DanielsonPress Article.pmd - SSRN papers

When Does R&D Expense Distort Profitability Estimates? Morris G. Danielson and Eric Press Financial analysts and researchers use accounting-based measures of profitability to assess the past performance of firms and as a starting point in forecasting future performance. Research and development costs can distort profitability measures because firms are required to expense R&D immediately, even if the investment creates future benefits. This article introduces a new model identifying two observable firm characteristics that must both be present for R&D costs to distort return on assets (ROA): large R&D expenditures and a sizable difference between ROA and the firm’s growth rate. Because these conditions typically do not occur together, there is frequently little difference between unadjusted ROA and ROA estimates obtained by reversing, capitalizing, and depreciating historical R&D costs, and the two ROA measures produce similar rankings of firm profitability. Our approach identifies cases for which unadjusted ROA could be misleading, letting analysts and researchers target these firms for further analysis. [G31, M21, M41]

Research and development is a critical input in the creation of new products in many firms, particularly technology and science-based companies. Although the purpose of R&D investment is to generate future cash flows, Statement of Financial Accounting Standards No. 2 (1974) requires firms to expense all R&D costs in the year they are incurred. If R&D yields future benefits, the immediate expensing of its costs makes a firm’s accounting rate of return and the underlying economic return on investment differ, which complicates the evaluation of profitability (Scherer, 1993 and Damodaran, 1998, 2001). Most analysts and researchers are aware of this problem, and know they can reverse, capitalize, and depreciate a firm’s historical R&D costs, recasting financial statement information to obtain better measures of economic profitability. This adjustment is part of the economic value-added calculation (Stewart, 1991). It is also recommended as a stand-alone adjustment (Damodaran, 1998 and 2001), and is used in research studies (e.g., Lev and Sougiannis, 1996, and Morris G. Danielson is an Assistant Professor of Finance at Saint Joseph’s University in Philadelphia, PA 19131. Eric Press is an Associate Professor of Accounting at Temple University in Philadelphia, PA 19122. We acknowledge the helpful comments of Kirsten Anderson, Bill Baber, Fabrizio Ferri, Jayanthi Krishnan, Buryung Lee, and participants at the 2004 Financial Management Association and Eastern Finance Association Conferences. Press received research support from Temple University.

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Chan, Lakonishok, and Sougiannis, 2001, Chambers, Jennings, and Thompson, 2002, and Penman and Zhang, 2002). Although analysts and researchers know it is important to adjust profitability for R&D costs, the accounting and finance literatures provide no guidance as to when potential distortion in accounting-based return measures makes an adjustment necessary. The expensing of R&D costs affects both the income statement and balance sheet, and therefore has an uncertain impact on a firm’s accounting rate of return. It is possible for the income statement and balance sheet errors to cancel out— resulting in a small difference between adjusted and unadjusted accounting rates of return—even for a firm with high R&D costs. Thus, it is not obvious when complex and time-consuming adjustments for historical R&D costs are an essential step in a profitability analysis. This article introduces a new way to adjust accounting-based performance measures for R&D costs. The model is easy to implement, as it requires only a small amount of historical data—return on assets (ROA), the ratio of R&D to assets, and the geometric R&D growth rate over the estimated useful life of the firm’s R&D assets. The model identifies two conditions that must both be present for R&D costs to distort ROA: large R&D expenditures and a sizable difference between ROA and the firm’s growth rate.

DANIELSON & PRESS — WHEN DOES R&D EXPENSE DISTORT PROFITABILITY ESTIMATES?

Thus, some firms with large R&D costs have significant differences between unadjusted ROA and ROA adjusted for R&D costs; others do not. For example, in 1999 (see Section VII, below) the reverse, capitalize, and depreciate procedure adjusts the ROA of Glaxo by 6.9 percentage points (from 29.0% to 22.1%), but adjusts the ROA of Pfizer by only 1.4 percentage points (from 24.2% to 22.8%). Yet, the two firms had similar R&D investments (as a percent of assets) that year. An analyst using our model would quickly identify Glaxo as having the greater ROA distortion, because there is a much greater difference between the R&D growth rate and unadjusted ROA for Glaxo (16.2 percentage points for Glaxo, but only 6.5 percentage points for Pfizer). We use panel data and exemplar firms from the pharmaceutical industry to show that average differences between unadjusted ROA and ROA estimates obtained by reversing, capitalizing, and depreciating historical R&D costs are small, and that both ROA measures typically rank firm profitability in a similar order. For a sizable subset of firms, however, the average difference between adjusted and unadjusted ROA exceeds 10 percentage points. We demonstrate that R&D intensity and the difference between a firm’s growth rate and ROA can identify these firms (which include over 5% of a sample of established firms, and almost 30% of a sample of young firms). We also show that our model yields ROA adjustments similar to those from the reverse, capitalize, and depreciate procedure.1 For many R&D-intensive firms, our results indicate that adjusting ROA for historical R&D investments is not necessary to interpret profitability properly. At the same time, the immediate expensing of R&D costs has the potential to create large ROA distortions in a certain subset of firms. Our model enables analysts to identify these firms, and quickly generate first-cut estimates of the underlying economic rates of return. This approach thus efficiently directs analyst efforts to firms requiring further analyses.

I. Accounting and Economic Returns The accounting treatment of R&D costs is one The purpose of both adjustment procedures—our short-cut method and the more involved reverse, capitalize, and depreciate procedure—is to move a firm’s accounting-based profits and returns closer to their (unobservable) economic counterparts. The adjusted profitability measures can then be used to assess manager performance and as a starting point in estimating future economic investment returns. We are not advocating changes to accounting rules allowing firms to capitalize R&D because, as Watts (2003) notes, the capitalization of R&D costs requires a firm to make assumptions that cannot be verified. Our objective is to promote a more complete understanding of how and when the immediate expensing of R&D costs affects accounting-based performance measures, and to provide a tool for quick estimation of the extent of any distortions. 1

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element of a broader problem created when accountingdetermined depreciation schedules are used in calculating ROA. Unless depreciation schedules for a firm’s tangible and intangible assets precisely represent economic depreciation, a firm’s accounting ROA will not equal the underlying internal rate of return (IRR) on the firm’s past investments. For R&D assets, the accounting depreciation schedule calls for immediate expensing of the entire cost. Thus, if R&D investment creates benefits spread over more than one year, the accounting and economic depreciation schedules differ, and the accounting treatment of R&D costs contributes to the difference between ROA and IRR. Fisher and McGowan (1983), Salamon (1985), and Fisher (1988) conclude such depreciation differences are sufficient to render accounting ROA meaningless as a representation of IRR. In Danielson and Press (2003), however, we show how to adjust ROA for the effects of firm growth and accounting conservatism, enabling its use for estimating IRR. We modify the Danielson and Press IRR estimation model to focus solely on the distortion to ROA created by the immediate expensing of R&D costs. In doing so, we ignore other factors that contribute to the difference between ROA and IRR. For example, to calculate IRR in an economic value-added computation, over 100 different adjustments are required (Weaver 2001). However, the adjustment for R&D costs is one of the most significant for science and technology firms, and is one of the few adjustments analysts can make without access to proprietary data. Thus, limiting our analysis to R&D has both theoretical and practical justification.

II. A Modified IRR Estimation Model In Danielson and Press (2003), we show that a firm’s IRR can be estimated as a function of its accounting return on assets ROA, investment growth rate g, beginning of period accounting book value ABV, and beginning-of-year economic book value EBV. We define ROA as income (before interest, but after taxes) divided by beginning-of-period net assets (total assets less the non-interest-bearing portion of current liabilities). Growth is measured historically, and is used in the model to determine the average age of a firm’s asset portfolio. ABV is beginning-of-period net assets. Finally, EBV is a firm’s ABV if it were to depreciate its assets using the theoretically correct economic depreciation schedules.2 EBV is related to market value, but the two measures are distinct. A firm’s market value is the present value of expected cash flows from assets in place, plus the net present value of cash flows from growth opportunities, discounted using the risk-adjusted cost of capital. In Danielson and Press (2003), we define EBV as the present value of the remaining cash flows from a firm’s assets in place, calculated using the IRR as the discount rate. Thus, a firm’s EBV and asset market value will be the same only when the IRR of its assets in place equals its cost of capital, and the net present value of its growth opportunities is zero. 2

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JOURNAL OF APPLIED FINANCE — FALL/WINTER 2005

EBV is unobservable, but we sidestep the problem of its direct measurement by noting that the ratio ABV/ EBV gauges accounting conservatism, arguing that generally accepted accounting principles (GAAP) pressure the ratio to be lower than one (Danielson and Press, 2003). The treatment of R&D investments is an excellent example of the consequences of conservatism. GAAP requires R&D to be expensed immediately, meaning its cost is not included in a firm’s ABV. However, if the investments create future benefits, their economic value depreciates over time. Thus, the unamortized portion of a firm’s R&D investments is part of a firm’s EBV, causing EBV to exceed ABV. Lev and Sougiannis (1996) and Penman and Zhang (2002) quantify the magnitude of this potential distortion using empirical data, and conclude that accounting book values can understate economic book values by over 20% for some R&D-intensive firms. Using the definitions of g, ABV, and EBV—and assuming that the firm’s historical growth rate has been constant and it invests in the same mix of assets over time—we estimate IRR as: (1) The two cases in which IRR equals ROA are readily apparent from Equation (1). First, IRR equals ROA when a firm’s ABV/EBV ratio is 1, meaning the accounting and economic depreciation schedules for the firm’s assets are the same. Second, IRR equals ROA when a firm’s growth rate equals ROA. For a firm’s IRR and ROA to differ materially, both the differences between 1) ABV/EBV and 1 and 2) ROA and g must be great. Although conservatism typically causes ABV/ EBV to be less than 1, the difference between ROA and g can be either positive or negative. To refocus Equation (1) on the distortion to ROA created by expensing R&D costs, we now modify four definitions. First, the growth rate gR&D measures the historical annual growth rate of R&D costs.3 Second, economic book value is defined more narrowly as EBV*, the firm’s accounting book value adjusted only for the reversing, capitalizing, and depreciating of R&D costs. Third, ROA is defined as a return measure before adjustment for R&D costs, and is renamed ROAUNADJ. Finally, IRR is replaced in the equation with ROAADJ, 3 In Danielson and Press (2003), we assume the historical growth rate is constant, and that the firm’s mix of tangible and intangible assets does not change. If these assumptions are met, the historical growth rates for tangible assets and R&D costs will be equal. If the two growth rates differ, as they may for actual firms, the R&D growth rate is the better measure for estimating the distortion to ROA created by immediate R&D expensing because the R&D growth rate more closely captures the evolution of the firm’s R&D assets over time.

to acknowledge that the revised model does not completely adjust ROA (calculated directly from a firm’s financial statements, ROAUNADJ) to the underlying IRR. Substituting these definitions in Equation (1) yields: (2) Equation (2) writes adjusted ROA as a function of two variables that can be easily estimated from a firm’s historical financial statements, ROAUNADJ and g R&D. Estimating ABV/EBV* is trickier. One way is to reverse, capitalize, and depreciate a firm’s historical R&D costs. But, at that point, a firm’s adjusted ROA could also be estimated, and there is no need for a short-cut method. Instead, we derive another short-cut procedure. To estimate ABV/EBV*, we first assume the growth in a firm’s R&D costs has been constant. Although observed growth rates are never perfectly stable, in Danielson and Press (2003) we demonstrate our model produces plausible IRR estimates when growth is not perfectly constant. We also assume that the economic depreciation of a firm’s R&D assets occurs on a straight-line basis over N years. While actual firms meet this assumption imperfectly, it is recommended for use in practice (Damodaran, 2001) and in research (e.g., Chan et al., 2001 and Chambers et al., 2002). Supporting this practice, Baber and Kang (1996) report small differences—typically less than one percentage point—in average IRR estimates using five distinct economic depreciation schedules. Using these assumptions, we show in the appendix that ABV/EBV* can be written as:

(3) where

(4) and

(5)

In Equation (4), the subscript t identifies R&D as the accounting expense during the test year, and τ is the marginal tax rate. As with ROA UNADJ, R&D t is divided by beginning of year net assets—total assets


less the non-debt portion of current liabilities. The growth rate g is the geometric growth in R&D costs over an N-year period ending in year t, meaning an analyst needs only to know the firm’s R&D costs at the beginning and the end of the period to use our model. The term PVAF (g, N) is the well-known formula for the present value of an annuity (for a $1 annuity) using g as the discount rate, and N as the annuity length (useful life). Thus, Equations (3), (4), and (5) allow analysts to estimate ABV/EBV* with minimal data requirements.4 Exhibit 1 displays ABV/EBV * estimates from Equations (3) – (5), using assumed gR&D and R&D/ Assets values ranging from 5% to 50%. R&D investments are assumed to have a three-year useful life in Panel A and a nine-year life in Panel B. In each panel, the marginal tax rate is 35%. The values show greater distortion to a firm’s accounting book value (in terms of driving ABV/EBV* lower) as either R&D intensity or N becomes larger, or as the growth rate declines. A high growth rate implies high current period R&D costs relative to historical R&D costs. Thus, a high value of g means fewer dollars are being capitalized from prior periods, resulting in a smaller adjustment to ABV. In the two panels, ABV/ EBV* drops below 0.75 (meaning the ROA adjustment is 25% of the difference between ROAUNADJ and gR&D) only when the R&D/Assets ratio is 20% or higher. Equations (2) – (5) suggest that the difference between a firm’s ROAUNADJ and ROAADJ will depend on two observable variables (holding the R&D useful life constant): R&D/Assets and the difference between g R&D and ROA UNADJ . Holding g R&D – ROA UNADJ constant, the difference between ROA UNADJ and ROAADJ will increase as R&D/Assets increases.5 If the difference gR&D – ROAUNADJ is not constant across firms, a firm with larger R&D investments will not necessarily have the greater ROA adjustment. 6 4 Equation (4) is undefined when the growth rate equals exactly zero. To apply the model to firms with growth rates equal to zero, the growth rate should be entered into the formula as a value just slightly different from zero. 5 To illustrate, consider two hypothetical firms, X and Y, each with ROA UNADJ equal to 15%. X is the more R&D-intensive firm; R&D/Assets is 20% for X, and 10% for Y. For each firm, the R&D useful life is 9 years and the marginal tax rate is 35%. If g R&D is 10% for each firm (g R&D – ROA UNADJ = –5 percentage points), ROA ADJ is 13.4% for X (1.6 percentage points less than ROA UNADJ ), and 14.1% for Y (0.9 percentage points less than ROA UNADJ ). In this example, the firm with greater R&D intensity (X) has the larger ROA adjustment. 6 To illustrate this possibility, assume that firms X and Y are defined as in footnote 5, except now gR&D is 15% for X (gR&D = ROA UNADJ ) and 5% for Y (g R&D – ROA UNADJ = –10 percentage points). With these growth rates, ROAADJ is 15% for X (ROAADJ = ROA UNADJ ), and 12.9% for Y (2.1 percentage points less than ROAUNADJ). Although Y makes smaller R&D investments than X (as a percent of assets), the immediate expensing of R&D distorts the unadjusted ROA of Y by a greater amount in this case.

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Therefore, large R&D expenditures are not a sufficient condition for R&D expense to distort a firm’s ROA by a great amount. Instead, the size of the misrepresentation also depends on the difference between the firm’s growth rate and its unadjusted ROA. Adjusting a firm’s ROA using Equations (2) – (5) is much simpler than reversing, capitalizing, and depreciating the firm’s actual R&D costs, and requires less data. To provide evidence about the usefulness of our approach, we consider three empirical questions. First, how great are the average distortions to ROA created by the immediate expensing of R&D costs? Second, can our model predict when the reverse, capitalize, and depreciate procedure will yield large (or small) ROA adjustments? Third, does Equation (2) produce ROA estimates that are typically close to those generated by the more detailed computation?

III. Data and Descriptive Statistics To investigate these questions, we use samples of R&D-intensive firms over 1993-1999. The size and composition of the samples depend on the assumed useful life of R&D.

A. Useful Life of R&D Before we can adjust a firm’s balance sheet and ROA for R&D investments, we must make an assumption about the average useful life of a firm’s R&D investments. Although some studies assume an R&D useful life as long as 30 years (e.g., Baber and Kang, 1996), useful lives are typically shorter, as a firm’s R&D investments include both successful ventures (providing benefits over several years) and unsuccessful ventures (requiring immediate expensing). Chan et al. (2001) and Chambers et al. (2002) assume a useful life of R&D assets of five years. Lev and Sougiannis (1996) provide empirical support for this practice, estimating a productive R&D useful life of nine years or less, depending on the industry. From a practical standpoint, the use of a shorter (five years or less) life helps maintain large sample sizes, because the financial statement adjustments to capitalize R&D investments require fewer historical data. However, the estimated adjustment increases with the assumed useful life, holding R&D spending constant. Thus, the use of artificially short R&D lives could understate the adjustment in some firms. To illustrate this trade-off, and to evaluate the sensitivity of our results to the choice of R&D useful life, we use two samples. In the established firms sample, we focus on firms with historical data available to perform the reverse, capitalize, and depreciate procedure over nine years. In the young firms sample,

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JOURNAL OF APPLIED FINANCE — FALL/WINTER 2005 *

Exhibit 1. ABV/EBV Estimates ABV/EBV* estimates obtained using Equations (3) and (4) and a range of values of the historical annual growth rate g, the ratio of current year R&D expense to assets (R&D/Assets), and the useful life of R&D investments. The marginal tax rate τ is 35 %.

R&D/Assets = g = 5%

Panel A. ABV/EBV* Estimates with Three-Year R&D Useful Life 5% 10% 20% 0.943 0.893 0.807

50% 0.625

g = 10%

0.947

0.643

0.900

0.818

g = 20%

0.953

0.912

0.837

0.673

g = 50%

0.967

0.935

0.879

0.743

g = 5% g = 10% g = 20% g = 50%

Panel B. ABV/EBV* Estimates with Nine-Year R&D Useful Life 0.880 0.785 0.647 0.895 0.810 0.681 0.918 0.848 0.736 0.952 0.908 0.831

we include firms with sufficient historical data to perform the reverse, capitalize, and depreciate procedure over three years.

B. Sample Description A firm in the established firm sample has data available on Compustat Research Insight for ten years prior to the test year; a firm in the young firm sample has data available for four years. Firms must have positive R&D expenses, at least $10 million in total assets at the beginning of the estimation period, and positive net assets (total assets less the non-interestbearing portion of current liabilities) during the test year. There are two additional restrictions in the young firms sample. First, the firm cannot be included in the established firms sample (i.e., the firms do not have data available for ten years prior to the test year). Second, no firm is included in the young firms sample if it had more than $100 million in total assets when first listed on Compustat. This screen excludes large firms that entered Compustat after a spin-off or as a new ADR listing, and creates a sharper contrast between the firms in the established firms and young firms samples. Exhibit 2 summarizes the sample definitions and defines the variables. We report data for the established firms sample in three test years 1999, 1996, and 1993 and also data for pooled samples of both types of firm over the 1993 – 1999 period. Because the observations in the pooled samples are not independent, we limit our statistical tests to the individual year samples. Exhibit 3 reports the number of firms in each sample, and lists sample means (and standard deviations) for seven test variables (see Exhibit 2 for definitions),

0.423 0.461 0.527 0.663

including size, profitability, R&D intensity, and growth. The last line reports Spearman correlations between ROA and ROARCD. Exhibit 3 confirms that shortening the R&D useful life allows us to greatly increase the sample size. Requiring only four years of historical data increases the firms for which we can estimate an R&D adjustment by 63 %, from 5,514 firms (established firms) to 8,967 firms (established firms plus young firms). The firms we gain by relaxing the historical data requirement are much smaller, report higher R&D spending levels, and experience greater R&D growth. Exhibit 3 also provides information about the extent of the distortions created by the immediate expensing of R&D. ABV/EBVRCD decreases as the assumed R&D useful life lengthens (holding other factors constant), as predicted by Exhibit 1. In the established firms sample, the estimated ABV/EBV ratios fall to between 0.83 and 0.85 using nine-year R&D lives, but average 0.91 using a three-year life. Although the young firms sample uses a three-year R&D life, the high average R&D/Asset ratio (20.8%) in this sample pushes the ABV/EBV ratio down to 0.83.7 In the established firms sample, the average ROA adjustments, |ROA – ROARCD|, fall between 1.9 and 3.4 percentage points. The ROA adjustments tend to be The average ABV/EBV ratios approximate the ratios predicted by Exhibit 1, using the average R&D/Assets and R&D growth rates from Exhibit 3 as inputs. For example, for the young firms sample, Exhibit 3 reports an average R&D growth rate of 19.6% and an average R&D/Assets ratio of 20.8%. Exhibit 1, Panel A, predicts an ABV/EBV ratio of 0.837 when g is 20% and R&D/Assets is 20%. The two ABV/EBV ratios differ because Exhibit 1 reports estimates obtained using Equations (3) and (4), while Exhibit 3 reports sample averages of ABV/EBV ratios obtained using the reverse, capitalize, and depreciate procedure.

7


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Exhibit 2. Sample and Variable Definitions Panel A. Sample Definitions Established Firms: Firms must have financial statement data available on Compustat Research Insight for a ten-year period prior to the test year and at least $10 million in total assets at the beginning of this period. The firms must also have positive R&D expenses during the test year. Young Firms: Firms must have financial statement data available on Compustat Research Insight for a four-year period prior to the test year and at least $10 million in total assets at the beginning of this period. The firms must also have positive R&D expenses during the test year. This sample excludes firms in the established firms sample and excludes firms appearing on Compustat for the first time between four and nine years before the test year if the firm’s total assets exceeded $100 million at that time. Panel B. Variable Definitions Net Assets: Total assets less the non-interest-bearing portion of current liabilities. R&D/Assets: R&D in the test year, divided by the firm’s beginning of year net asset balance. ROA: Earnings before extraordinary items and discontinued operations plus interest expense, net of tax, for the sample year, divided by net assets at the beginning of the sample year. R&D growth rate (gR&D): Annual geometric growth in R&D spending over a nine-year (or three-year) period ending with the test year’s R&D expense. ROARCD: Adjusted ROA calculated by reversing, capitalizing, and depreciating historical R&D costs on a straight-line basis over nine (or three) years, depending on the assumed useful life of the R&D assets. ABV/EBVRCD: Ratio of accounting book value (Net Assets) to adjusted accounting book value, calculating adjusted book value using the reverse, capitalize, and depreciate procedure. |ROA – ROARCD|: Absolute value of difference between each firm’s ROA and ROARCD. R&D Growth Std. Dev.: Standard deviation of individual firm R&D growth rates calculated using one-year growth rates over nine- and three-year periods ending with the test year R&D expense. ROAADJ: Adjusted ROA calculated using Equations (2) through (5) with Actual ROA, gR&D, a nine-year (or three-year) useful life, and a marginal tax rate of 35%. *

ABV/EBV : Ratio of accounting book value (Net Assets) to adjusted accounting book value, calculated using Equations (3) through (5) with Actual ROA, gR&D, a nine-year (or three-year) useful life, and a marginal tax rate of 35%. |ROAADJ – ROARCD|: Absolute value of difference between each firm’s ROAADJ and ROARCD. |ABV/EBV Difference|: Absolute value of difference between each firm’s ABV/EBV* and ABV/EBVRCD.

greater in the young firms sample, averaging 6.2%. However, the Spearman correlations between ROA and ROARCD in the pooled samples all exceed 0.95.8 Thus, the two return measures typically rank the profitability of R&D-intensive firms in the same order, meaning unadjusted ROA will often be a good proxy for ROARCD in empirical studies using large samples. Nonetheless, the difference between ROA and ROARCD exceeds 10 or 20 percentage points for some firms. When analysts must evaluate the profitability 8 When the two samples are combined, and a three-year R&D useful life is employed, the Spearman correlation between ROA and ROA RCD exceeds 0.96 (not reported in Exhibit 3).

of individual firms—such as in bank lending decisions or stock valuation exercises—it is critically important to identify these firms (for which unadjusted ROA could be a misleading profitability measure).

IV. Identification of Large R&D Adjustments Equations (2) – (5) identify two conditions necessary for R&D costs to distort ROA. First, a firm must have high R&D costs, reducing the ratio ABV/EBV (holding the R&D useful life constant). Second, the firm’s ROA and historical growth rate must differ. In Exhibit 4, we separate each sample into nine groups according to

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Exhibit 3. R&D Samples – Descriptive Statistics Descriptive statistics for two samples of R&D intensive firms over the 1993-1999 test years. The first four columns report results for individual year and pooled samples of established firms, assuming a nine-year useful life for R&D investments. The last two columns list information for pooled established firms and young firms samples, assuming a three-year useful life for R&D investments. Values listed are means and standard deviations (in parentheses) of each variable. The exhibit also reports Spearman correlation coefficients between ROA and ROARCD. All values are percentages, except for the number of firms, Net Assets, and correlations.

Nine-Year R&D Life (Established Firms) 1999 1996 1993 1993-1999 # of Firms

Three-Year R&D Life Established Young Firms Firms 1993-1999 1993-1999

761

820

734

5,514

5,514

3,453

3,191 (14,439)

2,925 (11,228)

2,423 (9,416)

2,888 (11,867)

2,888 (11,867)

96 (187)

R&D/Assets

10.2 (13.4)

9.8 (12.6)

8.6 (12.6)

9.6 (12.7)

9.6 (12.7)

20.8 (22.8)

ROA

5.5 (19.0)

8.2 (17.1)

4.9 (14.7)

7.0 (17.0)

7.0 (17.0)

-3.3 (29.2)

gR&D

8.9 (14.2)

8.6 (13.0)

7.2 (12.6)

8.2 (12.9)

9.4 (23.9)

19.6 (34.9)

ROARCD

6.8 (13.7)

8.7 (12.1)

5.3 (11.7)

7.5 (12.6)

7.4 (14.5)

0.4 (22.7)

ABV/EBVRCD

83.1 (15.3)

83.5 (13.7)

83.6 (13.9)

83.3 (14.3)

91.1 (8.9)

83.4 (13.5)

|ROA – ROARCD|

3.4 (6.9)

3.3 (7.3)

2.4 (4.7)

3.0 (6.3)

1.9 (4.5)

6.2 (9.9)

Correlation: ROA v ROARCD

0.954

0.949

0.954

0.951

0.973

0.964

Net Assets ($ millions)

firms’ R&D/Asset ratios and the absolute values of the difference between the R&D growth rate and ROA (|gR&D – ROA|). The goal is to determine whether we can use these observable characteristics to isolate firms with the largest ROA adjustments. We predict the largest ROA adjustments will occur when both R&D intensity and |gR&D – ROA| are high, and the smallest ROA adjustments will occur when one or both of these measures is closer to zero. For this test, we calculate adjusted ROA by reversing, capitalizing, and depreciating historical R&D costs. In doing so, the ROA adjustments are calculated outside the model we use to generate the predictions. Exhibit 4, Panel A, describes high-intensity R&D users—firms with R&D/Assets in excess of 20%; Panel B firms with R&D intensity between 10% and 20%; and Panel C firms with R&D intensity less than 10%. Each panel is further subdivided to report descriptive data for firms with high, medium, and low values of |gR&D – ROA|. For the 1999, 1996, and 1993 established firm samples (nine-year R&D life), we use a two-tailed t-test to determine if the level of R&D spending and the ROA adjustments in each panel are significantly different from the overall sample means (in Exhibit 3).

While we do not perform this test on the pooled samples because the observations are not independent, the pooled samples display the same patterns as the three individual year samples. In each panel, the average ROA adjustment decreases with |gR&D – ROA|, as predicted in Equation (2). For example, moving from Panel A.1 to A.3 (in all three individual years and in the pooled samples), the average ROA adjustment drops progressively, even though the average values of R&D/Assets remain large. The average ROA adjustment also drops as R&D/Assets declines, holding |gR&D – ROA| constant. This pattern can be seen by comparing Panels A.1 to B.1 to C.1. In many cases in Exhibit 4, firms with greater R&D intensity show similar (or smaller) ROA adjustments than firms with lower R&D/Asset ratios. For instance, the average ROA adjustments for 1999 and 1993 (established firms) in Panel A.2 are 1.7 and 2.7 percentage points higher than those in Panel A.3, even though R&D intensity is higher in Panel A.3 those years. In another example, the established firms in Panel B.3 have significantly higher levels of R&D intensity than the sample averages, but significantly smaller ROA adjustments.


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Exhibit 4. Analysis of ROA Adjustment Size Average ROA adjustments for nine subsets of firms, identified by the relative values of R&D/Assets and absolute value of the difference between a firm’s R&D growth rate and its ROA, |gR&D – ROA|. Panel A describes high-intensity R&D firms, Panel B medium-intensity R&D firms, and Panel C low-intensity R&D firms. Means and standard deviations (in parentheses) are listed for two variables: R&D/Assets and the absolute value of the ROA adjustment, |ROA – ROARCD|. Two-tailed t-tests indicate whether the subsample mean of each variable is significantly different from the corresponding sample mean from Exhibit 3. Significant positive differences are denoted ++ (1% level) or + (5% level); significant negative differences are denoted −− (1% level) or − (5% level). All entries are percentages, except for number of firms and correlations. Panel A. High-Intensity R&D Firms (R&D/Assets > 20%)

Nine-Year R&D Life (Established Firms) 1999 1996 1993 1993-1999


A.1: |g – ROA| > 20 percentage points # of Firms

59

330

350

979

R&D/Assets

++

61

40.3 (22.4)

++

33

36.6 (22.2)

++

36.4 (25.6)

39.0 (24.5)

38.0 (22.8)

42.3 (28.1)

|ROA – ROARCD|

20.2++ (14.1)

20.0++ (16.5)

16.8++ (11.9)

19.8 (14.5)

13.6 (11.3)

16.2 (13.6)

22

16

155

149

181

A.2: |g – ROA| > 10 and < 20 percentage points # of Firms

24 ++

++

++

R&D/Assets

28.9 (8.5)

30.5 (13.7)

28.0 (7.4)

29.6 (11.2)

33.0 (23.6)

34.7 (22.1)

|ROA – ROARCD|

5.3+ (3.5)

4.9 (3.9)

5.3++ (2.3)

5.6 (3.5)

3.8 (2.8)

3.9 (3.0)

27

28

20

204

190

183

30.2 (17.4) 3.2 (3.2)

28.8 (9.4) 1.8 (2.1)

31.9 (15.5) 2.3 (2.6)

A.3: |g – ROA| < 10 percentage points # of Firms R&D/Assets |ROA – ROARCD|

++

31.2 (10.6) 3.6 (3.9)

++

30.0 (13.7) 4.1 (5.4)

The most striking example is the comparison of Panel C.1 (low R&D/Assets, high |gR&D – ROA|) to Panel A.3 (high R&D/Assets, low |gR&D – ROA|). In each of these categories, the ROA adjustments (in the pooled samples) fall between 1.6 and 3.7 percentage points. Yet, the ratio R&D/Assets is over seven times higher in Panel A.3 than it is in Panel C.1. Clearly, analysts cannot identify firms for which ROA is potentially distorted by focusing on R&D intensity alone. Instead, the difference between g and ROA must also be considered. As expected, the greatest average ROA adjustments are in Panel A.1 (high R&D/Assets; high |g R&D – ROA|). The established firm adjustments average almost 20 percentage points with a nine-year R&D life and exceed 13.6 percentage points for a three-year R&D useful life. In each case, the average ROA adjustments in Panel A.1 are at least twice as high as those in all other panels of Exhibit 4. Although this panel includes

++

34.0 (38.9) 2.6 (1.8)

only 6.0% (= 330/5,514) of the established firms (nineyear life), it includes 28.4 % (= 979/3,453) of the young firms sample. Panel A.1 shows analysts can use the combination of high R&D intensity and a large |g R&D – ROA| difference to quickly identify firms for which the reverse, capitalize, and depreciate procedure will yield a great ROA adjustment. Distortions to a firm’s ROA of 13.6 or 20 percentage points (the estimated ROA distortions for established firms in Panel A.1) are certainly great enough to alter assessment of an individual firm’s creditworthiness or estimation of its value. Yet, the extent of the true ROA distortion depends on the specific useful life of a firm’s R&D assets. If this R&D useful life is close to zero (meaning immediate expensing of R&D is appropriate), unadjusted ROA is not misleading (even if calculations assuming a longer R&D useful life suggest a large hypothetical adjustment).

84


Exhibit 4. Analysis of ROA Adjustment Size (Continued) Panel B. Medium-Intensity R&D Firms (R&D/Assets between 10% and 20%)

Nine-Year R&D Life (Established Firms) 1999 1996 1993 1993-1999


B.1: |g – ROA| > 20 percentage points # of Firms R&D/Assets |ROA – ROARCD|

35 ++

14.5 (2.8) 8.8++ (6.1)

32 ++

27 ++

236

279

421

14.9 (2.7) 7.1++ (3.8)

14.6 (3.1) 8.3++ (5.3)

14.6 (2.9) 8.5 (6.0)

14.5 (3.0) 4.9 (3.2)

15.2 (2.9) 5.1 (3.8)

43

43

286

279

194

B.2: |g – ROA| > 10 and < 20 percentage points # of Firms

26 ++

++

++

R&D/Assets

15.5 (2.8)

13.7 (2.8)

14.0 (2.9)

14.2 (2.9)

14.1 (2.8)

14.2 (2.9)

|ROA – ROARCD|

3.9 (2.3)

3.6 (1.8)

3.8++ (2.4)

3.5 (2.3)

2.0 (1.0)

2.2 (1.7)

68

79

80

537

501

233

B.3: |g – ROA| < 10 percentage points # of Firms

++

++

++

R&D/Assets

14.1 (2.7)

13.8 (2.8)

13.7 (2.4)

13.9 (2.7)

14.0 (2.7)

14.6 (2.8)

|ROA – ROARCD|

1.6−− (2.0)

1.5−− (1.3)

1.5−− (1.2)

1.5 (1.3)

0.8 (0.7)

1.2 (1.4)

Panel C. Low-Intensity R&D Firms (R&D/Assets < 10%) C.1: |g – ROA| > 20 percentage points # of Firms

98

536

955

540

R&D/Assets

−−

70

3.8 (3.1)

−−

56

3.8 (2.9)

−−

3.8 (2.4)

3.9 (2.8)

3.7 (2.7)

4.5 (2.9)

|ROA – ROARCD|

3.1 (3.9)

4.6 (7.5)

3.8++ (3.6)

3.7 (4.7)

1.6 (1.9)

2.2 (2.9)

150

130

C.2: |g – ROA| > 10 and < 20 percentage points # of Firms

135

985

1,030

288

R&D/Assets

−−

3.6 (2.7)

−−

3.7 (2.7)

−−

3.8 (2.6)

3.7 (2.7)

3.7 (2.7)

4.3 (2.7)

|ROA – ROARCD|

1.2−− (1.3)

1.4−− (1.2)

1.4−− (1.3)

1.3 (1.2)

0.6 (0.6)

0.8 (0.8)

289

335

329

C.3: |g – ROA| < 10 percentage points # of Firms

2,245

1,781

434

R&D/Assets

−−

3.8 (2.6)

−−

4.0 (2.6)

−−

3.6 (2.6)

3.9 (2.7)

4.0 (2.7)

4.5 (2.8)

|ROA – ROARCD|

0.6−− (0.6)

0.6−− (0.7)

0.5−− (0.7)

0.6 (0.7)

0.3 (0.4)

0.4 (0.6)


Equation (2) is useful because it identifies two observable firm characteristics—a high R&D/Asset ratio, and a sizeable difference between ROA and the R&D growth rate—that must both be present for ROA to be misleading. After isolating firms with these characteristics, an analyst can then perform additional procedures to estimate the useful life of the firm’s R&D assets and to adjust ROA for these investments.9

V. ROAADJ versus ROARCD The model described by Equations (2) through (5) can also provide first-cut estimates of firms’ adjusted ROA. But, are these estimates close to those obtained after reversing, capitalizing, and depreciating historical R&D costs? Exhibit 5 identifies observable characteristics that describe firms for which the estimates ROAADJ and ROARCD are similar, and those that describe firms for which the estimates differ. Panel A lists the overall sample means (and standard deviations) of seven variables. R&D Assets and |gR&D – ROA| are identified by Equations (2) through (5)— and empirically by Exhibit 4—as factors that determine the size of the ROA distortion created by R&D expensing. The absolute value of the difference between the nine- and the three-year R&D growth rates (|g R&D (9) – g R&D (3)|) is a volatility measure that identifies whether changes in a firm’s growth rate follow a trend. The standard deviation of the firms’ individual year R&D growth rates is a second volatility measure. Equations (2) – (5) assume that a firm’s growth rate is constant; these variables allow us to assess whether the difference between ROAADJ and ROARCD becomes greater as growth volatility increases. The last two lines in Panel A report Spearman correlations between ABV/EBV* and ABV/EBVRCD and between ROAADJ and ROARCD. The average absolute value of the ABV/EBV differences is approximately 2.8 percentage points in the established firms sample (nine-year R&D life), and is 2.4 percentage points or less in the pooled samples with a three-year R&D life. The Spearman correlations between the two ABV/EBV measures all equal or exceed 0.95. Similarly, the average of the (absolute values) individual firms’ differences between ROAADJ and ROARCD is less than (or equal to) 1.1 percentage point in all cases in Exhibit 5. Thus, Equations (2) – (5) provide useful first-cut estimates of the distortion to a firm’s accounting book value created by the immediate expensing of R&D costs, and show that ROA ADJ typically is a good proxy for ROARCD. In all six columns of Exhibit 5, Panel A, the average 9 Depending on the level of the ROA distortions an analyst or researcher wishes to isolate, the cut-off values of R&D/Assets and |g R&D – ROA| can be set higher or lower than those in Exhibit 4, Panel A.

85

value of |ROAADJ – ROARCD| is less than the average value of |ROA – ROA RCD|. This demonstrates that adjustments produced by Equation (2) typically affect ROA in the same direction as the reverse, capitalize, and depreciate process. There is a high average difference between the two estimates as a percentage of the total adjustment, though. For example, the average |ROAADJ – ROARCD| for the pooled sample of established firms (nine-year R&D life) is 0.9, which is 30.0% of 3.0, the average value of |ROA – ROARCD|. Therefore, it is possible that ROAADJ and ROARCD could adjust the ROA of some firms in different directions. If this problem occurred frequently, or could not be linked to observable firm characteristics, it would make ROAADJ less useful as a proxy for ROARCD. Panels B through D of Exhibit 5 compare subset sample characteristics to the overall sample means in Panel A. In Panel B, both ROAADJ and ROARCD adjust ROA by less than one percentage point. In Panel C, both ROAADJ or ROARCD adjust ROA by more than one percentage point, and in the same direction. In Panel D, both ROAADJ or ROARCD adjust ROA by more than one percentage point, but in the opposite direction. For many R&D-intensive firms, Panel B indicates the immediate expensing of R&D causes only a modest distortion to ROA. The average absolute difference between ROA and ROARCD is 0.3 or 0.4 percentage points in this panel, and the average, absolute difference between ROA RCD and ROA ADJ is 0.2 percentage points. This panel includes 44.4% of the established firms (nine-year R&D life) sample, 59.4% of the established firms (three-year R&D life) sample, but only 25.8% of the young firms. Equation (2) predicts that the immediate expensing of R&D costs has minimal effect on a firm’s ROA if either R&D/Assets or |gR&D – ROA| is small. Panel B corroborates this, as the mean values of R&D/Assets and |gR&D – ROA| in the panel are considerably lower than the overall sample means. In addition, Panel B reveals that firms with small ROA adjustments tend to have lower R&D growth rates, a smaller difference |gR&D(9) – gR&D(3)|, and a lower growth-rate standard deviation. Thus, analysts should anticipate a minimal ROA adjustment if either R&D/Assets or |gR&D – ROA| is small, and the firm’s growth variability is low (meaning the firm conforms to the model’s assumptions). In Panel C, the average values of |ROA – ROARCD| are higher than in the overall samples, falling between 4.3 and 8.6 percentage points. These large differences are predictable, as both R&D/Assets and |gR&D – ROA| in this panel are much higher than the overall sample means. Even though the growth-rate standard deviations approach or exceed 40%, and the absolute difference |gR&D(9) – gR&D(3)| exceeds 13 percentage points, the estimation differences |ROAADJ – ROARCD|

86


Exhibit 5. Estimation Differences – ROARCD v. ROAADJ Average differences between ROA adjustments for R&D using the reverse, capitalize and depreciate procedure, and those using Equations (2) through (5). Means and standard deviations (in parentheses) are listed for of each variable. In Panels B, C, and D, two-tailed t-tests indicate whether the sub-sample mean (for 1999, 1996, and 1993) of each variable is significantly different from the corresponding sample mean (from Panel A). Significant positive differences are denoted ++ (1 % level) or + (5 % level); significant negative differences are denoted −− (1 % level) or − (5 % level). All entries are percentages, except for number of firms and correlations. Panel A. All Firms

# of Firms R&D/Assets |gR&D – ROA| |gR&D(9) – gR&D(3)| R&D Growth Std. Dev. |ROA – ROARCD| |ROAADJ – ROARCD| |ABV/EBV Difference| Correlations: ABV/EBV (* v RCD) ROARCD v ROAADJ # of Firms R&D/Assets |gR&D – ROA| |gR&D(9) – gR&D(3)| R&D Growth Std. Dev. |ROA – ROARCD| |ROAADJ – ROARCD| |ABV/EBV Difference|

Nine-Year R&D Life (Established Firms) 1999 1996 1993 1993-1999 761 820 734 5,514 10.2 (13.4) 15.0 (16.0) 12.0 (13.1) 39.9 (57.7) 3.4 (6.9) 0.9 (1.7) 2.7 (3.7)

9.8 (12.6) 13.7 (16.1) 12.8 (17.5) 35.1 (57.1) 3.3 (7.3) 1.0 (1.8) 2.9 (4.3)

9.6 (12.7) 17.7 (22.0) --32.8 (96.0) 1.9 (4.5) 0.5 (1.0) 1.0 (1.9)

20.7 (22.8) 34.0 (34.6) --53.2 (123.0) 6.2 (9.9) 1.1 (1.9) 2.4 (3.3)

0.963 0.950 0.958 0.956 0.988 0.984 0.986 0.985 Panel B. ROA Adjustment less than 1 Percentage Point 332 344 347 2,405

0.980 0.994

0.963 0.994

3,215

891

3.4−− (3.8) 7.5−− (7.4) 9.2−− (9.5) 33.2− (44.5) 0.3−− (0.3) 0.2−− (0.3) 0.9−− (1.0)

4.7 (4.6) 9.8 (10.0) --23.2 (92.5) 0.3 (0.3) 0.2 (0.2) 0.3 (0.5)

5.6 (5.8) 12.8 (18.9) --41.7 (103.8) 0.4 (0.3) 0.3 (0.3) 0.5 (0.6)

3.4−− (3.3) 6.0−− (4.9) 8.0−− (7.9) 23.8−− (26.0) 0.4−− (0.3) 0.2−− (0.3) 0.9−− (0.9)

are typically less than 25% of the total adjustment to ROA. Thus, ROAADJ can approximate ROARCD even if the model’s constant growth assumption is violated. Panel D identifies cases for which the adjustments implied by ROA ADJ and ROA RCD conflict. This is relatively uncommon, affecting only 4.0% of the established firms (nine-year R&D life) sample, 2.3% of the established firms (three-year R&D life) sample, and 4.2% of the young firms.

8.6 (12.6) 11.9 (12.7) 11.4 (11.9) 30.3 (34.3) 2.4 (4.7) 0.8 (1.2) 2.7 (3.4)

3.6−− (3.6) 5.7−− (4.7) 8.6−− (8.3) 26.5 (33.3) 0.3−− (0.3) 0.2−− (0.3) 1.0−− (1.1)

9.6 (12.7) 13.4 (14.7) 12.3 (16.0) 35.3 (63.1) 3.0 (6.3) 0.9 (1.5) 2.8 (3.8)

Three-Year R&D Life Established Young Firms Firms 1993-1999 1993-1999 5,514 3,453

3.6 (3.9) 6.3 (5.9) 8.7 (9.3) 28.1 (66.0) 0.4 (0.3) 0.2 (0.3) 0.9 (1.0)

What is most important is that Panel D shows the conflict generally occurs in identifiable circumstances—these problem firms have extremely volatile R&D growth rates. The firms tend to have large differences between the nine- and three-year growth rates (approximating or exceeding 25 percentage points), and the R&D growth-rate standard deviations average 67 % in the established firms (nine-year R&D life) sample, and over 130% in the pooled (three-year


87

Exhibit 5. Estimation Differences – ROARCD v. ROAADJ (Continued)

# of Firms

Panel C. Adjustment Exceeds 1 Percentage Point; ROARCD and ROAADJ Adjust ROA in Same Direction Three-Year R&D Life Established Young Nine-Year R&D Life (Established Firms) Firms Firms 1999 1996 1993 1993-1999 1993-1999 1993-1999 403 442 352 2,894 2,177 2,416

15.4++ 14.2++ 12.9++ 14.1 16.7 (15.7) (14.7) (16.3) (15.1) (16.6) |gR&D – ROA| 20.3++ 18.7++ 19.8 29.6 21.7++ (18.4) (19.1) (14.9) (17.2) (28.9) 13.6 15.2+ 13.1+ 14.3 -|gR&D(9) – gR&D(3)| (14.6) (19.1) (13.4) (18.7) -41.0 32.4 38.9 41.4 R&D Growth Std. Dev. 43.5 (64.2) (60.5) (29.0) (53.6) (87.5) 6.0++ 5.6++ 4.6++ 5.3 4.3 |ROA – ROARCD| (8.7) (9.2) (6.1) (8.0) (6.4) 1.3++ 1.2++ 1.3 0.9 1.2++ |ROAADJ – ROARCD| (1.6) (1.6) (1.4) (1.7) (1.2) 4.1++ 4.0++ 4.0 1.7 |ABV/EBV Difference| 3.8++ (4.2) (4.8) (4.1) (4.4) (2.2) Panel D. Adjustment Exceeds 1 Percentage Point; ROARCD and ROAADJ Adjust ROA in Opposite Directions # of Firms 26 34 35 215 122

26.1 (24.2) 43.0 (35.8) --51.0 (111.0) 8.6 (11.0) 1.2 (1.8) 2.7 (3.2)

++

24.8 (22.3) 15.6 (20.8) --158.4 (276.9) 2.5 (2.8) 4.5 (3.4) 7.6 (5.3)

R&D/Assets

R&D/Assets |gR&D – ROA| |gR&D(9) – gR&D(3)| R&D Growth Std. Dev. |ROA – ROARCD| |ROAADJ – ROARCD| |ABV/EBV Difference|

15.7 (14.8) 8.2−− (8.6) 24.6++ (17.6) 69.7 (83.0) 2.3 (4.1) 3.8++ (4.9) 8.3++ (6.1)

17.1 (15.4) 6.0−− (5.0) 31.1++ (37.8) 72.2 (147.9) 2.4 (5.0) 3.8++ (5.3) 8.5++ (6.2)

R&D life) samples. When growth variability is high, the geometric mean growth rate over an estimation period may not provide an appropriate weighting of each cohort of historical R&D investments, meaning g R&D is measured with much error. As the difference |gR&D – ROA| diminishes (and the average absolute differences |gR&D – ROA| is much lower in Panel D than in the overall samples), the measurement error embedded in g R&D can be large enough to reverse the sign of the raw difference (gR&D – ROA). If so, ROAADJ and ROARCD may adjust ROA in opposite directions, as they do for the firms in Panel D. Thus, analysts should use Equations (2) through (5) cautiously if a firm has extremely variable historical R&D growth, and if there is little difference between the estimated average growth rate and ROA.

VI. Non-Constant Depreciation So far, we have used straight-line depreciation to

++

14.6 (8.5) 5.8−− (4.7) 21.2++ (16.7) 46.5 (53.4) 1.4−− (1.1) 2.5++ (1.2) 6.7++ (2.9)

16.3 (14.4) 7.0 (6.6) 25.1 (22.3) 67.6 (114.6) 1.8 (2.7) 3.2 (3.1) 8.0 (4.5)

18.5 (13.4) 13.5 (15.0) --134.4 (202.8) 2.0 (2.2) 3.5 (2.8) 5.8 (5.0)

146

perform the reverse, capitalize, and depreciate calculation for sample firms. However, the economic depreciation of a firm’s R&D investments may not follow a straight-line schedule. Lev and Sougiannis (1996), for example, estimate economic depreciation of R&D-intensive firms from empirical data, and propose a non-constant schedule to depreciate R&D assets over a nine-year life.10 Exhibit 6 investigates whether Equations (2) through (5) would produce meaningful ROA estimates if a firm’s historical R&D investments do not depreciate on a straight-line basis. On average, the Lev and Sougiannis schedule and straight-line depreciation produce nearly identical ROA adjustments in the reverse, capitalize, and depreciate calculation (ROARCD). Not surprisingly, 10 In the Lev and Sougiannis (1996) schedule, R&D investments are depreciated over a nine-year useful life, and the annual depreciation charges range from 5.2% to 16.1%. The peak charge of 16.1% occurs in year 6.

88


Exhibit 6. IRR Estimates for R&D-Intensive Firms: Straight-Line vs. Non-Constant Depreciation Comparison of ROA adjustments—for the established firms samples—using straight-line depreciation to those obtained using the Lev and Sougiannis (1996) empirical depreciation schedule (year 1: 5.2%; year 2: 6.0%; year 3: 8.6%; year 4: 12.1%; year 5: 14.7%; year 6: 16.1%; year 7: 15.8%; year 8: 13.3%; and year 9: 8.2 %). The four absolute values measure differences between unadjusted ROA and the two measures of ROARCD, and between ROAADJ and the two measures of ROARCD. Means and standard deviations (in parentheses) are listed for each variable. All entries (other than the number of firms) are percentages.

# of Firms ROA ROARCD (straight-line) ROARCD (Lev & Sougiannis) ROAADJ |ROA – ROARCD (s-l)| |ROA – ROARCD (L&S)| |ROAADJ – ROARCD (s-l)| |ROAADJ – ROARCD (L&S)|

1999 761

Nine-Year R&D Life (Established Firms) 1996 1993 820 734

5.5 (19.0) 6.8 (13.7) 6.7 (14.0) 6.9 (14.0) 3.4 (6.9) 3.2 (6.6) 0.9 (1.7) 1.0 (1.8)

there is more difference between ROARCD and ROAADJ when we use the Lev and Sougiannis depreciation schedule to estimate ROARCD. However, the average difference is only 0.1 percentage points higher than when straight-line depreciation is used to estimate ROA RCD . Thus, our short-cut estimation method produces meaningful ROA adjustments even if the economic depreciation schedule for R&D investments is not straight-line.

VII. Interpreting ROA for Pharmaceutical Firms We illustrate the effects of the immediate expensing of R&D on reported ROA using a sample of pharmaceutical firms in SIC 2834. We select the ten pharmaceutical firms from the established firms sample 11 The question of how R&D expense affects the profits of pharmaceutical firms has policy implications because of periodic proposals to impose price controls on the industry. Early studies investigating the links between R&D expense, pharmaceutical firm profits, price controls, and innovation typically focus on two questions. First, do pharmaceutical firms earn economic returns commensurate with the risks they bear? Second, what effect would price controls have on innovation in this industry? Comanor (1986) and Scherer (1993) review this literature. We focus on the years before industry consolidation because the Danielson and Press (2003) model is a steady-state model, which might produce less reliable results in the years immediately following a significant acquisition or divestiture.

8.2 (17.1) 8.7 (12.1) 8.7 (12.3) 8.7 (11.9) 3.3 (7.3) 3.1 (7.0) 1.0 (1.8) 1.1 (2.0)

4.9 (14.7) 5.3 (11.7) 5.3 (11.9) 5.5 (11.7) 2.4 (4.7) 2.3 (4.5) 0.8 (1.2) 0.9 (1.3)

1993-1999 5,514 7.0 (16.9) 7.5 (12.6) 7.5 (12.8) 7.6 (12.6) 3.0 (6.3) 2.8 (6.0) 0.9 (1.5) 1.0 (1.7)

with the largest total assets in 1999, which precedes widespread industry consolidation since that date.11 Exhibit 7 compares the firms’ unadjusted ROA in three years to ROARCD and ROAADJ, both calculated using a nine-year useful life. Firms are listed in descending order of R&D/Assets. The results illustrate that high R&D costs do not by themselves create problematic distortions to a firm’s unadjusted ROA. In 1999, the firm with the greatest ROA adjustment (Bristol-Myers Squibb) has the sixth-highest R&D/ Assets ratio. In 1993, the firm with the greatest ROA adjustment (Merck) is tied for the fifth-highest R&D/ Assets ratio. These results again illustrate that the extent of the ROA adjustment depends on both R&D intensity and the difference gR&D – ROA. Firms with high R&D costs such as Pfizer can have modest ROA adjustments if there is little difference between the firm’s R&D growth rate and its unadjusted ROA. Conversely, firms with lower levels of R&D spending (such as Abbott Laboratories in 1999 and BristolMyers Squibb in 1996) can have large ROA adjustments if there is a great difference the R&D growth rate and unadjusted ROA. Exhibit 7 also shows that Equations (2) through (5) provide useful estimates of the distortion to a firm’s ROA. For 21 of the 30 observations, the absolute value of the difference between ROARCD and ROAADJ is less than 1 percentage point; for an additional 5, the difference is less than 2 percentage points. For the


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Exhibit 7. ROA Estimates for Pharmaceutical Firms This exhibit compares the unadjusted ROA of 10 pharmaceutical firms (SIC 2834)—in three different test years—to ROARCD and ROAADJ, assuming a nine-year R&D life. Firms are listed in descending order of R&D/Assets each year. All entries are percentages. Panel A. 1999

Schering-Plough Eli Lilly Pfizer Warner-Lambert Glaxo Bristol-Myers Squibb Johnson & Johnson Abbott Laboratories Amer. Home Products Merck

R&D/Assets 22.2% 21.8% 20.1% 20.1% 18.9% 16.8% 12.5% 11.9% 10.3% 8.0%

gR&D 13.5% 10.9% 17.7% 14.3% 12.8% 8.5% 13.5% 8.6% 18.8% 10.6%

Schering-Plough Abbott Laboratories Glaxo Pfizer Johnson & Johnson Bristol-Myers Squibb Warner-Lambert Eli Lilly Amer. Home Products Merck

23.0% 18.1% 17.9% 17.6% 13.8% 13.2% 12.1% 10.5% 8.5% 8.0%

12.5% 14.3% 25.2% 17.3% 13.3% 15.8% 10.2% 11.0% 21.5% 11.3%

Schering-Plough Abbott Laboratories Warner-Lambert Bristol-Myers Squibb Merck Glaxo Eli Lilly Pfizer Johnson & Johnson Amer. Home Products

18.4% 17.4% 15.9% 14.3% 14.1% 14.1% 13.9% 12.8% 12.5% 11.7%

15.0% 16.7% 10.1% 20.4% 12.9% 31.3% 12.1% 16.2% 12.1% 15.3%

ROA 39.8% 32.9% 24.2% 29.4% 29.0% 38.8% 20.9% 25.4% -5.9% 23.1% Panel B. 1996 39.7% 29.1% 33.6% 21.3% 21.8% 30.0% 19.4% 15.3% 13.4% 21.4% Panel C. 1993 27.6% 28.3% 11.4% 25.4% 26.8% 23.7% 8.1% 9.7% 20.0% 26.5%

ROARCD 32.2% 26.0% 22.8% 28.2% 22.1% 31.0% 19.1% 19.9% -2.4% 20.6%

ROAADJ 31.4% 25.6% 22.4% 25.0% 24.3% 30.0% 19.3% 21.6% -2.0% 21.1%

|ROA – ROARCD| 7.6% 6.9% 1.4% 1.2% 6.9% 7.8% 1.8% 5.5% 3.5% 2.5%

|ROAADJ – ROARCD| 0.8% 0.4% 0.4% 3.2% 2.4% 1.0% 0.2% 1.7% 0.4% 0.5%

29.5% 24.4% 28.7% 20.6% 20.4% 24.3% 16.6% 14.2% 14.7% 19.5%

30.6% 25.1% 31.7% 20.3% 19.8% 27.0% 17.4% 14.4% 14.4% 19.9%

10.2% 4.7% 4.9% 0.7% 1.4% 5.7% 2.8% 1.1% 1.3% 1.9%

1.1% 0.7% 3.0% 0.3% 0.6% 2.7% 0.8% 0.2% 0.3% 0.4%

24.1% 25.0% 10.9% 22.9% 23.1% 23.7% 8.9% 10.9% 18.2% 25.1%

24.2% 25.4% 11.1% 24.4% 23.5% 24.9% 9.0% 11.0% 18.3% 24.4%

3.5% 3.3% 0.5% 2.5% 3.7% 0.0% 0.8% 1.2% 1.8% 1.4%

0.1% 0.4% 0.2% 1.5% 0.4% 1.2% 0.1% 0.1% 0.1% 0.7%

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four cases when ROAADJ is less accurate, the first-cut estimates still adjust ROA in the correct direction. The greatest difference is for Warner-Lambert in 1999. This difference is large because the firm did not conform well to the model’s constant growth assumption in the years before 1999—its nine-year R&D growth rate was 14.3%, but its three-year growth rate was 31.4%. These results indicate that our first-cut method correctly identifies both the direction and the approximate magnitude of the distortion to ROA created by expensing R&D costs. And, the factors that impair the model’s accuracy are often observable.

VIII. Conclusions Statement of Financial Accounting Standards No. 2 (1974) has been criticized for creating a potential mismatch between the recognition of R&D expenses and the realization of the future benefits. If a portion of a firm’s R&D expenses will ultimately be successful, the accounting requirement to expense R&D can distort a firm’s return on assets. We present a new tool for estimating the distortion to ROA created by the immediate expensing of R&D costs. The method has minimal data requirements, and does not entail creating pro forma depreciation schedules and financial statements. Our model can help analysts quickly identify firms with potentially large ROA distortions, and to estimate the direction of the distortion. We show that sizable R&D investments are a necessary but not sufficient condition for a firm’s unadjusted ROA to be distorted; both high R&D investments and a great difference between the firm’s growth rate and its ROA are required. Therefore, firms with high levels of R&D intensity can have very small differences between adjusted and unadjusted ROA. We also show that unadjusted ROA is likely to be overstated if the growth rate is lower than unadjusted ROA, and understated if greater. The evidence indicates we can distinguish between firms with larger and smaller ROA adjustments and identify the correct direction of the distortion, unless the firm’s


past R&D growth rate has been extremely volatile. Thus, our approach generates useful first-cut estimates of a firm’s underlying economic rate of return that can guide more detailed analysis of firm profitability. Our model also has implications for research studies. Researchers use earnings as an independent variable to explain relative valuation levels or valuation changes in response to a range of economic conditions. They often standardize earnings by dividing it by accounting book value, making it a return measure. When used in this way as an explanatory variable, the implicit assumption is that the historical accounting rate of return provides information about a firm’s economic profitability. Other researchers have challenged this assumption, arguing that unadjusted accounting information provides little information about a firm’s economic returns. In response, sophisticated adjustment methods (such as the economic value added calculation) have been developed as means to obtain economically meaningful profitability measures, starting from a firm’s financial statements. However, our results show that unadjusted ROA and adjusted (for R&D costs) ROA typically rank firm profitability in a similar order. Thus, unadjusted ROA is a reasonable proxy for firms’ underlying economic profitability in many research applications, and complex adjustment procedures are often unnecessary. Our approach can quickly direct a researcher’s attention to the small subset of firms where the potential distortion created by the immediate expensing of R&D is greatest. For example, average differences between adjusted and unadjusted ROA exceed 10 percentage points in subsets including more than 5% of our established firms sample, and almost 30% of the young firms sample. Future research studies could investigate the question of whether unadjusted accounting returns are less value-relevant for these firms. By motivating this type of study, the method described in the paper can assist researchers in understanding the relations between historical earnings, expected future investment returns, and firm value more completely.


Appendix: Derivation of Balance Sheet Adjustment

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present value annuity factor for a $1 annuity lasting n years, with a discount rate of g:

To derive a formula to estimate how much expensing R&D distorts (understates) a firm’s balance sheet, we assume a firm’s R&D costs create benefits over the N years following the investment, and that the economic value of this investment declines annually on a straightline basis over the period. To calculate depreciation, we assume that no economic depreciation occurs in the year of the investment, and the investment loses 1/N of its economic value in each of the next N years. These assumptions keep the math tractable, and provide a firstcut approximation of the balance sheet adjustment. We define a firm’s current year R&D expense as R&D t , and assume that R&D costs increased at constant annual rate g% over the last N years. Thus, the R&D investment in year t – n is R&Dt/(1 + g)n. We first derive the dollar adjustment to a firm’s beginning-of-year balance sheet (i.e., the ending balance sheet for year t – 1), as a function of the current year R&D investment, $ADJ t-1 . This adjustment comprises the unamortized balances of R&D investments made in year t – N, defined as R&Dt/(1 + g) N, through year t – 1, defined as R&D t/(1 + g). Because R&D investments do not depreciate in the year of investment (by assumption), the balance sheet must be adjusted for the entire dollar amount, R&Dt/(1 + g). Similarly, the undepreciated R&D investment in each earlier year t – n is (N + 1 – n)/N of that year’s dollar R&D investment. The total adjustment is shown net of its deferred tax effect, calculated using the marginal tax rate τ , as the reversing of R&D expenses for analysis purposes does not affect the timing of the firm’s tax payments:

(A.2) Equation (A.3) substitutes the formula for PVAF into Equation (A.2), and Equation (A.4) simplifies the equation:

(A.3)

(A.4) The summation term in Equation (A.4) is the present value of a $1 annuity over an N-year period. This insight, and additional simplification, yields Equation (A.5):

(A.5) To calculate the balance sheet adjustment as a percentage of the year t – 1 ending accounting book value, %ADJt-1, we divide each side of Equation (A.5) by ABVt-1. This step yields Equation (A.6), which is Equation (4) in the text:

(A.6)

(A.1) To simplify Equation (A.1), we note that the numerator N + 1 – n in the last term decreases from N in year n = 1 to 1 in year n = N. Thus, the summation term can also be expressed as the sum of the present value of N $1 annuities. The first annuity lasts one year, and the Nth annuity lasts N years. Equation (A.2) shows this form of the model, where PVAF (g, n) is the

The beginning-of-year accounting book value (which is the ending balance from year t – 1) is adjusted for the capitalization of R&D costs to estimate the firm’s beginning-of-year economic book value: EBVt-1 = ABV t-1 + $ADJt-1. The ratio of beginning-of-year accounting book value to this estimate of economic book value (EBV*) is calculated in Equation (A.7). This is Equation (3) in the text:

(A.7)

92


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