0.0321 1.7805* 0.2544 1.3190 7 0.4655 1.6026 0.0378 1.7977* -0.5871 -1.0070 8 0.4720 1.6348 0.0419 1.9123* -0.0478 -0.1551

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Table XI. Matching-Firm Comparison of Stock Repurchase Activities

This table compares stock repurchase activities of firms that take advantage of universal-banking services by hiring previous lenders as lead-bond underwriters (treatment firms), and that of their matching controls that do not use previous lenders’ underwriting service. The “event” of interest is the instance when the “treatment” firm uses a previous lender as a lead-underwriter for the first time. See Section 5.1. for the details of identifying matching firms. The data used are the stock repurchase activities during the five years before and five years after the event. “Amount” is the total dollar amount of stock repurchases announced, in millions of constant 1990 dollars. “Frequency” is the count of separate repurchase programs in the 5-year period. “Percent of Shares” is the percent of shares outstanding repurchased during the 5-year period.

Before Event After Event Variable Used Control T Statistic Used Control T Statistic Amount 1397.35 2242.29 -1.8947* 730.59 767.03 -0.1014

Frequency 11.36 36.18 -3.3993*** 6 7.1 -0.4793 Percent of

Shares 913.53 2231.55 -2.1726** 548.54 358.47 0.9470

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Table XII. Matching Firm Stock Return Comparison

This table presents the difference in annual buy-and-hold stock returns between firms that take advantage of universal-banking services by hiring previous lenders as lead-bond underwriters (treatment firms), and their matching controls that do not use previous lenders’ underwriting service. The “event” of interest is the instance when the “treatment” firm uses a previous lender as a lead-underwriter for the first time. Panel A shows the difference when the matching firm is found by matching on industry, size and book-to-market ratio. Panel B shows the difference when the matching criteria is refined to also include past performance, measured by two quarters of operating returns on asset before the event. See Section 5.1. for details of identifying the matching firms. “ABHR” stands for annual buy-and-hold return, which is taken as the difference between the return on the treatment firm and the return on the control firm.

Panel A: Matched by Industry, Size, B/M Event Year ABHR p-value no obs %positive

-5 -0.038 0.79 71 42.25% -4 -0.787 0.83 76 50.00% -3 -0.050 0.83 78 43.59% -2 0.066 0.06 86 55.81% -1 -0.072 0.88 93 49.46% 1 0.087** 0.05 95 56.84%

Panel B: Matched by Industry, Size, B/M & Performance Event Year ABHR p-value no obs %positive

-5 -0.032 0.76 72 47.22% -4 -0.746 0.83 78 51.28% -3 -0.043 0.80 83 46.99% -2 0.094 0.01 90 61.11% -1 -0.083 0.95 95 46.32% 1 0.076* 0.06 97 51.55%