the voluntary adoption of international accounting...

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).

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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.

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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.

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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.

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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.

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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

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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

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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

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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.

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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

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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.

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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:

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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.

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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.

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(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

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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.

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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.

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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.

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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


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.


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.


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

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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)

*


-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)


-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)


0.00 (0.68)

Leverage (LEV) -0.66 (-0.35)

-0.12 (-2.24)

**


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.

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