Jurnal 2 ttg Cost of Equity Capital

ORI GINAL RESEARCH

International Financial Reporting Standards,institutional infrastructures, and implied cost of equitycapital around the world

Jeong-Bon Kim • Haina Shi • Jing Zhou

Published online: 23 February 2013� Springer Science+Business Media New York 2013

Abstract Using a sample of 21,608 firm-years from 34 countries during 1998–2004, this

study evaluates the impact of voluntary adoption of the International Financial Reporting

Standards (IFRS) on a firm’s implied cost of equity capital. We find that the implied cost of

equity capital is significantly lower for the full IFRS adopters than for the non-adopters

even after controlling for potential self-selection bias and firm-specific and country-level

factors that are known to affect the implied cost of capital. This result holds irrespective of

institutional infrastructure determining a country’s governance and enforcement mecha-

nisms. We also find that the implied cost of equity capital decreases with the efficacy of

institutional infrastructure. Moreover, we provide evidence that the cost of capital-reducing

effect of IFRS adoption is greater when IFRS adopters are from countries with weak

institutional infrastructures than when they are from countries with strong infrastructures.

The above results are robust to a battery of sensitivity checks.

Keywords International financial reporting standards (IFRS) � Cost of equity capital �Institutional infrastructure � Governance mechanism � Enforcement mechanism

JEL Classification M16 � G12 � M48

J.-B. KimCollege of Business, City University of Hong Kong, Kowloon, Hong Kong

H. Shi (&)School of Management, Fudan University, Shanghai 200433, Chinae-mail: [email protected]

J. ZhouSchool of Accountancy, Shanghai University of Finance and Economics, Shanghai, China

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Rev Quant Finan Acc (2014) 42:469–507DOI 10.1007/s11156-013-0350-3

1 Introduction

Proponents of International Financial Reporting Standards (IFRS)1 argue that accounting

standards harmonization via IFRS enhances the quality and comparability of financial

disclosures, which in turn facilitates a firm’s access to international capital markets and

thus cross-border investment flows (e.g., Covrig et al. 2007; Barth et al. 2008; Kim et al.

2011). In particular, proponents claim that a major benefit of IFRS adoption lies in its

enhanced ability to reduce the cost of raising funds from international capital markets. For

example, Fritz Bolkestein, the former European Commissioner for Internal Markets, claims

that IFRS adoption is ‘‘vital because a single set of accounting standards will help reduce

the cost of capital’’.2

Recently, several researchers have examined the effect of voluntary IFRS adoption on

the cost of equity capital, using the implied cost of capital estimates for a firm.3 Cuijpers and

Buijink (2005) examine the voluntary adoption of IFRS or non-local GAAP by European

Union (EU) firms, but they fail to document a lower cost of capital for IFRS adopters in the

EU. Daske (2006) investigates a sample of German firms with IFRS adoption for the period

of 1993–2002, and finds a higher cost of equity capital for IFRS adopters or US GAAP

adopters than for local GAAP adopters, which is inconsistent with the argument advanced

by proponents of IFRS. More recently, Daske et al. (2012) examine whether IFRS adoption

leads to a decrease in the cost of capital, and find that the effect of voluntary IFRS adoption

on the cost of capital is generally modest, while the effect is stronger for ‘‘serious’’ adopters

than for ‘‘label’’ adopters. In short, the extant evidence on the cost-of-capital effect of IFRS

adoption is weak and at best mixed, and is silent on the role of a country’s institutional

infrastructure in determining economic consequences of IFRS adoptions.

Given this inconclusive evidence, this paper aims to provide evidence on the effect of

voluntary IFRS adoption on the cost of equity capital using firm-level data from 34

countries with different institutional infrastructures. In so doing, we first investigate

whether and how the adoption lowers the cost of equity capital after controlling for a

country’s institutional infrastructure and other factors that are known to affect the cost of

capital. For this purpose, we construct a sample of 21,608 firm-year observations that

voluntarily adopt IFRS (hereafter IFRS adopters) and that do not (hereafter non-adopters)

from 34 countries over the period of 1998–2004, and then assess the difference in the cost

of equity capital between the IFRS adopters and the non-adopters. Second, we also

investigate whether the cost-of-capital effect of IFRS adoption is influenced by the efficacy

of institutional infrastructure that determines a country’s corporate governance and

1 For convenience, this paper uses the term IFRS to refer to both IAS and IFRS. The IAS refers to standardsissued by IASC and revised by IASB, and the IFRS refers to standards issued by the IASB. In 2001, theIASB succeeded the IASC and assumed its standard-setting responsibilities from the IASC. The IFRSencompasses all standards issued by the IASC and the IASB.2 See Bolkestein, F. ‘‘One currency, one accounting standard: Unless the European Union adopts a singleset of rules, it risks losing the benefits of the euro’’, Financial Times (June 14, 2000).3 Daske et al. (2008) and Li (2010) study the effect of mandatory IFRS adoption on cost of capital. We areinterested in examining the voluntary IFRS adoption effect because voluntary adoption can be viewed as astronger and more credible commitment to enhance disclosure by a firm. While EU mandated the use ofIFRS in the preparation of consolidated financial statements starting in 2005, many other countries are stillin the process of converging local GAAP with IFRS. On the other hand, studying the effect of IFRS adoptionin a mandatory setting can create other problems: using a single year (2005) as the benchmark ignores otherregulatory changes that can occur simultaneously with mandatory IFRS adoption. In this regard, our sampleof both voluntary adopters and non-adopters is less likely to suffer from this problem, since firms decide tovoluntarily adopt IFRS in different years.

470 J.-B. Kim et al.

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enforcement mechanisms.4 The issue is interesting and timely given that previous research

on economic consequences of IFRS adoptions has paid little attention to the role of a

country’s institutional infrastructure in determining the effect of IFRS adoption on the cost

of capital.

Briefly, our results reveal the following. First, we find that the cost of equity capital is

significantly lower for IFRS adopters than for non-adopters, suggesting that the IFRS adopters

benefit from enhanced disclosures via IFRS by enjoying a lower cost of external financing from

the equity market. Moreover this result holds even after controlling for cross-country differ-

ences in institutional infrastructures proxied by country-level corporate governance and

enforcement mechanisms such as disclosure regulations, auditing infrastructures and investor

protection. This finding lends support to the claim that accounting standard harmonization via

IFRS adoption lowers the cost of capital. Second, we find that the cost of capital is lower in

countries with strong institutional infrastructures than in countries with weak institutional

infrastructures. This corroborates the finding of previous corporate governance research that

countries with better legal institutions enjoy higher stock market valuation and easier access to

equity financing (La Porta et al. 1997, 1998, 2000, 2002; Claessens et al. 2002). Third, we find

that the cost of capital-reducing effect of IFRS adoption is greater in countries with weak

institutional infrastructures than in countries with strong institutional infrastructures. Finally,

we find that the above results are robust to a variety of sensitivity checks.

Our study contributes to the existing literature in the following ways. First, using a

multi-country, multi-period sample from around the world, this study provides evidence

that voluntary IFRS adoption leads to lower cost of equity capital, irrespective of a

country’s institutional infrastructure. Given the mixed evidence on the issue,5 our results

improve our understanding of the economic consequences of accounting standards har-

monization via IFRS. Second, to our knowledge, our study is one of the few, if not the first,

that examines the role of institutional infrastructures in determining the effect of voluntary

IFRS adoption on the cost of equity capital. Third, our finding of the cost of capital-

reducing effect of IFRS adoptions being greater (less) for firms in countries with weak

(strong) institutional infrastructures contributes to an evolving stream of research on the

role of institutional infrastructures in shaping firm-level governance mechanisms (e.g., Ball

2001; Ball et al. 2003; Berkowitz et al. 2003; Doidge et al. 2004; Durnev and Kim 2005;

Burgstahler et al. 2006; Chen et al. 2009). This literature is concerned with whether, and

how, country-level governance mechanisms such as a country’s disclosure regulations and

auditing infrastructures reinforce or substitute firm-level governance mechanisms in

determining a firm’s information quality and economic performance. For example, Chen

et al. (2009) find that firm-level governance is negatively related to cost of equity capital

and that the cost of capital-reducing effect is more pronounced in countries with poor legal

protection. Our study differs from theirs in that Chen et al. (2009) focus on the effect of

firm-level governance on cost of equity, while our analysis focuses on the effect of vol-

untary IFRS adoption on the cost of equity.6 In this sense, our study complements theirs by

4 Throughout the paper, a country’s enforcement mechanism refers to a country’s institutions that enforceaccounting standards, contractual rights and/or laws.5 For example, Daske (2006) examines the effect of IFRS adoption on cost of capital using a sample offirms from a single country, Germany, while Cuijpers and Buijink (2005) examine the effect of non-localGAAP adoption on cost of capital using a sample of European Union firms. Both studies fail to documentevidence on the cost of capital-reducing effect of IFRS or non-local GAAP adoption.6 On the other hand, while Chen et al. (2009) is an interesting study using the Credit Lyonnais SecuritiesAsia survey data, it is also a limitation which restricts the sample within 17 countries in 2001 and 2002. Incomparison, our study covers 34 countries and a longer time period from 1998 to 2004.

IFRS institutional infrastructures, and implied cost of equity capital 471

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providing additional evidence on the substitutive relation between firm-level and country-

level corporate governance. In particular, we document that the effect of a firm’s voluntary

IFRS adoption on lowering the cost of equity capital becomes weakened (strengthened)

when IFRS adopters are from countries with strong (weak) corporate governance and

enforcement mechanisms.

Finally, our study complements and extends Daske et al. (2012) who find only a modest

effect of IFRS adoption on the cost of capital.7 Their analysis relies on a comprehensive

coding for IFRS, and emphasizes the distinction between the ‘‘serious’’ adoptions and the

‘‘label’’ adoptions. They find a negative association of the cost of capital with the serious

adoption of IFRS, but not with the label adoption. The intuition behind the classification of

label versus serious adoptions is that firm-level reporting incentives play a significant role

for the economic effects of IFRS adoption and they hypothesize that only ‘‘serious

adoption’’ results in higher quality reports, compared with the use of local GAAP. How-

ever, evidence from the classification used by Daske et al. (2012) should be interpreted

with caution for the following reasons. First, there is no direct measure to differentiate

‘‘serious’’ and ‘‘label’’ adopters. As explained in their paper, ‘‘serious’’ adopter is an IFRS

adopter with above-median changes around IFRS adoption in reporting incentives,

reporting behavior, and reporting environment. However, changes in these proxies do not

necessarily lead to an improvement in reporting quality. It is possible that firms with strong

reporting behaviors, reporting incentives, and reporting environment are likely to provide

high-quality reports regardless of what type of accounting standards are followed. Suppose,

for example, that a firm has already provided financial reports with low accruals under

local GAAP. By Daske et al. (2012)’s classification scheme, such a firm will be classified

as a ‘‘label adopter’’ but in substance it is with high reporting quality. Thus, ‘‘label’’

adopters are not necessarily firms with poor reporting quality. Second, with an overall

trend to improve report quality, one cannot rule out the possibility that local GAAP

followers (non-IFRS-adopters) also improve report quality during a specific period. In such

a case, a firm may be classified as a ‘‘serious adopter’’ because of factors not related with

IFRS adoption. Therefore, the focus of Daske et al. (2012) is to investigate the different

economic consequences by ‘‘serious’’ and ‘‘label’’ adopters while ours is to examine the

cost of capital effect caused by voluntary IFRS adoption. Different from their paper, our

study distinguishes the full IFRS adopters from the partial IFRS adopters,8 and finds that

the difference in the cost of capital between full (as opposed to partial) IFRS adopters and

the non-adopters is statistically significant, irrespective of a country’s institutional infra-

structures. In this context, our study complements their study. In addition, our study

extends Daske et al. (2012) by documenting that the weaker is the institutional infra-

structure, the stronger is the cost of capital-reducing effect of IFRS adoption, a finding that

has not been examined by previous research, including Daske et al. (2012).

7 Our study and Daske et al. (2012) focus on different aspects of IFRS adoption effects. The main focus ofDaske et al. (2012) is on the heterogeneity in economic consequences of the ‘‘label’’ versus ‘‘serious’’adoptions, while the main focus of our study is on the effect of institutional infrastructures on the cost-of-capital effect of full IFRS adoption. However, to provide a direct comparison with their study, we conduct asensitivity test in Sect. 6.4 by classifying IFRS adopters into ‘‘serious’’ and ‘‘label’’ adopters.8 As shown in ‘‘Appendix 2’’, the Worldscope field 07536 includes 23 different codes that relate to a firm’schoice of accounting standards. As will be further explained in Sect. 4.1, among the 23 codes, this paperconsiders only two cases coded as 02 (IAS adoption) and 23 (IFRS adoption) as the full IFRS adoption case.We conduct a sensitivity test by classifying partial adoption as IFRS adopters. See Sect. 6.4 for furtherdiscussion.


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The remainder of the paper is organized as follows. Section 2 presents a brief history on

IFRS development and develops research hypotheses. Section 3 specifies empirical models

for hypothesis testing. Section 4 describes the sample and data sources, and performs

univariate tests. Section 5 presents the results of multivariate regressions. In Sect. 6, we

perform a battery of sensitivity tests. The final section concludes the paper.

2 Extant research and hypothesis development

2.1 Brief history on the IFRS development

The history of IFRS can be traced back to 1973 when representatives of the professional

accounting bodies from major developed economies reached an agreement to establish the

International Accounting Standards Committee (IASC) with no statutory mandates given

by political jurisdictions.9 In 1975, the IASC pronounced its first IAS. Since then, the IASC

had issued a total of 41 IAS until it was restructured into the International Accounting

Standard Board (IASB) in 2001. As of July 2012, the IASB has promulgated a total of 13

IFRS. A major task of the IASB is to cooperate with national accounting standard setting

bodies to achieve harmonization in accounting standards around the world. IFRS and IAS

are now widely accepted and considered by many to be one of the most prevalent

accounting standards around the world.10

In March 2002, the European Parliament broadly endorsed the proposal that all EU

companies listed on organized stock exchanges (about 9,000 companies in total) should,

from 2005 onwards at the latest, prepare and publish their consolidated accounts in

accordance with IFRS. This requirement applies not only to all 27 EU countries but also to

three European Economic Area (EEA) countries. In Switzerland (neither an EU nor EEA

member), most large companies already began to adopt IFRS in early 1990s. As will be

further explained in Sect. 4, prior to 2005, some firms partially adopted IFRS, i.e. in form

of adopting international standards with some EU or IASC guidelines. To be conservative,

we treat these partial adopters as non-adopters in our study, and classify only such firms

that fully adopt IFRS as full IFRS adopters.11 Throughout the paper, full IFRS adopters are

referred to as IFRS adopters.

2.2 Voluntary IFRS and cost of equity capital

While mandatory IFRS adoption is a country-wide regulatory event that aims to enhance

the quality of public disclosure, voluntary IFRS adoption can be viewed as an individual

9 Countries participated in the agreement are Australia, Canada, France, Germany, Japan, Mexico, Neth-erlands, the UK/Ireland and the US.10 For example, in 2002, the IASB and the Financial Accounting Standards Board (FASB) embarked on ajoint program to make US GAAP and IFRS converge to the maximum extent (Schipper 2005). Foreignissuers in the US are allowed to use IFRS without reconciliation to US GAAP since 2007. Foreign issuers inCanada are permitted to use IFRS instead of Canadian GAAP. Also, the IFRS has been widely adopted inthe Asia–Pacific region. For example, Bangladesh requires companies listed on local stock exchanges toadopt IFRS. Some countries (Australia, Hong Kong and New Zealand) have changed their local standardsinto new standards that are virtually similar to IFRS. Other countries (e.g. Singapore, India, Malaysia,Thailand, etc.) have changed most parts of local standards that are basically the same word-for-word withIFRS.11 More detailed discussions on this issue are provided in Sects. 4.1 and 6.3.


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firm’s strategic commitment to better reporting strategies (Leuz and Verrecchia 2000; Covrig

et al. 2007; Kim et al. 2011; Kim and Shi 2012). When compared with local accounting

standards in most countries, IFRS is considered to be more fair-value-oriented and more

effective in deterring fraud than local accounting standards. IFRS can also incorporate the

effects of economic events into financial statements in a timelier manner than local standards

(Coopers and Lybrand 1993; Dumontier and Raffournier 1998; Alexander and Archer 2001;

Carmona and Trombetta 2008). Moreover, several studies provide evidence suggesting that

financial disclosures under IFRS are of higher quality than those under local GAAP in most

financial reporting regimes. In particular, this line of research finds that the IFRS adoption

leads to 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. 2005), better accounting quality (Barth et al. 2008; Agostino et al. 2011), greater

investment flows by attracting more foreign mutual funds (Covrig et al. 2007), more favorable

loan contracting terms (Kim et al. 2011), and lower stock price synchronicity (Kim and Shi

2012). While the studies mentioned above focus on the impact of IFRS adoption on disclosure

quality, another stream of research focuses on the association between the disclosure quantity

or level and a firm’ voluntary adoption of IFRS. For example, Ding et al. (2007) report that

IFRS requires more comprehensive disclosures than do most countries’ local standards.

Using a sample of Swiss firms, Dumontier and Raffournier (1998) report that compliance with

IFRS is costly because the IFRS adoption requires additional disclosures and renunciation of

discretion in accounting practices. In short, the findings of the above studies, taken together,

directly or indirectly suggest that voluntary IFRS adoption leads to greater and higher-quality

disclosures.

Economic theories show an inverse relation between the level and/or quality of public

disclosures and the cost of capital. First, Diamond and Verrecchia’s (1991) model reveals

that greater disclosures reduce the amount of information revealed by a large trade. This

alleviates the adverse price impact associated with such trades, which in turn contributes to

lowering the cost of capital. Second, the enhanced disclosures via voluntary IFRS adoption

can result in an increase in public information available to investors, and thus lower the

profitability of acquiring private information. This effect would in turn lessen the profit-

ability of informed trading on private information, which in turn reduces the cost of capital

(Easley and O’Hara 2004; Foucault and Gehrig 2008). Several studies provide empirical

evidence supporting the above theoretical predictions. For example, Botosan (1997) and

Botosan and Plumlee (2002) use an annual report disclosure score as a proxy for a firm’s

disclosure level and find a negative association between the cost of equity and the dis-

closure level. Botosan et al. (2004) show that the cost of equity capital is inversely

associated with the quality or precision of public information. Francis et al. (2005) use a

sample of firms from different industries to examine the association between the cost of

capital and disclosure level, and find that a higher disclosure level leads to a lower cost of

equity and debt financing. Cheng et al. (2006) find that high level of financial transparency

and better shareholder protection lower firm’s cost of equity capital. Zhang and Ding

(2006) find a negative relation between disclosure and cost of capital in China. Eaton et al.

(2007) show that increased disclosure through accounting standards can reduce informa-

tion asymmetry, reduce the cost of financing and increase analyst following.

Building upon the findings of previous studies that show a positive relation between

voluntary IFRS adoption and the disclosure level or quality and that establish an inverse

relation between the disclosure level or quality and the cost of equity capital, we predict a

negative association between voluntary IFRS adoption and the cost of equity capital. To

provide empirical evidence on this issue, we test the following hypothesis:


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H1 The cost of equity capital is lower for firms that voluntarily adopt IFRS than those

that do not, other things being equal.

2.3 Effects of voluntary IFRS adoption on cost of equity capital and legal institutions

While our first hypothesis (H1) is concerned with the effect of voluntary IFRS adoption on

the cost of equity capital, our second hypothesis (H2) below is concerned with the interplay

between the firm’s IFRS adoption and a country’s institutional infrastructure. To proxy for

the efficacy of institutional infrastructure, our analysis focuses on a country’s corporate

governance and enforcement mechanisms.

One stream of research (e.g., Ball 2001; Ball et al. 2003; Berkowitz et al. 2003) suggests

that the adoption of high-quality accounting standards is not sufficient to improve the

quality of accounting information unless a country’s enforcement mechanisms work

effectively and/or firms have incentives to voluntarily communicate high-quality infor-

mation to the market. For example, Ball et al. (2003) provide evidence suggesting that in the

absence of appropriate incentives or effective enforcement mechanisms, high-quality

standards themselves do not produce desirable economic consequences such as timelier and

more transparent financial disclosures. Recent studies by Francis et al. (2005), Burgstahler

et al. (2006), Hope et al. (2009), Hossain et al. (2010) and Elbannan (2011) provide con-

sistent evidence. For example, Hope et al. (2009) document that the required rate of return

increases with excess auditor remuneration but this relationship holds only in countries with

stronger investor protection. This result implies that strong country-level enforcement

mechanisms reinforce firm-level governance. In this reinforcement scenario, one may

expect that the effect of IFRS adoption on the cost of capital is likely to be greater when

IFRS adopters are from countries with strong governance and enforcement mechanisms

than when they are from countries with weak governance and enforcement mechanisms.

A second stream of research, however, predicts and finds that country-level and firm-

level governance mechanisms act as substitutes for each other. A major argument is that

strong external governance significantly reduces potential agency problems associated with

weak firm-level governance, and thus, that the effect of country-level (firm-level) gover-

nance is of first-order (second-order) importance (e.g. Doidge et al. 2004; Lel and Miller

2008; Chen et al. 2009; Leuz et al. 2010; Kim and Shi 2012). In such a case, the effect of

firm-level governance (e.g., a firm’s voluntary IFRS adoption) on firm performance or the

cost of capital will be weak (strong) for firms in countries with strong (weak) country-level

governance and enforcement mechanisms. For example, Chen et al. (2009) find that firm-

level governance is negatively associated with the cost of equity in emerging markets and

the aforesaid association is stronger in markets with weak legal protection of investors,

which is consistent with the substitution argument. Similarly, Lel and Miller (2008) find

that the effect of bonding by US listing is greatest for firms domiciled in the countries with

the weakest investor protections, which also implies a substitution effect between firm- and

country-level governance mechanisms. In this substitution scenario, one may expect that

the effect of IFRS adoption on the cost of equity is likely to be greater in countries with

weak governance and enforcement mechanisms than in countries with strong governance

and enforcement mechanisms.

Given the two opposing lines of argument, it is an empirical question whether and how

the cost of capital-reducing effect of IFRS adoption is conditioned upon the efficacy of a

country’s governance and enforcement mechanisms. To provide empirical evidence on this

unresolved issue, we test the following hypothesis with no prediction on the directional

effect:


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H2 The effect of IFRS adoption on the cost of equity capital differs between firms in

countries with strong institutional infrastructures and firms in countries with weak insti-

tutional infrastructures, other things being equal.

3 Empirical model

To test hypotheses H1 and H2, we construct a sample from 34 countries covering the

period of 1998–2004 (before the EU’s requirement for listed companies to use IFRS when

preparing consolidated financial statements became effective). Thus, all IFRS adoptions in

our sample are voluntary. For this reason, examining the effect of IFRS adoption on the

cost of equity in a single-equation regression context may suffer from problems of self-

selection bias and/or reverse causality to the extent that both cost of equity capital and

IFRS adoption are influenced by unobserved factors. To address this issue, we adopt the

Heckman (1979)-type, two-stage treatment-effect approach. In particular, we specify the

following two-stage models:

DIFRS ¼ b0 þ b1Sizeþ b2Leverageþ b3Growthþ b4ForSales

þ b5Crossþ ðYear DummiesÞ þ ðIndustry DummiesÞþ Country Dummiesð Þ þ error term;

ð1Þ

LCoEPEG ¼ a0 þ a1DIFRSþ a2Institutionþ a3DIFRS�Institution

þ a4USGAAPþ a5Growthþ a6Sizeþ a7EVar

þ a8Leverageþ a9ROAþ a10Inflationþ a11Lamda

þ Year Dummiesð Þ þ Industry Dummiesð Þ þ error term

ð2Þ

where

LCoEPEG Natural log of one plus a firm’s cost of equity capital;

DIFRS 1 for full IFRS adopters and 0 otherwise as recorded in Worldscope;

Size Firm size measured by natural log of total assets;

Leverage The ratio of short-term and long-term debts to total assets;

Growth Natural log of long-term earnings growth as recorded in the IBES file;

ForSales Percentage of foreign sales;

Cross 1 if a firm’s shares are traded on foreign exchanges and 0 otherwise

USGAAP 1 for US-GAAP adopters and 0 otherwise as recorded in Worldscope;

EVar Earnings variability measured by the dispersion in analysts’ forecasts of

earnings;

ROA The ratio of earnings before interest and taxes to total assets;

Inflation The consumer price index reflecting changes in the cost of acquiring a fixed

basket of goods and services by the average consumer;

Lamda Inverse Mills ratio obtained from the probit IFRS-adoption model in Eq. (1);

Institution A proxy for the efficacy of a country’s institutional infrastructure related to

country-level governance and enforcement mechanisms. As shown in

‘‘Appendix 1’’, a total of seven proxies are used in this study

In the first stage, we estimate the probit IFRS-choice model in Eq. (1) with DIFRS as the

dependent variable, and compute the inverse Mills ratio. In Eq. (1), we include Size,

Leverage and Growth because larger, less levered, and growing firms are more likely to


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adopt IFRS (Dumontier and Raffournier 1998; Barth et al. 2008). ForSales is included in

the IFRS-choice model because the demand for IFRS-based reporting may increase as a

firm is getting more exposure to the foreign product market (Dumontier and Raffournier

1998). We include Cross to capture the effect of firm’s exposure to foreign capital market

on IFRS adoption. (Dumontier and Raffournier 1998; Cuijpers and Buijink 2005). In

Eq. (1), we include Country Dummies to control for cross-country differences in the

demand for better reporting strategies. Year Dummies and Industry Dummies are included

to control for the year and industry fixed effects.

In the second stage, we estimate Eq. (2) after including the inverse Mills ratio as an

additional explanatory variable to control for potential self-selection biases associated with

firms self-selecting the reporting strategy via IFRS adoption. The dependent variable,

LCoE, represents the ex ante, expected cost of equity capital measured by the implied cost

of capital under a certain valuation model. To ensure a normal distribution of the

dependent variable, the implied cost of equity, we use the natural logarithm of one plus

CoE.12 Previous research suggests several methods for measuring the ex ante cost of equity

capital implied by alternative equity valuation models with different assumptions (Claus

and Thomas 2001; Gebhardt et al. 2001; Easton 2004; Ohlson and Juettner-Nauroth 2005).

Hail and Leuz (2006) shows that alternative CoE estimates from different methods are

highly correlated with each other and are similar within a reasonable range. Botosan and

Plumlee (2005) evaluate five measures of ex ante cost of equity13 and find that only the

cost of equity measured by the PEG model of Easton (2004) and by the DIV model of

Botosan and Plumlee (2002) have stable association with firm risk measures (i.e., market

risk, leverage, information risk, firm size and growth) and in theoretically consistent

direction. In addition, only the cost of equity measured by the PEG model of Easton (2004)

consistently demonstrates positive correlation with 1-year ahead average realized risk

premium. Therefore, in this study, our main analyses rely on the implied cost of equity

capital that is measured using the price-earnings-growth (PEG) model of Easton (2004).14

To mitigate the effect of potential measurement errors of one particular model on our

results, we use two alternative measures of CoE in sensitivity analyses, which will be

further explained in Sect. 6.5.

Under the Easton PEG model, a firm’s expected cost of equity capital, denoted by

CoEPEG, is derived as follows:

CoEPEG ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

E0ðeps2Þ � E0ðeps1ÞP0

s

ð3Þ

where E0 is the expectation operator; the subscript 0 denotes the year of expectation

formation; epst is earnings per share in year t; and P0 is the price at the beginning of

12 Some countries in our cross-country sample, such as Brazil, experience high inflation and/or returnvolatility during our sample period, which results in extreme values in our cost of equity estimates.13 The five measures include (1) rDIVPREM employed in Botosan and Plumlee (2002), (2) rGLSPREM

employed in Gebhardt et al. (2001), (3) rGORPREM employed in Gordon and Gordon (1997), (4) rOJNPREM

derived in Ohlson and Juettner-Nauroth (2005), and (5) rPEGPREM also derived in Ohlson and Juettner-Nauroth (2005).14 We understand that there is no consensus on the best measure of ex ante cost of equity. For example,Easton and Monahan (2005) conclude that the rPEG is the worst performer. However, as pointed out byBotosan and Plumlee (2005), Easton and Monahan (2005)’s ‘‘preferred’’ metric is negatively related to betaand standard deviation of returns, which is contrary to the theory and calls into questions the validity of themetric. Prior cross-country studies also use PEG model to estimate cost of equity, for example, Francis et al.(2005), Chen et al. (2009), etc.


123

year 0.15 As indicated in Eq. (3), the use of the Easton method only requires data on stock

prices and 1-year ahead and 2-year ahead earnings expectations, and thus can be easily

implemented for cross-country studies like our study (Francis et al. 2005). We use 1-year

ahead and 2-year ahead earnings forecasts as recorded in the IBES International file to

proxy for expected earnings per share in Eq. (3).

We include Institution to control for cross-country differences in institutional infra-

structures determining country-level governance and enforcement mechanisms. To proxy

for the efficacy of institutional infrastructures related to a country’s corporate governance,

we consider two institutional variables, which are a country’s legal origin (Law), the

degree of mandatory disclosure requirement (Disclosure) and one composite variable that

captures the strength of investor protection environment (InvPro). To proxy for the efficacy

of institutional infrastructures related to a country’s enforcement mechanisms, we consider

two proxies for a country’s auditing infrastructures, i.e., the easiness of auditors being sued

(AudSue) and of auditors being sanctioned (AudSanction) and one composite variable, i.e.,

the effectiveness of securities regulation (SecReg). All institutional variables are measured

in such a way that higher (lower) scores indicate strong (weak) institutional infrastructure.

‘‘Appendix 1’’ provides a detailed description of these institutional variables and their data

sources. Unlike Eq. (1) which includes Country Dummies, we do not include Country

Dummies in Eq. (2) so that Institution captures country-specific factors influencing the

implied cost of capital.

In Eq. (2), the coefficient on DIFRS captures the difference in the implied cost of equity

between IFRS adopters and non-adopters. A negatively significant coefficient on DIFRS

(i.e., a1 \ 0) is consistent with our first hypothesis (H1). To test our second hypothesis

(H2), we include an interaction term, DIFRS*Institution. A positive coefficient on DIF-

RS*Institution indicates that the CoE-reducing effect of IFRS adoption becomes weaker

(stronger) when firms is from countries with strong (weak) governance and enforcement

mechanisms, which is consistent with the substitution view on the relation between firm-

level and country-level governance mechanisms. A negative coefficient on DIFRS*Insti-

tution indicates that the CoE-reducing effect of IFRS adoption becomes stronger for firms

in countries with strong institutional infrastructures than firms in countries with weak

institutional infrastructures. The negative coefficient is consistent with the reinforcement

view that country-level governance reinforces firm-level governance in determining the

quality of information, the cost of capital, and/or firm performance.

Similar to the argument advanced by proponents of IFRS adoption, non-IFRS firms with

US GAAP adoption are expected to improve disclosure, and are likely to have a lower cost of

equity (Daske et al. 2012). We therefore include the USGAAP indicator to differentiate the

effect of US GAAP adoption from that of IFRS adoption. As in many other studies on the

determinants of cost of equity capital (e.g., Botosan et al. 2004; Hail and Leuz 2006), we

include five firm-specific control variables that are known to affect the ex ante cost of capital,

that is Growth, Size, EVar, Leverage, and ROA, in order to isolate the effect of IFRS adoption

on the ex ante cost of equity capital from the effect of other firm-specific characteristics. We

also include a country-specific variable, Inflation (in addition to the country-level Institution

variable), to control for macro-economic conditions. The inverse Mills ratio, Lamda, is

included to control for potential self-selection biases. Finally, we include Year Dummies and

Industry Dummies to control for variations in the cost of equity capital over years and across

industries. Based on analysis and evidence reported in previous research (Francis et al. 2005;

15 Two important assumptions underlying the Easton formula are: (1) there is no change in abnormalearnings beyond the forecast horizon; and (2) there are no dividend payments prior to the earnings forecasts.


123

Hail and Leuz 2006; Botosan and Plumlee 2007), we predict positive (negative) coefficients

on Growth, EVar, Leverage and Inflation (Size and ROA).

4 Sample and descriptive statistics

4.1 Sample and data sources

The initial list of our sample consists of all non-US and non-Canadian firms that are

included in three databases Worldscope, IBES International, and Datastream during the

sample period of 1998–2004.16 Information about a firm’s IFRS adoption is obtained from

Worldscope. Worldscope has a data field 07536 which identifies accounting standards

followed by a specific firm. ‘‘Appendix 2’’ offers a detailed description of field 07536 as

recorded in Worldscope. We identify a firm as a full IFRS adopter if Worldscope indicates

that it adopts IAS (data field 07536 = 02) or IFRS (07536 = 23). This approach of

classification is different from Daske et al. (2012) which classify both full IFRS adoption

(07536 = 02 or 23) and partial IFRS adoption (international standards with some EU or

IASC/IASB guidelines, i.e., 07536 = 06, 08, 12, 16, 18 or 19) as IFRS adopters.

Our main analyses are based on the stricter classification of full IFRS adopters (07536 = 02 or

23) versus non-adopters for two major reasons: First, among the 23 classifications of data field

07536, only 02 and 23 unambiguously states a firm’s reporting strategy as IAS and IFRS,

respectively, implying a full IFRS adoption.17 Second, Daske et al. (2012) compare three data

sources of IFRS classification (Worldscope, Global Vantage and manual review of firms’ annual

report) and find that when the full IFRS adoption classification (07536 = 02 or 23, Global

Vantage data field ASTD = DI) is used, the contradiction among these databases is minimal. Our

approach is more conservative than Daske et al. (2012)’s in the following sense: to the extent that

both the partial and full adoptions yield identical or similar effects of IFRS adoption on reducing

the cost of capital, our approach of treating the partial adoptions as non-adoption introduces a

conservative bias relative to the Daske et al. (2012) approach in which partial adopters are treated

as full adopters.

We exclude firms in the banking, insurance and other financial industries from the sample (the

Worldscope general industry classifications 4, 5 and 6). We require that data on earnings forecasts

and stock prices necessary to compute the implied cost of capital be available from the IBES

International and Datastream databases, respectively. We delete firms from our sample if data

required to measure all firm-specific control variables are not available from the three databases.

After applying the above selection criteria, we obtain a sample of 21,608 firm-year

observations from 34 countries during 1998–2004. Panel A of Table 1 presents the dis-

tribution of our sample by country. The first column reports the number of IFRS adopters.

As shown in Table 1, our sample includes 1,327 firm-years with IFRS adoptions that

constitute about 6.14 % of total observations of 21,608 in our sample.18 Among 34

16 Year 1998 is chosen as the starting point because few IFRS adopters are identified before 1998. Oursample period ends at the end of 2004, because all listed firms in EU member countries are mandated toadopt IFRS starting January 2005. Therefore, our sample does not include firms with mandated IFRSadoptions.17 In Sect. 6.3, we conduct a sensitivity test based on Daske et al. (2012) classification of IFRS adopters.18 The size of our total sample and the percentage of IFRS adopters in our sample are comparable withthose in the sample of Covrig et al. (2007). They use a total sample of 24,592 firm-years with both IFRSadopters and non-adopters from the 1992–2002 period from 29 countries. In their total sample, the per-centage of IFRS adopters is 5% (See their Table 1).


123

countries in our sample, more than half of the sample firms from Austria, Hungary, Russian

and Switzerland are the IFRS adopters, while no firm from ten countries in our sample

adopts IFRS.19 Panel B of Table 1 describes the yearly distribution of the 1,327 IFRS

adopters and 20,281 non-adopters. As shown in Panel B, both the number and the per-

centage of IFRS adopters increase steadily over years, reflecting an increasing trend of

IFRS adoptions in many countries around the world.

Panel C of Table 1 reports data on all institutional variables considered in this paper. As

mentioned in Sect. 3, we use three institutional infrastructure variables, i.e., Law, Disclosure

and InvPro to proxy for the efficacy of a country’s governance mechanisms. Law is a binary

variable that is set equal to one if firms are from countries with common law origin and zero

otherwise. As in many other studies, we view common (code) law countries as being associated

with more (less) effective governance mechanisms. Data on Law are extracted from La Porta

et al. (1998). Disclosure refers to a disclosure requirement index that is developed to capture the

strength of a country’s stock exchange-mandated disclosures. The index ranges from 0 to 1,

with higher values indicating more extensive disclosure requirements (and therefore better

governance mechanisms). Data on Disclosure are extracted from La Porta et al. (2006). InvPro

is a composite score created by combining three indices which capture a country’s investor

protection environment, i.e. anti-director rights (AntiDir), efficiency of judicial system (EffJud)

and rule of law (LawRule). As explained in ‘‘Appendix 1’’, these three indices are measured

with different ranges. We therefore transform the original scores into percentage ranks and then

calculate the arithmetic mean of the ranks. Data on the three indices (AntiDir, EffJud and

LawRule) are obtained from La Porta et al. (1998, 2002).

The efficacy of a country’s enforcement mechanism is proxied by two auditing variables, i.e.,

AudSue and AudSanction, which capture the soundness of a country’s auditing infrastructure

(Guedhami and Pittman 2006), and one composite variable, SecReg, which measures the

effectiveness of securities regulation. AudSue is an index of the procedural difficulty in recov-

ering losses from the auditor in a civil liability case for losses due to misleading statements in the

audited financial information accompanying the prospectus, with higher values associated with

better enforcement mechanisms. AudSanction is an index of criminal sanctions applicable to the

auditor (or its officers) when the financial statements accompanying the prospectus omit material

information, with higher values representing better enforcement mechanisms. In this paper, we

assume that in countries with poor (good) auditing infrastructure countries, accounting and

auditing standards are less (more) likely to be enforced in a manner prescribed by accounting

standards. Data on AudSue and AudSanction are provided by The World Bank. SecReg is a

composite score incorporating three indices: (1) a disclosure requirement index (Disclosure); (2)

a liability standard index (LiabStd); and (3) a public enforcement index (PubEnf). As explained

in ‘‘Appendix 1’’, all three indices ranges from 0 to 1 with a higher (lower) score indicating that

investor have a better (poorer) enforcement mechanism. Our composite index, SecReg, is an

arithmetic mean of the three scores. As such, countries with higher scores of SecReg are more

likely to have a more effective securities regulation and a better functioning enforcement

mechanism in place.20 Data on LiabStd and PubEnf are also extracted from La Porta et al. (2006).

19 The inclusion of observations from these ten countries with no IFRS adopters into our sample is con-sistent with Covrig et al. (2007) and Kim et al. (2011). As will be further explained in Sect. 6.9, we also re-estimate our main regressions after excluding observations from these ten countries and find that theirexclusion does not alter our statistical inferences on the variables of interest.20 We follow Hail and Leuz (2006) approach to calculate SecReg. They argue that it is appropriate toconstruct the comprehensive measure in such a way because ‘‘the various enforcement mechanisms con-sidered by La Porta et al. (2006) could be substitutes……by aggregating the enforcement indices into acomprehensive measure we allow for substitution among the various enforcement mechanisms’’.


123

Table 1 Sample distribution

Country Number of

adopters

Number of

non-adopters

Total number

of observations

% of adopters

Panel A: Distribution of sample firms by country

Argentina 0 81 81 0.00

Australia 3 1,098 1,101 0.27

Austria 70 37 107 65.42

Belgium 38 194 232 16.38

Brazil 0 367 367 0.00

Czech Republic 10 17 27 37.04

Denmark 24 260 284 8.45

Finland 17 311 328 5.18

France 19 1,250 1,269 1.50

Germany 515 810 1,325 38.87

Greece 10 333 343 2.92

Hong Kong 34 753 787 4.32

Hungary 55 23 78 70.51

India 0 626 626 0.00

Ireland 0 119 119 0.00

Italy 2 409 411 0.49

Japan 0 5,241 5,241 0.00

Luxembourg 11 32 43 25.58

Malaysia 4 684 688 0.58

Mexico 0 225 225 0.00

Netherlands 20 491 511 3.91

New Zealand 0 185 185 0.00

Norway 0 262 262 0.00

Poland 19 55 74 25.68

Portugal 3 85 88 3.41

Russian Federation 18 15 33 54.55

Singapore 19 587 606 3.14

South Africa 29 385 414 7.00

Spain 3 290 293 1.02

Sweden 5 580 585 0.85

Switzerland 398 276 674 59.05

Taiwan 0 739 739 0.00

Thailand 0 427 427 0.00

United Kingdom 1 3,034 3,035 0.03

Total 1,327 20,281 21,608 6.14

Years Number

of adopters

Number of

non-adopters

Total number

of observations

% of adopters

Panel B: Distribution of sample firms by year

1998 65 2,315 2,380 2.73

1999 87 1,924 2,011 4.33

2000 155 2,504 2,659 5.83

2001 218 3,260 3,478 6.27


123

Table 1 continued

Years Number

of adopters

Number of

non-adopters

Total number

of observations

% of adopters

2002 238 3,335 3,573 6.66

2003 253 3,365 3,618 6.99

2004 311 3,578 3,889 8.00

Total 1,327 20,281 21,608 6.14

Country Law Disclosure InvPro AudSue AudSanction SecReg

Panel C: Country level corporate governance scores

Argentina 0 0.5 30.38 0.33 0 0.43

Australia 1 0.75 92.45 0.66 0.5 0.77

Austria 0 0.25 52.53 0 0.5 0.18

Belgium 0 0.42 45.80 0.66 1 0.34

Brazil 0 0.25 17.71 0.33 0 0.39

Czech Republic 0 N/A N/A N/A N/A N/A

Denmark 0 0.58 73.76 1 0 0.5

Finland 0 0.5 79.12 0.66 0.5 0.49

France 0 0.75 45.30 0.33 0.5 0.58

Germany 0 0.42 41.65 0 0.5 0.21

Greece 0 0.33 15.02 0.33 0.5 0.38

Hong Kong 1 0.92 73.49 0.66 1 0.82

Hungary N/A N/A N/A N/A N/A N/A

India 1 0.92 42.56 0.66 1 0.75

Ireland 1 0.67 39.81 0.66 1 0.49

Italy 0 0.67 15.62 0.33 0.5 0.46

Japan 0 0.75 83.71 0.66 0 0.47

Luxembourg N/A N/A N/A N/A N/A N/A

Malaysia 1 0.92 42.30 0.66 1 0.78

Mexico 0 0.58 8.00 0.33 0.5 0.35

Netherlands 0 0.5 73.76 1 0.5 0.62

New Zealand 1 0.67 92.45 0.66 0 0.48

Norway 0 0.58 92.45 0.66 1 0.43

Poland 0 N/A N/A N/A N/A N/A

Portugal 0 0.42 27.70 0.66 0 0.55

Russian N/A N/A N/A N/A N/A N/A

Singapore 1 1 73.41 0.66 1 0.84

South Africa 1 0.83 37.46 0.66 0.5 0.58

Spain 0 0.5 34.33 0.66 0.5 0.50

Sweden 0 0.58 79.12 0.33 0.5 0.45

Switzerland 0 0.67 73.76 0.66 0.5 0.48

Taiwan 0 0.75 25.83 0.66 1 0.64

Thailand 1 0.92 11.06 0 1 0.62

UK 1 0.83 80.97 0.66 0.5 0.72

Panel A reports the sample distribution by country. Panel B reports the sample distribution by year. In Panels A and B,

column 1 reports the number of firm-year observations of IFRS adopters. Column 2 reports the firm-year observations of

non-adopters. Column 3 reports the total number of firm-year observations. Column 4 reports the percentage of IFRS

adopters. Panel C reports the country level governance scores. The definition of the country level governance variables is

provided in ‘‘Appendix 1’’


123

As shown in Panel C of Table 1, our sample includes ten common law countries. Out of

34 countries in our sample, Singapore has the highest Disclosure score of 1, while Austria

and Brazil have the lowest score of 0.25. Three countries, Australia, New Zealand and

Norway have the highest score of InvPro. Regarding the auditing infrastructure variables,

half of the sample countries have the highest score of 0.66 for AudSue, while Austria,

Germany and Thailand have a zero score. Nine countries have the highest possible score of

1 for AudSanction, while six countries have the lowest possible scores of 0. Hong Kong

and Singapore have high SecReg scores which are larger than 0.8. Note that both countries

have an English common law origin. Austria is associated with a lowest SecReg score of

0.18. Overall, the information presented in Panel C of Table 1 reveals that the (country-

level) institutional variables we consider are highly correlated with each other, though

there is a reasonable variation across countries. As will be further explained in the next

section, we therefore include these institutional variables into our regression one by one to

avoid potential multicollinearity problems.

4.2 Descriptive statistics and univariate tests

Panel A of Table 2 presents descriptive statistics for the variables included in Eqs. (1) and

(2) for the full sample. As shown in Section A, LCoEPEG is reasonably distributed with a

mean (median) value of 0.183 (0.120). Sections B and C report the descriptive statistics for

our control variables and proxies for Institution. As can be seen from Sections B and C,

most variables are reasonably distributed. The mean values of Cross and USGAAP indicate

that about 27 % of our sample firms have their shares traded on foreign stock exchanges,

and about 3 % of our non-adopter samples use US GAAP. The distributions of the

Institution variables are in general skewed, though their standard deviations relative to

their mean values suggest that there is a reasonable level of cross-country variations in

these variables.

Panel B of Table 2 reports the mean, median and standard deviation of all the variables,

presented separately for the IFRS adopter sample and the non-adopter sample. We perform

univariate tests for the mean and median differences between the IFRS adopter and non-

adopter samples. As shown in Section A of Panel B, the mean (median) values of LCoEPEG

are 0.166 and 0.124 (0.184 and 0.120) for the IFRS adopter sample and the non-adopter

sample, respectively. The t test shows that LCoE–PEG is significantly lower for IFRS

adopters than for non-adopters. This is consistent with hypothesis H1, suggesting that the

IFRS adoption is associated with a significant reduction in a firm’s cost of equity capital.

As seen in Section B of Panel B, IFRS adopters are larger in size, less levered, have higher

growth potentials, more exposed to foreign product markets, more likely to have their

shares traded on foreign stock exchanges and less profitable, compared with non-adopters.

About 3 % of firms in the non-adopter sample use US GAAP as the reporting strategy. The

mean EVar difference between the adopter and non-adopter samples is insignificant,

though the median difference is significant, suggesting that earnings are more volatile for

IFRS adopters than for non-adopters. On average, IFRS adopters are more likely to come

from high inflation countries than non-adopters.

Section C of Panel B of Table 2 indicates that IFRS adopters have lower scores on Law,

Disclosure and InvPro than non-adopters. With regard to the institutional infrastructure

related to enforcement mechanisms, both the mean and median values of AudSue and

SecReg are lower for IFRS adopters than for non-adopters while the mean and median

values of AudSanction are higher for IFRS adopters than for non-adopters. The univariate

results in general suggest that firms in countries with weak institutional infrastructures are


123

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dif

fere

nce

bet

wee

nth

eIF

RS

ado

pte

rsan

dn

on

-ad

op

ters

.T

he

t-v

alu

ete

sts

the

dif

fere

nce

of

the

mea

nv

alu

eb

etw

een

the

two

gro

ups,

and

the

z-v

alu

ete

sts

the

dif

fere

nce

of

the

med

ian

val

ue

bet

wee

nth

etw

og

rou

ps.

Her

e*

,*

*,

and

**

*in

dic

ate

the

10

,5

,an

d1

%le

vel

so

fsi

gn

ifica

nce

,re

spec

tiv

ely

,fo

ra

two

-tai

led

test


123

more likely to adopt IFRS than those in countries with strong institutional infrastructures,

which is in line with the substitution view that firm-level governance and country-level

governance act as substitutes for each other.

Table 3 presents Pearson correlations and indicates the following. First, LCoEPEG is

negatively correlated with DIFRS, consistent with hypothesis H1. LCoEPEG is significantly

correlated with the firm-specific control variables, USGAAP, Growth, Size, EVar, Lever-

age, ROA and Inflation, suggesting a need to control for these variables when examining

the effect of IFRS adoption on the cost of equity capital. Second, DIFRS is positively

(negatively) correlated with Size, Growth, ForSales and Cross (Leverage), suggesting that

the demand for a better reporting strategy via IFRS adoption is greater for larger, high-

growth, less levered, cross-listed firms and firms having more foreign sales. Finally, as

expected, the coefficients of pair-wise correlation among the institutional variables are in

general quite high, suggesting that the inclusion of these variables together in the

regression may potentially create multicollearity problems.21

5 Empirical results

Panel A of Table 4 reports the results of the first-stage probit IFRS-adoption model in

Eq. (1). Consistent with our expectation, we find that the coefficients on Size, ForSales and

Cross are highly significant with a positive sign, while the coefficient on Leverage is

significantly negative, suggesting that the demand for a better reporting strategy via IFRS

is greater for larger, less levered, and cross-listed firms as well as firms with more

exposures to foreign product markets. The coefficient on Growth is positive, but insig-

nificant. The pseudo R2 of the model is quite high (about 54 %), suggesting that our probit

model explains a significant portion of a firm’s decision to adopt IFRS.

Panel B of Table 4 reports the results of the second-stage OLS regression. To address a

concern over potential serial correlation problems associated with unbalanced panel data,

we compute and report all t-values using robust standard errors adjusted for clustering at

the firm level. Column 1 presents the results using the full sample of 21,608 firm-years

with both IFRS adopters and non-adopters over the period 1998–2004. As shown in col-

umn 1 of the table, when Eq. (2) is estimated with no institutional variable included, the

coefficient on DIFRS is significantly negative with a magnitude of 0.029, which is con-

sistent with H1.

We then estimate Eq. (2) with inclusion of the institutional variables one by one. In so

doing, we exclude, from the full sample, such firms from countries with no country-level

score being available for a particular institutional variable considered. As such, the number

of firm-year observations varies across columns 2 to 7. As shown in columns 2 to 7, when

Institution and its interaction with DIFRS (i.e., DIFRS*Institution) are included, the

coefficient on DIFRS is highly significant with an expected negative sign across all cases

except when INST = AudSanction. The above results support hypothesis, H1, suggesting

that the implied cost of equity capital is significantly lower for full IFRS adopters than for

non-adopters, incremental to country’s institutional infrastructure.22

21 The unreported Spearman correlation shows qualitatively similar correlation.22 When we estimate Eq. (2) without DIFRS*Institution, the coefficient on DIFRS remains significantacross cases except when INST = AudSanction, while the coefficient on Institution are significantly negativeacross all cases.


123

Tab

le3

Pea

rso

nco

rrel

atio

nm

atri

x

Var

iable

LC

oE

PE

GD

IFR

SSiz

eL

ever

age

Gro

wth

ForS

ale

sC

ross

US

-

GA

AP

EV

ar

RO

AIn

flati

on

Law

Dis

clo

-

sure

InvP

roA

udSue

AudSanct

ion

DIF

RS

-0.0

17

(0.0

12)

Siz

e-

0.0

77

(\0.0

01)

0.0

34

(\0.0

01)

Lev

erage

0.1

02

(\0.0

01)

-0.0

16

(0.0

17)

0.2

95

(\0.0

01)

Gro

wth

0.1

46

(\0.0

01)

0.0

29

(\0.0

01)

-0.1

97

(\0.0

01)

0.0

50

(\0.0

01)

ForS

ale

s-

0.0

82

(\0.0

01)

0.1

27

(\0.0

01)

0.1

72

(\0.0

01)

0.0

38

(\0.0

01)

-0.0

52

(\0.0

01)

Cro

ss0.0

49

(\0.0

01)

0.1

57

(0.0

01)

0.3

11

(\0.0

01)

0.1

00

(\0.0

01)

-0.0

10

(0.1

61)

0.0

67

(\0.0

01)

USG

AA

P-

0.0

12

(0.0

82)

-0.0

46

(\0.0

01)

0.0

78

(\0.0

01)

-0.0

23

(\0.0

01)

0.0

57

(\0.0

01)

0.0

44

(\0.0

01)

0.1

43

(\0.0

01)

EV

ar

0.2

87

(\0.0

01)

-0.0

07

(0.3

30)

0.1

50

(\0.0

01)

0.0

15

(0.0

23)

-0.0

27

(\0.0

01)

-0.0

55

(\0.0

01)

0.1

13

(\0.0

01)

-0.0

07

(0.2

86)

RO

A-

0.1

67

(\0.0

01)

-0.0

23

(\0.0

01)

0.0

61

(\0.0

01)

-0.1

73

(\0.0

01)

-0.2

66

(\0.0

01)

0.0

21

(0.0

02)

-0.0

50

(\0.0

01)

-0.0

70

(\0.0

01)

-0.0

22

(0.0

02)

Inflati

on

0.1

16

(\0.0

01)

0.0

18

(0.0

08)

-0.0

49

(\0.0

01)

0.0

26

(\0.0

01)

0.0

19

(0.0

05)

-0.0

77

(\0.0

01)

-0.1

02

(\0.0

01)

-0.0

21

(\0.0

01)

-0.0

69

(\0.0

01)

0.0

31

(\0.0

01)

Law

-0.1

18

(\0.0

01)

-0.1

54

(\0.0

01)

-0.2

01

(\0.0

01)

-0.0

68

(\0.0

01)

-0.0

81

(\0.0

01)

0.1

26

(\0.0

01)

-0.2

72

(\0.0

01)

-0.1

21

(\0.0

01)

-0.2

60

(\0.0

01)

0.1

00

(\0.0

01)

0.2

48

(\0.0

01)

Dis

-

closu

re

-0.2

55

(\0.0

01)

-0.2

62

(\0.0

01)

-0.0

70

(\0.0

01)

-0.0

71

(\0.0

01)

-0.0

82

(\0.0

01)

0.1

04

(\0.0

01)

-0.2

09

(\0.0

01)

-0.1

80

(\0.0

01)

-0.1

15

(\0.0

01)

0.1

01

(\0.0

01)

-0.1

87

(\0.0

01)

0.6

39

(\0.0

01)

InvP

ro-

0.2

22

(\0.0

01)

-0.0

84

(\0.0

01)

0.0

04

(0.5

18)

-0.0

60

(\0.0

01)

-0.0

82

(\0.0

01)

0.1

43

(\0.0

01)

0.0

33

(\0.0

01)

-0.0

45

(\0.0

01)

0.0

26

(\0.0

01)

-0.0

56

(\0.0

01)

-0.3

43

(\0.0

01)

0.1

33

(\0.0

01)

0.2

99

(\0.0

01)

AudSue

-0.1

38

(\0.0

01)

-0.2

42

(\0.0

01)

0.0

38

(\0.0

01)

-0.0

18

(0.0

07)

-0.1

03

(\0.0

01)

0.0

97

(\0.0

01)

-0.1

09

(\0.0

01)

-0.1

94

(\0.0

01)

0.0

46

(\0.0

01)

0.0

81

(\0.0

01)

-0.1

08

(\0.0

01)

0.2

09

(\0.0

01)

0.4

02

(\0.0

01)

0.5

68

(\0.0

01)

Aud-

Sanct

ion

-0.0

65

(\0.0

01)

0.0

48

(\0.0

01)

-0.2

09

(\0.0

01)

-0.0

42

(\0.0

01)

0.0

15

(0.0

24)

0.0

82

(\0.0

01)

-0.2

96

(\0.0

01)

-0.0

30

(\0.0

01)

-0.3

12

(\0.0

01)

0.1

04

(\0.0

01)

0.1

79

(\0.0

01)

0.5

12

(\0.0

01)

0.2

75

(\0.0

01)

-0.1

47

(\0.0

01)

-0.1

19

(\0.0

01)


123

Tab

le3

con

tin

ued

Var

iable

LC

oE

PE

GD

IFR

SSiz

eL

ever

age

Gro

wth

ForS

ale

sC

ross

US

-

GA

AP

EV

ar

RO

AIn

flati

on

Law

Dis

clo

-

sure

InvP

roA

udSue

AudSanct

ion

Sec

Reg

-0.1

47

(\0.0

01)

-0.2

88

(\0.0

01)

-0.1

48

(\0.0

01)

-0.0

58

(\0.0

01)

-0.0

86

(\0.0

01)

0.1

33

(\0.0

01)

-0.3

33

(\0.0

01)

-0.2

19

(\0.0

01)

-0.2

15

(\0.0

01)

0.1

22

(\0.0

01)

0.0

96

(\0.0

01)

0.8

01

(\0.0

01)

0.7

80

(\0.0

01)

0.2

23

(\0.0

01)

0.5

19

(\0.0

01)

0.4

91

(\0.0

01)

The

var

iable

san

ddat

aso

urc

esar

edes

crib

edin

‘‘A

ppen

dix

1’’

.T

he

pval

ues

are

pre

sente

din

par

enth

eses


123

Tab

le4

Res

ult

so

ftw

o-s

tag

ere

gre

ssio

ns

of

cost

of

equ

ity

cap

ital

(LC

oE

PE

G)

on

IFR

Sad

opti

on,

inst

ituti

onal

infr

astr

uct

ure

and

oth

erdet

erm

inan

ts

Pre

d.

Sig

nC

oef

fici

ent

t- val

ue

Pan

elA

:R

esult

sof

the

firs

t-st

age

pro

bit

regre

ssio

no

fIF

RS

adopti

on

on

its

det

erm

inan

ts

Siz

e?

0.0

52

**

*4

.10

Lev

erage

–-

0.3

16

**

-2

.54

Gro

wth

?0

.026

1.3

1

Fo

rSa

les

?0

.971

**

*1

0.7

0

Cro

ss?

0.2

56

**

*5

.35

Inte

rcep

t?

-7

.826

-0

.00

Yea

rD

um

mie

sIn

clu

ded

Ind

ust

ryD

um

mie

sIn

clu

ded

Co

un

try

Du

mm

ies

Incl

uded

N2

1,6

08

Pse

ud

oR

25

4.0

4%

Pre

d.

Sig

n1 IN

ST

excl

uded

2 INS

T=

La

w3 IN

ST

=D

iscl

osu

re4 IN

ST

=In

vPro

5 INS

T=

Au

dS

ue

6 INS

T=

Au

dS

an

ctio

n7 IN

ST

=S

ecR

eg

Pan

elB

:R

esult

so

fth

ese

cond-s

tage

OL

Sre

gre

ssio

nof

cost

of

equit

yca

pit

al(L

Co

EP

EG

)o

nIF

RS

ado

pti

on

,in

stit

uti

on

alin

fras

tru

ctu

rean

do

ther

det

erm

inan

ts

Pa

nel

A:

Tes

tva

riab

les

DIF

RS

–-

0.0

29

**

(-2

.40)

-0

.020

*(-

1.8

8)

-0

.26

1*

**

(-8

.59)

-0

.111

**

*(-

4.9

7)

-0

.116

**

*(-

8.1

2)

-0

.008

(-0

.41

)-

0.1

02

**

*(-

6.1

4)

Inst

ituti

on

(IN

ST

)–

-0

.044

**

*(-

8.7

5)

-0

.35

4*

**

(-1

1.2

5)

-0

.002

**

*(-

17

.19

)-

0.1

70

**

*(-

12

.67

)-

0.0

03

(-0

.36

)-

0.1

76

**

*(-

10

.02

)

DIF

RS*In

stit

uti

on

?0

.06

2*

**

(3.1

5)

0.2

25

**

*(5

.79

)0

.001

**

*(3

.10

)0

.109

**

*(6

.16

)0

.047

(1.4

3)

0.1

03

**

*(3

.08

)


123

Tab

le4

con

tin

ued

Pre

d.

Sig

n1 IN

ST

excl

ud

ed2 IN

ST

=L

aw

3 INS

T=

Dis

closu

re4 IN

ST

=In

vPro

5 INS

T=

Au

dS

ue

6 INS

T=

Au

dSa

nct

ion

7 INS

T=

Sec

Reg

Pa

nel

B:

Co

ntr

ol

vari

ab

les

US

GA

AP

–-

0.0

02

(-0

.25)

-0

.012

(-1

.56)

-0

.040

**

*(-

4.4

9)

-0

.011

(-1

.48)

-0

.032

**

*(-

3.6

7)

-0

.00

6(-

0.8

0)

-0

.026

**

*(-

3.1

0)

Gro

wth

?0

.019

**

*(8

.39

)0

.01

7*

**

(7.7

8)

0.0

17

**

*(7

.58

)0

.015

**

*(7

.00

)0

.017

**

*(7

.81

)0

.01

9*

**

(8.3

9)

0.0

18

**

*(7

.78

)

Siz

e–

-0

.01

8*

**

(-1

1.7

6)

-0

.020

**

*(-

13

.17

)-

0.0

19

**

*(-

12

.77

)-

0.0

18

**

*(-

12

.06

)-

0.0

17

**

*(-

11

.28

)-

0.0

18

**

*(-

12

.12

)-

0.0

20

**

*(-

13

.08

)

EV

ar

?0

.022

**

*(1

2.0

7)

0.0

22

**

*(1

1.9

4)

0.0

21

**

*(1

2.3

5)

0.0

23

**

*(1

2.5

9)

0.0

23

**

*(1

2.6

3)

0.0

23

**

*(1

2.7

2)

0.0

22

**

*(1

1.8

0)

Lev

erage

?0

.141

**

*(8

.38

)0

.13

5*

**

(8.1

8)

0.1

21

**

*(7

.49

)0

.119

**

*(7

.40

)0

.130

**

*(7

.95

)0

.13

6*

**

(8.1

8)

0.1

32

**

*(8

.03

)

RO

A–

-0

.22

0*

**

(-1

2.4

6)

-0

.209

**

*(-

11

.97

)-

0.1

96

**

*(-

11

.14

)-

0.2

57

**

*(-

14

.00

)-

0.2

11

**

*(-

12

.00

)-

0.2

16

**

*(-

11

.98

)-

0.2

03

**

*(-

11

.55

)

Infl

ati

on

?0

.010

**

*(5

.79

)0

.01

5*

**

(7.3

0)

0.0

09

**

*(5

.99

)0

.007

**

*(4

.29

)0

.012

**

*(6

.58

)0

.01

4*

**

(6.7

0)

0.0

15

**

*(7

.19

)

La

mda

?0

.017

**

(2.1

0)

0.0

06

(0.7

6)

0.0

72

**

*(6

.41

)0

.026

**

*(3

.75

)0

.041

**

*(4

.76

)-

0.0

10

(-1

.46)

0.0

32

**

*(3

.49

)

Inte

rcep

t?

0.3

05

**

*(2

2.4

1)

0.3

19

**

*(2

3.5

0)

0.5

73

**

*(2

6.0

6)

0.4

44

**

*(3

2.3

2)

0.3

92

**

*(3

0.1

1)

0.2

99

**

*(2

2.4

7)

0.4

05

**

*(2

6.7

5)

Yea

rD

um

mie

sIn

clu

ded

Incl

ud

edIn

clu

ded

Incl

uded

Incl

uded

Incl

ud

edIn

clu

ded

Ind

ust

ryD

um

mie

sIn

clu

ded

Incl

ud

edIn

clu

ded

Incl

uded

Incl

uded

Incl

ud

edIn

clu

ded

N2

1,6

08

21

,45

42

1,3

53

21

,35

32

1,3

53

21

,35

32

1,3

53

R2

17

.07

%1

8.6

6%

22

.15

%2

1.6

5%

19

.85

%1

8.2

4%

19

.13

%

Th

ev

aria

ble

san

dd

ata

sou

rces

are

des

crib

edin

‘‘A

pp

endix

1’’

.H

ere

*,

**,

and

***

indic

ate

the

10,

5,

and

1%

level

sof

signifi

cance

,re

spec

tivel

y,

for

atw

o-t

aile

dte

st.

All

repo

rted

t-st

atis

tics

are

bas

edo

nst

and

ard

erro

rsad

just

edfo

rcl

ust

erin

gat

the

firm

lev

el.M

odel

1d

oes

no

tin

clu

de

inst

itu

tio

nal

var

iab

les;

Mo

del

2u

ses

La

was

the

inst

itu

tio

nal

var

iab

le;

Mo

del

3u

ses

Dis

clo

sure

asth

ein

stit

uti

on

alv

aria

ble

;M

odel

4u

ses

InvP

roas

the

inst

itu

tio

nal

var

iab

le;

Mo

del

5u

ses

Au

dSu

eas

the

inst

itu

tio

nal

var

iab

le;

Mo

del

6u

ses

Au

dSa

nct

ion

asth

ein

stit

uti

on

alv

aria

ble

;M

odel

7u

ses

Sec

Reg

asth

ein

stit

uti

on

alv

aria

ble

.In

all

reg

ress

ion

sw

ith

aco

un

try

-lev

el,in

stit

uti

on

alv

aria

ble

,fi

rms

are

del

eted

from

the

sam

ple

ifa

countr

y’s

score

for

each

inst

ituti

onal

var

iable

are

not

avai

lable


123

The coefficient on Institution is highly significant with a negative sign across all cases

except when INST = AudSanction. This indicates that firms in countries with strong insti-

tutional infrastructures raise equity funds at a significantly lower cost, compared with firms in

countries with weak infrastructures. This finding is consistent with previous research (e.g.,

Hail and Leuz 2006). More importantly, the coefficient on the interaction term, DIF-

RS*Institution, is significantly positive in all estimation models except when INST = Aud-

Sanction.23 This result supports hypothesis H2 that the CoE-reducing effect of IFRS adoption

differs systematically between countries with strong and weak institutions. The significantly

positive coefficient on DIFRS*Institution can be interpreted in such a way that the effect of

IFRS adoption on lowering the cost of equity capital is significantly greater for firms in

countries with weak institutional infrastructures than for firms with strong infrastructures.

This result is consistent with the substitution (as opposed to reinforcement) view that country-

level governance and enforcement mechanisms such as the disclosure regulation is a sub-

stitute for firm-level governance mechanisms such as a firm’s voluntary adoption of IFRS.

With respect to the sign and significance of firm-specific control variables, namely

USGAAP, Growth, Size, EVar, Leverage, ROA and Inflation, our results are consistent with

the findings of previous research. The coefficient on USGAAP is negative and significant in

three out of seven cases (when INST = Disclosure, AudSue and SecReg), providing some

evidence that the adoption of US GAAP lowers the cost of equity. The coefficients on

Growth, EVar, and Leverage are significantly positive, suggesting that equity investors

demand higher returns on their investment for higher growth, greater earnings variability,

and more levered firms. The coefficients on Size and ROA are significantly negative,

indicating that the equity market considers large and profitable firms as having a lower level

of risk, and thus demands lower returns. The significantly positive coefficient on Inflation

shows that investors from high inflation countries demand a high return on their investment.

The coefficient on Lamda is significant in all the cases except when INST = Law and

AudSanction, suggesting that there is a need to correct for self-selection bias.

In short, consistent with our hypothesis H1, the coefficient on DIFRS is significantly

negative across all cases except when INST = AudSanction, suggesting that voluntary

IFRS adoption is associated with a lower cost of equity capital, irrespective of a country’s

institutional infrastructures. In addition, we provide evidence that the cost of equity-

reducing effect of the IFRS adoption is greater in countries with weak institutional

infrastructures than in countries with strong institutional infrastructures. Taken together,

our results support the view that countries with weak (strong) institutional infrastructures

benefit more (less) from the voluntary IFRS adoption.

6 Sensitivity tests

6.1 Matched sample results

As shown in Table 1, our full sample used for regressions in Table 4 has an unequal

distribution of observations between the IFRS adopter sample of (maximum) 1,327 and the

non-adopter sample of (maximum) 20,281. To address a concern over potential problems

associated with this unbalanced group membership, we construct a matched sample, and

then re-estimate Eq. (2) using the matched sample. For this purpose, in each sample year,

23 A possible reason why results are weak when INST = AudSanction may be that the effects of AudSue andAudSanction substitute for each other.


123

IFRS adopters are matched to non-adopters in terms of size, industry and country. In each

sample year and country, each adopter is matched to a non-adopter in the same industry as

closely as possible on total assets with non-adopters with a maximum permissible dif-

ference in total assets of 10 percent. In so doing, a non-adopter, once matched to an IFRS

adopter in a certain year, is not allowed to be used for matching to other IFRS adopters in

that year. An adopter is discarded if we fail to find a non-adopter that matches with the

adopter within the prescribed matching criteria. After applying the above matching pro-

cedures, we obtain a matched sample of 2,228 firm-years which consist of 1,114 IFRS

adopters and 1,114 non-adopters.

As shown in Panel A of Table 5, the coefficient on DIFRS is negative across all cases,

and significantly negative in all cases except when INST = Law and AudSanction. The

coefficient on Institution is overall significantly negative in all cases except for the same

two cases (INST = Law and AudSanction). The coefficient on DIFRS*Institution is sig-

nificant in two of out six cases, i.e., when INST = InvPro and AudSue.

The use of the matched sample causes a substantial decrease in the sample size from

N = 21,608 in Table 4 to N = 2,288, which in turn leads to the significance of the

coefficients being weakened in some cases. Nevertheless, the matched-sample results

reported in Panel A of Table 5 is, overall, in line with the full sample results reported in

Table 4. This lends further support to hypotheses, H1 and H2.

6.2 Are observed differences due to pre-existing differences?

The focus of our full-sample regressions reported in Table 4 is on cross-firm differences in the

cost of equity (CoE) between voluntary IFRS adopters and non-adopters. Recall that all firms,

whether IFRS adopters or non-adopters, are included into our full sample used for the

regressions reported in Table 4, as long as they are covered by Worldscope, IBES, and Data-

stream during the sample period and they meet the data requirements described in Sect. 4.1.

One may argue that the CoE differences between IFRS adopters and non-adopters that

we observe in Table 4 may be driven by inherent differences in CoE that had pre-existed

between the two distinct groups of firms even before the firm-year observations adopted

IFRS, rather than they are a result of the IFRS adoption itself. To examine this possibility,

we investigate whether and how the CoE differences between the IFRS adopters and the

non-adopters change significantly from the pre-adoption period to the post-adoption per-

iod. For this purpose, we first identify the year of adoption for IFRS adopters (say, year t),

match IFRS adopters to non-adopters in each year during the post-adoption period (year

t ? 1 and onward until the last sample year 2004). We then re-estimate Eq. (2) using this

post-adoption sample. We also construct a pre-adoption sample for the pre-adoption period

(years t - 1, t - 2, …, 1998, i.e., year t - 1 and backwards to 1998), using the same set

of both IFRS adopters and matched non-adopters during the post-adoption period (years

t ? 1, t ? 2, …, 2004). We then re-estimate Eq. (2) using these firm-years during the pre-

adoption period (years t - 1, t - 2, …, 1998).24

24 We prefer evaluating the change in CoE associated with IFRS adoptions in this fashion to comparing theCoE only for adopters between the two periods, i.e., pre-adoption and post-adoption years for the followingreasons: (1) the use of only the adopters does not allow us to compare the intergroup (adopters vs. non-adopters) difference in CoE between the pre- and post-adoption periods, it also causes a substantialreduction in our sample size, and thus the statistical power of our tests drastically; and (2) the changeanalysis requires changes in Institution, but our proxies for Institution do not vary over time. This pre-adoption and post-adoption comparison approach is similar to Barth et al. (2008) approach to examine theeffect of adopting IFRS on accounting quality.


123

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123

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123

The above procedures allow us to identify a total of 460 firms that adopted IFRS in year

t during our sample period, 1998–2004, continue to adopt during the post-adoption period

(i.e., year t ? 1, t ? 2, …, 2004), and continue to exist during the pre-adoption period (i.e.,

year t - 1, t - 2, …, 1998). We then match these adopters with non-adopters by size,

country, industry and year. Each adopter is matched as closely as possible on total assets

with non-adopters with a maximum permissible difference in total assets of 10 percent. An

IFRS adopter is discarded if we fail to find a non-adopter that meets the above matching

criteria. After this matching process, we obtain a total of 1,894 firm-years for the post-

adoption period (year t ? 1, t ? 2, …, 2004) and a total of 750 firm-years for the pre-

adoption period. Note here that: (1) the same firms identified as the IFRS adopters during

the post-adoption period are treated as the IFRS adopters in the pre-adoption sample

period; (2) both the pre-adoption and post-adoption samples include an equal number of

IFRS adopters and non-adopters as an IFRS adopter is matched to a non-IFRS adopter; and

(3) the observations in the year of adoption are excluded to make a cleaner comparison

between the pre- and post-adoption samples.

We first re-estimate Eq. (2) using the matched post-adoption firm-years (N = 1,894),

and report the estimated coefficients on the variable of interest in Panel B of Table 5. We

then estimate Eq. (2) using the matched pre-adoption firm-years (N = 750), and report the

same in Panel C of Table 5. As shown in Panel B, during the post-adoption period, the

coefficient on DIFRS is significantly negative in all cases except when INST = Law and

AudSanction. This is, overall, in line with the results reported in Table 4. Further the

coefficient on DIFRS*Institution is positive in all cases, and significantly positive in two

out of six cases when INST = InvPro and AudSue. Overall, this is consistent with the

results reported in Table 4. On the other hand, the results reported in Panel C reveal that

the coefficients on DIFRS and DIFRS*Institution are both insignificant across all cases,

which is in sharp contrast with the post-adoption results reported in Panel B.

The overall significance of our two test variables for the post-adoption sample (Panel

B), along with their insignificance for the pre-adoption sample (Panel C), indicates that our

results reported in Table 4 are not driven by possible inter-group differences in other firm

characteristics that had pre-existed during the pre-adoption period. Rather the results

reported in Panels B and C of Table 5 support the view that the change in reporting

strategies to IFRS adoptions leads to lowering the cost of equity capital. In short, there is

little indication that IFRS adopters have some inherent advantages in raising capital at a

lower cost during the pre-adoption period (i.e., even before they adopted IFRS), compared

with non-adopters.

6.3 Full versus partial adoption of IFRS

As mentioned earlier, Daske et al. (2012) use a different approach for identifying IFRS

adopters, i.e. they classify a firm as an IFRS adopter if Worldscope data field 07536 = 02,

06, 08, 12, 16, 18, 19 or 23. This broader classification of IFRS adopters considers not only

the full IFRS adoption (07536 = 02 or 23) (that this study considers as the IFRS adoption),

but also the partial adoption (07536 = 06, 08, 12, 16, 18, or 19) (that this study considers

as a non-adoption) as the IFRS-adoption. To check the sensitivity of alternative classifi-

cation schemes to our test results, we re-estimate Eqs. (1) and (2) using Daske et al.

(2012)’s broader classification scheme. The results are presented in Panel D of Table 5.

As shown in Panel D, the regression results are mixed. When INST = Disclosure,

InvPro, AudSue and SecReg, the coefficient on DIFRS is significantly negative, suggesting

a negative association of CoE with the broadly classified IFRS adoptions (which include


123

both full and partial adoptions). In all other cases (INST = excluded, Law and AudSanc-

tion), however, the coefficient on DIFRS is insignificant. The above results may possibly

explain a reason why Daske et al. (2012) find an insignificant association between IFRS

adoption and cost of equity capital. Similar to the coefficient on DIFRS, the coefficient on

DIFRS*Institution is also mixed: it is significant with an expected positive sign when

INST = Law, Disclosure, AudSue, and SecReg, while it is insignificant in other cases.

The inconclusive evidence using Daske et al. (2012)’s broader classification of IFRS

adopters, along with the significant results (as reported in Table 4) using a stricter clas-

sification of IFRS adopters, suggests that suppliers of equity capital consider the full

(partial) adoption as a more (less) credible commitment. As a result, investors in the equity

market require a lower cost of capital for the full adopters than for the partial adopters.

6.4 Difference between ‘‘serious’’ and ‘‘label’’ IFRS adopters

Daske et al. (2012) classify IFRS adopters into ‘‘serious’’ and ‘‘label’’ adopters and find

that only ‘‘serious’’ IFRS adopters are associated with decreased cost of capital and

increased market liquidity. In our study, we effectively treat all the IFRS adopters as

‘‘serious’’ adopters. In this section, to provide a comparison with Daske et al. (2012) study,

we attempt to classify full IFRS adopters into ‘‘serious’’ and ‘‘label’’ adopters as Daske

et al. (2012) do. For this purpose, we differentiate between serious versus label adopters by

comparing the change in accruals from the pre-adoption period to the post-adoption period,

which is similar to the approach used by Daske et al. (2012). The intuition behind this

approach is that firms that make serious changes to their reporting strategies should exhibit

an improvement in earnings quality from the pre-adoption period to the post-adoption

period. We thus compare the magnitude of accruals 1 year before IFRS adoption with that

2 years after the adoption. As a proxy for the magnitude of accruals, we use the ratio of

absolute accruals to absolute operating cash flows. We identify a firm as a serious IFRS

adopter if it exhibits a decline in the magnitude of accruals after the adoption of IFRS.

We then re-estimate Eqs. (1) and (2) by including an additional binary variable ‘‘Serious

Adopter’’. This variable captures the differential effect on CoE between serious and label

adopters, while the variable DIFRS captures the effect of label adopters on CoE. The

results with Serious Adopter are presented in Panel E of Table 5. The p value (in paren-

thesis) from an F-test indicates joint statistical significance of the coefficients on DIFRS

and Serious Adopter. We find that coefficient on DIFRS remain significantly negative in

five out of seven cases (except when INST = Law and AudSanction) and coefficient on

Serious Adopter is significantly negative across all the cases except when INST is excluded.

This suggests that even the label adoption leads to lowering cost of equity, although the

serious adoption decreases CoE more than the label adoption. The p values for the F-tests

are significant at less than the 1 % level across all the cases except when INST = Aud-

Sanction, suggesting that DIFRS and Serious Adopter, taken together, add an incremental

explanation power to the model, i.e. the CoE-reducing effect of IFRS adoption by serious

adopter is highly significant. Coefficient on DIFRS*Institution remains significantly

positive in all the cases except when INST = AudSanction.

6.5 Regression results using alternative proxies for implied cost of capital

The results reported in Table 4 use the CoE estimate calculated by the Easton (2004)

model. To check whether our empirical results are robust to different measures of CoE, we

consider two alternative valuation models: (1) the Ohlson and Juettner-Nauroth (OJ: 2005)


123

model; and (2) the Gebhardt, Lee and Swaminathan (GLS: 2001) model.25 Similar to the

Easton (2004) model, the OJ model is a special case of the abnormal earnings growth

model. It uses 1-year ahead forecasted earnings and dividends per share as well as forecasts

of short-term and long-term abnormal earnings growth. Dividends are set equal to a

constant fraction of forecasted earnings. Following OJ, the cost of equity (CoEOJ) is

estimated as:

CoEOJ ¼ Aþ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

A2 þ eps1

P0

� eps2 � eps1

eps1

� c� 1ð Þ� �

s

where A ¼ 12

c� 1ð Þ þ dps1

P0

� �

: OJ suggest that c - 1 is well represented by economy-wide

growth, and so, like Gode and Mohanram (2003), we assume that c is equal to 1 ? (risk

free rate - 3 %). The earnings forecasts data are obtained from the IBES International file,

and price and risk-free rate data are extracted from Datastream. The sample size used for

computing CoEOJ is 19,456.

The GLS model is a special case of the residual income valuation model. Following

GLS, the cost of equity (CoEGLS = rGLS in the model below) is estimated using the

following model:

Pt ¼ bvt þX

T

s¼1

ðepstþs � rGLS � bvtþs�1Þð1þ rGLSÞs

þ ðepstþTþ1 � rGLS � bvtþTÞrGLS � ð1þ rGLSÞT

In the above, due to data limitations, we use actual book values per share and forecasted

earnings per share up to 2 years ahead to impute future expected residual income for an

initial 2-year period. The earnings forecasts data are obtained from the IBES International

file and the actual book value data are obtained from Worldscope. Due to the more

demanding data requirement, our sample size reduces to 11,227 for estimating rGLS in the

above GLS model.

Though not reported for brevity, we find that the natural logarithm of one plus the implied

cost of equity measure based on the OJ valuation model, i.e., LCoEOJ, has a mean of 0.184 for

the full sample (which includes both IFRS adopters and non-adopters). The mean LCoEOJ is

0.152 and 0.186 for the IFRS adopter sample and the non-adopter sample, respectively. This

difference in LCoEOJ between the adopter and non-adopter samples is significant at less than

the 1 % level (t = -9.18). LCoEGLS has a mean of 0.111 for the full sample, while it is 0.088

and 0.112 for the IFRS adopter sample and the non-adopter sample, respectively. Again, this

difference in LCoEGLS between the two samples is significant at less than 1 % level (t =

-4.51). Moreover, we find that the three alternative proxies for the implied cost of equity

capital under different valuation models (LCoEPEG, LCoEOJ and LCoEGLS) are highly and

positively correlated with one another. For example, the correlation between LCoEPEG and

LCoEOJ (LCoEGLS) is as high as of 0.887 (0.566) and that between LCoEOJ and LCoEGLS—is

0.429. This is consistent with the findings of Hail and Leuz (2006).

We repeat all the regressions reported in this paper, using the two additional proxies for

the implied cost of equity capital (LCoEOJ and LCoEGLS). The results of using LCoEOJ and

LCoEGLS as dependent variables are reported in Panels F and G of Table 5. When LCoEOJ

is used to proxy for implied cost of equity, coefficient on DIFRS is significantly negative

across all the cases and that on DIFRS*Institution remains significantly positive in all the

25 An important assumption is clean surplus, i.e., future book values are imputed from current book values,forecasted earnings and dividends. Dividends are set equal to a constant fraction of forecasted earnings.


123

cases except when INST = AudSanction. Similarly, when LCoEGLS is employed as the

dependent variable, coefficient on DIFRS remains negative in all the cases and is signif-

icant in four out of seven cases (when INST = Disclosure, InvPro, AudSue and SecReg).

Coefficient on DIFRS*Institution is significant and positive in four out of six cases (when

INST = Law, Disclosure, InvPro and AudSue). Overall, our empirical results reported in

Table 4 are robust to alternative proxies for implied cost of equity, suggesting that our

results are not affected by different measures of cost of equity.

6.6 Results of two-stage least squares regressions

In our main regression, we follow the Heckman (1979) procedure to control for potential

self-selection bias. To check whether the choice of econometric method affects our results,

we adopt an alternative approach, i.e. the two-stage least squares (2SLS) procedure, to

control for self-selection bias or reverse causality. For this purpose, we obtain the fitted

value of DIFRS from the first-stage probit regression for Eq. (1), and then use it as an

instrumental variable for DIFRS in the second-stage regression for Eq. (2). Results of the

2SLS regression are reported in Panel H of Table 5. The coefficient on DIFRS (DIF-

RS*Institution) remain significantly negative (positive) across all cases, indicating that our

results are robust to alternative treatments of an endogeneity concern.

6.7 Results of Fama–MacBeth regressions

To address potential problems arising from residual cross-correlation, we re-estimate Eq. (2) for

each of seven sample years using annual observations. We then compute the average of seven

annual regression coefficients and standard errors using the Fama–MacBeth (FM: 1973) proce-

dure. As shown in Panel I, the coefficient on DIFRS remains significantly negative in four out of

seven cases (when INST = excluded, Disclosure, AudSue and SecReg) and is marginally

insignificant in the other cases. The coefficient on DIFRS*Institution is significant with a positive

sign in three out of six cases (when Institution = InvPro, AudSue and AudSanction). Overall, the

FM results are qualitatively similar to those presented in Panel B of Table 4, suggesting that our

main results are robust to potential problems associated with residual cross correlation.

6.8 Results of weighted least squares regressions

As shown in Table 1, the number of firms for each country varies from 5,241 for Japan to

27 for Czech. The results of our OLS regressions therefore may be affected by a large

number of sample firms from a few countries such as Japan. To check whether our results

are unduly influenced by the unequal size of sample firms across different countries, we

re-estimate Eq. (2) using the weighted least square (WLS) procedure with an equal weight

assigned to each sample country. The results are reported in Panel J of Table 5. The

coefficients on variables of interest, i.e. DIFRS, Institution and DIFRS*Institution, remain

qualitatively similar to our main results reported in Table 4, suggesting that our main

results are robust to the unequal distribution of samples across different countries.

6.9 Regression results using reduced samples

As shown in Panel A of Table 1, two countries, Germany and Switzerland, in our sample

cover over half of the total IFRS adopters, i.e. 68.8 % of the IFRS adopters are from the


123

two countries. To check whether the high adoption rate in these two countries unduly

influences our empirical results, we re-estimate Eq. (2) after removing observations from

the two countries. After deleting firms from Germany and Switzerland, we construct a

reduced sample of 19,609 firm-year observations. The regression results using this reduced

sample are reported in Panel K of Table 5. The coefficient on DIFRS (DIFRS*Institution)

is significantly negative (positive) across all cases except when INST = AudSanction,

suggesting that our main results reported in Table 4 are unlikely to be driven by obser-

vations in the two countries.

As shown in Table 1, ten countries have no IFRS-adopters throughout the sample

period. Following Covrig et al. (2007) and Kim et al. (2011), we include observations from

these ten countries with no IFRS adopters in our full sample. To check the sensitivity of

our main results to the inclusion or exclusion of these ten (no-IFRS adoption) countries, we

re-estimate Eq. (2) after removing observations from these countries. Panel L of Table 5

reports the regression results using this reduced sample of 13,336 firm-year observations.

Overall the regression results using this reduced sample are qualitatively similar to those

reported in Table 4. As shown in Panel L, the coefficient on DIFRS is highly significant

with a negative sign across all cases except when INST = AudSanction, thus supporting

H1. Also, the coefficient on DIFRS*Institution remains qualitatively similar to those

reported in Table 4, although it becomes a bit less significant: In four out of six cases

(when INST = Law, Disclosure, InvPro and AudSue), the coefficient on DIFRS*Institution

is significantly positive, while it is insignificant in the other two cases.

6.10 Other sensitivity checks

Though not reported for brevity, we also perform two other sensitivity analyses.26 To

address a concern over possible problems associated with residual heteroskedasticity and

autocorrelation, we re-estimate Eq. (2) using the Newey–West (1987) procedure. We find

that the results using the Newey–West corrected standard errors remain qualitatively

similar to the regression results reported in Table 4. To further check whether our results

reported in Table 4 are unduly influenced by the existence of outliers, we also repeat our

regression analyses after winsorizing the top and bottom 1 % of each variable’s distri-

bution. The regression results using the winsorized observations remain qualitatively

identical to those reported in Table 4.

In sum, the results of various sensitivity checks re-confirm that the implied cost of

equity capital is lower for IFRS adopters than for non-adopters (H1) and the cost of capital-

reducing effect of IFRS adoption is greater for IFRS adopters in countries with weak

institutions than for those in countries with strong institutions (H2).

7 Summary and concluding remarks

Using a large sample of 21,608 firm-years with voluntary IFRS adopters and non-adopters

from 34 countries over 1998–2004, we compare the implied cost of equity capital between

voluntary IFRS adopters and non-adopters after controlling for other relevant factors. We

also investigate whether and how the cost-of-capital effect of IFRS adoptions is differ-

entially influenced by the efficacy of institutional infrastructures determining a country’s

26 Though not reported in the paper for brevity, the tabulated results of the sensitivity tests in this section areavailable from the authors upon request.


123

corporate governance and (accounting standards, contractual rights, and laws) enforcement

mechanisms. Our results reveal the following.

We find that the implied cost of equity capital is significantly lower for the voluntary IFRS

adopters than for the non-adopters, suggesting that the voluntary IFRS adopters benefit from

greater and/or better disclosures via IFRS by having a lower cost of raising capital from the

equity market. This result holds, irrespective of a country’s institutional infrastructure.

Second, we find that the cost of capital decreases with the efficacy of a country’s institutional

infrastructure. We observe, however, that the cost of equity-reducing effect is insignificant or

marginally significant when firms with partial IFRS adoptions are treated as IFRS adopters.

Moreover, we find that the cost of capital-reducing effect of IFRS adoption is greater when the

IFRS adopters are from countries with weak institutional infrastructures than they are from

countries with strong infrastructures, suggesting a substitution effect between firm-level

voluntary IFRS adoption and country-level governance. Our results are robust to various

sensitivity checks. Overall, evidence documented in this study suggests that firms in countries

with weak governance and enforcement mechanisms benefit more from the IFRS adoptions,

compared with firms in countries with strong mechanisms.

A firm’s ability to raise fund from the equity market is crucial for firm performance,

investment, and growth, and thus the development of a country’s overall economy.

However, given the benefit of IFRS adoption documented in this study, the percentage of

IFRS adoption is still low (i.e. \7 % of our sample), it is then interesting for future

research to study why firms choose not to adopt IFRS, for example, whether it is due to

potential losses of managers’ private control benefits associated with IFRS adoption or

whether it is because adopting IFRS is too costly (e.g., high compliance cost27 or a loss of

proprietary information). In addition, evidence reported in this study indicates that greater

and better disclosures via voluntary IFRS adoption enhance a firm’s ability to raise equity

capital at a lower cost, in particular, when firms are operating in countries with weak

institutional infrastructures. This finding has an important policy implication to accounting

standard setters and stock market regulators, in particular, in emerging markets with poor

governance and enforcement mechanisms: improving firm-level governance such as better

or higher-quality disclosures via voluntary IFRS adoption appears to be as important as

improving country-level governance and enforcement mechanisms. Further research is

called on how the firm-level accounting and auditing standards interplay with country-level

institutional infrastructures, in particular, in emerging markets where institutional infra-

structures are not well developed yet.

Acknowledgments We thank helpful comments from Jong-Hag Choi, Francis Kim, Jung H. Lee, Cam-eron Morrill, Aini Qiu, Byron Song, Xiaodong Xu, Cheong H. Yi, Liandong Zhang and participants of Ph.D.and DBA Research Seminars at Concordia University, Fudan University, Shanghai University of Financeand Economics, The Hong Kong Polytechnic University, Seoul National University, the Annual Conferenceof CAAA, and the Annual Meeting of AAA. Jeong-Bon Kim acknowledges partial financial support for thisproject from the CERG of the Hong Kong SAR Government and the SSHRCC via the Canada ResearchChair program. Haina Shi is grateful for financial support from the Humanities and Social Science ResearchProject of the Ministry of Education in China (No. 12YJC630169). Both Haina Shi and Jing Zhou gratefullyacknowledge the financial support from the National Natural Science Foundation of China (No. 71202056,No. 71072036 and No. 71272012). All errors are our own.

Appendix 1

See Table 6.

27 For example, Kim et al. (2012) suggests that mandatory IFRS adoption leads to an increase in audit fee.


123

Table 6 Variable definitions and data sources

Variable Definition Data source

LCoEPEG Natural log of one plus a firm’s cost of equity capital, cost of equity

capital is calculated following Easton (2004) as CoEPEG ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

E0ðeps2Þ�E0ðeps1ÞP

0

q

; whereas E0 is the expectation operator, eps2 is

earnings per share in year t ? 1, eps1 is earnings per share in year t, andP0 is the price at the beginning of year t

IBES International,Datastream

DIFRS A dummy variable, which equals 1 if a particular firm-year observationadopts a full set of IFRS and 0 otherwise

Worldscope

Size Firm size, measured by natural log of total assets (in Euro) Worldscope

Leverage The ratio of short-term and long-term debt to total assets. Worldscope

Growth Natural log of long-term earnings growth, long-term earnings growth iscalculated as (E0(eps) - E0(eps0))/E0(eps0), whereas E0 is theexpectation operator, eps is long-term forecasted earnings per share inyear t and eps0 is long-term forecasted earnings per share at thebeginning of year t

IBES International

ForSales Percentage of foreign sales to total sales Worldscope

Cross A dummy variable, which equals 1 if a firm’s shares are traded on foreignexchanges and 0 otherwise

Worldscope

USGAAP A dummy variable, which equals 1 if a particular firm-year observationadopts US GAAP and 0 otherwise

Worldscope

EVar Earnings variability, measured as standard deviation of analysts’forecasts of earnings per share

ROA Return on assets, measured as the ratio of earnings before interest andtaxes to total assets

Worldscope

Inflation The consumer price index reflecting changes in the cost of acquiring afixed basket of goods and services by the average consumer

IBES International

Lamda Inverse Mills ratio obtained from the probit IFRS-adoption model inEq. (1)

Law A dummy variable, which equals 1 if a country has a common law legalorigin and 0 otherwise

La Porta et al. (1998)

Disclosure An index of disclosure requirements relating to: (1) prospectus; (2)compensation of directors and key officers; (3) ownership structure; (4)inside ownership; (5) contracts outside the ordinary course of business;and (6) transactions between the issuer and its directors, officers, and/orlarge shareholders. The index ranges from 0 to 1; with higher valuesindicating more extensive disclosure requirements


AntiDir An index of antidirector rights, which is formed by adding one when: (1)the country allows shareholders to mail their proxy vote, (2)shareholders are not required to deposit their shares prior to the GeneralShareholders’ Meeting, (3) cumulative voting or proportionalrepresentation of minorities on the board of directors is allowed, (4) anoppressed minorities mechanism is in place, (5) the minimumpercentage of share capital that entitles a shareholder to call for anExtraordinary Shareholders’ Meeting is less than or equal to 10 % (thesample median), and (6) when shareholders have preemptive rights canonly be waived by a shareholders’ meeting. The range for the index isfrom 0 to 6

La Porta et al. (1998,2002)


123

Appendix 2

See Table 7.

Table 6 continued

Variable Definition Data source

EffJud Assessment of the efficiency and integrity of the legal environment as itaffects business, particularly foreign firms, produced by the country riskrating agency Business International Corp. It ‘‘may be taken torepresent investors’ assessment of conditions in the country inquestion.’’ Average between 1980 and 1983. Scale from 0 to 10, withlower scores representing lower efficiency levels


LawRule Assessment of the law and other tradition in the country produced by thecountry risk rating agency International Country Risk (ICR). Averageof the months of April and October of the monthly index between 1982and 1995. Scale from 0 to 10, with lower scores for less tradition forlaw and order


InvPro Arithmetic mean of percentage rank of AntiDir, EffJud and LawRule

AudSue Index of the procedural difficulty in recovering losses from the auditors ina civil liability case for losses due to misleading statements in theaudited financial information accompanying the prospectus. Equals onewhen investors are only required to prove that the audited financialinformation accompanying the prospectus contains a misleadingstatement. Equals two-thirds when investors must also prove that theyrelied on the prospectus and/or that their loss was caused by themisleading accounting information. Equals one-third when investorsmust also prove that the auditor acted with negligence. Equals zero ifrestitution from the auditor is either unavailable or the liability standardis intent or gross negligence

The World Bank

AudSanctionAn index of criminal sanctions applicable to the auditor (or its officers)when the financial statements accompanying the prospectus omitmaterial information. Equals zero if the auditor cannot be heldcriminally liable when the financial statements accompanying theprospectus are misleading. Equals one-half if the auditor can be heldcriminally liable when aware that the financial statementsaccompanying the prospectus are misleading. Equals one if the auditorcan also be held criminally liable when negligently unaware that thefinancial statements accompanying the prospectus are misleading

The World Bank

LiabStd An index of liability standards equals the arithmetic mean of (1) liabilitystandard for the issuer and its directors; (2) liability standard fordistributors; and (3) liability standard for accountants. The index rangesfrom 0 to 1, with higher values indicating less procedural difficulty inrecovering losses from agents


PubEnf An index of public enforcement equals the arithmetic mean of (1)supervisor characteristics index; (2) rule-making power index; (3)investigative powers index; (4) orders index; and (5) criminal index.The index ranges from 0 to 1, with higher values indicating betterpublic enforcement


SecReg Arithmetic mean of Disclosure, LiabStd and PubEnf


123

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