Is It OK to Manage Earnings?
Jan Barton
Emory University
Marcus Kirk
University of Florida
David Reppenhagen
University of Florida
Jane Thayer
University of Georgia
9 March 2010
ABSTRACT: We examine the earnings management choices of ethical firms relative to a set of control
firms. We measure ethical behavior using various proxies of corporate social responsibility, and earnings
management using abnormal levels of accruals, cash flows, inventory production, and discretionary
expenses. We find that ethical firms do manage earnings, primarily through real actions rather than
accounting choices. They do so mainly to meet analysts’ earnings forecasts and to reduce financing and
tax costs, rather than opportunistically to increase management’s compensation or equity stakes. Our
findings suggest that whether firms view earnings management as ethical depends on the manner and
reasons for managing earnings. It seems OK to manage earnings using real actions rather than accounting
choices, but apparently only to increase shareholders’ rather than managers’ wealth.
Keywords: financial reporting quality; earnings management; accounting choice; ethics; corporate
social responsibility; propensity score matching; canonical correlation analysis.
Data Availability: Data are available from sources identified in the paper.
We thank John Graham for sharing his simulated marginal tax rates, Ron Harris for research assistance,
and our schools for generous funding.
Corresponding author—J. Barton: (404) 727.6398, jbarton@emory.edu.
Stop fooling around with my numbers! The No. 1 job of management is to smooth out earnings.
—CEO quoted in C. Loomis (Fortune, 1999)
Companies plainly have the right to choose the time periods in which they sell appreciated assets
and book capital gains. And, when all is said and done, what’s wrong with honest smoothing?
Why shouldn’t companies encourage investors to look at longer-term trends, rather than reacting
to sequential bumps in the road? The current campaign to demonize earnings management seems
odd at best, and in some dimensions—in claiming to be forestalling the demise of the stock
market—just plain silly.
—D. Seligman (Forbes, 2000)
[Some] companies assume that smoothing earnings is a perfectly acceptable goal. It should not
be. The reality of life is volatile, and accounting should reflect the truth.
—F. Norris (The New York Times, 2003)
Legitimate earnings management is arguably something that the board of directors is employed by
the shareholders to do.
—London Society of Chartered Accountants, quoted by P. Morris (FT.com, 2002)
In the corporate world, sometimes things aren’t exactly black and white when it comes to
accounting procedures.
—President George W. Bush
when pressed about how Harken Energy hid losses
while he was on its board of directors, quoted in N. Gibbs (Time, 2002)
I. INTRODUCTION
Talk about ethics, particularly relating to external financial reporting, has recently taken on a new
life, following pandemic corporate scandals during the first few years of the twenty-first century that have
shaken the international business community. Some of these scandals involved sham transactions (e.g., as
in the case of AIG, HealthSouth, Kmart, and Xerox). Most of them, however, involved the improper
application of GAAP regarding revenue recognition (Halliburton), mergers (Tyco), or off-balance-sheet
financing (Adelphia, Enron); questionable “real” transactions like channel stuffing (AOL Time Warner,
Bristol-Myers Squibb), bid-rigging (AIG), asset swaps and round-trip trading (Duke Energy, Enron,
Global Crossing); and even the misclassification of “hard” operating cash flows (WorldCom). What
makes these events scandalous is not only their flagrant violation of investor protection laws or even the
sheer magnitude of the amounts involved—several billion U.S. dollars in the case of Adelphia, Enron,
Bristol-Myers Squibb, Merck, WorldCom, and Xerox—but also their clash with accepted social norms
(Ball 2009). Few, if anyone, would disagree with these events epitomizing unethical financial reporting.
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It is less clear, though, where to draw the “ethics line” when it comes to more common, run-ofthe-mill forms of earnings management—those that are legal and do not violate GAAP, even if they are
not fully disclosed to investors (Ball 2009). For example, Sherron Watkins, a former Enron VP who
helped uncover the firm’s accounting scandal, describes this “ethics line” as “that tried and true ethical
litmus test: if a transaction or activity will not pass muster on the front page of the Wall Street Journal,
then just don’t do it…” (Watkins 2003). On the other hand, and also speaking from an accounting
practitioner’s point of view, Parfet (2000) argues, “‘Bad’ earnings management…is intervening to hide
real operating performance by creating artificial accounting entries or stretching estimates beyond a point
of reasonableness… However, there is also a ‘good’ kind of earnings management—reasonable and
proper practices that are part of operating a well-managed business and delivering value to shareholders…
It is a mark of skill and excellence that the market seeks and rewards.”
Our goal is to inform the debate on the ethics of earnings management by examining empirically
whether a large cross-section of highly ethical firms manage earnings and, if so, how they do it and for
what reasons. Empirical analysis of financial reporting ethics is important, if for no other reason than to
assess regulators’ assumptions about acceptable practices and to avoid unintended consequences of
regulation. The ethical nature of different ways to manage earnings, as well as the reasons for doing so,
appears to be a key input in the regulation of financial reporting. For instance, regulators tend to see
earnings management “as pervasive and problematic—and in need of immediate remedial action”
(Dechow and Skinner 2000). Indeed, concerns over earnings management expressed by former SEC
Chairman Arthur Levitt in his famous 1998 “The Numbers Game” speech foreshadowed new disclosure
requirements to “crack down” on firms that manage earnings (Healy and Wahlen 1999). Lax ethics
coming to light particularly during the Enron debacle prompted the enactment of the Sarbanes-Oxley Act
in 2002 (Bush 2002; Lucas 2004).1
1
When signing the act into law, then U.S. President George W. Bush stated, “This law says to every dishonest
corporate leader: you will be exposed and punished; the era of low standards and false profits is over; no boardroom
in America is above or beyond the law. This law says to honest corporate leaders: your integrity will be recognized
and rewarded, because the shadow of suspicion will be lifted from good companies that respect the rules” (Bush
[3]
We identify highly ethical firms by examining a large sample of firms’ involvement in corporate
social responsibility (CSR) activities. CSR is a self-regulated business strategy in which a firm takes an
active role in conducting transactions with stakeholders in an ethical manner. For example, CSR behavior
includes maintaining good employer-employee relations, producing goods that are safe for consumption
and processed in a sustainable manner, respecting human rights and the environment, and ensuring strong
governance (Vogel 2005; Crane et al. 2008). Focus on CSR has increased in recent years as both
individual and institutional investors have made investing in socially responsible companies a priority
(Cox et al. 2004). In fact, by 2007, there were about 260 socially responsible investment (SRI) funds in
the Unites States, totaling over $2.1 trillion of assets under management (Budde 2008). Clearly, CSR and
SRI are not fads. SRI reflects investors’ ethical and social convictions (Renneboog et al. 2008), and its
focus on CSR makes firms’ CSR strategies a good proxy for corporate ethical behavior.
We test three specific hypotheses. The first one predicts that ethical firms are less likely to
manage accruals than a matched control sample; the second hypothesis predicts that ethical firms are
more likely to engage in “real” earnings management; the third one predicts that ethical firms are less
likely to manage earnings for opportunistic reasons.
The first and second hypotheses follow from arguments and empirical evidence suggesting a
commonly held view in the business community that manipulating accruals is less ethical, even if within
the boundaries of GAAP, than managing “real” business transactions to achieve a desired level of
earnings (Merchant and Rockness 1994; Dechow and Skinner 2000; Ball 2009). This view seems to rely
on the notion that managing accruals is akin to misrepresenting the “true” financial position of the firm,
whereas managing earnings through “real” activities involves changing the firm’s operations. The
resulting bottom line of “real” earnings management represents actual operating results and, therefore,
financial statements are more likely to present the firm’s “true” financial position.
2002). Ethical and moral views also appear to affect public policy on a broad range of issues, not only those
involving economic markets (Hunt 1999; Friedman 2008; Zak 2008). See Hausman and McPherson (2006) for a
discussion of the interplay between ethics and economics.
[4]
Our third hypothesis follows from the many incentives to manage earnings and the consequences
of earnings management to investors and other stakeholders. Earnings management can benefit
shareholders by lowering the volatility in share prices, lowering corporate taxes, avoiding debt covenant
violations, and improving the information content of earnings; however, it can also increase opportunistic
managers’ bonuses and equity stakes (Healy 1985; Watts and Zimmerman 1986; Hunt et al. 1996; Healy
and Wahlen 1999; Fields et al. 2001; Matsumoto 2002; Goel 2003; Graham et al. 2005; Cohen et al.
2008; Ball 2009). The general perception in the business community seems to be that earnings
management is a zero-sum game (for an opposing view, see Arya et al. 2003; Tucker and Zarowin 2006).
The fundamental problem that we face in testing our hypotheses is that, even if we can measure
ethical behavior perfectly, we cannot observe the extent and purpose of earnings management of an
ethical firm had it not behaved ethically to begin with. In other words, we see an ethical firm’s behavior
but not its counterfactual. If the choice to behave ethically is systematically correlated with other
variables that affect the form and extent of earnings management, then our findings will likely suffer from
“selection on observables” bias, making any inferences unreliable. To avoid this bias, we construct
counterfactuals using propensity score matching methods developed in the treatment effect literature
(Rubin 1974; Rosenbaum and Rubin 1983; Heckman et al. 1998; Dhejia and Wahba 2002). Broadly
speaking, we match ethical firms to control firms based on their propensity to behave ethically. We also
impose a common support—a restriction that the propensity scores of ethical and control firms overlap—
to ensure that we compare firms that are indeed comparable. We then test our first and second hypotheses
by comparing several proxies of accruals-based and “real actions”-based earnings management. We test
our third hypothesis by performing a canonical correlation analysis between the set of earnings
management proxies and a set of variables measuring incentives to manage earnings. Unlike multiple
regressions, canonical correlation analysis allows us to examine simultaneously the association between
the variables in these two sets.
Using 2003–2008 data for over 1,000 firms, we find no support for our first hypothesis. In fact,
we find that ethical firms tend to have income-increasing levels of abnormal accruals. However, we do
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find support for our other two hypotheses. Ethical firms have more income-increasing levels of abnormal
operating cash flows and inventory productions costs. In the canonical correlation analyses, these proxies
also have strong correlations with variables measuring incentives to meet or beat analysts’ earnings
forecasts and to lower income taxes and financing costs. Our results are consistent with the notion that
ethical firms behave as if managing earnings is OK. These firms seem to manage accruals, cash flows,
and inventory production to report higher levels of earnings than had they not behaved as ethically to
begin with. Tax and financing costs, including costs of missing earnings expectations—and not
opportunistic reasons like increasing managers’ bonuses and equity stakes—seem to motivate ethical
firms to manager earnings.
Ball (2008) argues that an important barrier to answering the “big questions” in accounting
research is the absence of counterfactuals. We believe our paper contributes to the literature by
constructing counterfactuals to answer what we believe to be a couple of important questions—is it
acceptable to manage earnings and, if so, how and for what reasons? The answers we present, based on
empirical analysis of a large sample of firms, should help inform regulators, academics, and the broader
business community about the ethical nature of earnings management.
The current draft of this paper has several limitations that we plan to remedy in the next version.
First, we need to tone down the normative undercurrent of the paper and instead beef up and clarify our
discussion of social norms, especially the descriptive and injunctive types of norms (Caliendo and Trost
1998), the mechanisms by which they likely affect financial reporting choices, and the role they play in
moral reasoning (e.g., Kohlberg 1981, 1984; Gilligan 1982; Turiel 1983; Rest et al. 1999). Second, we
need to tie the development of our hypotheses more directly to the economics-based literature on the
causes and consequences of earnings management.2 Third, we need to ensure that measurement error in
our proxy for socially responsible behavior is not biasing our results toward a Type I error with respect to
the proxies we use for abnormal accruals and opportunistic incentives. To this end, we will run additional
robustness checks on our factor analyses and we will broaden our proxies to reflect whether a firm is
2
Ronen and Yaari (2008) present a recent, thorough review of this literature.
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consistently included in SRI funds. Fourth, we need to develop crisper hypotheses about the magnitude
and direction of earnings management. This also means that we need to reassess the propriety of
measuring real earnings management directionally rather than in absolute terms. If the magnitude of real
earnings management is the appropriate outcome, then we need to modify the real earnings management
models currently used in the literature to suit our tests better. Finally, our analyses are silent about the
over-time stability of social norms about earnings management. Anecdotal evidence suggests that
attitudes may vary with the business cycle—“While the boom proceeds, almost everyone tolerates its
excesses, including moral lapses” (Samuelson 2000). Also, Cohen et al. (2008) argue and find that the
Sarbanes-Oxley Act of 2002 changed the cost of accruals manipulation vis-à-vis real earnings
management. It would not surprise us to find that this regulation and the media coverage of the 2001–
2002 accounting scandals changed the business community’s perception of what constitutes acceptable
behavior regarding financial reporting (see, e.g., Hannah and Zatzick 2008). Therefore, we will reestimate
our analyses but for the 1995–2000 pre-scandals/regulation period.
The rest of the paper proceeds as follows. Section II presents our hypotheses and describes the
intuition and assumptions behind the methods we use to test them. Section III describes our data sources,
with particular attention to the CSR data we use. Section IV then describes how we factor analyze these
data to construct an overall proxy for ethical behavior; here, we also present the results of several
construct validity tests. Section V presents results of our matching approach and the first two hypothesis
tests. Section VI presents results of the canonical correlation analyses used to test our third hypothesis.
Section VII concludes.
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II. HYPOTHESES AND METHODOLOGY
Our goal is to evaluate whether earnings management—its methods and objectives—is OK. We
follow Dictionary.com’s definition of OK: “correct, permissible, or acceptable; meeting standards” (OK,
a).3 Notice that this definition does not necessarily imply an ethical meaning.4
Our definition of ethical behavior is on par with Dictionary.com’s definition: “being in
accordance with the accepted principles of right and wrong that govern the conduct of a profession.”
While the dictionary lists as a synonym the word “moral”, it distinguishes these two words by their usage:
Morals refers to generally accepted customs of conduct and right living in a society, and to the
individual's practice in relation to these: the morals of our civilization. Ethics now implies
high standards of honest and honorable dealing, and of methods used, especially in the
professions or in business: ethics of the medical profession. [Italics in original]
One could argue that any form of earnings management is unethical, if one views earnings
management as a deliberate misrepresentation of the firm’s “true” financial position, especially for the
opportunistic purpose of increasing managers’ wealth or human capital. Instead, we believe that people
hold a more subtle view and consider earnings management ethical if the methods used fall within
generally accepted business practices, and the objectives are not opportunistic to managers but rather
equitable to the firm’s other stakeholders. Our belief is consistent with the epigraphs to this paper, as well
3
According to Dictionary.com, “OK is a quintessentially American term that has spread from English to many other
languages. Its origin was the subject of scholarly debate for many years until Allen Walker Read showed that OK is
based on a joke of sorts. OK is first recorded in 1839 but was probably in circulation before that date. During the
1830s there was a humoristic fashion in Boston newspapers to reduce a phrase to initials and supply an explanation
in parentheses. Sometimes the abbreviations were misspelled to add to the humor. OK was used in March 1839 as an
abbreviation for all correct, the joke being that neither the O nor the K was correct. Originally spelled with periods,
this term outlived most similar abbreviations owing to its use in President Martin Van Buren's 1840 campaign for
reelection. Because he was born in Kinderhook, New York, Van Buren was nicknamed Old Kinderhook, and the
abbreviation proved eminently suitable for political slogans” (OK, b).
4
Donaldson and Prestor (1995) argue that questions of business ethics can be viewed as either positive (e.g., what
firms do regarding ethics), normative (e.g., what ought to be done or what objectives ought to be pursued), or
instrumental (e.g., how to achieve a particular objective). We take the first view. We rely on empirical analyses
based on the social scientific method, rather than on normative analysis, because people often judge whether an
action is ethical based on emotions rather than on the type of reasoning expounded in utilitarian, consequentialist,
and deontological theories of ethics (Nichols 2004; Hauser 2006; Prinz 2007; ). Indeed, social norms and specific
cultural practices seem to drive evaluations about the ethicality and morality of people and their actions (Cook 1999;
Appiah 2008; Knobe and Nichols 2008), suggesting a culturally relativist view of ethics (Driver 2007).
[8]
as survey evidence reported by Merchant and Rockness (1994) and Graham et al. (2005)—managers
seem to view accrual-based earnings management as less ethical than “real” earnings management.
Based on the foregoing, we test the following three (alternative) hypotheses:
H1:
Firms are less likely to manage earnings through accruals when they behave ethically.
H2:
Firms are more likely to manage earnings through real activities when they behave
ethically.
H3:
Firms are more likely to manage earnings for nonopportunistic reasons when they behave
ethically.
The methods we use for testing these hypotheses are not common in accounting research, so we
turn now to discussing the intuition behind them.
Tests of H1 and H2 Using Matched Samples
We test H1 and H2 by modeling the effect of ethical behavior on earnings management within a
potential-outcomes/counterfactual framework (Rubin 1974; Rosenbaum and Rubin 1983; Heckman
1997). Suppose we can observe whether a firm behaves ethically, and by which means and to what extent
it manages earnings (we discuss our proxies for ethical behavior and earnings management later in
Sections IV and V). Let ETHICALi measure whether firm i behaves ethically, Yi1 measure the amount of
earnings managed if firm i behaves ethically (given by ETHICALi = 1), and Yi0 measure the amount
managed if it does not behave ethically (ETHICALi = 0). We are interested in the difference, if any,
between Yi1 and Yi0. Our test variable for H1 and H2 is the average effect of ethical behavior on earnings
management for those firms that in fact behave ethically. The treatment effect literature refers to this type
of effect as the “average treatment effect for the treated” (ATT), and defines it as:
ATT = E[Yi1|ETHICALi = 1] – E[Yi0|ETHICALi = 1].
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(1)
The term Yi0|ETHICALi = 1 is the amount of earnings management that we would observe had the
firm not behaved ethically; Yi1|ETHICALi = 1 is the amount we actually observe when the firm behaves
ethically. We focus on the effect for ethical firms, rather than for unethical ones or randomly chosen firms
from the population, because ethical firms have taken costly and visible steps to behave ethically.
Therefore, we expect the means and extent to which they manage earnings to reflect what these firms
believe is ethical when it comes to managing earnings.
We face a missing-variables problem in estimating the ATT in that the last term in Equation (1),
E[Yi0|ETHICALi = 1], is unobservable. That is, we cannot observe the extent of which an ethical firm
manages earnings had it not behaved ethically—we can observe one or the other, but not both states of
nature at the same time for the same firm. If behaving ethically is a random choice, we can easily estimate
the ATT by comparing the sample mean of Yi for firms behaving ethically with that of a control group of
firms not behaving (as) ethically. However, if the choice to behave ethically is systematically correlated
with a set of observable variables that also affect the form and extent of earnings management, then our
estimate of ATT will reflect “selection on observables” bias (Heckman et al. 1998; Dhejia and Wahba
2002). This bias may be large enough to render the results misleading.
We address this “selection on observables” problem using propensity score matching methods
developed in the treatment effect literature.5 The main idea of matching is to use a control group to mimic
a randomized experiment. The key assumption we need to make when matching is that, conditional on a
vector of covariates X, the form and extent of earnings management is independent of whether the firm
behaves ethically; that is, both Y0 and Y1 ⊥ ETHICAL|X. Under this assumption, Equation (1) becomes:
5
See Caliendo and Kopeinig (2008) for detailed guidance on implementing propensity score matching estimators,
and Doyle et al. (2007) and Armstrong et al. (2010) for examples of their implementation in the accounting
literature. These estimators offer two noteworthy advantages over regression analysis when estimating treatment
effects (Heckman et al. 1998; Dehejia and Wahba 2002). First, they are nonparametric, so they do not impose
arbitrary functional forms like linearity when controlling for selection bias. Second, when properly implemented,
they ensure that comparisons between treated and control firms occur over areas of common support, that is, over
otherwise similar firms. In contrast, regression analysis uses all sample observations, including those outside the
area of common support.
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ATT = E[Yi1|ETHICALi = 1, Xi] – E[Yi0|ETHICALi = 0, Xi],
(2)
where the last term is now observable.
We match ethical firms to control firms with similar values of X, like industry, size, and
performance. However, as the number of covariates in X increases, this approach becomes difficult to
implement in practice. The solution that Rosenbaum and Rubin (1983) suggest is to match firms on their
propensity scores—the probabilities that firms will behave ethically conditional on X. We can easily
estimate these propensity scores, p(Xi), using a standard logistic regression. The goal here is not to find
the best statistical model to explain the probability of ethical behavior, but rather to include in the
regression variables that simultaneously predict the decisions to behave ethically and to manage earnings
(Heckman and Navarro-Lozano 2004). It is OK to exclude variables that systematically predict ethical
behavior but not earnings management, because such variables by definition will have no effect on
earnings management or the ATT. It is also OK to exclude variables predicting earnings management but
not ethical behavior; one would expect those variables to be distributed identically between ethical and
control firms and, therefore, not introduce selection bias. As Section V describes in more detail, we
choose conditioning variables based on theories and empirical evidence of CSR and accounting method
choice.
We also impose a common support, requiring that some comparable control firms exist for each
ethical firm. Common support ensures that we do not “compare the incomparable.” Using propensity
score matching, we then estimate the ATT as:
ATT = E[Yi1|ETHICALi = 1, p(Xi)] – E[Yi0|ETHICALi = 0, p(Xi)].
(3)
To estimate the second term on the right-hand side of Equation (3), we match with replacement
each ethical firm to the control firm with the closest propensity score.
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If ethical firms view earnings management through accruals as unethical and, therefore, do not
manage accruals (or do so, but to a lesser extent), then we expect ATT < 0 for accruals-based earnings
management measures. This finding would support H1. In contrast, if these firms view earnings
management through real actions as ethical, then we expect ATT ≥ 0 for real earnings management
measures, supporting H2.
Tests of H3 Using Canonical Correlation Analysis
We test H3 by performing a canonical correlation analysis between a set of variables measuring
the extent of earnings management and a set of variables measuring incentives to manage earnings. We
then compare the results of this analysis for ethical firms (i.e., those with ETHICALi = 1) with those for
control firms (ETHICALi = 0).
Multiple regression allows us to examine the association between a single dependent variable
(e.g., abnormal accruals) and multiple independent variables (e.g., managerial compensation, leverage,
incentives to meet analyst expectations, etc.). In contrast, canonical correlation allows us to examine
simultaneously the association between multiple dependent variables (e.g., abnormal accruals, absolute
value of abnormal accruals, abnormal inventory production costs, etc.) and those multiple independent
variables.6 Therefore, canonical correlation analysis allows us to identify and quantify the type and extent
of earnings management that has the strongest association with various incentives to manage earnings.
6
Which set of variables is the dependent set and which the independent one does not matter since the technique is
symmetrical. As the broadest form of the general linear model, canonical correlation analysis subsumes virtually all
of the parametric methods commonly used in accounting research, including two-sample t tests, Pearson productmoment correlations, simple and multiple regressions, and analysis of variance (Knapp 1978). For instance,
canonical correlation analysis for two sets of one variable each is the same as a Pearson correlation between the two
variables; the analysis for a set of just one variable and another set of several variables is equivalent to a multiple
regression. Because the parameters φ and γ are estimated simultaneously, canonical correlation analysis has the
added benefit of limiting the probability of committing a Type I error. For example, if we perform 10 hypothesis
tests separately using multiple regression and claim significant results using a typical 0.10 significance level, we
might actually end up with a 65 percent chance of falsely rejecting a null [i.e., 1 – (1 – 0.10)10 = 0.65]. See LeClere
(2006), Fornell and Larcker (1980), and Stowe et al. (1980) for examples of canonical correlation analysis in
accounting research. Alpert and Peterson (1972) provide a discussion of how to interpret the results of canonical
correlation analysis.
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Given a set of earnings management proxies Y = {Y1, Y2, Y3, …, Yh} and a set of earnings
management incentives W = {W1, W2, W3, …, Wj}, the goal of our canonical correlation analysis is to find
pairs of canonical variates Ŷmi and Ŵmi that are maximally correlated, where the variate:
Ŷmi = φ1mY1mi + φ2mY2mi + φ3mY3mi + … + φhmYhmi
(4)
is a linear combination of the h earnings management measures and the variate:
Ŵmi = γ1mW1mi + γ2mW2mi + γ3mW3mi + … + γjmWjmi,
(5)
is a linear combination of the j earnings management incentives. The result is a set of coefficients φm and
γm that maximize the correlation between Ŷm and Ŵm. The first canonical correlation RC1 = Corr(Ŷ1, Ŵ1) is
the largest possible correlation between any linear combination of the earnings management proxies and
any linear combination of earnings management incentives. The algorithm proceeds by extracting in turn
additional pairs of maximally correlated canonical variates, each orthogonal to those already extracted.
The analysis extracts m = min(h, j) canonical correlations RCk = Corr(Ŷk, Ŵk) and pairs of canonical
variates Ŷk and Ŵk, where k = {1, 2, …, m}. We test each RCk for statistical significance using a likelihood
ratio test; we then assess its economic significance using Wilk’s λ criterion. Specifically, the λk associated
with each RCk measures the variance left unexplained by canonical functions k through m.
To evaluate H3, we focus only on statistically significant RCks, and interpret their respective
loadings and cross-loadings.7 Loadings are the correlations between a canonical variate and each variable
in its set [e.g., Corr(Ŷ2, Y2,3)]; cross-loadings are the correlations between the variate and each variable in
the opposite set [e.g., Corr(Ŷ2, W2,1)]. We interpret only variables with coefficients that are significant at
the 0.1 level or better and with loadings larger than 0.4 in absolute terms. We perform the canonical
7
Loadings and cross-loadings are more appropriate when interpreting results than standardized coefficients because
the latter are typically unstable, especially in the presence of collinearity.
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correlation analysis separately for the sample of ethical firms (with ETHICALi = 1) and the sample of
matched controls (with ETHICALi = 0), and compare their results to draw inferences on H3.
III. SAMPLE AND DATA SOURCES
Our sample selection begins with firms included in the 2003–2008 versions of KLD STATS, a
database on CSR performance put together by KLD Research & Analytics, Inc. KLD is the leading
provider of social research and indexes to institutional investors, financial analysts, consultants, the
media, and academic researchers. Their data have been used in peer-reviewed articles published in
journals like Journal of Banking and Finance, Financial Analyst Journal, Academy of Management
Journal, Strategic Management Journal, Journal of Business Ethics, and Accounting, Organizations, and
Society.
KLD’s research staff collects information from various sources including corporate officers, SEC
filings, annual financial reports, sustainability reports, news media, research partners covering non-U.S.
markets, and government agencies and NGOs such as the Departments of Labor and Defense, the
Occupational Safety and Health Administration, the Environmental Protection Agency, the Interfaith
Center on Corporate Responsibility, and Human Rights Watch. Using proprietary screens, KLD staff then
rate a firm’s performance along two sets of CSR criteria. The first set measures environmental, social, and
governance (ESG) performance across seven broad categories of issues affecting the firm’s
stakeholders—environmental footprint, employee relations, diversity, human rights, product quality and
safety, community involvement, and corporate governance. For each ESG criterion, KLD staff measure
several positive parameters or “strengths” and several negatives ones or “concerns.” The second set of
criteria measures the firm’s involvement in six controversial business issues (CBIs) that socially
responsible investors may find objectionable—alcohol, firearms, gambling, the military, nuclear power,
and tobacco.
The 2008 KLD STATS database summarizes annual ratings for 85 ESG and 15 CBI criteria (see
Appendix A). The ratings go back to 1991 for firms included in the S&P 500 and the Domini 400 Social
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indexes. Ratings for firms in the Russell 1000 index go back to 2001 and for firms in the Russell 2000
index back to 2003. Our initial sample consists of the 1,795 firms consistently included in the 2003–2008
databases. We begin our sample period in 2003 not only to maximize the number of public firms in our
analyses, but also because the Sarbanes-Oxley Act was enacted in 2002, following unprecedented
accounting scandals in 2001 and 2002. Cohen et al. (2008) show that this legislation affected firms’
earnings management behavior.
We restrict our sample to firms consistently included in the database for the entire 2003–2008
period to ensure that changes in KLD’s rating methodology throughout this period have the same effect
on all sample firms. By requiring a constant sample, we trade off ratings comparability across firms for
potential survivorship bias. If anything, we expect survivorship bias to limit the generalizability of our
inferences to larger firms.
From this initial sample of 1,795 firms, we drop 478 firms without required data on Compustat,
CRSP, I/B/E/S, or ExecuComp to calculate the variables described in Appendix B. Our final sample
consists of 1,317 firms; we calculate most of our variables using six annual observations for each firm, for
a total of 7,902 firm-year observations.
IV. IDENTIFYING FIRMS BEHAVING ETHICALLY
We use a firm’s involvement in CSR activities as a proxy for its ethical behavior. CSR is a selfregulated business strategy in which a firm takes responsibility for its impact on various stakeholders,
including investors, employees, consumers, communities, and the environment (Vogel 2005; Crane et al.
2008). In using a firm’s CSR behavior, we make a fundamental assumption: if a firm shows a high degree
of social responsibility, it will also show highly ethical behaving when reporting earnings. According to
Peter Bakker, CEO of the Dutch logistics group TPG, “[y]ou cannot go out claiming that you are going to
help save some hungry people, and make a complete mess of your accounting, or take a huge pay raise as
an executive, or pollute the environment” (Maitland 2004).
[15]
Firms are necessarily mindful of their conduct vis-à-vis stakeholders, in part to preserve
reputation with the investing public. Retail and institutional investors focused on socially responsible
investing (SRI) screen companies for both positive and negative CSR activities. Examples of positive
activities include good employer-employee relations, goods that are safe for consumption and processed
in a sustainable manner, operations that are respectful of human rights, and good corporate governance
policies. Conversely, examples of negative CSR activities include products and business practices that are
harmful to consumers, employees, or the environment. Appendix A shows how KLD rates these
activities.
Both institutional and retail investors are mindful of firms’ CSR activities (Cox et al. 2004).
Ethical investing has its ancient origins in Christian, Islamic, and Jewish traditions (Renneboog et al.
2008), but its growth has skyrocketed in the last few decades as both institutional and retail investors have
made SRI a priority. As of 2007, roughly 11 percent of assets under professional management in the U.S.
were involved in SRI. In 2007, there were approximately 260 socially screened investment funds, totaling
over $2.1 trillion of assets under management (Budde 2008). Clearly, CSR and SRI are not fads. SRI
reflects investors’ ethical and social convictions (Renneboog et al. 2008), and its focus on CSR makes
firms’ CSR strategies a good proxy for their ethical behavior.
A firm issues financial reports mainly to help set up and monitor contracts with its various
stakeholders: financing and governance contracts with creditors and investors, compensation contracts
with managers and employees, credit and product warranty contracts with customers and suppliers, and
(oftentimes implicit) operating contracts with governments and local communities. As such, we argue that
a firm’s ethical behavior regarding financial reporting, particularly with respect to earnings management,
reflects the extent to which the firm behaves ethically in its interactions with diverse stakeholders. Our
view is consistent with the view underpinning the CSR literature that corporate ethical behavior is
fundamentally a multidimensional construct (Carroll, 1979), reflecting the identity of the particular
stakeholders interacting with the firm. This literature identifies a large number of dimensions of ethical
[16]
behavior, ranging from the firm’s employee and community relations, to environmental responsibility and
product safety (Griffin and Mahon 1997; Johnson and Greening 1999, Hillman and Keim 2001).
We use KLD STATS to measure firms’ CSR behavior along several of these dimensions. We
calculate the total number of strengths and concerns over the test period for each of the ESG dimensions
listed in Appendix A: environmental footprint, employee relations, diversity, human rights, community
involvement, product quality and safety, and corporate governance. We then standardize each of the
variables to eliminate scale effects, and subtract the standardized number of concerns from the
standardized number of strengths to end with a net measure of performance on that particular ESG
dimension. We also calculate the standardized number of CBIs in which a firm is involved during the test
period. In the rest of the paper, we refer to these eight variables as “standardized KLD ratings.” Panels A
and B in Table 1 summarize these variables, showing that they have fairly wide distributions.
We operationalize ethical behavior at a higher level of abstraction than the eight KLD dimensions
by extracting the highest-order common factor from the standardized KLD ratings.8 We obtain this
highest-order factor by performing an initial common factor analysis on the eight ratings, obliquely
rotating (using promax 4) the resulting first-order factors, and then continuing this factoring/rotation
process until we end up with only one factor.9 This highest-order factor is less accurate but more
generalizable than lower-order factors (Gorusch 1983, p. 240–255). Because we are interested in the
shared variance among the standardized KLD ratings, we extract common factors rather than principal
components; therefore, our estimated common factors should exclude unique and measurement error
8
Estimating an overall measure of ethical behavior by simply adding up ratings on the various dimensions implies
that each dimension is equally important. Graves and Waddock (1993), Ruf et al. (1993), and Waddock and Graves
(1997) show that independent judges do not consider various CSR dimensions as equally important. We could also
cluster firms along these dimensions, but doing so would unnecessarily complicate our analysis. Not only is the
implementation of clustering methods arguably ad hoc, but we are not confident that we can offer ex ante precise
hypotheses about the number of clusters or differences in earnings management behavior across them. For example,
Lindgreen et al. (2009) use survey data to cluster about 400 U.S. firms into four homogenous groups of comparable
size. The authors use Ward’s hierarchical algorithm to find clusters, a method well known to produce clusters of
similar size (Hair et al. 2010); a different algorithm would likely have produced clusters of largely different sizes or
even a different numbers of clusters.
9
See Gorsuch (1983, Chapter 11) and Hair et al. (2010, Chapter 15) for detail discussions of higher-order factor
analysis and its implementation.
[17]
variances in the eight standardized KLD ratings. We determine the number of factors to extract at each
level using parallel analyses (Horn 1965).10
Panel C of Table 1 shows a moderate degree of correlation between most of the eight
standardized KLD ratings. A Bartlett (1950) test of the significance of the correlation matrix rejects the
null that the ratings are not correlated (χ2[28 df] = 587.26; p < 0.001). The last column of Panel C shows
that multiple correlations between each variable and the rest range from 0.17 to 0.36, suggesting that
between only 3 and 13% of the variation in each rating is shared by the other seven ratings.
Panel D shows the correlation structure underpinning the factors and the standardized KLD
ratings. The first-order analysis yields three factors; the correlations between them are 0.42, 0.83, and
–0.07, suggesting some further generalization is possible. The second-order analysis on these three firstorder factors yields two with a correlation of 0.25. Finally, the third-order analysis yields a final factor
(which we label CSR_FACTOR), whose correlations with the eight standardized KLD ratings range from
0.22 to 0.75, with a mean of 0.41. Its average correlation with the first- and second-order factors is 0.74
and 0.79.
We classify firms in the top quintile of this third-order CSR_FACTOR as behaving ethically,
ETHICAL = 1. To mitigate measurement error, we drop firms in the fourth quintile, and classify as
control firms, ETHICAL = 0, those firms in the bottom three quintiles. We then use ETHICAL as our
proxy for ethical behavior in tests of H1, H2, and H3. As a robustness check, we also report results using
DS400, an indicator coded 1 (0 otherwise) for sample firms included in the Domini Social 400 Index, a
well-respected stock market index of high-CSR firms.
Table 2 shows that the means of the eight standardized KLD ratings differ statistically between
ethical and control firms, both individually based on two-sample t tests and jointly based on Hotelling’s
10
We only retain factors whose eigenvalues are larger than the mean eigenvalue from a parallel analysis. We
implement this analysis by factor analyzing 1,000 randomly constructed data sets with the same number of
observations and variables as ours. Because the variables in these parallel sets are random, any resulting eigenvalues
should be just noise. Thus, we consider meaningful only those factors that we extract from our “real” dataset that
have eigenvalues larger than those we get from the “random” dataset. Parallel analysis yields relative unbiased
estimates of the correct number of (first-order) factors underlying a correlation matrix (Zwick and Velicer 1986).
[18]
T2 tests. Together, the results in Table 2 suggest that both ETHICAL and DS400 are reasonable proxies
for measuring corporate ethical behavior.
V. DO ETHICAL FIRMS MANAGE EARNINGS?
This section describes our tests of H1, the hypothesis that firms are less likely to manage earnings
through accruals when they behave ethically, and H2, the hypothesis that ethical firms are instead more
likely to manage earnings through real activities. First, we estimate a firm’s propensity to behave
ethically, next we match ethical firms to control firms based on these propensity scores, and finally we
estimate and compare the earnings management proxies for ethical and control firms.
Estimating Propensity Scores
The goal of estimating propensity scores is to include in the estimation model only variables that
affect simultaneously the probability of behaving ethically and the extent of managing earnings.11 To this
end, we draw on the theoretical and empirical literature on CSR and accounting method choice to identify
appropriate independent variables. For instance, theory and empirical evidence suggest that firms are
more likely to engage in CSR activities—and thus behave ethically—if they are large, highly reputable,
profitable, and in the public eye, with not only strong growth prospects, but also with strong corporate
governance and labor representation (see, e.g., Waddock and Graves 1997; Johnson and Greening 1999;
Jensen 2001; Cox et al. 2004; Sharfman et al. 2004; Vogel 2005; Fauver and Fuerst 2006; Crane et al.
2008; Renneboog et al. 2008). These firms also are more likely to be owned by institutional investors and
to operate in litigious industries, abroad (particularly in the European Community where adherence to
CSR norms is expected), or in developing countries where they may be subject to activist NGO oversight.
These variables have also been linked theoretically and empirically to earnings management behavior
(see, e.g., Watts and Zimmerman 1986; Bowen et al. 1995; Dechow et al. 1996; Hunt et al. 1996; Bushee
11
When specifying the propensity score model, it is better to err on the side of including too few rather than too
many variables. Irrelevant variables not only increase the variance of predicted propensity scores, but also may
make it more difficult to find matching observations over a common support (Caliendo and Kopeinig 2008).
[19]
1998; Barton 2001; Fields et al. 2001; Barton and Simko 2002; Matsumoto 2002; Graham et al. 2005;
Cohen et al. 2008).
Along with industry indicators, our propensity score model includes proxies for size (SIZE),
reputation and visibility (PRESS, ANALYSTS), profitability and growth prospects (ROA, LOSS, and PB),
corporate governance (%INSTITUTIONS, %ACTIVISTS, %BLOCKS, and BIG_N), labor intensity
(LABOR_INTENSITY), litigation (LITIGATION), and foreign sales and degree of international
diversification (FOREIGN_SALES, and MULTINATIONAL). Appendix B describes in detail the
construction of these variables. We estimate firm i’s propensity score using the following logit model:
Pr(ETHICALi = 1) = β0 + β1SIZEi + β2PRESSi + β3ANALYSTSi + β4ROAi + β5LOSSi
+ β6PBi + β7%INSTITUTIONSi + β8%ACTIVISTSi + β9%BLOCKSi
+ β10BIG_Ni + β11LABOR_INTENSITYi + β12LITIGATIONi
+ β13FOREIGN_SALESi + β14MULTINATIONALi
+ β′Σ(2-digit NAICS indicator)i + εi,
(6)
where ETHICAL is coded 1 if firm i is in the top quintile of the highest-order factor CSR_FACTOR
extracted from the standardized KLD ratings, 0 if it is in the bottom three quintiles (see Section IV). The
propensity score pi is simply the prediction Pr(ETHICALi = 1) using the parameters estimated for
Equation (6).12 For sensitivity purposes, we also report results with DS400 rather than ETHICAL as the
dependent variable.
Table 3 presents the regression results. As expected, most variables are significantly associated
with the probability that firms will behave ethically. The variables whose coefficients are not significant
tend to be significant in untabulated simple regressions, consistent with collinearity affecting the results in
Table 3. The diagnostic metrics reported at the bottom of the table suggest that the models do a decent job
12
We use a logit model rather than a probit one because the logit distribution has more density mass in the bounds
than the normal distribution, which might increase our chances of finding good matches for firms at the bounds.
[20]
at estimating the propensity to behave ethically, especially given that they include only variables that are
supposed to predict jointly ethical behavior and earnings management.
For ETHICAL, we estimate propensity scores ranging from 0.01 to 0.84, with a mean of 0.22 and
a standard deviation of 0.13. For DS400, the propensity scores range from 0.01 to 0.75, with a mean of
0.21 and a standard deviation of 0.14.
Matching Control Firms to Ethical Firms on Propensity Scores
We estimate the counterfactual amount of earnings management by matching each ethical firm to
its nearest neighbor in terms of propensity scores. We allow for replacement, thus using some control
firms more than once, to maximize the average quality of matching while reducing potential bias. These
benefits are particularly important in our case since the distributions of the estimated propensity score for
ethical and control firms are very different and do not overlap much (see Fig. 1). The tradeoff of using
matching with replacement is that reducing the number of actual control firms increases the variance of
the estimated counterfactual earnings management values. We further reduce the risk of bad matches by
using controls that are indeed close to the ethical firm; we do so by imposing a 0.01 caliper, thus
constraining the nearest neighbor to be within ± 0.01 from the ethical firm (e.g., if firm i’s propensity
score is 0.72, its match must have a propensity score between 0.71 and 0.73).13 The caliper constraint has
the potential effect of further increasing the variance and lowering the bias of the counterfactual
estimates. Overall, our matching approach is a bit conservative and may lead to less power in estimating
the ATTs.
Technically, the ATT is defined only for the region of common support; violating this condition
may be the primary source of bias in our analyses (Heckman et al. 1998; Dehejia and Wahba 1999). To
avoid “comparing the incomparable,” we impose a common support constraint by deleting all firms in one
group with propensity scores smaller than the minimum or larger than the maximum propensity score of
13
Rosenbaum and Rubin (1985) suggest using a caliper size equal to one quarter the standard deviation of the
estimated propensity score. In our case, this would suggest a caliper of 0.03. The 0.01 that we use is more
conservative and should result in higher-quality matches.
[21]
firms in the other group.14 We then check whether our matching procedure balances the distributions of
the matching variables in the ethical and control firms by testing the equality of the variables’ means
using two-sample t tests and an overall Hotelling’s T2 test (Rosenbaum and Rubin 1985). Table 4 reports
the results of these tests. Both Panels A and B show that, without matching, ethical and control firms (in
terms of either ETHICAL or DS400) differ on most of the variables jointly determining the probability of
behaving ethically and managing earnings. In contrast, ethical and control firms do not differ along these
dimensions after matching on their propensity scores. We take this last result as evidence that our
matching procedure is successful.
Measuring Earnings Management
We follow prior literature (e.g., Jones 1991; Dechow et al. 1995; Roychowdhury 2006) by
defining our proxies of earnings management as the firm’s abnormal levels of accruals, operating cash
flows, production costs, and discretionary expenses. Consistent with Cohen et al. (2008), we use the
following models to estimate the normal levels for these variables; we use the models’ residuals as
proxies for the abnormal levels:
ACCRUALSit/ASSETSit–1 = α1kt(1/ASSETSit–1) + α2kt([∆SALESit – ∆ARit]/ASSETSit–1)
+ α3kt(PPEit/ASSETSit–1) + α4kt(OCFit/ASSETSit–1) + ϖit,
(7)
OCFit/ASSETSit–1 = δ1kt(1/ASSETSit–1) + δ2kt(SALESit/ASSETSit–1)
+ δ3kt(∆SALESit/ASSETSit–1) + ωit,
14
(8)
Specifically, the propensity scores lie within the interval [0.02, 0.71] for firms coded ETHICAL = 1 and within
[0.01, 0.84] for firms coded ETHICAL = 0. Therefore, we delete from further analysis firms with propensity scores
outside the common support interval [0.02, 0.71]. Similarly, the propensity scores are within the interval [0.03, 0.75]
for firms with DS400 = 1 and within [0.01, 0.74] for firms coded DS400 = 0, so we restrict the sample to firms with
propensity scores within the common support [0.03, 0.74] when running our analyses with DS400 as the proxy for
ethical behavior.
[22]
PRODUCTION_COSTSit/ASSETSit–1 = θ1kt(1/ASSETSit–1) + θ2kt(SALESit/ASSETSit–1)
+ θ3kt(∆SALESit/ASSETSit–1) + θ4kt(∆SALESit–1/ASSETSit–1) + ξit,
(9)
and
DISC_EXPENSESit/ASSETSit–1 = ϕ1kt(1/ASSETSit–1) + ϕ2kt(SALESit–1/ASSETSit–1) + ζit,
(10)
where ACCRUALS is income before extraordinary items minus operating cash flows; ASSETS is total
assets; ∆SALES is the change in total revenue (SALES) over the fiscal year; ∆AR is the change in accounts
receivable; PPE is gross property, plant and equipment; OCF is operating cash flows;
PRODUCTION_COSTS is cost of sales plus the change in inventory over the year; and
DISC_EXPENSES is the sum of SG&A, R&D, and advertising expenses. Unlike Cohen et al. (2008), we
include OCF in Equation (7) because of operating cash flows’ inherent negative relation with accruals
and accrual volatility (Dechow 1994). This control becomes particularly important when estimating the
magnitude (as opposed to the direction) of accruals management (Hribar and Nichols 2007).
We estimate each model cross-sectionally by fiscal year t and 2-digit NAICS industry k, requiring
at least 10 annual observations per regression. Our proxies for the type and extent of earnings
management are abnormal accruals, ABN_ACCRUALS, measured by the residual ϖ in Equation (7); the
absolute value of abnormal accruals, |ABN_ACCRUALS|; abnormal operating cash flows, ABN_OCF,
measured by the residual ω in (8); abnormal production costs, ABN_PRODUCTION_COSTS, measured
by ξ in (9); and abnormal discretionary expenses, ABN_DISC_EXPENSES, measured by ζ in (10).
Appendix B reports descriptive statistics for these various proxies.
[23]
Estimating Effects of Ethical Behavior on Earnings Management
Table 5 presents the sample means of the earnings management proxies for ethical firms and their
matched samples, the differences in these means (i.e., the ATTs), and the statistical significance of these
differences based on bootstrapped standard errors. The results for ETHICAL in Panel A are essentially the
same as those for DS400 in Panel B. We discuss only the results for the matched samples.
We find no support for H1. The ATTs for ABN_ACCRUALS and |ABN_ACCRUALS| are not
negative as we predicted. In fact, the ATTs for ABN_ACCRUALS are significant at the 0.10 level but in
the opposite direction. Ethical firms tend to have less negative abnormal accruals than do matched control
firms. Indeed, ethical firms’ abnormal accruals are about half as negative as the abnormal accruals of
matched controls.
Regarding real earnings management, we do find support for H2. As predicted, the ATTs for
ABN_OCF are significantly positive (p < 0.10) and the ATTs for ABN_PRODUCTION_COSTS are
statistically negative (p < 0.01). However, the ATTs for ABN_DISC_EXPENSES are not significant at the
0.10 level. Relative to matched controls, ethical firms have higher levels of income-increasing abnormal
operating cash flows and inventory productions costs. In fact, their abnormal production costs are about
twice as large in absolute terms.
Taken together, the results reported in Table 5 are consistent with the notion that ethical firms
behave as if managing earnings is OK. Ethical firms seem to manage accruals, cash flows, and inventory
production to report higher levels of earnings than had they not behaved (as) ethically to begin with. The
remaining question is whether their objectives for managing earnings are more acceptable. We address
this question next.
VI. WHY DO ETHICAL FIRMS MANAGE EARNINGS?
H3 predicts that firms are less likely to manage earnings for opportunistic reasons when they
behave ethically. We evaluate this prediction by examining the canonical correlations between the set of
earnings management variables {ABN_ACCRUALS, |ABN_ACCRUALS|, ABN_OCF,
[24]
ABN_PRODUCTION_COSTS, ABN_DISC_EXPENSES} and the set of earnings management incentives
{BONUS, STOCK, OPTIONS, NEW_FINANCING, MEET_OR_BEAT, LEVERAGE, TAX}. These
incentives are among the most important factors identified in the literature for motivating earnings
management (see, e.g., Watts and Zimmerman 1986; Bowen et al. 1995; Dechow et al. 1996; Hunt et al.
1996; Bushee 1998; Barton 2001; Fields et al. 2001; Barton and Simko 2002; Matsumoto 2002; Graham
et al. 2005; Cohen et al. 2008). Because incentives related to managerial compensation and equity stakes
are usually viewed as opportunistic, we interpret significant coefficients and large loadings on BONUS,
STOCK, and OPTIONS as support for H3.
Table 6 presents separate results for the sample of firms coded ETHICAL = 1 and ETHICAL = 0,
while Table 7 presents results for firms coded DS400 = 1 and DS400 = 0. The results for ETHICAL and
DS400 are slightly different, so we discuss them separately.
Regarding firms coded ETHICAL = 1, Panel A in Table 6 shows the resulting five canonical
functions, with canonical correlations ranging from 0.41 to 0.04. Collectively, the full model of five
functions is statistically significant (F = 2.18, p < 0.01). The model explains about 29.8% of the total
variance shared between the set of earnings management proxies and the set of earnings management
incentives. Only the first two functions are significant though, accounting for about (14.42 + 8.03)/29.78
= 75.39% of the model’s explanatory power. Therefore, we only interpret these two canonical functions.
Table 6, Panel B presents standardized canonical function coefficients, loadings, and crossloadings for Functions 1 and 2 for the sample of firms coded ETHICAL = 1. We interpret only variables
with significant coefficients and loadings greater than 0.4 in absolute terms; we mark these variables ith a
checkmark in the tables.
The first canonical function suggests a strong association between abnormal accruals, production
costs, and discretionary expenses on one hand, and new financing, tax convexity, and the frequency of
meeting or beating analysts’ consensus earnings forecasts. The second canonical function suggests a
strong association between abnormal production costs and discretionary expenses on one hand, and
meeting or beating expectations and tax convexity on the other. Taken together, the results in Panel B
[25]
suggest that ethical firms do in fact engage in earnings management, but through “real” activities rather
than mere accrual manipulation. Ethical firms manage earnings primarily to meet earnings expectations
and to lower tax and financing costs.
Panel C in Table 6 presents the results for the sample of control firms, coded ETHICAL = 0. The
first canonical function suggests a strong association between abnormal accruals and leverage, while the
second function suggests a strong association between the magnitude of abnormal accruals and operating
cash flows on one hand, and meeting expectations, leverage, and tax convexity on the other.
Taken together, the results in Table 6 suggest that tax and financing costs, including costs of
missing earnings expectations—and not opportunistic reasons like increasing managers’ bonuses and
equity stakes—seem to motivate earnings management decisions.
When we use DS400 as our proxy for ethical behavior, we find slightly different results. We
report these in Table 7. The canonical correlations and explanatory power of the models are not much
different, at least qualitatively, from those reported in Panel A of Table 6. The main difference between
these two tables, however, centers on the role of equity-based compensation in motivating earnings
management, especially for the control firms coded DS400 = 0. The first canonical function for these
firms, tabulated in Panel B of Table 7, suggest that when not behaving ethically, firms are likely to
manage accruals to affect managers’ equity-based compensation.
We believe that the results of the canonical correlation analyses presented in Tables 6 and 7
support H3, at least qualitatively.
VII. CONCLUSION
We examine empirically the extent to which a large cross-section of highly ethical firms manage
earnings. Despite recent accounting scandals bringing renewed interest on the ethicality of earnings
management, it is not clear whether more common, run-of-the-mill forms of earnings management are
viewed as unethical.
[26]
The ethical nature of different ways to manage earnings, as well as the reasons for doing so,
appears to be a key input in the regulation of financial reporting. Understanding perceptions of ethical
behavior regarding earnings management should inform standard setters about the unintended potential
effects of regulation aimed at curbing earnings management.
We identify a sample of highly ethical firms by using their involvement in CSR activities as a
proxy for ethical behavior. Specifically, we derive a proxy of ethical behavior through a higher-order
factor analysis of CSR ratings that we purchased from KLD Research & Analytics, Inc. We then match
highly ethical firms to a set of controls using propensity score matching methods recently proposed in the
treatment effect literature. Ethical firms and their matches do not differ in any of the observable variables
we used to predict simultaneously ethical behavior and earnings management. This (near) perfect
matching allows us to attribute differences in accrual-based and real-activities-based earnings
management proxies to firms’ differences in ethical behavior.
Using 2003–2008 data for over 1,000 firms, we find that ethical firms tend to have higher levels
of income-increasing abnormal accruals compared to the matched control firms. Ethical firms also have
higher levels of income-increasing abnormal operating cash flows and inventory productions costs.
Canonical correlation analyses show that these variables also have the strongest associations with proxies
for incentives to meet or beat analysts’ earnings forecasts and to lower income taxes and financing costs.
We interpret our results as consistent with the notion that ethical firms behave as if managing
earnings is OK. Ethical firms seem to manage accruals, cash flows, and inventory production to report
higher levels of earnings than had they not behaved as ethically to begin with. Tax and financing costs,
including costs of missing earnings expectations—and not opportunistic reasons like increasing
managers’ bonuses and equity stakes—seem to motivate these ethical firms’ earnings management
decisions.
[27]
APPENDIX A
KLD STATS VARIABLES USED TO ESTIMATE ETHICAL BEHAVIOR
The KLD STATS database includes the ESG and CBI variables described below. A coding of 0 reflects the
absence of strengths and weaknesses for that particular criterion. Kinder (2007) provides a detailed description of
the various criteria.
Variable
Description
Environmental Ratings
ENV_STR
Number of environmental strengths (1 point for strengths in each of these areas: recycling,
environmentally beneficial products and services, pollution prevention, alternative fuels,
environmental transparency, fixed assets, management systems, other)
ENV_CON
Number of environmental concerns (1 point for weaknesses in each of these areas: hazardous
waste, regulatory problems, ozone-depleting chemicals, substantial emissions, agricultural
chemicals, climate change, other)
Social Ratings
EMP_STR
Number of employee relations strengths (1 point for strengths in each of these areas: union
relations, no-layoff policy, cash profit sharing, retirement benefits, health and safety, other)
EMP_CON
Number of employee relations concerns (1 point for weaknesses in each of these areas: union
relations, health and safety, workforce reduction, pension and benefits, other)
DIV_STR
Number of diversity strengths (1 point for strengths in each of these areas: CEO, promotion, board
of directors, family benefits, women and minority contracting, employment of the disabled,
progressive gay/lesbian policies, other)
DIV_CON
Number of diversity concerns (1 point for weaknesses in each of these areas: employee
discrimination, non-representation, other)
HUM_STR
Number of human rights strengths (1 point for strengths in each of these areas: positive operations
in South Africa through 1995, international community involvement, indigenous peoples
relations, labor rights, other)
HUM_CON
Number of human rights concerns (1 point for weaknesses in each of these areas: negative
operations in South Africa through 1995, Northern Ireland through 1994, Burma since 1995,
Mexico between 1995and 2002, international labor, indigenous peoples relations, other)
PRO_STR
Number of product strengths (1 point for strengths in each of these areas: quality,
R&D/innovation, benefits to economically disadvantaged consumers, other)
PRO_CON
Number of product concerns (1 point for weaknesses in each of these areas: product safety,
marketing/contracting controversy, antitrust, other)
COM_STR
Number of community strengths (1 point for strengths in each of these areas: generous giving,
innovative giving, support for housing, support for education, indigenous peoples relations, nonU.S. charitable giving, volunteer programs, other)
COM_CON
Number of community concerns (1 point for weaknesses in each of these areas: investment
controversies, negative economic impact, indigenous peoples relations concern, tax disputes,
other)
[28]
APPENDIX A (Continued)
Variable
Description
Corporate Governance Ratings
GOV_STR
Number of corporate governance strengths (1 point for strengths in each of these areas: limited
compensation, ownership structure, transparency, political accountability, public policy, other)
GOV_CON
Number of corporate governance concerns (1 point for weaknesses in each of these areas: high
compensation, tax disputes, ownership structure, accounting, transparency, political
accountability, public policy, other)
Controversial business involvement ratings
ALC_CON
Alcohol (1 if manufacturer, retailer, licensor, manufacturer of products use for production of
alcoholic beverages, owner of or owned by alcohol company)
FIR_CON
Firearms (1 if manufacturer, retailer, owner of or owned by firearms company)
GAM_CON
Gambling (1 if owner, operator, manufacturer, licensor, supporting products or services, owner of
or owner by gambling company)
MIL_CON
Military (1 if manufacturer or supplier of weapons, weapons systems, and components; owner of
or owned by military company)
NUC_CON
Nuclear power (1 if owner of nuclear power plants; construction and design of nuclear power
plants, nuclear power fuel, and key parts; nuclear power service provider; owner of or owned by
nuclear power company; or owner of or owned by nuclear power-related products and services
company)
TOB_CON
Tobacco (1 if manufacturer, retailer, licensor, manufacturer of products use for production of
tobacco products, owner of or owned by tobacco company)
[29]
APPENDIX B
VARIABLE DEFINITIONS
Unless otherwise indicated, we first calculate each variable on an annual basis over the 2003–2008 test period. We then average over the test period by
taking the median observation, which also helps mitigate the effects of potential outliers. Balance sheet and market data are as of the fiscal year-end.
Variable
Description
Source
Mean
St. Dev.
Variables Used to Estimate Ethical Behavior (See Appendix A for Variables Ending in _STR and _CON)
ENVIRONMENT
EMPLOYMENT
DIVERSITY
HUMAN_RIGHTS
PRODUCT
COMMUNITY
GOVERNANCE
CONTROVERSY
Standardized sum of ENV_STR over test period, less standardized sum of
ENV_CON over period
KLD STATS
0.00
1.08
Standardized sum of EMP_STR over test period, less standardized sum of
EMP_CON over period
KLD STATS
0.00
1.31
Standardized sum of DIV_STR over test period, less standardized sum of
DIV_CON over period
KLD STATS
0.00
1.52
Standardized sum of HUM_STR over test period, less standardized sum of
HUM_CON over period
KLD STATS
0.00
1.20
Standardized sum of PRO_STR over test period, less standardized sum of
PRO_CON over period
KLD STATS
0.00
1.32
Standardized sum of COM_STR over test period, less standardized sum of
COM_CON over period
KLD STATS
0.00
1.23
Standardized sum of GOV_STR over test period, less standardized sum of
GOV_CON over period
KLD STATS
0.00
1.50
Sum of ALC_CON, FIR_CON, GAM_CON, MIL_CON, NUC_CON, and
TOB_CON over test period, standardized and then multiplied by –1
KLD STATS
0.00
1.00
[30]
APPENDIX B (Continued)
Variable
CSR_FACTOR
Description
Source
Mean
St. Dev.
Highest-order factor extracted from ENVIRONMENT, EMPLOYMENT,
DIVERSITY, HUMAN_RIGHTS, PRODUCT, COMMUNITY, GOVERNANCE,
and CONTROVERSY
KLD STATS
0.00
0.50
ETHICAL
1 if firm is in top quintile of CSR_FACTOR, 0 otherwise
KLD STATS
0.21
0.41
DS400
1 if firm is included in the Domini Social 400 index anytime during test period, 0
otherwise
KLD STATS
0.22
0.14
Natural logarithm of market capitalization of common equity, calculated as share
price multiplied by shares outstanding
Compustat
7.47
1.58
Number of articles published about firm during test period, based on Factiva’s
database of major U.S. news and business publications. To be counted, an article
must include firm’s name in headline or lead paragraph, and cannot be a
republished article or a recurring pricing/market data news item (Barton 2005)
Factiva
0.00
0.00
ANALYSTS
Number of analysts following firm during year
I/B/E/S
9.08
6.35
ROA
Income before extraordinary items, divided by total assets
Compustat
0.02
0.40
LOSS
1 if income before extraordinary items is negative in at least three of the six years
in test period, 0 otherwise
Compustat
0.11
0.32
PB
Ratio of price to book value of equity
Compustat
2.90
3.12
%INSTITUTIONS
Percentage of total common shares held by institutional owners
CDA/Spectrum
0.78
0.18
%ACTIVISTS
Percentage of total common shares held by activists, identified as CDA/Spectrum
managers 12000, 12100, 12120, 18740, 38330, 81590, 49050, 54360, 57500,
58650, 63600, 63850, 63895, 66550, 66610, 66635, 82895, 83360, 90803, and
93405 (Cremers and Nair 2005; Larcker et al. 2007)
CDA/Spectrum
0.02
0.01
Variables Used to Estimate Propensity Scores
SIZE
PRESS
[31]
APPENDIX B (Continued)
Variable
%BLOCKS
Description
Source
Mean
St. Dev.
Percentage of total common shares held by institutions each owning at least 5
percent of common shares outstanding
CDA/Spectrum
0.21
0.11
BIG_N
1 if firm used a Big N auditor anytime during test period, 0 otherwise
Compustat
0.98
0.14
LABOR_INTENSITY
1 minus net property, plant and equipment, divided by total assets
Compustat
0.73
0.22
LITIGATION
1 if 4-digit SIC code is any of the following: 2833−2836, 8731−8734, 3570−3577,
7370−7374, 3600−3674, or 5200−5961; 0 otherwise
Compustat
0.34
0.48
FOREIGN_SALES
Foreign sales divided by total sales
Compustat
0.24
0.26
MULTINATIONAL
Entropy index for diversification in geographic segment sales, calculated as
ΣjPjln(1/Pj), where Pj is the proportion of firm’s sales generated in geographic
segment j (Palepu 1985)
Compustat
0.43
0.46
Variables Used to Measure Extent of Earnings Management
ABN_ACCRUALS
Abnormal accruals, estimated by residual in Equation (7)
Compustat
–0.25
1.08
|ABN_ACCRUALS|
Absolute value of ABN_ACCRUALS
Compustat
0.69
0.78
ABN_OCF
Abnormal operating cash flows, estimated by residual in Equation (8)
Compustat
0.19
0.32
Abnormal production costs, estimated by residual in Equation (9)
Compustat
–0.22
0.61
Abnormal discretionary expenses, estimated by residual in Equation (10)
Compustat
–0.58
0.99
Bonuses paid to five highest compensated managers, divided by their total
compensation
ExecuComp
0.10
0.09
Number of shares owned by five highest compensated managers, divided by total
common shares outstanding
ExecuComp
1.71
4.10
ABN_PRODUCTION_COSTS
ABN_DISC_EXPENSES
Variables Used to Measure Incentives to Manage Earnings
BONUS
STOCK
[32]
APPENDIX B (Continued)
Variable
OPTIONS
Description
Source
Mean
St. Dev.
Number of exercisable options owned by five highest compensated managers,
divided by total common shares outstanding
ExecuComp
1.06
1.17
NEW_FINANCING
Net proceeds from issuance of debt and equity, divided by total assets
Compustat
0.04
0.06
MEET_OR_BEAT
Number of times during test period that firm met or beat analysts’ consensus
forecast of quarterly earnings, divided by number of quarters with at least one
analyst forecast
Compustat
0.72
0.20
LEVERAGE
Total short- and long-term debt, divided by total assets
Compustat
0.22
0.19
TAX
1 if marginal tax rate (Graham 1996, 2007) is larger than effective tax rate (i.e.,
income tax expense divided by pretax income), 0 otherwise
Compustat
0.73
0.44
[33]
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[38]
TABLE 1
Factor Analysis of KLD’s Corporate Social Responsibility Ratings
Panel A: Distribution of Annual KLD Ratings over 2003–2008 (n = 10,770)
Strengths
Max
Max
Criterion
Possible
Mean Std. Dev. Max
Possible
ENVIRONMENT
8
0.15
0.51
4
7
EMPLOYMENT
7
0.29
0.62
5
5
DIVERSITY
8
0.69
1.13
7
3
HUMAN_RIGHTS
5
0.01
0.08
1
7
PRODUCT
4
0.06
0.25
3
4
COMMUNITY
8
0.16
0.52
5
5
GOVERNANCE
6
0.18
0.40
3
8
CONTROVERSY
―
―
―
―
6
Concerns
Mean
0.25
0.51
0.34
0.08
0.28
0.10
0.49
0.09
Std. Dev.
0.71
0.69
0.49
0.29
0.64
0.32
0.64
0.30
Max
5
4
2
3
4
3
4
2
Panel B: Distribution of Total and Net Standardized KLD Ratings over 2003–2008 (n = 1,795)
Total Concerns
Net Standardized
Total Strengths
Criterion
Mean
Std. Dev.
Mean
Std. Dev.
Min
Max
ENVIRONMENT
0.91
2.69
1.51
4.01
–6.45
7.11
EMPLOYMENT
1.76
3.22
3.04
3.32
–5.35
7.52
DIVERSITY
4.16
6.32
2.05
2.45
–4.72
5.88
HUMAN_RIGHTS
0.04
0.39
0.47
1.49
–10.55
15.64
PRODUCT
0.34
1.30
1.70
3.50
–6.36
8.72
COMMUNITY
0.96
2.85
0.60
1.64
–6.08
8.09
GOVERNANCE
1.07
1.81
2.91
3.05
–4.55
4.23
CONTROVERSY
―
―
0.53
1.73
–6.63
0.31
Panel C: Pearson and Multiple Correlations RM among Net Standardized KLD Ratings (n = 1,795)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(1) ENVIRONMENT
1.00
(2) EMPLOYMENT
0.12
1.00
(3) DIVERSITY
0.15
0.20
1.00
(4) HUMAN_RIGHTS
0.13
0.05 –0.01
1.00
(5) PRODUCT
0.17
0.13 –0.03
0.09
1.00
(6) COMMUNITY
0.27
0.10
0.18
0.06
0.06
1.00
(7) GOVERNANCE
0.08
0.02 –0.16
0.10
0.13 –0.01
1.00
(8) CONTROVERSY
0.10
0.02 –0.04
0.09
0.11
0.05
0.06
1.00
[39]
RM
0.36
0.26
0.33
0.19
0.26
0.31
0.23
0.17
TABLE 1 (Continued)
Panel D: Pearson Correlations between Net Standardized KLD Ratings and Factors (n = 1,795)
First-Order Factor
Variable
1
2
Third-order factor (CSR_FACTOR)
Second-order factors:
1
2
First-order factors:
1
1.00
2
0.42
1.00
3
0.83
–0.07
CSR variables:
ENVIRONMENT
0.72
0.42
EMPLOYMENT
0.44
0.10
DIVERSITY
0.48
–0.36
HUMAN_RIGHTS
0.30
0.45
PRODUCT
0.35
0.59
COMMUNITY
0.64
0.11
GOVERNANCE
0.06
0.60
CONTROVERSY
0.23
0.43
3
Second-Order Factor
1
2
Third-Order
Factor
(CSR_FACTOR)
1.00
1.00
0.25
1.00
0.79
0.79
1.00
0.95
0.16
0.96
0.53
0.93
–0.02
0.94
0.69
0.59
0.47
0.63
0.74
0.05
0.18
0.49
–0.22
–0.04
0.62
0.55
0.65
0.17
0.26
0.60
–0.10
0.09
0.56
–0.08
–0.29
0.47
0.45
0.33
0.50
0.47
0.75
0.30
0.22
0.40
0.45
0.59
0.25
0.35
Variable definitions and basic descriptive statistics are in Appendix B. The multiple correlations RM in Panel C are the squared
root of the multiple correlation coefficient R2 from a regression of each variable on the remaining variables. The RM measures the
maximal correlation between a variable and the linear combination of the other variables. The factors in Panel D are principal
common factors, with oblique promax (4) rotation. Correlations in Panels C or D greater than 0.04 in absolute value are
statistically significant at the 0.10 level or better, based on two-tailed tests.
[40]
TABLE 2
Differences in Means of Corporate Social Responsibility Variables between Ethical and Control Firms
Variable
CSR_FACTOR
1
0.58
ETHICAL =
0
–0.24
ENVIRONMENT
EMPLOYMENT
DIVERSITY
HUMAN_RIGHTS
PRODUCT
COMMUNITY
GOVERNANCE
CONTROVERSY
0.85
0.73
0.89
0.37
0.77
0.94
0.67
0.21
–0.31
–0.34
–0.30
–0.18
–0.32
–0.33
–0.42
–0.17
359
1,077
n
Hotelling’s T2
t-statistic
29.47 †
14.72
12.41
12.14
5.37
10.06
14.96
10.48
8.16
***
***
***
***
***
***
***
***
1,508.94 ***
1
0.31
DS400 =
0
–0.07
0.56
0.39
1.01
0.14
0.32
0.47
–0.39
0.26
–0.13
–0.09
–0.24
–0.03
–0.08
–0.11
0.09
–0.06
344
1,451
t-statistic
10.81 ***
8.82
4.59
13.14
1.70
3.49
6.33
–5.38
9.89
***
***
***
*
**
***
***
***
422.06 ***
***, ** and * denote significant at the 0.01, 0.05 and 0.10 levels, respectively, based on two-tailed tests.
† The difference in CSR_FACTOR is significant between the two groups by construction, since the coding of ETHICAL is based
on the distribution of CSR_FACTOR.
Variable definitions and basic descriptive statistics are in Appendix B. The t-statistics test whether the mean of a CSR variable
differs between ethical and control firms, assuming unequal variances for the two groups of firms. The Hotelling’s T2 test is the
multivariate equivalent of the two-sample t test, and tests whether the vector of means differs between the two groups.
[41]
TABLE 3
Logistic Regression Results for Propensity to Behave Ethically
Dependent Variable
Pr(ETHICAL = 1)
Pr(DS400 = 1)
Expected
% Change
% Change
Independent Variable Sign Coefficient in Odds
z-statistic
Coefficient in Odds z-statistic
SIZE
+
–0.01
–1.2
–0.16
0.12
20.9
2.36 ***
PRESS
+
—
—
—
—
—
—
ANALYSTS
+
0.03
20.3
2.10 **
0.08
65.3
5.87 ***
ROA
+
0.03
1.2
0.16
0.02
0.7
0.10
LOSS
–
–0.69
–49.7
–2.20 **
–1.08
–66.0
–2.84 ***
PB
+
0.07
24.7
2.14 **
0.04
11.7
1.36 *
%INSTITUTIONS
+
–2.62
–36.3
–3.22 •••
–1.50
–23.5
–2.11 ••
%ACTIVISTS
+
4.27
3.6
0.32
50.22
52.9
3.93 ***
%BLOCKS
+
1.58
18.3
1.29 *
0.10
1.1
0.09
BIG_N
+
–0.04
–3.6
–0.06
–0.17
–15.7
–0.28
LABOR_INTENSITY
+
0.69
16.3
1.19
–0.37
–7.8
–0.79
LITIGATION
+
0.29
34.0
1.41 *
–0.41
–33.9
–1.92 ••
FOREIGN_SALES
+
1.19
36.1
2.67 ***
0.19
4.9
0.44
MULTINATIONAL
+
0.07
3.4
0.30
0.31
15.3
1.29 *
n
Wald χ2
Pseudo R2
% firms correctly classified
% firms correctly classified as ethical (sensitivity)
% firms correctly classified as not ethical (specificity)
% positive predictive value
% negative predictive value
1,028
87.78 ***
0.11
78.89
9.87
98.01
57.89
79.70
1,297
165.69 ***
0.14
80.57
18.15
96.98
61.25
81.84
***, ** and * denote significant at the 0.01, 0.05 and 0.10 levels, respectively, based on two-tailed tests and heteroskedasticityconsistent standard errors.
••• and •• denote significant at the 0.01 and 0.05 levels, but opposite to our predicted sign.
Variable definitions and basic descriptive statistics are in Appendix B. The regressions include industry indicators based on 2digit NAICS codes; we do not tabulate their coefficients or the intercept. We drop 39 firms when estimating the regression with
ETHICAL as the dependent variable because their industry indicators predict failure perfectly; 223 of the 1,028 firms we use are
coded ETHICAL = 1. Similarly, we drop 20 firms when estimating the regression with DS400 as the dependent variable; 270 of
the 1,297 we use are coded DS400 = 1. The percentage change in odds is the effect of a change in the independent variable on
the odds of reporting ETHICAL = 1 or DS400 = 1 (rather than 0). For continuous variables, it is based on a change in one
standard deviation and it is calculated as 100×[exp(sjbj) – 1], where sj is the sample standard deviation of variable j (see Appendix
B) and bj is its estimated regression coefficient. For indicator variables, the percentage change is based on a change from 0 to 1
and it is calculated as 100×[exp(bj) – 1].
Sensitivity is the probability that the model correctly classifies a firm as ethical. It measures the model’s power: a higher
sensitivity means a lower Type II error rate. Specificity is the probability that the model correctly classifies a control firm as
such. It measures the model’s overall confidence level: a higher specificity means a lower the Type I error rate. Positive
predictive value is the probability that a firm predicted to behave ethically actually does so. Negative predictive value is the
probability that a firm predicted to behave unethically actually does so. Sensitivity and specificity reflect model quality, whereas
predictive values reflect the prevalence of ethical firms in the population. When calculating these metrics, we predict a firm to be
ethical if its propensity score is at least 0.50.
[42]
TABLE 4
Balancing Tests of Matching Quality
Panel A: Differences in Means between Ethical and Control Firms, Based on ETHICAL
Matched Controls
Unmatched Controls
Variable
ETHICAL = 1
ETHICAL = 0 t-statistic
ETHICAL = 0 t-statistic
SIZE
7.63
7.57
0.35
7.47
–0.83
ANALYSTS
10.30
9.17
2.11 **
10.05
–0.37
ROA
0.01
0.03
–0.23
0.04
0.66
LOSS
0.09
0.11
–1.00
0.08
–0.17
PB
3.52
2.76
3.06 ***
3.20
–1.18
%INSTITUTIONS
0.76
0.80
–3.09 ***
0.76
0.02
%ACTIVISTS
0.02
0.02
0.05
0.02
–0.76
%BLOCKS
0.19
0.21
–2.37 **
0.20
0.25
BIG_N
0.98
0.98
0.29
0.98
–0.34
LABOR_INTENSITY
0.78
0.71
4.92 ***
0.77
–0.51
LITIGATION
0.39
0.31
2.13 **
0.41
0.49
FOREIGN_SALES
0.35
0.22
6.80 ***
0.35
–0.18
MULTINATIONAL
0.60
0.41
5.20 ***
0.63
0.62
n
Hotelling’s T2
218
844
218
81.70 ***
5.20
Panel B: Differences in Means between Ethical and Control Firms, Based on DS400
Matched Controls
Unmatched Controls
Variable
DS400 = 1
DS400 = 0
t-statistic
DS400 = 0
t-statistic
SIZE
8.01
7.31
4.83 ***
8.21
–1.28
ANALYSTS
12.05
8.19
7.85 ***
12.18
–0.21
ROA
0.05
0.02
1.38
0.03
0.44
LOSS
0.04
0.13
–6.38 ***
0.04
–0.46
PB
3.44
2.75
2.98 ***
3.20
0.94
%INSTITUTIONS
0.79
0.78
0.27
0.79
–0.48
%ACTIVISTS
0.03
0.02
8.26 ***
0.03
–0.37
%BLOCKS
0.19
0.22
–3.87 ***
0.18
0.65
BIG N
0.99
0.98
0.52
0.99
–0.82
LABOR_INTENSITY
0.72
0.73
–1.11
0.73
–0.69
LITIGATION
0.34
0.34
–0.24
0.34
–0.19
FOREIGN_SALES
0.28
0.23
2.51 **
0.28
0.05
MULTINATIONAL
0.50
0.41
2.67 ***
0.54
–0.86
n
Hotelling’s T2
259
1,047
259
143.04 ***
8.07
***, ** and * denote significant at the 0.01, 0.05 and 0.10 levels, respectively, based on two-tailed tests.
Variable definitions and basic descriptive statistics are in Appendix B. The t-statistics test whether the mean of a variable used in
estimating propensity scores differs between ethical and control firms, assuming unequal variances for the two groups of firms.
The Hotelling’s T2 test is the multivariate equivalent of the two-sample t test, and tests whether the vector of means differs
between the two groups. Controls are matched on propensity scores, as the nearest neighbor (with replacement) within a 0.10
caliper over a common support. The sample sizes in this table differ from those in Table 3 because we drop five firms from the
223 coded ETHICAL = 1 and 11 from the 270 coded DS400 = 1 for being outside the range of common support.
[43]
TABLE 5
Effect of Ethical Behavior on Extent of Earnings Management
Panel A: Differences in Means between Ethical and Control Firms, Based on ETHICAL
Unmatched Controls
Expected
ETHICAL =
Variable
Sign of ATT
1
0
ATT
%
t-statistic
ETHICAL =
1
0
ATT
%
ABN_ACCRUALS
|ABN_ACCRUALS|
ABN_OCF
ABN_PRODUCTION_COSTS
ABN_DISC_EXPENSES
–0.22
0.73
0.25
–0.26
–0.63
0.19
0.01
0.06
–0.13
0.01
46.4
1.9
29.0
–104.7
2.3
–
–
+
–
–
n
–0.22
0.73
0.25
–0.26
–0.62
–0.20
0.68
0.17
–0.19
–0.58
223
805
–0.02
0.04
0.08
–0.08
–0.04
–11.0
6.6
46.6
–40.9
–6.6
–0.34
0.78
3.37 ***
–1.80 **
–0.52
218
Panel B: Differences in Means between Ethical and Control Firms, Based on DS400
Unmatched Controls
Expected
DS400 =
Variable
Sign of ATT
1
0
ATT
%
t-statistic
ABN_ACCRUALS
|ABN_ACCRUALS|
ABN_OCF
ABN_PRODUCTION_COSTS
ABN_DISC_EXPENSES
n
–
–
+
–
–
–0.26
0.69
0.25
–0.27
–0.58
270
–0.25
0.69
0.18
–0.20
–0.58
Matched Controls
–0.02
–0.01
0.07
–0.07
0.01
–7.0
–1.3
42.4
–33.7
1.0
1,027
–0.23
–0.17
3.48 ***
–1.67 **
0.09
–0.41
0.72
0.20
–0.13
–0.64
t-statistic
1.81 •
0.18
1.61 *
–2.95 ***
0.14
218
Matched Controls
DS400 =
1
0
–0.25
0.68
0.25
–0.27
–0.58
–0.46
0.74
0.19
–0.14
–0.53
259
259
ATT
%
0.21
–0.06
0.06
–0.13
–0.04
45.1
–8.1
34.5
–96.5
–8.1
t-statistic
1.78 •
–0.79
2.04 **
–2.80 ***
–0.44
*** and ** denote significant at the 0.01 and 0.05 levels, based on one-tailed tests.
• denotes significant at the 0.10 level, but opposite to our predicted sign.
Variable definitions and basic descriptive statistics are in Appendix B. The ATT is our estimate of the average treatment effect on the treated, calculated as the difference in means
between ethical and control firms. The ATT measures the effect of ethical behavior on the extent of earnings management. The t-statistics test whether the ATT is statistically
significant in the predicted direction. The columns labeled "%" contain the ATTs expressed as a percentage of the respective means for the control firms.
[44]
TABLE 6
Canonical Correlation Analysis Based on ETHICAL Subsamples
Panel A: Canonical Correlations (RC) and Explanatory Power (%)
Ethical Firms (ETHICAL = 1)
Canonical Function
RC
F-statistic Wilk’s λ
1
0.41
2.18 ***
0.70
2
0.29
1.48 *
0.85
3
0.26
1.07
0.93
4
0.08
0.21
0.99
5
0.04
0.11
1.00
Panel B: Canonical Functions for Ethical Firms (ETHICAL = 1; n = 218)
First Function
Standardized
Coefficient
t-statistic
Loading
Set of earnings management measures:
ABN_ACCRUALS
–0.38
–2.01 **
–0.46 9
|ABN_ACCRUALS|
–0.59
–3.25 ***
–0.29
ABN_OCF
0.53
2.78 ***
0.15
ABN_PRODUCTION_COSTS
–0.55
–3.08 ***
–0.66 9
ABN_DISC_EXPENSES
0.49
2.56 **
0.42 9
Set of earnings management incentives:
BONUS
STOCK
OPTIONS
NEW_FINANCING
MEET_OR_BEAT
LEVERAGE
TAX
–0.14
0.18
0.12
–0.50
0.64
–0.03
–0.49
–0.83
1.15
0.76
–3.16 ***
3.93 ***
–0.16
–3.14 ***
0.18
0.21
0.21
–0.64 9
0.66 9
–0.10
–0.44 9
[45]
%
14.42
8.03
6.55
0.63
0.15
29.78
RC
0.29
0.27
0.21
0.15
0.10
Matched Firms (ETHICAL = 0)
F-statistic Wilk’s λ
1.48 **
0.78
1.36
0.86
1.07
0.93
0.86
0.97
0.75
0.99
%
7.37
6.96
4.13
2.15
1.06
21.68
Second Function
Crossloading
Standardized
Coefficient
t-statistic
–0.19
–0.12
0.06
–0.27
0.17
0.38
0.10
0.16
–1.76
–0.77
1.38
0.39
0.57
–2.92 ***
–2.74 ***
–0.22
0.48
0.51
–0.51 9
–0.72 9
–0.06
0.14
0.15
–0.15
–0.21
0.07
0.09
0.09
–0.27
0.27
–0.04
–0.18
0.14
–0.27
–0.29
0.16
0.59
–0.39
0.53
0.56
–1.14
–1.23
0.70
2.49 **
–1.70 *
2.34 **
0.10
–0.36
–0.26
0.04
0.63 9
–0.32
0.59 9
0.03
–0.11
–0.08
0.01
0.19
–0.09
0.17
Loading
Crossloading
TABLE 6 (Continued)
Panel C: Canonical Functions for Matched Control Firms (ETHICAL = 0; n = 218)
First Function
Standardized
Coefficient
t-statistic
Loading
Set of earnings management measures:
ABN_ACCRUALS
–0.86
–3.73 ***
–0.76 9
|ABN_ACCRUALS|
–0.16
–0.67
0.05
ABN_OCF
–0.52
–2.12 **
–0.30
ABN_PRODUCTION_COSTS
–0.36
–1.58
–0.22
ABN_DISC_EXPENSES
–0.56
–2.21 **
–0.22
Set of earnings management incentives:
BONUS
STOCK
OPTIONS
NEW_FINANCING
MEET_OR_BEAT
LEVERAGE
TAX
–0.16
–0.16
–0.18
–0.42
–0.30
0.82
0.38
–0.64
–0.68
–0.72
–1.78 *
–1.31
3.50 ***
1.67 *
–0.09
–0.23
–0.26
–0.15
–0.30
0.76 9
0.31
Second Function
Crossloading
Standardized
Coefficient
t-statistic
–0.22
0.01
–0.09
–0.06
–0.06
–0.24
–0.92
0.55
–0.01
–0.11
–0.96
–3.61 ***
2.09 **
–0.02
–0.40
–0.06
–0.78 9
0.48 9
–0.08
–0.08
–0.02
–0.21
0.13
–0.02
–0.02
–0.03
–0.07
–0.08
–0.05
–0.09
0.22
0.09
0.28
0.44
–0.15
–0.09
0.52
0.52
–0.54
1.06
1.72
–0.54
–0.34
2.12
2.07
–2.18
0.32
0.31
–0.05
–0.08
0.46 9
0.48 9
–0.51 9
0.09
0.08
–0.01
–0.02
0.13
0.13
–0.14
*
**
**
**
Loading
Crossloading
***, ** and * denote significant at the 0.01, 0.05 and 0.10 levels, respectively, based on two-tailed tests.
9 denotes that we use this variable in interpreting the results, because its coefficient is significant at the 0.10 level or better and its loading is larger than 0.40 in
absolute terms.
Variable definitions and basic descriptive statistics are in Appendix B. We run the canonical correlation analyses separately for the set of firms coded ETHICAL
= 1 and their matched controls coded ETHICAL = 0. A canonical function is the relationship between a linear combination of the earnings management measures
and a linear combination of the earnings management incentives. Each of these linear combinations is a canonical variate. The canonical correlation RC measures
the strength of the relationship between the two variates in the canonical function. Wilk’s λ measures the total variance left unexplained by the canonical
functions, taken together and sequentially. The numbers in the columns labeled “%” are the percentage of total shared variance explained the respective
canonical function. The F-statistic tests whether the RC is statistically significant. A variable’s loading is the correlation between the variable and its respective
canonical variate. A cross-loading is the variable’s correlation with the opposite canonical variate in the function. We use both standardized coefficients and
canonical loadings to interpret the results.
[46]
TABLE 7
Canonical Correlation Analysis Based on DS400 Subsamples
Panel A: Canonical Correlations (RC) and Explanatory Power (%)
Ethical Firms (ETHICAL = 1)
Canonical Function
RC
F-statistic Wilk’s λ
1
0.31
1.87 ***
0.78
2
0.29
1.66 **
0.86
3
0.21
1.16
0.93
4
0.14
0.83
0.97
5
0.05
0.28
1.00
Panel B: Canonical Functions for Ethical Firms (DS400 = 1; n = 259)
First Function
Standardized
Coefficient
t-statistic
Loading
Set of earnings management measures:
ABN_ACCRUALS
–0.37
3.25
–0.32
|ABN_ACCRUALS|
–0.40
2.01
–0.13
ABN_OCF
0.69
3.08 ***
0.35
ABN_PRODUCTION_COSTS
–0.56
–2.56 ***
–0.69 9
ABN_DISC_EXPENSES
0.62
–2.78 **
0.32
Set of earnings management incentives:
BONUS
STOCK
OPTIONS
NEW_FINANCING
MEET_OR_BEAT
LEVERAGE
TAX
–0.10
0.27
0.12
0.28
0.92
–0.14
–0.24
0.83
–0.76
–1.15
3.14
0.16 ***
–3.93
3.16
0.01
0.17
0.18
0.25
0.86 9
–0.21
–0.17
[47]
%
7.82
7.51
4.07
2.22
0.33
21.96
RC
0.39
0.29
0.23
0.17
0.09
Matched Firms (ETHICAL = 0)
F-statistic Wilk’s λ
2.54 ***
0.61
1.94 ***
0.76
1.38
0.87
1.17
0.93
0.88
0.98
%
15.73
10.55
6.25
5.35
1.59
39.47
Second Function
Crossloading
Standardized
Coefficient
t-statistic
–0.10
–0.04
0.11
–0.21
0.10
0.37
–0.21
–0.05
–0.60
–0.95
1.46
–0.79
–0.17
–2.79 ***
–3.63 ***
0.12
0.03
0.38
–0.45 9
–0.74 9
0.04
0.01
0.11
–0.13
–0.21
0.00
0.05
0.06
0.08
0.26
–0.06
–0.05
0.15
–0.62
–0.12
0.64
0.06
0.06
0.43
0.67
–2.82 ***
–0.56
2.97 ***
0.28
0.27
2.02 **
0.00
–0.64 9
–0.13
0.60 9
0.17
0.27
0.41 9
0.00
–0.19
–0.04
0.17
0.05
0.08
0.12
Loading
Crossloading
TABLE 7 (Continued)
Panel C: Canonical Functions for Matched Control Firms (DS400 = 0; n = 259)
First Function
Standardized
Coefficient
t-statistic
Loading
Set of earnings management measures:
ABN_ACCRUALS
–0.89
–5.29 ***
–0.60 9
|ABN_ACCRUALS|
–0.83
–4.50 ***
–0.19
ABN_OCF
0.04
0.26
–0.01
ABN_PRODUCTION_COSTS
0.43
2.73 ***
0.38
ABN_DISC_EXPENSES
–0.55
–3.08 ***
–0.28
Set of earnings management incentives:
BONUS
STOCK
OPTIONS
NEW_FINANCING
MEET_OR_BEAT
LEVERAGE
TAX
0.18
0.63
–0.54
–0.14
–0.14
0.49
0.19
1.08
3.98 ***
–3.45 ***
–0.94
–0.89
3.10 ***
1.27
0.29
0.64 9
–0.41 9
–0.13
–0.31
0.52 9
0.08
Second Function
Crossloading
Standardized
Coefficient
t-statistic
–0.23
–0.07
0.00
0.15
–0.11
–0.22
0.40
0.21
–0.44
–0.40
–0.97
1.59
0.90
–2.07 **
–1.63 *
–0.42
0.69
0.55
–0.51 9
–0.71 9
–0.12
0.20
0.16
–0.15
–0.21
0.11
0.54
–0.16
–0.05
–0.12
0.20
0.03
0.19
0.16
–0.11
–0.05
0.48
–0.23
0.79
0.83
0.72
–0.51
–0.24
2.26 **
–1.08
3.77 ***
0.11
0.06
–0.05
–0.10
0.53 9
–0.36
0.79 9
0.03
0.02
–0.02
–0.03
0.15
–0.11
0.23
Loading
Crossloading
***, ** and * denote significant at the 0.01, 0.05 and 0.10 levels, respectively, based on two-tailed tests.
9 denotes that we use this variable in interpreting the results, because its coefficient is significant at the 0.10 level or better and its loading is larger than 0.40 in
absolute terms.
Variable definitions and basic descriptive statistics are in Appendix B. We run the canonical correlation analyses separately for the set of firms coded DS400 = 1
and their matched controls coded DS400 = 0. A canonical function is the relationship between a linear combination of the earnings management measures and a
linear combination of the earnings management incentives. Each of these linear combinations is a canonical variate. The canonical correlation RC measures the
strength of the relationship between the two variates in the canonical function. Wilk’s λ measures the total variance left unexplained by the canonical functions,
taken together and sequentially. The numbers in the columns labeled “%” are the percentage of total shared variance explained the respective canonical function.
The F-statistic tests whether the RC is statistically significant. A cross-loading is the variable’s correlation with the opposite canonical variate in the function. We
use both standardized coefficients and canonical loadings to interpret the results.
[48]
FIGURE 1
Propensity Score Density Functions for Ethical and Matched Control Firms
Panel A: Using ETHICAL as Proxy for Ethical Behavior
y
y
0
1
Density
2
3
4
p
0
.2
.4
Propensity Score
Ethical
.6
.8
Control
Panel B: Using DS400 as Proxy for Ethical Behavior
y
y
0
1
Density
2
3
4
p
0
.2
.4
Propensity Score
Ethical
[49]
.6
Control
.8