ayers 2008
TRANSCRIPT
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1/50Electronic copy available at: http://ssrn.com/abstract=1316564Electronic copy available at: http://ssrn.com/abstract=1316564
Credit Ratings and Taxes: The Effect of Book/TaxDifferences on Ratings Changes
Benjamin C. AyersThe University of Georgia (706) 542-3772
Stacie K. LaplanteThe University of Georgia
(706) [email protected]
and
Sean T. McGuireTexas A&M University
(979) [email protected]
November 30, 2008
JEL Classification: G29, H25, H32, M41Key Words: Credit ratings, taxable income, book income, tax planning
______________ Ayers and Laplante gratefully acknowledge the support of the Terry College of Business
and the J.M. Tull School of Accounting. McGuire gratefully acknowledges the supportof the Mays Business School. All three authors gratefully acknowledge support of thePwC INQuires Program. We thank Dan Collins, Michelle Hanlon, Bruce Johnson, KenKlassen, Mark Laplante, Lillian Mills (Associate Editor), Tom Omer, Terry Shevlin,Ryan Wilson, and workshop participants at the University of Iowa, University of Texas,the 2007 American Accounting Association Annual Conference, the 2008 ContemporaryAccounting Research Conference, and two anonymous referees for helpful comments.
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Credit Ratings and Taxes: The Effect of Book/TaxDifferences on Ratings Changes
1. Introduction
This paper examines whether credit analysts utilize the information contained in the
difference between book and taxable income (the book- tax difference) in analyzing a firms credit
risk (i.e., credit rating). The importance of credit ratings continues to rise with the globalization
of financial markets and the increased use of credit ratings in financial regulation and contracting
(Frost 2007). The importance of the analysis performed by credit rating agencies is also
underscored by the magnitude of debt verses equity financing for public corporations. In 2005,
corporate bond issuances exceeded $771 billion, whereas firms issued less than $127 billion in
equity (Thomson Financial 2005a, b).
Despite the vital role that credit ratings play in capital markets, relatively little is known
about the information used by credit analysts in making rating recommendations. At recent
hearings by the Securities and Exchange Commission (SEC), buy- side firms stressed that the
market-place needs to more fully understand the reasoning behind a ratings decision and the type
of information relied upon by rating agencies in their analysis (SEC 2003, p. 33).1
Indeed,Holthausen and Watts (2001) call for research analyzing the use of financial statement
information by lenders and note that it is not clear that investors and lenders utilize financial
statement information in the same manner.
Investigating the use of the information in the book-tax difference by credit analysts is
important for several reasons. First, as described above, firms rely heavily on debt issuances as a
major source of financing. Likewise, chief executive officers identify credit ratings as one of
1 In response to recent corporate scandals, Section 702(b) of the Sarbanes-Oxley Act of 2002 called for areport on the role and function of credit rating agencies in the operation of the securities market thatcoincided with a report already underway at the SEC. Specific issues studied by the SEC included, amongother things, whether rating agencies should disclose more information about their ratings decisions (SEC2003). This anecdotal evidence reinforces the notion that the specific type of information used by agenciesis not commonly understood.
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their highest concerns when making capital structure decisions (Graham and Harvey 2001).
Changes in credit ratings have significant consequences to firms because credit ratings are often
used in financial contracts. For example, Kisgen (2006) notes that a downgrade in Enrons credit
rating triggered $3.9 billion in accelerated debt payments. Changes in credit ratings also trigger
significant reactions in equity markets that have become more pronounced since the
implementation of Regulation Fair Disclosure (Jorion et al. 2005). Accordingly, analyzing
factors that influence credit ratings provides insight into a process that has significant economic
consequences to firms, investors, executives, etc.
Second, it is not clear whether credit analysts utilize the information in book-tax
differences. Indeed, the rating criteria description provided by Standard and Poors, Moodys,
and Fitch do not specifically identify book-tax differences as a factor they consider in evaluating
a firms credit risk. Ganguin and Bilardello (2005) note that some analysts view a firms taxes as
a set percentage payment a nd fail to incorporate a firms tax burden into their analysis, which
implies that some analysts likely ignore the information contained in book-tax differences.
Anecdotal evidence is also consistent with this view. For example, prior research suggests that
Enron paid little tax on its inflated earnings from 1996 through 2000 (e.g., Citizens for Tax
Justice 2002; Desai 2005). It is quite clear that neither the equity or debt market understood the
consequences of Enrons book -tax differences. Finally, Maydew (2005) suggests that it is an
empirical question whether rating agencies are able to discern fully the obligations of off-balance
sheet financing, one source of positive book-tax differences. Maydew notes that rigorous
empirical evidence on this issue is in short supply. 2 We answer this call (at least in part) by
investigating whether changes in book-tax differences are systematically associated with credit
rating changes.
2 Mills and Newberry (2005) find that firms with lower prior period credit ratings are more likely to usehigher amounts of off-balance sheet financing. They do not investigate whether ratings agencies adjustcredit ratings to reflect the off-balance sheet financing.
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We anticipate that book-tax differences may be informative for credit rating agencies for
at least two reasons. First, as book and taxable income diverge, credit analysts may interpret this
divergence as a signal of decreased earnings quality, and thus, higher credit risk. Though rating
agencies do not provide a comprehensive description of the data they review in assessing
financial statement quality, Standard and Poors Corporate Rating Criteria (2006) indicates that
concerns about data quality would lead to lower ratings. 3, 4 In turn, financial accounting texts,
extant literature, and anecdotal evidence suggest that book-tax differences may be especially
useful in assessing earnings quality. For example, Palepu et al. (2005, p. 3-10) suggests that a
widening book-tax difference represents a potential danger as it might indicate deteriorating
earnings quality, and Hanlon (2005) finds that firms with large positive and large negative book-
tax differences have less persistent earnings. Thus, book-tax differences may be a potentially
parsimonious variable to assess earnings quality, especially given the pressure that rating
agencies are under to issue ratings in a timely fashion (e.g., Mahoney 2002). Second, as
mentioned above, off-balance-sheet financing is one source of positive book-tax differences (i.e.,
book income exceeds taxable income). To the extent that book-tax differences (e.g., large
positive changes in book-tax differences) convey information regarding off-balance sheet
financing, such differences may be especially relevant to credit rating agencies.
To examine whether credit analysts use the information contained in book-tax
differences, we investigate whether changes in book-tax differences are associated with credit
3 Standard and Poors Corporate Rating Criteria (2006, pp. 23 -24) states that their analysis of financialstatements begin with a review of a firms accounting characteristics to determine whether ratios andstatistics derived from the financi al statements can be used appropriately to measure a companys
performance. The rating criteria further states (p. 117) that qualms about data quality (dubbed
information risk) would translate into a lower rating. Moodys (Mahoney 2002, p. 6) and Fit ch (2006, p. 4) also indicate that the qualitative characteristic of accounting quality is taken into consideration in therating process. Standard and Poors Rating Criteria, Moodys, and Fitch do not specifically identify book -tax differences as a variable they use to assess earnings quality; however, rating agencies provide littledetail on the measures they use. Thus, it is an empirical question whether rating agencies incorporate theinformation contained in book-tax differences in their analysis.4 Whether information risk is diversifiable or not (e.g., Core, Guay, and Verdi 2007; Francis, LaFond,Olsson, and Schipper 2005; Hughes, Liu, and Liu 2007; Lambert, Leuz, and Verrecchia 2007, etc.) has no
bearing on this studys research question. Specifically, because credit ratings represent a specific firmscredit risk (i.e., risk of loss) and not simply nondiversifiable sources of risk, the debate over thediversifiability of information risk is not central to this study.
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rating changes. We utilize a changes specifica tion because credit ratings are inherently sticky,
and thus, a changes specification provides a more powerful test of whether credit rating agencies
incorporate the information contained in book-tax differences in their analyses. 5, 6 Large positive
changes in book-tax differences may signal decreased earnings quality or increased off-balance
sheet financing, both of which should be interpreted as negative information for credit rating
agencies. Accordingly, we expect a negative association between positive changes in book-tax
differences and credit rating changes (i.e., the larger the positive change in book-tax differences,
the more likely a rating downgrade). The prediction for negative changes in book-tax differences
is less clear. Large negative changes in book-tax differences may signal decreased earnings
quality (Hanlon 2005), which may be interpreted as negative information for credit rating
agencies. However, negative changes in book-tax differences may also signal decreased off-
balance sheet financing, which would be viewed as positive information. Given these potentially
offsetting signals, we make no prediction for the effect of negative changes in book-tax
differences on credit ratings changes.
We rank positive changes in book-tax differences yearly into deciles and do the same for
negative changes in book-tax differences. Using an ordered logit model, we regress rating
changes that occur between the end of fiscal year t and the end of fiscal year t+1 on the decile
rank of positive changes in book-tax differences, the decile rank of the absolute value of negative
changes in book-tax differences, and control variables (e.g., changes in leverage, size,
profitability, cash flows, interest coverage, etc.) for year t . Consistent with large positive changes
in book-tax differences signaling decreased earnings quality and/or increased off-balance sheet
financing, results suggest a significant negative association between positive changes in book-tax
differences and credit rating changes. That is, large positive changes in book-tax differences are
associated with less favorable rating changes. We estimate that moving from the first to the third
5 Ceteris paribus , the sticky nature of ratings would bias against our predictions that rating agenciesincorporate the information in book-tax differences into their rating decisions in a timely manner.6 The level of differences in book and taxable income is also significantly correlated across adjacent years(i.e., =0.29 , Spearman correlation), which further suggests that a changes approach is appropriate.
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quartile of positive changes in book-tax differences decreases the probability of a rating upgrade
by 20.3% (from 6.4% to 5.1%) and increases the probability of a rating downgrade by 23.4%
(from 11.1% to 13.7%). We also find that large negative changes in book-tax differences are
associated with less favorable rating changes, consistent with these changes signaling decreased
earnings quality (instead of decreased off-balance sheet financing). Given the pattern of the
association between credit rating changes and both large positive and large negative changes in
book-tax differences, on balance, we interpret our results as being consistent with large changes
in book-tax differences (positive or negative) signaling decreased earnings quality. Results
suggest that moving from the first to the third quartile of negative changes in book-tax differences
decreases the probability of a rating upgrade by 15.2% (from 6.6% to 5.6%) and increases the
probability of a rating downgrade by 16.8% (from 10.7% to 12.5%).
As a refinement of our primary test, we examine situations where book-tax differences
likely result from tax planning to determine if the association between book-tax differences and
rating changes varies based on the source of the change in book-tax difference. 7 Prior research
suggests that taxable income is less useful to equity investors in evaluating firm performance (i.e.,
less value-relevant) when firms engage in tax planning, which is consistent with equity investors,
at least in part, distinguishing sources of book-tax differences (Ayers, Jiang and Laplante 2009).
In our setting, we anticipate the change in book-tax differences to be less informative regarding
earnings quality for high tax planning firms. Specifically, changes in book-tax differences,
whether positive or negative, may be more representative of variations in tax planning (increases
or decreases) than earnings quality. Thus, we expect tax planning to attenuate the relation
between changes in book-tax differences and credit ratings changes. We identify firms as high
tax planners using two separate measures: cumulative current effective tax rates and cumulative
7 From a research design standpoint, this analysis is particularly appealing as it allows us to investigatewhether the association between changes in book-tax differences and credit ratings changes is attenuated ina setting where book-tax differences are less likely to signal poor earnings quality. Thus, this analysis
provides some assurance that our primary tests capture the effect of book-tax differences on credit ratingsinstead of other correlated information.
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cash effective tax rates. We calculate the cumulative current (cash) effective tax rate as the ratio
of the sum of current tax expense (cash taxes paid) over the five years from year t-5 to year t-1 to
the sum of pre-tax book income less special items calculated over the same period (Dyreng,
Hanlon and Maydew 2008; Ayers et al. 2009).
Results suggest that for firms classified as non -tax planners using either tax planning
measure (i.e., those in the top four quintiles of cumulative current (cash) effective tax rates), both
large positive and large negative changes in book-tax differences are associated with less
favorable rating changes. For high tax-planning firms (i.e., those firms in the bottom quintile of
cumulative current (cash) effective tax rates), we find no association between changes in book-tax
differences and rating changes. These results suggest that for high tax planning firms, large
changes in book-tax differences do not signal poor earnings quality and thus, are not associated
with less favorable rating changes.
This study makes several contributions. Maydew (2005) notes that rigorous empirical
evidence is in short supply on whether rating agencies are able to discern fully the obligations of
off-balance sheet financing, one source of positive book-tax differences. This study answers this
call more broadly by investigating whether changes in book-tax differences are systematically
associated with credit rating changes. On balance, our evidence is consistent with large positive
and large negative book-tax changes signaling lower earnings quality, which results in less
favorable credit ratings. In addition to enhancing our understanding of the information used by
credit agencies, this study contributes to the emerging literature on the use of book-tax
differences by various financial statement users (e.g., investors, analysts, the IRS, and auditors). 8
Among other implications, this studys evidence may be useful to researchers or others interested
in modeling the credit rating process as it suggests that the change in book-tax differences is a
parsimonious variable that captures a construct of interest to credit analysts. Finally, recent
8 Prior research examines whether investors (Hanlon 2005, Lev and Nissim 2004), the IRS (Mills 1998),analysts (Weber 2006), and auditors (Hanlon and Krishnan 2006) utilize the information contained in book-tax differences.
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research concludes that equity investors, at least in part, are able to distinguish sources of book-
tax difference (Ayers, Jiang and Laplante 2009). This study extends this research by
investigating whether the association between credit rating changes and changes in book-tax
differences varies based on the source of the change in book-tax differences. Consistent with the
association varying by source of the change in book-tax differences, we find that tax planning
attenuates the relation between the change in book-tax differences and credit rating changes.
Our paper proceeds as follows. Section 2 develops our hypotheses, while Section 3
describes our research method. Section 4 discusses our sample, descriptive data and results.
Section 5 presents sensitivity analyses, and Section 6 concludes.
2. Background and Hypothesis Development
A. Credit Rating Agencies and Credit Analysis
Credit rating agencies engage in credit analysis to form an opinion on the
creditworthiness of either a debt issuer or a specific debt obligation. Credit rating agencies may
assign any one of the following credit ratings: AAA, AA, BBB, BB, B, CCC, CC, C, and D
where AAA is the highest rating and D is the lowest. Ratings of BBB or higher are considered
investment grade, meaning they are generally eligible for bank investment, while ratings of BB or
lower are speculative. Plus or minus signs or numbers are often added to the rating to show
relative standing within each major category, which provides more detailed indications of credit
quality. 9
Unlike equity holders who invest with an eye towards sharing in the upside profitability
of a firms success, creditors are concerned with the debtors ability to repay the debt plus an
acceptable return in a timely manner. Credit ratin gs reflect agencies long -term assessments of
both qualitative and quantitative risk profiles through macro and micro economic cycles, and are
therefore quite sticky (Pettit et al. 2004). Credit analysts have a different relationship than
9 See Frost (2007) and SEC (2003) for more details on credit rating agencies.
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other stakeholders with firms in that they have access not only to the information that the firm
makes publicly available, but they also have access to private information (Regulation FD did not
affect credit rating agencies access to private information) . Credit agencies typically gather
information from the firm before making a credit rating decision, and they also offer firms the
opportunity to provide additional information to the rating agency before publicly releasing a
credit rating or change in rating. Nonethe less, a firms financial statements (and related
disclosures) serve as rating agencies primary source of information regarding the financial
condition and financial performance of companies (Standard and Poors Corporate Rating
Criteria 2006, p. 23). 10
While rating agencies have been criticized for their lack of timeliness in identifying
recent corporate problems (e.g., Enron, Worldcom, K-Mart), the provision of credit over the past
decade in the United States has shifted away from commercial banks to the rated capital markets,
making public debt ratings generally more important to issuers than in the past (Pettit et al. 2004).
In fact, Graham and Harvey (2001) report that firms identify credit ratings as their second highest
concern when determining capital structure, i.e., firms care about credit ratings (Kisgen 2006).
B. Accounting Information and Credit Ratings
Despite the inherent importance of the credit ratings process, relatively little is known
about the specific information used by credit analysts in making rating recommendations, hence,
the calls for more disclosures regarding ratings decisions (SEC 2003) and research investigating
the use of accounting information by lenders (Holthausen and Watts 2001). Early studies
investigating the use of accounting information by credit rating agencies typically focused on
various ratios, such as interest coverage, long-term debt to total assets, profitability and size
10 Credit ratings presumably have access to a firms tax returns. Rating agencies use of tax returns toevaluate differences in book and taxable income would be consistent with our expectations but alsosuggests that our proxy for book-tax differences (described later) is measured with error. We know of noreason why this measurement error would bias in favor of our predicted relations. Nonetheless, we conductseveral sensitivity analyses (described in Section 5) to provide assurance that measurement error in taxableincome does not significantly influence our results.
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potentially parsimonious variable to assess earnings quality, especially given the pressure that
rating agencies are under to issue ratings in a timely fashion. 15
Second, off-balance-sheet financing is one source of positive book-tax differences (i.e.,
book income exceeds taxable income) (Mills and Newberry 2005). For example, book-tax
differences capture differences in the gain or loss treatment for book and tax purposes arising
from the initial asset transfer to create synthetic leases or securitizations. Off-balance-sheet
financing can be a significant threat to debtors ability to repay outstanding obligations with
disastrous financial consequences to the firms creditors. To the extent that changes in book -tax
differences convey information regarding off-balance sheet financing, such differences are likely
relevant to credit rating agencies because of their focus on the creditworthiness of debtors. While
rating agencies indicate they incorporate off-balance sheet financing into their analyses (Standard
and Poors Rating Criteria 2006, pp. 28 -29), Maydew (2005) suggests that it is an empirical
question whether rating agencies are able to discern fully the obligations of off-balance sheet
financing and notes that rigorous empirical evidence on this issue is in short supply. 16
D. Hypotheses
To examine whether credit analysts use the information contained in book-tax
differences, we investigate whether changes in book-tax differences are associated with the
probability of a change in credit ratings. As discussed above, large positive changes in book-tax
differences may signal decreased earning quality or increased off-balance sheet financing, both of
which should be interpreted as negative information for credit ratings agencies. Thus, we
15 We do not contend that taxable income is a better performance measure than book income as prior
research finds that book income better reflects investors perception of firm perform ance than taxableincome (Hanlon, Laplante, and Shevlin 2005; Ayers, Jiang, and Laplante 2009). Instead, as suggested in prior research (e.g., Hanlon 2005; Dhaliwal, Gleason, and Mills 2004; Phillips et al. 2003; Ayers, Jiang,and Young et al. 2006) and anecdotally (financial statement analysis texts), we expect that book-taxdifferences may be an informative signal of earnings quality because firms manage earnings upward viatemporary and permanent differences (Phillips et al. 2003; Dhaliwal et al. 2004), large book-tax differencesare likely if firms take a big bath, and firms with large book -tax differences have less persistent earnings(Hanlon 2005).16 To the extent that credit rating agencies utilize other sources of off-balance sheet financing not captured
by book-tax differences, our tests should be biased against finding a significant association between ratingchanges and changes in book-tax differences.
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expect that the larger the positive change in book-tax differences, the more likely a firm will
experience a less favorable rating change. Our first hypothesis stated in the alternative is:
H1: There is a negative association between positive changes in book-tax differences andcredit rating changes [i.e., the larger the positive change in book-tax difference the more(less) likely a firm is to experience a credit rating downgrade (upgrade)].
The effect of negative changes in book-tax differences on credit ratings is less clear. As
discussed above, large negative changes in book-tax differences might signal decreased earnings
quality (Hanlon 2005), which would be interpreted as negative information for credit ratings
agencies. However, large negative changes in book-tax differences could also represent a
significant decrease in off-balance sheet financing, which should be interpreted as a positive
signal of the firms credit worthiness. Given these potentially offsetting signals, we make no
prediction for the effect of negative changes in book-tax differences on credit rating changes.
Prior research suggests that taxable income is less informative as a performance measure
to equity investors when firms engage in tax planning (Ayers, Jiang and Laplante 2009). This
evidence is consistent with tax planning resulting in taxable income that obscures the firms
actual performance and suggests that equity investors, at least in part, distinguish sources of
book-tax differences. In our setting, we anticipate the change in book-tax differences to be less
informative regarding earnings quality for high tax planning firms. 17 Specifically, changes in
book-tax differences, whether positive or negative, may be more representative of variations in
tax planning (increases or decreases) than earnings quality. Thus, we expect tax planning to
attenuate the relation between changes in book-tax differences and credit ratings changes. Our
second hypothesis stated in the alternative is:
17 We note that off-balance-sheet financing is one form of tax planning. To the extent that tax planningreflects off-balance-sheet financing, we would not expect the association between book-tax differences andcredit ratings to decline. Likewise, to the extent that high tax planning firms also have low earnings qualitythis would also bias against our second hypothesis. Ayers, Jiang and Laplante (2009) find that the book income for high tax planning firms explains a similar proportion of returns as that of other firms,suggesting that book income for tax planning firms is as informative as book income for other firms.
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H2: Relative to low tax planning firms, the association between positive changes in book-tax differences and the likelihood of credit ratings changes is less pronounced (or notstatistically significant) for high tax planning firms.
3. Research Methods
To examine whether credit analysts use the information contained in book-tax
differences, we investigate whether changes in book-tax differences are associated with credit
rating changes. We utilize a changes specification because credit ratings are inher ently sticky,
so a changes specification provides a more powerful test of whether credit rating agencies
incorporate the information contained in book-tax differences in their analyses. Our primary
model is as follows:
(1)
where all variables are defined in Table 1 and discussed below.
Our dependent variable ( Rate it+1 ) captures changes in firm is Standard & Poors senior
debt rating that occur between the end of fiscal year t and the end of fiscal year t+1. Standard &
Poors rates a firms debt from AAA (indicating a strong capacity to pay interest and repay
principal) to D (indicating actual default). We translate ratings letters into numbers, with a larger
number indicating a better rating, and subtract the end of year t rating from the end of year t+1
rating to calculate Rate it+1 .18 A positive value for Rate it+1 indicates a rating upgrade, a negative
value indicates a rating downgrade, and a zero value indicates no rating change. Bond ratings do
not represent equally spaced discrete intervals, so ordinary least squares estimation is not
appropriate in our setting. For example, the change in risk in moving two rating categories (e.g.,
between an AAA and an AA bond) is greater than the change in risk in moving one rating (e.g.,
18 We convert credit ratings to a numerical scale from 1 to 20 with higher numbers equaling better ratings.For example, AAA = 20, AA+ = 19, AA= 18, AA- = 17, and D = 1.
1 1 2 3 4 5
6 7 8 9 10 11
12 13 14 15 25 26 76
Pr( )
&
it it it it it it
it it it it it it
it it it t it it
Rate P BTD N BTD AQ Size Loss
E CFO IntCov BTM StdROA StdRet
Lev R D CapInt Year Industry
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between an AAA and an AA+ bond), but it is not twice as great. Therefore, we utilize ordered
logistic analysis to examine the relation between book-tax differences and credit rating
decisions. 19
We investigate the effects of book-tax differences on credit rating decisions by separately
analyzing the association between positive and negative changes in book-tax differences
( PBTD it and NBTD it ) and credit rating changes. PBTD it is the decile rank of BTD it for firm-
years when BTD it is greater than or equal to zero, and zero otherwise. Likewise, NBTD it is the
decile rank of BTD it for firm-years when BTD it is less than zero, and zero otherwise (the larger
the negative change in book-tax differences, the higher the rank). 20 We calculate BTD it as the
change in firm is book-tax difference from year t-1 to year t , where book-tax difference is book
income minus estimated taxable income deflated by average total assets (data 6). 21 We examine
total book-tax differences because both temporary and permanent book tax differences may signal
decreased earnings quality or changes in off-balance sheet financing. Regarding earnings quality,
prior research suggests that changes in permanent book-tax differences (i.e., decreases/increases
in effective tax rates) are viewed as transitory by investors (Schmidt 2006) and that effective tax
rates are used to manage earnings (e.g., Dhaliwal et al. 2004; Krull 2004). Likewise, other
studies (e.g., Phillips et al. 2003 and Hanlon 2005) suggest that temporary book-tax differences
signal poor earnings quality. Changes in book-tax differences also incorporate some forms of
off-balance sheet financing and other tax shelters. For example, book-tax differences capture
differences in the gain or loss treatment for book and tax purposes arising from the initial asset
transfer to create synthetic leases or securitizations. 22 Following Hanlon et al. (2005), we
calculate taxable income as the sum of federal tax expense (data 63) and foreign tax expense (data
19 Our results are robust to using a generalized ordered logit model which relaxes the proportional oddsassumption imposed by the standard ordered logit model. See Fu (1998) for additional details.20 We rank PBTD it and N BTD it because descriptive statistics (untabulated) show that the means of both
PBTD it and N BTD it are just below the 75th percentile, which suggests both variables are highly skewed.
Inferences are similar if we use unranked values.21 Unless otherwise noted, data refers to Compustat data items.22 See Mills and Newberry (2005) for more details on off-balance sheet financing, and Treasury (1999),Graham and Tucker (2006) and Wilson (2009) for additional details on specific tax shelters.
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64) divided by the top U.S. statutory tax rate less the change in net operating loss carryforward
(data 52). If federal or foreign tax expense is missing, we measure tax expense as the difference
between total income tax expense (data 16) and deferred income tax expense (data 50). Book
income is pre-tax book income (data 170) less minority interest (data 49).
We utilize an estimate of taxable income derived from the financial statements. Plesko
(2000 and 2006) provides evidence that taxable income calculated from financial statements is
highly and significantly correlated with firms actual taxable income, thus providing some
assurance that taxable income estimated from financial statements is a reasonable proxy for a
firms actual taxable income. 23 As in prior research, however, this proxy for taxable income
contains measurement error due largely to seven major problems in estimating a firms tax
liability and hence taxable income from financial statement disclosures (Hanlon 2003). Briefly,
the problems include reserves for uncertain tax positions, intraperiod tax allocation,
consolidation, employee stock option exercises, tax credits, foreign operations, and negative
taxable income. Because we compute changes in book-tax differences, measurement error in
estimating taxable income is less of a concern in our setting to the extent that any error is similar
across years. Nonetheless, we conduct several sensitivity analyses (described in Section 5) to
provide assurance that measurement error in taxable income does not significantly influence our
results.
Our setting differs from prior research because credit analysts potentially have access to
actual tax return information and do not need to rely on financial statement disclosures to
estimate taxable income. To the extent that credit analysts utilize actual tax disclosures, our
23 Plesko (2000 and 2006) restricts the sample of firm years used in his primary tests to exclude firms withforeign operations and any apparent differences in consolidation for book and tax purposes. Plesko (2006)also compares financial statement and tax return data for an unrestricted sample of 37,853 firm years from1994 to 2001. For this sample, he is unable to reject the null hypothesis of no difference between taxableincome estimated from the financial statements (grossing up federal tax expense (data 63)), and either taxable income from the Form 1120 (line 28) or taxable income subject to tax from the Form 1120 (line 28less special deductions and NOL carryovers). Lisowsky (2009) also finds that total tax after credits fromthe tax return (Form 1120, Line 31) is highly and significantly correlated with federal tax expense (data 63)for 4,011 publicly traded U.S. corporations (excluding foreign-controlled firms) from 2000 to 2004.
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proxy for taxable income is subject to additional measurement error (i.e., it is arguably further
away from the measure of taxable income used by credit analysts). Given that tax returns are
private and we have no way of determining for which firms rating agencies incorporate actual tax
return data, we cannot control for this issue. Nonetheless, we know of no reason that this
measurement error should systematically bias analyses in favor of our expectations.
H1 predicts that the larger the positive change in book-tax difference the more (less)
likely a firm is to experience a ratings downgrade (upgrade). Thus, we expect a negative
association between PBTD it and Rate it+1 . We make no predictions regarding the association
between NBTD it and Rate it+1 . However, to the extent that negative changes in book-tax
differences signal decreased earnings quality, we would expect a negative association between
NBTD it and Rate it+1 . Likewise, to the extent that negative changes in book-tax differences
represent a decrease in off-balance sheet financing, we would expect a positive association
between NBTD it and Rate it+1 .
H2 predicts that tax planning attenuates the relation between the changes in book-tax
differences and credit rating changes. To investigate this hypothesis we expand our model to
include an indicator variable for high tax planning firms ( TaxPlan it ) and interact this indicator
with our measures of changes in book-tax differences ( PBTD it and NBTD it ).
1 1 2 3 4
5 6 7 8 9
10 11 12 13 14 15
16
Pr( )
&
it it it it it it
it it it it it it
it it it it it it
it
Rate P BTD N BTD TaxPlan P BTD TaxPlan
N BTD TaxPlan AQ Size Loss E
CFO IntCov BTM StdROA StdRet Lev
R D
17 18 28 29 78it t it it CapInt Year Industry
(2)
where variables are as previously defined or discussed below.
Dyreng et al. (2008) contend that tax avoidance firms are those that are able to sustain a
low tax rate over multiple years. Following Dyreng et al. (2008), we identify high tax planning
firms as firms in the lowest quintile of accumulated effective tax rates ( ETR) for each year and
two-digit SIC industry calculated as follows:
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1
51
5
.
t
imm t
t it
im imm t
(X) ETR
PTBI Special Items
(3)
We calculate ETR two ways. In our first measure, Trad ETR , the numerator, X , is current taxexpense calculated as total tax expense (data 16) less deferred tax expense (data 50) for firm i
summed over the five year period from t-5 through t-1 . The denominator is the difference
between PTBI , pre-tax book income (data 170), and Special Items (data 17) accumulated for firm
i over the five year period from t-5 through t-1 . If Special Items is missing, it is set equal to zero.
In our second measure of ETR, Cash ETR , the numerator, X , in equation (3) is cash taxes paid
(data 317) from the cash flow statement for firm i summed over the five year period from t-5
through t-1 , while the denominator remains the same. If cash taxes paid is missing for a
particular year, it is set equal to current tax expense for that year. Both measures of ETR capture
the effects of tax planning strategies that defer or permanently avoid taxable income (i.e.,
deferred tax expense is excluded from the calculation of Trad ETR , and Cash ETR only reflects
taxes once they are paid). Using taxes actually paid to calculate Cash ETR addresses some of the
known limitations of using current tax expense, such as ignoring the tax benefits associated with
stock options. 24
In equation (2), TaxPlan it equals one for firms in the lowest quintile of accumulated ETR
for each year and two-digit SIC industry, and zero otherwise. To examine the effects of tax
planning on the association between changes in book-tax differences and credit rating changes,
we analyze the coefficients for PBTD it x TaxPlan it as well as the sum of the coefficients for
PBTD it and PBTD it x TaxPlan it . A positive and significant coefficient for PBTD it x TaxPlan it
or evidence that the sum of the coefficients for PBTD it and PBTD it x TaxPlan it is not
significantly different from zero would be consistent with H2.
24 In a one period setting, using taxes actually paid creates measurement error because it captures paymentsthat are not necessarily applicable to income generated in the current period (e.g., estimated tax payments).However, because we aggregate both cash taxes paid and pre-tax book income over a five year period, it ismore likely that the income to which the tax payments relate is in the denominator (Dyreng et al. 2008).
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In addition to our variables of interest, equations (1) and (2) include controls for factors
previously demonstrated to be associated with credit rating changes. Francis et al. (2005) predict
and find that credit ratings decline with firm information risk represented by poor accruals
quality. We include a control variable, change in accruals quality ( AQ it ), to ensure that book-tax
differences do not simply capture the same construct represented by accruals quality (i.e., to
ensure that book-tax differences provide unique information to credit analysts). AQ it is defined
as the decile rank of firm is change in accruals quality from year t-1 to year t . We calculate
accruals quality using Dechow and Dichevs (2002) measure as modified by McNichols (2002)
and Francis et al. (2005). Specifically, we estimate the following model for each of Fama and
Frenchs (1997) 48 industry groups with at least 20 firms in year t :
1 1 2 3 1 4 5it it it it it it it TCA CFO CFO CFO Rev PPE (4)
where:
TCAit = firm is total current accruals in year t , defined as ( CAit - CL it - Cash it + STDEBT it );
CA it = firm is change in current assets (data 4) between year t-1 and t ;
CL it = firm is change in current liabilities (data 5) between year t-1 and year t ;
Cash it = firm is change in cash (data 1) between year t-1 and year t ;
STDEBT it = firm is change in current liabilities (data 34) between year t-1 and year t;
CFO it = cash flow from operations in year t, calculated as net income beforeextraordinary items (data 18) less total accruals ( TA It );
TAit = CA it - CL it - Cash it + STDEBT it DEPN it ;;
DEPN it = firm is depreciation and amortization expense (data 14) in year t;
Rev it = firm is change in revenues (data 7) between year t-1 and year t ;
PPE it = firm is gross value of property, plant, and equipment (data 12) in year t.
We deflate all variables in equation (4) by average total assets in year t and t-1 and
winsorize all independent variables at the 1 st and 99 th percentiles of the sample distribution to
remove the effects of outliers. We define accrual quality, AQ it , as the standard deviation of firm
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i s residuals, it ,. We estimate the change in accruals quality as the difference in accruals quality
from year t-5 to year t-1 relative to accruals quality from year t-4 to year t . A positive change
corresponds to an improvement in accruals quality, while a negative change corresponds to a
decrease in accruals quality. We expect a positive association between AQ it , and Rate it+1 .
Prior research notes that the cost of debt is associated with firm characteristics such as
size, profitability, and risk (e.g., Kaplan and Urwitz 1979; Sengupta 1998; Ahmed et al. 2002;
Jiang 2008). We control for the relation between size and cost of debt by including the change in
the natural logarithm of total assets from year t -1 to year t ( Size it ). We expect a positive
association between Rate it+1 and Size it . We include two variables to control for the association
between firm profitability and the cost of debt. Jiang (2008) finds that creditors place greater
emphasis on beating the zero -earnings benchmark relativ e to other earnings benchmarks. We
control for the effect that a loss has on the cost of debt using a dichotomous variable equal to one
if a firm has negative earnings per share in the current year ( Loss it ) and zero otherwise. We
include the change in earnings from year t-1 to year t deflated by total assets at the end of year t-1
( E it ) to control for the effects of changes in firm profitability (earnings growth) on credit rating
changes. We expect a negative coefficient on Loss it and a positive coefficient on E it .25
To
control for the association between firm risk and cost of debt we include proxies for changes in
operating uncertainty ( StdRoa it ), equity risk ( StdRet it ), overall leverage ( Lev it ), and firm
growth opportunities ( R&D it ). We expect negative coefficients on each of the variables
proxying for risk. 26 We also include the change in capital intensity, ( CapInt it ), because
significant capital acquisitions are likely to lead to large book-tax differences due to different
depreciation for book and tax purposes. While firms with greater capital intensity are generally
less risky for lenders, this may not be true if the additional assets do not have a ready market
25 In sensitivity analysis, we estimated the regressions after separating E into positive and negative E .Inferences are the same.26 In sensitivity analysis, we also include beginning of year credit ratings as a control for any effect of exante credit risk on ratings changes. Results are similar to those presented in the tables, and we find that
beginning of year ratings are negatively associated with ratings changes.
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(e.g., Sea Launchs dedicated launch platform that is used to launch rockets from oceanic
equatorial locations). Thus we do not make a prediction for sign of the coefficient on CapInt it .27
Ganguin and Bilardello (2005) note that cash generation and firm performance, as well as
industry specific and macro-economic events, influence credit analysis. We include changes in
cash flow from operations ( CFO it ), interest coverage ( IntCov it ), and the book-to-market ratio
( BTM it ) from year t-1 to year t as controls for firms ability to repay debt and gener al firm
performance. We expect positive coefficients on CFO it and IntCov it and a negative coefficient
on BTM it .28 We include year ( Year it ) and industry ( Industry it ) indicator variables to control for
year and industry-specific events that may influence credit ratings including changes in credit
rating standards through time (Blume et al. 1998). 29
4. Sample and Results
A. Sample Selection
Our primary sample consists of the intersection of the 2005 Compustat database and
CRSP stock return files. We begin our sample period in 1993 (i.e., the effective date of SFAS
No. 109) and exclude financial institutions (SIC codes 6000 6999), public utilities (SIC codes
4900 4999), and firms incorporated outside of the United States because these firms face
different tax, regulatory, and/or financial reporting issues than the remaining Compustat
population. We eliminate all firm- year observations missing Standard & Poors (S&P) senior
27 In sensitivity analysis, we also include a control for changes in corporate governance measured as thechange in the Gompers Index from period t-1 to period t because ratings agencies state that they consider the qualitative characteristic of governance in their ratings. Inferences remain the same, and the coefficientfor this variable is insignificant.28 To the extent that changes in book-to-market ratios capture changes in accounting conservatism, wewould also expect a negative coefficient for BTM it . 29 Ahmed et al. (2002) find that bond ratings are associated with conservatism. In sensitivity tests, wefollow Ahmed et al. (2002) and Givoly and Hayn (2000) and proxy for conservatism using the average of total accruals from t-4 to t (to match this studys accumulation period) multiplied by negative one.Inferences remain the same.
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debt rating (data 280) for two consecutive years. 30 We require that firm-year observations have
sufficient data to allow us to estimate the change in the book-tax difference and control variables
described in Section 3. Finally, we limit our sample to firms with positive values in the
denominator of equation (3) (sum of pre-tax book income less special items from year t-5 to year
t-1).31 These criteria result in a final sample consisting of 3,132 firm-year observations from
1994 to 2004. 32
B. Descriptive Statistics
Table 2 presents the distribution of credit rating changes for our main sample. The
aggregate sample shows that the majority of firm-years do not experience a change in credit
ratings (78%). When firm-years experience a change in credit rating, downgrades (15%) are
more prevalent than upgrades (7%). To examine whether firm-years with large positive
(negative) book-tax difference changes experience a different pattern of ratings changes
compared to firm-years with small book-tax difference changes, we subdivide our sample based
on the sign and magnitude of the change in book-tax differences. We classify firm-years as
having large positive ( LPBTD ) or large negative ( LNBTD) changes in book-tax differences if
they are in the upper (lower) 30 percent of all firm-years with a positive (negative) book-tax
difference change. We designate firm-years as having small changes in book-tax differences
(SBTD) if they are in the lower (upper) 30 percent of all firm-years with a positive (negative)
book-tax difference change. 33 The frequency distribution presented in Table 2 shows that the
pattern of credit ratings changes is relatively consistent regardless of the sign and magnitude of
the book-tax difference change. The one exception is that the frequency of downgrades for firms
30 Following Jiang (2008), we exclude observations with dramatic rating changes (i.e., | Rate it+1 | > 3) because such changes may be due to coding errors or significant events (e.g., merger and acquisitionactivity) which our model does not capture.31 We repeat all tests using a sample that does not require a positive denominator for equation (3). Thesample size is 3,369, and all inferences remain.32 Our final sample spans the years 1994 2004 due to the changes specification used in our tests.33 We replicate our ordered logistic analyses using this classification scheme. Specifically, we restrict thesample to firms with LPBTD , LNBTD , and SBTD , and then in equation (1) set PBTD equal to one for
LPBTD firms (zero otherwise) and NBTD equal to one for LNBTD firms (zero otherwise). Results aresimilar to those presented in tables 4 and 5.
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in the LNBTD is more than twice that of firms in the SBTD subsample (24% compared to
11%). Descriptive statistics presented in Table 3, Panel A suggest that this result may be due to
the high proportion of losses experienced by firms in the LNBTD subsample relative to the other
subsamples. In sum, frequency analyses provide little evidence that changes in book-tax
difference influence credit ratings. However, because frequency analyses do not incorporate
controls for other factors known to influence ratings changes (changes in profitability, risk, etc.),
they are not particularly powerful tests of our predictions. Accordingly, we focus our attention on
logistic regression analyses that include controls for other important factors known to influence
credit ratings.
Table 3, Panel A reports descriptive statistics for the overall sample as well as the
LPBTD , LNBTD , and SBTD subsamples. 34 The descriptive statistics for the overall sample
suggest that changes in the book-tax difference are positive on average. Univariate analysis
suggests that firms in both the LPBTD and LNBTD subsamples are smaller and have a higher
proportion of losses relative to the SBTD subsample. Surprisingly, firms in the LPBTD
subsample have similar ETRs relative to firms in the SBTD subsample, while firms in the
LNBTD subsample have lower ETRs relative to firms in the SBTD subsample. However, firms
in the LPBTD subsample have improved earnings performance, cash flow, interest coverage,
and leverage relative to the SBTD subsample. At the same time, firms in the L NBTD
subsample have deteriorating earnings performance, cash flow, interest coverage, and leverage
relative to the SBTD subsample. This evidence reiterates the importance of controlling for these
factors in our analyses.
Panel B of Table 3 presents univariate correlations for the aggregate sample with Pearson
(Spearman) correlations reported above (below) the diagonal. For these analyses, we compute
the correlation between PBTD it and other variables only for those observations with positive
values of PBTD it . We do the same for NBTD it . PBTD it is not significantly correlated with
34 We winsorize (reset) all continuous variables at 1 percent and 99 percent to mitigate the influence of extreme observations.
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Rate it+1 while NBTD it is negatively correlated with Rate it+1 . However, both PBTD it and
NBTD it are significantly correlated with E it . In combination, these correlations suggest that
univariate analysis is not appropriate when evaluating the association between changes in the
book-tax difference and Rate it+1 . All control variables are significantly correlated with Rate it+1
in the predicted direction except for R&D it .
C. Main Results
Panel A of Table 4 presents the results of estimating equation (1). 35 The first column in
Panel A includes results without the change in accruals quality control variable ( AQ). While the
coefficients on PBTD it and NBTD it are significantly negative ( p-value = 0.006, one-tailed test
on PBTD it , and p-value = 0.053, two-tailed test on NBTD it ) consistent with H1, we are
interested in capturing the information provided by changes in book-tax differences that is
incremental to previous accruals quality measures. 36 Thus, we focus on the results of estimating
equation (1) after controlling for the change in accruals quality.
The second column in Panel A of Table 4 presents the results of tests of our first
hypothesis. Consistent with H1, we find a negative and significant association between PBTD it
and credit rating changes ( p-value = 0.007, one-tailed test). This result suggests that increasingly
positive changes in book-tax differences signal negative information (e.g., decreased earnings
quality, increased off-balance-sheet financing) to credit analysts. Given the competing
predictions for what the information in negative changes in book-tax differences signals to credit
analysts (i.e., decreased earnings quality or decreased off-balance sheet financing), we make no
35 We use Rogers standard errors in all analyses to correct for serial correlation among multipleobservations per firm (Rogers 1993).36 We also consider alternative measures of accruals quality. Specifically, we recalculate the AQ it as thechange between the absolute residual from equation (4) in year t-1 and the absolute residual in year t (Dechow and Dichev 2002). As an additional sensitivity test, we proxy for accruals quality using thechange in the absolute value of discretionary accruals from year t-1 to year t (and alternatively, the absolutevalue of abnormal accruals), where discretionary accruals are calculated using both a modified Jones modeland a cross-sectional modified Jones Model with lagged return-on-assets (Kothari et al. 2005). Resultsfrom these analyses remain similar to those presented in the tables.
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prediction for the association between NBTD it and rating changes. Consistent with large
negative changes in book-tax differences indicating negative information to credit analysts, we
find a negative and significant association between NBTD it and rating changes ( p-value = 0.060,
two-tailed test). We interpret this evidence to suggest that credit analysts view large negative
changes in book-tax differences as a signal of decreased earnings quality (as opposed to
decreased off-balance-sheet financing). Given the pattern of the association between credit rating
changes and both large positive and large negative changes in book-tax differences, on balance,
we interpret our results as being consistent with large changes in book-tax differences (positive or
negative) signaling decreased earnings quality. Table 4 also indicates that the coefficients on the
majority of control variables are significantly different from zero in the predicted directions.
To assess the significance of changes in book-tax differences on ratings changes, Panel B
of Table 4 reports the difference in predicted probabilities of ratings upgrades and downgrades
when changes in book tax differences vary from the first to third quartile of PBTD and NBTD ,
holding all other variables at their means. 37 Results in Panel B indicate that moving from the first
to third quartile of PBTD decreases an average firms probability of a rating upgrade from 6.4%
to 5.1%, which represents a 20.3% decrease in the probability of an upgrade. In contrast, moving
from the first to third quartile of PBTD increases an average firms probability of a rating
downgrade from 11.1% to 13.7%, a 23.4% increase. For firms with negative changes in book-tax
differences, moving from the first to third quartile of NBTD decreases an average firms
probability of a rating upgrade by 15.2% (from 6.6% to 5.6%), and increases the probability of a
rating downgrade by 16.8% (from 10.7% to 12.5%). 38, 39
37 These probabilities are calculated separately for P BTD and N BTD because, by definition, N BTD = 0when changes in book-tax differences are positive and P BTD = 0 when changes in book-tax differencesare negative.38 Results are similar when assessing the changes in predicted probabilities while holding all other variablesat median levels. Moving from the first to third quartile of P BTD ( N BTD) decreases the probability of arating upgrade by 19.1% (14.8%) and increases the probability of a rating downgrade by 25.9% (19.2%).39 In sensitivity analysis, we analyzed the association between rating changes and changes in book-taxdifferences for those firms just below/above investment grade (i.e., BB+/BBB-). Results are similar tothose reported in the paper. However, the differences in predicted probabilities are much larger. For
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Table 5 presents the results of the tests of H2 for both measures of tax planning, Trad
ETR and Cash ETR .40 H2 predicts that the association between book-tax differences and changes
in credit ratings will be less pronounced (or not statistically significant) for high tax-planning
firms relative to low tax-planning firms. From a design standpoint this test is important as it
suggests that if the change in book-tax differences is capturing a correlated omitted variable, the
correlated omitted variables association with credit ratings must also vary with tax planning. In
this analysis, the coefficient on PBTD it represents the association between positive changes in
book-tax differences and changes in credit ratings for low tax-planning firms (i.e., firms in the top
four quintiles of accumulated ETR). The coefficient for PBTD it x TaxPlan it represents the
incremental association between credit rating changes and positive changes in book-tax
differences for high versus low tax planning firms. Finally, the sum of the coefficients for
PBTD it and PBTD it x TaxPlan it represents the association between positive changes in book-
tax differences and changes in credit ratings for high tax planning firms (i.e., firms in the lowest
quintile of accumulated ETR).
For H2, we test the significance of the coefficient for PBTD it x TaxPlan it and the sum of
the coefficients for PBTD it + PBTD it x TaxPlan it . The coefficient for PBTD it x TaxPlan it is
positive in both columns of Table 5 as predicted but is only statistically significant when we
identify high tax planning firms using cash taxes paid ( p-value = 0.046, one-tailed test).
However, the sum of the coefficients for PBTD it + PBTD it x TaxPlan it , is not significantly
different from zero when tax planning is defined either way ( p-values = 0.494 and 0.637, two-
tailed tests). These results suggest that for high tax planning firms, large changes in book-tax
differences do not signal poor earnings quality and thus, are not associated with less favorable
example, moving from the first to third quartile of P BTD increases an average firms probability of arating downgrade from 9.1% to 14.0%, which represents a 53.8% increase in the probability of adowngrade.40 To insure that our tax planning analyses are not simply capturing a firm size effect, in sensitivity analysiswe include a variable, LARGE , equal to one for firms in the top 20% of market value for year t , and zerootherwise, and interact it with PBTD and NBTD . Results are similar to those in Table 5, suggestingthat our tax planning results do not capture a firm size effect.
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results are robust to measurement error from employee stock option exercises, tax credits, foreign
operations and negative taxable income. 42
During our sample period, most firms recognized a deduction for tax purposes when
employees exercised stock options. For book purposes, however, firms were not required to
recognize compensation expense. In addition, tax benefits for stock option deductions were
added directly to equity and did not reduce tax expense in the financial statements. Therefore, tax
expense is overstated as is our estimate of taxable income because it is based on total tax expense.
Tax credits affect our calculation of taxable income because tax expense is reported after tax
credits (e.g., research and development, and foreign tax credit), which reduces current tax
expense, potentially understating our measure of taxable income. For firms with foreign
operations there is an issue as to which tax rate is appropriate to use in grossing up tax expense to
arrive at taxable income. To the extent tax expense reflects amounts paid to foreign governments
at different tax rates than in the United States, our measure of taxable income contains
measurement error. Finally, current tax expense is truncated at zero for firms with negative
taxable income.
We address the effects of employee stock options on our analyses by estimating taxable
income using cash taxes paid (data 317), instead of current tax expense. Cash taxes paid
represents total taxes paid across all jurisdictions (federal, state, local, foreign) in a given year
thus properly reflecting any deductions for employee stock options. 43 Inferences using this
alternative measure of taxable income are similar.
We address tax credits and foreign operations by re-estimating our analyses after
eliminating multinational firms and firms with high levels of research and development activities.
We classify firms as multinationals if their ratio of foreign pre-tax income (data 273) to total pre-
42 We do not address the effects of uncertain tax positions, intraperiod tax allocation, or consolidation onmeasurement error in taxable income due to data availability. See Hanlon et al. (2005) for more details.43 To the extent that firms 1) face tax rates in other jurisdictions that differ from the statutory maximumfederal tax rate, 2) receive tax credits, or 3) pay taxes in the current year that apply to different years, thismeasure of taxable income also contains measurement error.
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tax income (data170) is greater than 50 percent. We classify firms as high research and
development firms if their ratio of research and development expenses (data 46) to sales (data 12)
is in the upper quartile for a given year. Inferences remain the same. We address the issue of
negative taxable income in our main analysis by subtracting the change in NOL carryforward
from our estimate of taxable income. In sensitivity tests, we re-estimate our analyses using firm-
years in which the change in NOL is zero. Inferences are unchanged.
Finally, we estimate the regressions using only the change in temporary differences (data
50) and alternatively, only the change in permanent differences (total change in book-tax
differences less the change in temporary differences) to calculate the change in book-tax
differences. Results using the change in temporary book-tax differences are similar to those
presented in the tables, though not quite as strong (i.e., the coefficient for P BTD is statistically
significant in each analysis whereas the coefficient for N BTD is statistically significant when we
incorporate the controls for tax planning but is negative and insignificant without these controls
( p-value = 0.118, two-tailed test). When we use the change in permanent book-tax differences,
the coefficients P BTD and N BTD are both negative but not significant. Taken together, these
results suggest that individually the change in temporary or permanent book-tax differences are
not as informative as the change in total book-tax differences.
B. Alternative specifications of high tax planning firms
To test the robustness of the results for high tax planning firms, we performed additional
sensitivity analyses using alternative proxies for high tax planning. We first re-ran our analyses
using a two-year period to estimate Trad ETR , and Cash ETR instead of using a five-year period.
Second, we use the following parameter estimates from the investments in tax planning model
in Mills, Erickson, and Maydew (1998) to classify firms as high tax planning firms. 44
44 Based on a confidential survey data for a sample of large corporations, Mills et al. (1998) find that tax planning costs (i.e., in-house and outsourced tax-related expenditures) decrease with firm size and increasewith foreign operations, capital intensity, and the firms number of legal entities.
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C. Alternative Effect of Book-Tax Differences on Credit Ratings
In our primary analyses we find that both large positive and large negative changes in
book-tax differences result in less favorable rating changes, consistent with these changes
signaling decreased earnings quality. In addition, large changes in book-tax differences may
influence how ratings agencies specifically incorporate a firms earnings into their analyses. In
particular, to the extent that large changes in book-tax differences signal lower earnings
persistence, large changes in book-tax differences may mitigate the association between ratings
changes and earnings (i.e., rating agencies may place less weight on earnings for firms with
large book-tax differences). Consistent with this expectation, Ayers, Laplante, and Li (2008) find
that equity investor demand (selling pressure) around earnings announcements for firms with
positive (negative) earnings surprises declines for firms with large book-tax differences.
To investigate whether book-tax differences influence how ratings agencies incorporate
earnings information into their analyses, we examine the interaction of changes in book-tax
differences and changes in earnings ( P BTD* E and N BTD* E ). To test this, we modify
equation (1) as follows:
(5)
where all variables are as previously defined.
The results (not tabulated) are consistent with our primary analyses. As before, we find
negative and significant coefficients for PBTD it and NBTD it ( p-value = 0.021, one-tailed test,
and p-value = 0.069, two-tailed test, respectively). In addition, the coefficient on the interaction
of positive changes in book-tax differences and changes in earnings ( P BTD* E ) is significantly
negative ( p-value = 0.006, one-tailed test) as is the coefficient on the interaction of negative
changes in book-tax differences and changes in earnings ( N BTD* E , p-value = 0.009, one-
1 1 2 3
4 5 6 7 8
9 10 11 12 13
14 15 17 2716
Pr( )
&
**
it it it it
it it it it it
it it it it it
it it
it
it
it
Rate P BTD N BTD P BTD N BTD AQ Size Loss E
CFO IntCov BTM StdROA StdRet
Lev R D Year
E E
CapInt
28 78t it it Industry
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tailed test). Thus, this evidence suggests that in addition to signaling decreased earnings quality,
book-tax differences influence how creditors weight earnings in their rating decisions. 45, 46
D. Changes in book-tax differences that move the firm closer to the industry median
In additional analyses we test whether the effect of the change in book-tax differences on
credit ratings varies based on the level of book-tax differences after the change. Specifically, we
test whether the association between credit ratings and changes in book-tax differences varies for
firms whose book-tax differences are further away (closer to) the industry median book-tax
difference at the end of year t compared to year t-1 . The theory is that changes in book-tax
differences that result in a firms book -tax difference moving away from the book-tax differences
of its peer firms would be viewed more negatively than changes that move the firms book -tax
difference closer to its peer group. We use the industry median as a benchmark as book-tax
differences vary across industry due to underlying differences in the financial reporting and tax
rules across industries. Consistent with our main analyses, for firms with an end of year t book-
tax difference that is further away from the industry median book-tax difference we find that the
coefficients for PBTD, and NBTD are both negative ( p-value = 0.028, one-tailed test; p-value
= 0.011, two-tailed test, respectively). For firms with an end of year t book-tax difference that is
closer to the industry median book-tax difference we find that the coefficients for PBTD, and
NBTD are both negative but insignificant ( p-value = 0.126, one-tailed test; p-value = 0.425,
two-tailed test, respectively). In combination, these results suggest that both the change in book-
45 In additional tests, we find that the coefficients on the interaction of positive changes in book-taxdifferences and changes in earnings ( P BTD* E ) and the interaction of negative changes in book-taxdifferences and changes in earnings ( N BTD* E ) are both negative and significant for low tax planning
firms ( p-value = 0.006, one-tailed test; p-value = 0.003, one-tailed test, respectively) while thesecoefficients are not significant for high tax planning firms.46 In additional analyses, we re-estimate our regressions after de-composing E into CFO ,
ConformingAccruals (defined as Pre-Tax Book Income less CFO , N BTD, and P BTD), P BTD and N BTD (both unranked). We estimate this analysis to test whether P BTD and N BTD simply capture aless positive weighting on nonconforming accruals (i .e., accruals with book-tax differences) versusconforming accruals or cash flows. Consistent with our main analyses, we find that the coefficient for
P BTD is negative and statistically significant in all specifications. Likewise, the coefficient for N BTD isnegative and significant after controlling for tax planning. The negative coefficients (instead of simplysmaller positive coefficients) suggest that P BTD, and N BTD capture more than just nonconformingaccruals that receive a lesser or no weight.
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tax difference and the level of book-tax differences after the change (relative to the industry
median) influence how book-tax information is incorporated into credit ratings.
E. Levels analysis
In a contemporaneous study, Crabtree and Maher (2008) examine the relation between
the level of book-tax differences and ratings for new bond issuances and find that large positive
and large negative temporary and total book-tax differences are associated with lower ratings for
these bonds. We utilize a changes specification in our primary analysis because it provides a
more powerful test of how the information contained in book-tax differences influences credit
ratings and is less susceptible to correlated omitted variables than a levels study. In sensitivity
tests, we re-estimate our analysis using a levels specification and all inferences remain.
Specifically, the coefficients on the levels of positive and negative book-tax differences are both
negative and significant ( p-value = 0.014, one-tailed test; p-value = 0.026, two-tailed test,
respectively). In addition, while the coefficients on the interactions between the level of positive
book-tax differences and tax planning, PBTD it x TaxPlan it , and the level of negative book-tax
differences and tax planning and tax planning, NBTD it x TaxPlan it , are not significant, the sums of
the coefficients PBTD it + PBTD it x TaxPlan it and NBTD it + NBTD it x TaxPlan it , respectively, are
not significantly different from zero.
6. Conclusion
This paper examines whether credit analysts utilize the information contained in the
difference between book and taxable income (the book- tax difference) in analyzing a firms credit
risk (i.e., credit rating). Specifically, we investigate whether changes in book-tax differences are
associated with credit rating changes. Book-tax differences may be informative for credit rating
agencies for at least two reasons. First, as book and taxable income diverge, credit analysts might
interpret this divergence as a signal of decreased earnings quality. Second, off-balance-sheet
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financing is one source of positive book-tax differences (i.e., book income exceeds taxable
income) that can be particularly informative to credit analysts.
Using an ordered logit model, we regress ratings changes that occur from the end of fiscal
year t to the end of fiscal year t+1 on the decile rank of positive changes in book-tax differences,
the decile rank of negative changes in book-tax differences, and control variables (e.g., changes in
leverage, size, profitability, cash flows, interest coverage, beta, etc.). Consistent with large
positive changes in book-tax differences signaling decreased earnings quality and/or increased
off-balance sheet financing, we find a significant negative association between positive changes
in book-tax differences and credit rating changes. We also find that large negative changes in
book-tax differences are significantly associated with less favorable rating changes, consistent
with these changes signaling decreased earnings quality (instead of decreased off-balance sheet
financing). Given the pattern of the association between credit rating changes and both large
positive and large negative changes in book-tax differences, on balance, we interpret our results
as being consistent with large changes in book-tax differences (positive or negative) signaling
decreased earnings quality. Our analysis suggests that moving from the first to third quartile of
positive (negative) changes in book-tax differences decreases the probability of a rating upgrade
by 20.3% (15.2%) and increases the probability of a rating downgrade by 23.4% (16.8%).
We also examine situations where book-tax differences are likely to be the result of tax
planning to determine if the association between book-tax differences and rating changes varies
based on the source of the change in book-tax difference. Results suggest that for firms classified
as non -tax planners (i.e., those in the top four quintiles of cumulative curren t effective tax rates
and alternatively, cumulative cash effective tax rates), both positive and negative changes in
book-tax differences are associated with an increased probability of a less favorable ratings
change. For high tax-planning firms (i.e., those firms in the bottom quintile of cumulative
effective tax rate), we find no association between changes in book-tax differences (positive or
negative) and rating changes.
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This study makes several contributions. Maydew (2005) notes that rigorous empirical
evidence is in short supply on whether rating agencies are able to fully discern the obligations of
off-balance sheet financing, one source of positive book-tax differences. This study answers this
call more broadly by investigating whether changes in book-tax differences are systematically
associated with credit rating changes. Our evidence is consistent with book-tax changes signaling
negative information to credit rating agencies. On balance, our results suggest that a large change
in book-tax differences is a signal of lower earnings quality, which results in less favorable credit
ratings. In addition to enhancing our understanding of the information used by credit agencies,
this finding contributes to the emerging literature on the use of book-tax differences by various
financial statement users (e.g., investors, analysts, the IRS, and auditors). Among other
implications, this studys evidence may be useful to researchers or others interested in modeling
the credit rating process as it suggests that the change in book-tax differences is a parsimonious
variable that captures a construct of interest to credit analysts. Finally, recent research concludes
that equity investors, at least in part, distinguish sources of the book-tax difference. This study
extends this research by investigating whether the association between credit ratings changes and
changes in book-tax differences varies based on the source of the change in book-tax differences.
Consistent with the association varying by source of the change in book-tax differences, we find
tax planning attenuates the relation between the changes in book-tax differences and credit rating
changes.
Our study is subject to limitations. As discussed in Sections 3 and 5, we estimate taxable
income from publicly available financial statements. While this calculation contains
measurement error, given the limitations in obtaining actual taxable income and in knowing
which analysts utilize actual versus estimated taxable income, this measure appears to be a
reasonable proxy for our setting. In addition, sensitivity analyses suggest that results are robust to
alternative methods of estimating taxable income, providing additional comfort in the inferences
we draw. Second, we cannot eliminate the possibility that credit analysts do not use book -tax
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differences in their analyses but instead, utilize information correlated with our book-tax
difference measures. However, the use of a changes specification, inclusion a wide range of
controls for firm performance and other factors hypothesized to influence ratings, estimation of a
variety of sensitivity analyses, and evidence that the association between changes in book-tax
differences and rating changes is attenuated for high tax planning firms (e.g., where book-tax
differences more likely reflect tax planning than decreased earnings quality) provides some
comfort that our analyses capture the effects of book-tax differences on credit ratings. Thus,
while we are unable to conclude with certainty that creditors specifically use book-tax differences
in their analyses, our analyses, at the minimum, suggest that rating agencies incorporate data that
are closely related.
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