WORKING PAPER SERIES No. 30 | June 2020
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WORKING PAPER SERIES
No. 30 | June 2020
Andreicovici, Ionela | van Lent, Laurence | Nikolaev, Valeri |
Zhang, Ruishen
Accounting Measurement Intensity
TRR 266 Accounting for Transparency
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation):
Collaborative Research Center (SFB/TRR) – Project-ID 403041268 – TRR 266 Accounting for Transparency
www.accounting-for-transparency.deAccounting Measurement Intensity∗
Ionela Andreicovici† Valeri Nikolaev§
Laurence van Lent‡ Ruishen Zhang¶
June 2020
Abstract
We propose an empirical measure of metering problems, i.e., the difficulties of measur-
ing productivity and rewards in firms. We build on the insight that these metering
problems are reflected in the intensity with which firms apply Generally Accepted
Accounting Principles when preparing their financial statements to capture economic
transactions. We adapt a simple computational linguistics algorithm to identify textual
patterns that uniquely signify heightened use of accounting measurement in prepara-
tion of accounting reports. We validate the output of this algorithm before computing
time-varying, firm-level scores of accounting measurement intensity (AM I). We then
show that AM I is associated with the decisions of professional users of accounting
information. We also document that AM I is correlated with the cross-section of ex-
pected equity returns and with the cost of debt and non-price terms in the private loan
market. In CEO compensation contracts, we see lower pay-performance sensitivity
to accounting performance metrics as AM I increases. Finally, we report that AM I
correlates with investment and hiring decisions in firms; factor productivity, as well as
the efficiency of resource allocation. Together, these findings are consistent with the
predictions in Alchian and Demsetz (1972) about how metering problems affect the
boundary of the firm.
Keywords: metering problem, accounting measurement, stewardship, theory of the firm
JEL codes: D22, D23, D24, G12, J23, M40
∗
Preliminary. Van Lent and Zhang gratefully acknowledge funding from the Deutsche Forschungsge-
meinschaft Project ID 403041268 - TRR 266.
†
Frankfurt School of Finance and Management, Postal Address: Adickesallee 32-34, 60322 Frank-
furt am Main, Germany; E-mail: i.andreicovici@fs.de.
‡
Frankfurt School of Finance and Management; Postal Address: Adickesallee 32-34, 60322 Frank-
furt am Main, Germany; E-mail: l.vanlent@fs.de.
§
The University of Chicago; Postal Address: Booth School of Business, 5807 South Woodlawn Avenue,
Chicago, IL 60637; E-mail: valeri.nikolaev@chicagobooth.edu.
¶
Frankfurt School of Finance and Management; Postal Address: Adickesallee 32-34, 60322 Frank-
furt am Main, Germany; E-mail: r.zhang@fs.de.1. Introduction
When Alchian and Demsetz (1972) characterized the key problem of economic organization
as “the economical means of metering input productivity and metering rewards” (p. 778),
they squarely placed accounting at the heart of the theory of the firm (Ball, 1989). This
theory views a firm as a nexus of contracts that coordinate cooperation and the allocation of
economic resources (see also, Coase (1937); Jensen and Meckling (1976); Demsetz (1988)).
The boundaries of the firm and its productivity are directly affected by the informational
costs related to the metering of the inputs to production as well as the metering of their
productivity (Holmstrom and Milgrom, 1994; Kanodia, 2007). Despite this early recognition,
quantifying “the metering problem” and its effect on firms has been hampered by the lack
of a validated, large-sample metric of a given firm’s production of information through the
accounting measurement system (Gao, 2013). Indeed, so far little guidance has been offered,
even conceptually, on how such a measure should be constructed in an effort to determine
the (intensity of the) metering that actually takes place in firms.
In this paper, we use a simple machine-learning algorithm to construct a new, firm-level
and time-varying metric of accounting measurement intensity (henceforth, AM I). Intu-
itively, the metric capitalizes on the idea that accounting uses rules to convert a firm’s
transactions into information that meters the economic substance of these transactions. We
use a two-step procedure to quantify the degree of such metering. First, we rely on textual
analysis of US Generally Accepted Accounting Principles to identify unique language (word
combinations) that is used to lay out and discuss the rules of converting transactions into
accounting reports. We then, in the second step, measure the degree to which this unique
measurement-related language is encountered in the firm’s annual reports (10-K filings) ad-
justed for the total length of the report, to obtain a measure of the “intensity” of accounting
measurement in the firm’s disclosures.
An implicit assumption underlying this approach is that, when firms communicate fi-
1nancial information to outside investors, they have incentives, either due to mandatory
disclosure requirements or voluntary motives, to explain in sufficient detail the specific ac-
counting principles and procedures followed to construct the reported numbers. We present
four sets of findings that aim to underpin our interpretation that AM I indeed meaningfully
captures accounting measurement intensity. First, we list the top bigrams (two word com-
binations) culled from US GAAP to mark accounting measurement discussions in financial
statements. We observe that these bigrams correspond intuitively to the accountant’s notion
of measurement-related issues. Prime examples include word combinations such as “intan-
gible asset,” “estimate future,” “business combination,” “tax position,” “discount rate,”
among many others. We also identify top-scoring 10-Ks and verify that they indeed feature
extensive discussions of measurement-related issues.
Second, we show that our measure varies intuitively over time and across sectors. The
mean across firms of AM I increases significantly between 2000 and 2008, coinciding with US
GAAP moving increasingly towards fair value-based measurement (as opposed to historical
cost measurement). Subsequently from 2009, the mean AM I varies somewhat over the
years but appears to have reached a plateau. We find high mean AM I for sectors such as
the financial services and several manufacturing related businesses (paper, textiles), but low
mean accounting measurement intensity in tobacco and coal.1
Third, we examine firm-level associations between AM I and firm characteristics that
are commonly thought to be associated with accounting measurement issues (Francis et al.,
2008). For example, frequent changes in the business model and the volatility of the firm’s
operating environment are expected to add to the magnitude of the metering problem, when
the firm uses rules to convert the economic substance of its transactions to an accounting
report. Consistent with these predictions, we find that accounting measurement intensity
exhibits a positive cross-sectional relation with the variability of sales, presence of intangibles
1
Indeed, the financial services sector has the across-sectors highest AMI score. We exclude it, for this
reason and given that its metering problems are likely qualitatively different from other sectors, from our
further analyses.
2assets, such as goodwill, as well as with the length of its operating cycle.
Further, we examine the associations between AM I and key accounting properties, such
as earnings persistence, predictability, and accrual quality (e.g., Francis et al. (2008)). AMI
exhibits significant negative associations with persistence and predictability of earnings, as
well as with an indicator variable that identifies whether a firm has reported a restatement.
Interestingly, firms with lower accrual quality, as captured by higher variance of discretionary
accruals, exhibit lower AMI. This result suggests that measurement intensity, which aims
to capture the degree of metering problems, is distinct from the well-researched notion of
accounting quality. To further our argument that the traditional accounting properties are
conceptually and empirically distinct from our concept of accounting measurement intensity,
we first note that they have only a modest incremental explanatory power with respect to
AM I.
More importantly, we demonstrate that professional users of accounting reports, such as
auditors and financial analysts, respond to accounting measurement intensity. Indeed, in line
with the notion that audit effort and the associated risk depends on the extent of metering
problems in the firm, we show that auditors charge significantly higher audit fees when a
company has elevated levels of accounting measurement intensity. This effect holds in the
presence of controls for accounting properties and characteristics of the firm’s business model
and operating environment discussed above. This lends further support to our argument that
AM I and accounting quality properties are distinct and empirically separable.
We conclude the validation of our measure by showing that external users of accounting
information, such as investors and financial analysts, respond to greater accounting mea-
surement intensity. In line with increased information frictions between the firm and its
investors, we show that AM I is associated with a higher probability of informed trading and
and lower analyst following.
Together, this evidence strongly suggests that we are capturing an economically signif-
icant variation in firm-level accounting measurement intensity and that this construct is
3distinct from more traditional measures of accounting quality. This metric enables us to
address the question of central importance in the theory of the firm, namely, whether the
presence of metering problems influences the boundaries of organizations and determines
their productivity.
Our analysis is motivated by the theory in Alchian and Demsetz (1972) and Jensen and
Meckling (1976), which views a firm as a nexus of contractual relationships that directly
addresses metering problems existing among contracting parties. Metering, thus permeates
the economic organization of firms, giving rise to frictions in the firm’s interactions with
capital markets, in the rewarding and provisioning of incentives to employees, and in the
allocation of resources (Kanodia, 2007; Kanodia and Sapra, 2016). Ultimately, these frictions
place bounds on the productivity of the firm and on the scope of its operations.
We begin our exploration of how metering frictions affect the boundaries and produc-
tivity of firms by providing evidence on the pricing of AM I in capital markets. In Alchian
and Demsetz’ (1972) view, metering problems engender a need for monitoring and make the
selling of promises of future returns to prospective capital providers more difficult. Similar
predictions follow from the classic agency theory developed in Jensen and Meckling (1976),
among others. To test this prediction, we examine the link between AM I and the cost of
external capital. Using asset pricing tests, we show that accounting measurement intensity
is positively associated with expected equity returns in the cross section of firms. Turning to
debt markets, we also show that AM I has a positive and statistically significant association
with the cost-of-debt. Further, we document a positive link between AM I and the use of
accounting-based covenants, suggesting a contractual response to metering problems. Over-
all, the evidence supports the view that metering problems create contracting frictions that
are reflected in the pricing of corporate securities.
Having shown that accounting measurement intensity has associations with the pricing of
capital and is implicated in the contractual terms agreed with capital providers, we consider
another major contractual arrangement within the firm: compensation contracts. Alchian
4and Demsetz (1972) predict that one way to address the metering problems is to make the
rewards of top management depend on the fluctuations in the residual value of the firm.
Indeed, CEO compensation varies strongly with stock performance. However, to the extent
that accounting measurement produces precise or more sensitive signals informative of the
residual value, compensation should be responsive to accounting measures as well (Lambert
and Larcker, 1987; Banker and Datar, 1989; Barclay et al., 2005). In line with this conjecture,
we find that higher AM I scores are associated with a lower sensitivity of CEO compensation
to performance as measured by using accounting numbers. To alleviate an omitted variable
explanation, we also show that the sensitivity of compensation to a popular alternative
metric, namely, stock returns, does not change with the level of AM I.
Having established that AM I is associated with contracting frictions, we turn attention to
the analysis of resource allocation and productivity. Specifically, Alchian and Demsetz (1972)
predict that metering problems, with rewards and productivity only loosely correlated, will
lower firm-level productivity and distort resource allocation. Insofar metering problems lead
to lower productivity and limited access to finance, firms are expected to lower investments
and hiring. Consistent with this prediction, we find that increases in accounting measurement
intensity are associated with a significant decreases in the capital and R&D investment
rates as well as with reductions in employment growth. Further, in a sign of distorted
resource allocation, higher accounting measurement intensity increases the investment-cash
flow sensitivity. Finally, yet importantly, we show that AM I is associated with erosion in
firm-level total factor productivity.
We make several contributions to the literature. First, we introduce and validate empir-
ically a new measure that captures the construct of accounting measurement intensity, i.e.,
the intensity with which the Generally Accepted Accounting Principles are applied to convert
a firm’s transactions into reported numbers. We show that this measure varies intuitively
with firm characteristics and is distinct from more traditional indicators of accounting qual-
ity. Second, we use the proposed measure to test several interesting predictions that follow
5from the theory of the firm and that establish the link between metering problems, access
to capital, and firm productivity. Our evidence suggests that measurement is a significant
determinant of firms’ emergence and growth, as well as their allocation of resources.
The idea that accounting measurement is essential to understanding the efficient orga-
nization of production has been well-recognized in the accounting literature. Early work
pointing out the “stewardship” role of accounting aims to describe how different accounting
measurement rules are related to governance outcomes (Gjesdal, 1981; Paul, 1992; Bushman
et al., 2006; Bushman and Indjejikian, 1993). These studies generally also distinguish be-
tween the role of accounting in addressing the “metering problem” of measuring the marginal
productivity of inputs and rewarding the owners of those inputs accordingly and provid-
ing information to investors making decisions. Indeed, the trade-off between valuation and
stewardship when developing new measurement rules is one of the fundamental issues policy-
makers need to consider. And, often without much empirical evidence, critiques are leveled
against proposed rules that are seen to sway too much in one direction or the other. We
offer insights on the trade-offs faced by policy-makers inasmuch as we show that accounting
measurement intensity is associated with contracting frictions that manifest themselves in
the performance sensitivity of compensation contracts and in the way debt contracts are
written. In addition, we provide evidence that these metering problems are priced in equity
and debt markets, suggesting a valuation role as well as another mechanism (namely the
funding of the firm’s operations) through which accounting measurement might be impli-
cated in the scope of the firm. This approach is consistent with the ideas in Kanodia (2007),
who emphasizes that alternative accounting measurements have real effects inasmuch as the
question of how accountants measure and report a firm’s economic transactions to capital
markets has substantial effects on decision-making within the firm and ultimately on resource
allocation in the economy. This effect materializes as accounting measurement is reflected
in the capital market’s pricing of the firm, which managers, in turn, anticipate when making
decisions.
6In more recent work, Barrios et al. (2019) examine the relation between financial measure-
ment practices and firm-level productivity. These authors conceptualize the firm’s investment
in having audited GAAP-based financial statements as a good managerial practice that aids
in executive decision-making, and in turn increases productivity. In a related study, Breuer
(2018) examines the effect of tougher financial reporting regulation on resource allocation in
product and capital markets. These important studies focus on private (or limited liability)
firms and rely on stark differences between groups of firms (having audited financial state-
ments or not) to measure reporting quality. We offer a metric based on an algorithm that
can be applied to any sample of firms that have publicly available disclosures, thus com-
plementing what can be learned from (proprietary) data sets of private firms. Our measure
does not reflect reporting quality, but rather centers on metering problems in firms, which
is conceptualized to be the root cause for inefficiencies in productivity. What’s more, rather
than focusing on how better measurement can assist managers to improve their productivity,
we highlight the role of accounting measurement in countering the metering problem and
describe its function in the governance of firms. Doing so is important in view of the find-
ings in Choi (2018), whose general equilibrium analysis suggests that (accrual) accounting
systems improve resource allocation and aggregate productivity.
2. Measuring Accounting Measurement Intensity
Our method to obtain a metric of Accounting Measurement Intensity builds on the insight
that the process of transforming transactions into accounting data has been described in a
purposeful vocabulary in the pronouncements of standard setting bodies. The degree then
to which companies use the selfsame purposeful vocabulary to describe their accounting
practices should be a valid measure of the intensity of the process that transforms business
transactions into meaningful accounting reports.
While this premise is intuitive, its practical implementation faces three distinct challenges.
First, when identifying uniquely-accounting vocabulary in the standard setter’s pronounce-
7ments we need to identify expressions that are used much more frequently in accounting
documents than in every day language. Following Hassan et al. (2019), we achieve this
objective by comparing two-word combinations, i.e., bigrams, in a comprehensive set (“a
library”, A) of FASB pronouncements, described more completely below, with a library of
documents that capture non-business English.
We use a large set of (open source) English language novels to capture non-business En-
glish. Just removing non-business English from the set of bigrams, however, is unlikely to
be sufficient. Complicated transactions such as M&A deals can give rise to metering prob-
lems, but are separate from the same. Thus, as a second step, we need to supplement our
non-accounting training library (N) with text that reflects language used to discuss business-
related issues, without using accounting terms to do so. This poses a not straightforward
research design problem as accounting is the language of business and many non-accounting
texts about business issues tend to be steeped in accounting language. Including such “con-
taminated” texts into the training library would amount to “throwing the baby out with
the bathwater”. To overcome this issue, we augment our non-business English library with
a collection of texts taken from a BBC news dataset (Greene and Cunningham, 2006) and
from Webhose Datasets.2 These texts capture a broad range of topics spanning entertain-
ment, sports, technology, and politics. And while they likely use business-related words,
these texts, catering to a generalist audience, at the same time, eschew accounting-specific
terms. Together, this collection of text documents is our non-accounting training library N.
Third, conceptually, AM I should reflect variation in metering problems between firms. A
substantial proportion of transactions that firms need to map into accounting data, however,
is common to all and/or pose few measurement issues. These transactions are routine, their
implications well understood, and little judgment is needed to capture them in accounting
output. Accordingly, they should have little if any contribution to a metric of Accounting
Measurement Intensity. We reflect these considerations in our choices related to the pre-
2
Webhose is a web data provider turning unstructured web content into machine-readable data feeds.
8processing of the text data in the A training library. Specifically, we then remove bigrams
that appear in more than 90 percent of the 10-Ks in an effort to filter out “boilerplate”
accounting terminology. Additionally, in accordance with best-practice in textual analysis,
we first lemmatize and stem the texts, removing digits, punctuation, and stop words.
Having constructed the libraries N and A, we can now define the set of uniquely-accounting
bigrams as A\N. It is this set of bigram that forms the basis for computing our AM I metric.
In a nutshell, we take our set of A \ N bigrams to a given firm’s annual report (10-K) and
simply count their presence. We then scale each count by the total number of bigrams in
the 10-K yielding a statistic that represents the degree of accounting measurement intensity
of the firm. We do so for every 10-K in our sample, which we obtain from the SEC Edgar
database for all US publicly listed firms with a 10-K filed between 2001 and 2018. Thus,
1 X
Bit
(1) AM Iit = (1[b ∈ A \ N]),
Bit b
where b = 0, 1, ...Bit are the bigrams contained in the 10-K of firm i in year t and 1[·] is the
indicator function. We opt to equally weigh bigrams in the construction of AM I, although
we have experimented with alternative weighing schemes.3 The reason for equal weighting
is to avoid largely capturing few frequent terms but instead, in line with our objective,
emphasize the ‘long tail’ of heterogeneous measurement specific terminology encountered in
annual reports. To facilitate interpretation, we standardize AM I by subtracting its sample
mean and dividing by the sample standard deviation, such that a one-unit change in AM I
represents a standard deviation.4
3
In particular, weighing by a bigram’s tf-idf is a common practice in the textual analysis literature (see,
Loughran and McDonald (2011)). The “term frequency-inverse document frequency” weighs a bigram by
the frequency of its occurrence in the training library (tf ) scaled by its discriminatory power across training
libraries (idf ). In our setting, with only two training libraries, the idf term reduces to a constant scalar and
we would effectively be weighing by the term frequency of the bigram. Doing so yields very similar AM I
rankings and our inferences are not materially impacted.
4
When standardizing, and throughout most of the analysis we drop observations from the Financial
Services industry, which typically have very high raw AM I scores.
92.1. Training libraries
The validity of AM I, which we will subject to an extensive set of tests below, depends
on effective identification of the set of uniquely-accounting (A \ N) bigrams. We aim to
do so using a procedure that requires as little human involvement as possible, to avoid
contaminating the measure with researcher biases. The only subjective intervention needed
is in the researcher’s choice of the training libraries. We justify our choices more fully next.
Accounting training library. We use a comprehensive set of statements issued by the
Financial Accounting Standards Board to capture the unique vocabulary accountants use
to transfer economic transactions into financial reports. We use the Financial Accounting
Standards Original Pronouncements, Updates (“as amended” and “as issued”), the FASB In-
terpretations (as amended and as issued), FASB staff position papers, as well as the Emerging
Issues Task Force Abstracts (including Other Technical Matters), from 1973 to 2019. Collec-
tively, this library contains 70.2 MB of documents, i.e., 4.7 million unique bigrams excluding
digits, punctuation, and stop words, outlining and discussing Generally Accepted Accounting
Principles in the United States, which together shape the then-current accounting practices
of firms.5
Non-accounting training library. Our starting point in defining the non-accounting
library N is a set of English-language novels taken from Project Gutenberg.6 We supple-
ment this base library with news articles about sports, entertainment, politics, and tech-
nology.These text include such bigrams as “investment economics”, “share price”, “equity
owner”, and “credit financial”, illustrating their effectiveness in identifying generic business
language despite discussing sports and entertainment. Alternatively, we could have supple-
mented our base library with news articles that are more directly related to business articles
(taking stories from the finance and economics pages of news papers). Doing so, however,
would likely have removed important measurement related phrases from our ultimate A \ N
5
The corresponding pdf files that are transformed into txt format for use in our machine learning algorithm
are 211.7 MB.
6
We provide a list of the selected novels in Appendix, Table 1.
10set of bigram, as news about business topics is often steeped in accounting language. On
the other hand, our preferred method errs on the side of leaving too many business-related,
but not uniquely accounting bigrams in the ultimate set of uniquely-identified accounting
bigrams. Nevertheless the overlap between these two alternative N libraries is about 90 per-
cent, which leads us to conclude that set of A \ N bigrams is not very sensitive to the choice
of non-accounting training libraries. Ultimately, we adopt the version of the non-accounting
training library that has news articles aimed at a generic, non-business audience. After re-
moving non-accounting bigrams, our library of uniquely-identified accounting bigrams (A\N)
contains 490,397 terms.
2.2. Validation of AM I as a metric for Accounting Measurement Intensity
In this section, we start validating the output of our method by first simply evaluating pat-
terns of bigrams contained in A \ N. This is an important precaution to take as without face
validity of the building blocks to the AM I score, we cannot hope to develop an economically
meaningful statistic.
2.2.1. Face validity of AMI
We first list the top 200 bigrams (measured by their occurrence in 10-Ks across the
sample period) in Appendix Table 2. Reassuringly, we find that these top bigrams include
mostly word combinations that accountants would agree reflect instances in which metering
problems are present in transforming business transactions to financial reports. Prominent
examples include “intangible asset”, “business combination”, “loan loss”,“measure fair”,
“deferred income”,“unrecognized tax”, and “variable rate”, among others. Indeed, in this
list of 200 bigrams, there is very little evidence of clear-cut “false positives”, i.e., bigrams
that are intended to capture accounting measurement intensity, but are unrelated to the
same. Perhaps the only exception are bigrams referring to corporate officers, such as “officer
principal” and “director officer”. Interestingly, among the top bigrams are those which
capture an important aspect of accounting measurement intensity, namely the extent to
11which managerial judgement is required to adequately reflect the economics of the transaction
that needs to be mapped into accounting reports. For example, frequently used bigrams are
“management estimate”, “measure fair”, and “company determine”.
We provide further texture by listing a top-10 of bigrams for each sample year in Ap-
pendix Table 3. Doing so reveals some interesting patterns in the frequency of certain
bigrams that to some extent appears to mirror time-series patterns in accounting standard
setters’ concerns. For example, between 2001 and 2004, two top bigrams were “option plan”
and “stock purchase” consistent with the FASB’s stated intend to regulate stock option
plans and other executive compensation instruments in a response to the 2001 crash of the
internet bubble. We then conduct a similar exercise, but rather than listing the top bigrams
by year, Appendix Table 4 records the same by industry (using the Fama-French 17 industry
classification). Again, the patterns in bigrams make intuitive sense. In the Financial Ser-
vices Industry, top bigrams include “loan loss”, “mortgage loan”, and “capital requirement”,
whereas in the Oil and Gas Industry, signature bigrams are “gas reserve’, “prove reserve”,
and “asset retirement”.
While it is useful to establish face validity, focusing on individual bigrams can be mislead-
ing insofar as each individual bigram only contributes a little to the final AM I score given to
a firm’s 10-K (recall that we weight bigrams equally). Indeed, what should be evaluated in
the end is whether using these uniquely-accounting bigrams leads to AM I scores for 10-K’s
that accurately portray the heterogeneity in accounting measurement intensity across firms
(and within a given firm over time). That is, we wish to examine the properties of the
AM I measure created by our algorithm. To that end, we turn to testing the face validity
of the AM I score. This analysis is particularly important as some of the individual bigrams
plausibly also reflect the nature of the firm’s business model and its economic transactions in
addition to accounting measurement. Having such bigrams selected by our algorithm might
increase measurement error, but again—given the sheer volume of bigrams used to compute
AM I, their individual effect is likely to be very small.
12We aggregate AM I scores computed for each firm-year by taking the yearly cross-
sectional means and plotting them over time in Fig. 1. Next we compute average AM I
scores by industry (across years) and present these averages in Fig. 2. The former figure
shows the longitudinal development of accounting measurement intensity during our sample
period. The trend of yearly mean AM I scores is clearly upward, in particular until the start
of the Great Recession in 2008 after which the mean AM I plateaus at the level of about
0.1. The pattern could reflect the increased attention for “measurement intensive” fair-value
accounting in the early 2000s and the reduced emphasis on the same when the financial cri-
sis hit in 2008. At the industry-level, we observe high AM I values for Oil, Machinery, and
Construction, while Consumer Goods and Mining represent the opposite of the spectrum.
More so perhaps than the difference in mean AM I scores, one should take notice of the vari-
ation across industries.7 Our proposed measure captures between-industry heterogeneity in
measurement intensity as we would expect to observe.
We probe the relative contributions of aggregate (i.e., time series), sectoral, and firm-level
Accounting Measurement Intensity more systematically by performing variance decomposi-
tion, reported in Table 2. This analysis asks how much of the variation in AM Iit is accounted
for by various sets of fixed effects. We find that time fixed effects explain a modest degree
of the variation in AM I. The trend in aggregate AM I shown in Fig. 1 accounts for only
5.5 percent of the total variation. Sector fixed effects (at the three-digit SIC level) and the
interaction of sector and time fixed effects account for another 21.18 percent and 4.14 per-
cent respectively. The remaining variation in AM I scores, 69.18 percent plays out at the
firm-level rather than at the level of the sector or the economy as a whole. Only part of
this variation, namely, 21.18 percent, is not explained by time or firm fixed effects. Adding
granularity to our sector definition increases, as expected, the explanatory power of sector
fixed effects (which range between 19.91 and 23 percent, moving from SIC 2 to 4 digit pre-
cision). This increase in power is mirrored by a decrease in the explanatory power of firm
7
Note that we have dropped observations from the Financial Services industry.
13fixed effects (from 54.61 to 45.41 percent), putting bounds on the amount of variation in
AM I classified as “firm-level.”
The aggregate patterns discussed above are reassuring as they conform our prior of the
longitudinal trends in accounting measurement and heterogeneity in the degree of metering
problems across industries. Further comfort can be taken from the identity of S&P500
firms with AM I scores in the top 15. Appendix Table 5 presents an overview; the table
also includes the top bigrams in that firm’s 10-K as well as a top-scoring “snippet”, which
presents the sentence with the highest number of accounting bigrams scaled by sentence
length. A number of noteworthy observations follow from the table. First, top bigrams
include “measurement heavy” concepts such as “value hierarchy”, “remeasurement gain”,
“impairment charge”, and “defer compensation”. What’s more, consistent with measurement
intensity deriving from both the measurement of inputs as well as from metering rewards,
bigrams associated with the latter feature prominently: “post retirement”, “compensation
expense”, and “performance cash”. Third, the snippets capture indeed prime instances of
measurement. For example, the top snippet for Kroger, a supermarket chain, reads “the
company assesses, both at the inception of the hedge and on an ongoing basis, whether
derivatives used as hedging instruments are highly effective in offsetting the changes in the
fair value of cash flow of the hedged items”, highlighting measurement issues surrounding the
use of derivative instruments. Or consider the excerpt from the truck manufacturer Paccar
on the complications of dealing with loss reserves: “small balance impaired receivables with
similar risk characteristics are evaluated as a separate pool to determine the appropriate
reserve for losses using the historical loss information discussed below.”
2.2.2. Innate determinants, accounting quality, and AMI
We continue our validation efforts with further “sense” checks. We first perform a more
systematic examination of those firm characteristics that are correlated with AM I. Our
intuition is that AM I varies with the the firm’s business model and its operating environment
(Francis et al., 2008). We compute AM I for 10-Ks filed between 2001 and 2018 and hence
14our sample of AM I covers firms with a fiscal year ended between 2000 to 2018. Next, we
match the AM I data set with accounting information retrieved from Compustat via a linking
table provided by WRDS SEC Analytics Suite. This AMI-Compustat sample serves as the
primary data set for all following analyses. Descriptive statistics for the main independent
and dependent variables used in this and in the subsequent regression analyses are presented
in Table 3.
Our model specification takes the form
(2) AM Iit = δt × δs + γXit + ǫit ,
where δt , and δs represent a full set of time and sector fixed effects, and the vector Xit
contains a set of variables that prior work has thought to capture the firm’s “innate” factors
(i.e., its business model and operating environment), namely the log of the firm’s assets, the
variability of sales, the operating cycle, the incidence of losses, intangible asset intensity,
and capital intensity. All variable definitions and the data sources are detailed in Table
1. Standard errors are clustered by firm throughout the paper unless we state differently
explicitly.8
We report Ordinary Least Squares estimates for the relation between AM I and the
various innate factors in Table 4, panel A. We find that more variability in sales (σ(Salesi,t ))
is positively associated with AM I (in column 3, coefficient = 0.126, std. err. = 0.025),
suggesting that metering problems are higher when the operating environment of the firm
is more volatile. Larger firms within a sector (in a given year) also tend to have higher
measurement intensity, consistent with these firms having more complex operations that are
more difficult to map into accounting reports. Similarly, firms with longer operating cycles
(indicative of a more lengthy process to transform input into cash) have higher measurement
intensity (coefficient = 0.034, std. err. = 0.016). As one would also expect, we find that
8
All of our inferences are unaffected by clustering standard errors by firm-year. Further, we conduct
extensive analysis on the appropriate standard errors in our tests below.
15firms with greater fraction of intangible assets on their balance sheet (higher intangibles
intensity) exhibit higher AM I. We also find some evidence that capital intensive business
models, i.e., those characterized by a higher fraction of property, plant and equipment in
their asset base exhibit higher AM I.
Second, we examine whether and to what extent accounting measurement intensity
overlaps with proxies for well-known earnings quality attributes, such as the variability
of discretionary accruals (DA), earnings persistence (P ersistence), earnings predictabil-
ity (P redictability), or whether the financial statement was restated (1[Restatement]). We
do so by augmenting equation 2 with each of these variables separately in columns 1-4. We
consider the variation within sector-time by including the corresponding fixed effects. Our
findings indicate that AM I exhibits a negative association with variability of discretionary
accruals (column 1). While this result may seem surprising at first sight, it clearly indicates
that AM I is distinct from the notion of accruals quality based on the models of discre-
tionary accruals.9 It is also possible that, empirically, discretionary accruals models have
difficulties separating accounting quality from economic performance (Nikolaev, 2018); this
measurement error, in turn, could yield a negative correlation.
Unlike in the case of discretionary accruals, we find evidence of some intuitive “overlap”
between AM I and earnings persistence (coefficient = -0.125, std. err. = 0.032), consistent
with higher earnings persistence being associated with lower accounting measurement inten-
sity. Similarly, more predictable earnings are associated with lower AM I. We also find a
positive correlation between AM I and the presence of a restatement, such that firms that
display high measurement intensity are more likely to also have restated financial statements.
One important observation following from this table is that the explanatory power of
regressions that add the individual earnings quality proxies remains virtually unchanged
compared with our estimates of R2 in an equivalent specification in Panel A (column 3) that
does not include these variables. We conclude, therefore, that AM I and earnings quality
9
We verify that this result is not driven by a particular choice of discretionary accrual model nor is
sensitive to the use of control variables.
16are distinct concepts.
2.2.3. AMI and audit fee premiums
Our second “sense” check builds on the idea that auditors, putatively the most expert
professionals in the accounting measurement process, recognize firms with high accounting
measurement intensity and price the added effort required when delivering their service
accordingly. If our measure is valid, we expect to observe a positive correlation between
audit fees, i.e., the price charged by auditors to client firms, and AM I, while controlling for
other determinants of audit fees. We examine this prediction using the intersection of the
AMI-Compustat sample and the sample of firms with audit fee information between 2000
and 2018 available on Audit Analytics. We then estimate the following regression,
(3) Auditf eeit = δi + δt × δs + αAM Iit + βAQit + γXit + ǫit
In our preferred specification, not only do we examine the correlation between audit fees
and accounting measurement intensity (as we do in column (1)), but we also add accounting
attributes that proxy for accounting quality (columns 2-4), the vector X contains a standard
set of control variables. Prior literature has shown that audit fees reflect the increased effort
exerted by auditors when firms have poor accrual quality, higher earnings uncertainty, or
have incurred restatements. To the extent that auditors recognize “accounting measurement
intensity” as a different feature of the firm’s accounting system from those accounting quality
measures, as suggested by our results in Table 4, AM I should be correlated with fees even
in the presence of those controls.
Our evidence, presented in Table 5, shows that AM I is positively and significantly cor-
related with audit fees. When we build up to our preferred specification that adds all the
controls for accounting quality at the same time in column (4) as well as year and firm
fixed-effects, we find an estimated coefficient on AM I of 0.044 (std. err. = 0.008). This
estimate is somewhat attenuated compared with the estimate using within-sector and time
17variation in column 3, but still imply an economically meaningful role for over time changes
in AM I within the same firm in explaining audit fees. Based on the estimate in column 3,
a one standard deviation change in AM I increases audit fees by $55,000 or by about nine
percent of the sample mean.10 These findings indicate that AM I is priced by the auditors
and that this effect cannot be explained by the correlation of AM I with either firm innate
determinants or proxies for accounting quality.
2.2.4. How do users of accounting information respond to increased AMI?
Our final “sense check” exploits the intuition that higher level of accounting measurement
intensity should be more difficult to evaluate by users of these reports. We offer two sets of
results for this idea. First, we examine the correlation between AM I and the probability of
informed trade (PIN), a common statistic for the level of information asymmetry between
the firm and (equity) investors. Metering problems (i.e., high levels of AM I) may hamper
the ability of investors to judge the economic performance of the firm and increase the
information asymmetry between outsiders and firm “insiders”. Second, we ask whether
“metering problems” are correlated with the coverage of the firm by financial analysts.
While these analysts as professional information intermediary may help overcome problems
in conveying the performance of the firm when metering problems are abound, their ability
to do so under these circumstances is plausible compromised. We thus expect lower coverage
for firms with high levels of AM I.
We proceed by estimating PIN following Easley et al. (2002, 2010) for all firms on the
intersection of NYSE Trade and Quote and Compustat from 2000 to 2018. From IBES, we
collect the number of analysts covering a given firm in each fiscal year ending between 2000
and 2018. We then merge our AMI-Compustat sample with the PIN and the analyst data.
10
As we have standardized AM I by its panel standard deviation, we compute the economic magnitude
by using the estimated coefficient from column (3), which includes sector-year fixed effects. Using estimates
from the firm fixed effect regression would be inappropriate as, within firm, changes over time of one (panel)
standard deviation are rare (Mummolo and Peterson, 2018).
18Our specification takes the form,
(4) yit = δi + δt × δs + αAM Iit + γXit + ǫit
where yit is either PIN or the number of analysts covering the firm (Coverage), and X always
includes the log of the firm’s assets in the prior period.
Table 6 presents our estimates. In panel A, we find that AM I is positively associated with
the probability of informed trading, consistent with the idea that high scores of measurement
intensity increase the information asymmetry between the firm and its investors. While the
coefficient of interest drops from 0.006 (std. err. = 0.001) to 0.003 (std. err. = 0.001) as we
move from sector-year to firm and year fixed effects, which suggests that part of the variation
is driven by differences between firms within a sector in a given year, the association remains
significant at the one percent level.
In panel B, we examine the association between AM I and Coverage. Our preferred
estimate in the most stringent specification in column (4) equals -0.045 (std. err. = 0.007)
and is again significant at the one percent level. Firms with higher accounting measurement
intensity have lower financial analysts following. Compared with column (3), the drop in the
estimate of interest is about 54 percent, once more indicating that a significant part of the
variation in AM I plays out at the sector-year level.
Our validation strategy so far has been to show that our algorithm identifies bigrams
that are intuitively associated with metering problems and that the frequency of use of these
bigrams varies over time and across sectors as one would expect. Consistent with these
observations, the bigrams produce Accounting Measurement Intensity scores that vary (on
average) in an economically meaningful way, both longitudinally and in the cross-section.
Indeed, the AM I scores are associated with firm characteristics that imply more complex
business models and operating environments, attributes of accounting information, such as
persistence and the variance of discretionary accruals, as well as with fundamental decisions
19by professionals such as auditors and financial analysts.
3. Accounting Measurement Intensity in Capital Markets
Our validation tests provide some first evidence that AM I is a valid firm and time specific
measure of metering problems. Having such a measure available, allows us to address funda-
mental questions about the role of accounting measurement in the theory of the firm. Recall
that the theory of the firm views organization as a nexus of contracts that co-ordinates eco-
nomic activities and alleviates metering problems (Coase, 1937; Alchian and Demsetz, 1972;
Demsetz, 1988; Jensen and Meckling, 1976). Metering problems create contracting frictions
both within organizations as well as in the contracting between the firm and the outside
world. We examine the latter first, and consequently start by exploring the link between
accounting measurement intensity and contracting with external capital providers. As a first
step, we test for the presence of a link between AM I and the cost of equity capital. We
conduct a series of asset pricing tests to examine whether AM I is associated with a higher
cost of equity capital. In addition, we examine credit market outcomes, including the associ-
ation between AM I and the cost-of-debt as well as between AM I and non-price contractual
terms.
3.1. Cost of equity capital
Do metering problems influence the cost of equity capital? The answer to this question
in not obvious. Metering problems add a layer of uncertainty about the fundamental firm
characteristics that determine the objective distribution of returns (Barry and Brown, 1985).
For example, metering problems hamper the investors’ ability to observe and evaluate the
economic performance of a firm. To the extent that the measurement error present in ac-
counting data is diversifiable, classic portfolio theory suggests that it should not be priced
(Fama, 1976). As we have shown in the variance decomposition exercise, a substantial part
of the variation in AM I is at the firm-level. At the same time, however, AM I exhibits
20considerable time and sector variation too, which indicates that metering problems are cor-
related across firms with similar characteristics. To the extent that the imprecisely measured
fundamentals, which determine future cash flows and risks, are correlated across companies,
equity investors are likely to make systematic errors when making decisions. Because such
errors cannot be diversified, stocks with higher measurement problems should be priced at
a higher discount. The latter argument is in line with the theory in Lambert et al. (2007),
who argue that higher precision of accounting information reduces the assessed covariance
of a firm’s cash flow with the cash flows of other firms and hence reduce the cost of eq-
uity. What’s more, recent evidence also indicates that idiosyncratic volatility is priced (Ang
et al., 2006; Chordia et al., 2017), admitting potentially a pricing effect of purely firm-specific
measurement problems.
To test whether expected stock returns are increasing in AM I, we follow the Fama-
MacBeth (1973) methodology. We retrieve monthly stock returns of U.S.-listed firms over
2000 to 2019 from the Center for Research in Security Prices (CRSP). We then match these
monthly stock returns with the AMI-Compustat sample. We further require non-missing
stock returns for the prior one-year period.
As a first step, we estimate the following cross-sectional regression for each month (Novy-
Marx, 2013; Ball et al., 2020)
(5) Reti,t+1 = α + βAM Ii,t + γXi,t + ǫi
where Ret is the one-period ahead monthly return (including delisting returns when appli-
cable), the vector X contains natural logarithm of the market value of equity (M E), the
book-to-market ratio (BM ), both of which are lagged by six months following Ball et al.
(2020), the prior one-month return (Ret0,1 ) and the prior one-year (starting before Ret0,1 )
return (i.e., Ret2,12 ). We also lag AM I by six month, in such a way that firms with a fiscal
year that ends in June have both AM I and control variables from January to December of
21the preceding year. In the second step, the resulting time-series of monthly coefficients are
averaged and the associated t-statistics are computed based on Newey-West standard errors,
correcting for auto-correlation in returns up to two lags.
The results of the Fama-MacBeth regressions are reported in Table 5, using Newey-West
(1987) adjusted standard errors. We find a positive and significant (at the ten percent level)
association between AM I and expected returns. The estimated coefficient on the variable of
interest equals 0.001, implying that a one-standard deviation increase in AM I is associated
with a 10 bps increase in monthly expected returns.
3.2. Debt markets
In this section, we examine the extent to which the cost of debt capital as well as the use
of accounting information in debt contracts vary with the degree of metering problems. We
first report the association between AM I and the cost of debt. Our sample for this exercise
uses pricing and contract details of commercial loans. We collect information on the all-in-
drawn spreads and financial covenants for loans issued over the period 2000-2018 from the
Thomson Reuters Dealscan database. We then use the intersection of the resulting data set
with our firm-level AMI-Compustat sample aligned by the year in which the loan facility
starts. Our regressions specification is given by,
(6) yit = δi + δt × δs + αAM Iit + γXit + ǫit
where y represents the price or non-price term of interest and X includes the log of total
assets, the accounting return on assets (ROA), leverage (Leverage), and contract terms such
as M aturity and the F acility amount.
We turn attention first to the pricing of debt reported in Table 8. Our proxy for the
cost-of-debt is all-in-drawn spread, which describes the amount the borrower pays in basis
points over LIBOR for each dollar drawn down. We find a positive association between AM I
22and the all-in-drawn spread; the estimated coefficient ranges between 5.7 and 7.1, depend-
ing on whether we include only sector-and-time fixed effects or time and firm fixed effects.
Economically speaking, this estimate translates into an approximately 5.7 bps increase in
the cost-of-debt for a one standard deviation increase in AM I (using the specification with
sector-and-time fixed effects to ensure that the effect size calculation reflects the standard-
ization of AM I). The magnitudes of these results are lower as compared to the results for
the cost of equity capital. This result may be due to (1) debt securities being less sensi-
tive to information and (2) the presence of non-price terms, which also respond to metering
problems. We investigate this latter possibility next.
We consider the number of financial covenants, which are known to substitute for in-
creased loan pricing, included in the loan agreement. In particular, instead of increasing
the cost of debt, lenders can impose tighter covenants giving them earlier control over the
company in case its reported performance is low. We investigate this hypothesis in Table 8.
Indeed, we find a significant positive association between AM I and the number of financial
covenants. Our estimates on the variable of interest imply that a one-standard deviation in-
crease in AM I increases the number of covenants by about 0.05, or by 4.0 percent compared
to the sample mean.
Taken together, our findings show consistent correlations between AM I, our measure of
a firm’s metering problems, and capital markets outcomes. These correlations exist both in
the equity market, in which AM I is associated with expected returns, as well as in private
debt markets, where we find that AM I is positively associated with the cost-of-debt and
with the use of accounting-based covenants. The effect sizes are economically meaningful and
consistent with the notion that metering problems are associated with financing frictions.
In what follows we study whether the effects of metering problems extends beyond capital
market frictions, having a first-order role in contracting issues within the firm as well as
productivity and resource allocation outcomes.
234. Metering rewards: compensation contracts
Motivated by Alchian and Demsetz’ (1972) characterization of the metering of rewards,
which includes such activities as measuring output performance, apportioning rewards, and
detecting and estimating the marginal productivity of employees, we turn to the question
of how AM I correlates with the use of accounting performance measures in compensation
contracts. To the extent that metering problems (indicated by high AM I scores) provide
a stumbling block to efficient contracting with employees, we expect that firms rely less on
accounting-based performance measures. In theory, this can happen if metering problems
are associated with greater noise in accounting performance measures (Banker and Datar
(1989)) or when such problems arise due to the presence of accounting manipulations (Baker
(1992)). Stock price based measures, on the other hand, should be unaffected by these
metering problems (Lambert and Larcker, 1987). We test this prediction in a standard
performance sensitivity framework (see, e.g., Morse et al. (2011)), in which we regress the
(log of) total compensation onto two summary measures of performance, namely accounting
return on assets (ROA) and stock returns (Ret). For this analysis, we add to our AMI-
Compustat sample data taken from Execucomp on the total compensation of the CEO.
We then allow the compensation sensitivity to depend on AM I and estimate the following
regression
(7) Compensationi,t = δi + δs × δt + αAM Ii,t + βzROAi,t
+ ζ(AM Ii,t × zROAi,t ) + ηzReti,t + κ(AM Ii,t × zReti,t ) + λXi,t + ǫ,
where z indicates that the variable is standardized by subtracting the mean of each two-digit
SIC-year group and dividing by the group standard deviation. Compensation is the log of
total CEO compensation and the vector X contains, as before, the log of the firm’s assets.
If metering problems reduce the “usefulness” of accounting performance measures in re-
warding executives, we expect that ζ̂ < 0. At the same time, we expect that compensation-
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