the determinants and value relevance of the choice of
TRANSCRIPT
The Determinants and Value Relevance of the Choice of Accounting forResearch and Development Expenditures in the United Kingdom*
Dennis R. Oswald(London Business School)
email: [email protected]
August 2000
* This paper is based on my dissertation at the University of Chicago Graduate School of Business. I thank mythesis committee: Jennifer Francis (chair), Katherine Schipper, Toshiyuki Shibano, and Linda Vincent. I havealso benefited from comments and discussions with Kathleen Fitzgerald, Arthur Kraft, Jennifer Milliron, PerOlsson, Peter Pope, David Robinson, Matthew Rothman, Cathy Shakespeare, Terry Warfield, Stephen Young,and workshop participants at the British Accounting Association Conference (2000), the European AccountingAssociation Conference (2000), Lancaster University, London Business School, University of Wisconsin -Madison, University of Southern California, and Washington University. I am grateful to the University ofChicago Graduate School of Business and to the London Business School for financial support. Data areavailable from sources indicted in the text.
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The Determinants and Value Relevance of the Choice of Accounting for Researchand Development Expenditures in the United Kingdom
Abstract
This paper investigates the determinants and value relevance implications of the accounting method choice fordevelopment expenditures for firms with research and development (R&D) programs in the United Kingdom(UK). Using a sample of 1,780 UK firm-year observations over 1993-1997, of which I classify 231 (1,549) firm-year observations as Capitalizers (Expensers), I find that the decision to expense versus capitalize is influencedby firm size, the intensity of the firm's R&D programs, and whether the firm is in a steady-state with respect toits R&D programs. Results of value relevance tests indicate that Expensers have little to gain, if anything, interms of value relevance from adjusting their reported earnings and book value of equity to reflect as-if-capitalized numbers. For the Capitalizers, the value relevance of as-if-expensed earnings and book value ofequity is not substantially lower than the value relevance of their reported earnings and book value of equity.Additional analysis indicates that the steady-state status and R&D intensity of a firm's R&D programs maymarginally influence the value relevance of their financial information. These results are in contrast to those inUS studies that find that capitalization of R&D expenditures greatly enhances the value relevance of firms'financial statements.
Key Words: Research and Development, Accounting choice, Value relevance
Data Availability: Data are available from sources indicted in the text.
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The Determinants and Value Relevance of the Choice of Accounting for Researchand Development Expenditures in the United Kingdom
I. Introduction
This paper investigates the determinants and value relevance implications of the accounting method choice
for development expenditures for firms with research and development (R&D) programs in the United Kingdom
(UK). Under UK GAAP certain development expenditures may be either capitalized or expensed, with the
choice left to the discretion of management. Using the reported disclosures of UK firms (including their
capitalization and amortization schedules), I examine two questions related to the expensing versus capitalizing
decision: (1) What factors distinguish expensing firms from capitalizing firms?; and (2) Conditional on the
choice to expense or to capitalize, does the accounting treatment of development expenditures affect the value
relevance of these firms’ financial statement information, as captured by the contemporaneous association
between security prices and earnings and book value of equity?1
UK GAAP permits, but does not require, the capitalization and subsequent amortization of development
expenditures if five conditions are met: (1) There is a clearly defined project; (2) The related expenditure is
separately identifiable; (3) The outcome of the project is examined for its technical feasibility and its ultimate
commercial viability considered in the light of factors such as likely market conditions (including competing
products), public opinion, and consumer and environmental legislation; (4) The aggregate of deferred
development costs, any further development costs, and related production, selling and administrative costs is
reasonably expected to be exceeded by related future sales or other revenues; and (5) Adequate resources exist,
or are reasonably expected to be available, to enable the project to be completed and to provide any
consequential increases in working capital [Statement of Standard Accounting Practice (SSAP) No. 13, 1989].
Throughout this paper, I refer to development expenditures that meet conditions (1) - (5) as qualifying
development expenditures. If conditions (1) - (5) are met, UK GAAP does not require capitalization - firms may
choose to expense these costs in the period incurred. Thus, my investigations (of both management’s decision to
select one accounting method over another and the possible valuation implications of that decision) assume that
UK expensing firms meet the conditions to capitalize certain development expenditures and choose not to.
3
My study is related to three areas of research. The first is research on the determinants of choices
among generally accepted accounting rules. In particular, the first part of my analysis examines UK firms’
elections to capitalize versus expense qualifying development expenditures. The second set of studies to which
my paper contributes is research investigating whether and how the choice of accounting method influences the
informativeness or value relevance of intangibles. Finally, is research on the value relevance of financial
information when it is affected by the accounting treatment for R&D expenditures. My paper adds to the extant
literature in two main ways. First, by focusing on the capitalization versus expensing decisions made by a
sample of UK firms, I am able to examine the value relevance effects of actual (rather than hypothetical)
capitalization and amortization rules for qualifying development expenditures. Second my analysis of the value
relevance of reported and adjusted accounting information for UK firms allows me to explore the association
between measures of firm value and financial statement information conditional on a firm’s choice of one
accounting treatment over another.
My first set of tests examines factors that I hypothesize influence the choice of accounting for
development expenditures. Based on prior research, I posit that both an industry-specific factor (product life
cycle) and firm-specific factors (profitability, leverage, R&D intensity, R&D steady-state, systematic risk and
firm size) influence this choice. Using a sample of 1,780 UK firm-year observations over 1993-1997, of which I
classify 231 (1,549) firm-year observations as Capitalizers (Expensers)2, I find that the decision to expense
versus capitalize qualifying development expenditures is influenced by firm size (smaller firms are more likely
to capitalize), the intensity of the firm's R&D programs (firms with greater R&D intensity are more likely to
capitalize), and whether the firm is in a steady-state with respect to its R&D programs (firms not in steady-state
are more likely to capitalize).
My second set of tests compare the value relevance of both reported earnings and book values of equity
versus adjusted values of these variables (where the adjustments restate the reported financial information to the
1 Specifically, I measure value relevance as the model fit from a regression of contemporaneous share price onearnings per share and book value of equity per share and the variance of the log of the ratio of the predictedshare price to observed share price.2 I define a firm to be a Capitalizer in year t if in that year the firm reported intangible development assets;otherwise it is classified as an Expenser.
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alternative accounting method3). The results indicate that Expensers have little to gain, if anything, in terms of
value relevance from adjusting their reported numbers to reflect as-if-capitalized numbers. For the Capitalizers,
the value relevance of as-if-expensed earnings and book value of equity is not substantially lower than the value
relevance of the reported financial data; the magnitude of any difference in explained variability is small.
Additional analyses examine the influence of firms' steady-state status and level of R&D intensity on the value
relevance of their earnings and book value of equity. The results indicate that steady-state status and R&D
intensity may play a minor role in influencing the value relevance of earnings and book value of equity of firms
with R&D programs. Specifically, the explained variation is generally significantly higher under a policy of
capitalizing versus a policy of expensing, with the difference being larger for firms either not in steady-state or
with high R&D intensity; however the magnitude of the difference remains small.
The rest of the paper is organized as follows. The related literature and motivation for this study are
provided in section 2. Section 3 develops the hypotheses and research design. The sample and data
requirements are discussed in section 4. Section 5 presents the analysis of the determinants of the expense
versus capitalization decision, and Section 6 reports results on the value relevance of earnings and book value of
equity under each accounting method. Section 7 summarizes the results and concludes.
II. Motivation and Relation to Prior Literature
My study is related to three areas of research. The first is research on the determinants of choices
among generally accepted accounting rules. In particular, the first part of my analysis examines UK firms’
elections to capitalize versus expense qualifying development expenditures. My investigation of this question is
most closely related to Aboody and Lev’s [1998] examination of the determinants of US firms’ choices of the
amount of software development costs to expense or capitalize. 4 Using data from 163 software companies over
1987-1995, Aboody and Lev identify four variables that are significantly associated with the amount of software
development costs (as a percentage of market value of equity) that have been capitalized. Size and profitability
3 The adjustment for Expensers is based on inferred capitalization and amortization rates (50.12% and 15.22%,respectively) calculated from the note disclosures provided by Capitalizers.
4 In addition, a number of other studies have examined choices among alternative accounting policies. Examplesinclude Cushing and LeClerc [1992] and Hand [1993] who examine the LIFO versus FIFO inventory valuation
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are positively associated with the annual amount of capitalized development cost; software development
intensity and leverage are negatively related to the capitalized amount. They find no relation between the
capitalized amount and systematic risk. My study differs from Aboody and Lev’s in that I examine a broader set
of industries and technologies, a different accounting rule, a different accounting jurisdiction and I examine the
impact on value relevance of using as-if financial data (i.e., I adjust reported numbers to reflect the accounting
data that would had been reported had the alternative accounting method been used). In the next section I
elaborate on why the decision to capitalize versus expense may have implications for the value relevance of
firms’ financial statement information.
The second set of studies to which my paper contributes is research investigating whether and how the
choice of accounting method influences the informativeness or value relevance of intangibles. These studies
include Aboody and Lev’s [1998] examination of US firms’ capitalized software development costs; Loudder
and Behn’s [1995] study of capitalized US firms’ R&D expenditures prior to SFAS No. 2; Ely and Waymire’s
[1999] examination of US firms’ intangible assets in the pre-SEC period; Barth and Clinch’s [1998]
investigation of intangible asset revaluations in Australia; and Green, Stark and Thomas’ [1996] study of R&D
expenditures by UK firms. The findings of these studies generally support the claim that intangible assets are
value relevant. My study differs from these in that I examine the choice of accounting method for development
expenditures and the value relevance of firms' financial information conditional on that choice for a recent
sample of UK firms distributed across a broad range of industries.
The third area of research to which my paper relates is research on the value relevance of financial
information – primarily earnings and book value of equity – when this financial information is affected by the
accounting treatment for R&D expenditures. In particular, several recent studies (Lev & Sougiannis [1996],
Monahan [1999], Chambers, Jennings and Thompson [1999] and Healy, Myers and Howe [1999]) investigate
the value relevance of US firms’ financial statements (where GAAP requires expensing of R&D) by examining
the value relevance of both reported numbers and adjusted numbers, the latter created by applying hypothetical
capitalization and amortization rules for R&D expenditures. These studies generally conclude that capitalizing
R&D expenditures increases the value relevance of firms’ financial statements. My paper adds to this last body
choice; Holthausen [1981] who studies the decision to switch depreciation methods; and Zmijewski andHagerman [1981] and Bowen, DuCharme and Shores [1995] who examine income strategy choices.
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of research in several ways. First, by focusing on the capitalization versus expensing decisions made by a
sample of UK firms, I am able to examine the value relevance effects of actual capitalization and amortization
rules for qualifying development expenditures. Relatedly, using the UK data allows me to calibrate the accuracy
of the hypothetical rules used in the US studies. Specifically, the disclosures made by UK firms who choose to
capitalize provide evidence on the amount of R&D expenditures that generate intangible assets. In contrast,
studies using US data implicitly assume that all R&D expenditures (i.e., expenditures on both development and
research) create intangible assets. Second my analysis of the value relevance of reported and adjusted
accounting information for UK firms allows me to explore the association between measures of firm value and
financial statement information conditional on a firm’s choice of one accounting treatment over another.
III. Hypotheses and Research Design
Distinguishing Capitalizers from Expensers
My analysis of the determinants of the decision to capitalize or to expense qualifying development
expenditures provides information about firms’ reasons for selecting one method over another. My results may
have implications for debates over R&D accounting in particular, or over standards that allow alternative
treatments for similar transactions/events in general. For example, a finding that firms choose among acceptable
accounting alternatives to communicate value relevant information to the market supports a standard setting
philosophy of allowing for alternative treatments. In addition, if managers of firms in steady-state with respect
to their R&D programs select the expensing option while those mangers of firms not in steady-state select
capitalization, a flexible standard that permits either expensing or capitalization of qualifying development
expenditures may enhance the value relevance of financial information by allowing managers to condition their
reporting based on the firm's position in its life cycle.
My analysis assumes that the primary benefit form capitalization is the ability to communicate value
relevant information to investors about R&D programs (e.g., the creation of assets expected to provide future
benefits). Consistent with US studies that find that capitalization increases the value relevance of financial
statement data, I predict that voluntary capitalization increases the value relevance of earnings and book value of
equity, relative to restated numbers based on full expensing. A secondary benefit is that, given growing R&D
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expenditures, capitalization produces larger income and larger book value of equity than does a policy of
expensing.5
There are also a number of costs associated with capitalization. First, managers may find it costly to
deviate form analyst preferences, which favor expensing. 6 Second, managers may be concerned with how
accounting choices affect the quality of current and future earnings. 7 Finally, there may be measurement and
record keeping costs. 8
I assume that development expenditures in general will produce income-generating (intangible) assets.
I further expect that industry- and firm-specific factors influence both the economic value and life of these
intangible assets and the information environment of the R&D programs, and therefore affect the capitalize
versus expense decision. In terms of industry-specific information, I expect that shorter product life cycles will
be associated with the expensing alternative, because capitalizing short-lived intangible development asets does
not provide information about long-term future benefits to the firm. I use industry indicator variables to proxy
for product life cycles because I do not have direct measures for this variable.
I also examine the five firm-specific variables included by Aboody and Lev [1998] in their analysis of
development capitalization decisions: profitability, leverage, R&D intensity, systematic risk and firm size. I
hypothesize that firms with lower profitability capitalize their qualifying development expenditures in order to
avoid reducing their net income by expensing R&D.9 I further predict that profitable firms are more likely to
expense development expenditures to signal their financial health. I do not predict a directional relation between
leverage and the expense versus capitalize decision, since the effects of intangible assets on debt covenants is
5 Goodacre [1991] suggests that one reason managers may prefer capitalization (holding R&D expendituresconstant) is that, if management believes “[market] sentiment responds to the lower profits with a lower shareprice, it may be more difficult for the company to raise equity finance or, at least, equity finance may be morecostly.” [p. 78]. Additionally, McGrath [1996] claims that the requirement of expensing R&D expenditures inthe US hampers innovation since firms reduce their R&D expenditures to avoid reporting lower income.6 Goodacre [1991] reports UK analysts prefer firms to expense R&D in the period incurred. Additionally, theAssociation for Investment Management and Research also argues for expensing. [AIMR, 1994].7 Freeburn [1998, p.41] cites a manager from Dow Chemical as stating that "…promising projects that werecapitalized would have to be expensed if they did not work out, distorting future earnings."8 Specifically, SSAP No. 13 (paragraph 13) requires different persons with different types of judgment to assessthe technical, commercial and financial viability of capitalized projects at each accounting date. Additionally,Nixon and Lonie [1990] provide anecdotal evidence that suggests that tracking costs are not inconsequential:many mangers prefer to expense because “they can forget about it.” [p.90].9 This reasoning holds only for firms with increasing development expenditures. Alternatively, firms with lossesin a given year may prefer to expense their qualifying development expenditures in that year to increase future
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uncertain.10 I also include systematic risk and firm size as control variables; I do not specify directional
predictions for these variables.
I include two variables to capture the extent to which the choice of accounting method communicates
value relevant information. R&D intensity is included to determine whether the magnitude of R&D
expenditures influences the decision to capitalize versus expense. One argument is that managers of firms who
are heavily engaged in R&D activities (i.e., high R&D intensity) will capitalize their successful development
projects to communicate the success of their programs to market participants. This argument assumes that high
R&D intensity firms are engaged in many R&D projects and that the capitalization choice (which provides an
aggregate or summary indicator of the projects with future benefits) is preferred to disclosure through other
means. This argument also assumes that managers of firms with few projects (i.e., low R&D intensity firms)
find it less costly to communicate R&D information through other means (e.g., direct communication with the
market or note disclosures), than to incur the costs of capitalization. Alternatively, based on past experience,
managers of high R&D intensity firms may have developed summary statistics that convey information about
the success of their R&D projects. If so, it may be less costly to disclose information about the R&D programs
using these summary statistics than through capitalization. Based on these conflicting arguments, I do not predict
the sign of the relation between R&D intensity and the choice of accounting method.
I also include a measure of the firm's R&D steady-state status. I expect steady-state firms (resources
expended on development only to maintain the current level of the intangible development assets) are more
likely to expense qualifying development expenditures. This hypothesis follows from the observation that for
steady-state firms, the amount of development expenditures capitalized will equal the amount of amortization;
for these firms, income and book value of equity are the same under the expensing and capitalizing alternative.
Given the lower costs from expensing qualifying development expenditures, I expect steady-state firms to
choose the expense option. In contrast, for firms not in steady-state, the capitalization option may allow for
income. However, if the firm has adopted a policy of capitalization, UK Financial Reporting Standard No. 3[1992] requires over-time consistency of accounting treatment.10 Citron [1991,1992] provides evidence that intangible assets are excluded from UK bank loan and public debtcontracts. Alternatively, Goodacre [1991] states that capitalization “…may also have the benefit of reducing theimpact of restrictive loan covenants…”(p. 78).
9
value relevant information to be conveyed to the market that cannot be conveyed through expensing option (e.g.,
information about the success of specific R&D projects).11
I define a firm as being in steady-state with respect to its R&D programs in year t if the amount of
development capitalization is similar in magnitude to the amount of amortization of the intangible development
assets. Capitalizers disclose both amounts, but Expensers do not report development expense separately and the
economic life of the 'as-if-capitalized' intangible development assets must be estimated. To estimate these
amounts for the Expensers, I apply capitalization and amortization rules derived from the capitalization and
amortization schedules inferred from the Capitalizers' note disclosures (as required by SSAP No. 13, paragraph
31). Specifically. the inferred capitalization rate and amortization rate (for the Capitalizers) are calculated as
follows:
CAP_RATEit = DEVCAPit / RD_EXPENDit (1a)
AMORT_RATEit = DEVAMORTit / BEG_COSTit (1b)
where: CAP_RATEit = capitalization rate for firm i in year t,
DEVCAPit = amount of development expenditure capitalized for firm i in year t,
RD_EXPENDit = amount of R&D expenditure for firm i in year t,
AMORT_RATEit = amortization rate for firm i in year t,
DEVAMORTit = amortization of the intangible development assets for firm i in year t,
BEG_COSTit = beginning historical cost of the intangible development assets forfirm i in year t.
I apply the median capitalization and amortization rule calculated from the Capitalizer sample (the inferred rule),
to the R&D expense disclosed by the Expensers to estimate the annual "as-if" capitalized amount, "as-if"
amortization and the "as-if" intangible development assets for Expensers as follows::
DEVCAPit = CAP_RATEit * RDEXPit (2a)
DEVAMORTit = AMORT_RATEit * INTDA it (2b)
11 This assumption may not hold for firms who are decreasing their intangible development assets. I perform asensitivity analysis to determine whether the expense versus capitalize decision is different for non-steady-statefirms who are increasing versus decreasing their intangible development assets. (The results of this analysisindicate that both increasing and decreasing non-steady-state firms are more likely to capitalize their qualifyingdevelopment expenditures.)
10
INTDAit = ∑−
t
ytiDEVCAP - ∑
−
t
ytiDEVAMORT (2c)
where: RDEXPit = R&D expense for firm i in year t,
INTDAit = intangible development assets for firm i in year t,
y = the estimated life of the intangible development assets, equal to[1 / AMORT_RATEit] rounded up to the nearest integer.
To determine steady-state status, I calculate the absolute value of the difference betweent he amounts
capitalized (DEVCAPit) and amortized (DEVAMORTit) scaled by the intangible development assets (INTDAit)
reported for the Capitalizers and estimated by equations 2a - 2c for the Expensers. Firms in the lower half of the
distribution of this variable are classified as steady-state (STATE=1) and firms in the upper half of the
distribution are classified as non-steady-state (STATE=0).
In summary, I predict that the choice to expense versus capitalize qualifying development expenditures
is negatively associated with longer product life cycles, and positively related to profitability and steady-state
status. I examine these predictions using univariate comparisons and the following logit regression:
ititBETAitSTATE
itLEVitRDINTitPROFITitSIZEjitINDjj
itEXP
εββ
ββββββ
+++
++++∑=
+=
76
54321
20
10
(3)
where: EXPit = dummy variable equal to 1 if firm i is an Expenser firm in year t; 0 if it is aCapitalizer,
INDjit = indicator variable equal to 1 if firm i is in industry j in year t; 0 otherwise,
SIZEit = log of market value of equity for firm i, measured three months after fiscal yearend in year t,
PROFITit = net income divided by total assets (both variables converted to fullexpensing) for firm i in year t,
RDINTit = amount expended on R&D divided by total assets (converted to full expensing)for firm i in year t,
LEVit = total debt divided by book value of equity less the intangible development assetsfor firm i in year t,
STATEit = indicator variable equal to 1 if firm i is in steady-state in year t, 0 otherwise,
11
BETAit = beta for firm i in year t, calculated using monthly stock returns using themaximum number of returns over a 60 month period ending one month prior to thefiscal year end of year t (at least 12 monthly returns required) and an equally weightedmarket index,
εit = residual term for firm i in year t.
Value Relevance of Financial Information
Prior research on the value relevance of financial information for firms with material R&D expenditures
generally takes the view that capitalization of these expenditures results in more value relevant information than
does expensing (Lev and Sougiannis [1996]; Monahan [1999]; Chambers, Jennings and Thompson [1999]; and
Healy, Myers and Howe [1999]). As discussed in section two, prior researchers create as-if-capitalized earnings
and book value of equity, which adjust reported earnings and book values for the estimated amount of R&D
capitalized and amortized, and test the restated numbers for incremental value relevance.
I hypothesize that the decision to capitalize versus expense qualifying development expenditures
conveys value relevant information, and therefore influences the association between measures of firm value and
earnings and book value of equity. Specifically, two variables hypothesized to influence the expense versus
capitalize decision are expected to provide firm-specific information about R&D programs. Particularly for
firms not in steady-state, but also possibly for steady-state firms, a policy of capitalization may provide value
relevant information. For example, by capitalizing the qualifying development expenditures, management is
disclosing information about the creation of intangible development assets which are expected to provide future
benefits (e.g., increased revenues or decreased costs). Additionally, the decision to expense versus capitalize,
conditional on the R&D intensity may provide information about the success of R&D projects. In general, if
managers select an R&D accounting rule to convey information to the market, I expect the value relevance of the
firms' earnings and book value of equity to be higher using report numbers than using adjusted numbers.12
12 The idea that managers communicate value relevant information through their choices of accounting methodfor qualifying development expenditures is based on the following assumptions: (1) UK standard setters haveselected appropriate guidance (i.e., by providing the option to capitalize development expenditures, under certainconditions), and (2) managers appropriately exercise the choice provided in SSAP No. 13 to convey to themarket the most value relevant information. My research design confounds these two assumptions.Additionally, since I use inferred capitalization and amortization rules (discussed in section 3) my designintroduces measurement error when creating as-if-capitalized numbers for Expensers. For the Capitalizers, themeasurement error is minimized since the adjustments to their reported numbers are based on disclosed figures.
12
My analysis emphasizes the ability of financial statement information, conditional on accounting
choice, to convey value relevant information to the capital market. Following prior studies in this area, I define
value relevance as the explained variability of the contemporaneous relation between stock prices and earnings
and book value of equity.13 To measure the impact of capitalizing versus expensing on the value relevance of
accounting information, I compare the model fit from these regressions estimated using reported accounting data
and, separately, using accounting data restated to the alternative (i.e., not select) accounting method. The
comparison of model fit is based on the Vuong [1989] likelihood ratio test for non-nested model selection.14
In addition, I also compare the variance of the ratio of the log of the predicted share price (from the
regression of contemporaneous price on earnings and book value of equity) to observed share price based on
Chang’s [1999] argument that this measure is superior to adjusted R2 as a value relevance metric (I refer to this
metric as the ‘fitted price ratio’).15 To provide further evidence of value relevance, I estimate the fitted price
ratio using both reported and adjusted numbers. I use a F-statistic and a Wilcoxon test comparing the yearly
medians of the fitted price ratios to compare between reported and adjusted numbers.16
Because my sample UK firms contains both firms that expense and firms that capitalize, I am able to
estimate reported and adjusted regressions for both samples. If capitalization increases value relevance, I expect
that the explained variability will be the highest, and the fitted price ratio will be the lowest, using reported
(adjusted) data for Capitalizers (Expensers). If expensing R&D provides the most relevant information, I expect
to observe higher explained variation and a lower fitted price ratios using adjusted (reported) numbers for
Capitalizers (Expensers). However, if the choice of accounting method conveys value relevant information
(either about the firms' R&D programs or about future firm prospects) then I expect to observe higher explained
variation and a lower fitted price ratio using reported numbers for both Capitalizers and Expensers.
13 For example, see Easton and Harris [1991], Francis and Schipper [1999], Collins, Maydew and Weiss [1997],Kothari and Zimmerman [1995] and Barth and Clinch [1996, 1998].14 For a detailed explanation of this test see Dechow [1994], Appendix 2.15 Specifically, Chang [1999] argues that adjusted R2 is based on an additive error assumption, whereas the fittedprice ratio is based on a multiplicative error assumption.16 The F-test assumes that the two samples are independent (an assumption violated in my tests). The Wilcoxontest has low power since it is based on only five observations. Since all of the value relevance tests I use are
13
In summary, I separately estimate the following regressions for Expensers and for Capitalizers:
ititBVPS
itEPS
itP µλλλ +++=
210(4a)
ititBVPSADJ
itEPSADJ
itP κθθθ +++= _
2_
10(4b)
where: Pit = stock price for firm i in year t, observed three months after the fiscal year endof year t,
EPSit = earnings per share for firm i in year t,
BVPSit = book value of equity per share for firm i in year t,
ADJ_EPSit = adjusted earnings per share for firm i in year t,
ADJ_BVPSit = adjusted book value of equity per share for firm i in year t,
µit, κit = residual terms for firm i in year t.
Equation (4b) requires adjustments to earnings and book value of equity. For the firms in the
Capitalizer sample, I adjust reported earnings and book value of equity to reflect the numbers that would have
been reported had these firms expensed all development expenditures. Formally:
ADJ_EARNit = EARNit + DEVAMORTit – DEVCAPit (5a)
ADJ_BVEit = BVEit – INTDAit (5b)
where: ADJ_EARNit = adjusted earnings for firm i in year t,
EARNit = earnings for firm i in year t.
ADJ_BVEit = adjusted book value of equity for firm i in year t,
BVEit = book value of equity for firm i in year t.
For the Expenser firms, I adjust the earnings and book value of equity to reflect numbers that would
have been reported had these firms always capitalized their qualifying development expenditures. I follow the
same procedures used to estimate a firm's steady-state status (equations 2a - 2c) to estimate the amounts
capitalized and amortized and value of the intangible development assets. Formally:
ADJ_EARNit = EARNit + DEVCAPit – DEVAMORTit (6a)
ADJ_BVEit = BVEit + INTDAit (6b)
subject to potential econometric problems, I do not emphasize the results of the explained variability tests more
14
For both Capitalizers and Expensers, I scale ADJ_EARNit and ADJ_BVEit by the number of firm i’s shares
outstanding at three months after fiscal year-end of year t to obtain ADJ_EPSit and ADJ_BVPSit, respectively.
In addition to the tests that examine summary financial statement information, I estimate regressions
which include the R&D components as explanatory variables (i.e., the level of R&D expense, amortization
expense and intangible development assets). To the extent that the R&D components are valued differently from
other items, tests that aggregate the components (such as those in 4a-b) are biased against finding that R&D
items are value relevant. The R&D components regressions are as follows:
ititPSINTDAitPREBVPS
itPSDEVAMORTitPSRDEXPitPREEPSitP
ναα
αααα
+++
+++=
_5_4
_3_2_10(7a)
ititPSINTDAADJitPREBVPS
itPSDEVAMORTADJit
PSRDEXPADJitPREEPSitP
ϖχχ
χχχχ
+++
+++=
__5_4
__3__2_10 (7b)
where:
EPS_PREit = earnings per share before R&D expense and amortization of theintangible development assets for firm i in year t,
RDEXP_PSit = R&D expense per share for firm i in year t,
DEVAMORT_PSit = amortization of the intangible development assets per share for firm i inyear t,
BVPS_PREit = book value of equity per share before the intangible development assetsfor firm i in year t,
INTDA_PSit = intangible development assets per share for firm i in year t,
ADJ_RDEXP_PSit = adjusted R&D expense per share for firm i in year t,
ADJ_DEVAMORT_PSit = adjusted amortization of the intangible development assets pershare for firm i in year t,
ADJ_INTDA_PSit = adjusted intangible development assets per share for firm i in year t,
νit, ϖit = residual terms for firm i in year t.
than the fitted price ratio tests, or vice versa.
15
IV. Data and Sample Selection
The initial sample includes all UK firms on Datastream that disclosed either intangible assets or R&D
expense in any year t = 1993 – 1997; this search yields 2,232 firm-year observations (617 firms).17 I exclude
firms in the oil and gas exploration and production industry and firms in the mining industry (69 firm-year
observations in total) because these firms’ intangible assets likely relate to exploration and development not to
R&D. I further require data on industry membership, net income, sales, debt, book value of equity, stock price
(measured three months after fiscal year end), number of shares outstanding, and monthly stock returns (to
calculate compounded annual returns and beta) to be available on Datastream. Missing data on these items
reduces the sample by 383 firm-year observations, resulting in a final sample of 1,780 firm-year observations,
ranging from 327 firms in 1993 to 398 firms in 1997.
Table 1, Panel A reports descriptive statistics on the full sample (pooled across time and across
accounting treatment for qualifying development expenditures), and the sub-samples of Expensers and
Capitalizers. In total, the sample contains 1,549 Expenser firm-year observations and 231 Capitalizer firm-year
observations.18 These simple statistics suggest either that development expenditures rarely meet the five
conditions necessary for capitalization or that, when the conditions are met managers are reluctant to capitalize
development costs. Statistical tests (results not reported) indicate that the average Capitalizer firm and average
Expenser firm are similar in size (average market values of £1,125 million BP for Expensers versus £861 million
for Capitalizers, with the difference not significant at the .10 level). However, median values of market value,
sales, assets, book value of equity and earnings show that Expensers tend to be larger than Capitalizers (median
17 I choose to study UK firms for two reasons. First, UK GAAP provides an option for the capitalization ofqualifying development expenditures. This option allows for the examination of potential facots that influencefirms' decisions to select one accounting method over the other. Additionally, it allows for the examination ofthe value relevance of earnings and book value of equity conditional on the accounting method choice. Second,both the UK capital market and UK financial reporting system are similar to those in the US, therby enhancingthe comparability of my study to prior studies' examinations of R&D accounting using US data.18 Only 27 firms are classified as an Expenser in at least one year and as a Capitalizer in a different year. Fifteenof these firms maintained a policy of capitalization throughout the sample period, but in at least one of thesample years did not report an intangible development asset. The other 12 firms changed their accountingpolicy; three (nine) firms changed from a policy of capitalization (expensing) to expensing (capitalization). Asrequired by Financial Reporting Standard No. 3, the three firms changing to expensing disclosed the effects ofthe change and restated their prior period financial statements using the new policy (i.e., they wrote off theintangible development asset). Eight of the nine firms changing to capitalization did not disclose the impact ofthe new policy on prior financial statements; the other firm disclosed that the impact on prior asset balances wasimmaterial.
16
differences of these variables are significant at the .00 level). Expensers have a larger average and median share
price (significant at the .00 level). Finally, the average stock return is the same; however, the median return is
significantly (at the .09 level) larger for the Expensers.
Table 1, Panel B provides descriptive statistics on the intangible development assets, R&D expense and
amortization amounts of the pooled sample observations.19 Expensers have an average R&D expense of about
£20 million, or 3-4% of assets or market value of equity and 33% of reported earnings.20 The median R&D
expense is much smaller, about £2 million or about 1% of assets or market value of equity and 16% of reported
earnings. Capitalizers report an average R&D asset of £2 million (6%, 5% and 54% of reported assets, market
value of equity and earnings respectively), an average R&D expense of about £5 million and average
amortization of £520,000. Similar to the Expenser sample, the median amounts of these items are much smaller,
suggesting that a few large observations skew the mean statistics for both samples.
Table 1, Panel C details the industry membership for each subsample and for the combined sample.
Engineering and electronic manufacturing account for about 30% of the observations in each sample. I also
classify industries as either high-technology or low-technology in an attempt to separate firm-year observations
into groups with different product life cycles (based on the dichotomization used in Francis and Schipper
[1999]). Thirty-seven percent of the full sample observations are in industries classified high-technology (37%
(40%) of Expensers (Capitalizers)); 63% of the full sample observations belong to industries classified as low-
technology (63% (60%) of Expensers (Capitalizers)).
Table 2, Panel A reports the descriptive statistics on the gross and net balance sheet values of the
intangible development assets and the changes in book value (including the amount of development expenditure
capitalized and the amount of amortization) as reported in the Capitalizer firms’ note disclosures. The average
(median) historical cost of the development intangible assets is £3.09 million (£0.6 million), and the average
(median) capitalization and amortization are £0.8 million (£0.2 million) and £0.5 million (£0.07 million),
respectively. Table 3, Panel B presents information on the inferred capitalization and amortization rates from the
19 For the 225 of the 231Capitalizer firm-year observations, the amounts of capitalization, amortization andintangible development assets are collected from the notes to their annual reports (for the remaining six firm-yearobservations the values from Datastream are used). These values are used because Datastream reports only theamount of total intangible assets; this includes the capitalized development expenditures and capitalized costsrelating to patents, trademarks, licenses, rights, brands, goodwill, etc.
17
Capitalizers’ note disclosures. 21 The median capitalization rate, calculated using equation (1a) is 50.12% for the
full sample, and ranges from 18.15% to 100% across industries. 22 The median amortization rate, calculated
using equation (1b) is 15.22% (suggesting an asset life of 6.6 years) and ranges from 5.06% (inferred asset life of
19.8 years) to 24.32% (inferred asset life of 4.1 years) across the industries.23
V. Distinguishing Capitalizers from Expensers
Univariate and Logistic Results
Table 3, Panel A presents univariate comparisons of the variables hypothesized to influence the
decision to capitalize versus expense qualifying development expenditures. Results in Panel A indicate that,
relative to the average Capitalizer, the average Expenser is significantly (at the .00 level) larger in size and more
profitable, with lower R&D intensity, lower systematic risk and a greater propensity to be in steady-state with
respect to its R&D programs. In particular, 53% of Expensers are classified as being in steady-state compared to
30% of Capitalizers, and the median Expenser is in steady-state while the median Capitalizer is not (details not
reported in Table 4).24 There is no difference in leverage between the average Expenser and the average
Capitalizer. Similar differences in size, profitability, systematic risk and steady-state status are found from
comparisons of median values (Panel B); there is no median difference in R&D intensity.
Table 3, Panel B presents the results of estimating equation (3) for the full sample (i.e., the results are
based on a pooled time-series cross-sectional estimation). Results are presented both for the full model which
includes the industry indicator variables, and for a reduced model which excludes these variables. In the full
model, three variables are significantly related to the choice to expense qualifying development expenditures.25
20 Figures scaled by earnings exclude observations with net losses and earnings less than one million pounds (toavoid a small denominator).21 Industry median capitalization and amortization rates are reported only if there are more than 5 firm-yearobservations with available data to calculate the respective rates.22 In comparison, Whiteley, Bean and Russo [1996] and Wolff [1995] report that approximately 19%, 61%, and19% of R&D expenditures are spent on basic and applied research, product and process development, andtechnical service, respectively23 Lev and Sougiannis [1996] report inferred asset lives between five and eight years.24 For the full sample, the median absolute difference between the amounts capitalized and amortized, scaled bythe intangible development assets, is 0.20. The mean of this variable is 0.28; the upper and lower quartiles are0.36 and 0.10 respectively. For Expensers, the mean (median) value is 0.25 (0.18); for Capitalizers the mean(median) value is 0.49 (0.33).25 For any results that are significant (in this section and section 6) I report the significance level; results thathave a significance level greater than the 0.10 level are considered not significant, and the level is not reported.
18
The steady-state variable (STATE) is positively related to the expense option (at the .00 level), consistent with
the prediction that firms in steady-state with respect to their R&D programs are more likely to expense their
qualifying development expenditures as incurred. Firm size is positively related to the expense versus capitalize
choice (significant at the .00 level), indicating that larger firms are more likely to expense their qualifying
development expenditures. R&D intensity is significantly (at the .01 level) negatively related to the expense
option, suggesting that firms with lower R&D intensity expense their qualifying development expenditures.
Firm profitability, firm leverage and systematic risk are not significant in explaining the expense versus
capitalize decision.26 Finally, several industry indicator variables are statistically (at less than the .10 level)
negatively related to the expense versus capitalize decision (results not tabulated). These include Transportation,
Clothing and Textiles, Engineering, Media and Broadcasting, Business Support, Defense, Electronic
Manufacturing, Distributors (other), Diversified Industrials, Medical Equipment, Oil Service, Retailing, and
Software.27 Chi-square tests indicate that fit of the full model is significant at the .00 level.
The results from estimating the reduced model are similar to those from estimating the full model,
suggesting that the inclusion of industry indicator variables does not alter inferences drawn from the other test
and control variables. SIZE and STATE remain significantly (at the .00 level) positively related to the expense
option; RDINT is significantly (at the .01 level) negatively related to the expense versus capitalize choice;
PROFIT and LEV remain insignificant in explaining the expense decision. BETA is now significantly (at the
.08 level) negatively related to the expense decision, suggesting that systematic risk is related to industry
membership. Finally, the fit of the reduced model is significant at the .00 level.
Sensitivity Analyses
The results in Table 3, Panel B are based on a pooled cross-sectional and over-time estimation.
Because the same firms appear multiple times in the pooled sample and because their values of the dependent
variable generally do not change over time, the pooled sample significance levels are overstated due to
dependence among observations. To mitigate this concern, I perform three sensitivity analyses: (1) I estimate
both the full model and the reduced model by year; (2) I estimate both the full model and the reduced model
26 Based on the uncertainty surrounding the influence of firm leverage (see footnote 10), I re-estimate equation(3) after excluding leverage. The results are qualitatively similar.
19
measuring all variables as the median value for each firm i over 1993-1997; and (3) I estimate both the full and
reduced model after adjusting for the 27 firms classified as both an Expenser and Capitalizer during the sample
period.28 Overall, the results (not reported) from these analyses are consistent with the results reported in Table
3, with the exception that in the yearly estimation, R&D intensity is only significantly negatively related to the
expense decision in only one year.
I also examine the sensitivity of the results to the estimation of steady-state status. Specifically, I
perform five analyses: (1) I reclassify the cutoff points to the bottom / top 40%; (2) I use one-year sales growth
to assess steady-state status;29 (3) I reclassify firms into increasing non-steady-state, decreasing non-steady-state
and steady-state; (4) I implement an inferred rule based on the median industry-specific capitalization and
amortization rates (reported in Table 3);30 and (5) I use a hypothetical capitalization and amortization rule which
is similar to the rule used in a number of the US studies (100% capitalization of the R&D expense and straight-
line amortization over six years). Overall, the results (not reported) from examining alternate classifications of
steady-state status are consistent with the results reported in Table 3, with an exception of limited evidence
regarding the role of systematic risk and firm profitability in the expense versus capitalize decision. Specifically,
PROFIT (BETA) is significantly negatively related in the third (fourth) analysis.
Additionally, I examine the sensitivity of the results to an alternative proxy for product life cycle; I
estimate a modified equation (3) where the industry indicator variables are removed and I include an indicator
variable that equals one for firm-year observations classified as high-technology, and 0 otherwise. The results
(not reported) show the indicator variable for high-technology is not significantly related to the decision to
expense qualifying development expenditures, and that SIZE, RDINT and STATE remain significant as
previously documented. I also examine the full and reduced models for firms with "significant" R&D
27 No industry dummy is significantly positively related to the expense option. However, industries with nofirm-year observations classified as Capitalizers are excluded from the logistic regression. The excludedindustries are Aerospace, Automobiles and Parts, General Manufacturing, Paper and Packaging, and Steel.28 In the sensitivity analysis, I reclassify the 15 firms with a consistent capitalization policy as Capitalizers inevery year (22 firm-year observations are reclassified), and I delete the 12 firms that changed their accountingpolicy (53 firm-year observations are deleted). See footnote 21 for a discussion of these firms.29 Firms with sales growth less than (greater) than the median sample sales growth are classified as steady-state(non-steady-state).30 Industry median capitalization and amortization rates are used to estimate the amount of capitalization,amortization and the intangible development asset for 822 of the 1,549 Expenser firm-year observations withavailable time series of R&D expenditures to estimate the intangible development asset; estimates for the
20
expenditures, defined as R&D expenditures exceeding 1% of total (non R&D) assets.31 Results (not reported)
are consistent with the results reported in Table 3.
Finally, I examine the sensitivity of the results to the definition of the dependent variable. First, I use an
alternate definition of Capitalizers.32 The results (not reported) are similar to those in Table 3, with the exception
that in the full model firm profitability is significantly (at the .02 level) is negatively related to the expense
decision. Second, I modify equation (3) and regress the amount of the intangible development asset scaled by
total assets on the independent variables. The results (not reported) suggest that larger firms, less profitable
firms, and firms with lower R&D intensity capitalize less qualifying development expenditures than smaller
firms, more profitable firms and firms with greater R&D intensity.
Summary
Overall, I conclude that the decision to capitalize versus expense qualifying development expenditures
is influenced by firm size (with larger firms more likely to expense), by whether the firm is in a steady-state with
respect to its R&D program (with steady-state firms more likely to expense), and the intensity of the R&D
programs (with low R&D intensity firms more likely to expense). One prediction that follows from these results
is that the value relevance of earnings and book value of equity for Expenser firms, who are documented as more
likely to be in steady-state, may not be improved from adopting a capitalization policy. Because firms who are
in steady-state with respect to their R&D programs have roughly equivalent earnings and book value of equity
under either method, so imposing as-if capitalization and amortization policies may only introduce noise into the
reported numbers. In contras, the value relevance of financial information of Expenser firms not in steady-state
may be enhanced by capitalization, despite the measurement error in the restated numbers.
A second prediction is that since the average Expenser firm has lower R&D intensity than the average
Capitalizer firm, it may not be beneficial for Expensers to capitalize their development expenditures. That is,
there may exist less costly mechanisms by which Expenser firms communicate information about their R&D
programs. Alternatively, for firms with greater R&D intensity capitalization may be the most convenient or least
remaining 727 firm-year observations are based on the sample-wide median capitalization and amortizationrates.31 There are 1,077 firm-year observations classified as having "significant" R&D expenditures.32 A firm-year observation is classified as a Capitalizer in year t if in that year the firm reported both capitalizeddevelopment expenditure and an intangible development asset (this decreases the Capitalizer sample by 53 firm-year observations).
21
costly method to communicate R&D information. I conjecture that the value relevance of Capitalizers firms’
earnings and book value of equity will be the higher under a policy of capitalization if this is the most
appropriate method of communication. I investigate the influence of both steady-state status and R&D intensity
on the value relevance of Expensers’ and Capitalizers’ earnings and book value of equity in the next section.
VI. Value Relevance of Financial Information
Results Conditional on Accounting Choice
My second set of tests examines the value relevance of financial information separately for the
Expenser and Capitalizer samples. Table 4, Panel A reports univariate statistics for the independent variables
used in the value relevance tests. Using the as-if capitalize figures for the Expensers, the mean (median)
earnings per share increase from £0.15 (£0.12) to £0.16 (£0.13). The average (median) book value of equity per
share increases from £1.28 (£0.38) to £1.36 (£0.90). Using the as-if expense figures Capitalizers mean (median)
earnings per share remain at a loss of £0.05 (£0.05), and mean (median) book value of equity per share decreases
from £0.76 (£0.50) to £0.71 (£0.48). Table 4, Panel B reports the univariate statistics of the independent
variables used in the value relevance of the R&D components tests. For the Expensers the average (median)
earnings per share before R&D expense is £0.20 (£0.16). Applying the inferred capitalization and amortization
rules results in an average (median) adjusted R&D expense per share and amortization per share of £0.03 (£0.01)
and £0.01(£0.01), respectively. For Capitalizers the average (median) earnings per share before R&D expense
and amortization is about £0.00 (£0.07 million). The average (median) book value of equity per share before the
intangible development assets is about £0.71 (£0.48). The average (median) adjusted R&D expense per share
from creating as-if expense numbers is £0.05 (£0.02).
Table 5, Panel A reports the results from estimating equations 4a and 4b.33 These results show that EPS
and BVPS are highly significant in explaining the cross-sectional variation in contemporaneous stock prices for
both reported and adjusted numbers. For Expensers, the adjusted numbers provide a significantly better fit than
the reported numbers (the Vuong Z-statistic is significant at the .00 level); the adjusted R2 based on reported
numbers is 59.7%, compared to 61.0% using adjusted numbers. However, both the F-statistic and Wilcoxon test
33 To mitigate the influence of extreme observations, I report the results of all regressions after excludingobservations with studentized residuals greater than two in absolute value.
22
indicate no significant difference in fit as measured by the fitted price ratios. For Capitalizers, Vuong tests show
that reported numbers provide a significantly (at the .07 level) higher adjusted R2 than the adjusted numbers
(adjusted R2 is 49.4%using reported numbers and 46.0% using adjusted numbers), but hte fitted price ratio tests
indicate no significant difference.
Table 5, Panel B reports the results from estimating equations 7a and 7b. For Expensers, all reported
numbers are significantly (at the .00 level) positively related to observed share price; the adjusted R2 is 63.2%.
Using adjusted numbers only EPS_PRE, ADJ_RDEXP_PS and BVPS_PRE are significantly (at the .00 level)
positively related to observed share price; the adjusted R2 is 63.2%.34 Neither the Vuong Z-statistic test of
adjusted R2 nor the F-statistic / Wilcoxon tests of the fitted price ratios indicate a significant difference in fit
based on reported and adjusted numbers. For Capitalizers, the R&D component regression results indicate that
reported numbers (except amortization per share) are significantly (at less than the .09 level) positively related to
observed share price (amortization per share is significantly (at the .02 level) negatively related). The results
from using adjusted numbers indicate that all variables are significantly (at less than the .02 level) positively
related to contemporaneous share price. Neither the Vuong Z-statistic test of adjusted R2 (equal to 50.7% using
reported numbers and 49.8% using adjusted numbers) nor the F-statistic / Wilcoxon tests of the fitted price ratios
indicate a significant difference in fit based on reported and adjusted numbers.
Overall, the results in Table 5, Panels A and B are mixed concerning the impact on value relevance of
adjusting reported earnings and book value of equity to the alternative accounting rule for qualifying
development expenditures. The evidence in Panel A indicates that adjusted (reported) numbers provide more
highly value relevant earnings and book value of equity for Expensers (Capitalizers), as measured by the
adjusted R2; however, the magnitude of any difference from using reported versus adjusted numbers is small. In
Panel B there is no differene in explained variation between adjusted and reported numbers for either sub-
sample. Finally, there is no difference documented in the fitted price ratios between reported versus adjusted
numbers in either panel. From these results, I conclude there is little, if anything, to be gained from applying
hypothetical capitalization and amortization rules to the reported financial numbers of Expensers. Similarly, the
34 In contrast to Lev and Sougiannis' [1996] finding, the coefficient on the estimated intangible developmentassets is indistinguishable from zero.
23
evidence suggests that for Capitalizers the value relevance of their as-if-expensed earnings and book value of
equity is not substantially lower than the value relevance of their reported values of these variables.35
Results Conditional on Steady-State Status and R&D Intensity
The results from the determinants of the decision to expense versus capitalize qualifying development
expenditures (section five) indicate that non-steady-state firms are more likely to capitalize their qualifying
development expenditures. There is also limited evidence that firms with higher R&D intensity are more likely
to capitalize their qualifying development expenditures. Based on these findings and my hypothesis that the
decision to capitalize versus expense qualifying development expenditures may convey value relevant
information to the market (section three), I examine the influence of both steady-state status and R&D intensity
(separately and jointly) on the value relevance of firms' earnings and book value of equity.
Steady-State Status
I hypothesize that firms in steady-state with respect to their R&D programs have little to gain from
capitalizing qualifying development expenditures. For these firms, amounts capitalized will be similar in
magnitude to amounts amortized. In contrast, managers of firms not in steady-state with respect to their R&D
programs may view capitalization as a method of revealing value relevant information about their R&D
programs. I predict that for steady-state Expensers (Capitalizers) the value relevance of their earnings and book
value of equity will be higher using the reported (adjusted) numbers, and that for Expensers (Capitalizers) not in
steady-state value relevance will be the higher using the adjusted (reported) numbers. To test these conjectures,
I estimate equations 4a and 4b for Expensers and Capitalizers conditional on the steady-state status.
The results in Table 5, Panel C indicate that steady-state status may marginally influence the value
relevance of firms’ earnings and book value of equity. Specifically, for Expensers adjusted numbers always
provide a significantly (at the .00 level) better model fit than reported numbers, regardless of steady-state status.
The largest difference in adjusted R2 is reported for non-steady-state Expensers (increase of 1.9% - versus an
increase of 0.9% for steady-state Expensers). For steady-state Capitalizers there is no significant difference in
model fit between reported and adjusted numbers. However, for non-steady-state Capitalizers the model fit is
35 My results are consistent with those documented by Chambers, Jennings, and Thompson [1999], who findmodest increases in adjusted R2 from regressions of contemporaneous stock price on earnings and book value ofequity based on adjusted versus reported numbers.
24
significantly (at the .06 level) better using reported rather than adjusted numbers. Similar to the results in Panel
A no significant difference is documented between using reported and adjusted numbers for any sub-sample.
R&D Intensity
I hypothesize that firms with high R&D intensity may capatilize qualifying development expenditures
as one method to communicate information about the success of their R&D programs to investors. This
hypothesis implies that the value relevance of high R&D intensity Expensers' (Capitalizers') earnings and book
value of equity using adjusted (reported) numbers. For low R&D intensity Expensers (Capitalizers), I predict the
value relevance of their earnings and book value of equity will be higher using reported (adjusted) numbers.36
To test these conjectures, I estimate equations 4a and 4b for Expensers and Capitalizers conditional on R&D
intensity.
The results in Table 5, Panel D provide limited evidence that R&D intensity may influence the value
relevance of firms’ earnings and book value of equity. Specifically, for both high and low R&D intensity
Expensers, the model fit is significantly (at less than the .03 level) better using adjusted versus reported numbers.
The difference in adjusted R2 is larger for high R&D intensity Expensers relative to low R&D intensity
Expensers (difference of 1.6% (0.2%) for high (low) R&D intensity Expensers). For Capitalizers, the model fit
is significantly (at less than the .05 level) better using reported numbers versus adjusted numbers for both sub-
samples. Similar to Expensers the difference in adjusted R2 is larger for high R&D intensity Capitalizers
(difference of 2.9%) relative to low R&D intensity Capitalizers (difference of 1.0%). The results from tests of
the fitted price ratios indicate only one significant difference (based on the F-test); the fitted price ratio is
significantly (at the .01 level) smaller using adjusted versus reported numbers for high R&D intensity Expensers.
Interaction of Steady-State Status and R&D Intensity
If steady-state status and R&D intensity do influence the value relevance of firms’ earnings and book
value of equity conditional on accounting method choice for qualifying development expenditures, then the
ability to detect any influence should be enhanced by examining the interaction of these two characteristics.
Specifically, I hypothesize that high R&D intensity firms not in steady-state will benefit the most (as measured
36 Firm-year observations are classified as high R&D intensity if they are in the upper (lower) half of the sampledistribution of R&D intensity, where R&D intensity equals the amount expended on R&D divided by total assets(converted to full expensing).
25
by value relevance) from selecting a policy of capitalization.37 Additionally, I predict that low R&D intensity
firms in steady-state will have higher value relevance by adopting a policy of expensing. For the remaining
firms (either not in steady-state with low R&D intensity, or in steady-state with high R&D intensity) it is not
obvious which characteristic dominates; therefore I make no predictions for these firms.
Conditioning on accounting choice, I predict that high R&D intensity Expenser (Capitalizer) firms not
in steady-state will have higher value relevant earnings and book value of equity using adjusted (reported)
numbers. For low R&D intensity Expenser (Capitalizer) firms in steady-state I make the opposite prediction. To
test these predictions I estimate equations 4a and 4b conditional on steady-state status and R&D intensity for
Expensers and Capitalizers.
The results in Table 5, Panel E indicate that the only significant difference in model fit is for high R&D
intensity Expensers not in steady-state; the model fit is significantly (at the .00 level) better using adjusted versus
reported numbers. This result supports the hypotheses that steady-state status and R&D intensity influences the
value relevance of firms' earnings and book value of equity conditional on accounting method choice for
qualifying development expenditures. However, no significant difference in explained variability is
documented for the other sub-samples. Additionally, no significant differences are documented in the fitted
price ratios for any sub-sample.
Sensitivity Analyses
I perform a number of sensitivity analyses of the value relevance results. First, I estimate all
regressions after including yearly fixed effects to control for the dependence of observations. Second, I estimate
all regressions after making adjustments for firms that appear in both the Expenser sample and the Capitalizer
sample during the sample period. Third, I use the industry inferred, and separately the US hypothetical,
capitalization and amortization rule to create adjusted numbers for the Expensers. Fourth, I use the alternate
definition of Capitalizers (i.e., they reported and intangible development asset and capitalization). Fifth, I
separate non-steady-state firms into those increasing and those decreasing their intangible assets. Sixth, I use
37 This prediction is consistent with Monahan's [1999] findings that the increase in value relevance from usingadjusted numbers versus reported numbers is greater for firms not in steady-state (he refers to this as high pastgrowth in R&D expenditures) and with high R&D intensity (he refers to this as high conservatism).
26
beta and market adjusted returns in the return regressions.38 Seventh, I examine the results for the sample of
firms with “significant” R&D programs (i.e., R&D expenditures in excess of 1% of total assets net of the
intangible development asset).39 Finally, I examine the influence of steady-state status and R&D intensity on the
value relevance of firms’ financial statements regardless of their accounting method choice for development
expenditures (i.e., I combine all firms into either steady-state / non-steady-state, or into high / low R&D
intensity). The results of the sensitivity analyses are qualitatively similar to those reported in Tables 9 through
13.
I also estimate regressions of contemporaneous returns on the level and change in earnings per share
(the return regressions). Similar to the previous price regressions, I estimate the return regressions for Expensers
and Capitalizers separately, and I estimate the return regressions after conditioning on the steady-state status and
R&D intensity of firms' R&D programs. In general, the results of the return regressions do not indicate any
difference in the adjusted R2 from using reported versus adjusted numbers for any sample of firms. From these
results, I conclude that the value relevance of Expensers' (Capitalizers) earnings and book value of equity is not
increased (decreased) from imposing an as-if-capitalize (as-if-expense) policy.
VII. Summary and Conclusion
Using the reported disclosures relating to development expenditures of 1,780 UK firm-year
observations over 1993-1997, I investigate two specific questions relating to the capitalize versus expense
decision: (1) What factors distinguish expensing firms from capitalizing firms?; and (2) Conditional on the
choice to expense or to capitalize, does the accounting treatment of development expenditures affect the value
relevance of these firms' earnings and book value of equity?
My first set of tests examines factors that I hypothesize influence the choice of accounting for
qualifying development expenditures. Specifically, I use a logit regression of the decision to expense qualifying
development expenditures on industry indicator variables (to proxy for product life cycle), profitability, leverage,
systematic risk, firm size, R&D intensity and R&D steady-state. The results indicate that larger firms, firms in
38 Beta (market) adjusted returns are calculated by subtracting the 12 month compounded equally weighted betaportfolio compounded return (market return) from the 12 month compounded firm return.39 This analysis effectively removes firm-year observations classified as having low R&D intensity; therefore, Ido not examine value relevance conditional on R&D intensity.
27
steady-state and firms with lower R&D intensity are more likely to expense their qualifying development
expenditures.
My second set of tests compares the value relevance of reported earnings and book value of equity to
the value relevance of the adjusted values of these variables (where the adjustments restate earnings and book
value of equity to the alternative accounting method). The results conditional on the choice of accounting
method for qualifying development expenditures are mixed regarding which method generates the higher value
relevant earnings and book value of equity. Specifically, in some instances, adjusted (reported) financial data
explain significantly more of the variation in share prices than do reported (adjusted) financial data for the
Expensers (Capitalizers); however, the difference in adjusted R2 is small in magnitude. The results of the other
tests (i.e., the variance of the log of the ratio of predicted share price to observed share price and the explained
variability from the price and return regressions on the R&D components) indicate no difference in value
relevance between using reported and adjusted numbers. Finally, the results of additional analyses that examine
the influence of steady-state status and R&D intensity of firms' R&D programs are also mixed. In some cases
there is limited evidence that for firms not in steady-state or with high R&D intensity the value relevance of their
earnings and book value of equity is higher using numbers based on a policy of capitalization versus expensing.
Overall, I conclude from these results that Expensers have little to gain, if anything, in terms of value
relevance from adjusting their reported numbers to reflect as-if-capitalized numbers. For the Capitalizers, the
value relevance of as-if-expensed earnings and book value of equity is not substantially lower than the value
relevance of the reported values of these items. Finally, neither steady-state status nor R&D intensity plays an
important role in influencing the value relevance of the earnings and book value of equity of firms with R&D
programs. These conclusions are in contrast to the results of US studies (including Lev and Soguiannis [1996],
Monahan [1999] and Healy, Myers and Howe [1999]) which document large increases in value relevance from
comparing adjusted financial statement numbers to reported numbers (where the adjustments are made using
hypothetical capitalization and amortization rules).
28
Table 1Descriptive Statistics On Sample UK Firmsa
Panel A: Selected Financial Informationb
Expensers CapitalizersMean Median Mean Median
Market Price 2.66 2.14 1.83 1.13Market Value 1,125 137 861 31Return 16% 11% 18% 5%Sales 1,080 138 1,303 44Assets 1,097 113 804 34Book Value of Equity 475 47 303 16Earnings 69 7 39 1
Panel B: Research and Development Variablesc
Expensers CapitalizersMean Median Mean Median
INTDA 2.00 0.50INTDA / Assets 0.06 0.01INTDA / Market Value 0.05 0.01INTDA / Earningsd 0.54 0.12
R&D Expense 19.87 1.82 5.03 0.17R&D Expense / Assets 0.04 0.01 0.03 0.00R&D Expense / Market Value 0.03 0.01 0.03 0.01R&D Expense / Earningsd 0.33 0.16 0.37 0.08
INTDA Amortization 0.52 0.07INTDA Amortization / Assets 0.01 0.00INTDA Amortization / Market Value 0.01 0.00INTDA Amortization / Earningsd 0.10 0.02
29
Table 1 - ContinuedDescriptive Statistics On Sample UK Firmsa
Panel C: Industry Membershipe
Industry Expensers Capitalizers Full SampleHigh-Tech Industries:f
Aerospace 40 0 40Software 61 16 77Telecommunications 16 11 27Chemicals 92 6 98Pharmaceuticals 45 6 51Medical Equipment 64 15 79Defense 12 4 16Electronic Manufacturing 203 31 234Home Appliances 36 4 40
569 93 662Low-Tech Industriesf
Hotels and Leisure 30 6 36Business Support 89 19 108Oil Service 6 5 11Paper and Packaging 55 0 55Media and Broadcasting 34 15 49Retailing 33 13 46Steel 7 0 7Engineering 253 39 292Building and Construction 92 9 101Transportation 4 3 7Automobiles and Parts 51 0 51Clothing and Textiles 39 10 49Diversified Industrials 23 4 27Utilities 86 1 87General Manufacturing 56 0 56Distributors, other 41 12 53Food Manufacturers 81 2 83
980 138 1118Total 1549 231 1780
a The sample consists of UK firms who disclosed either an intangible asset or R&D expense in any year t = 1993-1997, with the following data available on Datastream: industry membership, net income, sales, debt, book valueof equity, stock price, number of shares outstanding and monthly stock returns (to calculate annual returns andbetas). The full sample (Panel A) includes 1,780 firm-year observations, ranging from 327 firms in 1993 to 398firms in 1997. A firm-year observation is classified as a Capitalizer (231 firm-year observations) if in that yearthe firm reported an intangible development asset; otherwise the firm-year observation is classified as anExpenser (1,549 firm-year observations).
b Market price per share and market value are measured 3 months after the fiscal year end. Return is the annualreturn (compounded monthly), measured from nine months before the fiscal year end to three months after thefiscal year end. Market price per share is reported in pounds sterling; market value, sales, assets, book value ofequity and earnings are measured in millions of pounds sterling.
30
Table 1 - ContinuedDescriptive Statistics On Sample UK Firmsa
c INTDA = intangible development assets; R&D Expense = the reported R&D expense; INTDA Amortization =the amortization of the intangible development assets. All three variables are measured in millions of poundssterling.
d When Earnings is used as the scaling variable, observations with earnings less than one million (including netlosses) are deleted.
e This panel details firm-year observations classified by industry membership. Datastream provides industrymembership at a fairly disaggregated level. Some of the reported industries are aggregated for purposes of thisstudy.
f Industries are separated into high-technology (High Tech) and low-technology (Low Tech) based on theclassification in Francis and Schipper (1999).
31
Table 2Intangible Development Assets and the Capitalization and Amortization Schedulesa
Panel A: Descriptive Statistics on the Intangible Development Assetsb
mean median std. dev range
BEG_INTDA 1.64 0.38 3.70 28.40
END_INTDA 1.96 0.50 4.03 23.80
DEVCAP 0.83 0.21 1.77 12.44
DEVAMORT 0.54 0.07 1.52 12.64
BEG_COST 3.09 0.64 8.81 70.82
Panel B: Median Capitalization and Amortization Ratesc
Industry CAP_RATE AMORT_RATE Asset Life
Full Sample 50.12 15.22 6.57
Clothing and Textiles 18.15 6.85 14.60
Engineering 19.07 24.32 4.11
Building and Construction 100.00 18.00 5.56
Media and Broadcasting 100.00 20.00 5.00
Business Support 50.00 20.77 4.81
Electronic Manufacturing 44.38 16.41 6.09
Distributors, other 100.00 24.25 4.12
Hotels and Leisure 94.73 5.06 19.76
Medical Equipment 73.57 6.19 16.16
Retailing 100.00 10.83 9.23
Software 22.52 14.68 6.81
Telecommunications 100.00 11.03 9.07
a The data is from the footnotes to the annual financial statements of 225 of the 231 Capitalizer firm-yearobservations. See Table 1 for sample selection and for the definition of Capitalizers.
b BEG_INTDA (END_INTDA) equals the beginning (ending) net value of intangible development assets;DEVCAP equals the annual amount of development expenditure capitalized; DEVAMORT equals theamortization of the intangible development assets; and BEG_COST equals the beginning historical cost ofintangible development assets. All numbers are reported in millions of pounds sterling.
c CAP_RATE = capitalization rate, calculated as DEVCAP divided by the R&D expenditure; AMORT_RATE =amortization rate, calculated as DEVAMORT divided by BEG_COST; Asset Life = inferred life of intangibledevelopment assets, calculated as one divided by the AMORT_RATE. CAP_RATE and AMORT_RATE are inpercentages; Asset Life is in number of years. The reported numbers are the median for either the full sample, orfor the respective industry. Industry rates and asset lives are reported only if there are more than five firm-yearobservations in the industry with data to calculate the rates.
32
Table 3Determinants of the Decision by UK Firms to Expense Qualifying Development Expenditures
Panel A: Univariate Statisticsa
Independent Mean Values: Median Values:Variable Expenser Capitalizer Differenceb Expenser Capitalizer Differenceb
SIZE 11.91 10.63 0.00 11.82 10.35 0.00PROFIT 0.03 -0.03 0.00 0.06 0.05 0.00RDINT 0.04 0.08 0.00 0.01 0.02 0.89LEV 1.43 1.59 0.88 1.10 1.22 0.12STATE 0.53 0.30 0.00 1.00 0.00 0.00BETA 0.97 1.15 0.00 0.90 1.02 0.02
Panel B: Logit Regression Resultsc
Independent Predicted Full Equationd Reduced Equatione
Variable Sign Coefficient Significance Coefficient Significance
INTERCEPT ? -0.74 0.23 -1.33 0.00
SIZE ? 0.32 0.00 0.29 0.00
PROFIT + -0.50 0.27 -0.23 0.59
RDINT ? -2.39 0.01 -1.84 0.01
LEV - 0.00 0.92 0.00 0.76
STATE + 0.75 0.00 0.74 0.00
BETA ? -0.17 0.11 -0.18 0.08
Chi-Square 195.80 116.51
# Observations 1,780 1,780
See Table 1 for sample selection and for the definition of Expensers and Capitalizers. There are 1,549 (231)Expenser (Capitalizer) firm-year observations.
a Panel A reports univariate statistics on the predicted determinants hypothesized to influence the decision tofollow a policy of expensing (versus capitalizing) qualifying development expenditures. The variables aredefined as follows: SIZE = the log of market value of equity, measured 3 months after the fiscal year end;PROFIT = net income divided by total assets (both variables converted to full expensing); RDINT = amountexpended on R&D divided by total assets (converted to full expensing); LEV = total debt divided by book valueof equity less the intangible development asset; STATE = dummy variable equal to 1 if the firm is in steadystate, 0 otherwise. A firm-year observation is defined to be in steady-state (non-steady-state) if it is in the lower(upper) half of the distribution of the absolute value of the difference between the amortization amount andcapitalization amount scaled by the amount of the intangible development asset; BETA = the firm beta,calculated using monthly stock returns using the maximum number of returns over a 60 month period ending onemonth prior to the fiscal year end (a minimum of 12 monthly returns are required) and an equally weightedmarket return.
33
Table 3 - ContinuedDeterminants of the Decision by UK Firms to Expense Qualifying Development Expenditures
b The Difference column reports the significance levels for F-tests (Wilcoxon tests) comparing the pooledsample mean (median) for the difference between Expensers and Capitalizers.
c Panel B reports the results of estimating the following logit regression:
ititBETAitSTATEitLEV
itRDINTitPROFITitSIZEjitINDj
jitEXP εββββββββ +++++++∑
=+=
7654321
20
10
EXPit = dummy variable equal to 1 if firm expenses development expenditures in year t, 0 otherwise; INDjit =dummy variable equal to 1 if firm is in industry j in year t, 0 otherwise; εit = residual term for firm i in year t; seenote a for the definition of all other variables.
d The third and fourth columns report the coefficients and significance levels from the estimation of the logitregression when all independent variables are included. Coefficients and significance levels for the industryindicator variables are not reported.
e The fifth and sixth columns report the coefficients and significance levels from the estimation of the reducedform logit regression when the industry indicator variables are excluded.
34
Table 4Univariate Statistics of the Independent Variables Used in the Value Relevance Tests
Panel A: Variables Used in Assessing Value Relevance of Earnings and Book Value of Equitya
Expensers CaptializersMean Median Mean Median
EPS 0.15 0.12 -0.05 0.05∆EPS 0.02 0.02 -0.03 0.01ADJ_EPS 0.16 0.13 -0.05 0.05∆ADJ_EPS 0.02 0.02 -0.03 0.01BVPS 1.28 0.83 0.76 0.50ADJ_BVPS 1.36 0.90 0.71 0.48
Panel B: Variables Used in Assessing Value Relevance of the R&D Componentsb
Expensers CaptializersMean Median Mean Median
EPS_PRE 0.20 0.16 0.00 0.07∆EPS_PRE 0.02 0.02 -0.02 0.01RDEXP_PS 0.05 0.02 0.03 0.01∆RDEXP_PS 0.01 0.00 0.01 0.00DEVAMORT_PS 0.00 0.00 0.01 0.00∆DEVAMORT_PS 0.00 0.00 0.00 0.00ADJ_RDEXP_PS 0.03 0.01 0.05 0.02∆ADJ_RDEXP_PS 0.00 0.00 0.01 0.00ADJ_DEVAMORT_PS 0.01 0.01 0.00 0.00∆ADJ_DEVAMORT_PS 0.00 0.00 0.00 0.00BVPS_PRE 1.28 0.83 0.71 0.48INTDA_PS 0.08 0.03 0.05 0.01
See Table 1 for sample selection and for the definition of Expensers and Capitalizers. There are 1,549 (231)Expenser (Capitalizer) firm-year observations.
a All numbers are reported in pounds sterling. EPS = earnings per share; ∆EPS = change in EPS; ADJ_EPS =adjusted earnings per share; ∆ADJ_EPS = change in ADJ_EPS; BVPS = book value of equity per share;ADJ_BVPS = adjusted book value of equity per share. Shares outstanding are measured three months afterfiscal year end.
b All numbers reported in pounds sterling. EPS_PRE = earnings per share before R&D expense and amortizationof the INTDA; ∆EPS_PRE = change in EPS_PRE; RDEXP_PS = R&D expense per share; ∆RDEXP_PS =change in RDEXP_PS; DEVAMORT_PS = amortization of the INTDA per share; ∆DEVAMORT_PS = changein DEVAMORT_PS; ADJ_RDEXP_PS = adjusted R&D expense per share; ∆ADJ_RDEXP_PS = change inADJ_RDEXP_PS; ADJ_DEVAMORT_PS = adjusted amortization of the INTDA per share;∆ADJ_DEVAMORT_PS = change in ADJ_DEVAMORT_PS; BVPS_PRE = book value per share before theintangible development assets; INTDA_PS = intangible development assets per share.
35
Table 5Value Relevance of Financial Informationa
Panel A: Value Relevance of Earnings and Book Value of Equityb
Expensers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 2.835 16.023* ADJ_EPS 2.869 16.714*BVPS 0.659 20.920* ADJ_BVPS 0.654 21.887*Adj. R-square 0.597 Adj. R-square 0.610Vuong's Z-Statistic 6.312*
Fitted Price Ratio 0.745 Fitted Price Ratio 0.728F-Statistic 1.020Difference 0.465
Capitalizers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 2.672 5.077* ADJ_EPS 2.428 4.528*BVPS 1.296 9.985* ADJ_BVPS 1.321 9.938*Adj. R-square 0.494 Adj. R-square 0.460Vuong's Z-Statistic -1.844*
Fitted Price Ratio 0.690 Fitted Price Ratio 0.770F-Statistic 1.120Difference 0.465
Panel B: Value Relevance of R&D Componentsc
Expensers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS_PRE 2.942 17.119* EPS_PRE 2.933 17.042*RDEXP_PS 3.095 6.495* ADJ_RDEXP_PS 5.913 3.243*BVPS_PRE 0.613 19.758* ADJ_DEVAMORT_PS 9.261 1.274
BVPS_PRE 0.613 19.470*INTDA_PS -2.233 -1.278
Adj. R-square 0.632 Adj. R-square 0.632Vuong's Z-Statistic 0.509
Fitted Price Ratio 0.678 Fitted Price Ratio 0.679F-Statistic 1.000Difference 0.347
36
Table 5 - ContinuedValue Relevance of Financial Informationa
Capitalizers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS_PRE 2.566 5.316* EPS_PRE 2.507 5.216*RDEXP_PS 2.521 2.572* ADJ_RDEXP_PS 2.006 2.399*DEVAMORT_PS -10.762 -2.262* BVPS_PRE 1.099 8.770*BVPS_PRE 1.134 8.933*INTDA_PS 2.743 1.709*Adj. R-square 0.507 Adj. R-square 0.498Vuong's Z-Statistic -1.186
Fitted Price Ratio 0.656 Fitted Price Ratio 0.680F-Statistic 1.020Difference 0.754
Panel C: Value Relevance Conditional on Steady-State Statusd
Expensers in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 2.785 12.184* ADJ_EPS 2.783 12.499*BVPS 0.726 16.900* ADJ_BVPS 0.721 17.463*Adj. R-square 0.616 Adj. R-square 0.625Vuong's Z-Statistic 3.223*
Fitted Price Ratio 0.586 Fitted Price Ratio 0.603F-Statistic 1.030Difference 0.465
Expensers not in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 4.636 15.034* ADJ_EPS 4.817 15.967*BVPS 0.348 7.419* ADJ_BVPS 0.332 7.380*Adj. R-square 0.594 Adj. R-square 0.613Vuong's Z-Statistic 4.053*
Fitted Price Ratio 0.977 Fitted Price Ratio 0.883F-Statistic 1.110Difference 0.602
37
Table 5 - ContinuedValue Relevance of Financial Informationa
Capitalizers in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 5.706 4.233* ADJ_EPS 6.541 4.504*BVPS 1.466 6.821* ADJ_BVPS 1.339 6.035*Adj. R-square 0.756 Adj. R-square 0.757Vuong's Z-Statistic 0.129
Fitted Price Ratio 0.315 Fitted Price Ratio 0.336F-Statistic 1.070Difference 0.917
Capitalizers not in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 3.239 4.712* ADJ_EPS 2.668 3.774*BVPS 0.963 5.866* ADJ_BVPS 1.017 5.951*Adj. R-square 0.381 Adj. R-square 0.320Vuong's Z-Statistic -1.967*
Fitted Price Ratio 0.853 Fitted Price Ratio 0.947F-Statistic 1.110Difference 0.251
Panel D: Value Relevance Conditional on R&D Intensitye
High R&D Intensity Expensers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 5.409 17.744* ADJ_EPS 5.554 19.096*BVPS 0.797 13.288* ADJ_BVPS 0.708 13.751*Adj. R-square 0.635 Adj. R-square 0.651Vuong's Z-Statistic 3.373*
Fitted Price Ratio 0.775 Fitted Price Ratio 0.636F-Statistic 1.220*Difference 0.347
38
Table 5 - ContinuedValue Relevance of Financial Informationa
Low R&D Intensity Expensers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 1.769 8.815* ADJ_EPS 1.772 8.859*BVPS 0.710 18.869* ADJ_BVPS 0.706 18.898*Adj. R-square 0.643 Adj. R-square 0.645Vuong's Z-Statistic 2.136*
Fitted Price Ratio 0.767 Fitted Price Ratio 0.760F-Statistic 1.010Difference 0.602
High R&D Intensity Capitalizers:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 0.247 0.405 ADJ_EPS 0.264 0.429BVPS 1.316 7.938* ADJ_BVPS 1.285 7.647*Adj. R-square 0.449 Adj. R-square 0.420Vuong's Z-Statistic -2.001*
Fitted Price Ratio 0.881 Fitted Price Ratio 0.946F-Statistic 1.070Difference 0.917
Panel E: Value Relevance Conditional on Steady-State Status and R&D Intensityf
High R&D Intensity Expensers not in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 5.016 10.107* ADJ_EPS 5.223 11.374*BVPS 0.669 7.596* ADJ_BVPS 0.601 8.090*Adj. R-square 0.553 Adj. R-square 0.584Vuong's Z-Statistic 2.838*
Fitted Price Ratio 0.869 Fitted Price Ratio 0.742F-Statistic 1.170Difference 0.465
39
Table 5 - ContinuedValue Relevance of Financial Informationa
Low R&D Intensity Expensers in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 1.226 4.775* ADJ_EPS 1.239 4.844*BVPS 0.771 14.453* ADJ_BVP
S0.766 14.450*
Adj. R-square 0.603 Adj. R-square 0.605Vuong's Z-Statistic 1.193
Fitted Price Ratio 0.646 Fitted Price Ratio 0.640F-Statistic 1.010Difference 0.917
High R&D Intensity Capitalizers not in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 0.083 0.095 ADJ_EPS 0.012 0.014BVPS 1.231 5.535* ADJ_BVPS 1.230 5.730*Adj. R-square 0.415 Adj. R-square 0.401Vuong's Z-Statistic -1.162
Fitted Price Ratio 1.105 Fitted Price Ratio 1.158F-Statistic 1.050Difference 0.917
Low R&D Intensity Capitalizers in Steady-State:
Reported AdjustedInd. Variable Coefficient t-stat Ind. Variable Coefficient t-statEPS 7.651 4.114* ADJ_EPS 7.618 3.973*BVPS 1.827 5.497* ADJ_BVPS 1.808 5.360*Adj. R-square 0.882 Adj. R-square 0.877Vuong's Z-Statistic -1.461
Fitted Price Ratio 0.128 Fitted Price Ratio 0.134F-Statistic 1.050Difference 0.917
40
Table 5 - ContinuedValue Relevance of Financial Informationa
a This table reports the results of the value relevance tests where contemporaneous share price is regressed onearnings per share and book value of equity per share (expect Panel B where the independent variables are theR&D components). See Table 1 for sample selection and for the definition of Expensers and Capitalizers. Thereare 1,549 (231) Expenser (Capitalizer) firm-year observations. See the notes to Table 4 for the definition of theindependent variables; share price for firm i (Pit) is observed three months after the fiscal year of year t. Twotests are used to assess the difference in value relevance. First, the Vuong Z-statistic reports the results of thelikelihood ratio test developed by Vuong [1989] for non-nested model selection; a significant negative (positive)Z-statistic indicates that reported (adjusted) numbers is the model of choice. Second, the Fitted Price Ratio is thevariance of the log of the predicted share price dived by observed share; the F-statistic compares the two samplevariances and Difference is the significance level of the Wilcoxon test comparing the median of the annualsample variances. See the notes to Table 4 for the definition of the independent variables.
b Panels A, C, D and E report the coefficient estimates and t-statistics for the following two regressions:
Reported: Pit = λ0 + λ1EPSit + λ2BVPSit + µit,Adjusted: Pit = θ0 + θ1ADJ_EPSit + θ2ADJ_BVPSit + κit,
c Panel B reports the coefficient estimates and t-statistics for the following two regressions:
Reported: Pit = α0+α1EPS_PREit+ α2RDEXP_PSit + α3DEVAMORT_PSit + α4BVPS_PREit + α5INTDA_PSit + νit,Adjusted: Pit = χ0 + χ1EPS_PREit + χ2ADJ_RDEXP_PSit+ χ3ADJ_DEVAMORT_PSit +χ4BVPS_PREit + χ5INTDA_PSit + ϖit,
d Of the Expenser firm-year observations 820 (729) are defined to be in (not in) steady-state. Of the Capitalizerfirm-year observations 70 (161) are defined to be in (not in) steady-state. A firm-year observation is defined tobe in (not in) steady-sate if it is in the lower (upper) half of the distribution of the absolute value of the differencebetween the amortization amount and capitalization amount scaled by the amount of intangible developmentassets.
e Of the Expenser firm-year observations 774 (775) are defined as high (low) R&D intensity. Of the Capitalizerfirm-year observations 116 (115) are defined as high (low) R&D intensity. A firm-year observation is defined ashigh (low) R&D intensity if it is in the upper (lower) half of the distribution of R&D intensity, where R&Dintensity is measured as the amount expended on R&D divided by total assets (converted to full expensing).
f Of the Expenser firm-year observations 416 (371) are classified as being in (not in) steady-state and having low(high) R&D intensity. Of the Capiralizer firm-year observations 29 (74) are classified as being in (not in)steady-state and having low (high) R&D intensity.
41
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