the voluntary adoption of international accounting...
TRANSCRIPT
The Voluntary Adoption of International Accounting Standards and Loan Contracting around the World
By
Jeong-Bon Kim, Judy S. L. Tsui and Cheong H. Yi
Current Version March 2007
____________ Kim is at Concordia University and The Hong Kong Polytechnic University. Both Tsui and Yi are at The Hong Kong Polytechnic University. We thank Jong-Hag Choi, Annie Qiu, Byron Song, Haina Shi, Yoon S. Zang, and participants of Ph.D. and DBA Research Seminars at The Hong Kong Polytechnic University for their useful comments on the earlier version of the paper. We acknowledge financial support for this research obtained through the 2006 Competitive Earmarked Research Grant (CERG) of the Hong Kong SAR Government. Correspondence: Jeong-Bon Kim, John Molson School of Business, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, PQ, H3G 1M8, Canada (E-mail: [email protected]; Phone: 514-848-2424 Ext. 8933).
1
The Voluntary Adoption of International Accounting Standards and Loan Contracting around the World
Abstract
Using a sample of non-US borrowers from 30 countries over the 1997-2005 period, this paper investigates the effect of the voluntary adoption of International Accounting Standards (IAS) on the price and non-price terms of bank loan contracts and the mix of domestic vs. foreign lenders who participate in loan deals. Our results reveal the following. First, we find that lenders charge significantly lower loan rates to IAS adopters than they do to non-adopters. The rate difference between the two groups amounts to nearly 25 basis points. Second, we find that lenders impose more favorable or less restrictive non-price terms on IAS adopters than they do on non-adopters. In particular, our results show that IAS adopters have a larger amount of loan facility, and are less likely to have restrictive covenants in their loan contracts, compared with non-adopters. Finally, we find that voluntary IAS adoption by borrowers attracts more suppliers of loans, and this increase in the number of lenders is due to IAS adopters attracting more foreign lenders from the international loan market. In conclusion, our results, taken as a whole, support the view that voluntary IAS adoption improves the contracting efficiency in the market for private debts such as bank loans by enabling lenders to assess borrowers’ credit quality more accurately and improving lenders’ familiarity with borrowers in the international loan market. Keywords: International accounting standards, loan spreads, debt covenants, lender mix.
2
The Voluntary Adoption of International Accounting Standards and Loan Contracting around the World
1. Introduction
In 1973, representatives of the professional accounting bodies from major developed
economies 1 reached an agreement to establish the International Accounting Standards
Committee (IASC) with no statutory or regulatory mandate given by political jurisdictions.
Since then, the IASC and its successor, the International Accounting Standards Board
(IASB), have issued a total of 41 International Accounting Standards (IAS) and a total of 7
International Financial Reporting Standards (IFRS), respectively, in an effort to harmonize
financial reporting standards around the world. Since the IASC was restructured into the
IASB in 2001, this private sector-based voluntary effort for developing a common
language of business has made significant progress as manifested in the 2002 cooperative
agreement between the IASB and the Financial Accounting Standards Board (FASB) to
“work together to develop high quality, fully compatible financial reporting standards that
could be used for domestic and cross-border reporting” (Schipper 2005, p. 102). IAS and
IFRS (hereafter IAS for convenience) have now emerged as the most popular financial
reporting model in the world (Barth et al. 2005; Covrig et al. 2007) as thousands of
companies around the world voluntarily adopted the standards. Further, IAS has
increasingly received wide support from securities regulators across different political
jurisdictions as culminated in the European Union (EU)’s decision to mandate all
companies listed on organized securities exchanges in EU countries to prepare their
financial statements in accordance with IAS starting from January 1, 2005.
1 Countries which participated in the agreement are Australia, Canada, France, Germany, Japan, Mexico, Netherlands, the UK/Ireland and the US.
3
Several studies have examined economic consequences of IAS adoption, and
provide evidence suggesting that financial disclosures under IAS are, in general, of higher
quality than those under domestic accounting standards. In particular, these studies find
that voluntary IAS adoption leads to less accounting flexibility and smaller analysts
forecast errors (Ashbaugh and Pincus 2001), higher market liquidity and trading volume
(Leuz and Verrecchia 2000), higher earnings response coefficients (Bartov et al. 2004),
and better accounting quality in terms of less aggressive earnings management, more
timely recognition of economic losses and greater value relevance of accounting amounts
(Barth et al. 2005), a convergence of accounting amounts under IAS with those under US
GAAP (Barth et al. 2006), and greater investment flows by attracting more foreign mutual
funds (Covrig et al. 2007).
A major argument in favor of accounting standards harmonization via IAS is that
IAS adoption enables firms to get easier access to external financing, in particular, by
facilitating external financing from the global equity and debt markets and cross-border
investment flows. Surprisingly, however, previous research has paid little attention to
examining the effect of IAS adoption on the cost of equity or debt financing. Given that
private debts such as bank loans are the most important source of external financing to
most firms around the world, this paper aims to provide systematic evidence on the effect
of voluntary IAS adoption on the price and non-price terms of bank loan contracts as well
as on the lender mix (the composition of foreign vs. domestic lenders). To do so, we
construct a sample of non-US borrowers from 30 countries who engaged in loan deals
during the nine-year period from 1997 to 2005, and then compare various features of loan
contracts between IAS adopters and non-adopters to address the following questions.
4
First, we investigate whether, and how, voluntary IAS adoptions by borrowers lead
lenders to charge lower loan rates. Voluntary IAS adoptions, which give rise to greater and
higher-quality disclosures (e.g., Ashbaugh and Pincus 2001), provide researchers with an
ideal setting for evaluating economic consequences of a borrower’s commitment to better
disclosure. In this paper, we argue that the voluntary IAS adoption reduces ex ante
information uncertainty faced by lenders and/or information asymmetries between
borrowers and lenders. As a result, lenders are better able to assess borrowers’ credit
quality, and thus, to save ex post costs associated with monitoring borrowers’ credit quality
and re-negotiating contractual terms when credit quality changes.2 We predict and find that,
after controlling for borrower-specific credit risk, loan-specific characteristics, and
country-level factors, lenders charge a lower loan rate to borrowers who voluntarily adopt
IAS (hereafter IAS-adopters) than they do to non-adopters. The rate difference between the
two groups amounts to nearly 25 basis points, and is not only statistically significant but
also economically significant as well. We also find that the loan rate-reducing effect of
voluntary IAS adoption holds, irrespective of a country’s property rights, creditor rights,
credit market development, and economic development.
Second, we investigate whether voluntary IAS adoptions have an impact on the non-
price terms of loan contracts: loan size, maturity, securitization, and restrictive covenants.
Commercial banks and other lenders use loan size and maturity to ration credit in
equilibrium (Chava et al. 2005). Studying the effect of IAS adoption on loan size and
maturity is interesting and important because it provides useful insights into how the
2 An important feature of private debts such as bank loans that distinguishes from public debts such as public bonds is the ability of lenders to renegotiate the loan terms when credit quality changes after the loans are granted. For public bonds, the re-contracting costs are, in general, prohibitively high due to diverse, wide-spread, arms-length bondholders, compared with those for private debts.
5
voluntary IAS adoption plays a role in credit rationing by lenders. Moreover, lenders often
require loans to be secured by collateral and/or impose protective covenants in an effort to
reduce the agency cost of debt (Smith and Warner 1979; Bradley and Roberts 2004). In
this paper, we also examine whether voluntary IAS adoptions influence the presence of
loan securitization and restrictive covenants in loan contracts to provide evidence on the
effect of IAS adoption on the overall design of loan contacts. To the extent that higher-
quality disclosures via IAS adoption alleviate information asymmetries between lenders
and borrowers and facilitate more efficient monitoring, we expect that lenders impose
more favorable or less restrictive non-price terms on borrowers who use IAS than they do
on borrowers who use local accounting standards. Overall, our results show that IAS
adopters enjoy more favorable non-price terms than non-adopters. In particular, we find
that firms that apply IAS, on average, have a larger amount of loan facility, and are less
likely to have restrictive covenants in their loan contracts, compared with firms using local
accounting standards. With respect to loan maturity and the likelihood of loans being
secured by collateral, however, we find no significant difference between IAS users and
non-IAS users.
Finally, we investigate whether voluntary IAS adoption by borrowers leads to an
increase in the number of lenders and a change in the lender mix, i.e., the composition of
domestic vs. foreign lenders who participated in loan deals. To the extent that voluntary
IAS adoption mitigates information problems faced by lenders participating in syndicate
loans, and enhances lenders’ familiarity with borrowers’ accounting standards in the
international loan market, one can predict that the voluntary IAS adoption increases the
number of participant lenders and attracts more foreign lenders. Consistent with the
6
prediction, we find that the voluntary IAS adoption attracts more suppliers of loans, and
this increase in the number of lenders is due to IAS adopters attracting more foreign
lenders from the international loan market.
In summary, our results support the view that voluntary IAS adoption improves the
contracting efficiency in the market for private debts such as bank loans by enabling
lenders to assess borrowers’ credit quality more accurately and improving lenders’
familiarity with accounting standards adopted by borrowers in the international loan
market. Our study adds to the existing literature in the following ways. First, this paper is,
to the best of our knowledge, the first study that investigates the impact of IAS adoption on
the price and non-price terms of loan contracts and the lender mix. We provide direct
evidence that voluntary IAS adoption leads lenders to charge lower loan rates, increase
loan size, impose less restrictive covenants, and attracts more foreign lenders in each loan
deal. Second, our study contributes to the loan contracting literature as well. Our finding is
consistent with the notion that the commitment to higher quality disclosures via IAS
adoption mitigates ex ante information risk faced by lenders and/or information
asymmetries between lenders and borrowers, and thus lowers loan rates. Previous studies
in the loan pricing literature examine a variety of borrower-specific factors determining
various features of private debt contracting (e.g., Strahan 1999; Esty and Megginson 2003).
However, no previous research has investigated how a commitment to a better reporting
strategy such as voluntary IAS adoption improves the efficiency of private debt contracting.
Finally, recent studies by Bharath et al. (2006) and Kim et al. (2006) provide evidence that
banks take into account the quality of financial reporting, proxied by accrual quality and
audit quality, respectively, when assessing borrowers’ credit risk. However, the focus of
7
these studies is on loan contracts in the US where the quality of financial reporting is
considered the highest, and thus a voluntary commitment to a better reporting strategy is
likely to be of second-order importance. Thus, international (non-US) evidence reported in
this study sheds right on the role of greater and higher-quality disclosures in private debt
contracting under financial reporting environments that are significantly different from the
US.
The remainder of the paper is structured as follows. In section 2, we develop
research hypotheses. Section 3, we specify an empirical model for hypothesis testing. In
Section 4, we describe our sample and data sources, present descriptive statistics on major
research variables, and conduct univariate tests. In Section 5, we present the results of
various multivariate tests. The final section concludes the paper.
2. Hypothesis Development
2.1. The effect of IAS adoption on bank loan contracting
Economic theory suggests that higher quality accounting information and disclosure
effectively reduce information asymmetries between corporate insiders and outsiders and
thereby lowers the cost of capital. A firm’s decision to voluntarily switch from local
GAAP to IAS is an important strategic commitment that typically causes an increase in the
quantity and quality of accounting disclosures in most financial reporting regimes
(Ashbaugh and Pincus 2001; Covrig et al. 2007). This commitment is costly, and thus
credible, because it is difficult for IAS adopters to reverse the decision once made, and IAS
adoption requires nontrivial efforts and resources on the part of preparers of financial
statements and their auditors. Higher quality disclosures via IAS alleviate the degree of
8
uncertainty faced by lenders concerning borrowers’ ability to pay the interests and
principal of bank loans. This reduction in ex ante information risk faced by lenders leads to
lowering the cost of external financing (e.g. Diamond and Verrecchia 1991; Baiman and
Verrecchia 1996). From an ex post standpoint, higher quality disclosures via IAS reduce
costs associated with monitoring borrowers’ performance or credit quality and
renegotiating contractual terms subsequent to credit quality changes. It is thus likely that
voluntary IAS adoption enables lenders to charge a lower loan rate to borrowers in
equilibrium.
Recently, Leuz and Verrecchia (2005: LV) provide another reason why high quality
information reduces the cost of external financing. They analyze the role of information in
aligning the interests of firms and outside capital suppliers with respect to capital
investment decisions, and establish an inverse relation between the quality of performance
reports to outside capital suppliers and a firm’s cost of external financing. Their analysis
shows that high-quality reports improve the coordination between firms and capital
suppliers with respect to capital investment decisions. On the other hand, poor-quality
reports lead to a misaligned capital investment due to the impaired coordination.
Anticipating this, rational capital suppliers demand a higher risk premium to firms with
poor-quality reports. The LV theory suggests that higher quality disclosures via IAS
adoption give rise to a saving in the cost of coordination between borrowers and lenders,
which in turn enables lenders to charge lower loan rates to IAS adopters than they do to
non-adopters. Recently, Francis et al. (2005) report that firms with better accrual quality
pay lower interest rates on borrowing, a finding consistent with the above view.
9
In sum, we predict that lenders charge lower loan rates to IAS adopters than they
do to non-adopters, because voluntary IAS adoption leads to: (1) lowering ex ante
information risk faced by lenders and ex post monitoring and re-contracting costs; and (2)
improving the coordination between lenders and borrowers with respect to capital
investment decisions. We therefore hypothesize in alternative form:
H1: Loan spreads, measured by loan rates in excess of a benchmark rate, are lower for borrowers who voluntarily use IAS than those who do not, other things being equal.
Bank loan contracts include not only price terms, but also non-price terms such as
loan size, maturity, securitization, and restrictive covenants. Lenders use various non-
price terms (as well as price terms) when designing loan contracts in an attempt to
mitigate information problems and potential conflicts between lenders and borrowers.
Faced with information problems, lenders may control their risk exposure to low quality
borrowers by limiting the size of loans and/or shortening the maturity of loans (Strahan
1999). To the extent that voluntary IAS adoption reduces information uncertainty or the
associated information asymmetry faced by lenders, lenders are better able to assess
borrowers’ credit quality. As a result, for IAS adopters, lenders are faced with lower ex
ante information asymmetry than they are for non-adopters. One may therefore expect
that lenders offer more favorable contractual terms in terms of loan size and maturity to
IAS using firms than they do to firms using local GAAP.
H2: Loan size is larger, and loan maturity is longer, for borrowers who voluntarily use IAS than those who do not, other things being equal.
The presence of collaterals and restrictive covenants in loan contracts. Lenders may
also be associated with information problems faced by lenders. Lenders are more likely to
10
require collaterals or the inclusion of various restrictive covenants in bank loans to
borrowers with opaque information or requiring intense monitoring (e.g., Rajan and
Winston 1995; Bradley and Roberts 2004). Higher quality accounting information via IAS
will lower ex post costs associated with monitoring borrowers’ credit quality and re-
negotiating contractual terms in response to credit quality changes. Thus, we expect that
lenders are less likely to require loan securitization and restrictive covenants for IAS users
than for local GAAP users.3
H3: The likelihood of loans being secured by collateral and restrictive covenants being imposed on borrowers is greater for those who use IAS than for those who do not, other things being equal. .
2.2. The effect of IAS adoption on the number of lenders and the mix of domestic vs.
foreign lenders
We now turn our attention to the effect of voluntary IAS adoption on the number of
lenders participating in each loan and the lender mix (the composition of foreign vs.
domestic lenders). Dennis and Mullineaux (2000) show that fewer lenders are involved in
loans to borrowers with severe information problems. Sufi (2006) also demonstrates that
loans to opaque borrowers have less participant lenders. These studies suggest that credible
financial reports of borrowers may mitigate adverse selection and moral hazard problems
among syndicate loan participants, thereby attracting more participants in a syndicate.
3 Evidence shows that IAS adoption not only increases the quantity and quality of financial disclosures, but also reduces accounting flexibility by restricting a firm’s choice of accounting measurement methods (e.g., Ashbaugh and Pincus 2001). Ashbaugh and Pincus report that this reduced accounting flexibility improves the ability of analysts to forecast future earnings more accurately. Bharath et al. (2006) provide evidence suggesting that lenders use more stringent (non-price) contractual terms for borrowers with poor reporting quality. IAS adoption may cause a decrease in the agency cost of debt to the extent that this reduced accounting flexibility via IAS adoption increases reporting quality and thus enables lenders to save ex post costs associated with loan monitoring and re-contracting. To this extent, lenders are also likely to offer more favorable non-price terms, or impose less restrictive covenants, for IAS adopters than for non-adopters.
11
Based on the above evidence, we expect that more lenders are involved in loans to firms
that use IAS.
We also expect that voluntary IAS adoption attracts more foreign lenders into a
syndicate by increasing lenders’ familiarity with a borrower. On one hand, IAS-based
reporting makes it relatively easier for borrowers to explain and communicate their
financial results and credit quality to foreign lenders in a more user-friendly way. On the
other hand, IAS-based reporting makes it less costly for foreign lenders to assess
borrowers’ credit risk prior to loan origination and to monitor credit quality and renegotiate
the contractual terms subsequent to credit quality changes. The “home-bias” literature in
international finance suggests that foreign investors are faced with higher information costs
than domestic investors when making portfolio decisions, and thus prefer to invest in firms
they are familiar with (Chan et al. 2005; Kang and Stulz 1997; Dalhquist and Robertsson
2001; Covrig et al. 2006; Kim and Yi 2005). These studies provide evidence suggesting
that an increase in a firm’s exposure to foreign investors (for example, by making them
more familiar with the firm) expands investor base by attracting more foreign investors. In
the international loan market one may therefore expect that an increase in a borrower’s
visibility or lenders’ familiarity via IAS adoption draws more foreign lenders by
alleviating their perceived uncertainty about the borrower.
In sum, we predict that the enhanced credibility of financial reports and the
improved familiarity via IAS increase the number of participant lenders and attract more
foreign lenders into a syndicate. To provide empirical evidence on this unexplored issue,
we test the following hypothesis in alternative form:
12
H4: The number of lenders and the percentage of foreign lenders who participate in each loan are greater for borrowers who voluntarily use IAS than those who do not, other things being equal.
3. Empirical Model
To test our hypotheses, H1 to H4, we specify the following regression model
linking various features of loan contracts, one by one, with our test variable and control
variables:
Loan Featuret = α0 + α1.DIASt-1 + α2. Borrower-specific Controlst-1
+ α3. Loan-specific Controlst + α4.Country-level Controls (1)
+ (Industry Dummies) + (Year Dummies) + error term
where the dependent variable, Loan Feature, denotes one of the proxies for price and non-
price terms of loan contracts, the number of lenders, or the mix of foreign vs. domestic
lenders, and empirical definitions of all variables are summarized in Appendix I.
To test H1, we estimate Eq. (1) using the price term, Spread, as the dependent
variable. The Spread variable is measured by the drawn all-in spread in basis points. This
all-in spread represents the interest rate charged by lenders (plus the annual fee and the
upfront/maturity fee) over the benchmark rate, i.e., LIBOR. We measure the cost of loan
using a spread over LIBOR because most loans in the international loan market are priced
in terms of the floating rate in excess of LIBOR. Commercial banks and other lenders
typically assess the risk of a loan based upon the information on the business nature and
performance of borrowing firms, and then set a markup over a prevailing benchmark rate
such as LIBOR to compensate for the credit risk. The Spread variable thus reflects lenders’
perceived level of risk on a loan facility provided to a specific borrower.
13
To test H2, we use, as the dependent variable, two non-price terms of loan contracts,
i.e., the size and maturity of loan facility, denoted by LoanAMT and Maturity, respectively.
The LoanAMT variable is measured by the natural log of the amount of each loan facility
granted to a borrower. The Maturity variable is measured by the natural log of the loan
maturity period which is defined as the difference in months between the loan origination
date and the maturity date.
To test H3, we first estimate Eq. (1) using, as the dependent variable, three
indicator variables, i.e., the probabilities of a loan being secured by collateral, financial
covenants being imposed, and general (non-financial) covenants being imposed, denoted
by DSecured, DFinCov and DGenCov, respectively. These indicator variables take the
value of 1 for secured loans, loans with at least one financial covenant included, and loans
with at least one general covenant included, respectively, and 0 otherwise. When one of
these indicator variables is used as the dependent variable, Eq. (1) is estimated using the
probit regression procedure. In addition, we also construct a covenant index (CovIndex) as
explained below, and then estimate Eq. (1) using CovIndex as the dependent variable. For
our international sample, loan covenants included in the loan contracts are classified into
three broad categories: (1) the requirement of loan securitization by collateral; (2) financial
covenants that are typically linked to accounting numbers; (3) general covenants which
include all other non-financial covenants such as restrictions on prepayment,4 dividend
payment, and voting rights. To obtain a composite measure of the strength of various
covenants included in the loan contract, we construct the covenant index by assigning the
value of 1 for a secured loan (DSecured = 1), for a loan facility with financial covenants
4 The prepayment restriction includes asset sweep, excess cash flow sweep, debt issue sweep, equity issue sweep, and insurance proceeds.
14
(DFinCov = 1) and for a loan facility with general covenants (DGenCov = 1), and then
adding up the values for each loan facility to obtain our empirical measure of the covenant
index. We estimate Eq. (1) by running a Poisson regression of CovIndex on DIAS and
other control variables.
Finally, to test H4, we estimate Eq. (1) using the number of lenders who
participated in each loan facility, denoted by NLender, and the composition of foreign vs.
domestic lenders, which is measured by the ratio of foreign lenders to NLender and is
denoted by %Foreign. To determine whether a lender is foreign or domestic, we check
manually whether commercial banks and other financial institutions who participated in
each loan facility are headquartered in the same country where borrowers are
headquartered. We identify the nationality of the headquarter office of each bank
participating in each loan facility using The Bankers’ Almanac 2005.
Our test variable, DIAS is a dummy variable which equals 1 if the borrower
voluntarily adopted IAS in fiscal year t - 1 (i.e., a year immediately before loans are made)
during the 1996-2004 period when loans are made in year t during our sample period,
1997-2005; and 0 otherwise. Recall that the EU mandated all listed firms to adopt IAS
starting from January 1, 2005, while some of these firms voluntarily adopted IAS prior to
2005. As the lagged DIAS is used in Eq. (1), we effectively link various measures of Loan
Feature in 2005 to DIAS in 2004 in our regression.5, 6 In Eq. (1), the coefficient on DIAS
5 Since the IAS adoption dummy, i.e., DIAS, (as well as all borrower-specific, financial statements variables) is measured in year t – 1 and the dependent variable, i.e., Loan Feature, is measured in year t, there is no two-way causation between DIAS (our test variable) and Loan Feature (our dependent variables). This approach mitigates a concern over reverse causality in Eq. (1) with respect to the Loan Feature-DIAS relation. 6 To correct for a potential self-selectivity problem, we also use the Heckman-type, two-stage treatment effects model. In the first stage, we run a probit model that links a firm’s IAS adoption to explanatory variables and then obtain the inverse Mills ratios. Following Barth et al. (2005), we include firm size, leverage, cash flows, sales growth, percentage change in common stock and percentage change in total debt in the probit IAS-adoption model. We then estimate the probit model using the maximum likelihood
15
captures the difference in the value of each dependent variable representing Loan Feature
between IAS adopters and non-adopters. When Loan Feature is one of Spread, DSecured,
DFinCov, DGenCov, or CovIndex, a negative coefficient on DIAS (i.e., α1 < 0) is
consistent with H1 and H3, while when Loan Feature is either LoanAMT or Maturity, a
positive coefficient on DIAS is consistent with H2. When Loan Feature is NLender
or %Foreign, a positive coefficient on DIAS is consistent with H4.
To isolate the effect of voluntary IAS adoption on Loan Feature from the effect of
borrower-specific characteristics, we include four borrower-specific control variables, ROA,
Size, MB, and Leverage, that are known to affect borrowers’ credit quality and thus the
price and non-price terms of loan contracts. In addition to these four variable, we also
consider an additional borrower-specific variable, namely asset maturity (ASM) when Eq.
(1) is estimated using loan maturity (Maturity) as the dependent variable. All borrower-
specific variables are measured in a year immediately before loan deals are made.
Previous research on bank loan contracts shows that several loan-level
characteristics are related to the loan rate charged by lenders (e.g. Strahan 1999; Dennis et
al. 2000; Bharath et al. 2006). To control for potential confounding effects of these loan
characteristics on our results, we include five loan-specific variables, that is LoanAMT,
Maturity, NLender, DForCurr, and DPPricing. 7 Here, LoanAMT and NLender are as
defined earlier. DForCurr is a dummy variable that equals 1 for a loan facility quoted in
foreign currency and 0 otherwise. DPPricing is a dummy variable that equals 1 for a loan
procedure and obtain the inverse Mills ratio. In the second stage, we include in Eq. (1) the inverse Mills ratio as an additional control variable to correct for potential self-selection biases. Though not tabulated, the results from the two-stage treatment effects model are qualitatively identical with the results reported in the paper. 7 As will be further explained later on, when one of these loan-specific variables (e.g., LoanAMT) is used as the dependent variable, the same variable is, of course, not used as an independent variable.
16
facility with performance pricing options and 0 otherwise. In addition to these loan-
specific variables, we also consider an additional loan-specific variables, namely the term
loan indicator (TLoan) when Eq. (1) is estimated using loan maturity (Maturity) as the
dependent variable. Since we do not have a clear theory predicting the directional effect of
these variables on the price and non-price terms of loan contracts, we do not predict the
signs of the coefficients on these loan-specific variables. All loan-specific variables are
measured in the same year when loan deals are made.
Previous research suggests that a country’s protections of property rights and
creditor rights and a country’s credit market development influence bank loan contracting.
For example, Bae and Goyal (2003) examine the effect of various institutional variables on
loan spreads and find that a country’s property rights protection is the most important
institutional variable determining loan spread.8 Esty and Megginson (2003) find that a
country’s creditor rights protection is an important factor determining the size and structure
of loan syndicates in the syndicated project finance loans. In estimating Eq. (1), we
consider four country-level variables, that is PRights, CRights, CMktDev, and LGDP,
representing the levels of a country’s property rights protection, creditor rights protection,
credit market development, and economic development, respectively.9 Both property rights
and creditor rights protections are measured using the property rights index and the
creditor rights index, respectively, developed by La Porta et al. (1998). The level of a
country’s credit market development in year t is measured by the amount of credits
8 In their cross-country regressions of loan spreads on country-level determinants of loan spreads, Bae and Goyal (2003) consider several country-level variables, including property rights, creditor rights, language, religion, and legal origin. Overall, they find property rights are the most important determinant of the loan rates across different regression specifications. 9 We also consider other institutional variables considered in Bae and Goyal (2003), but find that they are, overall, insignificant in our regressions.
17
supplied by financial intermediaries to the private sector in year t deflated by a country’s
GDP in year t.10 LGDP denotes the natural log of a country GDP per capita in year t.
Finally, we include Industry Dummies and Year Dummies to control for potential
differences in various proxies for the Loan Feature variable across industries and over
years.
4. Sample and Data
4.1. Sample and data sources
The initial list of our sample consists of all firms that are included in the
Worldscope database and the Loan Pricing Company’s Dealscan database during the
sample period, 1997-2005. The data on a firm’s IAS adoption and all borrower-specific
variables for the 1996-2004 period are obtained from Worldscope. The Dealscan database
is an online database which contains a variety of historical bank loan data and other
financial arrangements.11 The database includes the loan data starting from 1986, and
expands its coverage over time, in particular, after 1995. We select 1997 as the starting
year of our sample period because there are few IAS-adopters that are included in the
Dealscan database prior to 1997. Our sample period ends in 2005 because loan-related data
are available to us only up to year 2005 and to exclude all EU firms that are mandated to
adopt IAS starting from January 1, 2005.12
10 CMktDev and LGDP are measured using the data obtained from the International Monetary Fund (IMF). 11 Other papers using the LPC Dealscan database include Strahan (1999), Bae and Goyal (2003), Bharath et al. (2006), Asquith et al. (2005), Ivashina et al. (2005), and Kim et al. (2006). 12 Note that all EU listed firms are mandated to prepare their financial statements in accordance with IAS starting in January 2005. As shown in Eq. (1), empirical measures of our dependent variables (Loan Feature) in 2005 are linked to voluntary IAS adoption (DIAS) in 2004. As such, all EU firms that are mandated to adopt IAS in 2005 are effectively excluded from our sample.
18
The loan data in the Dealscan database are compiled for each deal and facility.
Each deal, i.e. a loan contract between a borrower and bank(s) at a specific date, may have
only one facility or have a package of several facilities with different price and non-price
terms.13 We consider each facility as a separate observation in our sample because many
loan characteristics and loan spreads vary across facilities, and require that all loan
facilities in our sample are senior debts.14 We then match the loans with borrowers’
financial statement data in Worldscope, using the ticker symbol and name of each borrower.
This procedure leads to a substantial reduction in the number of available loan facilities
because many borrowers included in the Dealscan database are subsidiaries of public firms,
private firms and government entities rather than publicly traded companies, and some
public companies are not covered by Worldscope (Strahan 1999; Dichev and Skinner
2002). We require that all the relevant annual financial statements data needed to compute
all borrower-specific characteristics be available in the fiscal year immediately before the
loan initiation year.
As shown in Panel A of Table 1, we obtain a sample of 2,425 facility-year
observations from 30 countries after applying the above selection procedures. Out of 2,425,
166 observations are from borrowers that voluntarily adopted IAS.15 As shown in column
1 of Panel A, the number of facility-year observations in the total sample of borrowers
with both IAS adopters and non-adopters is widely distributed across countries, ranging
13 For instance, a deal may comprise a line of credit facility and a term loan with longer maturity. 14 This selection criterion is similar to those used by Bharath et al. (2006) and Kim et al. (2006). 15 The percentage of IAS adopters in our sample, which is about 6.8%, is greater than that in the sample of Covrig et al. (2007). They use a total sample of 24,592 firm-years with both IAS adopters and non-adopters in the 1992-2002 period from 29 countries to examine the effect of IAS adoption on foreign mutual fund holdings in the global equity market. In their total sample, the percentage of IAS adopters is about 5% (See their Table 1). It should be noted that that their focus is on the global equity market while our focus is on the international market for private debts, primarily loans by commercial banks and other institutional lenders such as investment banks and insurance companies.
19
from the lowest of 1 for Austria to the highest of 477 for the United Kingdom. Column 2
of Panel A shows the distribution of facility-years using IAS across countries which ranges
from 0 for 16 countries to the largest of 65 for Germany.16
Columns 3 and 4 of Panel A show the property rights index and the creditor rights
index by country, respectively, that are developed by La Porta et al. (1998). Similar to
Morck et al. (2000) and Bae and Goyal (2003), a country’s property rights index is
measured by adding three indices from La Porta et al. (1998) representing the extent of
government corruption, the risk of expropriation by the government, and the risk of the
government repudiating contracts. Each of the three indices ranges from 0 to 10, and thus
the property rights index ranges from 0 to 30 with high values indicating more respect for
private property rights. As shown in column 4, the property rights index ranges from the
lowest of 12.9 for Philippines to the highest of 29.6 for Norway. A country’s creditor
rights index is measured by adding four dummy variables representing “no automatic stay
on assets,” “secured credit first,” “restrictions for going into reorganization,” and “current
management does not stay in the reorganized firm.” It is measured in such a way that
creditor rights are better protected in a country with a higher value of the creditor rights
index. As shown in column 4, the creditor rights index ranges from the lowest value of 0
for France and Philippines to the highest value of 4 for six countries including Hong Kong,
Netherlands, Singapore, and the UK.
16 We have also estimated all regressions reported in the paper after excluding observations from 16 countries with no IAS adoptors. Though not report, we find that the results using this reduced sample are qualitatively similar to those reported in the paper. Note that Covrig et al. (2007) also include in their sample observations from 9 (out of 29) countries with no IAS adopters when examining the effect of IAS adoption on foreign mutual fund holdings.
20
Columns 5 and 6 of Panel A show the mean levels of a country’s credit market
development and GDP per capita in US dollars. As shown in Column 5, the average
amount of credits supplied by financial intermediaries varies widely across countries
ranging from 20% of GDP for Israel and Turkey and to 164% of GDP for Switzerland. As
expected, Column 6 shows that average GDP per capita during our sample period, 1997-
2005, varies widely across countries, ranging from US$508 for India to US$40,412 for
Switzerland.
Panel B of Table 2 reports that the yearly distribution of 166 IAS adopters and
2,259 non-IAS adopters. As shown in Panel B, the number of IAS adopters, overall,
increases over the years with a slight decline only in 1998. The number of non-IAS
adopters in our sample also increases over the years with a decline in 2001, which reflects
an increasing trend in the Dealscan coverage over the years. Panel C of Table 2 presents
the distribution of IAS adopters and non-adopters across 8 different industries, and reveals
that both IAS adopters and non-adopters are most heavily concentrated in the
manufacturing industry.
[INSERT TABLE 1 ABOUT HERE!]
4.2. Descriptive statistics and univariate tests
Table 2 presents descriptive statistics for all borrower-specific and loan-specific
variables considered in this study, separately, for the IAS adopters and the IAS non-
adopters, and performs univariate tests for the mean and median differences between the
two groups. As shown in Panel A, the mean (median) drawn all-in spread (Spread) is about
52 (36) basis points for the IAS adopters while it is about 103 (75) basis points for non-
21
adopters. The mean and median differences are both significant at less than the 1% level,
which is in line with H1. Consistent with H2, the amount of loan facility (LoanAMT) is
significantly larger for IAS adopters than non-adopters. Note, however, that IAS adopters
have a shorter loan maturity (Maturity) than non-adopters, which is inconsistent with H2.
A comparison of DSecured, DFinCov, DGenCov, and CovIndex between the two samples
reveals that IAS adopters are less likely to have their loans secured, and to have restrictive
covenants, which is consistent with H3. When compared with non-adopters, IAS adopters
have not only more lenders but also more foreign lenders who participate in each loan
facility, a finding consistent with H4. Finally, we find that IAS adopters are more likely to
have their loans quoted in foreign currency (DForCurr), and are less likely to have a term
loan (TLoan), compared with non-adopters. We find, however, that there is no significant
difference in the likelihood of loans with performance pricing options between the two
samples.
As shown in Panel B, the mean profitability (ROA) is not significantly different
between IAS-adopters and non-adopters with the same median ROA of 5% for both
samples. The mean borrower size (Size) is not significantly different between the two
samples, though its median is significantly larger for IAS-adopters than for non-adopters at
the 5% level. The growth potential, measured by the market-to-book ratio (MB) is, on
average, smaller for IAS adopters, compared with non-adopters, though its median is not
significantly different between the two samples. Both mean and median of the debt-to-total
asset ratio (Leverage) are not significantly different between the two groups. Both mean
and median asset maturity (ASM) is significantly shorter for IAS adopters than for non-
adopters.
22
[INSERT TABLE 2 ABOUT HERE!]
Table 3 reports a Pearson correlation matrix. Our test variable, DIAS, is negatively
correlated with the drawn all-in spread (Spread), which is consistent with H1. Note that
DIAS is positively correlated with LoanAMT, which is consistent with H2, but it is
negatively correlated with Maturity, which is inconsistent with H2. We find that DIAS is
negatively correlated with DSecured, DFinCov, DGenCov, and CovIndex, which is in line
with H3. Consistent with H4, DIAS is positively correlated with NLender and %Foreign.
Consistent with our priors, Spread is negatively correlated with ROA, Size, and LoanAMT.
A significantly negative correlation of Spread with DForCurr and DPPricing suggests that
borrowers with foreign currency loans and performance pricing options in their loans are
likely to pay lower loan rates. The correlation between LoanAMT and NLender is 0.46.
This high correlation is not surprising given that large loans are often provided through a
loan consortium or syndicate with multiple lenders.
[INSERT TABLE 3 ABOUT HERE!]
5. Results of Multivariate Tests
5.1. Tests for the effect of IAS adoption on loan spread
To test H1, we estimate Eq. (1) using Spread as the dependent variable. Table 4
presents the results of the OLS regressions in Eq. (1) using the full sample of 2,425
facility-years with both IAS adopters and non-adopters over the 1997-2005 period.
Reported t-values are computed using standard errors adjusted for heteroskedasticity and
23
clustering at the firm level.17 In column 1, we estimate Eq. (1) after excluding the country-
level control variables but including country dummies. In columns 2 and 3, we estimate Eq.
(1) after including only one of the two institutional variables, i.e., PRights and CRights,
while in column 4, we include both of them.
As shown in columns 1 to 4, the coefficients on DIAS are significant with an
expected negative sign at less than the 1% level across all cases after controlling for all
other factors. These significantly negative coefficients on DIAS strongly support H1. The
coefficient on DIAS captures the loan rate difference in basis points between IAS adopters
and non-adopters. The results in columns 1 to 4 show that the magnitude of the DIAS-
coefficient ranges from about 20 basis points in column 1 to about 25 basis points in
column 3. This suggests that the IAS adopters, on average, pay lower loan rates than the
non-adopters by more than 20 basis points even after controlling for borrower-specific and
loan-specific characteristics and country-level factors. This loan rate difference is
economically significant as well. Overall, the above results suggest that voluntary IAS
adoption mitigates ex ante information risk faced by lenders and ex post loan monitoring
and re-contracting costs, which in turn translates into significantly lower loan rates charged
to IAS adopters.
With respect to the estimated coefficients on borrower-specific variables (Panel B),
the following is apparent. First, the coefficients on both ROA and Size are highly
significant with an expected negative sign across all cases. This is consistent with the view
that lenders consider both large and high-ROA borrowers as having less credit risk or better
17 We also estimated Eq. (1) using the weighted least squares (WLS) procedure with an equal weight assigned to each country to address a concern over potential problems arising from unequal distribution of samples across different countries. Though not reported for brevity, we found that the WLS results are qualitatively identical with those reported in the paper.
24
capacity to repay the loan, and thus charge lower loan rates to such borrowers. Second, the
coefficient on MB is insignificant, albeit positive, across all cases. Borrowers with high
growth potential (as reflected in high MB) may have a lower credit risk or higher credit
quality because they have a greater ability to generate future cash flows, compared with
borrowers with low growth potential. In such case, the coefficient on MB should be
positively significant. On the other hand, borrowers with high growth potential could be
viewed as having a higher risk because cash flows of high growth firms tend to be more
volatile over time than those of low growth firms. These two opposing effects may cancel
out each other, leading us to observe an insignificant coefficient on MB. Finally, the
coefficient on Leverage is highly significant with an expected positive sign, which is
consistent with evidence reported in many other studies (e.g., Bharath et al. 2006; Kim et
al. 2006). High-leverage firms are likely to have higher default risks, and thus have
relatively poor credit quality, compared with low-leverage firms. To compensate for this
potential credit risk, banks are likely to charge a higher loan rate for high-leverage firms
than for low-leverage firms.
With respect to the estimated coefficients on loan-specific variables (Panel C), the
following is noteworthy. The coefficient on LaonAMT is highly significant with an
expected negative sign across all cases, suggesting that lenders charge lower loan rates on
large loans than they do on small loans. The coefficient on Maturity is insignificant except
for column 1. The coefficient on NLender is significantly negative in columns 2 and 4,
suggesting that loan rates decreases as more lenders participate in a loan deal. The
coefficients on DForCurr are significantly negative except for column 1 with its
magnitude ranging from -11.97 to -15.27. This suggests that loan rates are significantly
25
lower for foreign currency loans than for local currency loans by more than 11 basis points.
The coefficient on DPPricing is insignificant across all cases, indicating that for our
international sample, no significant difference in loan rates exists between loans with and
without performance pricing options.
With respect to country-level control variables, we find that the coefficients on
PRights are significant, ranging from -5.81 in column 4 to -6.01 in column 2. The
significantly negative coefficients on PRights suggest that borrowers from countries with
strong property rights pay lower loan rates than borrowers from countries with weak
property rights. We find, however, that the coefficients on CRights and CMktDev are
insignificant. This is consistent with Bae and Goyal (2003) who report that the extent of a
country’s property rights protection is the most (and the only in most cases) significant
institutional variable determining loan rates among several other institutional variables
they consider. In the next two subsections, when we examine the effect of IAS adoption on
various non-price terms of a loan contract and the lender mix, we therefore report the
results of regressions that include only PRights, but not CRights, or CMktDev, along with
LGDP.
In sum, consistent with our hypothesis H1, the coefficients on DIAS are
significantly negative across all cases, after controlling for all borrower-specific and loan-
specific characteristics and country-level factors. Our results reported in Table 4, taken
together, suggest that voluntary IAS adoption enables borrowers in the international loan
market to save a significant amount of borrowing cost.
[INSERT TABLE 4 ABOUT HERE!]
26
5.2. Tests for the effect of IAS adoption on non-pricing terms of loan contracts
To assess the effect of IAS adoption on loan size and maturity, we estimate Eq. (1)
using LoanAMT and Maturity, respectively, as the dependent variable, and report the
regression results in columns 1 and 2 of Table 5, respectively.18 Note that in column 2, we
include ASM and TLoan as additional determinants of loan maturity, because previous
research shows that loan maturity is associated with asset maturity (Barcley and Smith
1995; Bharath et al. 2006), and loan maturity differs in general between term loans and
non-term loans such as revolvers and 364-day facilities. As shown in column 1, the
coefficient on DIAS is significant with an expected positive sign at less than the 1% level,
indicating that IAS adopters, on average, have significantly larger loans, compared with
non-adopters. However, as shown in column 2, there is no significant difference in loan
maturity between IAS adopters and non-adopters after controlling for all other factors. In
short, the above results, along with those reported in Table 4, indicate that IAS adopters
have not only cheaper loans, but also larger loans, compared with non-adopters.
To evaluate the effect of IAS adoption on the likelihood of loans being secured by
collateral, we estimate Eq. (1) with DSecured as the dependent variable, using the probit
regression procedure. As shown in column 3, we find no significant difference in the
likelihood of loans being secured between IAS adopters and non-adopters. To examine the
effect of IAS adoption on covenant restrictions, we estimate Eq. (1) with DFinCov,
DGenCov, or CovIndex as the dependent variable. Columns 4 and 5 report the probit
18 As mentioned earlier, for brevity, Table 4 reports the results of regressions which include only PRights and LGDP as the country-level control variables because CRights and CMktDev are found to be both insignificant in most cases. Though not reported, the inclusion of CRights and CMktDev as additional control-level controls in our regressions of various non-price terms on DIAS does not alter the results of hypothesis tests and other statistical inferences.
27
regression results using DFinCov and DGenCov as the dependent variable, respectively.
Column 6 reports the Poisson regression result using CovIndex as the dependent variable.
All reported t-values are based on standard errors adjusted for heteroskedasticity and
clustering at the firm level. As shown in columns 4 to 6, the coefficients on DIAS are
significant with an expected negative sign at less than the 5% level across all three
columns. This suggests that IAS adopters are less likely to have restrictive covenants than
non-adopters, irrespective of whether the restrictive covenant is of financial or general (i.e.,
non-financial) type or of the combination thereof.
Our results reported in Table 5, taken as a whole, support H2 in that IAS adoption
is associated with an increase in loan size, and is consistent with H3 in that IAS adoption
leads to a decrease in the likelihood of financial or general covenants being imposed and a
decrease in the overall strength of covenant restrictions measured by our covenant index.
Stated differently, our results suggest that the voluntary IAS adoption by borrowers in year
t - 1 increases loan size in year t, and motivates lenders to use less restrictive covenants in
year t.
With respect to the estimated coefficients on control variables, they are, overall, in
line with the results reported in Table 4, though the level of significance is weaker. Recall
that the coefficient on DPPricing is insignificant in all cases in Table 4. Interestingly, we
find the coefficients on DPPricing, overall, become significant when the non-price terms
of loan contracts are regressed on DIAS and all control variables, as shown in Table 5. This
finding suggests that the provision of performance pricing options is more associated with
the non-price terms of loan contracts such as loan size, the likelihood of having restrictive
28
covenants, and the strength of covenant restrictions than it is with the price term, i.e., loan
spreads.
[INSERT TABLE 5 AROUND HERE!]
5.3. Tests for the effect of IAS adoption on the mix of domestic vs. foreign lenders
We investigate whether IAS adoption induces more foreign lenders relative to
domestic lenders to participate in each loan deal. For this purpose, we estimate Eq. (1),
using NLender and %Foreign as the dependent variable, and report the regression results
in columns 1 and 2 of Table 6, respectively. As shown in column 1, the coefficient on
DIAS is 3.68 which is significant at less than the 5% level. This means that IAS adoption
attracts, on average, a total of nearly 4 additional lenders per loan facility. As shown in
column 2, the coefficient on DIAS is 0.10 which is significant at less than the 1% level.
This indicates that, on average, IAS adoption leads to a 10% increase in the number of
foreign lenders (relative to NLender) who participate in a loan contract. The above findings
are consistent with H4. In sum, our results suggest that voluntary IAS adoption increases
the number of loan suppliers, and this increase is associated with IAS adopters attracting
more foreign lenders from the international loan market.
With respect to the estimated coefficients on control variables, the following is
noteworthy. First, borrower-specific control variables are, overall, insignificant in both
columns except for Leverage when %Foreign is used as the dependent variable (column 2).
The negatively significant coefficient on Leverage in column 2 indicates that foreign
lenders are less likely to participate in loan deals with a borrower with high leverage.
29
Second, the coefficients on loan-specific control variables suggest that foreign lenders are
more likely to be attracted to large loans and foreign currency loans. The significantly
positive coefficient on PRights in column 2, coupled with the significantly negative
coefficient on PRights in column 1, suggests that local banks play a more important role in
providing bank loans in countries with weak property rights, while foreign banks
participate in loan deals more actively in countries with strong property rights protection.
[INSERT TABLE 6 AROUND HERE!]
6. Summary and Concluding Remarks
Using a sample of non-US borrowers from 30 countries over the 1997-2005 period,
this study investigates the effect of voluntary IAS adoption on loan rates charged by
lenders in the international loan market. We compare the price and non-price terms of loan
contracts and the composition of domestic vs. foreign lenders who participate in each loan
facility between borrowers who voluntarily adopted IAS and those who did not after
controlling for borrower-specific, loan-specific, and country-level variables that are
deemed to affect bank loan contracting.
Our results reveal the following. First, we find that lenders charge significantly
lower loan rates to IAS adopters than they do to non-adopters. The rate difference between
the two groups amounts to nearly 25 basis points, which is not only statistically significant
but also economically significant as well. We also find that the loan rate-reducing effect of
voluntary IAS adoption holds, irrespective of a country’s property rights, creditor rights,
credit market development, and economic development. Our results suggest that lenders
view voluntary IAS adoptions by borrowers as reducing ex ante information risk and ex
post monitoring and re-contracting costs.
30
Second, we find that lenders impose more favorable or less restrictive non-price
terms on IAS adopters than they do on non-adopters. In particular, our results show that
IAS-adopters have a larger amount of loan facility, and are less likely to have restrictive
covenants in their loan contracts, compared with non-adopters. With respect to loan
maturity and the likelihood of loan securitization, however, we find no significant
difference between IAS adopters and non-adopters.
Finally, we find that voluntary IAS adoption by a borrower increases the number of
loan suppliers, and this increase is due to IAS adopters attracting more foreign lenders
from the international loan market. This supports the view that foreign lenders are likely to
be more (less) familiar with IAS (local accounting standards), compared with domestic
lenders, and thus IAS adoption increases borrowers’ visibility or lenders’ familiarity with
borrowers’ financial statement in the international loan market.
In conclusion, our results, taken as a whole, suggest that lenders in the
international loan market view voluntary IAS adoption as a credible commitment to a
better reporting strategy by borrowers. Our evidence reported in this paper supports the
notion that voluntary IAS adoption improves the contracting efficiency in the market for
private debts such as bank loans by enabling lenders to assess borrowers’ credit quality
more accurately and improving lenders’ familiarity with financial statements prepared by
borrowers in the international loan market. Overall, our results corroborate the claim
(advanced by proponents of IAS) that accounting standards convergence via IAS facilitates
cross-border investment flows and reduces the cost of external financing. To our
knowledge, this paper is the first study that provides direct evidence on the impact of IAS
adoption on the price and non-price terms of loan contracts and the lender mix. Warranted
31
is further research on the role of accounting standard convergence via IAS in the
international equity and public bond markets, given the scarcity of empirical evidence on
the issue. We leave this issue to future research.
[INSERT APPENDIX I AROUND HERE!]
32
References
Ashbaugh, H. and M. Pincus. 2001. Domestic accounting standards, international
accounting standards, and the predictability of earnings. Journal of Accounting Research 39: 417-434.
Asquith, P., A. Beatty, and J. Weber. 2005. Performance pricing in bank debt contracts.
Journal of Accounting and Economics 40: 101-128. Bae, K. H. and V. K. Goyal. 2003. Property rights protection and bank loan pricing. SSRN
Working paper. Baiman, S. and R. Verrecchia. 1996. The relation among capital markets, financial
disclosure, production efficiency and insider trading. Journal of Accounting Research 34: 1-22.
Barcley, M. J. and C. W. Smith. 1995. The maturity structure of corporate debt. Journal of
Financial Economics 50-2: 609-631. Barth, M. and W. Landsman, and M. Lang. 2005. International accounting standards and
accounting quality. Working Paper (Stanford University and University of North Carolina).
Barth, M. and W. Landsman, M. Lang, and C. W. Williams. 2006. Accounting quality:
International accounting standards and US GAPP. Working Paper (Stanford University and University of North Carolina).
Bartov, E., S. Goldberg, and M. Kim. 2005. Cost of capital and financial statement
transparency. Working Paper (New York University). Bharath, S. T., J. Sunder and S. Sunder. 2006. Accounting quality and debt contracting.
Working Paper (University of Michigan and Northwestern University). Bradley, M. and M. R. Roberts. 2004. The structure and pricing of corporate debt
covenants. Working Paper (Duke University and University of Pennsylvania). Chan, K., V. M. Covrig, and L. Ng. 2005. Home bias and the cost of capital. Working
Paper (Hong Kong University of Science and Technology). Chava, S., M. Dierker and D. Livdan. 2005. Do shareholder rights affect the cost of bank
loans? Working Paper (University of Houston). Covrig, V. M., M. L. DeFond, and M. Hung. 2007. Home bias, foreign mutual fund
holdings, and the voluntary adoption of international accounting standards. Journal of Accounting Research (forthcoming).
33
Covrig, V., S. Lau, and L. Ng. 2006. Do domestic and foreign fund managers have similar
preferences for stock characteristics? Journal of International Business Studies 37 (2006): 407-429.
Dahlquist, M. L. and G. Robertsson. 2003. Direct foreign ownership, institutional investors,
and firm characteristics. Journal of Financial Economics 59: 413-440. Dennis, S. and D. J. Mullineaux. 2000. Syndicated loans. Journal of Financial
Intermediation 9: 404-426. Diamond, D. and R. Verrecchia. 1991. Disclosure, liquidity, and the cost of capital.
Journal of Finance 66: 1325-1355. Dichev, I. and D. Skinner. 2002. Large-sample evidence on the debt covenant hypothesis.
Journal of Accounting Research 40: 1091-1123. Esty, B. C. and W. L. Megginson. 2003. Creditor rights, enforcement, and debt ownership
structure: Evidence from the global syndicated loan market. Working Paper (Harvard Business School and University of Oklahoma).
Francis, J., R. LaFond, P. M. Olsson, and K. Schipper 2005. The market pricing of accrual
quality. Journal of Accounting and Economics 30: 295-327. Green, W. 1990. Econometric analysis. Macmillian Publishing Companies, New York,
New York. Ivashina, V., V. B. Nair, A. Saunders, and N. Z. Massoud. "Bank debt and corporate
governance" (August 2005). EFA 2004 Maastricht Meetings Paper No. 4601 Available at SSRN: http://ssrn.com/abstract=557082
Kang, J. K. and R. Stulz. 1997. Why is there a home bias? An analysis of foreign portfolio
equity ownership in Japan. Journal of Financial Economics 46: 3-28. Kim, J.-B. B. Y. Song, and J. S. L. Tsui. 2006. Auditor quality, tenure and bank loan
pricing. Working paper (The Hong Kong Polytechnic University.). Kim, J.-B. and C. H. Yi. 2005. Foreign equity investment and corporate transparency.
SSRN Working Paper. La Porta, R., F. Lopez-de-Salines, A. Shleifer, and R. W. Vishny. 1998. Law and Finance.
Journal of Political Economy 106: 1113-1155. Leuz, C. and R. Verrecchia. 2000. The economic consequences of increased disclosure.
Journal of Accounting Research 38: 91-124.
34
____, and ____. 2005. Firms’ capital allocation choices, information quality, and the cost of capital. SSRN Working Paper.
Morck, R., B. Yeung, and W. Yu. 2000. The information content of stock markets: why do
emerging markets have synchronous stock price movements? Journal of Financial Economics 58: 215-260.
Rajan, R. and A. Winston. 1995. Covenants and collateral as incentives to monitor.
Journal of Finance 50-4: 1113-1146. Schipper, K. 2005. The introduction of International Accounting Standards in Europe:
Implications for international convergence. European Accounting Review 14: 101-126. Smith C. W. and J. B. Warner. 1979. On financial contracting: An analysis of bond
covenants. Journal of Financial Economics 7: 117-161. Strahan, P. E. 1999. Borrower risk and the price and non-price terms of bank loans.
Working Paper (Federal Reserve Bank of New York). Sufi, A. 2006. Information asymmetry and financing arrangements: Evidence from
syndicated loans. Journal of Finance (forthcoming).
35
Appendix I Variable
Definition
Panel A: Loan-specific variables Spread The amount of a borrower pays in basis points over LIBOR for each dollar
drawn down. LoanAMT The log of the amount of a loan facility. Maturity The log of maturity measured in months. DSecured One if a facility is secured and zero otherwise. DFinCov One if a facility has financial covenants and zero otherwise. DGenCov One if a facility has general covenants and zero otherwise. CovIndex The sum of DSecured, DFinCov, and DGenCov NLender The total number of lenders in each loan facility. %Foreign The ratio of foreign lenders to total number of lenders. DForCurr One for a facility in the foreign currency and zero otherwise. DPPricing One for a facility with a performance pricing option and zero otherwise TLoan One for a term loan and 0 otherwise; Panel B: Borrower-specific variables DIAS One for firms voluntarily adopting IAS and zero otherwise. ROA Net income/total assets. SIZE The log of total assets. MB The ratio of market capitalization to book equity. LEV The ratio of total debts to total assets. ASM
onDepreciati
PPEPPECA
PPECOGS
CAPPECA
CA **+
++
, where CA=current
assets; PPE=property, plant, and equipment; COGS=cost of goods sold. Panel C: Country-level variables Propery rights index (PRights)
The property rights index aggregating three indices measuring government corruption, the risk of expropriation by the government and the risk of government repudiating contracts. Source: LLSV (1998).
Creditor rights index (CRights)
The creditor rights index. Source: LLSV (1998).
Credit market development (CMktDev)
Credits by financial intermediaries to the private sector/GDP. Average of 1999-2003. Source: Djankov et al. (2005).
Economic Development (LGDP)
The log of per capita GDP. Source: International Monetary Fund.
36
Table 1 Sample profile
Panel A: Distribution of total samples, IAS adopters and non-adopters by country and country-
level variables Country
(1) Total
facility-year obs.
(2) Facility-year obs. using IAS
(3) Property
rights index
(4) Creditor
rights index
(5) Credit market
develop’t Average
1999-2003
(6) Mean of per capita GDP over 1997-2005 (US$)
Australia 38 0 26.5 1 0.88 24,094 Austria 1 1 27.9 3 1.04 28,705 Belgium 16 2 27.9 2 0.78 27,097 Canada 188 0 28.6 1 0.81 25,284 Denmark 19 3 29 3 1.23 35,977 Finland 36 1 28.8 1 0.58 28,215 France 254 11 27.9 0 0.87 26,263 Germany 104 65 28.6 3 1.18 27,396 Greece 26 8 21 1 0.6 13,640 Hong Kong 129 2 25.6 4 1.54 24,837 India 117 0 18.4 4 0.3 508 Israel 11 0 24.1 4 0.2 18,622 Italy 59 0 24.7 2 0.79 23,369 Japan 70 2 27.9 2 1.07 33,672 Korea 137 0 22.2 3 0.93 11,551 Malaysia 39 0 22.8 4 1.38 4,067 Netherlands 60 3 29.3 2 1.42 28,838 Norway 38 0 29.6 2 0.83 43,374 New Zealand 7 0 29 3 1.17 17,692 Philippines 39 0 12.9 0 0.41 1,019 Portugal 11 0 24.8 1 1.4 13,248 Singapore 50 0 26.4 4 1.17 22,949 South Africa 23 2 23.1 3 0.76 3,447 Spain 72 0 25.3 2 1.06 18,451 Sweden 65 2 29 2 0.72 30,810 Switzerland 35 33 30 1 1.64 40,412 Taiwan 208 0 25.13 2 0.99 13,685 Thailand 10 0 20.2 3 1 2,165 Turkey 86 31 18.13 2 0.2 3,296 United Kingdom
477 0 28.4 4 1.36 27,894
Total 2,425 166 Mean 25.4 2.3 0.94 20,686 Median 26.5 2 0.96 23,731 Std. dev. 4.1 1.2 1.37 11,884
37
Panel B: Sample distribution by year Year IAS-Adopters Non-adopters
1997 5 60 1998 2 121 1999 9 201 2000 16 284 2001 17 229 2002 19 291 2003 30 347 2004 34 383 2005 34 343 Total 166 2,259
Panel C: Sample distribution by industry Industry Facility-years
For IAS adopters Facility-years for
non-adopters Mining 1 98 Construction 0 71 Manufacturing 85 886 Utilities 18 372 Trade 12 168 Finance, Insurance and Real Estate 36 422 Services 14 241 Public Administration 0 1 Total 166 2,259
38
Table 3: Descriptive statistics and univariate test for mean and median differences between IAS adopters and non-adopters IAS adopters
(N = 166) Non-IAS adopters
(N = 2,259)
Variable N Mean Median Std. dev. Mean Median Std. dev.
Panel A: Loan-specific variables Loan spread (Spread) 2,425 51.85 36.25 44.57 103.37*** 75.00*** 89.04 Log of the amount of loan facility (LoanAMT) 2,425 20.08 20.12 1.27 19.06*** 19.08*** 1.45 Loan maturity (Maturity) 2,425 3.37 3.58 0.79 3.63*** 4.09*** 0.71 Secured loan dummy (DSecured) 2,425 0.02 0.00 0.15 0.12*** 0.00*** 0.33 Financial covenant dummy (DFinCov) 2,425 0.02 0.00 0.13 0.19*** 0.00*** 0.40 General covenant dummy (DGenCov) 2,425 0.02 0.00 0.13 0.08*** 0.00*** 0.28 Covenant index (CovIndex) 2,425 0.06 0.00 0.40 0.40*** 0.00*** 0.72 Number of lenders (NLender) 2,425 24.21 22.00 14.08 15.81*** 13.00*** 11.10 Percent of foreign lenders (%Foreign) 2,425 0.77 0.77 0.20 0.57*** 0.63*** 0.32 Foreign currency dummy (DForCurr) 2,425 0.92 1.00 0.27 0.66*** 1.00*** 0.47 Performance pricing dummy (DPPicing) 2,425 0.17 0.00 0.38 0.16 0.00 0.37 Term loan dummy (TLoan) 2,425 0.16 0.00 0.36 0.35*** 0.00*** 0.47 Panel B: Borrower-specific variables Return on assets (ROA) 2,425 0.05 0.05 0.05 0.06 0.05 0.09 Firm size (SIZE) 2,425 9.25 9.28 1.37 9.27 8.84** 2.85 Market-to-book ratio (MB) 2,425 2.11 1.79 1.30 2.68*** 1.65 11.24 Leverage (LEV) 2,425 0.30 0.27 0.12 0.32 0.31 0.17 Asset maturity (ASM) 1,948 0.97 0.86 0.42 3.81*** 0.90** 12.45 See Appendix I for the definitions of all variables.
***, **, *: Significant at 0.01, 0.05, and 0.10, respectively.
39
Table 3: Pearson correlation matrix Variable DIAS Spread LoanAMT Maturity DSecured DFinCov DGenCov CovIndex NLender %Foreign DForCurr DPPricing Tloan 1. Spread -0.15*** 1 2. LoanAMT 0.18*** -0.34*** 1 3. Maturity -0.09*** 0.11*** -0.06*** 1 4. DSecured -0.08*** 0.30*** -0.20*** 0.16*** 1 5. DFinCov -0.11*** 0.17*** -0.22*** 0.09*** 0.28*** 1 6. DGenCov -0.06*** 0.17*** -0.10*** -0.01 0.12*** 0.38*** 1 7. CovIndex -0.12*** 0.30*** -0.25*** 0.12*** 0.65*** 0.82*** 0.64*** 1 8. NLender 0.18*** -0.22*** 0.46*** -0.08*** -0.13*** -0.03* -0.00 -0.07*** 1 9. %Foreign 0.16*** -0.16*** 0.26*** -0.10*** -0.25*** -0.24*** 0.04** -0.22*** 0.29*** 1 10. DForCurr 0.14*** -0.15*** 0.13*** -0.07*** -0.19*** -0.15*** 0.05*** -0.15*** 0.20*** 0.40*** 1 11. DPPricing 0.01 -0.03* 0.27*** 0.02 -0.01 0.08*** 0.10*** 0.08*** 0.13*** 0.05*** 0.00 1 12. TLoan -0.10*** 0.18*** -0.29*** 0.20*** 0.13*** 0.13*** 0.06*** 0.16*** -0.06*** -0.04** 0.10*** -0.14*** 1 13. ROA -0.00 -0.12*** -0.00*** -0.01 -0.07*** 0.00 -0.01 -0.03* -0.00 0.03* 0.04** 0.00 0.01 14. Size -0.00 -0.14*** 0.05** -0.15*** -0.02 0.00 0.07*** 0.02 0.15*** 0.09*** 0.16*** -0.12*** 0.13*** 15. MB -0.01 0.05** 0.05** -0.00 -0.02 -0.02 -0.00 -0.02 0.00 0.05** 0.03 0.01 -0.04** 16. Leverage -0.02 0.15*** -0.00 0.12*** 0.11*** 0.01 0.05** 0.07*** -0.02 -0.10*** -0.03* 0.01 0.12*** 17. ASM -0.05** 0.02 0.06*** -0.03 -0.05** -0.00 -0.01 -0.03 -0.01 0.01 -0.03 0.08*** -0.03*
Variable ROA Size MB Leverage ASM 13. ROA 1 14. Size -0.15*** 1 15. MB -0.00 -0.06*** 1 16. Leverage -0.00 0.08*** -0.08*** 1 17. ASM -0.02 -0.05** 0.00 -0.00 1
See Appendix I for the definitions of all variables.
***, **, * : Significant at 0.01, 0.05, and 0.10, respectively.
40
Table 4: Effect of IAS adoption on loan spread Variable
(1) Country dummies
included with no country-level
controls
(2) Creditor
rights index excluded
(3) Property
rights index excluded
(4) Both
property and creditor
rights indices included
Panel A: Test Variable IAS adoption dummy (DIAS) -18.54
(-2.67) *** -23.03
(-3.51) *** -24.78
(-3.66) *** -24.56
(-3.57) ***
Panel B: Borrower-specific controls Return on assets (ROA) -161.98
(-3.49) *** -131.34
(-3.05) *** -120.62
(-2.80) *** -126.88
(-2.91) ***
Firm size (Size) -14.92 (-6.69)
***
-5.52 (-5.24)
*** -4.53 (-4.08)
*** -5.46 (-5.13)
***
Market-to-book ratio (MB) 0.55 (1.03)
0.63 (1.12)
0.62 (1.11)
0.63 (1.13)
Leverage (Leverage) 77.36 (5.40)
*** 74.22 (5.04)
*** 73.79 (4.88)
*** 73.49 (4.96)
***
Panel C: Loan-specific controls Log of the amount of loan facility (LoanAMT)
-10.47 (-4.55)
*** -15.36 (-8.22)
*** -17.42 (-9.65)
*** -15.29 (-8.19)
***
Loan maturity (Maturity) 9.22 (2.55)
** 4.07 (1.20)
3.60 (1.05)
3.57 (1.05)
Number of lenders (NLender) -0.05 (-0.18)
-0.45 (-1.80)
* -0.31 (-1.28)
-0.44 (-1.78)
*
Foreign currency dummy (DForCurr)
10.77 (1.31)
-15.09 (-2.50)
** -11.97 (-1.98)
** -15.27 (-2.54)
**
Performance pricing dummy (DPPricing)
3.71 (0.74)
8.42 (1.53)
6.59 (1.23)
8.31 (1.52)
Panel D: Country-level controls Property rights index (PRights) -6.01
(-3.20) *** -5.81
(-3.16) ***
Creditor rights index (CRights)
-4.46 (-1.49)
-3.85 (-1.34)
Credit market development (CMktDev)
-2.77 (-0.30)
7.30 (0.58)
10.42 (0.87)
GDP per capita (LGDP) 10.07 (2.07)
** -8.78 (-1.99)
** 6.22 (1.08)
Constant 354.91 (9.37)
*** 507.12 (14.32)
*** 561.53 (12.45)
*** 536.11 (12.73)
***
Country dummies Yes No No No Industry dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes R-squared 0.35 0.25 0.24 0.25 N 2,425 2,425 2,425 2,425
See Appendix I for the definitions of all variables. ***, **, and *: Significant at 0.01, 0.05 and 0.10, respectively. Standard errors are heteroskedasticity- robust, adjusted for clustering at the firm level.
41
Table 5: Effect of IAS adoption on non-pricing terms Dependent Variable =
(1) Loan amount (LoanAMT)
(2) Loan maturity
(Maturity)
(3) Secured loan
indicator (DSecured)
(4) Financial
covenant indicator (DFinCov)
(5) General covenant
indicator (DGenCov)
(6) Covenant index
(CovIndex)
Panel A: Test variable IAS adoption dummy (DIAS)
0.43 (3.28)
*** -0.05 (-0.83)
-0.25 (-0.88)
-1.06 (-2.95)
*** -0.69 (-2.02)
** -1.42 (-2.07)
**
Panel B: Borrower-specific controls Return on assets (ROA) 0.46
(1.27) -0.00
(-0.71) -1.41
(-2.11) ** 0.15
(0.27) -0.70
(-0.95) -0.82
(-1.18)
Firm size (SIZE) 0.08 (3.47)
*** -0.04 (-4.58)
*** 0.00 (0.04)
0.01 (0.69)
0.04 (2.04)
** 0.03 (1.65)
*
Market-to-book ratio (MB) 0.00 (0.58)
-0.00 (-1.38)
0.00 (0.03)
0.00 (0.07)
0.00 (0.69)
0.00 (0.01)
Leverage (LEV) -0.23 (-1.10)
0.19 (1.86)
* 0.90 (2.77)
*** -0.11 (-0.36)
0.45 (1.39)
0.59 (1.92)
*
Asset maturity (ASM) -0.01 (-1.02)
Panel C: Loan-specific controls Log of the amount of loan facility (LoanAMT)
-0.01 (-0.18)
-0.17 (-3.69)
*** -0.27 (-6.53)
*** -0.25 (-4.69)
*** -0.33 (-8.06)
***
Loan maturity (Maturity) -0.06 (-1.60)
0.37 (3.81)
*** 0.16 (2.56)
** -0.01 (-0.25)
0.24 (2.93)
***
Number of lenders (NLender)
0.05 (12.57)
*** 0.01 (0.35)
-0.01 (-1.05)
0.01 (2.59)
*** 0.01 (1.87)
* 0.01 (1.66)
*
Term loan (TLoan) 0.28 (7.78)
***
Foreign currency dummy (DForCurr)
0.32 (4.25)
*** -0.04 (-1.17)
-0.58 (-5.14)
*** -0.38 (-3.66)
*** 0.34 (2.53)
** -0.39 (-3.59)
***
Performance pricing dummy (DPPicing)
0.47 (5.27)
*** 0.06 (1.24)
0.19 (1.37)
0.72 (5.46)
*** 0.80 (5.26)
*** 0.77 (5.52)
***
42
Panel C: Country-level controls Property rights index (PRights)
0.20 (6.86)
*** -0.03 (-1.61)
-0.03 (-0.81)
-0.02 (-0.73)
0.03 (1.02)
-0.01 (-0.47)
Log of GDP per capita (LGDP)
-0.08 (-0.90)
0.03 (0.51)
-0.01 (-0.09)
-0.02 (-0.25)
-0.10 (-1.09)
-0.05 (-0.50)
Constant 12.94 (24.28)
*** 4.67 (12.53)
*** 2.24 (2.15)
** 4.19 (5.22)
*** 2.23 (2.24)
** 4.84 (5.53)
***
Industry dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes R-squared 0.45 0.12 0.18 0.17 0.13 0.14 N 2,425 1,945 2,425 2,425 2,425 2,425 See Appendix I for the definitions of all variables. ***, **, and *: Significant at 0.01, 0.05 and 0.10, respectively. (1) and (2) are OLS regressions and (3), (4) and (5) are Probit regressions. (6) is a Poisson regression. Standard errors are heteroskedasticity robust, adjusted for clustering at the firm level.
43
Table 6: Effect of IAS adoption on number of total lenders and percent of foreign lenders Dependent variable =
(1) Number of lenders
(NLender)
(2) percent of foreign lenders
(%Foreign) Panel A: Test variable IAS adoption dummy (DIAS) 3.68
(2.39) ** 0.10
(3.91) ***
Panel B: Borrowrer-specific controls Return on assets (ROA) 0.55
(0.19) 0.01
(0.05)
Firm size (SIZE) 0.05 (0.35)
-0.00 (-0.10)
Market-to-book ratio (MB) 0.00 (0.54)
0.00 (0.68)
Leverage (LEV) -0.66 (-0.35)
-0.12 (-2.24)
**
Panel C: Loan-specific controls Log of the amount of loan facility (LoanAMT)
4.10 (16.10)
*** 0.05 (7.40)
***
Loan maturity (Maturity) -0.35 (-0.83)
-0.00 (-0.53)
Foreign currency dummy (DForCurr) 1.25 (1.93)
* 0.22 (10.94)
***
Performance pricing dummy (DPPicing)
1.91 (2.22)
** 0.02 (1.13)
Panel D: Country-level controls Property rights index (PRights)
-1.01 (-3.94)
*** 0.02 (3.31)
***
Log of GDP per capita (LGDP) 0.79 (1.21)
-0.12 (-6.94)
***
Constant -44.53 (-9.42)
*** 0.08 (0.67)
Industry dummies Yes Yes Year dummies Yes Yes R-squared 0.31 0.31 N 2,425 2,425
See Appendix I for the definitions of all variables. ***, **, and *: Significant at 0.01, 0.05 and 0.10, respectively. Standard errors are heteroskedasticity robust, adjusted for clustering at the firm level.