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How Rules-Oriented Are Accounting Standards?
By
Paul E. Madsen Assistant Professor
Fisher School of Accounting University of Florida
and
Devin Williams Doctoral Student
Fisher School of Accounting University of Florida
Draft as of: 7/2012 We thank Stephen Moehrle for his helpful comments on a predecessor paper. We also thank Charlie Costello for research assistance.
How Rules-Oriented Are Accounting Standards?
ABSTRACT: Recent accounting scandals have prompted an effort to identify features of accounting that may have contributed. One prominent candidate has been the “rules-based,” as opposed to “principles-based,” system of financial reporting standards. But on what basis can the accounting community judge the extent to which its standards are indeed rules-based? In this study, we model the rules-orientation of occupational standards in a large cross-section of U.S. occupations as a means of putting the rules-orientation of accounting standards in context. We find that the standards used by professional auditors are extremely rules-oriented even after controlling for a number of occupational features that theory suggests influence the costs and benefits of constraining worker choices through standardization, especially when it is rules-based. We then examine how the rules-orientation of standards governing specific financial reporting practices changes the dialogue among accounting professionals with respect to that practice. A lower quality dialogue is one of the theorized consequences of increasing rule-boundedness. We find evidence consistent with standard-setters “crowding out” other groups that had previously participated in accounting debates which is especially pronounced when the topic under discussion shifts from a principles-based to a rules-based standard. Keywords: accounting standards; principles-based standards; rules-based standards; principles
versus rules; O*NET; Financial Accounting Standards Board (FASB); Public Company Accounting Oversight Board (PCAOB)
Data Availability: The occupation data in this study are collected and distributed by the U.S.
Department of Labor in the O*NET 4.0 and Occupational Employment Statistics (OES) databases and on the Career One Stop website. Contact the authors for the data collected from the Accountants’ Handbook.
1
I. INTRODUCTION
Recent accounting scandals have prompted an effort to identify the features of accounting
that may have contributed. One prominent candidate has been the “rules-based,” as opposed to
“principles-based,” system of financial reporting standards in the United States (FASB 2002, SEC
2003, Batson 2003 40, Benston et al. 2006, Ball 2009).1 But on what basis can the accounting
community judge the extent to which its standards are indeed rules-based? Or in other words, to what
extent might one expect U.S. GAAP to be rules-based given the characteristics of the work of
accountants and the institutional, economic, and legal context within which the profession has
developed? Currently, a scientifically robust answer to these questions does not exist. There are very
few systems of auditing and financial reporting standards in the world, and current systems of
standards cannot be considered independent observations because standard-setting organizations
have frequently imitated one another when establishing them. Legitimate counterfactuals against
which to compare the U.S. system of accounting standards are, therefore, scarce (Ball 2008).
Benchmarks discussed in the literature for studying rules- versus principles-based standards include
U.S. standards of the past (Mergenthaler 2009), the national accounting standards of other countries
(Broshko and Li 2006, Bennett et al. 2006), or the standards of the IASB (Shortridge and Myring
2004, Ball 2009). While these comparisons have yielded valuable insights, each approach is limited
by the small numbers of systems of accounting standards available for study and the large number of
potentially confounding variables that are present when making longitudinal or cross-country
comparisons to study them.
In this study we tap a largely unexplored source of variation in standards, which is available
in the cross-section of U.S. occupations, to empirically model the rules-orientation of the standards of
1 Consistent with Madsen (2011) we use Baer’s (1986) definition of standards: “Standards are formalized and codified decision rules that join professional knowledge to action” (Baer 1986, 533). See Section II for more discussion of our definitions of standards and of rules- versus principles-oriented standards.
2
occupations and the economic, social, and political forces influencing it. The data come primarily
from a U.S. Department of Labor database called O*NET 4.0, which contains data provided by a
team of “occupational analysts” describing the characteristics of the work performed by every
significant U.S. occupation. The strengths of our approach are that the occupations in our sample
share a common institutional, economic, and legal environment reducing the number of potential
confounders; are characterized in the O*NET database by a large set of common variables collected
consistently across occupations, facilitating quantitative cross-sectional comparisons; use standards
whose characteristics differ widely; and each represents a competitively successful occupation which
has survived a long history of competition against other occupations for control of work
“jurisdictions” (Abbott 1988) and whose success is likely partially attributable to the relatively
efficient use of occupational standards (He and Wong 2004, Raisch and Birkinshaw 2008, Uotila et
al. 2009).
Our measure of the rules-orientation of occupational standards comes from an O*NET
variable describing the complexity of the standards workers use to make decisions on their job. We
consider standards which are rated as more complex by O*NET’s occupational analysts to be more
rules-oriented and standards that are rated as simpler to be more principles-oriented.2
Our model generally explains the rules-orientation of U.S. occupational standards well, with
R2 statistics of nearly 88% when the model is estimated on the whole sample of occupations and of
91% when the sample is restricted to include a smaller set of high-status professions.3 There is a
2 The tests in this study follow those in Madsen (2011), which examines the overall level of standardization in financial reporting and the nature of the professional dialogue about financial versus managerial accounting. In contrast to Madsen (2011), our purpose is to study the nature of financial reporting standards (their location on the principles versus rules spectrum after controlling for the perceived importance of standards) rather than the absolute level of accounting standardization. 3 There is no well accepted list of occupations that are considered high status professions. We subjectively selected the following 32 SOC occupations as our sample of high status professions: accountants; actuaries; aerospace engineers; agricultural engineers; airline pilots, copilots, and flight engineers; anesthesiologists; architects, except landscape and naval; auditors; chemical engineers; civil engineers; clergy; commercial pilots; computer hardware engineers; economists; education administrators, postsecondary; electrical engineers; family and general practitioners; financial analysts; financial managers, branch or department; judges, magistrate judges, and
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positive and statistically significant association between rules-orientation and the importance of
standards to an occupation’s work, the level of complexity of the occupation’s work, the extent to
which the work of the occupation puts property at risk, the extent to which an occupation performs
inspections or audits of other peoples’ work, and the size of the occupation. Rules-orientation is
negatively associated with the extent to which the occupation manages other workers, the importance
of adaptability on the job, and a measure of the prevalence of state licensing requirements required to
work in the occupation.
Using the model, we estimate a benchmark of the level of rules-orientation one would expect
in the auditing profession, the O*NET occupation whose work is most influenced by financial
reporting and auditing standards, given its characteristics. We find that the observed rules-orientation
of accounting standards is far greater than the benchmark predicted by the model.4 The regression
residual for the auditing profession is among the largest in the sample, falling in the 92nd percentile.
Assuming that the model does not omit variables that are especially critical to explaining the rules-
orientation of accounting standards, this result is consistent with claims that accounting standards are
extremely rules-oriented.
Commentators that are concerned about the rules-orientation of accounting standards argue
that increasingly constraining the choices of accountants and auditors through standardization and
especially rules-oriented standardization has a number of negative consequences that could
potentially contribute to poorer quality financial reports and audits (FASB 2002, Nelson et al. 2002,
SEC 2003, Batson 2003 40, Benston et al. 2006, Ball 2009). One alleged negative consequence of
increasing the standardization of financial reporting and auditing is that it changes the intellectual magistrates; lawyers; mechanical engineers; nuclear engineers; obstetricians and gynecologists; pediatricians, general; personal financial advisors; pharmacists; physicists; registered nurses; surgeons; and treasurers, controllers, and chief financial officers. 4 We interpret regression residuals from our model as a valuable measure of the extent to which an occupation has deviated from the level of rules-orientation that would maximize its competitive efficiency. The concept of competitive efficiency was developed to describe how economic agents compete for scarce resources in imperfect markets and so it differs from the popular concept of allocative efficiency, which is applicable to perfect markets. See Section II for further explanation of competitive efficiency.
4
climate of the discipline by steering opinion about which topics are worthy of debate and by
crowding non-standard-setter groups out of the public professional dialogue (Baxter 1962, Zeff 1986,
White 2005, Sunder 2010, Madsen 2011). Following our occupation analysis, we test whether the
impact of standardization on the professional dialogue in accounting is especially pronounced for
rules-based standards, which we assume constrain choices more than principles-based standards,
using citations from several editions of a reference manual called the Accountants’ Handbook. The
Accountants’ Handbook is a long-running and popular reference manual that is intended to explain
the essentials of accounting to a broad audience of consumers and producers of accounting
information. We argue that the citations in the Handbook are informative about the extent to which
various interest groups participate in the public dialogue about a given accounting topic.
We define a citation as a mention of a source of information which is not the author. We
begin by identifying several topics for which the primary governing standard shifted from relatively
principles-oriented to relatively rules-oriented (switch topics) and we also identify several topics that
did not undergo such a change (principles-only topics) as a control group. We collect citations from
the sections of the Handbook discussing each switch topic beginning three editions before the switch
and ending three editions after the switch. For principles-only topics, we collect citations from
sections of the Handbook discussing them in every edition from which we collect data for any of the
switch topics.
Consistent with prior research, we find that when we consider our switch and control topics
together, the citations per page to standard-setters generally increased and the citations per page to
non-standard-setters generally declined over time (Madsen 2011). This finding is consistent with the
claim that standard-setters crowd non-standard-setter participants out of the professional dialogue.
When we consider how these trends differed in the switch topics versus the principles-only topics,
we find that both the increase in citations-per-page to standard-setters and the decline in citations-
per-page to non-standard-setters were statistically significantly larger for the switch topics. This
5
evidence is consistent with rules-based standards having a particularly strong negative impact on the
professional dialogue in accounting.
The results in this study depend on the quality of both the O*NET and Accountants’
Handbook data, and the validity of the model of occupational rules-orientation. O*NET 4.0 is
particularly well suited to making cross-occupation comparisons because the data in it were provided
by objective “occupational analysts” rather than incumbent workers who self-selected into their
occupation.5
Our analysis of the effect of rules-based standards on the professional dialogue in accounting
requires us to identify a set of accounting topics that had once been governed by principles-based
standard but switched to rules-based standards and a set of topics that have been consistently
governed over the sample period by principles-based standards. We used existing literature to guide
our choice of topics to include in each set, but such decisions are inevitably subjective. The
interpretability and generalizability of our results regarding the professional accounting discourse
depend on the quality of these classifications.
Section II discusses theory that is relevant to both the O*NET and the Accountants’
Handbook analysis. Section III discusses the analysis of the O*NET data describing the extent to
which occupational standards are rules-oriented. Section IV presents the study of the impact of
switches from principles-oriented to rules-oriented standards on the professional accounting
dialogue. Section V discusses conclusions.
II. THEORY
In this study we employ theory for two purposes. The first is to identify occupational
characteristics that might be expected to explain the extent to which occupational standards evolve
toward the rules or principles end of the standards continuum. The second is to identify a testable
5 We discuss this further in the O*NET data section. See http://www.onetcenter.org/reports/AnalystProc.html for more information on O*NET’s occupational analysts.
6
implication of deviations from the level of rules-orientation predicted by the model. In this section,
we discuss some general theory on occupations and efficiency. We leave the theoretical discussions
of occupational characteristics and their expected association with rules-orientation to Section III and
the discussion of the consequences of deviations from predicted levels of rules-orientation to Section
IV.
Following Madsen (2011), we use Baer’s (1986) definition of standards: “Standards are
formalized and codified decision rules that join professional knowledge to action” (Baer 1986, 533).
This definition is broad and encompasses the pronouncements of formal standard-setting bodies like
the FASB and PCAOB, but also encompasses decision rules with labels like laws, regulations, or
guidelines that have been formalized in national or state law or that are only present in firm specific
practice guides. We conceive of standards existing on a continuum with “rules” at one end and
“principles” at the other and assume that standards toward the rules end of the spectrum constrain
behavior more than standards at the principles end. We follow prior literature, especially
Mergenthaler (2009) and Kohlbeck and Warfield (2010), in defining standards as rules-based when
they have bright-line thresholds, scope and legacy exceptions, large volumes of implementation
guidance, and a high level of detail, and as principles-based when they are concise, have few
exceptions or internal inconsistencies, limited implementation guidance, and few bright-lines (see
also SEC (2003)). In the occupational context, the principle of nonmaleficence from medical ethics,
“first do no harm” (Smith 2005) is an example of a standard near the “principles” end of the
rules/principles continuum, while the 2007 California Building Code for Masonry6 is an example of a
standard near the “rules” end of the spectrum.
In order to model the rules-orientation of occupational standards, we require theories to
identify occupational features that are likely associated with the relative costs and benefits of using
rules-oriented standards. This requires us to specify a theory of standard-setting which considers the 6 Available at: http://www.rumford.com/code/2007CBCCh21A.pdf.
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forces influencing standard-setter decisions to make standards more or less rules-oriented. We reject
the view that accounting standard-setters could effectively promote the public interest, even if it was
their sole goal to do so. They are constrained from doing so by a lack of information about the social
consequences of their decisions (Sunder 1988, Watts and Zimmerman 1986; FASB 1991; Meeks and
Swann 2009), by the difficulty of specifying a broadly acceptable definition of social welfare
(Buchanan and Tullock 1962, Sunder 1988), and by the threat of political intervention in standard-
setting if their decisions are sufficiently repugnant to any constituent group (Zeff 2002, 2005; Kothari
et al. 2010). Rather, we view accounting standard-setters as political agents whose role is primarily to
manage conflicts among constituent groups and between constituents and supervisory government
agencies by making socially acceptable, rather than the most socially efficient, choices (Horngren
1972, Zeff 1978, Sunder 1988, McLeay et al. 2004 p. 286, Kothari et al. 2010). We assume that
members of the occupation whose work is subject to a given set of standards have significant power
to influence the nature of the standards and that they use this power to improve their competitive
positions relative to one another and relative to other occupations with overlapping skill sets
(Buchanan and Tullock 1962, Arrow 1963, Olson 1965, Stigler 1971, Mahoney 2003, Kleiner 2006,
Kothari et al. 2010).
Given competition between occupations for jurisdictional control, workers in occupations
have incentives to pursue institutional innovations that enhance the competitive fitness of the
occupation.7 Effective use of standards is one potential means of enhancing occupational competitive
efficiency. Occupational standardization likely involves a number of tradeoffs. The management 7 The sociology literature views occupations as social groups that are more or less cohesive and have gained control of areas of economic activity, which have been called work “jurisdictions” (Larson 1977, Abbott 1988, Macdonald 1995). The jurisdictions controlled by any given occupation overlap at the margins with the jurisdictions controlled by occupations with similar skills. For example, the knowledge and skills of engineers and accountants working in manufacturing firms overlapped during the early 20th century and the two occupations battled for control of the work jurisdiction that is now called cost accounting (Abbott 1988). Workers in occupations benefit if demand for the work of the occupation increases, whether through growth in the size of jurisdictions it controls or growth in the number of jurisdictions it controls. It is in this sense that they are in perpetual competition with one another, with successful occupations growing in size and income and unsuccessful occupations shrinking or disappearing (Abbott 1988, MacDonald 1995).
8
literature on “exploration” versus “exploitation” suggests that, while standardization can increase
operational efficiency in the short run, this comes at the cost of a lower rate of radical innovation,
which can threaten organizational survival in the long-run (March 1991, Benner and Tushman 2002,
He and Wong 2004, Cole and Matsumiya 2007, Raisch and Birkinshaw 2008, Uotila et al. 2009). In
addition, occupational standardization can add value by increasing the consistency with which the
occupation’s services are delivered (Abbott 1988, Freidson 2001, Meeks and Swann 2009), but this
comes at the cost of replacing expert judgment with a routinized “check the box” mentality (Baxter
1962, 1979; Jamal et al. 2005; Sunder 2005, 2010).
Given the poor information environment in which they operate, standard-setters are likely
unable to manage these tradeoffs in a manner that maximizes social welfare. Rather, the best they can
hope to do is arrive at “competitively efficient” solutions through trial and error experimentation
combined with competition to sort the effective from the ineffective solutions (Alchian 1950, Hayek
1968). Competitive efficiency is an idea that was developed by economists interested in “imperfect
markets” which are characterized by uncertainties, asymmetric information, barriers to entry and exit,
market power, and unequal access to technology (Alchian 1950, North 1990, Kohn 2004).
Competitively efficient behavior can be defined as the application by agents in competition for scarce
resources of the set of known strategies that most effectively enhance the survival prospects of their
users in the existing competitive environment. While allocative efficiency involves finding a global
maximum along an objective function, competitive efficiency cannot be said to do so. Competitive
efficiency applies to situations where there is too little information to maximize an objective function
and the problem agents face is primarily one of effective adaptation to unpredictable changes in
circumstances rather than effective allocation of a set of known resources to a set of known
consumers of resources (Alchian 1950, Hayek 1968, Kling 2007). While the characteristics of U.S.
stock markets approximate those of perfect markets and so can be effectively understood using
concepts like allocative efficiency, the characteristics of the market for occupational standards often
9
deviate from perfect market assumptions and so are better understood using the concept of
competitive efficiency.
Competitive success is the reward enjoyed by occupations that manage the tradeoffs of
standardization in a competitively efficient manner, but there are barriers to identifying competitively
efficient solutions that can prevent an occupation from doing so. These include the presence of
entrenched special interest groups that benefit from the status quo and resist change and
anticompetitive legal protections that prevent the discovery and correction of deviations from
competitive efficiency (North 1990, 2005; Kleiner 2006). Because occupations do not inevitably
arrive at competitively efficient levels of rules-orientation, we do not assume that all occupations
have arrived at a competitively efficient “equilibrium” level of rules-orientation. But we assume that,
because of competition among occupations, the rules-orientation of the standards of an average
occupation in our sample is approximately competitively efficient.
III. RULES-ORIENTATION OF OCCUPATIONAL STANDARDS
In this section, we develop and estimate a model of the rules-orientation of occupational
standards. This section begins with a discussion of theory linking occupational characteristics to
likely costs and benefits of rules-oriented occupational standards. We then discuss our data, methods,
and results from the O*NET analysis.
Theory of Occupational Standards
Madsen (2011) develops a model of the overall level of occupational standardization and
estimates it using the O*NET 12.0 database. Our model is a modified and expanded version of the
Madsen (2011) model. Following Madsen (2011), we expect that the complexity of occupational
work, the extent to which the work of the occupation puts people or property at risk of harm, the
importance of adaptability, the extent to which the occupation is professionalized, and the presence
10
of network externalities will explain variation in the extent of the rule-boundedness of occupational
standards (which is increasing in the level of rules-orientation).8
One proposed benefit of occupational standards is that they routinize complex tasks, enabling
non-experts to perform them more like an expert would (Larson 1977, Brunsson and Jacobsson 2000,
Meeks and Swann 2009). If they can do this, workers may be able to provide complex services at a
lower cost or higher quality by standardizing the task. We, therefore, expect that occupations that
perform complex tasks will use standards and that those standards will tend toward the rules end of
the rules/principles continuum.
Theory from accounting suggests that occupational standards can be used to defend auditor’s
decisions when an audit firm is sued (Healy and Palepu 2001, Schipper 2003, Healy 2003, Watts
2003, Benston et al. 2006, Bratton 2007). If standards are indeed effective tools for defending
workers against litigation costs, workers in occupations that put people or property at risk might be
expected to demand more standards and standards that are more detailed. For this reason, we expect a
positive association between the extent to which the work of an occupation puts people or property at
risk and the degree to which their standards are rules-oriented.
Standardization brings with it a number of potential benefits but many of its potential costs
come from its effect on adaptability. Standards can reduce the rate of innovation by reducing
variation in practice (Benner and Tushman 2002, Cole and Matsumiya 2007) or lock an occupation
into practices that may be competitively efficient today but will not be in the future if the competitive
environment changes (Farrell and Saloner 1985, Meeks and Swann 2009). Workers in occupations
that experience high levels of environmental uncertainty may, therefore, resist standardization in an
8 We expect that the relationship between complexity and rules-orientation will be positive in situations that do not involve strategic interaction between opposing sets of agents. If the job is complex because it involves strategic interactions, we expect a negative association between complexity and rules-orientation. In our tests, we control for the extent to which the occupation performs inspection or auditing services because these services are strategic in nature. Because we believe that this controls for strategic complexity, we predict a positive coefficient on our complexity measure. We discuss our inspection variable further below.
11
effort to retain the flexibility to adapt to change. For this reason we expect a negative association
between the environmental uncertainty experienced by an occupation and the degree of occupational
standardization, which we assume is increasing in the rules-orientation of standards.
The literature on the sociology of professions describes how occupations transform
themselves into high-status professions through a coordinated set of initiatives, like controlling
occupational entry and education and lobbying for legislation that limits a work jurisdiction to
licensed workers, which together are called a “professional project” (Larson 1977, Macdonald 1995,
Kleiner 2006). An increase in occupational standards can be a consequence of the pursuit by an
occupation of professional status because standards publicly display occupational knowledge and
could enhance occupational prestige (Abbott 1988, Lee 1995), and because standards are one tool
professions use to exercise control over their members (Abbott 1988, Friedson 2001). We, therefore,
expect a positive association between the extent of professionalization and the extent to which
occupational standards are rules-oriented.
Standardization is a means of increasing uniformity, which is particularly valuable in the
presence of network externalities, or situations in which a product or service becomes more valuable
as it is used by larger numbers of people. We control for occupation size as a means of controlling
for the magnitude of network effects (Katz and Shapiro 1985, Farrell et al. 1992, Dye and Sunder
2001, Healy 2003, Jamal et al. 2003, Meeks and Swann 2009). We expect a positive association
between the potential value of network effects to an occupation and the rules-orientation of
occupational standards.
In addition to these occupational features, which we expect to be associated with rules-
orientation following Madsen (2011), we add two new predictions. We expect that the rules-
orientation of occupational standards will be positively associated with the level of occupational
compensation and negatively associated with the degree to which an occupation performs inspection
work.
12
In addition to pursuing licensing requirements, occupations with professional power may use
it to artificially limit the entry of new workers to reduce competition and increase compensation
(Routh 1965, Kleiner 2006).9 Therefore, in addition to licensing requirements, we control for
compensation as a means of better capturing the extent to which an occupation is professionalized.
Madsen (2011) finds a positive association between the degree of occupational
standardization and the complexity of occupational work but his prediction with respect to the
complexity variable is non-directional because, while standardization can potentially enable a non-
expert to perform difficult tasks more like an expert, it may not do so in strategic situations. In
strategic situations, a non-expert applying a rules-oriented standard could likely be easily
outmaneuvered by a strategic opponent that is an expert and is not constrained by a detailed standard.
Transaction structuring is an example of this problem (Nelson et al. 2002). We expect that strategic
interactions in the occupational context are especially likely in occupations that perform inspection
services. We, therefore, expect that occupations that perform inspections of the work of another
person or group will be less likely to use rules-based standards than occupations that do not perform
inspections.
In the next section, we discuss our occupation data and how we use it to operationalize the
theoretical constructs we expect will explain the rules-orientation of occupational standards.
Occupation Data and Methods
Our occupations data come primarily from a dataset collected by the U.S. Department of
Labor called O*NET 4.0 which was released in 2002. We use the 4.0 version of the database because
it was the last in which the data were collected from “occupational analysts” rather than incumbent
workers in the occupations. While occupational analysts likely have knowledge about a job that is
more shallow than workers on the job, their advantages are that they did not self-select into the
9 For example, not all medical doctors are board certified, but the rigors of medical school ensure the supply of qualified graduates is limited.
13
occupations they describe and they provide ratings for multiple occupations with which they are
familiar with the likely consequence that the data they provide are more comparable across
occupations than those provided by incumbent workers. Occupational analysts were employees of the
O*NET project who were hired because they were experts in fields such as industrial and
organizational psychology, vocational psychology, human resources, or industrial relations. They
were required to have two or more years of work experience and at least two years of graduate
education in their respective fields.
O*NET 4.0 contains data describing 900 occupations which are defined according to the
Standard Occupation Classification (SOC) of the U.S. federal government.10 We restrict the sample
to the subset of O*NET occupations that do not have missing values for any of our required variables
leaving 643 occupations.
The dependent variable in our tests is the rules-orientation of occupational standards. O*NET
asked occupational analysts to rate both the level of importance and the level of complexity of the
skill “evaluating information to determine if it complies with standards” for each of the jobs in the
database. The full text of the standardization questions from the O*NET “Work Activities”
questionnaire is displayed in Figure 1. In part B of the question, analysts are asked to assess the
“level” (which is a term used in O*NET to denote the level of complexity) of the skill required to
perform the job. We label the analyst ratings of the level of importance of this skill “importance of
standards” and the level of complexity “rules-orientation of standards.” Madsen (2011) shows that in
the O*NET 12.0 database, the values for the importance and rules-orientation of standards are highly
correlated. In O*NET 4.0, their correlation is 0.75 (p = 0.00). In our tests, to isolate the rules-
orientation of occupational standards from the importance of standards, we control for the
importance of standards in all regressions. As a result, our measure of rules-orientation is increasing
10 See http://www.bls.gov/soc/ for more information on the SOC system.
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in the complexity of occupational standards after controlling for the importance of occupational
standards.
Figure 2 shows a scatter-plot of O*NET occupations with their rules-orientation (defined as
the complexity of occupational standards that is not explained by the importance of occupational
standards) on the y-axis and their occupation group (SOC code) on the x-axis. Occupations above the
horizontal line marking zero use standards that are more rules-oriented than average while
occupations below it use standards that are more principles-oriented than average. Several
occupations with extreme values and their occupational groups (two-digit SOC code title) are
labeled. Figure 1 shows that there is considerable variation in occupational rules-orientation.
Auditors’ level of rules-orientation is among the highest in the sample along with judges, nuclear
engineers, and nuclear medicine technologists. Office and administrative support occupations tend to
have standards that are particularly principles-oriented. While the data in Figure 2 are informative
about the variation in rules-orientation present in the sample, they are not informative about whether
these values are likely near a competitively efficient level. In the remainder of this section, we
construct and estimate an empirical model of the rules-orientation of occupational standards in order
to identify deviations from the levels of rules-orientation that would be expected given occupations’
characteristics.
O*NET contains variables describing the work of occupations in detail but does not contain
variables expressly measuring most of our constructs of interest. Our approach when this is the case
is to identify O*NET variables that we expect are highly correlated with our construct of interest and
factor analyze them to identify unobserved factors that best explain the shared variance in the
O*NET variables.11 We take the factor scores calculated from factors with eigenvalues greater than
one as the operationalizations of our constructs.
11 An alternative approach would be to use principal components analysis rather than factor analysis. Principal components analysis is a method that is similar to factor analysis in that it is useful for reducing the dimensionality
15
Theory suggests that the complexity of tasks performed by an occupation is likely to be
positively associated with rules-orientation. There is not a single O*NET variable measuring job
complexity. We identify seven O*NET variables that we expect are highly correlated with job
complexity. They are the complexity of information analyzed, the complexity of problem solving, the
complexity of decisions made and problems solved, the complexity of consultation and advice
provided, the complexity of information that is interpreted, the complexity of judgments and
decisions, and the training required for the job. Our measure of the degree to which the work of an
occupation puts people at risk, which we expect is positively associated with rules-orientation, is
based on three variables: the extent to which workers are responsible for the health and safety of
others, the importance of knowledge of public safety and security, and a measure we construct from
several O*NET variables measuring occupational exposure to hazards like heights and radiation.
O*NET has two measures that we expect are highly correlated with the degree to which the work of
an occupation puts property at risk, which we expect are positively associated with rules-orientation.
These are the potential seriousness of an error committed on the job and the extent to which workers
are responsible for work outcomes.
We construct a measure of the importance of adaptability, which we expect to be negatively
associated with rules-orientation, from six O*NET variables. These are the importance of updating
and using relevant knowledge and the complexity of active learning, of coordinating and adjusting
actions in relation to others’ actions, of thinking creatively, of idea fluency, and of original thought.
O*NET has a search feature on its website that enables one to search all O*NET occupation titles
and job descriptions for keywords. This search feature returns a list of occupations along with a
of a dataset and identifies latent “principal components” that explain variation in the input variables. The approaches differ in that factor analysis explains the shared variance of the input variables while principal components analysis explains the total variance of the input variables. O*NET 4.0 was not intended to measure many of our constructs of interest and, therefore, the variables we use as inputs are noisy proxies for them. Because factor analysis ignores variance that is idiosyncratic and focuses instead on variance that is shared across input variables, we expect that it will produce factors that measure our constructs of interest with less noise than principle components analysis.
16
“relevance score” that indicates the extent to which the term is relevant to each occupation. To
construct our measure of the extent to which an occupation performs inspection work, we perform a
number of keyword searches on the most recent O*NET database (16.0) and record the relevance
scores returned for each occupation. The terms we search are audit, inspect, examiner, examine,
investigate, verify, review, and check. Our measure of the extent to which an occupation performs an
inspection role is constructed from a factor analysis of the relevance scores from these eight keyword
searches. Our measure of compensation comes from a question in the O*NET values questionnaire
asking how much workers in the occupation value high levels of compensation relative to their peers.
State licensing is a variable between 0 and 5 which is a count of the number of U.S. states that
require a license to work in an occupation rounded to the nearest unit of 10. These data were
collected from the Career One Stop website in 2010.12 Finally, occupation size is the log of an
estimate of the number of people working in an occupation. It is collected from the Occupational
Employment Statistics database of the U.S. Department of Labor.
The results of the factor analyses which produce our complexity, risk of physical harm,
importance of adaptation, and inspection role variables are displayed in Table 1. We estimate them
using the iterated principal factor method.13 Panel A shows the factors produced by the factor
analyses, their eigenvalues, and the proportion of the shared variance in the input variables they
explain. The factor analyses for each of our constructs each produce a single factor with an
eigenvalue greater than one. We take this factor as our measure of each construct. Table 1 Panel B
shows the factor loadings for each input variable which can be interpreted as the correlation of the
input variable with the factor. It shows that the complexity factor is most strongly associated with the
12 See http://www.acinet.org/licensedoccupations/lois_state.asp?nodeid=16&by=occ. 13 In addition to the iterated principal factors estimation method, we use orthogonal quartimax rotation to ensure maximization of the variance of the factor loadings in each variable. Before rotation, one factor is retained from each factor analysis as only one factor retained an eigenvalue greater than one (latent root method). In addition, all variables loaded heavily on the retained factor. With rotation, only one factor was retained and the loadings on that factor were not significantly different from the prior-to-rotation loadings. The changes were marginal and did not affect the analysis. The reported factor loadings are unrotated for ease of interpretation and replication.
17
analyzing data, complex problem solving, and making decisions variables. The legal risk physical
factor is most strongly associated with the responsible for others’ health and safety variable. The
adaptation factor is most strongly associated with the fluency of ideas input variable. And the
inspection factor is most strongly associated with the relevance scores for the search term “examine.”
Table 2 shows descriptive statistics for each of the variables we use in our rules-orientation
regression as well as the value for the auditing profession and the location of the auditor value in the
distribution of all occupations expressed in percentile terms. It shows that the auditing profession has
among the highest values in the sample for rules-orientation (99.5th percentile), importance of
standards (97.5th percentile), job complexity (96.7th percentile), inspection role (98.4th percentile),
and state licensing requirements (99.8th percentile). The only variable we examine for which the
auditing profession is below average is the extent to which their work exposes people to physical
harm (21.3rd percentile).
Results
Univariate correlations among our variables are shown in Table 3. The univariate correlations
between the dependent and independent variables are all positive, which is contrary to expectations
in the cases of the adaptation and inspection variables. 14
Table 4 shows the results of an OLS regression estimation of our model of the rules-
orientation of occupational standards. We cluster errors by two-digit SOC code (22 groups) to correct
for possible correlation of the residuals across similar occupations. Importance of standards, job
complexity, consequence of error, and occupation size are all significantly positively associated with
rules-orientation, consistent with expectations.15 The coefficient on legal risk physical is also
14 Table 3 also shows that some of the independent variables are highly correlated, raising concerns about the risk of multicolinearity in our multivariate tests. In our multivariate analysis below, we compute variance inflation factors (VIFs) to determine presence of extreme multicollinearity. All of the VIFs are well below the “rule of thumb” cutoff of for individual variables of 10 (Baum 2006).14 The mean VIF is 2.63. 15 As a sensitivity analysis, we ran the model without the occupation size variable, which comes from the OES rather than the O*NET variable and may be measured with error. The results did not qualitatively differ from the original
18
positive, as predicted, but is not statistically significant. Also consistent with predictions, we find that
adaptation is significantly negatively associated with rules-orientation.
Three of our variables have coefficients that are large but have signs that are opposite of
predictions. These are responsibility for outcomes and state licensing, for which we expected a
positive sign but find a negative sign, and inspection, for which we expected a negative sign but find
a positive sign.16
The model’s high R2 (0.88) shows that the cross-sectional variation in the sample is
explained well by the model. Errors from the estimation of the model are differences between the
model’s predicted levels of rules-orientation and rules-orientation as reported by the occupational
analysts. Table 5 shows the regression errors for a selection of accounting related occupations (panel
A) and professions (panel B). The rows showing the auditing profession are bolded to highlight them.
We emphasize the results for auditors because this is the O*NET occupation whose work is most
directly affected by auditing and financial reporting standards. O*NET’s definition of auditors
explains that they “analyze data to detect...non-compliance with laws, regulations, and management
policies,” “prepare detailed reports on audit findings,” “inspect account books and accounting
model with one exception. The responsible for outcomes variable that was significant at the 0.05 level in the original model became significant at the 0.10 level with a p-value of 0.052. All else remained qualitatively identical. 16 We speculate that responsibility for outcomes may have a negative association with rules-orientation due to the latitude needed by workers with significant responsibility to accomplish their intended objectives. For example, the occupation with the highest score for the responsible for outcomes variable is “Sales Manager,” a job that likely requires adaptability and creativity in response to customer demands. State licensing is negatively associated with rules-orientation, against expectation, possibly because of the increasing prevalence of state licensing requirements, even in jobs that are not traditionally considered professions. For example, the Career One Stop website shows that to work in the “hairdressers, hairstylists, and cosmetologists,” “bus drivers,” or “interior designers” occupations requires a license in many states. These are not traditionally considered professions. The unexpected result for state licensing could be a consequence of the fact that the proliferation of state licensing requirements means that occupational licensing is not a strong indicator of professionalization. The coefficient on the inspection variable had a positive sign when we expected a negative sign. This is perhaps a mechanical consequence of the O*NET definitions of our variables. Inspection occupations apply not only the standards of their own profession but also the rules and standards of those occupations that they inspect. In addition, as a practical matter, the occupation being inspected deals with standards as only a portion of their occupation whereas the same standards capture virtually all of the attention of an inspector of that occupation. Thus, when answering an O*NET question regarding the complexity of rules and standards, an analyst may be more likely to rank an inspection-performing occupation higher than the occupation it inspects because of this compounding of occupational standards across two occupations.
19
systems for...use of accepted accounting procedures,” and “confer with company officials about
financial and regulatory matters,” all of which are functions that are likely directly impacted by
financial reporting or auditing standards.
Table 5, panel A shows that, of our five selected accounting occupations, three have extreme
positive errors, suggesting that their occupational standards are far more rules-oriented than the
model’s predictions. The accounting occupations with extreme positive errors are tax preparers (99th
percentile), auditors (92.5th percentile), and bookkeeping, accounting, and auditing clerks (88th
percentile). Table 5, panel B shows that of the 32 professions we study, auditors have the fifth largest
regression error. Other professions with exceptionally large positive errors are Judges, nuclear
engineers, and chemical engineers. Professions with exceptionally large negative errors, indicating
far less rules-orientation (or, equivalently, greater principles-orientation) than expected include
economists, financial advisors, and pharmacists.
Our analysis suggests that the work of auditors is governed by standards that are far more
rules-oriented than one might expect given the profession’s characteristics. In the next section, we
explore one of the possible consequences of deviating from a competitively efficient level of rules-
orientation, changes in the nature of the professional dialogue.
IV. PRINCIPLES, RULES AND THE PROFESSIONAL ACCOUNTING DIALOGUE
In this section, we examine how the professional dialogue in accounting changes over time
for topics governed by principles-based standards (principles-only topics) as compared to topics
governed by standards that switch from principles-based to rules-based (switch topics). We begin by
discussing the theory linking the rules-orientation of accounting standards to the accounting dialogue.
We then explain the data and methods and conclude the section by discussing results.
Theory
20
Concerns about the impact of standardization on the intellectual vitality of the accounting
discipline date back to at least the beginning of the 20th century (Uniformity 1906) and persist today
(Sunder 2010) but statistical tests of these claims are rare. Baxter (1962) argues that the presence of a
powerful standard-setting authority in a field, whether or not it is benevolent, inevitably reduces the
scope and intensity of intellectual inquiry in the field with the result that “the stream of new ideas
dries up...criticism loses its edge, and ideas are not put to a stern test” (Baxter 1962, 421). This is
consistent with Zeff (1986), which shows that the advocacy writing of the large audit firms declined
following the empowerment of the FASB, perhaps because of its greater authority to determine
which accounting practices have “substantial authoritative support.” We predict, consistent with
Baxter (1962) and Zeff (1986) that as standard-setters increasingly exercise their authority, they will
increasingly dominate accounting debates. Because we assume that rules-based standards by their
nature constrain behavior more than principles-based standards, we expect that Baxter’s (1962)
theory is especially applicable to them.
Data and Methods
Madsen (2011) tests Baxter’s (1962) theory that standardization, regardless of the level of
rules-orientation, narrows the accounting dialogue using citations from an accounting reference
manual called the Accountants’ Handbook. In this section, we present a refinement of the Madsen
(2011) approach that is intended to examine our prediction that Baxter’s (1962) argument is
especially applicable to accounting topics governed by rules-based standards.
The Accountants’ Handbook is an accounting reference manual that is intended for a broad
audience of accountants and financial statement users. It was first published in 1923 and most
recently published in 2012 (12th edition). It is attractive for use in characterizing the nature of
accounting discourse and its relationship to accounting standards because it has been repeatedly
updated over the time during which accounting standards emerged and it has passed the “market test”
by remaining in print. We, therefore, assume that it provides useful snapshots of the nature of the
21
accounting discourse through time. It has also used a somewhat stable set of chapters and sections
which facilitates time-series comparisons of data collected from them. Table 6 shows some
descriptive information about the editions of the Handbook that are used in this study.
We examined accounting standards applicable to a number of accounting topics to identify a
set of principles-only topics and a set of switch topics which are sufficiently covered by the
Accountants Handbook. The control topics we identified are inventory valuation, intangible asset
accounting, and accounting for bad debt. These three topics have been governed by general
principles rather than rules-based standards. As an example of the principles-based control group, the
current guidance for the accounting for bad debt expense for receivables is contained in the
Accounting Standards Codification topic 310, and is as follows:
ASC 310-10-35-8 Subtopic 450-20 requires recognition of a loss when both of the following conditions are met: a. Information available before the financial statements are issued or are available to be issued (as discussed in Section 855-10-25) indicates that it is probable that an asset has been impaired at the date of the financial statements. b. The amount of the loss can be reasonably estimated. ASC 310-10-35-9 Losses from uncollectible receivables shall be accrued when both of the preceding conditions are met. Those conditions may be considered in relation to individual receivables or in relation to groups of similar types of receivables. If the conditions are met, accrual shall be made even though the particular receivables that are uncollectible may not be identifiable.
The principles-orientation of this standard can be seen when compared to the rules-based standards
governing our switch topics, which are hedge accounting (switching between 1994 and 1998 with
SFASs 119 and 133), accounting for income taxes (switching in 1987 with SFAS 96), accounting for
pensions (switching in 1985 with SFAS 87), and lease accounting (switching in 1976 with SFAS 13).
Lease accounting has traditionally been the most prominent example of rules-based accounting.
Appendix A quotes the standards for lease accounting under APB 5 (principles-based) and also FAS
13 (rules-based) to illustrate the difference in rule-orientation between the two standards.
22
Madsen (2011) finds that the citations from financial accounting topics in the Handbook
become increasingly dominated by standard-setters while concurrently the citations to non-standard-
setter groups become less frequent, consistent with Baxter (1962) and Zeff (1986). This trend is not
apparent in managerial accounting topics suggesting that the cause is not something that is general to
all of accounting but is peculiar to financial accounting. In this study, we more closely examine
financial accounting topics to see whether the rules-orientation of the standard governing them is
predictably associated with the rate of the changes in citations documented in Madsen (2011).
Specifically, we test whether the increase in standard-setter citations and the decline in citations to
non-standard-setters have been larger in switch topics than in control topics.
To conduct our tests, we begin by collecting citations from the Handbooks’ discussions of
our switch topics from three editions prior to the switch to a rules-based standard and three editions
after the switch and for the control topics from each edition from which we coded any switch topic
data. Table 7 shows our control and switch topics and the Handbook editions from which we
collected citations data for each of them. Where possible, we collected citations from 10 randomly
selected pages discussing a given topic in a given Handbook edition. We require that selected pages
be at least three quarters text. For many of our topics, these pages were together in a single
Handbook chapter dedicated to discussing our topic. When this is the case, Table 7 shows the
relevant chapter number. In other cases, the pages were not collected in one chapter but were spread
over many chapters. In these cases, we use the Handbooks’ indices to identify pages and subsections
which discuss our topics. For these cases, Table 7 lists the word “index” instead of a chapter number.
The notes to Table 7 list our search terms. If there were more than 10 such pages, we randomly select
10 of them to code. Where there were fewer than 10 pages discussing one of our topics, we collect
data from every page or subsection discussing the given topic in the given Handbook edition and
record the number of lines of text making up the coded page or subsection. The result is a list of
23
citations hand collected from our control and switch topics along with the number of pages or
proportion of pages from which they were collected.17
Then, using the line counts, we adjust our citation counts, which are on a “citations-per-line”
basis for some topics and a “citations-per-page” basis for others, to be consistently on a “citations per
page” basis. The number of words on an average page of the Handbook differs from edition to
edition. The final step is to adjust all citations-per-page values to be in terms of 3rd edition pages so
that they are comparable across editions with different page lengths.
At the end of this process, we have a list of citations-per-page for each of our Handbook
edition/accounting topic pairs. We take these citations and group them into two groups: standard-
setters, which includes citations to the CAP, APB, and FASB, and non-standard-setters which
includes all other citations which were to specific individual experts, journal articles, books, AAA,
AIA, AICPA, NACA, and regulators like the SEC and IRS. There is some ambiguity in this
classification exercise because the AICPA has been the parent to financial accounting standard-
setting organizations (the CAP and APB) and the audit standard-setter. The SEC also issues standard-
like rules. In robustness tests, we include citations to the AICPA and SEC in our standard-setters
category. We discuss the results of the robustness tests in the results section.
Results
Table 8 shows the average citations per page for each edition/topic pair. For example, hedge
accounting in the 6th edition (1981) averaged 2.33 citations per page to non-standard-setter groups.
Table 8 is separated into Panel A, which shows the citations per page to non-standard-setter groups
and Panel B, which lists the citations per page to standard-setters. Each panel is also separated into
the switch topics (the switch occurs after the underlined number in the table and before the number
just below it) and principles-only topics. An increase in citations to standard-setters without a 17 An independent coder with no knowledge of this study’s predictions coded about 10% of the pages we coded to determine the reliability of our data collection process. The independent coder recorded the same citations as we did in 88% of cases.
24
corresponding decrease to non-standard-setter groups would indicate that standard setters are getting
more attention without crowding others out. An increase in citations to standard-setters with a
corresponding decline in citations to non-standard-setter groups would suggest that standard-setters
are crowding others out of the dialogue, reducing the breadth of accounting discourse.
In Table 8, the final two columns show the average citations-per-page for switch topics and
principles-only topics. These two columns show that over time there were declines in the citations-
per-page to non-standard-setters and increases in citations-per-page to standard-setters in both switch
and principles-only topics. It is not immediately apparent from Table 8 whether these trends were
stronger in switch topics than in non-switch topics.
We make statistical comparisons of the magnitude of the decline in citations-per-page to non-
standard-setters and the increase in citations-per-page to standard-setters in the switch versus
principles-only topics in Table 9.18 These statistical tests are informative about whether the finding
that standard-setters crowd other participants out of the professional accounting dialogue is more
pronounced in topics that switched to a rules-based standard. Table 9 differs from Table 8 in that it
aligns the switch topics in event time and shows values for each edition/topic pair for the switch
topics only. The values in Table 9 represent the difference between the citations-per-page in each
switch topic and the average citations-per-page in the principles-only topics from the same time-
period. For example, in Table 9 Panel A, the value at t-3 for hedge accounting is 1.23 which
represents the citations-per-page to non-standard-setters in the hedge section of the 6th edition of the
Handbook (2.33) less the average citations-per-page to non-standard-setters in the principles-only
sections of the 6th edition (1.10).
18 The first row of Table 8, representing the 3rd edition, has switch topic data from only the lease topic. The 3rd edition of the Handbook contained only one tenth of one page discussing leases and that passage contained only one citation (to an accounting expert). When we adjust the citations data to be on a citations-per-page basis, this single citation is inflated by a factor of 10. Because it is based on so little data, we drop the lease observation from the 3rd edition in our statistical tests in Table 9.
25
The last column in Table 9 shows the average of the differences before the switch to a rules-
based standard and after the switch. Table 9 Panel A shows that before the switch, the sections
discussing switch topics contained, on average, 0.28 more citations-per-page to non-standard-setters
than did the discussions of principles-only topics. But this changed after the switch to a rules-based
standard. After the switch, the discussions from the switch topics contained on average 0.37 fewer
citations-per-page to non-standard-setters than the discussions in the principles-only sections. This
represents a decline in citations-per-page to non-standard-setters that was greater by 0.65 citations-
per-page in switch topics than in non-switch topics. This is evidence that the decline in citations-per-
page to non-standard-setters was larger for accounting topics that switched to a rules-based standard
than for topics that were consistently governed by a principles-based standard. A t-test taking each of
the edition/switch topic observations from Table 9 as observations shows that this faster decline in
switch topics was statistically significant at the p = 0.05 level.19
Table 9 Panel B shows that before the switch to rules-based standards, the switch topics
contained, on average, 0.89 more citations-per-page to standard-setters than the principles-only
topics. The magnitude of this difference grew after the switch to a rules-based standard to 1.83
citations-per-page. The increase in the size of the difference amounts to 0.94 citations-per-page
which is statistically significant at the p = 0.05 level. This is evidence that the trend toward more
citations-per-page to standard-setters was stronger in the discussions of switch topics than in
discussions of principles-only topics. Together, the results in Table 9 are consistent with our
prediction that the “crowding out” effect of standardization on participants in the accounting
discourse is particularly strong when standards are rules-based.
In robustness test, we repeat these tests after reclassifying the AICPA and SEC into the
standard-setters group. The result is that the decline in citations-per-page to non-standard-setters and
the increase in citations-per-page to standard-setters become slightly less different in the switch and 19 We use unpaired t-tests assuming unequal variances in Table 9 Panels A and B.
26
principles-only topics than they were in our main tests. This can be interpreted as a weakening of our
result that the crowding out effect is stronger for rules-based than for principles-based standards. The
increase in citations to standard-setters was greater in switch topics than in principles-only topics by
0.64 citations-per-page (p = 0.05) and the decline in citations to non-standard-setters was greater in
switch topics than in principles-only topics by 0.35 (p = 0.11).
The results in this section are consistent with previous findings that in financial accounting,
the influence of standard-setters in the dialogue among professional accountants has increased over
time while concurrently the participation of non-standard-setter groups has declined (Madsen 2011).
This is consistent with standard-setters crowding other participants out of the professional dialogue
concerning financial accounting. Our results further suggest that this trend was stronger for financial
accounting topics that were governed by rules-based, rather than principles-based standards.
V. CONCLUSIONS
The claim that U.S. GAAP has become excessively rules-based and would improve if it were
to become more principles-based has recently received significant attention from accounting
regulators and researchers (FASB 2002, SEC 2003, Batson 2003 40, Benston et al. 2006, Ball 2009).
But such claims are inevitably based on scant evidence because of the scarcity of counterfactuals
against which to compare U.S. GAAP (Ball 2008). Another consequence so the scarcity of
counterfactuals is that the consequences of an extremely rules-based U.S. GAAP are not well
understood.
In this study, we first use the cross-section of U.S. occupations to put the rules-orientation of
financial reporting and auditing standards in perspective. We show that the rules-orientation of
occupational standards is a function of observable occupational characteristics including the
importance of standards to an occupation’s work, the complexity of the work, the extent to which the
occupation puts people or property at risk, the importance of adaptability on the job, the extent to
27
which the occupation is professionalized, whether they inspect others’ work, and occupation size. We
then show that the rules-orientation of the standards used by auditors is extremely high, even after
controlling for the characteristics of the auditing profession that likely influence the costs and
benefits of rules-oriented standards. This is consistent with the claim that U.S. GAAP is extremely
rules-oriented.
We then examine one potential consequence of rules-based standards, that they crowd non-
standard-setter participants out of the professional discourse about financial accounting with
distinctive potency. We test this claim using citations from several editions of a long popular
accounting reference manual called the Accountants’ Handbook. We collect citations from topics
which were consistently governed by principles-based standards and from sections that came to be
governed by rules-based standards and study how the citations changed in these sections over time.
In all of our sections, whether they were governed by principles- or rules-based standards, we find
that the influence of standard-setters steadily increased while the participation of non-standard-setter
groups steadily declined, consistent with standard-setters crowding other participants out of the
accounting dialogue. We then show that these trends were stronger for accounting topics that
switched to a rules-based standard, suggesting that the crowding out effect is particularly strong
when standards are rules-based.
There is a well recognized scarcity of research evaluating the quality of accounting’s
regulatory institutions. We do not have good answers to even some of the most basic questions about
them. Why is financial reporting regulated (Healy and Palepu 2001, Kothari et al. 2010)? What are
the costs and benefits of financial reporting regulation (Leuz and Wysocki 2008)? How do the
benefits of financial reporting regulation compare to the costs (Beyer et al. 2010)? These questions,
and many others, remain unanswered even after more than 70 years of federal disclosure regulation.
A primary reason for our ignorance is that world does not produce easily exploitable natural
experiments that we can use to discover the answers. “What does he know of England who only
28
England knows?”20 Or in other words, evaluation of anything requires that it be compared against
something else. But every option for comparison that has been identified by researchers interested in
accounting regulators has critical limitations (Ball 2008).
In this study we evaluate accounting standard-setters using a source of variation that has
received very little attention in the accounting literature, the cross-section of U.S. occupations. This
research setting, like the others, has limitations. Occupations differ. The differences are critically
important because they constitute variation, and researchers need variation to learn from data. But
one must carefully account for the effects of ancillary differences between occupations in order to
isolate the effects of the differences of interest. The cross-section of occupations has distinctive
strengths. The characteristics of the occupations have been carefully, and fairly exhaustively,
quantified. These data are publicly available. U.S. occupations have all developed under the same
national institutions, facing a similar cultural, legal, and economic environment, but are also subject
to widely varying regulatory systems. These features make the cross-section of U.S. occupations a
valuable source of counterfactual worlds against which to compare the existing accounting regulatory
system.
The present study corroborates the finding in Madsen (2011) that accounting standards are
extreme relative to the standards of other occupations. It also shows that this extremity could have
important detrimental long-term consequences. Why are accounting standards extreme? Is there a
benefit that offsets the costs? These are puzzles for future research. Research exploiting the variation
available in the cross-section of U.S. occupations will likely be a profitable source of answers.
20 This is an idiom that has been attributed to Kipling and Shakespeare but of which I have been unable to find the original source.
29
Appendix A Example of Switch from Principles-Based to Rules-Based
Lease Accounting
RULES-BASED – FAS 13 (Nov. 1976)
FAS 13 p7. ...If at its inception (as defined in paragraph 5(b)) a lease meets one or more of the following four criteria, the lease shall be classified as a capital lease by the lessee. Otherwise, it shall be classified as an operating lease…. A. The lease transfers ownership of the property to the lessee by the end of the lease term b. The lease contains a bargain purchase option... c. The lease term (as defined in paragraph 5(f)) is equal to 75 percent or more of the estimated economic life of the leased property... d. The present value at the beginning of the lease term of the minimum lease payments....equals or exceeds 90 percent of the excess of the fair value of the leased property
PRINCIPLES-BASED – APB 5 (Sept. 1964) APB 5 10. ...The presence...of either of the two following conditions will usually establish that a lease should be considered to be in substance a purchase: a. The initial term is materially less than the useful life of the property, and the lessee has the option to renew the lease for the remaining useful life of the property at substantially less than the fair rental value; or b. The lessee has the right, during or at the expiration of the lease, to acquire the property at a price which at the inception of the lease appears to be substantially less than the probable fair value of the property at the time or times of permitted acquisition by the lessee. 11. ...the existence... of one or more circumstances such as those shown below tend to indicate that the lease arrangement is in substance a purchase and should be accounted for as such. a. The property was acquired by the lessor to meet the special needs of the lessee and will probably be usable only for that purpose and only by the lessee. b. The term of the lease corresponds substantially to the estimated useful life of the property, and the lessee is obligated to pay costs such as taxes, insurance, and maintenance, which are usually considered incidental to ownership. c. The lessee has guaranteed the obligations of the lessor with respect to the property leased. d. The lessee has treated the lease as a purchase for tax purposes.
30
Appendix B Citation Definition
In general, a citation is coded when the author of the handbook references an external source of information (source of information other than the writer). Citations generally appear in 3 types.
1. Citation to a specific source. Examples include, “SFAS No. 109 allows partial recognition…” This would be coded as a citation to the FASB (Standard-setter). Another example would be a citation to The Wall Street Journal; this would be coded as a citation to a non-standard setting body.
2. Citation to a general source. Examples include, “The AICPA’s position on this topic has been…”, which would be coded as a citation to the AICPA (non-standard setter). Also, “…which matches current costs with sales, according to some writers, will tend to…” “Some writers” would be coded as a citation to non-standard setters.
3. An indirect citation to a specific source. Examples include, “as defined in the lease classification criteria.” This would be a citation to the FASB (standard-setting body) as it indirectly cites FAS 13’s capital lease criteria. Another example occurred when a paragraph mentions “the standard indicates” wherein the context a specific standards was being discussed. This would be coded according to the body which set the standard being discussed. There were very few of these types of citations.
Citation rules: 1. Only one instance of a specific citation was recorded if it appeared multiple times in the same
paragraph. For example, if APB Opinion No. 5 was referred to twice in one paragraph, only one citation would be recorded. On the other hand, if APB Opinion No. 5 and APB Opinion No. 7 were both referred to in one paragraph, two separate citations would be recorded.
2. As page numbers were selected to code, only the first full citation on a page was recorded. If a citation began on a non-selected page and finished on a selected page, it was not recorded.
3. If a citation contained an additional citation, only the first, or parent, citation was recorded.
31
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FIGURE 1 O*NET Question That Is the Source of the Standardization Measures
This figure shows the full text of the O*NET question from which data on the importance of standards and the rules-orientation of standards (level of complexity) are collected.
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FIGURE 2 Rules-Orientation of O*NET Occupations Organized by Occupation Group
Each dot represents an occupation from the O*NET 4.0 database. The y-axis shows the rules-orientation of occupational standards which is the portion of the complexity of occupational standards that is not explained by the importance of occupational standards.
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TABLE 1 Results of Factor Analysis to Create Proxies for Complexity, Physical Legal Risk, Adaptation,
and Inspection Panel A: Factors and Eigenvalues a Construct Factor Eigenvalue Proportion Explained Cumulative Explained Complexity 1 5.94 0.94 0.94 2 0.19 0.03 0.97 3 0.11 0.02 0.99 4 0.05 0.01 0.99 5 0.03 0.00 1.00 6 0.01 0.00 1.00 7 -0.00 0.00 1.00 Legal Risk Physical 1 1.52 0.94 0.94 2 0.09 0.06 1.00 3 -0.00 -0.00 1.00 Adaptation 1 4.44 0.91 0.91 2 0.27 0.06 0.97 3 0.13 0.03 0.99 4 0.03 0.01 1.00 5 0.00 0.00 1.00 6 -0.00 0.00 1.00 Inspection 1 2.53 0.63 0.63 2 0.59 0.15 0.77 3 0.51 0.13 0.90 4 0.22 0.05 0.95 5 0.11 0.03 0.98 6 0.07 0.02 1.00 7 0.00 0.00 1.00 8 -0.00 -0.00 1.00
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TABLE 1 (continued) Results of Factor Analysis to Create Proxies for Complexity, Physical Legal Risk, Adaptation,
and Inspection Panel B: Variable Loadings on Factors b Legal Risk Variable Complexity Physical Adaptation Inspection Analyzing data or information 0.95 Complex Problem Solving 0.95 Making Decisions and Solving Problems 0.95 Provide Consultation and Advice to Others 0.92 Interpreting the Meaning of Information for Others 0.90 Judgment and Decision Making 0.90 Training 0.83 Responsible for Others' Health and Safety 0.92 Knowledge of public safety and security 0.67 Physical harm 0.48 Fluency of ideas 0.96 Originality of thought 0.90 Active Learning 0.88 Thinking Creatively 0.85 Updating and Using Relevant Knowledge 0.77 Coordination and adjusting actions in relation to others' actions. 0.73 O*NET Relevance Search - "Examine" 0.93 O*NET Relevance Search - "Examiner" 0.88 O*NET Relevance Search - "Investigate" 0.43 O*NET Relevance Search - "Review" 0.40 O*NET Relevance Search - "Audit" 0.35 O*NET Relevance Search - "Inspect" 0.33 O*NET Relevance Search - "Verify" 0.33 O*NET Relevance Search - "Check" 0.16 a Eigenvalues in Panel A are taken from unconstrained, unrotated factor analyses. b Factor Loadings are taken from factor analyses constrained to one factor. This table shows results from factor analyzing variables from the O*NET 4.0 database maintained by the U.S. Department of Labor (DOL). The unit of analysis is the occupation. Factor analysis is performed using the iterated principal factors method. Training is labeled “Job Zone” in O*NET. Physical Harm is a variable we construct by taking the maximum value across the following O*NET variables which all measure occupational exposure to various hazards: very hot temperatures, very cold temperatures, extremely bright or inadequate lighting conditions, sounds and noise levels that are distracting and uncomfortable, whole body vibration, hazardous equipment, hazardous conditions, high places, diseases or infection, radiation, and contaminants. Variables labeled “O*NET Relevance Search” are scores for each occupation of the relevance of a given search term provided by the O*NET website in searches of the O*NET 16.0 database.
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TABLE 2 Descriptive Statistics, Values, and Rankings for Auditors
Auditors Variable Description Mean Median Min Max n Value Percentile Rules-Orientation 2.63 2.50 0.16 6.83 643 5.50 99.5% Importance of Standards 3.02 3.00 1.00 5.00 643 4.33 97.5% Complexity 0.00 -0.15 -1.71 2.28 643 1.89 96.7% Legal Risk Physical 0.00 -0.21 -1.23 2.73 643 -0.83 21.3% Consequence of Error 2.86 2.78 1.33 4.87 643 3.67 86.5% Responsibility for Outcomes 1.86 1.63 1.00 4.00 643 2.54 82.9% Adaptation 0.00 -0.12 -1.95 2.56 643 0.79 73.9% Compensation 3.10 3.12 1.62 4.62 643 3.75 91.3% Inspection 0.00 -0.31 -0.49 7.89 643 3.92 98.4% State Licensing 1.19 1.00 0.00 5.00 643 5.00 99.8% Occupation Size 10.74 10.72 6.49 15.19 643 12.98 92.1% The sample comes from the O*NET database maintained by the U.S. Department of Labor (DOL). The unit of analysis is the occupation. Rules-Orientation is a measure of the level of complexity of occupational standards. Importance of Standards is a measure of the importance of evaluating information to determine if it complies with standards to the work of the occupation. Complexity is a measure of the complexity of an occupation’s work. Legal Risk Physical is a measure of the extent to which the work of the occupation puts people at risk of physical injury. Consequence of Error measures the severity of a worker’s mistake if not readily correctable. Responsibility for Outcomes measures how responsible the worker is for outcomes and results of other workers. Adaptation is the measure of the need for innovation and adaptability on the job. Compensation is a measured of the extent to which workers in an occupation value receiving high levels of compensation relative to other occupations. Inspection is a measure of the extent to which an occupation performs inspection or audit tasks. State Licensing is a measure of the number of states that require a license to work in a given occupation (in units of ten). Occupation Size is the log of the number of workers in the occupation. The columns labeled “Auditors” show the value of each variable for auditors and the percentile location of that value among all occupations in the sample where 100th percentile is the largest value in the sample.
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TABLE 3 Correlations between Variables in Rules-Orientation Regression
1 2 3 4 5 6 7 8 9 10 1. Rules-Orientation 2. Importance of 0.75 Standards 0.00 3. Complexity 0.83 0.46 0.00 0.00 4. Legal Risk Physical 0.10 0.04 0.11 0.01 0.26 0.01 5. Consequence of Error 0.42 0.32 0.37 0.59 0.00 0.00 0.00 0.00 6. Responsibility for 0.44 0.29 0.57 0.31 0.39 Outcomes 0.00 0.00 0.00 0.00 0.00 7. Adaptation 0.68 0.39 0.90 0.12 0.31 0.63 0.00 0.00 0.00 0.00 0.00 0.00 8. Compensation 0.52 0.31 0.60 0.12 0.48 0.46 0.57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9. Inspection 0.29 0.31 0.12 -0.01 0.11 -0.03 0.00 0.04 0.00 0.00 0.00 0.76 0.00 0.53 0.92 0.32 10. State Licensing 0.27 0.13 0.34 0.28 0.28 0.18 0.27 0.30 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 11. Occupation Size 0.01 0.12 -0.08 -0.08 -0.03 -0.03 -0.05 -0.17 0.04 0.19 0.80 0.00 0.04 0.03 0.42 0.42 0.22 0.00 0.29 0.00 Reported coefficients are Pearson correlations. Spearman correlation coefficients are not reported because they are virtually identical to the reported Pearson correlations. Italicized rows are p-values. The sample comes from the O*NET database maintained by the U.S. Department of Labor (DOL). The unit of analysis is the occupation. Rules-Orientation is a measure of the level of complexity of occupational standards. Importance of Standards is a measure of the importance of evaluating information to determine if it complies with standards to the work of the occupation. Complexity is a measure of the complexity of an occupation’s work. Legal Risk Physical is a measure of the extent to which the work of the occupation puts people at risk of physical injury. Consequence of Error measures the severity of a worker’s mistake if not readily correctable. Responsibility for Outcomes measures how responsible the worker is for outcomes and results of other workers. Adaptation is the measure of the need for innovation and adaptability on the job. Compensation is a measured of the extent to which workers in an occupation value receiving high levels of compensation relative to other occupations. Inspection is a measure of the extent to which an occupation performs inspection or audit tasks. State Licensing is a measure of the number of states that require a license to work in a given occupation (in units of ten). Occupation Size is the log of the number of workers in the occupation.
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TABLE 4 Regression Explaining the Rules-Orientation of Occupational Standards
Variable Predicted Sign Coefficient Estimates Importance of Standards + 0.597*** Complexity + 0.854*** Legal Risk Physical + 0.019 Consequence of Error + 0.069** Responsibility for Outcomes + -0.073†† Adaptation - -0.236*** Inspection - 0.049†† Compensation + 0.063 State Licensing + -0.026†† Occupation Size + 0.018** Constant 0.415 R2 0.879 Observations 643 ** and *** Represent statistical significance at the p = 0.05, and 0.01 levels, respectively. †† represents statistical significance of the variable but with a sign opposite the predicted sign. P-values are one-tailed for all variables. The sample comes from the O*NET database maintained by the U.S. Department of Labor (DOL). The unit of analysis is the occupation. Rules-Orientation is the dependent variable and is a measure of the level of complexity of occupational standards. Importance of Standards is a measure of the importance of evaluating information to determine if it complies with standards to the work of the occupation. Complexity is a measure of the complexity of an occupation’s work. Legal Risk Physical is a measure of the extent to which the work of the occupation puts people at risk of physical injury. Consequence of Error measures the severity of a worker’s mistake if not readily correctable. Responsibility for Outcomes measures how responsible the worker is for outcomes and results of other workers. Adaptation is the measure of the need for innovation and adaptability on the job. Compensation is a measured of the extent to which workers in an occupation value receiving high levels of compensation relative to other occupations. Inspection is a measure of the extent to which an occupation performs inspection or audit tasks. State Licensing is a measure of the number of states that require a license to work in a given occupation (in units of ten). Occupation Size is the log of the number of workers in the occupation. Occupations in O*NET 4.0 are organized according to the SOC occupation classification system. Similar occupations in the SOC are grouped together in two-digit SOC codes. We cluster errors by two-digit SOC code to control for possible correlation of the errors terms across similar occupations.
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TABLE 5 Regression Residuals for Selections of Occupations
Panel A: Selection of Accounting Occupations Rules- Occupation Orientation Prediction Error Error Rank N Error %ile Tax Preparers 3.50 2.63 0.87 6 643 99.1% Auditors 5.50 5.01 0.49 48 643 92.5% Bookkeeping, Accounting, 3.66 3.27 0.39 78 643 87.9% and Auditing Clerks Accountants 4.50 4.49 0.01 300 643 53.3% Tax Examiners, Collectors, 4.33 4.55 -0.22 488 643 24.1% and Revenue Agents Panel B: Selection of Professions Rules- Occupation Orientation Prediction Error Error Rank N Error %ile Top 7 out of 32 Professions Ranked by Error Judges, Magistrate 6.83 5.16 1.67 1 643 99.8% Judges, and Magistrates Nuclear Engineers 6.00 4.99 1.02 5 643 99.2% Chemical Engineers 4.83 4.22 0.61 32 643 95.0% Physicists 5.16 4.55 0.60 33 643 94.9% Auditors 5.50 5.01 0.49 48 643 92.5% Electrical Engineers 4.60 4.18 0.42 68 643 89.4% Actuaries 4.83 4.43 0.40 76 643 88.2% Aerospace Engineers 5.16 4.84 0.32 117 643 81.8% Bottom 7 out of 32 Professions Ranked by Error Architects 4.00 4.11 -0.11 412 643 35.9% Surgeons 4.40 4.52 -0.12 425 643 33.9% Registered Nurses 3.58 3.78 -0.20 479 643 25.5% Clergy 2.66 2.91 -0.25 509 643 20.8% Anesthesiologists 4.00 4.28 -0.28 516 643 19.8% Pharmacists 3.66 3.99 -0.33 536 643 16.6% Personal Financial Advisors3.00 3.39 -0.39 560 643 12.9% Economists 1.66 3.08 -1.42 643 643 0% This table shows the rules-orientation of a selection of occupations as estimated by O*NET’s occupational analysts, the rules-orientation predicted for each occupation by our model of occupational rules-orientation (an estimate of the competitively efficient level), the difference between these two values (the regression error) and where this regression error falls in the whole sample of 643 regression errors. Regression errors that are among the largest in the sample are near the 100th percentile while errors that are among the smallest (most negative) in the sample are near the 0th percentile.
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TABLE 6 Descriptive Information about the Accountants’ Handbook
Edition Year Editor(s) Contributors % Ph.D. % CPA or CA Total Pages 3 1943 W. A. Paton 89 39%† -† 1505 4 1956 Rufus Wixon 30 67% 80% 1541 Walter G. Kell 5 1970 Rufus Wixon 50 72% 74% 1523 Walter G. Kell Norton M. Bedford 6 1981 Lee J. Seidler 57 30% 75% 2068 D. R. Carmichael 7 1991 D. R. Carmichael 56 21% 82% 1378 Steven B. Lilien Martin Mellman 8 1996 D. R. Carmichael 71 21% 90% 1723^ Steven B. Lilien Martin Mellman 9 1999 D. R. Carmichael 67 12% 91% 1735 Steven B. Lilien Martin Mellman 10 2003 D. R. Carmichael 64 20% 88% 1828 Paul H. Rosenfield 11 2007 D. R. Carmichael 69 20% 87% 1799 O. Ray Whittington Lynford Graham † Because the 3rd edition does not list contributors' degrees and designations, I count the number of contributors described as "Professor" or "Dean" as contributors with Ph.D.s. I was unable to count the number of CPAs or CAs for this edition. ^ Our copy of the eighth edition was spiral bound and was missing Section 4, a section that was not needed for our data collection. To produce an estimate of the total pages in the eighth edition, I assumed that Section 4 was 42 pages long, the average length of the other sections of the eighth edition. Some of the information in this table comes from Madsen (2011).
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TABLE 7 Switch and Control Topics and Their Location in the Handbooks from Which Data Were Collected
Switch Topics Control Topics Inventory Edition Year Tax Leases Hedge Pensions Valuation Intangibles Bad Debt 3 1943 index 10 15 index 4 1956 27 index index 12 19 index 5 1970 index index index 12 19 index 6 1981 13 26 index 27 18 23 index 7 1991 17 16 index 26 13 15 index 8 1996 18 17 20 32 14 16 index 9 1999 19 21 32 15 17 index 10 2003 24 18 20 index 11 2007 26 20 22 index The values in this table describe from where in each Handbook’s we collected citations. The top of the table shows seven accounting topics for which we collected data. The first four, tax, leases, hedge accounting, and pension accounting, are topics that are governed by standards that we consider to have switched from principles-based to rules-based. For “switch” topics, we collected data from three editions of the Handbook prior to the switch (ending with the edition whose value is underlined for a particular topic) and three editions of the Handbook after the switch. The final three topics, inventory valuation, intangibles, and bad debt, are accounting topics that were governed by a principles-based standard over the entire sample period. We collected data from “principles-only” topics for every edition in which we collected data for any switch topic. Numbers in the table represent the number of the Handbook section that discusses the topic. The word “index” in the table means that there was not a whole Handbook section dedicated to the topic so we identified relevant pages by searching the index. For the tax topic, our search terms were “taxes” and “income taxes.” For the leases topic, our search terms were “leases”, ”leasehold”, and “leases and leasing”. For the hedge accounting topic our search terms were “hedging”, “futures contracts”, forward exchange contracts”, and “hedges”. For the pensions topic, our search terms were “pension plans”. For the bad debt topic our search terms were “uncollectible accounts” and “uncollectibles”. For each index term used we focused only on those references to financial reporting matters. For example, personal income tax items were not searched.
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TABLE 8 Citations per Page to Non-Standard-Setters and Standard-Setters
Panel A: Non-Standard-Setter Groups (Experts, Profession, and Regulators) Switch Topics Principles-Only Topics Average Average Edition Hedge Tax Pension Lease Inventory Intangibles Bad Debt Switch Principles 3 10.00 3.20 2.30 2.90 10.00 2.80 4 1.18 1.96 1.36 1.82 3.00 2.50 1.50 2.44 5 3.38 2.40 1.97 1.08 2.25 2.37 2.58 1.90 6 2.33 1.18 3.55 0.97 1.29 1.08 0.93 2.01 1.10 7 0.88 0.92 0.28 0.09 0.64 1.28 1.18 0.54 1.03 8 1.32 1.32 0.96 0.00 1.49 1.32 0.56 0.90 1.12 9 1.93 0.96 0.88 0.88 1.32 1.11 1.26 1.10 10 0.09 2.26 0.87 0.56 0.09 1.23 11 0.09 0.96 1.14 0.53 0.09 0.88 Average 1.11 1.49 1.67 2.40 1.51 1.62 1.40 Panel B: Standard-Setters (CAP, APB, and FASB) Switch Topics Principles-Only Topics Average Average Edition Hedge Tax Pension Lease Inventory Intangibles Bad Debt Switch Principles 3 0.00 0.10 0.00 0.00 0.00 0.03 4 0.00 0.87 2.27 0.55 0.09 0.00 1.05 0.21 5 0.68 2.00 0.77 0.59 0.39 0.17 1.15 0.38 6 1.00 4.41 2.04 3.33 0.22 3.76 0.47 2.70 1.48 7 1.76 3.85 3.58 1.38 1.19 2.84 0.59 2.64 1.54 8 3.77 5.61 4.12 4.91 1.23 3.60 1.11 4.61 1.98 9 2.37 4.04 3.86 0.96 3.25 1.11 3.42 1.77 10 3.48 1.48 3.48 1.11 3.48 2.02 11 2.89 1.58 3.51 1.05 2.89 2.05 Average 2.55 3.10 2.75 2.11 0.88 2.32 0.62 Data are collected from editions of the Accountants’ Handbook. Values are citations-per-page to non-standard-setter groups (panel A) and standard-setters (panel B). Data are displayed for four topics which were once governed by a principles-based standard but switched to a rules-based standard. These “switch topics” include hedge accounting, tax accounting, pension accounting, and lease accounting. Values in the table come from three editions of the Handbook prior to the switch and three editions after the switch. The transition from principles- to rules-based standard occurs between the underlined value and the value beneath it. We also display data for three topics which were governed by standards that remained principles-based over the sample period. These were inventory accounting, accounting for intangibles, and accounting for bad debts. The table shows average citations-per-page for each accounting topic (in the bottom row) and average citations-per-page over time separately for the switch topics and principles-only topics (last two columns).
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TABLE 9 Citations per Page in Switch Topics less Citations per Page in Principles-Only Topics
Panel A: Differences in Citations per Page to Non-Standard-Setters Event Time Hedge Tax Pension Lease Average Diff. Pre/Post Average Diff. t-3 1.23 -1.26 -0.48 ^ -0.17 t-2 -0.15 1.48 0.50 -1.08 0.19 t-1 0.19 0.08 2.45 0.06 0.70 0.28 t+1 0.83 -0.12 -0.76 -0.13 -0.04 t+2 -1.14 0.19 -0.16 -0.94 -0.51 t+3 -0.79 -0.14 -0.22 -1.12 -0.57 -0.37 Post-Switch Average Difference less Pre-Switch Average Difference -0.65** Panel B: Differences in Citations per Page to Standard-Setters Event Time Hedge Tax Pension Lease Average Diff. Pre/Post Average Diff. t-3 -0.48 -0.21 0.66 ^ -0.01 t-2 0.22 0.29 1.62 2.06 1.05 t-1 1.79 2.93 0.56 0.39 1.42 0.89 t+1 0.59 2.31 2.04 1.85 1.70 t+2 1.46 3.64 2.14 -0.17 1.77 t+3 0.85 2.26 2.09 2.93 2.03 1.83 Post-Switch Average Difference less Pre-Switch Average Difference 0.94** ^ These values were based on a single passage equal in length to one tenth of one page in the Handbook. They were excluded from the statistical tests because they were based on so little data. Data come from editions of the Accountants’ Handbook. Column labels are accounting topics which were governed by principles-based standards but switched to rules-based standards. Values represent the difference between the citations-per-page in each switch topic and the citations-per-page in a set of topics that remained principles-based over the sample period (inventory accounting, accounting for intangible assets, and bad debt accounting). Average differences are shows for each year in event time (t-3 to t+3) and for the periods before the switch (t-3 to t-1) and the periods after the switch (t+1 to t+3) separately. At the bottom of each panel is the difference between the differences. It shows whether the change in the citations-per-page in the switch topics was different than the change in the principles-only topics.
