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THE ACCOUNTING REVIEW American Accounting AssociationVol. 87, No. 5 DOI: 10.2308/accr-501922012pp. 1767–1789

Does Mandatory IFRS Adoption ImproveInformation Comparability?

Rita W. Y. Yip

Lingnan University

Danqing Young

The Chinese University of Hong Kong

ABSTRACT: This study examines whether the mandatory adoption of International

Financial Reporting Standards (IFRS) in the European Union significantly improves

information comparability in 17 European countries. We employ three proxies—the

similarity of accounting functions that translate economic events into accounting data,

the degree of information transfer, and the similarity of the information content of

earnings and of the book value of equity—to measure information comparability. Our

results suggest that mandatory IFRS adoption improves cross-country information

comparability by making similar things look more alike without making different things

look less different. Our results also suggest that both accounting convergence and

higher quality information under IFRS are the likely drivers of the comparability

improvement. In addition, we find some evidence that cross-country comparability

improvement is affected by firms’ institutional environment.

Keywords: IFRS adoption; information comparability; institutional environment.

Data Availability: Data are available from commercial providers (Worldscope, Data-

Stream, and I/B/E/S).

I. INTRODUCTION

Demand for internationally comparable accounting information has increased significantly

in recent years due to rapid growth in cross-country investment. One reflection of this

trend is the widespread adoption of International Financial Reporting Standards (IFRS),

including the mandatory adoption of IFRS in the European Union (EU) in 2005. Because

mandatory IFRS are some of the most important financial reporting regulations in recent years,

many studies have examined the various effects of their adoption (e.g., Capkun et al. 2008; Daske et

We thank John Harry Evans III (senior editor), Steven Kachelmeier (senior editor), two anonymous reviewers, andworkshop participants at The Chinese University of Hong Kong and the 2009 American Accounting Association AnnualMeeting for helpful comments and suggestions.

Editor’s note: Accepted by John Harry Evans III, with thanks to Steven Kachelmeier for serving as editor on a previousversion.

Submitted: November 2009Accepted: April 2012

Published Online: April 2012

1767

al. 2008; Armstrong et al. 2010; Horton and Serafeim 2010; Clarkson et al. 2011). However, to the

best of our knowledge, the effect of IFRS adoption on cross-country information comparability has

not been thoroughly examined, although better comparability has been commonly cited as one of

the main benefits of IFRS adoption. The purpose of this study is, thus, to provide more empirical

evidence on this issue.

Information comparability is ‘‘the quality of information that enables users to identify

similarities in and differences between two sets of economic phenomena’’ (Financial Accounting

Standards Board [FASB] 1980, 9; International Accounting Standards Board [IASB] 2010, A36).

The Financial Accounting Standards Board further states that ‘‘greater comparability of accounting

information, which most people agree is a worthwhile aim, is not to be attained by making unlike

things look alike any more than by making like things look different’’ (FASB 1980, 42). These

statements assert that there are two equally important facets of information comparability: the

similarity facet, which indicates whether firms engaged in similar economic activities report similar

accounting amounts, and the difference facet, which indicates whether firms engaged in different

economic activities report dissimilar accounting amounts. Because improvement in one facet of

comparability does not automatically lead to improvement in the other facet, the overall benefit of

IFRS adoption on cross-country information comparability is contingent on whether adoption

improves both facets of comparability, or at least improves one without impairing the other.

It is intuitively appealing that mandatory IFRS adoption improves the similarity facet of

cross-country information comparability, and some empirical evidence from prior studies is

consistent with this intuition (e.g., Ashbaugh and Pincus 2001; Chi 2009; Barth et al. 2011; DeFond

et al. 2011). However, our view is that more research is required before a firm conclusion can be

drawn, as prior studies either use input-based comparability measures, such as accounting rule

variability and the number of accounting rules used on an announcement date (e.g., Chi 2009;

DeFond et al. 2011), or rely on samples of voluntary adopters that likely have different reporting

incentives from those of mandatory adopters (e.g., Ashbaugh and Pincus 2001; Barth et al. 2011).

More importantly, the effect of IFRS adoption on the difference facet of cross-country

comparability has not been addressed at all. This facet of comparability is important for mandatory

IFRS adoption because ‘‘an overemphasis on uniformity may reduce comparability by making

unlike things look alike’’ (IASB 2010, A36). For example, if IFRS allow fewer accounting choices

than local accounting standards, then their adoption could force firms to treat different economic

transactions in a more similar way, thus, diminishing the difference facet of comparability.1 In this

study, we test the effect of IFRS adoption on both facets of cross-country information

comparability.

Following previous comparability studies (e.g., Bradshaw et al. 2009; Barth et al. 2011;

DeFond et al. 2011; De Franco et al. 2011), we refer to firms in the same industry as similar firms

and those in different industries as different firms.2 We compare the cross-country information

comparability of the similarity facet among similar firms from different countries, and we compare

the cross-country information comparability of the difference facet among different firms from

different countries, across the pre- and post-IFRS periods. Because increased cross-country

comparability after IFRS, if any, could be affected by accounting convergence and/or higher quality

1 As an example, some local accounting standards allow firms to use the acquisition method for acquisitions andthe pooling method for mergers. However, IFRS allow only the acquisition method for all business combinationsthat are not under common control. As such, firms are forced to account for acquisitions and mergers in the sameway.

2 The underlying logic is that firms in the same industry have similar operational properties and face similareconomic shocks, whereas firms in different industries may have different operational properties and facedifferent industry-specific shocks. This definition is also supported by the common practice of analysts usingfirms in the same industry as benchmarks when analyzing a firm’s financial statements.

1768 Yip and Young

The Accounting ReviewSeptember 2012

financial information under IFRS than under local accounting standards, we also examine the two

facets of within-country comparability to identify the possible drivers underlying the comparability

improvement. As IFRS adoption generally reduces firms’ accounting choices (e.g., Ashbaugh and

Pincus 2001; Hoogendoorn 2006), an increase in both cross- and within-country comparability

would suggest that both accounting convergence and higher quality information are the underlying

drivers, whereas only an increase in cross-country comparability would suggest that convergence is

the likely driver.

We employ three proxies for information comparability. The first is the similarity of accounting

functions measure developed by De Franco et al. (2011).3 With this approach, accounting is

essentially the mapping of economic transactions to financial statements, and information

comparability can be defined as the similarity of firms’ accounting functions that translate economic

transactions into accounting data. The second proxy is the degree of information transfer, as

measured by the association between the earnings surprise of an announcing firm and the

contemporaneous stock price movements of other firms. The intuition underlying this measure is

that an earnings announcement by a firm conveys information that has not previously been publicly

available, and investors can abstract such information and adjust stock prices for other firms with

comparable accounting. The third proxy is the similarity of the information content of earnings

(ICE) and the information content of the book value of equity (ICBV), as measured by the

long-window association between stock price and earnings and the book value of equity. Because

ICE and ICBV capture the extent to which accounting earnings and the book value of equity reflect

a firm’s economic performance, firms that engage in similar economic activities should have a

similar ICE and ICBV if their accounting systems are comparable.4

To test our research questions, we use data from 17 European countries in which listed firms

must use IFRS to prepare their consolidated financial statements since 2005. We find that the

comparability of accounting information for similar firms from different countries is significantly

greater in the post-IFRS period (2005–2007) than in the pre-IFRS period (2002–2004), using all of

the three comparability measures. However, using the measure of the similarity of accounting

functions and information transfer,5 we find that there are no discernible comparability changes

among different firms from different countries across the two periods. Further, the comparability

change in the similarity facet is greater than that in the difference facet. These results, thus, suggest

that IFRS adoption improves the similarity facet of cross-country information comparability

without discernibly impairing the difference facet of comparability. For within-country analysis, we

find that the similarity facet of comparability increases for the accounting function measure, but not

for the information transfer measure. However, the increase in the similarity facet of within-country

comparability does not statistically differ from that of cross-country comparability using both

measures. We, thus, interpret our results as consistent with the view that both accounting

convergence and information quality play an important role in the observed IFRS effects.

As a supplemental test, we examine whether the improvement in the similarity facet of

cross-country comparability is affected by firms’ institutional environment. We classify a firm’s

institutional environment by the common law versus code law origin of the legal system in its home

country, because a country’s legal origin can proxy for a variety of institutional features, such as

3 De Franco et al. (2011) also develop another comparability measure that captures the similarity of two firms’economic events and accounting functions at the same time. Because we examine whether IFRS adoptionimproves the similarity of accounting functions, we do not use that measure here.

4 We summarize comparability measures for each test in Figure 1.5 As explained in ‘‘Similarity of ICE and ICBV’’ in Section III, the similarity of ICE and ICBV measure is not

applicable for the sample of different firms from different countries and the sample of similar firms from thesame country.

Does Mandatory IFRS Adoption Improve Information Comparability? 1769


financial reporting incentives and the effectiveness of legal enforcement. We find that among

similar firms from countries with the same legal origin, nearly all of the comparability measures

increase after IFRS adoption. However, among similar firms from countries with different legal

origins, only one comparability measure increases. Overall, these results provide some evidence

that the institutional environment in the home countries of firms influences the effect of IFRS

adoption on information comparability.

This study contributes to the literature by providing empirical evidence on the association

between mandatory IFRS adoption and information comparability. We address the impact of IFRS

adoption on both the similarity and difference facets of comparability, whereas previous

comparability studies address only the former. Our results suggest that mandatory IFRS adoption

has an overall benefit in enhancing investors’ ability to compare firms with similar fundamentals

without discernibly reducing their ability to distinguish firms with different fundamentals. This

study also extends the literature on the importance of institutions by documenting that IFRS

adoption is more likely to improve comparability among firms with similar institutional

environments. All of the findings may have practical implications for regulators and investors in

countries considering IFRS adoption.

Section II discusses the literature and the differences between our study and previous studies.

Section III describes the information comparability measures employed, and Section IV presents the

empirical tests and their results. Section V presents supplemental and sensitivity tests, and Section

VI concludes.

II. MOTIVATION AND LITERATURE REVIEW

Globalization in the last two decades has significantly increased the economic interaction

among countries, which, in turn, has created demand for more internationally comparable

accounting information. There are several potential benefits associated with enhanced information

comparability. For example, both the FASB and the International Accounting Standards Board

argue that more comparable information enables global markets to operate with less friction.6

Several studies also suggest that greater information comparability facilitates international

transactions and minimizes exchange costs (e.g., Turner 1983; Weber 1992; Choi et al. 1999).

The importance of comparable financial information in the global economy has led to

growing research interest in information comparability. De Franco et al. (2011) develop two

measures of accounting comparability and test their construct validity. They also use U.S. data to

examine the benefits of comparability, finding that comparability is positively associated with

analyst following and forecast accuracy, and negatively associated with forecast optimism and

dispersion. Bradshaw et al. (2009) examine the association between accounting method variability

(as a proxy for information comparability) and analyst characteristics using U.S. data, finding that

a lower level of information comparability is associated with greater analyst forecast error and

dispersion.

Several recent studies examine issues related to accounting comparability resulting from IFRS

adoption. Using data from 26 countries from 1995 to 2006, Barth et al. (2011) find that the value

relevance of earnings and equity book value is more comparable among non-U.S. firms after the

application of the International Accounting Standards than when local accounting standards were

used. Beuselinck et al. (2007) investigate the determinants of earnings comparability as proxied

by the association between accruals and cash flows for the period 1990–2005 using data from 14

EU countries, finding that the association is not affected by mandatory IFRS adoption. DeFond et

6 These arguments appeared in news releases by the FASB on February 27, 2006, and by the IASB on February22, 2006.

1770 Yip and Young


al. (2011) examine the benefits of increased accounting uniformity as a proxy for comparability,

using data from 14 European countries. They find that foreign mutual fund ownership increases

among mandatory adopters in countries with strong implementation credibility. Li (2010) cites

enhanced comparability as the likely mechanism behind the cost of equity reduction in the EU

after IFRS adoption. Wu and Zhang (2010) find an increased use of relative performance

evaluation based on foreign peer firms’ accounting information among European firms in the

post-IFRS period.

Our study differs from these recent studies in two important ways. First, we address both the

similarity and difference facets of comparability, whereas the previous studies only address issues

related to the former. Second, we examine a different research question and, thus, use different

comparability measures, sample periods, and sample firms. For example, whereas we examine the

relation between comparability and mandatory IFRS adoption in 17 European countries, Barth et al.

(2011) examine the comparability of mandatory and voluntary non-U.S. IFRS adopters and U.S.

firms. Beuselinck et al. (2007) rely on a non-price-related comparability measure and data from the

year of switch only (2005), whereas we use three price-related measures and three post-IFRS years

of data.7 Li (2010) and DeFond et al. (2011) use input-based and non-firm-specific comparability

measures, whereas we employ output-based and firm-specific measures.8

III. SAMPLES AND COMPARABILITY MEASURES

Initial Sample

We collect data on listed firms in Austria, Belgium, Denmark, Finland, France, Germany,

Greece, Ireland, Italy, Luxembourg, The Netherlands, Norway, Portugal, Spain, Sweden,

Switzerland, and the United Kingdom from the Worldscope database. The number of firms

included in the Worldscope database for each country is presented in Table 1, Panel A. We exclude

financial, insurance, and real estate firms (SIC codes 6000–6999) from the sample because they

have special operating properties and are subject to additional regulations. We also exclude firms

that adopted IFRS before 2005 and firms that do not report consolidated financial statements from

the sample. Finally, we restrict the sample to firms with a fiscal year ending in December to ensure

that each firm has the same sample period. Table 1, Panel B, summarizes the sample selection

procedure and the final sample size.

Comparability Measures and Related Samples

Similarity of Accounting Functions

This comparability measure was developed by De Franco et al. (2011), who argue that

accounting is essentially the mapping of economic transactions to financial statements, and that

accounting comparability can, thus, be defined as the similarity of accounting functions to translate

economic transactions into accounting data. We first estimate each firm’s accounting function by

applying the following equation:

7 Price-related comparability measures capture comparability from the perspectives of both investors and analysts.Because investors and analysts together are likely to be the largest groups using reported accounting informationto make investment decisions, it is important to examine comparability from their perspectives.

8 Following De Franco et al. (2011), we classify comparability measures that are computed using reportedaccounting data into the output-based category, and those that are based on accounting methods or accountingpolicies into the input-based category. For the dichotomy of firm-specific and non-firm-specific, measures thatare computed using firm-level data are classified into the category of firm-specific, and those computed usingindustry- or country-level data are classified into the category of non-firm-specific.



ROAit ¼ ai þ biRETit þ eit; ð1Þ

where ROAit is the return on assets (an accounting performance measure) of firm i in period t, and is

calculated as net income divided by total assets, and RETit, a proxy for economic events, is the

stock return of firm i in period t. The coefficients (ai and bi ) represent the estimated accounting

function of a firm. Using semiannual data, we estimate each firm’s accounting function in the pre-

and post-IFRS periods separately.

The similarity of accounting functions of firm i and firm j is then computed as follows. First,

we translate firm i’s economic activity into accounting ROA using its own accounting function (ai

and bi ) and the corresponding firm j’s accounting function (a j and bj) for each semiannual year.

Thus, we obtain the two expected ROAs, EðROAÞiit and EðROAÞjit; and the absolute value of their

TABLE 1

Sample Composition by Country and Initial Sample Procedures

Panel A: Sample Composition by Country from the Worldscope Database

Country Number of Firms

Austria 98

Belgium 153

Denmark 191

Finland 127

France 818

Germany 966

Greece 293

Ireland 64

Italy 281

Luxembourg 42

The Netherlands 171

Norway 206

Portugal 60

Spain 143

Sweden 419

Switzerland 273

United Kingdom 1,951

Total 6,256

Panel B: Initial Sample Selection Procedures

Number of firms obtained from Worldscope 6,256

Exclusions:

Financial institutions, insurance companies, and real-estate firms (1,538)

Fiscal year not ending in December (1,359)

Firms that voluntarily adopted IFRS before the mandatory requirement (573)

Firms without consolidated accounts (224)

Total number of firms in the initial sample 2,562

This table presents the sample composition by country and the initial sample selection procedure. Panel A presents thesample composition by country for the 6,256 firms collected from the Worldscope database. Panel B presents the initialsample selection procedure, which gives 2,562 firms.

1772 Yip and Young


difference for each semiannual year.9 This process yields six absolute value ROA differences for the

pre-IFRS period (2002–2004) and six differences for the post-IFRS (2005–2007) period. Second,

we translate firm j’s economic activity into accounting ROA using its own accounting function (a j

and bj) and firm i’s accounting function (ai and bi ), and obtain the two expected ROAs, EðROAÞjjtand EðROAÞijt; and the absolute value of their difference for each semiannual year. Similarly, there

are six differences for the pre-IFRS period and six differences for the post-IFRS period.10 Third, we

calculate the mean of the 12 differences in the pre-IFRS period (post-IFRS period) as the proxy for

the information comparability of firms i and j in the pre-IFRS period (post-IFRS period). We

multiply the mean by �1 so that a higher value represents greater comparability.

To generate a sample for a test of the similarity facet of cross-country comparability, we form

pairs of firms from the same industry, based on the three-digit SIC code, but different countries. We

rank the firms in each industry based on their total assets in 2006. For the largest firm, we choose a

foreign firm with the closest total assets as its pair. We also require the ratio of the smaller value of

total assets to the larger value in a pair to be greater than 50 percent. This procedure generates 133

pairs of firms. To form a sample to test the difference facet of cross-country comparability, we form

pairs from different industries and countries. We require one firm in the pair to be a manufacturing

firm (one-digit SIC code of 2 or 3) and the other to be a service firm (one-digit SIC code of 7 or 8).11

For each manufacturing firm, we choose a service firm in a different country that is closest in terms of

total assets. The ratio of the smaller value of total assets to the larger value must be greater than 50

percent. This procedure results in 148 pairs of firms. We employ the same procedure to form pairs

from the same country, and generate 61 pairs of similar firms and 125 pairs of different firms.

Degree of Information Transfer

Previous studies have found associations between the information released by announcing

firms and the returns of other firms in the same industry and country. For example, such information

transfer has been documented with respect to the news in the earnings announcements (Firth 1976;

Foster 1981; Clinch and Sinclair 1987; Han and Wild 1990; Hramnath 2002), stock split

announcements (Tawatnuntachai and D’Mello 2002), management earnings forecasts (Baginski

1987; Han et al. 1989), and corporate security offerings (Szewczyk 1992). Such transfers occur

because an announcement by a firm conveys information that has not previously been publicly

available, and the stock market responds by reevaluating the value of non-announcing firms and

adjusting their share prices accordingly. An important condition for information transfer through

earnings announcements is comparable accounting earnings. If earnings are not comparable, then

an earnings announcement by a firm is of little value in predicting the value of other firms, resulting

in a low degree of information transfer. As such, the degree of information transfer reflects the level

of information comparability.

We measure an earnings surprise for an announcing firm as the difference between reported

earnings and ex ante expected earnings. The ex ante expected earnings are proxied by the mean

analyst earnings forecast in the month immediately before the earnings release, which is obtained

from the I/B/E/S database.12 We calculate the abnormal stock returns of a non-announcing firm in

9 E(ROA)iit ¼ ai þ b iRETit and E(ROA) j

it ¼ a j þ b jRETit.10 E(ROA)i

jt ¼ ai þ b iRETjt and E(ROA) jjt ¼ a j þ b jRETjt.

11 Because we assume that similar (different) firms face similar (different) economic shocks and have similar(different) operational properties, we use the three-digit SIC code to define similar firms to ensure that these firmsare fundamentally similar, and use the one-digit SIC code to define different firms to ensure that they arefundamentally different.

12 Only annual earnings surprises are used in this test because the data on announcement date for semiannual orquarterly earnings are not available for most sample firms.



the three days around the earnings release of the announcing firm (Day �1 to Day þ1) using the

following model:

Uit ¼ RETit � ðai þ biRETmtÞ; ð2Þ

where Uit is the abnormal stock return of firm i on day t, and RETit and RETmt are the stock

return of firm i and the market return of firm i’s domestic market on day t, respectively.13 We

estimate the coefficients ai and bi separately for each fiscal year using data from Day �185 to

Day�6, where Day 0 is the earnings announcement date of the announcing firm. The cumulative

abnormal return of a non-announcing firm is the sum of its abnormal returns on the three days

surrounding the earnings release date of the announcing firm. To reduce the cross-sectional

correlation of prediction errors across clusters of non-announcing firms, we follow Baginski

(1987) and use the mean cumulative abnormal return of all of the non-announcing firms in the

empirical analyses.

To form a sample to test the similarity facet of cross-country comparability, we first identify all

of the industries at the three-digit SIC code level that contain at least two firms from different

countries. We rank the firms in each industry by their earnings announcement dates. To qualify as

an announcing firm, the firm’s earnings announcement window (Day �1 to Day þ1) must not

overlap with that of any other firm, to avoid the situation of non-announcing firms reacting to more

than one earnings announcement on the same day. Each announcing firm is paired with all similar

foreign firms with later earnings announcement dates that are of a similar size to the announcing

firm (a ratio of the smaller total asset value to the larger value of greater than 50 percent). This

approach yields 744 pair-year observations. To form a sample to test the difference facet of cross-

country comparability, we match each announcing firm in the manufacturing sector with all of the

foreign service firms with later earnings announcements and similar total assets, and vice versa.

This sample contains 910 pair-year observations. For the two within-country samples, we

implement the same procedure to form pairs within the same country, resulting in 195 and 630 pair-

year observations for the tests of the similarity facet and difference facet of within-country

comparability, respectively.

Similarity of ICE and ICBV

We employ the Ohlson (1995) model, in which a firm’s market value is regressed on net

income and equity book value, to test if ICE and ICBV are similar for two sets of firms:

MVit ¼ b0 þ b1NIit þ b2BVit þ b3Dx þ b4Dx�NIit þ b5Dx�BVit þ eit; ð3Þ

where MVit is the total market value of equity at the end of the fiscal year, NIit is the net income,14

and BVit is the book value of equity.15 These three variables are scaled by the number of

outstanding common shares. The variable Dx is an indicator where x equals 1 when it is a country

indicator, and 2 when it is an industry sector indicator. A significant b4 and b5 indicate that firms

from different sets have a different ICE and ICBV, respectively, and, thus, a low degree of

information comparability.

13 The market return indices for Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,Luxembourg, The Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom areATX, BEL, OMXC, OMXH, CAC, DAX, Athex, Ireland SE, MIB, LUX, AEX, OBX, PSI, IBEX, MXStockholm, Swiss Market, and FTSE, respectively.

14 In the Worldscope database, net income is defined as earnings (revenues less operating costs, depreciation,interest, taxes, and other expenses) less preferred dividends for non-U.S. firms.

15 We use annual data to estimate Equation (3) because data on the semiannual book value of equity are missing formost sample firms in the Worldscope database.

1774 Yip and Young


To examine the similarity facet of cross-country information comparability, we estimate

Equation (3) within each industry, as determined by three-digit SIC code, for every possible

combination of two countries in the pre- and post-IFRS periods separately. We assign an ICE

(ICBV) comparability score of 1 if b4 (b5) is insignificant (defined as a two-tailed p-value of more

than 5 percent), and 0 otherwise. We require at least three firms in an industry in a given country to

ensure that there are at least 18 observations in each regression. This process yields 210 regressions

and, hence, 210 comparability scores for ICE and ICBV in the pre- and post-IFRS periods,

respectively.

For the difference facet of within-country comparability analysis, we estimate Equation (3)

using a set of firms from the manufacturing sector (one-digit SIC code of 2 or 3) and the other set of

firms from the service sector (one-digit SIC code of 7 or 8), for every possible combination within a

country in the pre- and post-IFRS periods separately. This results in 64 comparability scores for

ICE and ICBV in each period. To estimate Equation (3), the sample firms must differ on only one of

the industry sector or country dimensions captured by indicator Dx. Thus, this approach is not

applicable to test either the difference facet of cross-country comparability, because sample firms

are from different industry sectors and countries, or to test the similarity facet of within-country

comparability, because sample firms are from both the same industry and the same country. Our

comparability measures for each test are summarized in Figure 1.

IV. RESULTS

Results Using the Similarity of Accounting Functions Measure

We perform multivariate Regression (4) for four samples: similar firms from different

countries, different firms from different countries, similar firms from the same country, and different

firms from the same country:

Compit ¼ b0 þ b1IFRSt þ b2TA Ratioi þ b3Com Codei þ b4Listingi þ IDi; ð4Þ

where Compit is the comparability of pair i in period t, and IFRSt is an indicator that equals 1 if t is

the post-IFRS period, and 0 otherwise. A significant b1 indicates that information comparability

changes between the pre- and post-IFRS periods. The variable TA_Ratioi is the ratio of the smaller

value of total assets to the larger value of the two firms in a pair in 2006. Com_Codei is an indicator

that equals 1 when the home countries of the two firms in the pair have different legal origins, and 0

otherwise, and Listingi is an indicator that equals 1 if the two firms in a pair are both listed on at

least one of the same stock exchanges, and 0 otherwise. These variables control for differences in

firm size, institutional environment, and stock listing between the two firms, all of which may affect

firms’ reporting incentives and, hence, the comparability of their accounting information. These

variables are all winsorized at the top and bottom 1 percent. IDi is an industry indicator of a pair.

For within-country analysis, we exclude Com_Code and Listing because they take the same value

for all of the observations. For difference facet of comparability analysis, IDi is excluded because

each of the pairs consists of one manufacturing firm and one service firm. Following the discussion

and practices in prior studies (e.g., Gow et al. 2010; DeFond et al. 2011), we adjust the standard

errors by country clusters in the within-country analyses.

Table 2, Panel A, presents descriptive statistics for the four samples. The mean and median of

Comp between cross-country similar and different firms, within-country similar and different firms,

cross- and within-country similar firms, and cross- and within-country different firms do not

statistically differ except that the mean of cross-country similar firms is smaller than that of cross-



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1776 Yip and Young


TABLE 2

Results of the Tests Using the Similarity of Accounting Functions Measure

Panel A: Descriptive Statistics for the Variables in the Various Samples

Variable n Mean Median Minimum Maximum Std. Dev.

Sample of Similar Firms from Different Countries

Compit 266 �0.282 �0.038 �2.950 �0.002 1.138

IFRSt 266 0.500 0.500 0.000 1.000 0.501

TA_Ratioi 266 0.810 0.838 0.500 0.995 0.153

Com_Codei 266 0.459 0.000 0.000 1.000 0.499

Listingi 266 0.045 0.000 0.000 1.000 0.208

Sample of Different Firms from Different Countries

Compit 296 �0.094 �0.030 �0.729 �0.002 0.071

IFRSt 296 0.500 0.500 0.000 1.000 0.500

TA_Ratioi 296 0.957 0.982 0.576 1.000 0.075

Com_Codei 296 0.628 1.000 0.000 1.000 0.484

Listingi 296 0.007 0.000 0.000 1.000 0.082

Sample of Similar Firms from the Same Country

Compit 122 �0.142 �0.040 �1.157 �0.001 0.278

IFRSt 122 0.500 0.500 0.000 1.000 0.502

TA_Ratioi 122 0.794 0.812 0.501 0.986 0.155

Sample of Different Firms from the Same Country

Compit 250 �0.112 �0.041 �0.696 �0.005 0.177

IFRSt 250 0.500 0.500 0.000 1.000 0.500

TA_Ratioi 250 0.904 0.943 0.551 1.000 0.102

Panel B: Regression Results

Variables

Cross-CountryAnalysis

Within-CountryAnalysis

Pooled SampleAnalysis

SimilarFirms

DifferentFirms

SimilarFirms

DifferentFirms

Cross-CountrySimilar and

Different Firms

Cross- andWithin-Country

Similar Firms

Intercept �0.680 �0.109 �0.446 �4.653 0.206 �0.192

(�1.332) (�1.248) (�1.027) (�1.313) (0.656) (�0.278)

IFRSt 0.474*** 0.009 0.655* 0.720 0.009 0.655***

(2.698) (0.969) (1.924) (1.347) (0.117) (3.274)

Similar_Firmi �0.542***

(�4.186)

Cross_Countryi �0.269

(�0.649)

IFRSt � Similar_Firmi 0.465***

(4.199)

IFRSt � Cross_Countryi �0.181

(�0.749)

TA_Ratioi �0.470 0.055 0.132 2.849 �0.247 �0.280

(�1.684) (0.657) (1.318) (1.119) (�0.785) (�0.599)

Com_Codei �0.143* 0.007 �0.033 �0.208

(�1.703) (0.768) (�0.494) (�1.116)

(continued on next page)



country different firms (results are not tabulated).16 Panel B reports the regression results. For the

two cross-country analyses, the coefficient of IFRS (b1) is positive and significant for the sample of

similar firms (coefficient of 0.474, p , 0.01), but is close to 0 (0.009) and statistically insignificant

for the sample of different firms.17 We also compare the coefficient of IFRS between the samples of

similar and different firms. We combine these two samples, and include an indicator for the

observations from the sample of similar firms (Similar_Firm) and its interaction with IFRS in

TABLE 2 (continued)

Variables




SimilarFirms

DifferentFirms

SimilarFirms

DifferentFirms


Different Firms


Similar Firms

Listingi �0.626 �0.075*** �0.483** �0.611

(�1.396) (�18.680) (�2.271) (�1.575)

IDi Yes No Yes No Yes Yes

Observations 266 296 122 250 562 388

Adj. R2 0.201 0.006 0.341 0.073 0.317 0.275

*, **, *** Indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, two-sided.This table presents the results of the tests using the similarity of accounting functions measure. Panel A presents thedescriptive statistics for the variables used in the multivariate test. All of the variables are winsorized at the top andbottom 1 percent. Panel B presents the regression coefficients (t-statistics in parentheses). The base model is as follows:

Compit ¼ b0 þ b1IFRSt þ b2TA Ratioi þ b3Com Codei þ b4Listingi þ IDi:

For within-country analyses, Com_Code and Listing are excluded from the model because all of the observations equal 1,and the standard errors are adjusted by country clusters. For difference facet of comparability analysis, IDi is excludedbecause each of the pairs consists of one manufacturing firm and one service firm. For analyses using a pooled sample ofcross-country similar and different firms, the model includes an indicator variable, Similar_Firm, and its interaction withIFRS. For analyses using a pooled sample of cross- and within-country similar firms, the model includes an indicatorvariable, Cross_Country, and its interaction with IFRS.

Variable Definitions:Compit ¼ information comparability between two firms in pair i in period t;IFRSt ¼ an indicator that equals 1 if t is a post-IFRS period, and 0 if t is a pre-IFRS period;TA_Ratioi ¼ ratio of the smaller value of total assets to the larger value of the firms in a pair in 2006;Com_Codei¼ an indicator that equals 1 when the home countries of the two firms in a pair have different legal system

origins, and 0 otherwise;Listingi¼ an indicator that equals 1 if the two firms in a pair are listed on at least one of the same stock exchanges, and 0

otherwise;IDi ¼ an industry indicator of a pair;Similar_Firmi¼ an indicator that equals 1 if Comp is the comparability between two similar firms, and 0 if it is between

two different firms; andCross_Countryi ¼ an indicator that equals 1 if Comp is the comparability between two similar firms from different

countries, and 0 if it is between two similar firms from the same country.

16 Seven pairs of firms have extremely small comparability values, mainly in the pre-IFRS period. When these pairsare excluded from the sample of cross-country similar firms, the mean comparability of the sample becomessimilar to those of the other samples. When we estimate Equation (4) using the reduced sample, the coefficient ofIFRS remains positive and significant (coefficient of 0.119, p , 0.000), and significantly greater than that of thesample of cross-country different firms.

17 We follow Milton (1986) to estimate a minimum sample size necessary to reach a t-value of 2.00 and 1.68 for thesamples used in the tests of the similarity of accounting functions and information transfer measures where theresult for the variable of interest is insignificant. We find that all of our sample sizes are greater than theircorresponding minimum sample sizes based on a t-value of 2.00.

1778 Yip and Young


Equation (4). We find that the coefficient on the interaction is positive and significant (coefficient of

0.465, p , 0.01). These results, thus, suggest that there is a significant improvement in the

similarity facet, but not in the difference facet of cross-country comparability in the post-IFRS

period.

For the two within-country analyses, the coefficient on IFRS is 0.655 and marginally

significant for similar firms, and 0.720 but insignificant for different firms. We combine the samples

of cross- and within-country similar firms, and include an indicator for the observations from the

cross-country sample (Cross_Country) and its interaction with IFRS in the model. We find that the

coefficient on the interaction is insignificant, suggesting that the effect of IFRS adoption on the

similarity facet of cross-country comparability is similar to that of within-country comparability.

Results Using the Information Transfer Measure

The regression model using the measure of information transfer is as follows:

jNAF CARitj ¼ b0 þ b1jAF UEitj þ b2IFRSt þ b3jAF UEitj�IFRSt þ b4AF Sizeit

þ b5AF Analystit þ b6AF Lossit þ IDi; ð5Þ

where jNAF_CARitj is the average absolute value of the cumulative abnormal returns of all the non-

announcing firms within the three-day earnings release window of an announcing firm, and

jAF_UEitj is the absolute value of the unexpected earnings per share of the announcing firm, scaled

by its stock price at the beginning of the fiscal year.18 We use the absolute value of these two

variables because information transfer can be positive or negative. A positive transfer takes place

when a positive (negative) earnings surprise for an announcing firm indicates an unexpected

improvement (deterioration) in market conditions, which positively (negatively) affects the stock

prices of similar firms. A negative information transfer occurs when a positive (negative) earnings

surprise for an announcing firm represents an increased (decreased) market share for that firm,

which negatively (positively) affects the stock prices of similar firms (Kim et al. 2008). The

indicator IFRSt equals 1 if t is a post-IFRS year, and 0 if it is a pre-IFRS year. We include the

logarithm of total assets in U.S. dollars of the announcing firm (AF_Sizeit) in the model because

previous studies find that the earnings surprises of larger firms affect the stock prices of similar

firms more than those of smaller firms (e.g., Atiase 1985; Firth 1996).19 The variable AF_Analystitis the number of one-year-ahead earnings forecasts issued and revised for the announcing firm,

controlling for the intensity of analyst activity. In addition, we include AF_Lossit in the model to

indicate whether the announcing firm is reporting a loss, as prior studies find that losses are less

informative than profits (e.g., Hayn 1995). The industry indicator IDi is also included in the cross-

and within-country analysis for similar firms. Further, we adjust standard errors by country clusters

based on the announcing firm’s country in all of the four analyses.

Our variable of interest is the interaction between the absolute value of the unexpected earnings

of the announcing firm and the indicator for the post-IFRS period (jAF_UEitj � IFRSt). The

coefficient on this interaction (b3) indicates whether information comparability differs across the

pre- and post-IFRS periods. The descriptive statistics for the variables for the four samples are

summarized in Table 3, Panel A. We winsorize all of the variables at the top and bottom 1 percent.

As Panel A shows, the mean absolute values of the cumulative abnormal return of the non-

announcing firms range from 0.023 to 0.028, and are all greater than zero.

Table 3, Panel B, reports the regression results. For cross-country analysis, the coefficient b3 on

the variable of interest (jAF_UEitj � IFRSt) is positive and significant (p , 0.05) for the sample of

18 NAF and AF in the variable names denote Non-Announcing Firms and Announcing Firms, respectively.19 The Worldscope database provides accounting data in both the local currency and U.S. dollars.



TABLE 3

Results of the Tests Using the Information Transfer Measure

Panel A: Descriptive Statistics for the Variables in the Various Samples

n Mean Median Minimum Maximum Std. Dev.


jNAF_CARitj 744 0.025 0.018 0.000 0.170 0.027

jAF_UEitj 744 0.237 0.016 0.000 2.420 0.996

IFRSt 744 0.472 0.000 0.000 1.000 0.500

AF_Sizeit 744 2.726 2.692 1.199 5.033 0.740

AF_Analystit 744 6.742 5.000 1.000 30.000 6.439

AF_Lossit 744 0.181 0.000 0.000 1.000 0.386

Sample of Different Firms from Different Countries

jNAF_CARitj 910 0.023 0.021 0.005 0.065 0.011

jAF_UEitj 910 0.201 0.016 0.000 2.232 0.505

IFRSt 910 0.488 0.000 0.000 1.000 0.500

AF_Sizeit 910 2.782 2.759 1.160 4.372 0.717

AF_Analystit 910 7.158 5.000 1.000 32.000 6.717

AF_Lossit 910 0.188 0.000 0.000 1.000 0.391

Sample of Similar Firms from the Same Country

jNAF_CARitj 195 0.028 0.019 0.000 0.128 0.029

jAF_UEitj 195 0.363 0.029 0.000 2.225 0.700

IFRSt 195 0.485 0.000 0.000 1.000 0.501

AF_Sizeit 195 2.696 2.629 0.890 4.512 0.825

AF_Analystit 195 7.441 5.000 1.000 39.000 7.561

AF_Lossit 195 0.170 0.000 0.000 1.000 0.377


jNAF_CARitj 630 0.025 0.020 0.001 0.106 0.019

jAF_UEitj 630 0.239 0.020 0.001 2.227 0.552

IFRSt 630 0.508 1.000 0.000 1.000 0.500

AF_Sizeit 630 2.681 2.684 1.085 4.194 0.702

AF_Analystit 630 6.897 5.000 1.000 28.000 6.289

AF_Lossit 630 0.205 0.000 0.000 1.000 0.404

Panel B: Regression Results

Variables




SimilarFirms

DifferentFirms

SimilarFirms

DifferentFirms


DifferentFirms


Similar Firms

Intercept 0.010 0.038*** 0.039 0.019* 0.036*** 0.025***

(0.272) (11.200) (1.027) (1.668) (19.560) (3.697)

jAF_UEitj 0.000 0.000 0.000 0.000 0.000 �0.000

(0.069) (0.354) (0.144) (0.478) (1.020) (�0.289)

IFRSt �0.002 �0.004 �0.008 �0.000 �0.001 �0.004

(�1.468) (�1.385) (�1.413) (�0.252) (�1.036) (�0.759)

Similar_Firmi 0.001

(1.089)


1780 Yip and Young


TABLE 3 (continued)

Variables




SimilarFirms

DifferentFirms

SimilarFirms

DifferentFirms


DifferentFirms


Similar Firms

Cross_Countryi �0.003

(�0.588)

jAF_UEitj � IFRSt 0.001** 0.001 0.007 0.000 0.001*** 0.003*

(2.430) (0.484) (1.554) (0.091) (3.566) (2.041)

jAF_UEitj � Similar_Firmi �0.001

(�0.431)

jAF_UEitj � Cross_Countryi 0.002

(1.334)

IFRSt � Similar_Firmi 0.002

(1.439)

IFRSt � Cross_Countryi 0.002

(0.389)

IFRSt � jAF_UEitj� Similar_Firmi

0.003**

(2.306)

IFRSt � jAF_UEitj� Cross_Countryi

�0.001

(�1.261)

AF_Sizeit 0.001 �0.005*** �0.009 �0.003 �0.004*** �0.002

(0.210) (�8.658) (�1.156) (�1.497) (�4.723) (�0.644)

AF_Analystit �0.000 0.000 0.001 �0.000 �0.000 0.000

(�0.492) (0.681) (0.954) (�1.712) (�0.018) (0.189)

AF_Lossit �0.000 0.002* 0.003 0.002 0.001 0.002

(�0.070) (2.197) (0.369) (0.865) (0.941) (1.651)

IDi Yes No Yes No Yes Yes

Observations 744 910 195 630 1,654 939

Adj. R2 0.046 0.154 0.029 0.075 0.124 0.030

*, **, *** Indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, two-sided.This table presents the results for the tests using the information transfer measure. Panel A presents the descriptive statistics forthe variables in the multivariate test. All of the variables are winsorized at the top and bottom 1 percent. Panel B presents theregression coefficients (t-statistics in parentheses). The standard errors are clustered at the announcing firm’s country level.The base model is as follows:

jNAF CARitj ¼ b0þ b1jAF UEitj þ b2IFRSt þ b3jAF UEitj�IFRSt þ b4AF Sizeit þ b5AF Analystit þ b6AF Lossit þ IDi:

For analysis using a combined sample of similar and different cross-country firms, the model includes an indicator,Similar_Firm, and its two- and three-way interactions with IFRS and jAF_UEj. For analysis using a pooled sample ofcross- and within-country similar firms, the model includes an indicator, Cross_Country, and its two- and three-wayinteractions with IFRS and jAF_UEj.Variable Definitions:jNAF_CARitj¼ average absolute value of the cumulative abnormal return of all of non-announcing firms within the three-

day earnings release window of the announcing firm;jAF_UEitj ¼ absolute value of the unexpected earnings per share of the announcing firm, scaled by its stock price at the

beginning of the fiscal year;IFRSt ¼ an indicator that equals 1 if t is a post-IFRS year, and 0 if t is a pre-IFRS year;AF_Sizeit¼ size of the announcing firm, measured by the logarithm of total assets in U.S. dollars;AF_Analystit ¼ number of one-year-ahead earnings forecasts issued and revised for the announcing firm;AF_Lossit ¼ an indicator of whether the announcing firm is reporting a loss;IDi ¼ an industry indicator of a pair;Similar_Firmi¼ an indicator that equals 1 if the announcing and the non-announcing firms are similar firms, and 0 if they

are different firms; andCross_Countryi¼ an indicator that equals 1 if the announcing and the non-announcing firms are from different countries,

and 0 if they are from the same country.



similar firms with a coefficient of 0.001, but insignificant for the sample of different firms. Similar

to the measure of accounting functions similarity, we pool the two samples and include an indicator

for the observations from the sample of similar firms (Similar_Firm) and its interaction with

jAF_UEitj � IFRSt in the model to test the equality of b3 across the similar and different firms. We

find that the coefficient on IFRSt �jAF_UEitj � Similar_Firmi has a value of 0.003 and is significant

(p , 0.05), which suggests that IFRS adoption increases cross-country information transfer more

for similar firms than for different firms.

For within-country analysis, the coefficient b3 on jAF_UEitj � IFRSt is insignificant for both the

sample of similar firms and the sample of different firms. However, the result from the test that

examines the equality of b3 between the cross-country and within-country similar firms shows that

the coefficient on the variable of interest (IFRSt � jAF_UEitj � Cross_Countryi ) is insignificant,

suggesting that change in information transfer does not statistically differ between these two

samples. Thus, the results using the information transfer measure are generally consistent with those

using the similarity of accounting functions measure.

Results Using the Similarity of ICE and ICBV Measure

For the ICE and ICBV measures, we conduct tests for similar firms across countries and for

different firms within the country. The descriptive statistics for the variables used in Equation (3)

for these two samples are summarized in Table 4, Panel A. We winsorize all of the variables at the

top and bottom 1 percent. Table 4, Panel B, presents the aggregate results from estimating Equation

(3).

We perform a t-test to compare the mean comparability scores for ICE and ICBV across the

pre- and post-IFRS periods. Table 4, Panel C, reports the results. For the sample of cross-country

similar firms, the mean comparability scores for ICE and ICBV in the post-IFRS period are 0.890

and 0.862, respectively, indicating that among the 210 regressions of Equation (3), about 89.0

(86.2) percent show that the information content of earnings (book value of equity) does not

statistically differ between the two sets of similar firms from two different countries. The 0.890

score for ICE is marginally greater than the corresponding pre-IFRS period score 0.829 (p¼ 0.06),

and the 0.862 score for ICBV is significantly greater than the pre-IFRS period score of 0.748 (p ,

0.01). This again suggests that IFRS adoption increases the similarity facet of cross-country

information comparability. However, for the sample of different firms from the same country, the

mean comparability scores for ICE and ICBV in the post-IFRS period (0.688 and 0.688) are not

significantly different from the corresponding scores in the pre-IFRS period (0.594 and 0.625).

Summary of Results

Our comparability measures consistently indicate a significant improvement in the similarity

facet, but an indiscernible change in the difference facet of cross-country information comparability

after mandatory IFRS adoption. Further, the comparability improvement in the similarity facet is

consistently greater than that in the difference facet. Together, these results suggest that IFRS

adoption is, on the whole, beneficial, because it makes similar things look more alike without

making different things look discernibly less different.

We also find some evidence of improvement in the similarity facet of within-country

comparability. Importantly, our results show that there is no statistical difference between the

similarity facet of comparability improvement in the cross-country sample and that in the within-

country sample. Together, these results suggest that IFRS adoption improves information

comparability among similar firms from different countries, as well as similar firms within the same

country. As IFRS adoption generally reduces accounting choices for European firms (Hoogendoorn

1782 Yip and Young


TABLE 4

Results of the Tests Using the ICE and ICBV Measures

Panel A: Descriptive Statistics for the Variables in the Estimation of Equation (3)

Variable n Mean Median Minimum Maximum Std. Dev.


MVit 1,798 28.849 6.939 0.070 531.913 73.171

NIit 1,798 1.513 0.283 �12.690 39.300 5.719

BVit 1,798 25.459 3.252 �4.699 802.606 94.491


MVit 1,259 30.123 6.853 0.071 625.803 82.706

NIit 1,259 1.459 0.249 �15.144 45.312 6.370

BVit 1,259 26.988 3.216 �5.475 851.320 103.926

Panel B: Aggregate Summary Table of Estimates

Estimates

Full Sample Pre-IFRS Sample Post-IFRS Sample

Mean p-value Mean p-value Mean p-value

Estimates for the Sample of Similar Firms from Different Countries (n ¼ 420)

Intercept 10.327*** 0.000 10.187*** 0.000 10.467*** 0.000

NIit 3.313*** 0.000 1.917*** 0.000 4.709*** 0.000

BVit 0.690*** 0.000 0.623*** 0.000 0.757*** 0.000

D1 �6.657*** 0.000 �6.445*** 0.002 �6.868*** 0.000

D1 � NIit �0.000 0.970 0.000 0.366 �0.000** 0.016

D1 � BVit �0.000 0.496 �0.000 0.196 0.000* 0.072

Estimates for the Sample of Different Firms from the Same Country (n ¼ 128)

Intercept 5.642** 0.035 4.289* 0.074 6.996 0.145

NIit 2.384*** 0.002 0.761 0.483 2.076*** 0.000

BVit 0.924*** 0.002 0.897*** 0.000 �0.172* 0.096

D2 7.638*** 0.004 7.686** 0.031 �0.338* 0.060

D2 � NIit 1.345* 0.077 2.664** 0.014 �2.100 0.981

D2 � BVit �0.332 0.270 �0.460** 0.032 �1.332 0.718

Panel C: t-test Results

Similar Firms fromDifferent Countries

Different Firms fromthe Same Country

Mean Comparability Score for ICE

Pre-IFRS 0.829 0.594

Post-IFRS 0.890 0.688

Difference 0.061* 0.094

t-statistic (two-tailed p-value) in a paired t-test 1.868 1.515

(0.063) (0.135)

Observations 210 64

Mean Comparability Score for ICBV

Pre-IFRS 0.748 0.625

Post-IFRS 0.862 0.688

Difference 0.114*** 0.063

t-statistic (two-tailed p-value) in a paired t-test 3.412 0.942

(0.001) (0.350)

Observations 210 64




2006), our results are consistent with the view that IFRS adoption improves information

comparability via both accounting convergence and higher quality information.

V. SUPPLEMENTAL AND SENSITIVITY TESTS

Supplemental Tests for the Impact of Institutional Environment on Information

Comparability

These supplemental tests examine whether the impact of IFRS adoption on the similarity facet

of cross-country information comparability is influenced by a firm’s institutional environment. On

one hand, the importance of institutional factors to a firm’s reporting incentives suggests that the

comparability improvement resulting from IFRS adoption will be greater among firms from

countries with similar institutional environments than among firms from countries with different

institutional environments (e.g., Ball et al. 2003; Ball and Shivakumar 2005; Burgstahler et al.

2006). On the other hand, because countries with similar institutional environments usually had

similar local accounting standards in the pre-IFRS period, the comparability improvement may be

smaller among firms from these countries. We, thus, view this as an empirical question.

We classify a firm’s institutional environment according to the origins of the legal system in its

home country. For the tests using the accounting function measure, we form two samples. The first

(second) sample includes all pairs of two similar firms from countries with the same (different) legal

origin(s). We drop the variable Com_Codei from Equation (4), and estimate the modified model for

the two samples. Our results, presented in Table 5, Panel A, show that cross-country information

comparability increases after IFRS adoption for both samples. We also test the equality of

comparability improvement across the two samples by pooling the two samples and including an

indicator for the observations from the first sample and its interaction with IFRS. We find that the

comparability improvement does not statistically differ between the two samples.20

TABLE 4 (continued)

*, **, *** Indicate significance at the 0.10, 0.05, and 0.01 levels, respectively.This table presents the results of the tests using the ICE and ICBV measure. Panel A presents the descriptive statistics forthe variables in the estimation of the following equation:

MVit ¼ b0 þ b1NIit þ b2BVit þ b3Dx þ b4Dx�NIit þ b5Dx�BVit:

All of the variables are winsorized at the top and bottom 1 percent. The sample of cross-country similar firms comprises1,798 firms, and the sample of within-country different firms comprises 1,259 firms. Panel B presents the aggregatesummary table for the estimates, which includes the means and two-tailed p-values showing whether the means aresignificantly different from 0. Panel C presents the results for the t-test of the difference in ICE and ICBV across the pre-and post-IFRS period for the sample of cross-country similar firms (n¼ 210) and the sample of within-country differentfirms (n¼ 64), respectively. An ICE (ICBV) comparability score of 1 is assigned if b4 (b5) is insignificant (defined as ap-value of more than 5 percent), and 0 otherwise.

Variable Definitions:MVit¼ total market value of equity at the end of the fiscal year, scaled by the number of outstanding common shares;BVit¼ book value of equity, scaled by the number of outstanding common shares;NIit¼ net income, defined as earnings (revenues less operating costs, depreciation, interest, taxes, and other expenses)

less preferred dividends, scaled by the number of outstanding common shares; andDx¼ a country indicator when x equals 1, and an industry sector indicator when x equals 2.

20 Our results could be affected by the enforcement changes accompanied with IFRS adoption. For example, the EUpassed the Transparency Directive in 2004 to establish disclosure requirements and facilitate IFRS compliance.

1784 Yip and Young


TABLE 5

Impact of Institutions on Information Comparability

Panel A: Accounting Function Measure by Legal System Origin

VariableSame Origin of Legal System

Compit

Different Origins of Legal SystemCompit

Intercept 1.846 �1.642***

(1.441) (�8.498)

IFRSt 0.446*** 0.507**

(4.603) (2.689)

TA_Ratioi �1.245 �0.269**

(�1.143) (�2.578)

Listingi �1.123

(�1.391)

IDi Yes Yes

Observations 144 122

Adj. R2 0.116 0.274

Panel B: Information Transfer Measure by Legal System Origin

VariableSame Origin of Legal System

jNAF_CARitjDifferent Origins of Legal System

jNAF_CARitj

Intercept 0.027*** 0.019*

(5.637) (1.673)

jAF_UEitj 0.104 0.002

(1.620) (0.894)

IFRSt �0.003 �0.000

(�1.466) (�0.092)

jAF_UEitj � IFRSt 0.024** �0.001

(2.157) (�0.449)

AF_Sizeit �0.002 �0.003

(�1.449) (�1.510)

AF_Analystit 0.000 �0.000

(0.363) (�1.644)

AF_Lossit 0.001 0.002

(0.258) (0.834)

IDi Yes Yes

Observations 514 285

Adj. R2 0.016 0.075

Panel C: ICE and ICBV Measure by Legal System Origin

Legal Origins nPre-IFRS

MeanPost-IFRS

Mean Difference t-statistics p-value

t-test Results for the Difference in ICE Comparability Scores

Same 155 0.794 0.858 0.064a 1.550 0.123

Different 55 0.709 0.782 0.073a 0.893 0.376

0.009b 0.097 0.923




For the information transfer measure, we pair an announcing firm’s earnings surprise with the

average cumulative abnormal return of similar firms from countries with the same (different) legal

origin(s) to the home country of the announcing firm in the first (second) sample. We find that the

cross-country information transfer increases significantly in the post-IFRS period for the first

sample, but not for the second sample. These results are reported in Table 5, Panel B.

For the ICE and ICBV measure, the first (second) sample includes all of the comparability

scores estimated using the two sets of firms from countries with the same (different) legal origin(s).

We compare the mean comparability scores across the pre- and post-IFRS periods in the two

samples. We find that for ICE, there is no significant change in the mean comparability score across

the two periods. For ICBV, however, the mean comparability score is significantly higher in the

post-IFRS period than in the pre-IFRS period for the first sample with the same legal system origin,

but not for the second sample. Table 5, Panel C, reports these results.

Overall, we find consistent evidence for enhanced cross-country information comparability

among firms from countries with similar institutional environments. However, the results for firms

from different institutional environments are mixed. Although the results using the measure of

accounting function similarity suggest an improved comparability, the results using the other two

measures suggest the opposite. This provides some evidence that the institutional environment in

the home countries of firms is an important determinant of their information comparability.

Sensitivity Tests

We perform several sensitivity tests to assess the robustness of our results. First, because

unusual accounting choices could be reflected in financial statements due to the anticipation of, and

the transition to, IFRS during 2004 and 2005, we exclude these two years by defining the pre-IFRS

period as 2002–2003 and the post–IFRS period as 2006–2007. The untabulated results based on the

new sample periods are similar to those of the main tests.

Second, our results on the comparability improvement may be driven by increased similarity of

firms’ responses to economic shocks. To address this possibility, we examine whether there is a

significant increase in firms’ responses to economic shocks. Specifically, we compute the stock

TABLE 5 (continued)

Legal Origins nPre-IFRS

MeanPost-IFRS

Mean Difference t-statistics p-value

t-test Results for the Difference in ICBV Comparability Scores

Same 155 0.645 0.755 0.110**a 2.580 0.011

Different 55 0.745 0.818 0.073a 1.071 0.289

0.037b 0.450 0.653

*, **, *** Indicate significance at the 0.10, 0.05, and 0.01 levels, respectively.a Represents a test of mean difference across the pre- and post-IFRS periods.b Represents a test of mean difference between the first and second samples.This table presents the results for the additional tests of the impact of firms’ institutions on their informationcomparability. Panel A presents the coefficients (t-statistics in parentheses) for the tests using the accounting functionmeasure, in which the first (second) sample includes all of the pairs of two similar firms from countries with the same(different) legal origin(s). The variables are defined in Table 2. Panel B presents the coefficients (t-statistics inparentheses) for the tests using the information transfer measure. In the first (second) sample, the announcing firm’searnings surprise is paired with the average cumulative abnormal return of similar firms from countries with the same(different) legal origin(s) to the home country of the announcing firm. The variables are defined in Table 3. Panel Cpresents the results of tests using the ICE and ICBV measure, in which the first (second) sample includes all of thecomparability scores estimated using two sets of firms from countries with the same (different) legal origin(s). Thevariables are defined in Table 4.

1786 Yip and Young


return correlation between two sets of similar firms from different countries for the pre- and post-

IFRS periods separately. Because firms in the same industry face similar economic shocks, their

stock returns should be correlated if they react to economic shocks in similar ways. A significantly

higher mean correlation of stock return in the post-IFRS period than in the pre-IFRS period would

suggest that there is an increase in the similarity of firms’ responses to economic shock after

mandatory IFRS adoption. We find that the mean correlation is significantly lower in the post-IFRS

period (0.134) than in the pre-IFRS period (0.314), suggesting that firms’ responses to economic

shocks become less similar in the post-IFRS period. This, in turn, suggests that the similarity of

firms’ responses to economic shocks is unlikely to be the driver of improved information

comparability in the post-IFRS period.

Third, because level regressions can be subject to econometric problems, we use a returns

model to compare ICE across the pre- and post-IFRS periods. In our return model, annual stock

returns are regressed on earnings scaled by price, a loss indicator, a country indicator, and the

interaction between earnings and the country indicator. We run the regression for every possible

combination of two countries in an industry in the pre- and post-IFRS periods separately, and assign

a comparability score of 1 if the coefficient on the interaction term is insignificant, and 0 otherwise.

We find that the mean comparability score in the post-IFRS period (0.990) is significantly higher

than that in the pre-IFRS period (0.945), which is consistent with the finding from the level model

that IFRS adoption improves the similarity facet of cross-country information comparability.

Fourth, because all of our comparability measures are based on equity price, we use a non-

price-based comparability measure to examine the similarity facet of cross-country information

comparability. Following Beuselinck et al. (2007), we assume that earnings comparability is

affected by the timely recognition of losses by a firm’s accounting system. We use the following

modified model from Ball and Shivakumar (2005) to determine the comparability scores for every

possible combination of two countries in an industry in the pre- and post-IFRS periods separately:

ACCjt ¼ b0 þ b1CFjt þ b2NegCFjt þ b3CFjt�NegCFjt þ b4Dþ b5D�CFjt

þ b6D�NegCFjt þ b7D�CFjt�NegCFjt þ ejt; ð6Þ

where ACCt is accruals, defined as net income minus operating cash flow, and CFt is operating cash

flow. Both are scaled by the average total assets. NegCFt is an indicator of a negative CF, and D is a

country indicator. The coefficient on the last item (b7) indicates whether the accounting systems in

the two countries recognize losses in accounting accruals in a similar fashion. We assign a

comparability score of 1 if b7 is insignificant, and 0 otherwise. We find that the mean comparability

score in the post-IFRS period (0.861) is significantly higher than that in the pre-IFRS period

(0.764). This result is consistent with the findings from those equity price-based comparability

measures that cross-country information comparability improves for similar firms after mandatory

IFRS adoption.

VI. CONCLUSION

One of the most commonly mentioned benefits of IFRS adoption is the improvement in

cross-country information comparability. This study provides empirical evidence for this posited

benefit using data from 17 European countries that adopted IFRS in 2005. We use three proxies for

information comparability. The first is the similarity with which two firms translate economic

events into their financial statements. The second is the degree of information transfer, and the third

is the similarity of the information content of earnings and of the book value of equity.

Overall, our results are consistent with the view that mandatory IFRS adoption improves

information comparability across countries. In particular, our results indicate a significant increase

in the similarity facet of cross-country comparability in the post-IFRS period. However, we do not



find such improvement in the difference facet of cross-country comparability, which implies that the

mandatory IFRS adoption is not sufficient to achieve a full enhancement in comparability in the

EU. Our results also suggest that both accounting convergence and higher quality accounting

information are likely to be the mechanisms underlying the observed comparability improvement in

the similarity facet. Further, our results suggest that comparability improvement is more likely to

occur among firms from similar institutional environments than among firms from different

institutional environments.

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