The Optional Qualitative Assessment in Impairment Tests
Dirk Black
School of Accountancy
College of Business
University of Nebraska – Lincoln
Jake Krupa
Miami Business School
University of Miami
Miguel Minutti-Meza
Miami Business School
University of Miami
August 2019
*We acknowledge helpful comments from Michael Ettredge, Kurt Gee, Amanda Gonzales, Trevor Sorensen, Kyle Welch,
staff at the Financial Accounting Standards Board (FASB), and workshop participants at the Brigham Young University
Accounting Research Symposium, the University of Miami, and the Graduate Research Accounting Conference at Emory
University. Dirk Black acknowledges the support of the College of Business at the University of Nebraska – Lincoln.
Jake Krupa and Miguel Minutti-Meza acknowledge the support of the Miami Business School at the University of Miami.
The Optional Qualitative Assessment in Impairment Tests
ABSTRACT: We examine optional qualitative assessments in impairment tests of goodwill and
indefinite-lived intangibles. These assessments are intended to reduce impairment testing complexity,
but introduce accounting optionality. We find that firms performing qualitative assessments face
lower impairment risk and higher costs of performing quantitative impairment tests. Then, using a
difference-in-differences design, we find that qualitative assessment firms have a higher incidence of
impairments vs. firms disclosing nothing about qualitative assessments, suggesting that qualitative
analysis may make it more difficult for managers to manipulate quantitative tests to avoid
impairments. We also find that qualitative assessment firms exhibit no reduction in impairment
timeliness, and find no evidence of increased monitoring costs for auditors, regulators, and investors
surrounding the accounting standard change introducing qualitative assessments. Our findings inform
standard setters about the determinants and consequences of qualitative assessments and speak to the
broader issue of the costs and benefits of optionality in accounting.
JEL Classifications: M41; M42; M48
Data Availability: Data are available from public sources identified in the text. Hand-collected data
are available upon request.
Keywords: Goodwill; intangibles; impairment; qualitative assessment; fair value; ASC 350; SFAS
142.
1
I. INTRODUCTION
Companies face complex rules when determining the carrying value and potential impairment
of intangible assets with indefinite life, including goodwill.1 Since 2001, Intangibles – Goodwill and
Other (ASC 350, previously SFAS 142) requires companies to perform an annual impairment test for
these assets, comparing their carrying values to their estimated fair values (FASB 2001).2 Recent
Accounting Standards Updates (ASUs) issued by the Financial Accounting Standards Board (FASB)
modified the existing rules to introduce a qualitative assessment as an impairment indicator, a “Step
0” intended to reduce the complexity and costs of the quantitative two-step test in ASC 350.
ASU 2011-08 gives companies the option of beginning the goodwill impairment test by
performing a qualitative assessment at the reporting-unit level (FASB 2011). This standard introduces
a rare accounting situation where an entirely “unconditional option” is allowed in U.S. GAAP (FASB
2011, BC23, 22). This qualitative assessment considers events and circumstances informative about
the likelihood of impairment, such as economic conditions, industry and market considerations, and
financial performance. However, this assessment does not require estimating the fair value of a
reporting unit using discounted cash flows or another similar approach. If, after performing this
assessment, a company concludes that it is “more likely than not that the fair value of a reporting unit
is less than its carrying amount,” the company should perform the quantitative test. (FASB 2011, 1).
ASU 2012-02 provides the same option for indefinite-lived intangibles (FASB 2012), while ASU
2017-04 eliminates Step 2 of the impairment test and continues to allow the optional qualitative
assessment (FASB 2017), highlighting the increased prominence of the qualitative assessment.
1 Existing accounting standards make a distinction between intangible assets with finite and indefinite lives (see section
II for additional details). 2 Impairments of goodwill and other intangible assets are frequent. For instance, in the fiscal years ending between
December 15th, 2009 and December 15th, 2015, 6.8% of firms with non-missing assets included in Compustat reported
goodwill or other intangible impairments. Please refer to section II for additional details on the two-step quantitative
impairment test.
2
This study investigates the effects of the optional qualitative assessment using four research
questions. Our first research question is: What are the characteristics of firms using the qualitative
assessment option?3 This question is driven by standard setters’ intentions to reduce the cost of
impairment tests. Survey evidence from Duff and Phelps (2017) indicates that in the first three
calendar years since the adoption of ASU 2011-08 and ASU 2012-02, only 29, 43 and 54 percent of
all public firms performed a qualitative assessment. These statistics contrast with 81 percent of the
respondents expecting to implement the qualitative standard in 2011 (Duff and Phelps 2011). Among
the reasons given for bypassing this assessment were: 1) Step 1 is a “more robust analysis;” and, 2)
Step 0 is “cumbersome and/or time-consuming relative to Step 1” (Duff and Phelps 2012, 35).
In our analyses, we identify a set of firms with goodwill and intangibles on their balance sheet
from fiscal years 2009 to 2015. Next, using textual analysis and manual coding, we search annual 10-
K fillings on the SEC’s EDGAR database to identify a subsample of firms that disclose performing a
qualitative assessment or implementing ASUs 2011-08 and 2012-02.4 Using this data, we compare
three groups of firms: 1) Firms specifically disclosing that they perform a qualitative assessment; 2)
Firms mentioning the option to perform a qualitative assessment under ASUs 2011-08 and 2012-02,
discussing the change in standards, or disclosing bypassing the qualitative assessment; and, 3) Firms
remaining silent about the qualitative assessment.5 In our final sample of 1,228 firms, we classify 373
as “performing” firms, 592 as “mentioning” firms, and 263 as “silent” firms. Our study is among the
first to provide descriptive evidence on firms using the qualitative assessment option.
3 Previous research has studied determinants (Francis, Hanna, and Vincent 1996; Beatty and Weber 2006; Hayn and
Hughes 2006; Brochet and Welch 2011; Gu and Lev 2011; Ramanna and Watts 2012; Glaum, Landsman, and Wyrwa
2015; Li and Sloan 2017) and consequences of goodwill impairments (Bens, Heltzer, and Segal 2011; Li, Shroff,
Venkataraman, and Zhang 2011; Darrough, Guler, and Wang 2014). Other studies have also studied determinants of long-
lived asset impairments (Francis et al. 1996; Riedl 2004) and write-downs in general (Lawrence, Sloan, and Sun 2013). 4 We utilize financial statement disclosure to identify performers of the qualitative assessment. Per ASU 2011-08 (FASB
2011, BC24, 23), the FASB intends to have firms “make a positive assertion” about the conclusions reached when
performing a qualitative assessment. Thus, firms performing the qualitative assessment are intended disclose this action. 5 Our category classification is similar to that used in the Duff and Phelps (2014) survey. Appendix C provides examples
of the disclosures we use to classify firms into these categories.
3
In answer to our first research question, we document that firms performing the qualitative
assessment have higher past stock returns, lower book-to-market ratios, lower previous incidence of
impairment losses, and higher balances of goodwill and intangibles. These results suggest that firms
exercise the qualitative assessment option when they face comparatively lower impairment risk and
higher costs of performing the traditional quantitative impairment test.
Our second research question is: Has the incidence of impairments changed after the adoption
of the qualitative assessment standards? Agency theory and existing empirical evidence support
concerns about the impairment tests under the two-step model in ASC 350 and SFAS 142 (e.g., Beatty
and Weber 2006; Hayn and Hughes 2006; Li et al. 2011; Ramanna and Watts 2012; Li and Sloan
2017). The qualitative assessment can increase subjectivity and reduce timeliness of impairment tests.
However, there exists a tradeoff between increasing agency costs and subjectivity concerns and the
practical implementation costs of impairment testing. The benefits of impairment testing may not
always justify its costs, including fees paid to business valuation experts.6 Furthermore, a thorough
qualitative analysis may make it more difficult for managers to manipulate the inputs of the two-step
quantitative test to avoid impartments (e.g., projected cash flows and discount rates) and may allow
for signaling of the firm’s type due to the qualitative assessment’s optionality.
To answer our second research question, we examine impairments during the two and six
years (respectively) surrounding the effective date of ASU 2011-08, the earliest of the qualitative
assessment standards. We implement a difference-in-difference (DD) research design comparing
companies that disclose, mention, or are silent about the qualitative assessment. Finally, we examine
cross-sectional variation in a firm’s opportunity to manipulate impairment tests.
6 According to the Duff and Phelps (2011) survey, public companies listed the high cost of third party valuation experts
as one of the biggest challenges to performing impairment tests.
4
We find that following the effective date of ASU 2011-08, firms performing the qualitative
assessment have a higher incidence of impairment recognition relative to firms silent about the
qualitative assessment. Our results are robust to employing a model of qualitative assessment and
propensity score matching to improve covariate balance between treatment and control firms
Although the qualitative assessment requires additional judgment, the implementation of this option
results in an increase (not a decrease) in the overall incidence of impairment charges. The relative
increase in impairment incidence for a firm performing a qualitative assessment vs. a firm remaining
silent about a qualitative assessment is 9.7 percentage points for the two-year analysis and 4.9
percentage points for the six-year analysis based on the propensity-matched samples.
In cross-sectional tests examining opportunities to influence impairments, we fail to find that
the higher incidence of impairments following the standard change for firms performing qualitative
assessments varies between: 1) Firms with a high vs. low number of reporting segments; 2) Firms
with high vs. low number analyst following; and, 3) Firms with high vs. low market-to-book ratios.
Our third research question is: Has the adoption of the qualitative assessment made
impairments more difficult to predict and less timely? We compare Type I and Type II errors of an
impairment model and then examine the frequency of impairments in the first three quarters of the
fiscal year vs. the fourth quarter. Using the pre-standard-change period to train our model, we find
that the incidence of incorrectly predicting an impairment (Type I error) is lower for qualitative
assessment firms vs. other firms, while the incidence of incorrectly failing to predict an impairment
(Type II error) is higher for qualitative assessment firms vs. other firms. Using a sample of firm-years
with impairment charges, we find no difference in the incidence of early impairments between firms
using the qualitative assessment vs. other firms in the post-period. We cannot conclude that qualitative
assessments improve impairment predictability, but we conclude that qualitative assessments do not
adversely affect impairment timeliness.
5
Finally, our fourth research question is: Has the adoption of the new rules resulted in
unintended consequences reflected in audit fees, SEC enforcement, and investors’ reaction to
earnings news? The adoption of the new rules may have resulted in unintended consequences for firm
monitors as reflected in audit fees, SEC enforcement, and investors’ reaction to earnings news as
auditors, regulators, and investors adjust to the additional discretion available to management with
the qualitative assessment option. We find no evidence that qualitative assessment firms are more
likely to pay higher audit fees after the standard change or receive more SEC comment letters
pertaining to goodwill or intangibles vs. firms that are silent about the qualitative assessments. We
find no evidence that investors respond differently to earnings news from qualitative assessment firms
vs. other firms, suggesting that performing a qualitative assessment does not worsen investors’
perceptions of earnings quality. These results suggest that external monitors do not view qualitative
assessment firms as having higher financial reporting risk than other firms. Moreover, the results do
not provide consistent evidence that monitoring costs have shifted to auditors, the SEC, or investors.
Collectively, our results quantify the impact of the impairment standard change and shed light
on whether firms opportunistically use the discretion allowed by the qualitative assessment option.
Our setting is useful because the qualitative assessment option allowed under ASU 2011-08 and 2012-
02 is one of the few accounting situations where an unrestricted option is allowed to managers.
Limited evidence is available in the existing literature regarding discretionary choices in conducting
impairment tests. We provide evidence that subjectivity from the qualitative assessment option does
not decrease impairment incidence. Our findings inform the debate about the complexities and costs
of impairment tests, a topic of interest to the FASB. With the added importance of “Step 0” following
the adoption of ASU 2017-04 after December 15, 2019 and the removal of “Step 2” (FASB 2017),
this paper can help standard setters, regulators, and practitioners understand how qualitative
assessments in particular, and optionality in accounting in general, affect accounting information.
6
II. BACKGROUND, LITERATURE, AND RESEARCH QUESTIONS
The Goodwill Impairment Test
Goodwill recognized on the balance sheet represents expected future economic benefits from
intangible assets that are not identifiable and cannot be separately recognized following an
acquisition. Goodwill is typically recognized as the difference between the purchase price paid by the
acquirer and the fair value of the net assets acquired (ASC 805 Business Combinations, previously
SFAS 141, revised in 2007 (FASB 2007)). A relatively high proportion of the price paid for
acquisitions is allocated to goodwill. Shalev, Zhang, and Zhang (2013) examine a sample of 320
acquisitions by U.S. companies between 2001 and 2008. In their sample, the mean proportion
allocated to goodwill and other indefinite-lived intangible assets is 59 percent of deal value.
Impairment charges can be very large – Kraft Heinz recently recognized a $744 million goodwill
impairment loss and a $474 million intangible asset impairment charge for the six months ended June
29, 2019 (Kraft Heinz 2019; Trentmann 2019).
Starting after December 2001, ASC 350 Intangibles–Goodwill and Other (previously SFAS
142, Goodwill and other intangible assets) requires a two-step goodwill impairment test (FASB
2001). 7 ASC 350-20-35-28 requires annual goodwill impairment testing and interim goodwill
impairment testing when circumstances warrant the test. The goodwill impairment decision depends
on a two-step process involving a quantitative analysis of a company’s reporting units.8
Per ASC 350-20-35, in Step 1, the company must determine whether the fair value of the
reporting unit is less than its carrying value (including goodwill). If that is the case, the company must
7 ASC 350 (previously SFAS 142) was issued at approximately the same time as ASC 805 Business Combinations
(previously SFAS 141, Business Combinations, revised in 2007), which required the measurement of goodwill based on
the purchase method (http://www.fasb.org/summary/stsum141.shtml). 8 At the time of the acquisition, goodwill must be allocated to those reporting units of the acquiring company that are
expected to benefit from the synergies of the acquisition. Thus, goodwill is tested for impairment at the reporting unit
level. The identification of reporting units is unique to each company and begins with identifying operating segments.
7
proceed to Step 2 and determine the implied fair value of goodwill of the reporting unit by assigning
the fair value of the reporting unit used in Step 1 to all assets and liabilities of that reporting unit
assuming the reporting unit had been acquired. Then, the company must compare the implied fair
value of goodwill with the carrying amount of goodwill to determine whether goodwill is impaired.
The impairment loss is equal to the carrying value minus the implied fair value of goodwill, is
recognized in net income, and cannot be reversed in future periods.
The FASB released ASU 2011-08 Testing Goodwill for Impairment, applicable after
December 2011, introducing the option to perform a qualitative assessment of a reporting unit before
performing Step 1 of the goodwill impairment test (FASB 2011). The qualitative assessment involves
considering whether circumstances suggest that a goodwill impairment charge may need to be
recorded. ASU 2011-08 notes the following events and circumstances that may indicate the necessity
of an impairment charge: 1) Macroeconomic conditions; 2) Industry and market considerations; 3)
Cost factors; 4) Overall financial performance; 5) Other relevant entity-specific events; 6) Events
affecting a specific reporting unit; and, 7) A sustained decrease, both absolute and relative to a
company’s peers, in share price (FASB 2011).
A company must proceed with Step 1 of the quantitative goodwill impairment test only if the
qualitative assessment indicated more than a 50 percent chance that the carrying value of a reporting
unit is greater than its fair value (FASB 2011). An entity may decide to implement the qualitative
assessment or bypass it in any given period for any reporting unit (ASC 350-20-35-3B). Appendix B
shows a recommended flowchart for goodwill impairment decisions and the full list of suggested
events and circumstances that a firm should consider as part of the qualitative assessment taken from
ASU 2011-08 (FASB 2011).
Recently, the FASB released ASU 2017-04 Simplifying the Test for Goodwill Impairment,
applicable after December 2019 (FASB 2017). Per the changes in this ASU, a firm compares the
8
carrying amount of a reporting unit with its fair value (i.e., the previous Step 1 of the goodwill
impairment test). The goodwill impairment charge is equal to the excess carrying value over the fair
value of the reporting unit, up to the amount of goodwill for the reporting unit. Although ASU 2017-
04 eliminates the previous Step 2, firms are still allowed to use the optional qualitative assessment.
The Indefinite-lived Intangibles Impairment Test
The accounting standards make an important distinction between intangible assets with finite
and indefinite lives. Finite-lived intangible assets have a defined useful life. In contrast, indefinite-
lived intangible assets do not have a defined useful life. Finite-lived assets are amortized over their
useful life and subject to an impairment test similar to the one applied to other long-term assets with
finite lives, according to the rules in ASC 350 and 360. The FASB proposed the update ASU 2012-
02 Testing Indefinite-Lived Intangible Assets for Impairment, applicable after September 2012, giving
preparers the same qualitative assessment option for indefinite-lived intangible assets that ASU 2011-
08 gives for goodwill impairment testing (FASB 2012).
Research Questions
What Are the Characteristics of Firms Using the Qualitative Assessment Option?
Our first research question addresses the cost-benefit tradeoff in voluntarily implementing a
qualitative assessment: What are the characteristics of firms using the qualitative assessment option?
It is an open empirical issue as to why less than half of the firms opted to perform this assessment in
the first two years after the adoption of the new rules, despite the potential benefits of this approach
(Duff and Phelps 2017).
Per a recent practitioner article by Deloitte, due to the costs and complexity of performing
Step 1 of the impairment test, which involves a valuation exercise for each reporting unit, many firms
and auditors employ valuation experts to assist them. Several complexities arise during this analysis,
including “assignment of assets/liabilities to reporting units; supportability of forecasts from a market
9
participant perspective; discount rates and terminal value assumptions; choices of valuation multiples;
and environmental awareness with respect to financial reporting fair value estimates” (Deloitte 2011,
2). However, the Deloitte article also notes “developing appropriately detailed documentation of the
qualitative factors to support an assertion that goodwill is not impaired” could be a challenge (Deloitte
2011, 5). The article also implies that firms with poor performance might be less likely to use the
qualitative assessment option (Deloitte 2011). The Duff and Phelps (2012, 35) survey also identifies
key implementation challenges that may motivate firms to bypass the qualitative assessment,
including: 1) Step 1 is perceived as a “more robust analysis;” 2) Step 0 is more “cumbersome and/or
time-consuming relative to Step 1”; and, 3) There is “uncertainty about auditor requirements” for Step
0. Overall, we expect that small firms, high-performing firms, and firms with fewer indefinite-lived
intangibles will adopt the qualitative assessment due to the complexity of impairment tests.
Has the Incidence of Impairments Changed After the Adoption of the Qualitative Assessment
Standards?
Our second research question addresses the link between the incidence of impairments and
the adoption of the qualitative assessment rules: Has the incidence of impairments changed after the
adoption of the qualitative assessment standards? This question follows a relatively extensive prior
literature that examined the adoption of the goodwill impairment rules following SFAS 142.9 Agency
theory and existing empirical evidence support concerns about the impairment tests under the
previous two-step model in ASC 350 and SFAS 142 (e.g., Beatty and Weber 2006; Hayn and Hughes
2006; Li et al. 2011; Ramanna and Watts 2012; Li and Sloan 2017).
The qualitative assessment can increase the subjectivity and lack of timeliness of impairment
tests. In the discussion of possible costs of this qualitative option, ASU 2011-08 (FASB 2011, BC34,
9 For a detailed review of the literature on goodwill impairments, including U.S. and international standards, purchase
price allocations, determinants and consequences of goodwill impairments, and other issues, see Boennen and Glaum
(2014). In addition, see Glaum et al. (2015) for determinants of goodwill impairments under IFRS for non-U.S. firms.
10
26) explicitly mentions that: “The Board acknowledged that the amendments in this Update may
result in entities applying more judgment about when and how to perform this evaluation.” Thus, the
revised standards constitute a shock to the level of judgment required to perform impairment tests
and provide an interesting setting to examine impairment decisions. Managers have latitude in
deciding which events and circumstances should be considered, as well as in measuring and
aggregating these factors to determine whether it is “more likely than not” that the fair value of a
reporting unit is less than its carrying amount. Per ASU 2011-08 (FASB 2011, BC35, 26), “The Board
concluded that the qualitative assessment described in this Update will allow an entity to exercise
more judgment to reduce the recurring costs of calculating the fair value of a reporting unit.”
Moreover, ASUs 2011-08 and 2012-02 represent interesting challenges for firms and auditors
(Deloitte 2011). “Without a quantitative analysis of market data to justify a position, it is possible that
management could be more subjective in its interpretation of market factors to reduce the chances for
impairment, thus introducing more risk into the process of evaluating goodwill” (Deloitte 2011, 5).
The additional subjectively potentially arising from ASUs 2011-08 and 2012-02 may be exacerbated
in situations where greater opportunity is available to manipulate impairment testing, such as for firms
with many segments, low analyst following, and high market-to-book ratios.
However, implementing impairment tests annually (and more often if necessary) is a costly
endeavor. Private companies expressed concerns to the FASB over the “cost and complexity of the
first step of the two-step goodwill impairment test” (FASB 2011, 1), and public companies listed the
high cost of third party valuation experts as one of the biggest challenges to performing impairment
tests (Duff and Phelps 2011). Furthermore, the FASB’s conceptual framework states that, “Reporting
financial information imposes costs, and it is important that these costs are justified by the benefits of
reporting that information” (FASB 2010, QC35, 21). Moreover, a more principles-based qualitative
analysis may make it more difficult for managers to manipulate the inputs of the two-step test to avoid
11
impartments (e.g., projected cash flows and discount rates) and allow for better signaling of the firm’s
type due to the optionality of the qualitative assessment.
Has the Adoption of the Qualitative Assessment Made Impairments More Difficult to Predict and
Less Timely?
Our third research question, related to the incidence of impairment is: Has the adoption of the
qualitative assessment made impairments more difficult to predict and less timely? If judgment is
used to manipulate the timing of impairments, we would expect impairments to be more difficult to
predict using a statistical model based on observable firm characteristics, increasing the model’s Type
I and Type II errors. This expectation stems from a departure from past impairment test practice and
the introduction of optionality into the evaluation of goodwill and intangible assets for impairment.
Moreover, this increased subjectivity could lead to diversity of accounting practice, with may result
in less predictable impairment losses following the adoption of the standards.
Previous research argues that some managers have exploited the discretion afforded by SFAS
142 to delay goodwill impairments (Li and Sloan 2017). In addition, since the qualitative assessment
option allows management more opportunity to justify avoiding the quantitative impairment test
procedures or manipulate the timing of impairment losses, impairment tests may be performed less
frequently and/or thoroughly. If the qualitative assessment option introduces greater leeway for
delayed quantitative impairment testing and recognition, we expect more impairment losses to be
delayed to the fourth quarter after the adoption of the qualitative assessment standards.
Has the adoption of the new rules resulted in unintended consequences reflected in audit fees, SEC
enforcement, and investors’ reaction to earnings news?
With our fourth and final research question, we aim to determine whether the adoption of the
qualitative assessment standards had indirect consequences, shifting monitoring costs to auditors or
the SEC, or making earnings news less transparent to investors for firms using the qualitative
12
assessment option. Ayres, Neal, Reid, and Shipman (2018) argue that impairment tests are a difficult
task for auditors, given potential misalignment in incentives between managers and auditors. They
also document that the decision to record a goodwill impairment is associated with an increase in the
probability of auditor dismissal. The Public Company Accounting Oversight Board (PCAOB)
inspection reports reveal that goodwill impairment tests are a common audit deficiency (Hanson
2012). Moreover, a recent report by Ernst and Young (EY) examining trends in SEC comment letters
indicates that goodwill and intangibles were the sixth most common areas targeted by comment letters
in 2017 and 2018 (EY 2018, 6). Additionally, GE is facing two federal investigations of its accounting
practices – one by the SEC and one by the Justice Department – after taking a $22 billion goodwill
impairment charge in 2018 related to the 2015 acquisition of Alstrom SA (Shumsky 2018).
A potential unintended consequence of the qualitative assessment option is that auditors, SEC,
and investors will compensate for the added discretion available to managers in impairment testing
by performing more audit work and charging higher audit fees, increasing regulatory monitoring
effort and issuing more comment letters related to goodwill and intangible assets, or discounting
earnings news due to greater uncertainty related to impairment testing and loss recognition.
III. SAMPLE SELECTION AND DESCRIPTIVE STATISTICS
Sample Selection
Our sample is the intersection of the Compustat North America Annual file, CRSP, and the
panel of observations resulting from our text scraping of public companies’ 10-K filings. We begin
our sample construction with all annual observations from the Compustat North America Annual file
with non-missing, positive values of sales (SALE) and total assets (AT) with fiscal years ending
between December 15, 2009 and December 15, 2015. This yields an initial sample of 46,393 firm-
year observations, comprised of 10,671 unique firms. We then limit our sample to those firms with
13
goodwill or intangible balances (or write downs of goodwill, goodwill and other intangibles when
combined, or write downs of indefinite-lived intangibles) greater than or equal to 1 percent of total
assets, resulting in 27,306 firm-year observations. Next, we require a valid link to CRSP to calculate
the required return variables, which yields 18,542 firm-year observations. We then merge this dataset
with the panel data of our hand-collected qualitative assessment variables described in the section
below. As a result of this merge, we are left with 18,145 firm-year observations for 4,413 unique
firms. Next, we require our sample to have non-missing values of one-year lagged total assets, and
we remove firms in the Utilities industry (FF17 = 14) or firms missing an industry classification.10
This gives us a sample of 17,427 firm-year observations from 4,245 firms, which serve as the starting
point for each sample in our analyses.
We further restrict our sample to only those observations with non-missing values of our
control variables. Finally, we require firms to have three observations in both the pre- and post-
adoption periods of our sample. These restrictions result in a total of 7,368 firm-year observations for
1,228 unique firms. Table 1 details each step in our sample selection process.
Identifying Companies Adopting the Qualitative Assessment Option
Our panel of hand-collected data begins with the collection of all annual 10-K fillings on
SEC’s EDGAR database from 2010 to 2014 in an attempt to capture the first time a firm mentions
the adoption of the applicable standards (ASU 2011-08 for goodwill or ASU 2012-02 for intangibles).
Then, using regular expressions, we extract all paragraphs that mention key words (and derivations
of those words) such as “goodwill,” “intangible,” or “indefinite-lived” in conjunction with the
mention of words like “qualitative,” “step zero,” or specific mentions of the applicable standards
10 We thank Ken French for providing the Fama-French 17 industry definitions on his website at
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_17_ind_port.html.
14
(ASU 2011-08, ASU 2012-02, or Topic 350). As a result of this scraping, we identified 6,997 firm-
year disclosures meeting our search requirements from 3,046 unique firms.
Each of the extracted 10-K sections were then read by one of two people, a research assistant
and one of the co-authors, to determine how to classify the firm’s disclosure. Of the 3,046 unique
firms, we identified 76 firms that switched between performing and bypassing the qualitative
assessment. For a clean identification, we removed these 76 firms and their corresponding firm-year
observations from our sample.11 Next, we merge our hand-collected sample to our WRDS-based
sample using CIK and matching fiscal-year-end dates with DATADATE in Compustat. Based on our
search, we assign firms into one of three categories: 1) Firms specifically disclosing that they perform
a qualitative assessment; 2) Firms mentioning the option to perform a qualitative assessment under
ASUs 2011-08 and 2012-02, discussing the change in standards, or disclosing bypassing the
qualitative assessment; and, 3) Firms silent about the qualitative assessment. This approach is
consistent with the FASB’s basis for conclusions in ASU 2011-08, in that “the Board intends for an
entity to make a positive assertion about its conclusion reached and the events and circumstances
taken into consideration if it determines that the fair value of a reporting unit is not more likely than
not less than its carrying value” (FASB 2011, BC24, 23; Duff and Phelps 2014). Duff and Phelps
(2014, 4) assert, “Companies making such a positive assertion are unambiguously Step 0 Users.”
Appendix C provides examples of the disclosures that we used to classify firms into these categories.
Descriptive Statistics
Table 2, Panel A reports the descriptive statistics for the sub-samples of companies: 1)
Performing the qualitative assessment (N. Obs. = 2,238; N. Firms = 373); 2) Mentioning the standard
(N. Obs. = 3,552; N. Firms = 592); and, 3) Silent about the qualitative assessment (N. Obs. = 1,578;
11 Of these 76 firms, six are eliminated in sample screens leading up to and including merging with CRSP, and the
remaining 70 firms are eliminated after merging our hand-collected data with the Compustat/CRSP merged sample.
15
N. Firms = 263), including data from three years before and three years after the adoption of ASU
2011-08.12 The average incidences of goodwill and indefinite-lived intangible impairments or write-
downs (GW_WDi,t) are 11.3, 11.8, and 11.3 percent in the three sub-samples. The companies in the
three partitions have substantial balances of goodwill and other intangibles, ranging between 19.4 and
25.9 percent of total assets (Perc_GWi,t-1). The companies in the three partitions have total assets
ranging between 875 and 1,122 million (SIZEi,t-1). In terms of financial performance, firms
performing the qualitative assessment have the highest stock returns and the lowest book-to-market
ratios (RETi,t and BTMi,t-1).13
Table 2, Panel B, provides additional detail, partitioning the sub-sample of companies
performing the qualitative assessment (N. Obs. = 2,238) into the pre- (POSTt = 0) and post- (POSTt
= 1) adoption year. We note that that the incidence of impairments for these firms did not change
significantly surrounding ASU 2011-08 (GW_WDi,t).
To examine differences in observable variables across treatment (i.e. Performing) and control
(Mentioning and Silent) firms in the period preceding ASU 2011-08, we compare means of control
variables used in our regression analyses in Table 2, Panel C. We fail find to find that firms performing
a qualitative assessment recorded more or fewer impairment losses than firms mentioning the
qualitative assessment option.
Because some of the control variables differ between the treatment and control groups, we use
propensity score matching to improve covariate balance. Table 2, Panel D presents differences in
12 We focus our subsequent tests on years surrounding ASU 2011-08 since it is the earliest of the qualitative assessment
option standards. 13 In subsequent tables, we present sample selection information and descriptive statistics when our sample varies from
Table 2, Panel A in a particular test. For example, Table 7, Panel B presents sample selection information and descriptive
statistics for the subsample of firm-years with impairment losses.
16
means across treatment and control observations following the propensity score matching procedure,
none of which are significant.14 In subsequent tests, we control for these observable characteristics.
Table 2, Panel E provides the incidence of impairments before and after the adoption of ASU
2011-08 (POSTt = 0/1) separately for firms performing the qualitative assessment versus all other
firms (QUALi = 1/0). Within each pre/post comparison, firms are grouped into book-to-market ratio
quintiles, with quintile five containing firms with the highest book-to-market ratios. This table gives
a general sense for differences in the incidence of impairment losses for various sample partitions,
and whether that incidence changed surrounding the adoption of the new impairment standards. While
the incidence of impairments generally increases with the book-to-market ratio as expected, we find
no evidence in any comparison that the incidence of impairments significantly decreased after the
qualitative assessment option was allowed. These descriptive statistics suggest that the additional
discretion in impairment testing available after the adoption of ASU 2011-08 does not result in
significantly fewer impairment charges.
IV. RESEARCH DESIGN AND RESULTS
Characteristics of Firms Using the Qualitative Assessment Option
We examine the characteristics of firms performing a qualitative assessment, our first research
question. We estimate three determinant models, comparing firms performing the qualitative
assessment versus: 1) Firms mentioning the qualitative assessment option or silent about the
qualitative assessment; 2) Firms mentioning the qualitative assessment option; and, 3) Firms silent
about the qualitative assessment. We use only the first fiscal year following the effective date of ASU
2011-08 and estimate equation 1 using logistic regression:
14 Table 3, Panel B presents the propensity-score matching model used for the three-year pre-ASU-2011-08 period.
17
P(QUALi) = f(Perc_GWi,t-1, GW_WDi,t-1, Controlsi,t, Fixed Effectsi,t, ei,t) (1)
where QUALi is an indicator variable equal to one if firm i discloses performing a qualitative
assessment at any time in our search period of 2010 to 2014, and zero otherwise; Perc_GWi,t-1 is the
ratio of the goodwill and other intangibles balance (Compustat variables GDWL and INTANO,
respectively) to total assets for firm i in year t-1; GW_WDi,t-1 is an indicator variable equal to one if
firm i takes a write down in year t-1 (i.e., Compustat GDWLIP is less than zero), and zero otherwise.
The Controls vector includes established indicators of goodwill and intangible impairments,
plus other firm characteristics (Francis et al. 1996; Beatty and Weber 2006; Hayn and Hughes 2006;
Brochet and Welch 2011; Gu and Lev 2011; Ramanna and Watts 2012; Glaum et al. 2015; Li and
Sloan 2017). SIZEi,t-1 is the natural logarithm of total assets for firm i in year t-1; RETi,t is the annual
stock return for firm i minus the value-weighted cumulative market return during year t; RET2i,t-1 is
the two-year stock return for firm i minus the value-weighted cumulative market return from the
beginning of year t-2 to the end of year t-1; BTMi,t-1 is the ratio of the book value of equity to the
market value of equity for firm i in year t-1; BTM_INDi,t-1 is an indicator variable equal to one if the
book-to-market (BTM) ratio is greater than one for firm i in year t-1, zero otherwise; GROWTHi,t-1 is
the change in sales for firm i from year t-2 to year t-1, scaled by sales in year t-1; NASDAQAMi,t-1 is
an indicator variable equal to one if firm i’s shares are traded on the NASDAQ or AMEX exchanges
(EXCHG = 11 or 12) in year t-1, and zero otherwise; SEGSi,t-1 is the natural logarithm of the count of
business segment IDs (SID) for firm i in year t-1; HERFINDXi,t-1 is the Fama-French-17-industry-
year sum of squared sales shares for firm i in year t-1 (Sum(Share2)), where Share is lagged firm sales
divided by lagged total industry-year sales; and, INDROAi,t-1 is the average change in firm i's Fama-
French-17 industry median return-on-assets ratio over years t-5 to t-1. Fixed Effectsi,t are indicator
variables for the Fama-French 17 industries. Detailed variable definitions are in Appendix A.
18
Table 3, Panel A presents the results of estimating equation 1. Column 1 presents results
comparing firms performing the qualitative assessment vs. firms only mentioning the new standards
and firms silent about the new standards. Column 2 presents results comparing firms performing the
qualitative assessment vs. firms only mentioning the standard. Column 3 presents results comparing
firms performing the qualitative assessment vs. firms silent about the new standards. In two of three
columns, we find a positive association between past firm performance (RET2i,t-1) and the likelihood
of performing the qualitative assessment. We also find a negative association between book-to-market
ratios (BTMi,t-1) and the likelihood of performing the qualitative assessment in two of three columns.
Moreover, we find a modestly significant negative association between past recognition of
impairment losses and performing a qualitative assessment (GW_WDi,t-1) in column 3, suggesting that
firms with a low past propensity of recording goodwill impairment are more likely to exercise the
qualitative assessment option. We also find a higher likelihood of performing a qualitative assessment
when firms have more intangible assets as a percentage of total assets in columns 1 and 3 and more
segments in column 3 (Perc_GWi,t-1 and SEGSi,t-1), consistent with firms using this option when
relatively more assets and business units must be tested for impairment and more costs must be
expended to do so.
We find a negative association between sales growth and the use of the qualitative assessment
in columns 1 and 2 (GROWTHi,t-1, suggesting that slow-growing firms are more likely to perform a
qualitative assessment. We also find a negative association between firm size and the use of the
qualitative assessment in column 3 when we examine qualitative assessment firms vs. silent firms,
suggesting that smaller firms are more likely to employ the qualitative assessment option.
In Table 3, Panel B, we present estimations of equation 1 similar to Table 3, Panel A, but use
averages of the independent variables over the three-year pre-ASU-2011-08 period. We again find
some evidence of a higher likelihood of performing a qualitative assessment when firms have more
19
intangible assets as a percentage of total assets in column 2 (Perc_GWi,t-1), and some evidence that
firms currently performing well (RETi,t), but not previously performing well (RET2i,t-1), are more
likely to perform a qualitative assessment. Overall, Table 3 suggests that firms may use the qualitative
assessment option when they face comparatively lower impairment risk and higher costs of
performing the traditional quantitative impairment test.
Incidence of Impairments Pre- and Post-Adoption of the Qualitative Assessment Option
Pre/Post Analysis
We compare the incidence of impairments for the full sample and for firms performing the
initial assessment three years before and after the adoption of ASU 2011-08. We choose the adoption
date of ASU 2011-08 for our “post” measure since it preceded ASU 2012-02. We estimate the
following linear probability model of the likelihood of goodwill and indefinite-lived intangibles
impairments, controlling for several determinants of impairments suggested by prior studies:15
P(GW_WDi,t) = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (2)
All variables are as defined above with the addition of the POSTt indicator variable, which is equal
to one for the first fiscal year in which ASU 2011-08 became effective for all firms (i.e., the first
fiscal year beginning after December 15, 2011), and zero for the fiscal year immediately prior.
In Table 4, Panel A, columns 1 and 2, we estimate equation 1, controlling for determinants of
impairments suggested by prior studies. The main independent variable (POSTt) captures the average
difference in impairment less recognition surrounding ASU 2011-08. We do not find a statistically
significant coefficient for the variable POSTt for the full sample (column 1) or for firms performing
the qualitative assessment (column 2).
15 We use a linear probability model due to the interaction in the difference-in-differences model described below.
20
Difference-in-differences Analysis
We complement our pre/post analysis by implementing a difference-in-differences (DD)
research design, comparing outcomes between the categories in the six years surrounding the adoption
date of ASU 2011-08. This research design mitigates the effect of time-related trends associated with
the adoption of the new rules. We use the following linear probability model:
P(GW_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (3)
In Table 4, Panel A, columns 3-5, we present our DD analysis including three years before
and three years after the effective date of ASU 2011-08. The main independent variable is the
interaction POSTt * QUALi between the indicator POSTt, which captures the incremental effect of the
adoption of the qualitative assessment standards, and the indicator QUALi, equal to one for firms
performing a qualitative assessment at any time in our search period of 2010 to 2014 and zero for
firms only mentioning the standard and firms silent about the standard (column 3), zero for firms only
mentioned the standard (column 4), and zero for firms silent about the qualitative assessment (column
5). We find that in the post-adoption period, firms performing the qualitative assessment have an
incrementally higher likelihood of impairment loss recognition compared to firms that are silent about
the qualitative assessment (column 5).
In Table 4, Panel B, we estimate equation 3 using our propensity matched samples on the six
years (columns 1 and 2) and two years (columns 3 and 4) surrounding ASU 2011-08. Similar to Table
4, Panel A, we find that in the post-adoption period, firms performing a qualitative assessment have
a higher likelihood of impairment loss recognition compared to firms silent about the qualitative
assessment (columns 2 and 4).
21
Cross-sectional Analysis
To complement our DD analysis, we next examine whether firm managers with stronger
opportunities to avoid impairment losses use the additional discretion provided by qualitative
assessments. Specifically, we perform our DD tests allowing the coefficient on POSTt * QUALi to
vary with whether: 1) The firm has many reporting units/segments; 2) The firm has low analyst
following/external monitoring; and, 3) The firm has a high market-to-book ratio. We expect firms
with more opportunity (more reporting units), weaker external monitoring (lower analyst following),
and more valuation cushion (high market-to-book ratio) to be more likely to use the qualitative
assessment option to avoid impairment charges if managers take advantage of the greater subjectivity
allowed with the qualitative assessment option in impairment testing.
For this analysis, we stratify our sample based upon our three cross-sections of interest, the
number of reporting segments, the number of analysts issuing an EPS forecast for the firm’s fiscal
year, and the quartile ranking of the firm based on the market-to-book ratio. This stratification is
performed on the first year of adoption of the qualitative assessment (POSTt = 1) for the 1,228 firms
in our six-year pre- and post- sample, following the sample selection in Table 1. The result of the
stratification for reporting segments and analyst following is displayed in Table 5, Panel A. For the
market-to-book ratio, we sort firms into quartiles based on the value of this ratio. Since the number
of segments and analyst following are count variables, an ideal cutoff for top and bottom percentiles
cannot be formed. As such, for the reporting segments subsample, we compare firms with only one
reporting segment to firms with four or more reporting segments. For the analyst following
subsample, we compare firms with no analyst following to firms with five or more analysts following
the firm. For the market-to-book ratio subsample, we compare firms in the top quartile of the market-
to-book ratio distribution to firms in the lowest quartile of the market-to-book ratio distribution.
22
Three indicator variables are generated to identify these firms. Firms with four or more
reporting segments are assigned a value of one for High_SEGSi, and firms with one reporting unit are
assigned a value of zero for that variable. Firms with five or more analysts following are assigned a
one for High_FOLLOWi, and firms with no analyst following are assigned a zero. Firms in the top
quartile of the market-to-book ratio distribution are assigned a one for High_CUSHIONi, and firms
in the bottom quartile of the market-to-book ratio distribution are assigned a zero. With the firms
identified for each of the three subsamples, we then include six years surrounding ASU 2011-08 for
the regression estimates of equations 4-5. Highi represents High_SEGSi, High_FOLLOWi, or
High_CUSHIONi.
P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, Controlsi,t, Fixed Effectsi,t, ei,t) (4)
P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, QUALi, POSTt*QUALi, Highi*QUALi,t,
POSTt*Highi*QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (5)
Table 5, Panel B presents the results of the regression analysis for the reporting segments
subsample. Columns 1 and 2 present estimations of equation 4, while columns 3 and 4 present
estimations of equation 5. The results from the DD design in columns 3 and 4 show that the interaction
term POSTt * High_SEGSi * QUALi is not statistically significant. As such, we do not find that firms
with a high number of segments opportunistically use the qualitative assessment in a manner different
than firms with a low number of segments. Table 5, Panel C presents the results of the regression
analysis for the analyst following subsample. This results from the DD design in columns 3 and 4
show an insignificant negative coefficient for the POSTt * High_FOLLOWi * QUALi. As such, we
conclude that firms with low analyst following performing a qualitative assessment take no more
advantage of the additional discretion offered by ASUs 2011-08 and 2012-02 than firms with high
analyst following. Table 5, Panel D presents the results of the regression analysis for the market-to-
book ratio subsample. This results from the DD design in columns 3 and 4 show an insignificant
negative coefficient for the POSTt * High_CUSHIONi * QUALi. We conclude that managers of firms
23
with greater valuation cushion, as represented by high market-to-book ratios, take no more advantage
of the subjectivity available with the qualitative assessment in impairment testing than other firms.
The combined results from Tables 4 and 5 suggest that the judgment implied by the optional
qualitative assessment does not materially decrease the incidence of impairments among firms that
disclose performing an assessment. If anything, there is an increased incidence of impairment loss
recognition for firms exercising the qualitative assessment option relative to firms silent about the
qualitative assessment option, consistent with a thorough qualitative analysis making it more difficult
for managers to manipulate the inputs of the two-step quantitative test to avoid impairment losses.
The Predictability and Timeliness of Impairment Charges
We next examine whether the predictability of impairments changed with the adoption of the
revised impairment accounting standards. We use the largest possible with data available for our
control variables (N. Obs. = 12,212 from 2,811 firms). We estimate equation 2 (excluding the POSTt
variable), separately for the pre- and post-periods using logistic regression and present the results in
Table 6, Panel B. The results suggest a very modest decline in the predictability of impairment charges
in the post-period, as represented by lower Pseudo R2 and less area under the receiver operating
characteristic curve (AUC) in column 2 vs. column 1.
In Table 6, Panel C, we present results examining Type I error (incorrectly predicting an
impairment loss when a loss does not exist) versus Type II error (incorrectly failing to predict an
impairment loss when a loss exists). Using the pre-period as a hold-out sample to train our impairment
prediction model (equation 2, excluding the POSTt variable), we assign firm-year observations in the
highest 11 percent of estimated probabilities in each year of the post-period a value of one for
predicted impairment, and zero otherwise. We then perform two-sample tests of proportions for Type
I and Type II error comparing firms that perform a qualitative assessment with those that do not. We
find that the incidence of incorrectly predicting an impairment when an impairment is not recognized
24
(Type I error) is lower in the post-standard-change period for firms using the qualitative assessment
vs. other firms, while the incidence of incorrectly failing to predict an impairment when an
impairment is recognized (Type II error) is higher for firms using the qualitative assessment vs. other
firms. Given that Type I error is lower, while Type II error is higher, for firms using the qualitative
assessment after ASU 2011-08 adoption, we cannot make a definitive statement on the effect of the
accounting standard change on impairment predictability.
To examine impairment timeliness, we examine whether qualitative assessment firms are
more likely to recognize impairment charges in Q1, Q2, or Q3 than other sample firms, thus providing
evidence on whether the qualitative assessment aids or hinders early identification of impairment
losses. To do so, we estimate equation 6 using a linear probability model:
P(EARLY_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (6)
where EARLY_WDi,t is an indicator variable equal to one if an impairment charge is recorded in Q1,
Q2, or Q3, and equal to zero if an impairment charge is recorded only in Q4. In this test, we use only
firm-year observations with an impairment charge (N. Obs. = 1,416 from 897 firms). We present the
results in Table 7, Panel D. We find no difference in the incidence of early (non-fourth quarter)
impairment loss recognition between qualitative assessment firms vs. other firms (POSTt * QUALi).
While we cannot conclude that qualitative assessments improve impairment predictability, we
conclude that qualitative assessments do not appear to adversely affect impairment timeliness.
Unintended Consequences for Audit Fees, SEC Enforcement, and Investors
Audit Fees
Auditing accounting estimates is one of the largest risks faced by auditors (PCAOB 2017;
Chen, Keung, and Lin 2019). When auditing accounting estimates, auditors usually assess the
reasonableness of management’s quantitative judgments of fair value. However, with the adoption of
25
ASUs 2011-08 and 2012-02, managers can now solely rely on qualitative assessments to determine
whether an impairment exists or whether additional impairment testing is necessary. The qualitative
assessment option arguably allows more room for management bias in examining assets for possible
impairment. Auditing management's determination that an impairment does not exist based purely on
a qualitative assessment may pose more risk for auditors, since numbers (e.g., inputs to or outputs
from discounted cash flow models) that can be verified may no longer be present. If this is the case,
we expect an increase in audit effort and audit fees in response to increased audit risk for audits of
clients that perform a qualitative assessment. Conversely, if a qualitative assessment reduces the
complexity of the impairment assessment, then auditors may expend less audit effort on clients
performing a qualitative assessment. We examine whether there is a change in audit fees surrounding
the adoption of ASU 2011-08 and employ a research design similar to our main analyses as
represented by equations 7 and 8. The dependent variable is AFEEi,t, defined as the natural logarithm
of audit fees.
AFEEi,t = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (7)
AFEEi,t = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (8)
Table 9, Panel C presents the results using observations from the six years surrounding the
effective date of ASU 2011-08. New controls introduced in Table 8, Panel C include Big-4 Auditor
(BIGNi,t) and firm-specific accounting performance (ROAi,t). In our DD design in columns 3 and 4,
we find modest evidence that qualitative assessment firms experience no difference in audit fees vs.
other firms (POSTt * QUALi).
SEC Comment Letters
We examine whether firms that perform qualitative assessments face more regulatory scrutiny
in the form of SEC comment letters specifically related to goodwill or intangible assets. Increased
regulatory scrutiny may be a potential unintended consequence of performing a qualitative
26
assessment. In order to assess this possibility, we employ a research design similar to our main
analyses as represented by equations 9 and 10. The dependent variable is SEC_COMMi,t, an indicator
variable equal to one if the firm receives a comment letter related to goodwill or intangibles for fiscal
year t, zero otherwise.
P(SEC_COMMi,t) = f(POSTt, GW_WDi,t, Controlsi,t, Fixed Effectsi,t, ei,t) (9)
P(SEC_COMMi,t) = f(POSTt, QUALi, POSTt * QUALi, GW_WDi,t, Controlsi,t, Fixed Effectsi,t, ei,t)
(10)
Table 9, Panel C presents the results using observations from the six years surrounding the
effective date of ASU 2011-08. New controls introduced in Table 9, Panel C include an indicator for
merger and acquisition activity (MERGEi,t) and a loss indicator variable (LOSSi,t). In our DD design,
we find that qualitative assessment firms have a lower incidence of receiving a comment letter
compared to other firms (POSTt * QUALi).
Investor Reaction to Earnings News
Following Li et al. (2011), we explore whether investors react differently to earnings news
from qualitative assessment firms vs. other firms. If performing a qualitative assessment results in
less informative earnings, the investor reaction to earnings news is expected to be muted for
“performers” vs. other firms. We employ a DD research design similar to our main analyses as
represented by equations 11 and 12.
EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, LOSSi,t, Fixed Effectsi,t, ei,t) (11)
EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, QUALi, POSTt * QUALi, EarnSurpi,t
* QUALi,t, POSTt * EarnSurpi,t * QUALi, LOSSi,t, Fixed Effectsi,t, ei,t) (12)
The dependent variable is EarnCARi,t, defined as the three-day cumulative abnormal returns
from trading days -1 to +1 surrounding the earnings announcement date. We also construct EarnSurpi,t
defined as the difference between reported earnings per share and the last analyst forecast issued five
or more days before the earnings announcement, scaled by stock price. Table 10, Panel C displays
27
the results of the regression analysis. In our DD design in columns 3-4, we find no evidence that
investors respond differently to earnings news for qualitative assessment firms vs. other firms in the
post-adoption period. Collectively, the results from Tables 8, 9, and 10 suggest that external monitors
do not view qualitative assessment firms as having significantly higher financial reporting risk arising
from impairment testing than other firms.
VI. CONCLUSION
This study investigates the determinants and consequences of qualitative assessments in
annual impairment tests of goodwill and indefinite-lived intangibles allowed under ASUs 2011-08
and 2012-02. The qualitative assessment constitutes a preliminary test, often referred to as “Step 0,”
aimed at reducing the complexity and costs of a quantitative impairment test.
We identify firms with goodwill and intangibles on their balance sheets from 2009 and 2015.
Based on our search, we assign firms into one of three categories: 1) Firms specifically disclosing
that they perform a qualitative assessment; 2) Firms mentioning the option to perform a qualitative
assessment under ASUs 2011-08 and 2012-02, discussing the change in standards, or disclosing
bypassing the qualitative assessment; and, 3) Firms remaining silent about the qualitative assessment.
Our results suggest that firms use the qualitative assessment option when they face
comparatively lower impairment risk and higher costs of performing the quantitative impairment test.
We examine whether greater management discretion in impairment tests results in fewer recognized
impairment charges, as managers often have incentive to defer loss recognition. Perhaps surprisingly,
using a difference-in-differences analysis, we find that firms performing a qualitative assessment have
an incrementally higher likelihood of goodwill impairments compared to firms silent about the
qualitative assessment in the post-adoption period. This result does not vary with greater opportunity
to subjectively manipulate impairments tests, as represented by a high number of segments, low
28
analyst following, or a high market-to-book ratio. We also find that the availability of the qualitative
assessment option did not decrease the propensity of firms to record early impairment charges (i.e.,
in Q1-Q3 instead of Q4), suggesting that the timeliness of impairments was not harmed by the
introduction of this accounting option. Moreover, we find no evidence of increased monitoring costs
for auditors, regulators, and investors surrounding the accounting standard changes.
Our findings must be viewed within two limitations of an empirical study that relies on
publicly available data. First, we use firms’ disclosures to identify adopters of the qualitative
assessment option, though this concern is somewhat mitigated if: 1) Firms’ disclosures follow the
FASB’s statement that “the Board intends for an entity to make a positive assertion about its
conclusion reached and the events and circumstances taken into consideration if it determines that the
fair value of a reporting unit is not more likely than not less than its carrying value” (FASB 2011,
BC24, 23; Duff and Phelps 2014); and, 2) “Companies making such a positive assertion are
unambiguously Step 0 Users” (Duff and Phelps 2014, 4). Second, we cannot directly observe how
the qualitative assessment has impacted the complexity and internal costs of impairment testing.
Nevertheless, we advance the literature by providing evidence that the additional discretion available
with the optional qualitative assessment does not appear to decrease the overall incidence of
impairments. Our findings inform the standard-setting debate about the complexities and costs of
impairment tests, and whether recent changes in standards for the impairment of intangible assets
result in significant changes in accounting practice. Our findings also speak to the broader issue of
the costs and benefits of optionality in accounting.
29
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Ramanna, K., and R. Watts. 2012. Evidence on the use of unverifiable estimates in required goodwill
impairment. Review of Accounting Studies 17 (4): 749-780.
Riedl, E. 2004. An examination of long-lived asset impairments. The Accounting Review 79 (3): 823-
852.
Shalev, R., I. Zhang, and Y. Zhang. 2013. CEO compensation and fair value accounting: Evidence
from purchase price allocation. Journal of Accounting Research 51 (4): 819-854.
Shumsky, T. 2018. GE’s $22 billion charge intensifies regulatory scrutiny. Wall Street Journal
(October 30). Available at: https://www.wsj.com/articles/ges-22-billion-charge-intensifies-
regulatory-scrutiny-1540942603.
Trentmann, N. 2019. Stock price-related impairment charges expected to rise as markets fluctuate.
Wall Street Journal (CFO Journal – August 8). Available at: https://www.wsj.com/articles/stock-
price-related-impairment-charges-at-kraft-heinz-expected-to-rise-as-markets-fluctuate-
11565302563.
31
APPENDIX A – Variable Definitions
Variable Definition Source
Dependent Variables
GW_WDi,t = Indicator variable equal to one when firm i determines the
goodwill balance is impaired and takes a write down in year t
(GDWLIP is less than zero), and zero otherwise;
Compustat
Independent Variables
POSTt = Indicator variable equal to one for fiscal years beginning after
12/15/2011, and zero for fiscal year immediately prior;
Compustat
QUALi = Indicator variable equal to one if firm i discloses that a
qualitative assessment was performed at any point during the
sample period;
SEC EDGAR 10-K filings
Perc_GWi,t-1 =
Ratio of the balance of the total intangible balance
(GDWL+INTANO) to total assets (AT) for firm i in year t-1;
Compustat
GW_WDi,t-1
= The one-year lagged indicator variable of the dependent
variable;
Compustat
SIZEi,t-1 = The natural logarithm of total assets (AT) for firm i in year t-1; Compustat
RETi,t = The annual stock return for firm i minus the value-weighted
cumulative market return during year t;
CRSP
RET2i,t-1 = The two-year stock return for firm i minus the value-weighted
cumulative market return from the beginning of year t-2 to the
end of year t-1;
CRSP
BTMi,t-1 = Ratio of the book value of equity (CEQ) to market value of
equity (PRCC_F * CSHO) for firm i in year t-1;
Compustat
BTM_INDi,t-1 = Indicator variable equal to one if the book-to-market (BTM)
ratio is greater than one for firm i in year t-1, and zero
otherwise;
Compustat
GROWTHi,t-1 = The change in sales (SALE) for firm i from year t-2 to year t-
1, scaled by sales in year t-1;
Compustat
NASDAQAMi,t-
1
= Indicator variable equal to one if firm i's shares are traded on
the NASDAQ or AMEX (EXCHG = 11 or 12) in year
t-1, and zero otherwise;
Compustat
SEGSi,t-1 = The natural logarithm of the count of distinct business segment
IDs (SID) for firm i in year t-1;
Compustat
HERFINDXi,t-1 = The Fama-French-17-industry-year sum of squared sales
shares for firm i in year t-1 (Sum(Share2)), where Share is
equal to firm sales (SALE) divided by lagged total industry-
year sales;
Compustat
INDROAi,t-1 = Average percentage change in firm i's Fama-French 17
industry median return-on-assets ratio over years t-5 to t-1;
Compustat
32
APPENDIX A – Variable Definitions (continued)
Variable Definition Source
Variables Used in Additional Analyses
High_SEGSi = Indicator variable equal to 1 if a firm has four or more
reporting segments during the first year of ASU 2011-08
being effective, and 0 for firms with only one reporting
segment;
Compustat
High_FOLLOWi = Indicator variable equal to 1 if a firm has five or more
analysts issuing EPS forecasts for the first year of ASU
2011-08 being effective, and 0 for firms with no analyst
following;
I/B/E/S
High_CUSHIONi = Indicator variable equal to 1 if a firm is in the top quartile of
market-to-book for the first year of ASU 2011-08 being
effective, and 0 for firms in the bottom quartile of market-to-
book;
Compustat
EARLY_WDi,t = Indicator variable equal to 1 if an impairment charge is
recognized before the fourth quarter of fiscal year t for firm i
(GDWLIPQ is less than zero in the first, second, or third
quarter of the fiscal year), and 0 if an impairment charge is
recognized in only the fourth quarter;
Compustat
EarnCARi,t = Cumulative returns of firm i for trading days -1 to +1 around
the earnings announcement for fiscal year t minus the value-
weighted cumulative market return in the same window;
CRSP
EarnSurpi,t = Actual earnings per share for firm i in fiscal year t minus the
last analyst forecast issued 5 or more days before the
earnings announcement, scaled by stock price;
I/B/E/S, CRSP
LOSSi,t = Indicator variable equal to one for firm i which has negative
net income (NI) in fiscal year t, and zero otherwise;
Compustat
SEC_COMMi,t = Indicator variable equal to one for firm i which has received
an SEC comment letter pertaining to goodwill or intangibles
for fiscal year t, and zero otherwise;
Audit Analytics
MERGEi,t = Indicator variable equal to one for firm i which has a non-
zero balance for acquisitions (AQP) for fiscal year t, and
zero otherwise;
Compustat
BIGNi,t = Indicator variable equal to one for firm i which has engaged
a Big-4 auditor for fiscal year t, and zero otherwise;
Audit Analytics
AFEEi,t = The natural logarithm of the audit fees for firm i in fiscal
year t;
Audit Analytics
ROAi,t = Net income (NI) scaled by total assets (AT) for firm i for
fiscal year t;
Compustat
33
APPENDIX B – Excerpts from ASU 2011-08 about the Updated Impairment Test Approach
A. Flowchart for the new impairment test approach now codified in ASC 350-20-55-25
B. Examples of events and circumstances for the qualitative assessment now codified in ASC 350-
20-35-3C
a. Macroeconomic conditions such as a deterioration in general economic conditions, limitations on accessing
capital, fluctuations in foreign exchange rates, or other developments in equity and credit markets
b. Industry and market considerations such as a deterioration in the environment in which an entity operates, an
increased competitive environment, a decline in market-dependent multiples or metrics (consider in both
absolute terms and relative to peers), a change in the market for an entity’s products or services, or a regulatory
or political development
c. Cost factors such as increases in raw materials, labor, or other costs that have a negative effect on earnings and
cash flows
d. Overall financial performance such as negative or declining cash flows or a decline in actual or planned revenue
or earnings compared with actual and projected results of relevant prior periods
e. Other relevant entity-specific events such as changes in management, key personnel, strategy, or customers;
contemplation of bankruptcy; or litigation
f. Events affecting a reporting unit such as a change in the composition or carrying amount of its net assets, a more-
likely-than-not expectation of selling or disposing all, or a portion, of a reporting unit, the testing for
recoverability of a significant asset group within a reporting unit, or recognition of a goodwill impairment loss
in the financial statements of a subsidiary that is a component of a reporting unit
g. If applicable, a sustained decrease in share price (consider in both absolute terms and relative to peers).
34
APPENDIX C – Examples of Disclosure
Disclosure of the performance of the qualitative assessment (Performing)
Starbucks Corporation, 10-K filing for the fiscal year ended 9/30/2012
(https://www.sec.gov/Archives/edgar/data/829224/000082922412000007/sbux-9302012x10k.htm):
“Goodwill
We test goodwill for impairment on an annual basis during our third fiscal quarter, or more frequently
if circumstances, such as material deterioration in performance or a significant number of store
closures, indicate reporting unit carrying values may exceed their fair values. When evaluating
goodwill for impairment, we first perform a qualitative assessment to determine if the fair value of
the reporting unit is more likely than not greater than the carrying amount. If not, we calculate the
implied estimated fair value of the reporting unit. If the carrying amount of goodwill exceeds the
implied estimated fair value, an impairment charge to current operations is recorded to reduce the
carrying value to the implied estimated fair value.” (Footnote 1, 57)
Disclosure of the option to perform (Mentioning)
Coach, Inc., 10-K filing for the fiscal year ended 6/29/2013
(https://www.sec.gov/Archives/edgar/data/1116132/000114420413047469/v350111_10k.htm):
“Recent Accounting Pronouncements…
In September 2011, Accounting Standards Codification 350-20, “Intangibles — Goodwill and
Other — Goodwill,” was amended to allow entities to assess qualitative factors to determine if it is
more-likely-than-not that goodwill might be impaired, and whether it is necessary to perform the two-
step goodwill impairment test required under current accounting standards. This guidance was
effective for the Company’s fiscal year beginning July 1, 2012. The adoption of this amendment did
not have a material effect on the Company’s consolidated financial statements.”
(Footnote 2, 68)
35
TABLE 1 – Sample Selection
Total Obs. Firms
Observations in Compustat with fiscal years ending between
12/15/2009 and 12/15/2015, and with non-missing, positive
values of total assets (AT) and sales (SALE)
46,393 10,671
Observations remaining after requiring at least one non-missing
value greater than or equal to 1% of total assets of any of the
following variables: goodwill (GDWL), impairment of goodwill,
impairment of goodwill and other intangibles when combined,
and impairment of unamortized intangibles (GDWLIP), and
intangibles (INTANO)
27,306 6,810
Observations remaining after requiring a valid and unique link to
CRSP
18,542 4,483
Observations remaining after merging with hand-collected
qualitative assessment and removing firms that switched between
performing and bypassing the qualitative assessment
18,145 4,413
Observations remaining after removing firms with missing
lagged total assets
17,992 4,386
Observations remaining after removing firms in the Utilities
industry (FF17 = 14) or missing an industry classification
17,427 4,245
Sample starting point for all analyses 17,427 4,245
Observations remaining after requiring non-missing control
variables SIZE, RET, RET2, BTM, BTM_IND, GROWTH,
NASDAQAM, SEGS, HERFINDX, INDROA
12,212 2,811
Observations remaining after requiring a firm to appear in all six
years of the sample period (three pre and three post)
7,368 1,228
Final sample for main analysis 7,368 1,228
Firms performing a qualitative assessment (Performing) 2,238 373
Firms disclosing the new option, but not specifying (Mentioning) 3,552 592
Firms not disclosing any information (Silent) 1,578 263
Propensity Score Matched Sample
Performing 2,148 358
Mentioning 2,148 358
Performing 1,206 201
Silent 1,206 201
This table shows the sample selection for the observations used in the analyses with three fiscal years pre- and post-
effective date of ASU 2011-08.
36
TABLE 2 – Descriptive Statistics
Panel A: Partitions by Disclosure Category
Performing Mentioning Silent
Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median
GW_WDi,t 0.113 0.317 0.000 0.118 0.323 0.000 0.113 0.316 0.000
POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
Perc_GWi,t-1 0.259 0.189 0.220 0.259 0.201 0.204 0.194 0.179 0.129
GW_WDi,t-1 0.137 0.344 0.000 0.140 0.348 0.000 0.128 0.334 0.000
SIZEi,t-1 6.870 1.839 6.831 6.774 1.909 6.793 7.023 2.428 6.875
RETi,t 0.105 0.495 0.022 0.087 0.530 -0.004 0.067 0.587 -0.029
RET2i,t-1 0.157 0.725 0.022 0.132 0.765 -0.008 0.113 0.851 -0.063
BTMi,t-1 0.564 0.506 0.476 0.590 0.549 0.490 0.653 0.634 0.503
BTM_INDi,t-1 0.134 0.341 0.000 0.147 0.354 0.000 0.182 0.386 0.000
GROWTHi,t-1 0.088 0.280 0.058 0.097 0.306 0.061 0.099 0.314 0.060
NASDAQAMi,t-1 0.504 0.500 1.000 0.449 0.497 0.000 0.468 0.499 0.000
SEGSi,t-1 0.781 0.672 1.099 0.747 0.690 1.099 0.668 0.705 0.693
HERFINDXi,t-1 0.027 0.026 0.017 0.027 0.026 0.017 0.027 0.025 0.018
INDROAi,t-1 -0.120 0.638 -0.092 -0.141 0.641 -0.092 -0.111 0.747 -0.109
N. Obs. = 2,238 3,552 1,578
Panel B: Pre- and Post-Effective Date for Firms Performing a Qualitative Assessment
QUALi = 1, POSTt = 0 QUALi = 1, POSTt = 1 Difference
in Means Variable Mean S.D. Mean S.D.
GW_WDi,t 0.113 0.317 0.113 0.317 0.000
Perc_GWi,t-1 0.250 0.190 0.267 0.189 0.017*
GW_WDi,t-1 0.178 0.383 0.097 0.295 -0.081***
SIZEi,t-1 6.757 1.827 6.984 1.845 0.227**
RETi,t 0.158 0.569 0.053 0.401 -0.105***
RET2i,t-1 0.180 0.775 0.135 0.671 -0.045
BTMi,t-1 0.635 0.568 0.493 0.423 -0.142***
BTM_INDi,t-1 0.179 0.383 0.089 0.285 -0.090***
GROWTHi,t-1 0.082 0.318 0.094 0.235 0.012
NASDAQAMi,t-1 0.504 0.500 0.504 0.500 0.000
SEGSi,t-1 0.761 0.678 0.802 0.665 0.041
HERFINDXi,t-1 0.027 0.026 0.027 0.026 0.000
INDROAi,t-1 -0.324 0.752 0.084 0.406 0.408***
N. Obs. = 1,119
1,119
2,238
37
TABLE 2 – Descriptive Statistics (continued)
Panel C: Differences of Means in Firm Characteristics in the Pre Period
Mean in POSTt = 0 Difference in Means
Performing Mentioning Silent Perf. vs Ment. Perf. vs Silent Ment. vs Silent
Variable (1) (2) (3) (2) – (1) (3) – (1) (3) – (2)
GW_WDi,t 0.113 0.111 0.122 -0.002 0.009 0.011
Perc_GWi,t-1 0.250 0.249 0.191 -0.001 -0.059*** -0.058***
GW_WDi,t-1 0.178 0.156 0.139 -0.022 -0.039* -0.017
SIZEi,t-1 6.757 6.642 6.923 -0.115 0.166 0.281**
RETi,t 0.158 0.113 0.103 -0.045* -0.055 -0.010
RET2i,t-1 0.180 0.172 0.192 -0.008 0.012 0.020
BTMi,t-1 0.635 0.639 0.658 0.004 0.023 0.019
BTM_INDi,t-1 0.179 0.178 0.181 -0.001 0.002 0.003
GROWTHi,t-1 0.082 0.092 0.123 0.010 0.041* 0.031*
NASDAQAMi,t-1 0.504 0.449 0.468 -0.055** -0.036 0.019
SEGSi,t-1 0.761 0.734 0.645 -0.027 -0.116*** -0.089**
HERFINDXi,t-1 0.027 0.028 0.028 0.001 0.001 0.000
INDROAi,t-1 -0.324 -0.406 -0.306 -0.082** 0.018 0.100**
N. Obs. = 1,119 1,776 789 2,895 1,908 2,565
Panel D: Propensity Score Matched Samples
Mean in POSTt = 0
Difference in
Means Mean in POSTt = 0
Difference in
Means
Performing Mentioning Perf. vs Ment. Performing Silent Perf. vs Sil.
Variable (1) (2) (3) = (2) – (1) (4) (5) (6) = (5) – (4)
GW_WDi,t 0.113 0.115 0.002 0.109 0.138 0.029
Perc_GWi,t-1 0.253 0.252 -0.001 0.201 0.218 0.017
GW_WDi,t-1 0.174 0.178 0.004 0.174 0.158 -0.016
SIZEi,t-1 6.716 6.702 -0.014 6.817 6.861 0.044
RETi,t 0.139 0.133 -0.006 0.119 0.127 0.008
RET2i,t-1 0.169 0.145 -0.024 0.185 0.200 0.015
BTMi,t-1 0.632 0.659 0.027 0.665 0.633 -0.032
BTM_INDi,t-1 0.176 0.190 0.014 0.189 0.167 -0.022
GROWTHi,t-1 0.080 0.070 -0.010 0.100 0.105 0.005
NASDAQAMi,t-1 0.489 0.486 -0.003 0.453 0.493 0.040
SEGSi,t-1 0.743 0.758 0.015 0.658 0.670 0.012
HERFINDXi,t-1 0.026 0.026 0.000 0.025 0.025 0.000
INDROAi,t-1 -0.398 -0.398 0.000 -0.332 -0.303 0.029
N. Obs. = 1,074 1,074 2,148 603 603 1,206
38
TABLE 2 – Descriptive Statistics (continued)
Panel E: Incidence of Impairments Pre- and Post-Adoption of Qualitative Assessment by
Quintiles of Book-to-Market
Frequency of GW_WDi,t
QUALi = 1 QUALi = 0
Quintile of BTM POSTt=0 POSTt=1 Diff. POSTt=0 POSTt=1 Diff.
1 6.7% 7.4% 0.7% 5.1% 5.1% 0.0%
2 6.3% 7.9% 1.6% 8.1% 6.4% -1.7%
3 8.7% 8.7% 0.0% 10.0% 11.6% 1.6%
4 11.4% 15.5% 4.1% 11.0% 15.0% 4.0%*
5 18.1% 14.0% -4.1% 16.7% 15.1% -1.5%
Total 11.3% 11.3% 0.0% 11.5% 11.8% 0.4%
This table includes descriptive statistics for the sample of companies in our analysis. Panel A reports the descriptive
statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 2,238); 2) Mentioning
the standard (N. Obs. = 3,552); and, 3) Silent about the qualitative assessment (N. Obs. = 1,578), including the three years
before and after the adoption of ASU 2011-08.
Panel B shows a partition of the sample performing the qualitative assessment by pre and post adoption. Panel C displays
the mean of each variable in the three years before the adoption partitioned by group, and Panel D displays the means of
each variable in our propensity score matched samples in the three years before the adoption. Panel E reports the
frequencies of goodwill write downs pre- and post-adoption of ASU 2011-08 for firms performing the qualitative
assessment (QUALi = 1) compared to all other firms (QUALi = 0).
In Panels B, C, D, and E, differences in means are reported, and ***, **, and * indicate statistical significance at 0.01,
0.05, and 0.10 levels, respectively. Refer to Appendix A for variable definitions.
39
TABLE 3 – Determinants of Performing a Qualitative Assessment
Panel A: Determinants in the First Year after ASU 2011-08
Full sample
POST=1
Performing vs
Mentioning
POST=1
Performing vs
Silent
POST=1
(1) (2) (3)
Variables QUALi QUALi QUALi
Perc_GWi,t-1 0.599* -0.007 2.546***
[1.75] [-0.02] [4.54]
GW_WDi,t-1 -0.351 -0.286 -0.531*
[-1.60] [-1.21] [-1.94]
SIZEi,t-1 -0.034 0.002 -0.104**
[-0.86] [0.04] [-2.03]
RETi,t -0.101 -0.101 -0.104
[-0.67] [-0.60] [-0.53]
RET2i,t-1 0.227** 0.263** 0.221
[2.20] [2.22] [1.26]
BTMi,t-1 -0.330* -0.258 -0.479**
[-1.83] [-1.28] [-2.13]
BTM_INDi,t-1 -0.042 0.016 -0.148
[-0.16] [0.06] [-0.42]
GROWTHi,t-1 -0.730* -0.889** -0.845
[-1.75] [-2.01] [-1.35]
GROWTH2i,t-1 0.065 0.052 0.840
[0.22] [0.17] [1.34]
NASDAQAMi,t-1 0.177 0.155 0.310
[1.15] [0.98] [1.53]
SEGSi,t-1 0.083 0.003 0.257*
[0.83] [0.03] [1.85]
HERFINDXi,t-1 4.413 5.723 -13.260
[0.33] [0.44] [-0.39]
INDROAi,t-1 -0.083 0.221 -0.998
[-0.15] [0.39] [-1.32]
Constant -0.937 -0.614 0.906
[-1.36] [-0.87] [0.63]
Industry FE Yes Yes Yes
N. Obs. 1,228 965 636
Pseudo R2 0.031 0.028 0.094
AUC 0.609 0.600 0.699
40
TABLE 3 – Determinants of Performing a Qualitative Assessment (continued)
Panel B: Determinants using the Three Year Pre-period Average
Performing vs
Mentioning
POST=0
Performing vs
Silent
POST=0
(1) (2)
Variables QUALi QUALi
µ(Perc_GWi,t-1) 0.090 2.380***
[0.24] [4.34]
µ(GW_WDi,t-1) 0.283 0.275
[0.99] [0.71]
µ(SIZEi,t-1) 0.013 -0.080
[0.30] [-1.48]
µ(RETi,t) 0.919*** 1.400***
[2.84] [2.85]
µ(RET2i,t-1) -0.444* -0.641*
[-1.79] [-1.74]
µ(BTMi,t-1) -0.132 -0.429
[-0.59] [-1.29]
µ(BTM_INDi,t-1) -0.010 -0.032
[-0.03] [-0.06]
µ(GROWTHi,t-1) 0.249 -1.388
[0.40] [-1.57]
µ(GROWTH2i,t-1) -0.339 0.552
[-0.90] [0.96]
µ(NASDAQAMi,t-1) 0.146 0.153
[0.93] [0.76]
µ(SEGSi,t-1) -0.048 0.201
[-0.43] [1.38]
µ(HERFINDXi,t-1) -0.604 -10.638
[-0.05] [-0.80]
µ(INDROAi,t-1) -0.191 0.551
[-0.44] [1.05]
Constant -0.681 0.539
[-0.99] [0.70]
Industry FE Yes Yes
N. Obs. 965 636
Pseudo R2 0.023 0.083
AUC 0.598 0.691
This table includes results from the logistic regressions on the identified subsets with the following specification:
P(QUALi) =f(Perc_GWi,t-1, GW_WDi,t-1, Controlsi,t, Fixed Effectsi,t, ei,t) (1)
Panel A is limited to first fiscal year when the qualitative assessment became available as an option (post ASU 2011-08’s
effective date). Column 1 uses the whole sample of firms, while column 2 (3) subsets the sample to compare those firms
performing the qualitative assessment to those that are mentioning (are silent about) the option to perform the assessment.
Panel B uses the averages of the determinant variables in the three year pre-adoption, which is then used to perform
propensity score matching between the identified subsets.
Robust z-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.
41
TABLE 4 – Incidence of Impairments Three Years Pre and Post ASU 2011-08
Panel A: Full Sample
Full Sample Performing Full Sample Performing vs
Mentioning
Performing
vs Silent
(1) (2) (3) (4) (5)
Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t
POSTt 0.005 0.011 0.002 0.013 -0.023
[0.66] [0.83] [0.22] [1.31] [-1.38]
QUALi -0.006 0.002 -0.022
[-0.52] [0.17] [-1.49]
POSTt * QUALi 0.010 -0.002 0.036*
[0.66] [-0.14] [1.83]
Perc_GWi,t-1 0.151*** 0.138*** 0.151*** 0.152*** 0.146***
[6.90] [3.65] [6.89] [6.29] [4.89]
GW_WDi,t-1 0.213*** 0.194*** 0.214*** 0.208*** 0.209***
[11.95] [6.41] [11.96] [10.44] [8.71]
SIZEi,t-1 0.007*** 0.002 0.007*** 0.005** 0.005*
[2.97] [0.47] [2.97] [2.14] [1.82]
RETi,t -0.074*** -0.088*** -0.074*** -0.079*** -0.075***
[-9.83] [-5.87] [-9.81] [-8.50] [-7.75]
RET2i,t-1 -0.011** -0.011 -0.011** -0.014** -0.006
[-2.14] [-1.33] [-2.14] [-2.47] [-1.00]
BTMi,t-1 0.038*** 0.026 0.038*** 0.039*** 0.029
[2.98] [1.00] [2.99] [2.65] [1.64]
BTM_INDi,t-1 0.049*** 0.083** 0.050*** 0.049** 0.071***
[2.71] [2.41] [2.71] [2.42] [2.68]
GROWTHi,t-1 0.003 0.007 0.003 -0.007 0.017
[0.29] [0.31] [0.28] [-0.48] [1.12]
NASDAQAMi,t-1 -0.016* 0.010 -0.015* -0.010 -0.005
[-1.66] [0.61] [-1.66] [-1.00] [-0.37]
SEGSi,t-1 0.031*** 0.048*** 0.031*** 0.029*** 0.042***
[4.69] [4.05] [4.70] [3.83] [4.78]
HERFINDXi,t-1 -0.088 -1.903 -0.099 0.280 -1.994*
[-0.15] [-1.06] [-0.17] [0.43] [-1.92]
INDROAi,t-1 0.004 0.001 0.004 0.007 -0.001
[0.67] [0.05] [0.69] [0.93] [-0.16]
Constant -0.009 0.111 -0.007 0.003 0.062
[-0.28] [1.34] [-0.22] [0.07] [1.22]
Industry FE Yes Yes Yes Yes Yes
N. Obs. 7,368 2,238 7,368 5,790 3,816
Adj. R2 0.104 0.102 0.104 0.101 0.109
AUC of Logit Model 0.769 0.778 0.769 0.771 0.774
42
TABLE 4 – Incidence of Impairments Three Years Pre and Post ASU 2011-08 (continued)
Panel B: Propensity Score Matched Sample
Performing vs
Mentioning
Three Years
Performing
vs Silent
Three Years
Performing vs
Mentioning
One Year
Performing
vs Silent
One Year
(1) (2) (3) (4)
Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t
POSTt 0.022* -0.032 0.008 -0.038
[1.81] [-1.56] [0.36] [-1.09]
QUALi 0.001 -0.029 -0.017 -0.059**
[0.08] [-1.52] [-0.83] [-1.98]
POSTt * QUALi -0.012 0.049* 0.019 0.097**
[-0.67] [1.95] [0.58] [2.07]
Perc_GWi,t-1 0.163*** 0.123*** 0.157*** 0.142*
[5.81] [2.95] [3.39] [1.71]
GW_WDi,t-1 0.191*** 0.183*** 0.195*** 0.178***
[8.40] [5.65] [4.22] [2.86]
SIZEi,t-1 0.005* 0.008** 0.009* 0.006
[1.84] [2.20] [1.87] [1.15]
RETi,t -0.080*** -0.071*** -0.098*** -0.074***
[-6.77] [-5.65] [-4.50] [-2.79]
RET2i,t-1 -0.014** -0.004 -0.012 -0.004
[-2.20] [-0.42] [-1.32] [-0.29]
BTMi,t-1 0.041** 0.045* 0.053* 0.049
[2.15] [1.83] [1.95] [1.19]
BTM_INDi,t-1 0.050** 0.059* 0.047 0.029
[2.10] [1.74] [1.13] [0.47]
GROWTHi,t-1 -0.009 0.005 -0.022 -0.003
[-0.48] [0.25] [-0.77] [-0.08]
NASDAQAMi,t-1 -0.001 -0.006 -0.001 -0.006
[-0.11] [-0.37] [-0.05] [-0.24]
SEGSi,t-1 0.031*** 0.045*** 0.023* 0.054***
[3.52] [3.85] [1.74] [2.90]
HERFINDXi,t-1 0.533 -3.958* -2.455 -5.673*
[0.46] [-1.80] [-1.38] [-1.68]
INDROAi,t-1 0.005 -0.004 -0.013 -0.013
[0.54] [-0.31] [-0.57] [-0.61]
Constant -0.000 0.123 0.120 0.161
[-0.00] [1.36] [1.29] [1.17]
Industry FE Yes Yes Yes Yes
N. Obs. 4,296 2,412 1,432 804
Adj. R2 0.099 0.108 0.096 0.073
AUC of Logit Model 0.772 0.775 0.769 0.760
Panel A, Columns 1 and 2 of this table includes results from the linear probability model on the identified subsets with
the following specification:
P(GW_WDi,t) = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (2)
Panel A, Columns 3 through 5 and Panel B of this table includes results from the linear probability model on the identified
subsets with the following specification:
P(GW_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (3)
Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. The area under the receiver operating characteristic curve (AUC)
is displayed for the same specifications using a logistic regression model in order to assess goodness of fit. Variable
descriptions are included in Appendix A.
43
TABLE 5 – Opportunities to Manage Impairment Charges
Panel A: Number of reporting segments and analyst following in the first year of adoption
Reporting Segments Analyst Following
Number of Firms Perc. Number of Firms Perc.
0 - - 368 30.0
1 500 40.7 214 17.4
2 92 7.5 140 11.4
3 256 20.9 99 8.1
4 173 14.1 71 5.8
5 118 9.6 64 5.2
6 55 4.5 41 3.3
7 or more 34 2.7 231 18.8
Total 1,228 100.0 1,228 100.0
Panel A tabulates reporting segments and analyst following for each unique firm in our sample. The first year of adoption
for ASU 2011-08 is the year chosen to stratify the sample in order to maintain a balanced sample throughout all six years
of the analysis. For the subsamples in this analysis, we compare firms with only one reporting segment to firms with four
or more reporting segments, and firms with no analyst following to firms with five or more analysts following. Firms in
the respective top portion of the cross-section are assigned a one for the respective Highi variable, and firms in the bottom
are assigned a zero.
44
TABLE 5 – Opportunities to Manage Impairment Charges (continued)
Panel B: Regression analysis – High and low number of reporting segments
Full Sample Performing Full Sample Performing vs
Silent PSM
(1) (2) (3) (4)
Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t
High_SEGSi 0.009 0.039 -0.007 0.055
[0.33] [0.80] [-0.23] [0.86]
POSTt 0.007 0.033* -0.000 -0.021
[0.71] [1.93] [-0.02] [-0.83]
POSTt * High_SEGSi 0.003 -0.018 0.008 -0.018
[0.20] [-0.55] [0.42] [-0.39]
QUALi -0.034** -0.039*
[-2.57] [-1.84]
POSTt * QUALi 0.027 0.052*
[1.50] [1.70]
High_SEGSi * QUALi 0.049* -0.019
[1.80] [-0.37]
POSTt * High_SEGSi * QUALi -0.020 0.062
[-0.54] [0.94]
Perc_GWi,t-1 0.149*** 0.172*** 0.148*** 0.152***
[5.89] [3.67] [5.89] [3.11]
GW_WDi,t-1 0.237*** 0.190*** 0.237*** 0.172***
[10.82] [5.65] [10.78] [4.37]
Controls Included Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
N. Obs. 5,280 1,506 5,280 1,764
Adj. R2 0.123 0.112 0.123 0.122
Panel C: Regression analysis – High and low number of analysts following
Full Sample Performing Full Sample Performing vs
Silent PSM
(1) (2) (3) (4)
Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t
High_FOLLOWi -0.005 0.023 -0.007 -0.048
[-0.28] [0.77] [-0.39] [-1.40]
POSTt -0.013 0.033 -0.029* -0.084***
[-0.99] [1.43] [-1.85] [-2.69]
POSTt * High_FOLLOWi 0.023 -0.015 0.037* 0.088*
[1.29] [-0.50] [1.69] [1.77]
QUALi -0.038** -0.052
[-2.04] [-1.59]
POSTt * QUALi 0.052** 0.090**
[2.01] [2.12]
High_FOLLOWi,* QUALi 0.015 0.035
[0.53] [0.72]
POSTt * High_FOLLOWi * QUALi -0.047 -0.077
[-1.28] [-1.19]
Perc_GWi,t-1 0.136*** 0.121*** 0.138*** 0.113**
[4.56] [2.62] [4.62] [2.10]
GW_WDi,t-1 0.204*** 0.226*** 0.204*** 0.187***
[8.36] [5.31] [8.35] [4.35]
Controls Included Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
N. Obs. 4,224 1,356 4,224 1,548
Adj. R2 0.089 0.106 0.089 0.111
45
TABLE 5 – Opportunities to Manage Impairment Charges (continued)
Panel D: Regression analysis – High and low cushion
Full Sample Performing Full Sample Performing vs
Silent PSM
(1) (2) (3) (5)
Variables GW_WDi,t GW_WDi,t GW_WDi,t GW_WDi,t
High_CUSHIONi -0.005 0.013 -0.011 -0.027
[-0.27] [0.30] [-0.53] [-0.73]
POSTt 0.020 0.027 0.017 -0.011
[1.23] [0.74] [0.96] [-0.23]
POSTt * High_CUSHIONi -0.022 -0.036 -0.014 -0.001
[-1.13] [-0.87] [-0.65] [-0.02]
QUALi -0.014 -0.037
[-0.50] [-0.80]
POSTt * QUALi 0.011 0.022
[0.30] [0.36]
High_CUSHIONi,* QUALi 0.022 0.017
[0.62] [0.30]
POSTt * High_CUSHIONi * QUALi -0.028 0.005
[-0.61] [0.07]
Perc_GWi,t-1 0.184*** 0.135*** 0.185*** 0.093*
[6.31] [2.73] [6.32] [1.74]
GW_WDi,t-1 0.214*** 0.196*** 0.214*** 0.182***
[8.68] [3.94] [8.69] [3.71]
Controls Included Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
N. Obs. 3,570 924 3,570 1,770
Adj. R2 0.124 0.116 0.123 0.101
Panel B, C, and D includes results from the following OLS regressions on the identified subsets with the following
specifications:
P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, Controlsi,t, Fixed Effectsi,t, ei,t) (4)
P(GW_WDi,t) = f(Highi, POSTt, POSTt*Highi, QUALi, POSTt*QUALi, Highi*QUALi,t, POSTt*Highi*QUALi,
Controlsi,t, Fixed Effectsi,t, ei,t) (5)
Where Highi is replaced with the indicator variable of interest (segments, analyst following, or cushion) which identify a
cross-section of firms with a higher likelihood of exhibiting opportunistic behavior in impairment assessments.
High_SEGSi and High_FOLLOWi are defined in Panel A; High_CUSHIONi is an indicator variable equal to one if a firm
is in the top quartile of the market-to-book ratio in the first year after adoption of ASU 2011-08, and zero if a firm is in
the lowest quartile of market-to-book in that first year. These indicator variables are assigned to the same firms for all six
years in order to maintain a balanced sample.
Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.
46
TABLE 6 – Predictive Modeling of Impairments
Panel A: Sample selection
Sample starting point for all analyses 17,427 4,245
Observations remaining after requiring non-missing control
variables SIZE, RET, RET2, BTM, BTM_IND, GROWTH,
NASDAQAM, SEGS, HERFINDX, INDROA
12,212 2,811
Final sample for extended analysis 12,212 2,811
Firms performing a qualitative assessment (Performing) 3,366 731
Firms disclosing the new option, but not specifying (Mentioning) 5,859 1,346
Firms not disclosing any information (Silent) 2,987 734
Panel B: Logistic Regression Model
POST=0 POST=1
(1) (2)
Variables GW_WDi,t GW_WDi,t
Perc_GWi,t-1 1.665*** 1.452***
[8.01] [6.48]
GW_WDi,t-1 1.533*** 1.420***
[14.06] [12.16]
SIZEi,t-1 0.088*** 0.088***
[3.56] [3.37]
RETi,t -1.091*** -1.104***
[-8.56] [-6.85]
RET2i,t-1 -0.066 -0.265***
[-0.92] [-3.10]
BTMi,t-1 0.633*** 0.111
[5.73] [1.13]
BTM_INDi,t-1 0.196 0.585***
[1.26] [3.42]
GROWTHi,t-1 0.131 -0.188
[1.06] [-1.26]
NASDAQAMi,t-1 -0.045 -0.255**
[-0.47] [-2.45]
SEGSi,t-1 0.282*** 0.396***
[4.14] [5.43]
HERFINDXi,t-1 2.162 10.406*
[0.31] [1.91]
INDROAi,t-1 0.284*** -0.037
[2.81] [-0.22]
Constant -3.958*** -3.846***
[-11.00] [-10.62]
Industry FE Yes Yes
N. Obs. 6,033 6,179
Pseudo R2 0.153 0.135
AUC 0.778 0.764
47
TABLE 6 – Predictive Modeling of Impairments (continued)
Panel C: Type I and II Errors of Predicted Impairments Post ASU 2011-08
Full Sample GW_WDi,t
POSTt = 1 =0 =1 Total
Pred.
Imp.
=0 81.5% 7.5% 89.0%
=1 7.3% 3.7% 11.0%
Total 88.8% 11.2% 100.0%
QUALi = 1 GW_WDi,t
POSTt /= 1 =0 =1 Total
Pred.
Imp.
=0 83.6% 8.2% 91.8%
=1 5.8% 2.4% 8.2%
Total 89.4% 10.6% 100.0%
QUALi = 0 GW_WDi,t
POSTt = 1 =0 =1 Total
Pred.
Imp.
=0 80.7% 7.3% 88.0%
=1 7.9% 4.1% 12.0%
Total 88.6% 11.4% 100.0%
Two-sample test of proportions QUALi = 1 QUALi = 0 Difference
False Positive Rate (Type I) 6.5% 8.9% -2.4%***
False Negative Rate (Type II) 77.3% 64.0% 13.3%***
Panel A shows the sample selection for the observations used in the prediction modeling of impairment analysis. Panel
B reports the results of the from a logistic regression model on all observations pre- and post-adoption of ASU 2011-
08 in columns 1 and 2, respectively, using the following specification:
P(GW_WDi,t) = f(Perc_GWi,t-1, GW_WDi,t-1, Controlsi,t, Fixed Effectsi,t, ei,t) (Equation 2 without POSTt)
Robust z-statistics are shown in the brackets. Standard errors are clustered by firm, and the area under the receiver
operating characteristic curve (AUC) is displayed in order to assess goodness of fit.
Panel C displays the analysis of type I and II errors. The estimated coefficients from Panel B, column 1 (the pre-period)
are used to estimate the likelihood of an impairment for every observation in the post-period. Firm-year observations in
the highest 11% of estimated probabilities in each year of the post-period are assigned a value of 1 for predicted
impairment, while the remaining firm-year observations are assigned a 0. Two-by-two contingency tables are displayed
comparing the predicted impairment to actual impairments for the full sample in the post-period, as well as separately
for qualitative assessment firms (QUALi = 1) and for the remainder of firms (QUALi = 0). Lastly, a two-sample test of
proportions is performed to evaluate the difference between the type I and type II error rates between the two sub-
samples. Throughout the table, ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively.
Variable descriptions are included in Appendix A.
48
TABLE 7 – Impairment Timeliness and Qualitative Assessments
Panel A: Sample selection
Sample starting point for all analyses from Table 1 17,427 4,245
Observations remaining after requiring non-missing control
variables SIZE, RET, RET2, BTM, BTM_IND, GROWTH,
NASDAQAM, SEGS, HERFINDX, INDROA
12,212 2,811
Observations remaining after restricting the sample to firm-years
with an impairment charge (GW_WDi,t=1)
1,416 897
Final sample for impairment timeliness analysis 1,416 897
Firms performing a qualitative assessment (Performing) 365 234
Firms disclosing the new option, but not specifying (Mentioning) 722 463
Firms not disclosing any information (Silent) 329 200
Panel B: Descriptive statistics
Performing Mentioning Silent
Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median
EARLY_WDi,t 0.447 0.498 0.000 0.429 0.495 0.000 0.331 0.471 0.000
POSTt 0.507 0.501 1.000 0.481 0.500 0.000 0.486 0.501 0.000
QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
Perc_GWi,t-1 0.293 0.199 0.260 0.300 0.210 0.250 0.210 0.182 0.158
GW_WDi,t-1 0.356 0.480 0.000 0.368 0.483 0.000 0.362 0.481 0.000
SIZEi,t-1 6.987 1.740 6.892 6.804 1.896 6.803 7.523 2.387 7.234
RETi,t -0.065 0.523 -0.119 -0.097 0.600 -0.176 -0.129 0.556 -0.228
RET2i,t-1 -0.031 0.654 -0.145 -0.074 0.803 -0.213 -0.107 0.761 -0.249
BTMi,t-1 0.748 0.634 0.630 0.830 0.726 0.701 0.963 0.805 0.774
BTM_INDi,t-1 0.260 0.439 0.000 0.285 0.452 0.000 0.337 0.474 0.000
GROWTHi,t-1 0.101 0.332 0.050 0.080 0.335 0.031 0.104 0.324 0.048
NASDAQAMi,t-1 0.512 0.501 1.000 0.418 0.494 0.000 0.459 0.499 0.000
SEGSi,t-1 0.966 0.660 1.099 0.823 0.691 1.099 0.920 0.724 1.099
HERFINDXi,t-1 0.029 0.029 0.017 0.028 0.026 0.017 0.032 0.031 0.018
INDROAi,t-1 -0.149 0.609 -0.120 -0.159 0.644 -0.120 -0.148 0.719 -0.184
N. Obs. = 365 722 329
Panel A shows the sample selection for the observations used in the impairment timeliness analysis. Panel B reports the
descriptive statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 365); 2)
Mentioning the standard (N. Obs. = 722); and, 3) Silent about the qualitative assessment (N. Obs. = 329), including only
firm-year observations with an impairment charge (GW_WDi,t=1) in the three years before and three years after the
adoption of ASU 2011-08. Variable descriptions are included in Appendix A.
49
TABLE 7 – Impairment Timeliness and Qualitative Assessments (continued)
Panel C: Mean early impairment charges between groups, pre and post
POSTt =0 POSTt =1 Combined
Mean S.D. Mean S.D. Mean S.D.
Performing EARLY_WDi,t 0.428 0.496 0.465 0.500 0.447 0.498
N. Obs. = 180 185 365
Mentioning EARLY_WDi,t 0.427 0.495 0.432 0.496 0.429 0.495
N. Obs. = 375 347 722
Silent EARLY_WDi,t 0.367 0.483 0.293 0.457 0.331 0.471
N. Obs. = 169 160 329
Combined EARLY_WDi,t 0.413 0.493 0.409 0.492
N. Obs. = 724 692
Panel D: Impairment timeliness between groups, pre and post
Full Sample Performing Full Sample Performing
vs Silent
(1) (2) (3) (4)
Variables EARLY_WDi,t EARLY_WDi,t EARLY_WDi,t EARLY_WDi,t
POSTt 0.016 0.082 -0.006 -0.036
[0.56] [1.26] [-0.18] [-0.71]
QUALi 0.003 0.023
[0.05] [0.37]
POSTt * QUALi 0.080 0.119
[1.26] [1.57]
Perc_GWi,t-1 0.229*** 0.182 0.224*** 0.259**
[3.11] [1.24] [3.05] [2.23]
GW_WDi,t-1 0.035 -0.015 0.039 0.045
[1.21] [-0.25] [1.33] [1.07]
Controls Included Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
N. Obs. 1,416 365 1,416 694
Adj. R2 0.013 -0.038 0.014 0.010
Panel C reports the mean of (percentage of) companies taking an impairment charge in the first three quarters for each of
the subsamples of firms in the pre- and post-periods.
Panel D includes results from the following linear probability models on the identified subsets with the following
specification:
P(EARLY_WDi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (6)
Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.
50
TABLE 8 – Audit Fees and Qualitative Assessments
Panel A: Sample selection
Sample starting point for all analyses from Table 1 17,427 4,245
Observations remaining after requiring non-missing control
variables AFEE, SIZE, BIGN, ROA, BTM
12,129 3,018
Observations remaining after requiring a firm to appear in all six
years of the sample period (three pre and three post)
6,846 1,141
Final sample for audit fees analysis 6,846 1,141
Firms performing a qualitative assessment (Performing) 2,202 367
Firms disclosing the new option, but not specifying (Mentioning) 3,378 563
Firms not disclosing any information (Silent) 1,266 211
Panel B: Partitions by each type
Performing Mentioning Silent
Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median
AFEEi,t 14.271 1.101 14.206 14.187 1.153 14.210 14.265 1.539 14.101
POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
GW_WDi,t 0.114 0.317 0.000 0.128 0.334 0.000 0.117 0.321 0.000
QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
Perc_GWi,t-1 0.260 0.189 0.216 0.264 0.205 0.212 0.212 0.185 0.143
SIZEi,t-1 6.996 1.812 6.948 6.843 1.876 6.902 7.290 2.532 7.086
BIGNi,t 0.796 0.403 1.000 0.798 0.401 1.000 0.828 0.378 1.000
ROAi,t 0.043 0.097 0.053 0.032 0.126 0.049 0.025 0.137 0.048
BTMi,t-1 0.529 0.453 0.448 0.576 0.510 0.480 0.611 0.563 0.482
N. Obs. = 2,202 3,378 1,266
Panel A shows the sample selection for the observations used in the audit fee analysis. Panel B reports the descriptive
statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 2,202); 2) Mentioning
the standard (N. Obs. = 3,378); and, 3) Silent about the qualitative assessment (N. Obs. = 1,266), including three years
before and three years after the adoption of ASU 2011-08. Variable descriptions are included in Appendix A.
51
TABLE 8 – Audit Fees and Qualitative Assessments (continued)
Panel C: Regression analysis
Full Sample Performing Full Sample Performing vs
Silent PSM
(1) (2) (3) (4)
Variables AFEEi,t AFEEi,t AFEEi,t AFEEi,t
POSTt 0.003 0.027* -0.004 -0.014
[0.34] [1.89] [-0.44] [-0.51]
QUALi 0.041 0.178***
[1.37] [2.96]
POSTt * QUALi 0.023 0.032
[1.57] [0.97]
Perc_GWi,t-1 -0.041 -0.139 -0.046 0.022
[-0.55] [-0.98] [-0.63] [0.12]
GW_WDi,t 0.050* 0.066* 0.050* 0.039
[1.90] [1.66] [1.91] [0.70]
SIZEi,t-1 0.532*** 0.518*** 0.532*** 0.533***
[59.69] [31.95] [59.64] [29.70]
BIGNi,t 0.298*** 0.315*** 0.300*** 0.214***
[8.05] [4.74] [8.12] [2.84]
ROAi,t -0.509*** -0.666*** -0.519*** -0.597***
[-5.03] [-3.78] [-5.13] [-3.06]
BTMi,t-1 -0.112*** -0.092** -0.109*** -0.090**
[-4.48] [-2.06] [-4.36] [-2.08]
Constant 10.212*** 10.281*** 10.198*** 10.007***
[110.30] [47.76] [110.12] [34.69]
Industry FE Yes Yes Yes Yes
N. Obs. 6,846 2,202 6,846 1,596
Adj. R2 0.836 0.815 0.837 0.853
Panel C includes results from the following OLS model on the identified subsets with the following specification:
AFEEi,t = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (7)
AFEEi,t = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (8)
Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.
52
TABLE 9 – SEC Comment Letters and Qualitative Assessments
Panel A: Sample selection
Sample starting point for all analyses from Table 1 17,427 4,245
Observations remaining after requiring non-missing control
variables SEC_COMM, MERGE, BIGN, LOSS
16,717 4,112
Observations remaining after requiring a firm to appear in all six
years of the sample period (three pre and three post)
10,134 1,689
Final sample for comment letter analysis 10,134 1,689
Firms performing a qualitative assessment (Performing) 3,090 515
Firms disclosing the new option, but not specifying (Mentioning) 4,938 823
Firms not disclosing any information (Silent) 2,106 351
Panel B: Partitions by each type
Performing Mentioning Silent
Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median
SEC_COMMi,t 0.086 0.280 0.000 0.082 0.274 0.000 0.025 0.157 0.000
POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
GW_WDi,t 0.111 0.315 0.000 0.117 0.321 0.000 0.119 0.324 0.000
QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
MERGEi,t 0.396 0.489 0.000 0.375 0.484 0.000 0.220 0.415 0.000
BIGNi,t 0.790 0.407 1.000 0.777 0.416 1.000 0.852 0.355 1.000
LOSSi,t 0.189 0.391 0.000 0.218 0.413 0.000 0.238 0.426 0.000
N. Obs. = 3,090 4,938 2,106
Panel A shows the sample selection for the observations used in the SEC comment letter analysis. Panel B reports the
descriptive statistics for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 3,090); 2)
Mentioning the standard (N. Obs. = 4,938); and, 3) Silent about the qualitative assessment (N. Obs. = 2,106), including
three years before and three years after the adoption of ASU 2011-08. Variable descriptions are included in Appendix A.
53
TABLE 9 – SEC Comment Letters and Qualitative Assessments (continued)
Panel C: Regression analysis
Full Sample Performing Full Sample Performing vs
Silent PSM
(1) (2) (3) (4)
Variables SEC_COMMi,t SEC_COMMi,t SEC_COMMi,t SEC_COMMi,t
POSTt -0.046*** -0.053*** -0.042*** -0.027***
[-9.01] [-5.34] [-7.18] [-2.96]
QUALi 0.025*** 0.067***
[2.65] [4.17]
POSTt * QUALi -0.012 -0.016
[-1.07] [-0.88]
GW_WDi,t 0.050*** 0.007 0.050*** 0.040**
[4.99] [0.40] [4.99] [2.16]
MERGEi,t 0.032*** 0.021* 0.031*** 0.017
[5.33] [1.93] [5.17] [1.46]
BIGNi,t 0.010 0.025** 0.010 0.009
[1.53] [2.04] [1.60] [0.62]
LOSSi,t 0.012* 0.014 0.012* -0.010
[1.68] [0.92] [1.78] [-0.90]
Constant 0.032*** 0.059** 0.025** -0.018
[2.97] [2.54] [2.26] [-0.98]
Industry FE Yes Yes Yes Yes
N. Obs. 10,134 3,090 10,134 2,196
Adj. R2 0.025 0.023 0.026 0.027
Panel C includes results from the following linear probability model on the identified subsets with the following
specification:
P(SEC_COMMi,t) = f(POSTt, Controlsi,t, Fixed Effectsi,t, ei,t) (9)
P(SEC_COMMi,t) = f(POSTt, QUALi, POSTt * QUALi, Controlsi,t, Fixed Effectsi,t, ei,t) (10)
Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.
54
TABLE 10 – Market Reaction to Earnings of Users of Qualitative Assessments
Panel A: Sample selection
Sample starting point for all analyses from Table 1 17,427 4,245
Observations remaining after requiring non-missing control
variables EarnCAR, EarnSurp, LOSS
11,584 3,376
Observations remaining after requiring a firm to appear in all six
years of the sample period (three pre and three post)
5,070 845
Final sample for ERC analysis 5,070 845
Firms performing a qualitative assessment (Performing) 1,626 271
Firms disclosing the new option, but not specifying (Mentioning) 2,364 394
Firms not disclosing any information (Silent) 1,080 180
Panel B: Partitions by each type
Performing Mentioning Silent
Variable Mean S.D. Median Mean S.D. Median Mean S.D. Median
EarnCARi,t 0.007 0.062 0.006 0.006 0.063 0.002 -0.000 0.055 -0.001
EarnSurpi,t 0.000 0.021 0.000 0.001 0.020 0.000 0.000 0.039 0.000
POSTt 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
QUALi 1.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
LOSSi,t 0.125 0.331 0.000 0.123 0.328 0.000 0.151 0.358 0.000
N. Obs. = 1,626 2,364 1,080
Panel A shows the sample selection for the observations used in the ERC analysis. Panel B reports the descriptive statistics
for the sub-samples of companies: 1) Performing the qualitative assessment (N. Obs. = 1,626); 2) Mentioning the standard
(N. Obs. = 2,364); and, 3) Silent about the qualitative assessment (N. Obs. = 1,080), including three years before and three
years after the adoption of ASU 2011-08. Variable descriptions are included in Appendix A.
55
TABLE 10 – Market Reaction to Earnings of Users of Qualitative Assessments (continued)
Panel C: Regression analysis
Full Sample Performing Full Sample Performing vs
Silent PSM
(1) (2) (3) (4)
Variables EarnCARi,t EarnCARi,t EarnCARi,t EarnCARi,t
EarnSurpi,t 0.274*** 0.363*** 0.245*** 0.285***
[5.32] [3.56] [4.21] [2.76]
POSTt -0.003* -0.004 -0.003 0.011*
[-1.88] [-1.20] [-1.41] [1.89]
POSTt * EarnSurpi,t -0.148** -0.137 -0.140* -0.267***
[-1.98] [-0.75] [-1.66] [-3.59]
QUALi 0.003 0.013**
[1.07] [2.35]
POSTt * QUALi -0.001 -0.016*
[-0.16] [-1.86]
EarnSurpi,t * QUALi 0.117 0.816*
[1.02] [1.94]
POSTt * EarnSurpi,t * QUALi 0.001 -0.415
[0.00] [-0.55]
LOSSi,t 0.001 -0.001 0.001 0.002
[0.40] [-0.28] [0.42] [0.25]
Constant 0.005 0.012 0.004 -0.007
[1.00] [1.01] [0.79] [-1.63]
Industry FE Yes Yes Yes Yes
N. Obs. 5,070 1,626 5,070 1,050
Adj. R2 0.010 0.008 0.010 0.017
Panel C includes results from the following OLS regressions on the identified subsets with the following specification:
EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, LOSSi,t, Fixed Effectsi,t, ei,t) (11)
EarnCARi,t = f(EarnSurpi,t, POSTt, POSTt * EarnSurpi,t, QUALi, POSTt * QUALi, EarnSurpi,t * QUALi,t, POSTt
* EarnSurpi,t * QUALi, LOSSi,t, Fixed Effectsi,t, ei,t) (12)
Robust t-statistics are shown in the brackets. Standard errors are clustered by firm. ***, **, and * indicate statistical
significance at 0.01, 0.05, and 0.10 levels, respectively. Variable descriptions are included in Appendix A.
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