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Which Performance Measures Do Investors

Around the World Value the Mostand Why?

Jan Barton

Emory University

Thomas Bowe Hansen

University of New Hampshire

Grace Pownall

Emory University

ABSTRACT: We examine the value relevance of a comprehensive set of summary

performance measures including sales, earnings, comprehensive income, and operat-

ing cash flows. We find that, while value relevance peaks for measures above the line,

no single measure dominates around the world. Instead, a measure is more relevant

when it captures, directly and quickly, information about firms cash flows. Specifically,

for each performance measure by country, we estimate eight attributes commonly used

to assess earnings quality. We find these attributes highly correlatedmost of their

variance is explained by only two principal factors. A factor capturing articulation with

cash flows is positively associated with a measures value relevance; a factor reflecting

the measures persistence, predictability, smoothness, and conservatism is negatively

associated. Our results suggest that, when it comes to equity valuation, accounting

researchers and standard-setters should focus not on what performance measure is

best at a given point in time, but on the underlying attributes that investors find most

relevant.

Keywords: summary performance measure; earnings attributes; value relevance;

international accounting standard setting.

Data Availability: Data are available from sources identified in the paper.

We appreciate the helpful comments of Steve Kachelmeier senior editor, an anonymous reviewer, Linda Bamber, MaryBarth, Phil Berger, Andy Call, Jenny Gaver, Stacie Laplante, Katherine Schipper, Matt Wieland, workshop participants atThe University of Georgia, and participants in the Reporting Financial Performance workshops in Bordeaux, New York,Washington D.C., and Istanbul. We also thank Ron Harris for research assistance, and the Goizueta Business School, theIAAER Reporting Financial Performance Research Program, and the KPMG Foundation for generous funding.

Editors note: Accepted by Steven Kachelmeier.

THE ACCOUNTING REVIEW American Accounting AssociationVol. 85, No. 3 DOI: 10.2308/accr.2010.85.3.7532010pp. 753789

Submitted: February 2008 Accepted: August 2009

Published Online: May 2010

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I. INTRODUCTION

R

ecent research suggests that revenue exhibits value relevance that differs from that of

various expenses and other line items making up earnings on the income statement, per-

haps due to the differential persistence, heterogeneity, and ease with which those line items

can be misreported. Some studies e.g., Ertimur et al. 2003 find that revenues are more valuerelevant than earnings in at least some circumstances. Chandra and Ro 2008 extend the com-parison of both information content and value relevance between revenues and a performance

measure that includes expenses, gains, and losses, finding that the incremental value relevance of

revenues is pervasive rather than confined only to cases in which earnings are less informative.

Taken together, these findings raise validity concerns regarding the assumption underpinning the

literature that earnings is the most relevant performance measure for equity valuation. Consistent

with these concerns, in a study of the valuation implications of losses, Hayn 1995, 150 suggeststhe need for examining the degree of substitution between earnings numbers and alternative

accounting variables.

We expand the comparison of the value relevance of line items, subtotals, and totals on the

statements of financial performance to support broad generalizations about the usefulness of per-

formance measures and the reasons for the increased usefulness of some measures relative to

others. Our goal is to provide insight to standard-setters for the project on reporting financial

performance currently being pursued by the Financial Accounting Standards Board FASB andthe International Accounting Standards Board IASB.1 The joint FASB/IASB project on reportingfinancial performance proposes to explore whether certain line items, subtotals, and totals should

be defined in standards and required to be displayed in financial statements FASB 2001, 5 andto consider whether to require the display of summarized amounts such as operating income or

income from core activities, EBITDA, or operating cash flow FASB 2001, 2.We estimate and compare the value relevance of a comprehensive set of performance mea-

sures commonly disclosed in the financial statements of almost 20,000 firms from 46 countries

during 19962005. Our performance measures are operating cash flows, sales, EBITDA, operating

income, income before taxes, income before extraordinary items and discontinued operations, net

income, and total comprehensive income. We find that the value relevance of the performance

measures varies substantially across line items on the income statement as well as across coun-tries. In general, subtotals near the center of the income statement, such as operating income, have

the strongest association with contemporaneous stock returns; subtotals at the top or bottom of the

income statement, such as sales and total comprehensive income, have the weakest association

with stock returns. Although the weak association between sales and stock returns is fairly robust

across countries, we observe weak value relevance of net income and total comprehensive income

mostly in common-law countries.

We also estimate seven attributes of each performance measure that are assumed in the

literature to increase the value relevance of earnings. We draw our estimation methods for these

seven earnings attributespersistence, predictability, smoothness, contemporaneous and lagged

association with operating cash flows, timeliness, and conservatismfrom the literatures on the

properties of earnings and earnings quality, and consider whether these attributes reflect unique

underlying constructs. We find that estimates of the seven attributes are strongly correlated with

each other, making interpretation of the attributes and a regression of value relevance on theseattributes problematic. We then perform a principal components factor analysis to address the

collinearity among these attributes, thereby reducing the dimensionality of the data and developing

1 Barth, Beaver, and Landsman 2001 and Holthausen and Watts 2001 provide an insightful academic debate on the roleof the value relevance literature in financial reporting standard-setting.

754 Barton, Hansen, and Pownall

The Accounting Review May 2010American Accounting Association
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a parsimonious description of the underlying constructs. We find that two intuitively appealing

factorscorresponding to the notions of sustainability and articulation with cash flowsexplain

about 80 percent of the variance of the seven attributes. Each of these two factors is significantly

correlated with our proxy for value relevance: the articulation-with-cash-flows factor positively,

and the sustainability factor negatively. We interpret these findings as suggesting that performance

measures that reflect more closely and timely changes in current and expected future operating

cash flows are more useful to equity investors. On the other hand, performance measures that are

more persistent, predictable, smooth, and conservative are less useful for equity valuation.

Taken together, our evidence indicates that optimal performance measures useful in equity

valuation vary across different economic circumstances and accounting regimes as a function of

the underlying attributes of the performance measures. Therefore, researchers and standard-setters

should focus not on what performance measure is best at any one point in time, but rather should

focus on the underlying attributes demanded by financial statement users.

We contribute to the academic literature in three specific ways. First, we extend the literature

comparing the information content and value relevance of performance measures other than earn-

ings. One performance measure will convey more or less information relative to another if the twoare not perfectly correlated, that is, when the information they contain about future earnings or

cash flows is different. Ertimur et al. 2003 and Jegadeesh and Livnat 2006 find that stock pricesrespond to both revenue and earnings information contained in announcements. Chandra and Ro

2008 examine the incremental value relevance and information content of revenue given earn-ings to determine whether the superiority of revenue is pervasive or just confined to cases in which

earnings is less informative. Such cases include extreme earnings events, as well as settings for

which the accounting model is not well-specified see Lev and Zarowin 1999 for technologyfirms, and Davis 2002 and Hayn 1995 for periods of economic shocks or losses. Chandra andRo 2008 find that the incremental value relevance of revenues is pervasive and increasing overtime. They argue that revenues contain information about future earnings and cash flows that is

lost when revenues are aggregated with nonoperating items, gains, losses, unusual and infrequent

items, and other events and transactions with different persistence, stickiness, and flexibility forearnings management. We extend this literature by examining a comprehensive set of financial

performance measures, explicitly considering a broader set of attributes of performance measures

that may lead to differential value relevance, and expanding the capital market context beyond the

U.S. setting by including firms traded in the global capital market.

Second, we extend the literature comparing attributes and pricing effects of earnings, accruals,

cash flows, pro forma or core earnings, and nonfinancial performance measures. For example,

Vincent 1999 compares the relative and incremental information content of funds from opera-tions and earnings per share for real estate investment trusts. Francis et al. 2003 investigate theability of earnings and a variety of non-earnings performance measures identified at the industry

level to explain stock returns. Finally, Bradshaw and Sloan 2002 document the increased use ofpro forma earnings in the last two decades of the twentieth century, comparing the relative

association with stock returns of GAAP earnings and pro forma earnings. We add to this literature

by investigating why particular performance measures are useful, in addition to documenting

which performance measures are most useful in the global capital market and within each par-

ticular accounting regime. This analysis builds on the evidence presented by Francis et al. 2004,who investigate the relationship between the cost of capital and seven earnings attributes. We

expand their findings by using similar attributes to investigate the relationship between the infor-

Which Performance Measures Do Investors Around the World Value the Mostand Why? 755

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mation content of performance measures and their underlying attributes.2

Our evidence indicates

that performance measures that are optimal for equity valuation vary across different economic

and accounting regimes as a function of the underlying attributes of the performance measures.

Finally, we contribute to the substantial body of research on the properties of earnings and its

components, both in the United States and globally e.g., Lipe 1986; Rayburn 1986; Alford et al.1993; Ball et al. 2000; Barth, Cram, and Nelson 2001; Barth et al. 2002; Leuz et al. 2003. Muchof this literature treats each attribute as independent, either by including only one or two attributes

in the empirical tests, or by including several attributes as independent variables in regression

analyses. For example, Lipe 1990 examines the impact of the persistence of earnings and theability of current earnings to predict future earnings on a firms earnings response coefficient. In

contrast, Francis et al. 2004 examine the association between the cost of capital and sevenearnings attributes. However, empirical evidence indicates that earnings attributes are often

strongly correlatedChaney et al. 2008 find that a principal component analysis of earningsquality measured as in Dechow and Dichev 2002, persistence, predictive ability, and smooth-ness results in only one factor with an eigenvalue greater than 1.

3Our evidence indicates that

researchers should consider the potential relationships among performance attributes when using

them to test accounting theories.4

Section II discusses our performance measure attributes and their relationship with the mea-

sures value relevance. We describe our sample and data in Section III; Section IV presents

analyses regarding our value relevance measures and the attributes of our performance measures.

In Section V, we use a principal components factor analysis to examine the correlation structure

among the performance measure attributes, and we investigate the relationship between these

attributes factors intuitively corresponding to sustainability and articulation with cash flows andthe measures value relevance. Section VI offers concluding remarks.

II. PERFORMANCE MEASURE ATTRIBUTES AND VALUE RELEVANCE

In this section, we describe the seven performance measure attributes that we consider in our

analyses and explain why the presence or absence of each attribute would affect a performance

measures value relevance. We focus on a measures value relevanceits ability to explain varia-

tion in contemporaneous stock returnsbecause value relevance is generally viewed in the litera-

ture as a direct estimate of the measures usefulness in equity investors decision making e.g.,Collins et al. 1997; Francis and Schipper 1999; Lev and Zarowin 1999. Moreover, the FASBconsiders relevance as a primary quality that makes accounting information useful to investors

FASB 1980; Barth, Beaver, and Landsman 2001; Holthausen and Watts 2001.

2 Our attributes differ from those in Francis et al. 2004 in two ways. First, we use value relevance as our dependentvariable rather than as an explanatory variable because our research question focuses on which performance measure isuseful to investors. In addition, their dependent variable is cost of capital, measured using Value Line reports, a datarequirement that is unavailable for the majority of our sample firms. Second, they include quality measured as inDechow and Dichev 2002 as an earnings attribute. However, this attribute is theoretically designed to measure thequality of a particular performance measurenet income. As an analog to this measure, we include two attributes: theability of a performance measure to predict next periods cash flows, and the closeness of a performance measure to

current period cash flows.3 They retain a second factor in their analysis with an eigenvalue of 0.95, indicating that the majority of the variation inthese four attributes can be explained by essentially two factors.

4 This result represents an empirical observation rather than a theoretical relationship between the attributes. For example,the strength of the association between persistence and predictability is determined by the magnitude of the error termin the first-order autoregressive model commonly used to measure these variables. A firm may have very persistentearnings that are relatively unpredictable if the mean shock is large, or nonpersistent earnings that are quite predictableif the mean shock is small. See Lipe 1990 for a description of the conceptual difference between these variables, andSchipper and Vincent 2003 for a description of the relationships among a wide variety of earnings attributes.


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Consistent with Francis et al. 2004, we argue that, to the extent that a particular attributecaptures some aspect of the uncertainty about future cash flows available to equity holders, a

favorable outcome of that attribute for a particular performance measure will make that measure

more value relevant. We select our attributes based on their use in prior academic literature. In

particular, we are consistent with recent literature by considering the same or analogous attributesas those examined by Francis et al. 2004. We describe in turn each of the attributes we considerand provide intuition regarding each attributes relationship with value relevance.

Persistence

This construct captures in part the performance measures sustainability over time. We follow

Lev 1983, Ali and Zarowin 1992, and Francis et al. 2004 in measuring performance measurejs PERSISTENCE in country k as the slope coefficient 1 in the first-order autoregressive model:

Performance Measure jit+1,k = 0,k + 1,kPerformance Measure jit,k + it,k. 1

Values of1 close to 1 indicate highly persistent performance, while values close to 0 indicate

highly transitory performance. If 1 = 1, then the performance measure follows a random walk,with drift 0. If 1 1, the performance measure is expected to converge to a long-term meanof 0 / 1 1, with 1 capturing the speed of convergence. As usual, the subscripts i and tdenote firm and year, respectively.

Investors are more likely to view more persistent measures as desirable since those measures

are recurring e.g., Penman and Zhang 2002; Revsine et al. 2002, 245; Richardson 2003. Con-sistent with this view, Lipe 1986 shows that the information content of earnings components isincreasing in the components persistence. Similarly, Bhattacharya et al. 2003 and Brown andSivakumar 2003 show that core earnings, which tends to be more persistent than net income,is more value relevant than net income. Therefore, we expect more persistent performance mea-

sures to be more value relevant.

Predictability

A measures predictabilityits ability to predict itselfis not only valued in security analysisand equity valuation see, e.g., AIMR 1993; Lee 1999, but it is also an element of relevance in theFASBs conceptual framework and, hence, a desirable attribute from the perspective of standard-

setters FASB 1980. However, Francis et al. 2004 argue that the link between predictability andinformation content is unclear. For example, highly predictable earnings may indeed eliminate

uncertainty about future earnings. But if managers make opportunistic accounting choices to

increase the predictability of earnings, then such persistence might in fact lead to earnings num-

bers that are less value relevant, especially if managers accounting choices reduce the ability of

earnings to convey information about future cash flows to shareholders. Therefore, we make no

directional prediction regarding the relationship between PREDICTABILITY and value relevance.

Our proxy for a performance measures PREDICTABILITY is the adjusted R2 for the first-

order autoregressive model shown in Equation 1, similar in spirit to the square root of theregressions error variance used by Lipe 1990 and Francis et al. 2004. Larger values of PRE-

DICTABILITY imply more predictable performance.

Smoothness

Ronen and Sadan 1981, Chaney and Lewis 1995, Demski 1998, and many others arguethat smoothness is a desirable earnings attribute if managers use their private information about

future earnings to smooth out transitory fluctuations. Such smoothing behavior would lead to more

representative and useful reported earnings. However, if the smoothness of performance measures


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results from earnings management, as assumed by for example Leuz et al. 2003, then smooth-ness indicates that managers have added noise to the performance measure, thus reducing its value

relevance Ohlson and Penman 1992. We are therefore unable to make a prediction regarding theassociation between SMOOTHNESS and value relevance.

Our proxy for performance measure js SMOOTHNESS is the slope coefficient 1 from aregression of the standard deviation of the measure on the standard deviation of operating cash

flows sdOCF, with both standard deviations measured over the sample period:

sdPerformance Measure jit,k = 1,ksdOCFit,k + it,k. 2

We constrain the regression to have no intercept, such that the regressions slope is equivalent to

the smoothness measures used in previous work e.g., Francis et al. 2003; Leuz et al. 2003.Smaller values of SMOOTHNESS indicate a smoother performance measure relative to operating

cash flows, with the latter defined as SMOOTHNESS 1.

Predictability of Future Cash Flows

Investors tend to view performance measures that are more useful in predicting future cash

flows as being more desirable FASB 2002. Therefore, we expect performance measures with agreater ability to predict future cash flows to be more value relevant.

We estimate performance measure js ability to predict one-period-ahead cash flows, PRE-

DICT_OCF, as the adjusted R2 from the regression:

OCFit+1,k = 0,k + 1,kPerformance Measure jit,k + it,k, 3

where OCF is the firms operating cash flows.

Substitute for Cash Flows

Some practitioners and academics take the position that earnings mapping more closely to

operating cash flows is of higher quality Harris et al. 2000; Dechow and Dichev 2002; Penman2007. The underlying assumption is that if a performance measure is closer to the firms cashflows, then accrual accountingand therefore managers judgments and estimateswill have less

of an effect on the reported performance measure. For instance, the less discretion managers

exercise over the accrual process, the fewer opportunities they have to introduce noise into the

performance measure. Therefore, we expect measures that are closer to operating cash flows to

have greater value relevance.5 We measure each performance measures ability to substitute for

operating cash flows, SUBST_OCF, as the slope coefficient 1 in the regression:

OCFit,k =0,k +1,kPerformance Measure jit,k + it,k, 4

where OCF is the firms operating cash flows.

Conservatism

Conservatism reflects the differential ability of accounting earnings to capture economic

losses versus economic gains Basu 1997; Ball et al. 2000; Watts 2003. Managers have incentivesto disclose good news quickly and to delay the disclosure of bad news McNichols 1988. How-

ever, financial statement users likely view disclosures that are inconsistent with managers incen-tives as being more credible Mercer 2004. Therefore, conservative accounting is likely viewed asmore credible. Consistent with this view, LaFond and Watts 2007 argue that conservative finan-

5 An alternative argument is that accrual accounting reduces the noise resulting from lumpy cash flows, removing theability of managers to manipulate earnings by timing cash inflows and outflows. This interpretation would lead to theinference that performance measures that are closer to cash flows are less value relevant.


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cial statements serve an informational role by reducing managers incentives and ability to ma-

nipulate accounting numbers, thus reducing information asymmetry between managers and share-

holders. However, Francis et al. 2004 argue that conservatism, by definition, implies biasedinformation and, hence, does not necessarily improve the quality of accounting performance

measures. Therefore, we make no directional prediction regarding the relationship between con-servatism and value relevance.

Following Ball and Shivakumar 2005, we measure conservatism relative to operating cashflows as the asymmetric timeliness coefficient 3,k from the regression of performance measure j

on proxies for good news i.e., positive cash flows and bad news i.e., negative cash flows:6

Performance Measure jit,k = 0,k + 1,kNEGit,k + 2,kOCFit,k + 3,kNEGit,k OCFit,k + it,k.

5

The indicator NEG is coded 1 if OCF 0, and 0 otherwise. The coefficient is standardized i.e.,multiplied by the standard deviation of the interaction NEGit,k OCFit,k, then divided by the

standard deviation of the performance measure j in country k. We set CONSERVATISM to 0 forperformance measure OCF. Larger values of CONSERVATISM indicate performance measures

that capture changes in the firms economic performance sooner relative to cash flows.

Timeliness

Timeliness captures a performance measures ability to reflect quickly both good and bad

news about the firms performance. As a performance measure attribute, it assumes that account-

ing numbers are intended to measure changes in the firms economic position Ball et al. 2000.Timely information is considered not only more relevant in decision making, but also more

reliable. With respect to earnings, for example, Francis et al. 2004 argue that timeliness increasesthe reliability of the information reported. Because the FASB considers relevance and reliability as

the two primary attributes that make accounting information useful to investors FASB 1980,timeliness is likely a desirable attribute from the standard-setters point of view.

Our measure of TIMELINESS is the estimated adjusted R2 for Equation 5. This interpreta-tion is based on the role of accruals in capturing information regarding future cash flows as well

as the positive correlation between current cash flows and future expected cash flows Ball andShivakumar 2005. We set TIMELINESS to 1 in the case of performance measure OCF. Largervalues of TIMELINESS indicate performance measures that capture changes in the firms eco-

nomic performance in a more timely fashion.

III. SAMPLEWe begin constructing our sample with the 206,730 firm-years available during 19962005

for the 26,479 firms in the Compustat Global Vantage database. The largest number of observa-

tions come from Japan 38,731 firm-years and the United States 37,330 firm-years. Table 1presents the subsequent restrictions that we impose on the sample. We present data on the obser-

vations aggregated by country because country groups are arguably the most homogeneous sub-

units of the data. Value relevance may vary between line items, subtotals, and totals on the

statements of financial performance across countries due to economic fundamentals such as the

6 Conservatism and timeliness are typically measured in the literature following Basu 1997, with stock returns insteadof operating cash flows in the right-hand side of Equation 5. Ball and Shivakumar 2005 adapt Basus 1997 modelto private firms by measuring conservatism and timeliness relative to operating cash flows. We follow their approach toavoid inducing a mechanical correlation between the value relevance and the conservatism/timeliness attributes of theperformance measures we examine. As we report later in Section IV, using returns-based rather than cash-flow-basedmeasures of conservatism and timeliness yields similar conclusions.


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TABLE 1

Sample Selection

Country

Initial Sample Less Deleted Firm-Years

FirmsFirm-Years

MissingMarket

Data

MissingAccounting

DataInadequateSubsample

ExtremValues

Australia 1,553 9,769 3,836 1,437 0 303Canada 822 6,368 1,097 751 0 285U.K. 2,787 18,626 3,223 5,112 0 686U.S.A. 4,685 37,330 5,073 3,992 0 1,804France 1,013 7,429 1,470 1,482 0 326Germany 985 7,760 1,720 1,419 0 365Japan 4,269 38,731 6,198 2,053 0 2,582

Argentina 40 340 38 82 0 13Austria 145 1,088 149 307 0 34

Bahamas 1 7 4 0 3 0 Bahrain 2 20 7 10 3 0 Bangladesh 1 10 7 0 3 0 Belgium 195 1,443 264 450 0 52Belize 1 9 3 1 5 0 Bermuda 548 4,423 1,070 673 2,680 0 Botswana 1 5 2 1 2 0 Brazil 177 1,413 242 314 0 44Virgin Islands 4 26 6 5 15 0 Cayman Islands 317 1,733 645 232 856 0 Chile 125 1,065 136 203 0 54China 1,450 12,210 9,450 169 0 176Colombia 34 259 58 81 0 7Croatia 4 36 9 11 16 0

Cyprus 4 38 1 22 15 0 Czech Rep 33 229 31 76 0 6Denmark 247 1,909 486 475 0 67

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TABLE 1 (continued)

Country


FirmsFirm-Years

MissingMarket

Data

MissingAccounting


ExtremValues

Egypt 15 126 27 71 28 0 Estonia 3 24 3 12 9 0 Finland 156 1,231 333 185 0 55Gabonese Republic 1 10 0 3 7 0 Ghana 1 9 1 7 1 0 Gibraltar 1 3 3 0 0 0Greece 112 853 103 206 0 44Greenland 1 7 3 4 0 0Guyana 1 10 10 0 0 0Hong Kong 252 2,110 174 877 0 88Hungary 24 193 16 31 0 5Iceland 6 40 23 10 7 0 India 321 2,688 202 580 0 153Indonesia 308 2,485 310 594 0 126Ireland 103 753 142 163 0 30Israel 73 605 76 215 0 16Italy 391 2,815 614 761 0 106Jordan 4 35 0 26 9 0 Kenya 1 10 0 4 6 0 Korea 315 2,239 321 488 0 120Kuwait 2 20 14 1 5 0 Latvia 2 11 5 6 0 0Liberia 2 18 6 1 11 0 Liechtenstein 3 30 10 20 0 0Lithuania 3 18 7 3 8 0 Luxembourg 41 305 88 106 0 8Malaysia 910 7,546 1,272 1,360 0 390

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TABLE 1 (continued)

Country


FirmsFirm-Years

MissingMarket

Data

MissingAccounting


ExtremValues

Malta 3 29 0 29 0 0Marshall Islands 1 10 0 0 10 0 Mauritius 1 8 2 0 6 0 Mexico 113 838 140 173 0 42Monaco 3 22 4 7 11 0 Morocco 8 63 5 34 24 0 Namibia 1 2 0 1 1 0 Neth. Antilles 5 50 1 29 20 0 The Netherlands 296 2,279 387 498 0 91New Zealand 144 970 245 224 0 49Norway 241 1,685 506 269 0 62Pakistan 59 516 34 194 0 19Panama 3 28 1 6 21 0 Papua New Guinea 5 40 2 2 36 0 Peru 27 246 36 60 0 6Philippines 196 1,654 128 610 0 64Poland 55 436 44 159 0 13Portugal 76 543 64 133 0 28Qatar 2 4 0 4 0 0Romania 4 28 5 12 11 0 Russia 22 183 20 38 0 8Saudi Arabia 9 45 15 26 4 0 Singapore 546 3,959 778 516 0 216Slovakia 9 70 4 33 33 0 Slovenia 8 64 16 8 40 0 South Africa 329 2,235 292 840 0 70Spain 209 1,619 275 320 0 67

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TABLE 1 (continued)

Country


FirmsFirm-Years

MissingMarket

Data

MissingAccounting


ExtremValues

Sri Lanka 7 57 0 30 27 0 Sweden 401 2,988 858 397 0 144Switzerland 317 2,640 433 707 0 101Taiwan 342 2,575 393 460 0 131Thailand 448 3,527 304 809 0 189Turkey 62 532 24 204 0 25Ukraine 1 5 5 0 0 0United Arab Emirates 6 31 10 13 8 0 Venezuela 16 124 11 52 61 0 Zambia 1 10 5 1 4 0 Zimbabwe 8 55 17 15 23 0

Total 26,479 206,730 44,538 32,005 4,029 9,270

The final sample consists of 117,474 observations for 19,784 public firms over 19962005 with data available on Compustats Global Vanobservations without data to calculate stock returns; 32,005 observations without accounting data to calculate the performance measures; 4,02fewer than 100 firm-years during the sample period, from Bermuda and from the Cayman Islands; and 9,270 observations in the extreme 1st and of the performance measures.

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incidence of losses or industry composition, accounting standards, enforcement mechanisms, and

capital market incentives Ali and Hwang 2000; Ball et al. 2000; Ashbaugh-Skaife et al. 2006;Boonlert-U-Thai et al. 2006; La Porta et al. 2006; Jackson and Roe 2009.

To reduce the burden of interpreting the data disaggregated into 46 subunits, we arrange the

tables whenever possible so that the first seven rows of each panel describe the seven equity

capital markets most frequently studied in cross-national research of the properties of accounting

numbers. These seven markets together constitute about 66 percent of the global market for equity

securities as of April 2008 World Federation of Exchanges 2008; these seven markets and theirpercentage of the global equity market are Australia 2.1 percent, Canada 6.3, United Kingdom6.3, United States 32.6, France/Euronext 7.0, Germany 3.5, and Japan 8.1. The first fourof these are in common-law countries and the final three are in code-law countries.

7The remaining

rows in Table 1 and in all subsequent tables describing country-level data present statistics for all

other countries in the sample.

Table 1 shows that we lose 44,538 firm-years 21.5 percent of the sample because of missingstock return data. We also lose 32,005 firm-years 15.5 percent due to missing accounting data,

primarily EBITDA and sales. We exclude 4,029 firm-year observations 1.9 percent from twocountries with fewer than 100 firm-yearsBermuda and the Cayman Islands. Finally, we drop9,270 firm-years 4.5 percent in the extreme 1 percentiles of the distribution of fiscal-year stockreturn, sales, EBITDA, operating income, income before taxes, income before extraordinary items

and discontinued operations, net income, total comprehensive income, and operating cash flows.

The final sample consists of 117,474 firm-years representing 19,784 firms. The final distribution of

firm-years across countries is consistent with that of the original sample, with the largest numbers

of observations still from Japan 27,898 and the United States 26,461.Table 2 presents descriptive statistics for our sample. Panel A shows that the number of firms

covered by Global Vantage in the United States and Canada peaked in the late 1990s and has been

decreasing in the current decade. A less pronounced pattern, peaking early in this decade, is

apparent in the number of German and French companies covered. The decrease in Global Van-

tage coverage in these North American and European economies is similar to decreases in thenumber of companies listed in each of these jurisdictions during our sample period see also Smithand Cohen 2007. Coverage of companies in most other countries is fairly stable across time, withthe exception of extensive growth in Australia, China, Korea, Singapore, and Taiwan.

Table 2, Panel B shows that the observations from both common- and code-law countries

cover a wide range of industries. The largest number of firms in most countries is in the manu-

facturing sector SIC codes 2039. However, industry representation varies across countries, withthe mining and construction industries particularly well-represented in Australia and Canada, and

the utilities and communications industries each representing the largest percentage of observa-

tions in at least one country. Finally, Panel C shows median values for a selection of firm char-

acteristics. The sample exhibits a great deal of variation across countries in terms of the median

firms size, accounting performance, equity market performance, leverage, and percentage of loss

years. The tenor of the results we report later is largely invariant to these industry and scaledifferences.

7See Ali and Hwang 2000 and La Porta et al. 2006 for a discussion of code- and common-law countries.


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TABLE 2

Sample Description

Panel A: Distribution of Firms by YearCountry 1996 1997 1998 1999 2000 2001 2002 2003

Australia 194 211 226 263 295 330 436 561

Canada 438 438 444 429 439 418 417 394

U.K. 752 959 1,029 950 957 1,037 1,002 895

U.S.A. 2,859 2,923 2,908 2,730 2,777 2,642 2,581 2,378

France 261 361 357 372 409 531 508 466

Germany 258 317 332 382 492 568 544 445

Japan 2,544 2,634 2,615 2,645 2,809 2,877 2,876 2,648

Argentina 12 18 21 24 23 22 22 20

Austria 40 65 69 70 10 70 61 55

Belgium 45 56 55 61 10 76 82 78

Brazil 10 34 77 89 95 107 108 105

Chile 16 17 78 87 81 79 78 74 China 2 60 97 98 91 132 268 348

Colombia 5 7 14 15 15 12 13 13

Czech Republic 0 7 21 22 16 17 12 10

Denmark 58 80 91 87 83 86 106 91

Finland 33 59 59 65 73 91 95 93

Greece 9 21 38 48 56 65 69 61

Hong Kong 62 75 98 85 100 104 102 107

Hungary 0 10 18 19 18 17 16 16

India 92 118 209 214 213 209 205 169

Indonesia 65 94 145 144 169 154 171 187

Ireland 30 37 46 44 44 43 42 42

Israel 14 24 31 35 36 35 34 31

Italy 60 102 112 113 122 157 174 164

Korea 17 17 62 81 135 169 189 195 Luxembourg 2 5 6 8 11 13 14 14

Malaysia 198 314 400 363 469 467 490 535

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Panel A: Distribution of Firms by Year

Country 1996 1997 1998 1999 2000 2001 2002 2003

Mexico 33 38 58 52 47 46 50 49

The Netherlands 99 135 139 140 144 148 138 123

New Zealand 13 21 37 43 53 49 58 55

Norway 43 70 86 82 76 93 102 89

Pakistan 3 4 16 38 39 42 29 32

Peru 1 3 15 17 19 18 18 19

Philippines 18 40 98 103 97 88 101 103

Poland 0 10 28 35 36 32 25 19

Portugal 21 44 37 40 32 30 32 29

Russian Federation 0 6 9 10 13 14 17 18

Singapore 140 147 187 176 220 229 311 309

South Africa 46 73 75 100 103 131 147 147

Spain 79 97 94 96 102 102 97 96

Sweden 58 93 119 139 146 205 215 191

Switzerland 95 120 132 128 146 161 158 148

Taiwan 26 50 145 152 159 166 193 215

Thailand 141 179 198 238 234 228 235 232

Turkey 9 19 35 28 40 37 29 27

Total 8,901 10,212 11,166 11,160 11,874 12,347 12,670 12,097 1

Panel B: Distribution of Firm-Years by Industry (rows add up to 100%)

Two-Digit SIC Code

19 1017 2039 4047 48 49 5051

Agro/Forest Mining/Constr. Manuf. Transp. Comm. Util. Whls.

Australia 1.96% 35.56% 27.47% 2.69% 3.29% 2.41% 3.55%

Canada 0.17 27.27 38.18 2.36 5.12 3.78 3.71

U.K. 1.13 8.66 37.72 3.41 2.21 2.33 5.00

U.S.A. 0.31 5.09 49.38 2.69 3.47 5.20 3.46 France 1.25 4.19 48.49 2.72 2.14 1.81 5.03

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Two-Digit SIC Code

19 1017 2039 4047 48 49 5051

Agro/Forest Mining/Constr. Manuf. Transp. Comm. Util. Whls. Germany 0.54 2.70 55.59 2.35 1.83 4.39 3.78

Japan 0.32 7.26 54.41 4.33 0.60 0.79 10.22

Argentina 3.38 1.45 58.45 0.00 9.66 22.22 0.00

Austria 0.00 9.36 63.88 3.68 1.34 5.52 3.01

Belgium 2.81 8.27 52.88 1.77 1.18 4.14 6.06

Brazil 1.11 3.08 61.25 1.60 11.19 15.13 1.11

Chile 5.80 2.38 43.01 6.25 5.80 18.45 2.38

China 1.53 2.40 61.16 8.28 1.28 5.84 1.57

Colombia 0.00 0.00 77.88 0.00 0.00 4.42 8.85

Czech Republic 0.00 13.79 28.45 0.00 10.34 42.24 0.00

Denmark 0.00 5.68 59.70 7.72 1.70 1.70 7.95

Finland 0.00 2.51 63.59 6.46 1.58 3.69 3.43

Greece 1.60 12.60 53.00 4.80 4.80 3.00 2.80 Hong Kong 0.00 3.60 39.24 6.08 6.18 3.91 10.20

Hungary 0.00 0.00 67.38 2.84 9.22 8.51 0.00

India 0.00 1.65 81.06 2.00 1.20 3.75 0.57

Indonesia 2.51 5.84 63.30 5.36 2.47 0.07 9.07

Ireland 0.00 25.36 36.36 5.98 0.96 0.00 13.16

Israel 0.00 1.01 53.69 0.00 6.71 2.68 1.34

Italy 1.05 4.95 60.49 4.95 3.30 8.40 1.57

Korea 0.00 4.50 69.24 3.97 3.44 3.21 2.98

Luxembourg 0.00 5.83 23.30 10.68 33.01 8.74 0.00

Malaysia 5.53 9.79 53.54 4.24 0.97 2.17 5.42

Mexico 0.00 8.49 50.31 3.52 7.87 0.00 2.90

The Netherlands 0.00 6.83 49.19 4.91 2.15 0.00 9.75

New Zealand 8.41 1.99 29.87 12.39 5.09 10.40 5.31

Norway 0.71 12.15 38.92 18.75 2.36 3.18 1.65 Pakistan 0.00 7.81 66.91 4.09 5.20 10.04 0.00

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Two-Digit SIC Code

19 1017 2039 4047 48 49 5051

Agro/Forest Mining/Constr. Manuf. Transp. Comm. Util. Whls. Peru 0.00 30.56 43.06 0.00 6.94 13.19 6.25

Philippines 0.00 17.49 32.98 5.75 7.98 4.58 1.53

Poland 2.27 16.36 55.00 0.00 3.18 2.27 2.73

Portugal 0.00 12.58 47.80 3.77 7.86 2.20 4.09

RussianFederation

0.00 9.40 41.03 5.98 28.21 15.38 0.00

Singapore 0.49 5.92 45.81 6.78 1.10 0.98 10.09

South Africa 0.89 20.21 30.99 3.65 4.19 0.27 7.03

Spain 0.52 10.24 50.37 4.18 2.72 9.30 2.82

Sweden 0.00 5.54 50.28 3.78 3.08 0.88 3.84

Switzerland 0.00 1.36 63.97 4.50 0.50 6.65 5.22

Taiwan 0.00 4.90 82.59 4.27 1.19 0.00 1.76

Thailand 1.21 4.04 61.21 3.50 4.90 1.35 5.12

Turkey 0.00 2.15 75.99 4.66 3.23 3.23 4.66

Total 0.84% 8.20% 50.69% 3.86% 2.52% 3.22% 5.64%

Panel C: Median Values of Selected Firm Characteristics ($ Amounts in Millions)

Country

EquityMarket

Value Assets SalesMarket-to-

Book EquityStock

ReturnReturn on

EquityProfit

MarginRep

Lo

Australia $43 $45 $30 1.69 10.75% 4.49% 2.19% 44

Canada 273 295 185 1.74 11.79 6.74 4.27 31

U.K. 126 133 145 1.77 5.35 9.27 3.69 27

U.S.A. 501 475 445 2.07 6.09 9.19 3.56 28

France 119 191 184 1.66 7.96 9.35 2.83 22

Germany 100 181 194 1.65 0.63 6.54 1.63 29

Japan 112 285 295 1.00

2.40 3.81 1.39 20Argentina 381 844 463 0.90 0.01 7.26 6.33 28

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Country

EquityMarket


Book EquityStock

ReturnReturn on

EquityProfit

MarginRep

Lo

Austria 86 214 224 1.20 1.04 8.04 2.74 2Belgium 157 250 243 1.55 7.97 9.72 3.26 1

Brazil 202 795 603 0.65 5.57 8.54 4.79 2

Chile 217 322 156 1.16 7.35 8.31 8.08 1

China 229 294 147 1.75 13.87 6.18 4.54 1

Colombia 209 436 225 0.62 6.84 5.41 7.98 1

CzechRepublic

162 425 284 0.61 5.68 6.49 7.07 1

Denmark 95 157 170 1.28 6.42 8.62 3.02 1

Finland 266 296 315 1.59 13.78 11.49 4.31 1

Greece 300 348 240 2.10 4.13 10.18 5.98

HongKong

119 286 159 0.76 0.56 5.89 6.11 2

Hungary 132 206 207 1.09 3.78 9.98 6.50 1

India 116 208 195 1.31 9.43 13.78 6.73 Indonesia 24 79 61 0.85 6.01 7.99 2.39 3

Ireland 175 218 214 1.92 8.04 10.04 4.34 3

Israel 390 447 254 1.58 8.68 7.24 5.02 3

Italy 240 443 317 1.51 8.39 6.06 2.93 2

Korea 246 827 698 0.79 26.91 8.18 3.64 1

Luxembourg 834 1,363 848 1.48 9.10 3.13 0.89 3

Malaysia 34 72 42 0.95 6.25 5.23 3.98 2

Mexico 613 1,353 1,071 1.11 10.51 11.43 5.68 1

TheNetherlands

229 356 471 1.93 2.89 13.87 3.22 1

NewZealand

107 127 122 1.55 16.03 10.46 5.80 1

Norway 130 182 144 1.63 9.64 7.82 3.07 3

Pakistan 66 121 114 1.35 17.49 19.72 7.06 1

Peru 133 274 151 0.80 14.30 9.50 9.37 1

Philippines 18 71 224 0.58 3.42 2.14 2.95 4

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Country

EquityMarket


Book EquityStock

ReturnReturn on

EquityProfit

MarginRep

Lo

Poland 99 151 186 1.14 7.19 6.89 3.35 1Portugal 157 470 363 1.50 6.99 7.63 2.92 1

RussianFed

1714 3,750 2,027 1.03 47.47 8.15 8.82 1

Singapore 49 92 66 1.05 4.74 5.12 3.47 2

SouthAfrica

229 285 311 1.60 15.70 17.01 5.62 1

Spain 412 514 380 1.73 17.14 11.04 6.05

Sweden 125 148 162 1.86 7.20 8.97 3.10 3

Switzerland 272 415 380 1.55 8.90 9.46 4.21 1

Taiwan 323 508 343 1.38 2.68 7.82 4.92 2

Thailand 24 58 51 0.88 3.66 9.64 5.05 2

Turkey 351 251 311 2.05 2.09 15.87 5.89 1

The final sample consists of 117,474 observations for 19,784 public firms over 19962005 with data available on Compustats Global Vantageover the firms fiscal year.

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IV. WHICH PERFORMANCE MEASURES ARE MOST VALUE RELEVANT?We examine eight summary performance measures: total sales SALES; operating earnings

before interest, income taxes, depreciation and amortization EBITDA; operating income beforeincome taxes OPINC; income before income taxes IBTAX; income before extraordinary items

and discontinued operations IBXIDO; net income NI; total comprehensive income TCI; andoperating cash flows OCF. All measures are scaled by the lagged market value of commonequity.

Our proxy for summary performance measure js value RELEVANCE in country k is the

adjusted R2 from the regression:

RETURNit,k = 0,jk + 1,jkPerformance Measure jit,k + it,k, 6

where RETURNis firm is stock return for fiscal year t, net of the average stock return for that year

in the firms country k using all firms with available stock return data in that country, andperformance measure j SALES, EBITDA, OPINC, IBTAX, IBXIDO, NI, TCI, OCF. Largervalues of RELEVANCE imply that performance measure j is more relevant for equity valuation.

Table 3 reports the value relevance of each of our eight performance measures for each

country. To provide a visual summary, the greatest value of RELEVANCE in each country appearsin bold. The performance measure with the greatest value relevance varies substantially across

countries. IBTAX is the most value relevant measure in 25 of the 46 countries, including Australia,

Canada, the United States, and France. However, each of the other performance measures, with the

exception of SALES, is the most value relevant in at least one country. For instance, in the U.K.

and Germany, EBITDA is the most value relevant performance measure; in Japan, OPINC is the

most value relevant. Furthermore, the performance measure exhibiting the greatest value relevance

does not follow a simple, easily distinguishable pattern, such as a code-law versus common-law

dichotomization. The magnitude of our estimates of value relevance also varies widely between

performance measures for example, OPINC varies from 0.010 in Luxembourg to 0.295 inRussia. The distribution of value relevance measures among the seven countries at the top of thetable is considerably tighter, ranging from 0.003 for SALES in Canada to 0.117 for IBTAX in

France.

To formalize these relative patterns in value relevance, Table 4 presents results of estimating

these models:

RELEVANCEjk = 0 + 1DUM_SALESjk + 2DUM_EBITDAjk + 3DUM_OPINCjk

+ 4DUM_IBTAXjk + 5DUM_IBXIDOjk + 6DUM_NIjk + 7DUM_TCIjk

+ jk, 7

RELEVANCEjk = 1 + COMMONjk 0 + 1DUM_SALESjk + 2DUM_EBITDAjk

+ 3DUM_OPINCjk + 4DUM_IBTAXjk + 5DUM_IBXIDOjk

+ 6DUM_NIjk + 7DUM_TCIjk + jk, 8

RELEVANCEjk = 0 + 1UPTO_SALESjk + 2UPTO_EBITDAjk + 3UPTO_OPINCjk

+ 4UPTO_IBTAXjk + 5UPTO_IBXIDOjk + 6UPTO_NIjk

+ 7UPTO_TCIjk + jk, and 9


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TABLE 3

Value Relevance of Performance Measures across Countries

Country SALES EBITDA OPINC IBTAX IBXIDO NI

Australia 0.013 0.031 0.027 0.032 0.027 0.024

Canada 0.003 0.039 0.042 0.063 0.054 0.053

U.K. 0.012 0.081 0.073 0.065 0.053 0.053

U.S.A. 0.007 0.037 0.038 0.039 0.030 0.026

France 0.018 0.073 0.109 0.117 0.097 0.095

Germany 0.028 0.077 0.074 0.076 0.062 0.056

Japan 0.005 0.063 0.102 0.070 0.055 0.055

Argentina 0.039 0.089 0.085 0.031 0.030 0.031

Austria 0.015 0.038 0.056 0.091 0.089 0.089

Belgium 0.005 0.035 0.117 0.162 0.143 0.142

Brazil 0.001 0.001 0.001 0.001 0.001 0.001Chile 0.004 0.056 0.067 0.133 0.136 0.137

China 0.008 0.048 0.060 0.063 0.055 0.055

Colombia 0.007 0.106 0.072 0.170 0.137 0.137

Czech Republic 0.002 0.009 0.008 0.059 0.044 0.039

Denmark 0.002 0.050 0.104 0.127 0.117 0.117

Finland 0.028 0.109 0.150 0.180 0.158 0.167

Greece 0.002 0.019 0.035 0.059 0.059 0.059

Hong Kong 0.009 0.035 0.027 0.047 0.047 0.047

Hungary 0.006 0.082 0.079 0.177 0.156 0.155

India 0.023 0.040 0.048 0.066 0.060 0.057

Indonesia 0.027 0.138 0.095 0.038 0.030 0.049

Ireland 0.002 0.009 0.011 0.028 0.021 0.024

Israel 0.013 0.022 0.028 0.059 0.044 0.071

Italy 0.009 0.070 0.098 0.121 0.104 0.104 Korea 0.025 0.049 0.084 0.092 0.074 0.074

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TABLE 3 (continued)

Country SALES EBITDA OPINC IBTAX IBXIDO NI

Luxembourg 0.009 0.009 0.010 0.003 0.001 0.001

Malaysia 0.021 0.062 0.053 0.050 0.041 0.041

Mexico 0.002 0.031 0.050 0.021 0.006 0.009

The Netherlands 0.010 0.042 0.051 0.062 0.051 0.050

New Zealand 0.011 0.137 0.181 0.223 0.209 0.210

Norway 0.014 0.054 0.007 0.083 0.091 0.097

Pakistan 0.021 0.130 0.177 0.162 0.109 0.106

Peru 0.006 0.018 0.044 0.053 0.069 0.070

Philippines 0.007 0.016 0.005 0.001 0.001 0.001

Poland 0.016 0.049 0.055 0.137 0.127 0.127

Portugal 0.003 0.077 0.108 0.068 0.045 0.044

Russian Federation 0.106 0.273 0.295 0.216 0.204 0.209 Singapore 0.012 0.105 0.092 0.093 0.081 0.076

South Africa 0.062 0.210 0.205 0.168 0.140 0.113

Spain 0.032 0.080 0.121 0.131 0.126 0.126

Sweden 0.007 0.105 0.118 0.126 0.120 0.119

Switzerland 0.034 0.072 0.109 0.114 0.102 0.099

Taiwan 0.008 0.101 0.111 0.106 0.101 0.101

Thailand 0.031 0.084 0.059 0.030 0.025 0.032

Turkey 0.004 0.042 0.045 0.075 0.066 0.075

The sample consists of 117,747 observations for 19,784 public firms over 19962005 with data available on Compustats Global Vantageperformance measure j RELEVANCE as the adjusted R2 from the regression:

RETURNit,jk = 0,jk + 1,jkPerformance Measure jit,jk + it,jkfor country k, where performance measure j SALES, EBITDA, OPINC, IBTAX, IBXIDO, NI, TCI, OCF.

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TABLE 3 (continued)

Variable Definitions:RETURN buy-and-hold stock return over fiscal year t, net of the median stock return for that year in country k;

SALES total sales;EBITDA operating earnings before interest, income taxes, depreciation, and amortization;

OPINC operating income before income taxes;IBTAX income before income taxes;

IBXIDO income before extraordinary items and discontinued operations;NI net income;

TCI total comprehensive income; andOCF operating cash flows.

All variables are scaled by lagged market value of common equity. The largest value relevance estimate for each country appears in bold.

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TABLE 4

Relative and Incremental Value Relevance of Summary Performance Measures

Panel A: Relative Value RelevanceRELEVANCEjk01DUM_SALESjk2DUM_EBITDAjk3DUM_OPINCjk4DUM_IBTAXjk5DUM_IBXID

7DUM_TCIjk jk

RELEVANCEjk 1COMMONjk 01DUM_SALESjk2DUM_EBITDAjk3DUM_OPINCjk4DUM_IBTA

5DUM_IBXIDOjk6DUM_NIjk7DUM_TCIjk jk

Performance MeasureIndicator

Equation (7)

Equation (8)

COMMON 0

Coefficient p-value Coefficient p-value Co

Intercept i.e., DUM_OCF 0.033 0.00 0.030 0.00 DUM_SALES 0.022 0.00 0.019 0.00

DUM_EBITDA 0.027 0.00 0.036 0.00

DUM_OPINC 0.039 0.00 0.063 0.00

DUM_IBTAX 0.034 0.00 0.052 0.00

DUM_IBXIDO 0.023 0.00 0.039 0.00

DUM_NI 0.022 0.00 0.039 0.00

DUM_TCI 0.009 0.18 0.019 0.01

Adj. R2 0.32 0.45

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Panel B: Incremental Value Relevance

RELEVANCEjk01UPTO_SALESjk2UPTO_EBITDAjk3UPTO_OPINCjk4UPTO_IBTAXjk5UPTO_IB

7UPTO_TCIjk jk

RELEVANCEjk 1COMMONjk 01UPTO_SALESjk2UPTO_EBITDAjk3UPTO_OPINCjk4UPTO_I

6UPTO_NIjk7UPTO_TCIjk jk

Performance MeasureIndicator

Equation (10)

Equation (9) COMMON 0

Coefficient p-value Coefficient p-value Co

Intercept i.e., UPTO_OCF 0.033 0.00 0.030 0.00 UPTO_SALES 0.022 0.00 0.019 0.00

UPTO_EBITDA 0.049 0.00 0.055 0.00

UPTO_OPINC 0.013 0.10 0.028 0.00

UPTO_IBTAX 0.006 0.40 0.012 0.26

UPTO_IBXIDO 0.011 0.00 0.013 0.00 UPTO_NI 0.001 0.25 0.000 0.89

UPTO_TCI 0.013 0.00 0.019 0.02

Adj. R2 0.32 0.45

The sample consists of seven attributes for each of eight performance measures estimated for 46 countries, using 117,474 observations for 19,7on Compustats Global Vantage over 19962005. RELEVANCE and the performance measures SALES, EBITDA, OPINC, IBTAX, IBXIDO, NI, T3. COMMON is an indicator coded 1 if the observation pertains to a common-law country i.e., Australia, Canada, Hong Kong, India, IrelaPakistan, Singapore, South Africa, Thailand, the U.K., and the U.S.A. . The indicators DUM_j are coded 1 for observations where the dependemeasure j, and the indicators UPTO_j are coded 1 for observations where the dependent variable pertains to performance measure j or any oththe income statement. The regressions reflect the frequency of firm-years per country, as reported in Table 1. The coefficients are standardizedindependent variable Xis the regular coefficient on X, multiplied by the standard deviation ofXand then divided by the standard deviation of thAll p-values are two-tailed, based on heteroscedasticity-consistent standard errors clustered by country.

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RELEVANCEjk = 1 + COMMONjk 0 + 1UPTO_SALESjk + 2UPTO_EBITDAjk

+ 3UPTO_OPINCjk + 4UPTO_IBTAXjk + 5UPTO_IBXIDOjk

+ 6UPTO_NIjk + 7UPTO_TCIjk + jk, 10

where the dependent variable is the value relevance of measure j in country k estimated and

reported in Table 3, the performance measures j are as previously defined, COMMON is an

indicator coded 1 if the observation is from a common-law country, the indicators DUM_j are

coded 1 for observations where the dependent variable pertains to performance measure j, and the

indicators UPTO_j are coded 1 for observations where the dependent variable pertains to perfor-mance measure j or any other performance measure above it on the income statement.

8

Table 4 and Figure 1 present the results of estimating Equations 710. The results forEquation 7 reported in Table 4, Panel A show that RELEVANCE tends to increase as one movestoward the middle of the income statement, with OPINC having the largest value relevance

about twice as strong as our benchmark OCF. SALES is the only accrual-based summary perfor-

mance measure with value relevance statistically and economically lower than that of OCF. The

results for Equation 8, also reported in Panel A, show a similar pattern for firms in code-lawcountries, with value relevance about three times larger for OPINC than for cash flows. As

highlighted in Figure 1, this pattern is less pronounced for firms in common-law countriesvalue

relevance is essentially similar for EBITDA, OPINC, and IBTAX. Highly transitory items like

8 For example, if the observation relates to the value relevance of SALES, then DUM_SALES is coded 1 and all otherDUM_j indicators in Equations 7 and 8 are coded 0. In Equations 9 and 10, UPTO_SALES is coded 1 and allother UPTO_j indicators are coded 0. On the other hand, if the observation relates to the value relevance of IBXIDO,then DUM_IBXIDO is coded 1 and the other DUM_j indicators in Equations 7 and 8 are coded 0; in Equations 9and 10, UPTO_SALES, UPTO_EBITDA, UPTO_OPINC, UPTO_IBTAX, and UPTO_IBXIDO i.e., all UPTO_j indi-cators for performance measures as one moves down the income statement up to and including IBXIDO are coded 1,whereas UPTO_NI and UPTO_TCI which are reported below IBXIDO in the income statement are coded 0. Thebaseline in these regressions is for observations relating to OCF, in which case all DUM_j and UPTO_j indicators arecoded 0.

FIGURE 1

Value Relevance of Summary Performance Measures

0.0

0.0

0.0

0.0

0.0

0.1

0.1

0.1

0.1

ValueRelvance(in%)

0

2

4

6

8

0

2

4

6

OCF SALES EBITDA OPIN

Summary Pe

C IBTAX

rformance Me

IBXIDO

sure

NI TCI

Code

C om on


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extraordinary items, discontinued operations, and other comprehensive income do not appear

incrementally useful in common-law countries. Together, the results in Panel A show that value

relevance tends to peak for summary performance measures above the line, but less so for firms

in common-law countries. Performance measures that include operating costs appear to be much

more useful for valuation than measures without cost data or cash-based measures.Equations 9 and 10 are equivalent to Equations 7 and 8, but they highlight the finding

that some performance measures have statistically indistinguishable value relevance. For example,

results for Equation 9 reported in Panel B of Table 4 suggest that EBITDA, OPINC, and IBTAXare essentially similar in terms of value relevance, as the coefficients of the last two measures are

not statistically significant. Based on regression results for Equation 10, one can conclude that,as one moves down the income statement, IBTAX does not add to the value relevance of OPINC,

and that NI is essentially similar to IBXIDO. In common-law countries, no additional value

relevance is gained by reporting depreciation and amortization expenses or nonoperating income,

as evidenced by the coefficients on UPTO_OPINC, and UPTO_IBTAX not being statistically

significant. However, when included in a performance measure, income taxes and highly transitory

items like extraordinary items, discontinued operations, and comprehensive income appear to

garble information in that performance measure, decreasing its value relevance.

Table 5 reports descriptive statistics for the eight performance measures Panel A and theseven underlying attributes Panel B, along with the mean value of each attribute for eachperformance measure Panel C. SALES is the most persistent and predictable performance mea-sure, and both persistence and predictability decrease monotonically as one moves down the

income statement toward TCI. However, SALES is the least smoothed performance measure rela-

tive to OCF. In fact, while all other performance measures have smaller standard deviations than

OCF, SALES has an average standard deviation that is more than seven times the standard devia-

tion of OCF.

Panel C of Table 5 also shows that OCF, EBITDA, and OPINC are the best predictors of next

periods cash flows, and OPINCand EBITDA are the closest to current years OCF. The timeliness

of the performance measures peaks at EBITDA and then decreases as one moves down the income

statement toward TCI. This result is likely driven by the inclusion of more transitory items in the

later subtotals and TCI. In contrast, IBTAX, IBXIDO, and NI are the most conservative perfor-mance measures, possibly reflecting managers incentives to push bad news down the income

statement McVay 2006.The standard deviation of each performance measures attributes also appears in Table 5.

Since each attribute is measured at the country level, these standard deviations indicate that the

attributes vary across countries. Indeed, all of the attributes exhibit substantial variation for each

performance measure, with the exceptions ofSMOOTHNESS and SUSBT_OCF. The largest varia-

tion is for PREDICT_OCF, which has a coefficient of variation greater than 1 for IBTAX, IBXIDO,

NI, and TCI.

Together, the results shown in Tables 35 indicate that the relevance and underlying attributes

of the different performance measures we examine vary both within the statements of financial

performance and also across countries. In the following sections we investigate the association

between the attributes of a performance measure and its value relevance.

V. WHY ARE SOME PERFORMANCE MEASURES MORE VALUE RELEVANT THANOTHERS?

Relations among the Attributes

The literature usually views the various performance measure attributes as distinct see, e.g.,Leuz et al. 2003; Schipper and Vincent 2003; Francis et al. 2004. However, as Panel A of Table6 shows, most of these attributes are strongly correlated with each other. For example, PERSIS-


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TABLE 5

Descriptive Statistics of Performance Measures and Their Attributes

Panel A: Descriptive Statistics of Performance Measures (n 117,474)PerformanceMeasure Mean Std. Dev. Q1 Med

SALES 2.54 9.98 0.61 1.4

EBITDA 0.20 2.35 0.07 0.

OPINC 0.11 1.73 0.03 0.0

IBTAX 0.05 1.34 0.01 0.0

IBXIDO 0.01 1.13 0.00 0.0

NI 0.01 1.13 0.00 0.0

TCI 0.02 1.16 0.02 0.0

OCF 0.13 1.25 0.02 0.

Panel B: Descriptive Statistics of Attributes (n 939,792)

Attribute Mean Std. Dev. Q1 M

RELEVANCE 0.05 0.03 0.03

PERSISTENCE 0.52 0.19 0.39

PREDICTABILITY 0.28 0.19 0.13

SMOOTHNESS 1.62 2.25 0.62

PREDICT_OCF 0.09 0.10 0.02

SUBST_OCF 0.44 0.31 0.21

TIMELINESS 0.32 0.29 0.12

CONSERVATISM 0.01 0.23 0.15

Panel C: Means and Standard Deviations (in Parentheses) of Attributes by Performance Measure (n 117,474)

SALES EBITDA OPINC IBTAX IBXIDO NI

PERSISTENCE 0.83 0.66 0.61 0.49 0.47 0.44

0.07 0.08 0.08 0.09 0.10 0.10

PREDICTABILITY 0.66 0.43 0.36 0.21 0.18 0.16 0.09 0.09 0.08 0.07 0.07 0.07

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Panel C: Means and Standard Deviations (in Parentheses) of Attributes by Performance Measure (n 117,474)

SALES EBITDA OPINC IBTAX IBXIDO NI

SMOOTHNESS 7.31 0.77 0.63 0.82 0.70 0.75

1.79 0.16 0.17 0.22 0.22 0.26 PREDICT_OCF 0.07 0.21 0.12 0.05 0.04 0.03

0.07 0.08 0.08 0.07 0.06 0.06 SUBST_OCF 0.03 0.63 0.65 0.34 0.35 0.33

0.02 0.11 0.17 0.14 0.16 0.16 TIMELINESS 0.23 0.46 0.30 0.17 0.16 0.15

0.07 0.10 0.11 0.10 0.10 0.09 CONSERVATISM 0.37 0.20 0.02 0.15 0.20 0.19

0.08 0.19 0.16 0.12 0.13 0.12

The sample consists of seven attributes for each of eight performance measures estimated for 46 countries, using 117,474 observations for 19,7on Compustats Global Vantage over 19962005. All statistics are weighted by the number of firm-years in each country, listed in Table 1. The

EBITDA, OPINC, IBTAX, IBXIDO, NI, TCI, and OCF, all defined in Table 3.

Various Attributes:PERSISTENCE

slope coefficient from the regression of each performance measure j on its lagged value: Performance Measur+ 1,jk Performance Measure jit,jk + it,jk;PREDICTABILITY adjusted R2 from the regression of each performance measure j on its lagged value: Performance Measure jit

+ 1,jk Performance Measure jit,jk + it,jk;SMOOTHNESS slope coefficient from the regression: sd Performance Measure jit,jk = 1,jksdOCFit,jk + it,jk of the standard deviation

standard deviation of OCF, both deviations measured over the sample period.PREDICT_OCF adjusted R2 from the regression: OCFit+1,jk = 0,jk + 1,jk Performance Measure jit,jk + it,jk of operating cash flows o

measure j;SUBST_OCF slope coefficient from the regression;

OCFit,jk0,jk +1,jkPerformance Measure jit,jk + it,jk of operating cash flows on the contemporaneous value of performancTIMELINESS adjusted R

2from the regression: Performance Measure jit,jk = 0,jk + 1,jkNEGit,k + 2,jkOCFit,k+ 3,jkNEGit,kOCFi

NEG indicator coded 1 if OCF 0, 0 otherwise; andCONSERVATISM asymmetric timeliness coefficient 3,jk from the regression used to estimate TIMELINESS. The coefficient is standardi

standard deviation of the interaction NEGkOCFk, then divided by the standard deviation of the performance measu

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TABLE 6

Factor Analysis of Performance Measure Attributes

Panel A: Pearson and Multiple Squared Correlations R2

among Attributes(1) (2) (3) (4) (5)

1 PERSISTENCE 1.00 2 PREDICTABILITY 0.94 1.00 3 SMOOTHNESS 0.57 0.70 1.00 4 PREDICT_OCF 0.37 0.39 0.05 1.00 5 SUBST_OCF 0.15 0.19 0.50 0.51 1.00 6 TIMELINESS 0.02 0.02 0.08 0.61 0.79 7 CONSERVATISM 0.56 0.71 0.59 0.24 0.02

Panel B: Principal Component Factors

Factor Eigenvalue Percentage Explained

1 3.20 45.7%

2 2.39 34.1 3 0.66 9.4

4 0.41 5.8

5 0.26 3.8

6 0.06 0.8

7 0.03 0.4

Panel C: Rotated Factor Loadings (for Two Factors Retained) and Unique Variances of Attributes

Attribute FACTOR1 FACTOR2

PERSISTENCE 0.90 0.03

PREDICTABILITY 0.97 0.03

SMOOTHNESS 0.80 0.33

PREDICT_OCF 0.34 0.78

SUBST_OCF 0.24 0.92

TIMELINESS 0.02 0.90

CONSERVATISM 0.79 0.13

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Panel D: Pearson and Multiple Squared Correlations R2 between Attributes and Rotated Factors

AttributeFACTOR1

(Sustainability)

FACTOR2

(Articulation with CashFlows)

PERSISTENCE 0.90 0.05

PREDICTABILITY 0.97 0.05

SMOOTHNESS 0.80 0.31

PREDICT_OCF 0.35 0.79

SUBST_OCF 0.22 0.91

TIMELINESS 0.04 0.90

CONSERVATISM 0.80 0.14

Panel E: Pearson Correlations between Performance Measures and Rotated Factors

Performance MeasureFACTOR1

(Sustainability)

SALES 0.16

EBITDA 0.15

OPINC 0.16

IBTAX 0.17

IBXIDO 0.18

NI 0.18

TCI 0.16

OCF 0.12

The sample consists of seven attributes for each of eight performance measures estimated for 46 countries, using 117,474 observations for 19,7on Compustats Global Vantage over 19962005. The performance measures are SALES, EBITDA, OPINC, IBTAX, IBXIDO, NI, TCI, and OCFattributes are defined in Table 5. Oblique promax rotation allows the retained factors to be correlated with, rather than orthogonal to, each other. reflect the frequency of firm-years per country as shown in Table 1.

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TENCE is positively correlated with PREDICTABILITY and SMOOTHNESS; the latter is nega-

tively correlated with SUBST_OCF, TIMELINESS, and CONSERVATISM. The strongest correla-

tion between any two attributes is the 0.94 correlation between PERSISTENCE and

PREDICTABILITY; both the mean and median Pearson correlations, in absolute values, between

any two variables are 0.39. A Bartlett 1950 test of the significance of the correlation table rejectsthe null that the variables are not correlated 221 df = 1,496.4; p 0.001. The last column of

Panel A shows that multiple squared correlations between each attribute and the rest ranges from

0.68 to 0.96, suggesting that at least two thirds of the variation in each attribute can be explained

by the other attributes.

This strong correlation structure raises two issues. First, regression coefficient estimates may

be biased due to collinearity, so we may be unable to determine reliably which attributes are

associated with more value relevant performance measures. Second, and more important, the

seven attributes we focus on may indeed be capturing fewer underlying attribute dimensions of the

various performance measures.

We address these two issues by performing a principal components factor analysis on the

seven performance measure attributes. The analysis takes into account the number of firm-years

used to estimate each attribute; such frequency weighting reflects the relative number of firms in

each country that would be affected by changes in financial reporting standards involving the

performance measures definitions or attributes. Correlations and factor analyses without this

frequency weighting are qualitatively similar to those reported in Table 6. The eigenvalue-greater-

than-1 rule of thumb, a scree plot, and a parallel analysis all suggest that we retain the first two

factors for further analysis.9 Panel B shows that these first two factors account for about 80 percent

of the total variation in the original seven attributes.

To simplify the structure of the factors and facilitate interpretation, we perform an oblique

promax rotation of the factors. A benefit of oblique rotation is that it allows the factors to be

correlated with each other. In our case, the correlation between the two retained, rotated factors is

0.02, suggesting they are essentially orthogonal. Panel C of Table 6 shows the factor loading and

uniqueness for each of the seven attributes. Factor loadings are the standardized coefficients from

the regressions of each attribute on the factors and, therefore, they are equivalent to partial

correlations, and the uniqueness of each attribute is the proportion of its variance not explained bythe two retained factors. The latter are consistent with the pattern in the multiple squared coeffi-cients in the last column of Panel A.

PERSISTENCE, PREDICTABILITY, and SMOOTHNESS load strongly and positively on the

first factor, FACTOR1, whereas CONSERVATISM loads strongly and negatively on this factor. In

9 See Gorsuch 1983 for a discussion of factor analysis. The eigenvalues in Panel B of Table 6 are the variances of thefactors. The sum of the eigenvalues is equal to the number of variables, so that each eigenvalue divided by 7 is theproportion of the variance of the variables that is explained by that factor. Since the standardized variables each havevariance equal to 1, Guttman 1954 and Kaiser 1960 suggest retaining factors with eigenvalues greater than 1 anyfactor with an eigenvalue less than 1 adds less than would a single variable and, therefore, is not helpful in reducing thedimensionality of the data. Cattell 1966 proposes a scree test as an alternate method for determining how manyfactors to retain. This test plots the eigenvalues of each factor on the vertical axis and the factor number on thehorizontal axis. The determination of how many factors to retain is based on the slope of the graph. The last factor

retained is the one at which the slope of the line flattens. A parallel analysis Horn 1965 also can be used to determinethe number of factors retained. We have not seen it used in the accounting literature, although methodological studiessuggest it is the most accurate among common methods for determining the number of factors e.g., Zwick and Velicer1986. We implement this analysis by performing a factor analysis on a random data set with the same number ofobservations and variables as ours; because the variables in this parallel data set are random, any resulting eigenvaluesshould be uninterpretable. We repeat this random factor analysis 500 times. The results also suggest retaining the firsttwo factors because the first two eigenvalues from our real data set are larger than the means of the first twoeigenvalues from the random data set, and the last five eigenvalues in our real data set are smaller than the means ofthose from the random data set. Taken together, the criteria we examined indicate a two-factor solution.


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contrast, PREDICT_OCF, SUBST_OCF, and TIMELINESS load strongly and positively on the

second factor, FACTOR2. The two factors explain between 65 and 95 percent of the variance of

each of the underlying attributes.

Panel D of Table 6 presents Pearson correlations between each attribute and the rotated

factors. These results suggest some overlap among the various attributes and factors, and theimportance of considering all factors simultaneously in further analyses. More important, our

results indicate that several of the attributes of accounting performance measures that the prior

literature has treated as independent are better represented by a smaller number of underlying

factors. In particular, those measures related to the time-series properties of earnings, and mea-

sures of the relationship between current earnings and current and future cash flows, are strongly

related, capturing primarily only two underlying dimensions.

To further help interpret the factors, Panel E of Table 6 presents the Pearson correlations

between the factors and mean country-level performance measures. Both FACTOR1 and FAC-

TOR2 appear to have slightly higher correlations with performance measures that are further down

the income statement, and the lowest correlations with OCF. Based on the correlations in Panel D

and E, we conclude that FACTOR1 captures the notion of sustainability: higher values of FAC-

TOR1 suggest that the performance measure is more persistent, predictable, and smoothed out.

FACTOR2, on the other hand, seems to capture the notion of articulation with cash flows, in the

sense that higher values of FACTOR2 suggest a performance measure that is less smoothed out,

more correlated with current and future cash flows, and, therefore, more timely. It is important to

recognize that a high value of FACTOR2 does not imply that the performance measure is closer to

cash flows and therefore devoid of accruals, but rather that its accruals are more correlated with

cash flowsevery Pearson correlation between FACTOR2 and the accrual-based performance

measures reported in Panel E of Table 6 is substantially larger in magnitude than the correlation

between FACTOR2 and operating cash flows.

We extract our factors using principal component factoring, which attempts to explain the

total variance of the seven attributes. An alternative approach is to extract the factors using

principal common or axis factoring, which explains only the shared variance of the attributes. The

Pearson correlation between the principal component factors and the principal common factors is

0.95 for FACTOR1 and 0.98 for FACTOR2, so we only tabulate further results using the formersince they yield similar results to those using the latter. Also, we obtain similar inferences when

we measure TIMELINESS and CONSERVATISM using stock returns rather than cash flows, that is,

consistent with Basu 1997 rather than Ball and Shivakumar 2005. For instance, the Pearsoncorrelation between cash-flow-based and returns-based principal component factors is 0.91 for

FACTOR1 and 0.56 for FACTOR2.

Relations between Value Relevance and Performance Measure Attribute Factors

To provide descriptive evidence on the relations between value relevance and the two attribute

factors, we estimate the following model:

RELEVANCEjk = 0 + 1FACTOR1jk + 2FACTOR2jk + kkCountry indicatorjk + jk 11

where RELEVANCE is the value relevance of summary performance measure j in country k,

FACTOR1 is the sustainability factor, and FACTOR2 is the articulation-with-cash-flows factor. Themodel includes country indicators to control for country-specific fixed effects.

We evaluate the magnitude and sign of each standardized coefficient i.e., the coefficient multiplied by the ratio of the standard deviation of the independent variable to the standard

deviation of RELEVANCE to assess each factors relative importance in equity valuation. Doingso allows us to interpret the economic as well as statistical significance of each variable. A positive

negative coefficient can be interpreted as the number of standard deviations that the dependent


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variable will increase decrea

which performance measure do investors use

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