Capitalization vs Expensing of R&D and Earnings Management

Dennis R. Oswald

London Business School

and

Paul Zarowin

New York University

First Draft: November 24, 2004

1

Capitalization vs Expensing of R&D and Earnings Management

I. Introduction

We examine how a firm’s decision to capitalize vs expense R&D costs affects whether

and how the firm manages earnings thru its R&D decisions, and in particular, whether the firm

adjusts accruals or real transactions. There is much evidence that when R&D costs are expensed,

firms manage earnings by real transactions, cutting R&D expenditures to meet earnings

thresholds, such as avoiding losses or negative earnings changes.1 There also general evidence

(i.e., not related to R&D transactions) that firms manage earnings thru accruals to meet earnings

benchmarks.2 However, there is no research on how firms’ accounting choices affect their

subsequent earnings management decisions, and in particular, their decisions to use real vs

accrual earnings management. This paper provides the first evidence on this important issue.

We examine how firms use R&D decisions to manage earnings, by real vs accrual

earnings management, in the U.K. over the period 1992-2002, where firms have the option to

expense or capitalize R&D costs (subject to certain restrictions, discussed below). The U.K. R&D

accounting environment enables us to test whether firms use different earnings management tools,

given their predetermined R&D choices. Such tests are not possible in the U.S., where virtually

all internal R&D costs are expensed (except in the case of the software industry - SFAS #86).

Since the U.K. capital market is a major market that is similar to the U.S. (as well as to the stock

markets of other developed nations), our results might be generalized to other countries.3

Our research is motivated by the different ways in which capitalization vs expensing affect

earnings. When firms expense R&D costs, a reduction in R&D expenditures increases pre-tax

1This is referred to “real” or “transactions” earnings management. See Baber, Fairfield, and Haggard (1991), Dechow and Sloan (1991), Perry and Grinaker (1994), and Bushee (1998). 2See Healy and Wahlen (1999) for numerous examples.

23Ball, Kothari, and Robin (2000) group the U.K., U.S., Australia, and Canada as major common-law countries.

earnings one-for-one, and thus manipulating R&D expenditures is an effective means for

managing earnings to avoid losses or negative earnings changes. However, when firms capitalize

and amortize R&D costs, current period R&D expense is a combination of amortization of past

R&D expenditures plus the percentage of current expenditures that are expensed. Since

amortization of past R&D expenditures is a sunk expense of the current period, a reduction in

current R&D expenditures has less than a one for one effect on pre-tax earnings (how much less

depending on the fraction of costs capitalized), and reducing R&D expenditures is a relatively

ineffective tool for managing earnings to achieve benchmarks. Since reducing R&D expenditures

may not yield much earnings management benefit and may negatively impact the firm’s long term

growth and profitability, capitalizers may be reluctant to do it. For capitalizers, therefore,

managing accruals, such as changing the percentage of current expenditures that are capitalized,

may be a more effective, less costly, earnings management tool.

Based on the above arguments and the evidence for U.S. firms, we hypothesize and find

that when U.K. firms expense R&D costs, they manage earnings by real transactions to meet

earnings benchmarks; i.e., compared to the previous year, U.K. firms reduce actual R&D

expenditures, in order to avoid losses or negative earnings changes. However, when U.K. firms

capitalize R&D costs, they don’t reduce R&D expenditures to meet such earnings benchmarks.

Rather, they manage earnings with accruals, reducing R&D expenses by increasing the percentage

of R&D costs that are capitalized. Thus, the method of R&D accounting affects the subsequent

earnings management decisions.

Our findings are important, because there is a dearth of evidence on the economic

implications of accounting choices (Fields, Lys, and Vincent, 2002). As Fields, Lys, and Vincent

discuss, manipulating accruals may result in lower wealth loss to principals than manipulating

3

real transactions. Regarding R&D, Bushee (1998) points out that R&D expenditure cuts have real

implications for long-term value and are of greater concern to equity holders than manipulation of

discretionary accruals. In this case, full expensing of R&D may induce more costly earnings

management than capitalization. Moreover, since reductions in R&D expenditures might reduce

innovation, real earnings management thru R&D cutbacks might result in greater societal cost

than accrual manipulation under capitalization. Given the increasing importance of R&D

expenditures and innovation (Lev, 1999, Lev and Zarowin, 1999), the implications of R&D

accounting decisions are a major issue. More generally, our results suggest that the choice of

capitalizing vs expensing costs can impact the economy by affecting firms’ real production and

investment decisions.

The rest of the paper is organized as follows. Section 2 reviews the previous literature on

R&D and earnings management. Section 3 discusses our sample and data. Section 4 discusses our

hypotheses and test. Section 5 reports our test results. Section 6 provides evidence on how R&D

capitalizers manage earnings. Section 7 concludes.

2. Related Literature

A number of papers have examined whether firms use R&D expenditures to manage

earnings. All of the studies exclusively use data from the U.S., where R&D costs must be

expensed. Baber, Fairfield and Haggard (BFH, 1992) is one of the first papers to examine whether

managers who may be concerned about hitting earnings targets engage in real earnings

management by cutting their R&D expenditures. Specifically, they examine whether managers

reduce their R&D expenditures in relation to two earnings goals: (1) positive net income, and (2)

prior period’s net income. Based on these earnings goals, BFH partition their sample of 4,818

4

U.S. firms over the period 1977-1987 into three categories based on whether the firm will (1)

miss the goal regardless of their R&D expenditure, (2) exceed the goal regardless of their R&D

expenditure, and (3) meet the earnings goal by reducing their R&D expenditure. The authors find

that R&D spending is relatively lower for firms that can manage to hit their earnings goal by

reducing their R&D expenditures. The authors conclude that managerial decisions to invest in

R&D are influenced by a concern about reported earnings.

Bushee (1998) provides an extension to the above study by examining whether

institutional investors create or reduce incentives for managers to cut their R&D spending in order

to meet short-term earnings targets. He examines a sample of 13,994 firm-year observations over

the period 1983-1994 to determine whether the percentage of institutional holdings is associated

with the decision by managers to cut their R&D expenditures (after controlling for other variables

hypothesized to influence this decision) for three partitions of firms similar to BFH’s groupings

(those with an increase in pre-tax, pre-R&D earnings, those with a large decrease in pre-tax, pre-

R&D earnings, and those with a small decrease in pre-tax, pre-R&D earnings). His main finding

is that the percentage of institutional holdings is significantly negatively associated with the

decision to cut R&D expenditures for the sample of firms with a small decrease in pre-tax, pre

R&D earnings. In further analysis, Bushee examines whether the type of institutional investor is

associated with the decision to cut R&D expenditures; for the sample of firms with a small

decrease in pre-tax, pre-R&D earnings he finds that managers of firms with high ownership by

‘transient’ institutions are more likely to cut their R&D spend. Based on his results, he concludes

that managers of firms with high institutional ownership are less likely to cut their R&D

expenditures to increase earnings; however, if the institutional ownership is by ‘transient’ owners,

then managers are much more likely to cut their R&D spend to increase earnings.

5

Other evidence also indicates that managers reduce R&D expenditures in order to meet

earnings goals. Perry and Grinaker (1994) also examine the relation between R&D expenditures

and earnings, and they find an approximately linear relation between unexpected R&D spending

and unexpected earnings, suggesting that R&D expenditures may be cut when earnings fall short

of expectations. Dechow and Sloan (1991) examine whether CEOs in the final years of their

tenure manage discretionary investment to enhance short-term performance, and they find that the

growth in R&D expenditures is reduced over this horizon, but the reduction in expenditures is

mitigated through CEO stock ownership. In a study that examines a number of tools of real

earnings management, Roychowdhury (2003) finds that firms that report small profits have

unusually low discretionary expenses (advertising, R&D and selling, general and administrative)

expenses, suggesting that they manage earnings via R&D expenditures. Darrough and Rangan

(2004) document that the change in R&D expenditure is negatively associated with the level of

managerial selling in an initial public offering.

In summary, there is considerable evidence that U.S. firms engage in “real” earnings

management by cutting R&D expenditures to meet earnings targets.

3. Data and Sample

We examine U.K. firms because U.K. 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 light of factors such as likely market conditions (including

competing products), public opinion, and consumer and environmental legislation; (4) The

6

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]. Any expenditures on

research (pure or applied) must be expensed in the period incurred. In summary, the five

conditions are intended to ensure that an asset is indeed created by the R&D expenditures.4

Our initial sample includes all U.K. firms on Datastream (active and dead files) that

disclosed either a R&D asset or R&D expense in any year t = 1990 - 2002. We begin in 1990

because prior to the revised SSAP No. 13 in 1989 many firms did not voluntarily report their

R&D expenditure (the revised SSAP No. 13 made this disclosure mandatory). This search yields

5,976 firm-year observations (1,026 firms). For observations with a positive value of R&D asset,

we examine the firm's notes to the financial statements to ensure that the amount recorded by

Datastream in fact relates to an R&D asset.5 Removing inappropriate observations reduces the

sample by 402 firm-year observations (100 firms). Since we require one-year and two-year

lagged R&D expenditures (to calculate the prior change in R&D) we remove observations from

1990 and 1991 and firms that have not existed for the prior two years; this reduces the sample by

1,501 firm-year observations (206 firms). Finally we remove firm-year observations with

missing data needed to estimate equation (2), below. This removes 335 firm-year observations

4 There are a number of reasons why management may chose not to capitalize development expenditures which meet the five criteria outlined in SSAP #13. First, it may be costly to deviate from analyst preferences (AIMR, 1991). Second, managers may be concerned about the quality of current and future earnings (Freeburn, 1998). Finally, there are measurement and record keeping costs associated with capitalizing development expenditures (Nixon and Lonnie, 1990).

7

5 For example, for firms in the mining industry, Datastream often reports a positive amount for the firm's R&D asset; however, upon examination it is apparent that the amount reported relates to an exploration asset, not R&D. Similarly, for many firms in the Oil and Gas industry, the R&D asset relates to exploration and development.

(35 firms), resulting in our final sample of 3,738 firm-year observations (721 firms), ranging from

299 firms in 1992 to 390 firms in 2002. In total, there are twenty-seven industries represented;

however nearly 40% of the observations are from three industries: Engineering & Machinery,

Electronic & Electrical Equipment, and Software & Computer Services.

Table 1 reports many firm descriptive statistics on the sample firms after partitioning

observations by whether they choose to expense (the expensers) or capitalize (the capitalizers)

their development expense.6 The large number of expensers (3,247 firm-year observations)

relative to capitalizers (491 firm-year observations) suggests 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. Expensers appear to be larger, older and

more profitable than capitalizers (with larger market value, sales, assets, book value of equity, age

and earnings). Expensers report mean (median) R&D expense of £25.22 million (£2.28 million);

whereas capitalizers report mean (median) R&D expense of only £4.47 million (£0.14 million).

Finally, neither the expensers nor the capitalizers are highly levered (measured as total debt

divided by total assets).

4. Hypotheses and Tests

4.1 Basic tests

As pointed out above, our research is motivated by how the R&D accounting method

determines how R&D expenditures and parameters of the R&D capitalization decision affect pre-

tax earnings. In order to compare our results to previous evidence on U.S. data, we first examine

8

6 Although the accounting method choice is an endogenous firm decision, for the purposes of our tests it is exogenous, since it is predetermined at the time of the earnings management decision. Thus, we are not concerned about any self-selection bias, and we do not report whether expensers and capitalizers different significantly on the firm characteristics.

whether U.K. firms adjust R&D expenditures to meet earnings benchmarks, for both capitalizers

and expensers. For expensers, of course, R&D expenditures are the same as R&D expenses, by

construction. For capitalizers, however, R&D expenses depend not only on current expenditures,

but also on the percentage of these expenditures that are capitalized, and the amortization of past

capitalized expenditures (which is sunk and cannot affect current earnings). There are two

important parameters for any test of earnings management to meet benchmarks: the benchmarks

for earnings and for the variable being managed (R&D expenditures or expenses in our case). If

firms use R&D decisions to manage earnings, but our benchmarks are incorrect, we bias our tests

against finding significant results. Based on prior research, we assume that firms manage earnings

to avoid losses and earnings decreases (Burgstahler and Dichev, 1997). Thus, our earnings

benchmarks are zero earnings level and zero earnings change. Also based on previous research,

we examine whether firms decreased their R&D expenditures as compared to the previous year.

Thus, our R&D benchmark is zero change.

Using these benchmarks, analogous to BFH and Bushee (for the earnings change), we

consider three groups of firms, based on their pre-tax earnings before R&D expenditures in the

current year (EBRDt) or change vs the previous year (∆EBRDt), both compared to their R&D

expenditures in the previous year (RDt-1).7 Our first grouping assumes that firms manage earnings

to avoid losses (the earnings goal is zero earnings). Therefore, we create the following groupings

based on EBRDt compared to RDt-1:

Group 1 Group 2 Group 3 EBRDt < 0 0 < EBRDt < RDt-1 RDt-1 < EBRDt

9

7Like Bushee (1998), we don’t use analysts’ forecasts as an earnings benchmark, because it would affect the sample composition, require an estimate of analysts’ expected R&D expenditures, and create a specification problem if analysts anticipate earnings management.

Group 1 firms are performing so poorly that they show losses even before considering

current R&D expenditures. By contrast, group 3 firms are successful enough that they would

show current year pre-tax profits even if current year R&D expenditures maintained at last year’s

level. Group 2 would show losses if they maintained R&D expenditures at last year’s level, but

can show profits by cutting R&D expenditures.

Group 2 is the primary group of interest. Since firms in groups 1 and 3 will show losses

and profits, respectively, by maintaining R&D expenditures at last year’s level, they have less

incentive to cut R&D expenditures than firms in group 2, who can show profits only by cutting

R&D expenditures. Thus, our first hypothesis is:

H1: Expensers in group 2 are more likely to cut R&D expenditures than expensers in group 1 or 3.

We use H1 to replicate prior research on U.S. data cited above.

For capitalizers, however, reducing expenditures is a blunt instrument for managing earnings, and

so we do not expect differences between the groups in the percentage of firms that cut R&D

expenditures. Thus, the corollary to H1 for capitalizers is:

H1A: There are no differences in the percentage of firms that cut R&D expenditures between earnings groups for capitalizers.

Analogous to the first grouping, our second grouping assumes that firms manage earnings to

avoid earnings decreases (the earnings goal is zero earnings change). Therefore, we create the

following groupings based on ∆EBRDt compared to RDt-1:

Group 1 Group 2 Group 3 ∆EBRDt < -RDt-1 -RDt-1 < ∆EBRDt < 0 0 < ∆EBRDt

10

As with the first set, we hypothesize that expensers in group 2 are more likely to cut R&D

expenditures than expensers in groups 1 or 3, but we expect no differences between groups for

capitalizers.

In order to compare the percentages across earnings groups, we run the logistic regression

for expensers and capitalizers, respectively:

CutRDt = β0 + β1C1t+ β2 C3t + ε (1)

where CutRDt equals one if a firm cut its R&D expenditures in t, compared to t-1, and zero

otherwise, C1t is a dummy variable that equals one if a firm is in group 1in year t, and zero

otherwise, C3t is a dummy variable that equals one if a firm is in group 3 in year t, and zero

otherwise.

This logit regression framework allows us to easily test comparisons across groups, and

we can add controls as necessary. Note that since we define the dummy variables for groups 1 and

3, the intercept captures the probability of an R&D cut for group 2, and β1 and β2 capture the

incremental probabilities for groups 1 and 3, compared to group 2. We define the variables in this

way in order to measure each group’s deviation from group 2, which is our primary group of

interest.

By running separate regressions for expensers and capitalizers, we allow the differential

probabilities between the groups to differ between the capitalizers and the expensers. This is

necessary to test our hypotheses, since if U.K. expensers manage R&D expenditures to meet

earnings benchmarks, but U.K. capitalizers do not, as we hypothesize, then the expenser

probabilities, but not the capitalizer probabilities, should differ across the groups.

11

4.2 Expanded Tests

Our tests based on equation (1) assume that a firm’s decision to cut R&D expenditures is

based only on the firm’s decision to meet an earnings benchmark to avoid losses or earnings

declines. However, there are other factors that determine a firm’s R&D expenditures, such as the

firm’s growth opportunities (positive NPV investments), pattern of past R&D expenditures, and

profitability. Following Bushee (1998), we expand (1) to control for these other effects.

CutRDt = β0 + β1C1t + β2C3t + β3PCRDt + β4CIRDt + β5CCAPEXt + β6CSALESt

+ β7TOBQt + β8SIZEt + β9LEVt + β10FCFt + β11DISTt + ε (2) We first include the prior change in R&D expenditures (PCRDt), measured as one-year lagged

R&D expenditures minus two-year lagged R&D expenditures, all divided by current period sales,

as a proxy for changes in the firm’s R&D opportunity set over time. To control for changes in the

industry’s R&D opportunity set, we include the change in industry R&D intensity, measured as

the log of current industry R&D intensity (R&D expenditure divided by sales) in year t minus the

log of lagged industry R&D intensity (excluding the firm) in year t-1 (industry membership is

based on Datastream Level 4 Classification). Firms with an increasing (decreasing) R&D

opportunity set are expected to be less (more) likely to cut their R&D expenditures; therefore, we

expect β3 and β4 to be negative. To control for funds available to invest in R&D projects, we

include both the change in capital expenditures (CCAPXt) and the change in sales (CSALESt); we

expect a negative relation between the funds available for investment and the decision to cut R&D

expenditures (β5 and β5 should be negative). We measure the change in capital expenditures

(sales) as the log of current capital expenditures (sales) in year minus the log of capital

expenditures (sales) in year t-1. Tobin’s q (TOBQt) is included to capture the marginal benefit-to-

cost ratio of undertaking new investment. Firms with higher (lower) values should face a greater

12

(lower) cost associated with reducing investment; therefore we expect a negative association with

the decision to cut R&D expenditures (β7 should be negative). We measure Tobin’s q as the sum

of market value of equity (measured at fiscal year end of year t), the book value of preferred stock

and the book value of total debt divided by total assets. Firm size (SIZEt) is included to first proxy

for the firm’s information environment (larger firms should have fewer opportunities for earnings

management) and secondly to proxy for the likelihood that the firm faces cash constraints.

Therefore, we hypothesize that larger (smaller) firms will be less (more) likely to cut their R&D

expenditures (β8 should be negative). We measure size as the log of market value of equity at the

fiscal year end of year t. Leverage (LEVt) is included to proxy for the firm’s proximity to debt

covenants. Firms with higher (lower) leverage may be more (less) likely to engage in earnings

management, suggesting a positive relation between leverage and a cut in R&D expenditures (β9

should be positive). Leverage is measured as total debt divided by total assets. To proxy for the

amount of available cash we include free cash flow (FCFt); we hypothesize that lower (greater)

available cash will be positively (negatively) associated with the decision to cut R&D

expenditures (β10 should be negative). We measure free cash flow as cash flow from operations

less the average capital expenditures in the prior two years divided by current assets. For

observations after 1995 we use cash flow from operations as reported in the financial statements.

For observations between 1992 to 1995 we proxy for cash flow from operations as operating

profit plus depreciation and amortization less the net change in working capital (change in

receivables, inventories and other current assets less the change in payables, taxes payable and

other current liabilities). Finally we include a variable to measure the percentage of R&D that

would need to be cut in order to hit the earnings goal (DISTt). We hypothesize that the more (less)

R&D that needs to be cut in order to achieve the earnings goal the firm is more (less) likely to cut

13

their R&D expenditures (β11 should be positive). When the earnings goal is zero earnings, DISTt

equals pre-tax pre-R&D earnings divided by lagged R&D expenditure all minus one. When the

earnings goal is zero earnings change, DISTt equals the change in pre-tax pre-R&D earnings

R&D divided by lagged R&D expenditure.

5. Results

5.1 Basic Tests

Table 2 shows the percentage of expensers and capitalizers cutting R&D expenditures in

each group, for each earnings benchmark. The results in panel A (the earnings goal is zero

earnings) show that 47% of expensers in group 2 cut their R&D expenditures relative to only 40%

(32%) of firms in group 1 (group 3). However, for capitalizers, 38% of group 2 firms cut their

R&D expenditures relative to 43% (34%) of firms in group 1 (group 3). The results in panel B

(the earnings goal is zero earnings change) indicate that for expensers, 38% of group 2 firms cut

their R&D expenditures, relative to 40% (30%) of firms in group 1 (group 3). For capitalizers,

only 36% of firms in group 2 cut their R&D expenditures, relative to 39% (34%) of firms in

group 1 (group 3). The evidence in Table 2 suggests that there are differences in the probabilities

of firms cutting their R&D expenditures based on their pre-tax and R&D earnings and on their

R&D accounting method. Equation (1), formally tests whether this is so.

The results of equation (1) are shown in Table 3. For expensers using both the earnings

level and change benchmarks, group 2 has a higher probability of an R&D cut than group 3,

(coefficients on C3 are significantly negative), but we cannot reject the null hypothesis that

groups 1 and 2 are equal (coefficients on C1 are insignificantly different from zero). The

unexpectedly (based on our hypothesis) high tendency of group 1 firms to cut R&D expenses may

14

be due to their decreased profitability. Aside from this one exception, these results are as

Hypothesis 1 predicts, and show that U.K. R&D expensers use real R&D transactions to manage

earnings to meet earnings benchmarks. This is consistent with previous evidence on U.S. data.

For capitalizers using both earnings benchmarks, we cannot reject the null hypothesis that

group 2 is no different from either of the other groups. In fact, for capitalizers using the earnings

change benchmark, there are no differences between any of the 3 groups (using the earnings level

benchmark, the probability of a C1 firm is greater than the probability of a C3 firms at the .06

level).

Overall, using both of our earnings benchmarks, we find little evidence that the probability

of capitalizers cutting R&D expenditures differs across earnings groups, consistent with our

hypothesis. These results indicate that U.K. R&D capitalizers do not manage earnings using real

R&D transactions. Our findings on the differences between capitalizers vs expensers is the first

evidence that we are aware of that accounting choice influences subsequent earnings management

decisions.

5.2 Expanded tests

The results of equation (2) are also shown in Table 3. We find that for both expensers and

capitalizers that the change in sales (CSALES) and Tobin’s Q (TOBQ) are significantly

negatively associated with a cut in R&D expenditure (for both earnings goals), as hypothesized.

For the expensers we also find that the change in capital expenditures (CCAPEX) and leverage

(LEV) are significantly associated with a cut in R&D expenditure in the direction hypothesized

(change in capital expenditures (leverage) is negatively (positively) associated). For the

15

capitalizers, we find that the prior change in R&D expenditures is significantly positively

associated with a cut in R&D expenditures (opposite to our predictions).

The results examining the differences across earnings groups are consistent with the

results of estimating equation (1); when they differ, equation (2) provides even stronger support

for our hypotheses. For example, for expensers with the earnings level benchmark, the coefficient

of C1 is now significantly negative (it was insignificantly negative in the basic model), indicating

that group 2 expensers are more likely to cut R&D expenditures than group 1 expensers. For

capitalizers, all of the conclusions from the basic model hold up, showing no differences in the

probability of a R&D expenditure cut across groups. Overall, the results with the expanded model

(2) indicate that expensers manage R&D expenditures to meet earnings benchmarks, but that

capitalizers do not.

6. Do capitalizers use R&D to manage earnings?

6.1 Preliminary Evidence

Since capitalizers don’t use real R&D transactions to manage earnings, it is important to

examine whether they use R&D decisions for earnings management at all. As a first step in this

investigation, Table 4 shows the percentage of capitalizers in each earnings group that cut R&D

expenses as compared to the previous year. If we find no differences across the earnings groups,

then we must conclude that capitalizers do not manage earnings via R&D.

The data in Table 4 suggest that capitalizers manage R&D expenses similar to expensers,

as the percentage of firms cutting their R&D expense is greatest in group 2 (around 50%), and

both smaller and similar in groups 1 and 3 (around 20%). In order to compare the differences in

16

the probabilities across capitalizer groups, we modify equation (1) by using capitalizers’ R&D

expenses as the dependent variable.

The results, shown in Table 5, are very similar for both earnings benchmarks and for both

models. The coefficients of both C1 and C3 are significantly negative, indicating that capitalizers

in group 2, for whom reducing R&D expenses is crucial to meeting the benchmark, are much

more likely to cut R&D expenses than capitalizers in either groups 1 or 3. Importantly, we are

able to find significant differences between group 2 and the other groups, despite the small

number of group 2 firms. These results show that capitalizers do manage earnings by their R&D

accounting.

Overall, the combined evidence from the regression results in Tables 3 and 5 provide

strong evidence that both expensers and capitalizers use R&D decisions to manage earnings, but

in a different ways. Expensers manage real R&D transactions, cutting expenditures to meet

earnings benchmarks. Capitalizers manage R&D expenses to meet earnings benchmarks, but not

by managing transactions. So, how do capitalizers manage R&D expenses? We now address this

question.

6.2 How do capitalizers use R&D to manage earnings?

Capitalizers’ R&D expense in a given period is a combination of both amortization of past

R&D expenditures plus the uncapitalized portion of current expenditures. Since the amortization

component is fixed, capitalizers can manage their R&D expense by varying the percentage of

current expenditures that they capitalize. If cutting R&D expenditures is more costly than

managing an accrual such as adjusting the percentage of costs capitalized, capitalizers may

manage earnings by adjusting this percentage. Since our previous evidence showed that group 2

17

firms are more likely than group 1 or group 3 firms to cut R&D expense, but not R&D

expenditures, to meet earnings benchmarks, we expect that group 2 firms are more likely to

increase their percentage of costs capitalized, than group 1 or group 3 firms.

For each earnings benchmark and for each of the three earnings groups, Table 6, Panel A

shows the mean CAP% in years t-1 and t, and the percentage of firms that increased their CAP%.

Using zero earnings as the earning goal, we find that group 2 firms appear to be more likely to

increase CAP% than the other firms: group 1 firms show a decline in CAP%, group 3 show a

modest increase, whereas group 2 firms show a large increase. Additionally, it appears that many

more group 2 firms (50%) are likely to increase their capitalization percentage relative to group 1

(23.4%) and group 3 (32.6%) firms. However, when zero earnings change is used as the earnings

goal, our results are weaker. Group 1 firms have virtually no change in their capitalization

percentage, group 2 and group 3 firms show a modest increase. The percentage of group 2 firms

increasing their capitalization percentage is only slightly larger than the other two groups (39.3%

for group 2 versus 28.9% for group 1 and 31.3% for group 3).

To test for any significance between these group differences, we regress the increase in

capitalization percentage on group dummies:

INC-CAP% = β0 + β1C1+ β2 C3 + ε (3)

where INC-CAP% equals one if a firm increased its capitalization percentage in year t compared

to year t-1, and zero otherwise, and C1 and C3 are as before. Note that (3) is identical to (1), but

with a different dependent variable. The results of the logistic regression (Table 6, Panel B)

indicate that when zero earnings is used as the earnings goal (Levels column), group 2 firms are

significantly more likely to increase their capitalization percentage than group 1 firms; however

no difference is found between group 2 and group 3 firms. When zero earnings change is used as

18

the earnings goal (Changes column), there are no significant differences in the likelihood of an

increase in the capitalization percentage across the three groups.

Since the power of our tests may be diminished by such few firm observations and the use

of a dummy dependent variable, we estimate the following equation using OLS regression with

the magnitude of the change in capitalization percentage as the dependent variable:

∆CAP% = β0 + β1C1+ β2 C3 + ε (4)

The results in the ‘OLS’ columns indicate that when zero earnings is the earnings goal, group 2

firms have a significantly larger positive change in their capitalization percentage from year t-1 to

year t. However, no changes are found when zero earnings change is used as the earnings goal.

Overall, the results in Panel B provide modest support of our earlier results; in future work we

intend to further examine the means by which capitalizers may be using their R&D accounting, or

other management ‘tools’ to manage their earnings.

7. Conclusion

This paper investigates how a firm’s decision to capitalize vs expense R&D costs affects

whether and how the firm manages earnings thru its R&D decisions, and in particular, whether

the firm adjusts accruals or real transactions. There is considerable evidence that when faced with

earnings or other goals, managers of U.S. firms have reduced their R&D expenditures in order to

meet those goals. We examine a sample of U.K. firms, because U.K. GAAP permits the

capitalization of development expenditures provided certain conditions have been met. Using this

sample, we show firms’ accounting choices affect their subsequent earnings management

decisions, and in particular, their decisions to use real vs accrual earnings management.

19

To examine how U.K. firms manage their R&D expenditures, we first partition our sample

observations based on whether they choose to expense (expensers) or to capitalize (capitalizers)

their R&D expenditures. We then further partition our sample firms based on their proximity to

meeting their earnings target, where we both zero earnings level and zero earnings change as

prospective targets. Specifically, each firm-year observation is sorted into one of three groups.

The first group includes firms who will not meet their earnings goal even with cutting all of their

R&D expenditures; the second group includes firms who could meet their earnings goal with a

reduction in their R&D expenditures; the third group includes firms who will meet their earnings

goal even after maintaining their R&D expenditures from the prior period. For expensers, we

generally find that the likelihood of a cut in R&D expenditures is higher for firms in the second

group (these results hold even after controlling for other factors that determine a firm’s R&D

expenditures). For capitalizers, we find no difference in the likelihood of a cut in R&D

expenditures across the three groups. In further analysis of the capitalizers we find that firms in

the second group are more likely to cut their R&D expenditures than firms in the other two

groups. We find some limited evidence that these capitalizers are managing their earnings

through changing the percentage of expenditures capitalized.

Overall, we find evidence that both expensers and capitalizers use R&D decisions to

manage earnings, but in a different ways. Expensers manage real R&D transactions, cutting

expenditures to meet earnings benchmarks. Capitalizers manage R&D expenses to meet earnings

benchmarks, but not by managing transactions. We believe our findings are important, because

we provide some of the first evidence on the economic implications of accounting choices. In

future analysis, we intend to further examine the tools, if any, used by capitalizers to manage

earnings towards earnings goals.

20

References

Baber, W., P. Fairfield, and J. Haggard, 1991, “The Effect of Concern About Reported Income on Discretionary Spending Decisions: the Case of Research and Development”, The Accounting Review, 818-829.

Ball, R., S. P. Kothari, and A. Robin, 2000, “The Effect of International Institutional Factor on Properties of Accounting Earnings”, Journal of Accounting and Economics, 1-51. Burgstahler, D. and I. Dichev, 1997, “Earnings Management to Avoid earnings Decreases and

Losses”, Journal of Accounting and Economics, 99-126. Bushee, B., 1998, “The Influence of Institutional Investors on Myopic R&D Investment

Behavior”, The Accounting Review, 305-333. Darrough, M. and S. Rangan, 2004, “Do Insiders Manipulate Earnings When They Sell Their

Shares in an Initial Public Offering?”, working paper Baruch College. Dechow, P. and R. Sloan, 1991, “Executive Incentives and the Horizon Problem: An Empirical

Investigation”, Journal of Accounting and Economics, 51-89. Fields, Thomas, Thomas Lys, and Linda Vincent, 2001, “Empirical Research on Accounting

Choice”, Journal of Accounting and Economics, 255-307. Freeburn, C., 1998, “Minimum Enthusiasm for Capitalizing R&D,” Chemical Week,160: 41. Healy, P. and J. Wahlen, 1999, “A Review of the Earnings Management Literature and its

Implications for Standard Setting”, Accounting Horizons, 365-384. Lev, B., 1999, “R&D and Capital Markets”, Journal of Applied Corporate Finance, 21-35. Lev, B. and P. Zarowin, 1999, “The Boundaries of Financial Reporting and How to Extend

Them”, Journal of Accounting Research, 353-385. Nixon, B. and A. Lonie, 1990 “Accounting for R&D: The Need for Change,” Accountancy, 90-

91. Perry, S. and R. Grinaker, 1994, “Earnings Expectations and Discretionary Research and

Development Expenditures”, Accounting Horizons, 43-51. Roychowdhury, S., 2003, “Management of Earnings Through the Manipulation of Real Activities

That Affect Cash Flow from Operations”, working paper MIT.

21

Table 1 Descriptive Statisticsa

Expensers Capitalizers Mean Median Mean Median

Share Price 2.99 1.97 1.57 0.79 Market Value 1606.02 117.87 215.77 26.78 Sales 1170.65 119.80 188.40 36.99 Assets 1435.39 102.74 202.36 33.64 Book Value of Equity 618.47 43.56 60.34 11.31 Earnings 57.39 4.51 0.08 0.55 R&D Expense 25.22 2.28 4.47 0.14 Age 17.81 14.77 14.21 11.16 Leverage 0.18 0.16 0.23 0.21 # Observations 3247 491

aThe sample consists of U.K. firms who disclosed either a R&D asset or R&D expense in any year t=1992-2002, with all available data on Datastream to estimate the empirical tests. A firm-year observation is defined as a Capitalizer if in that year the firm reported either a non-zero value for the R&D asset or a non-zero amount for R&D amortization; otherwise the firm-year observation is classified as an Expenser. Share price per share and market value are measured at the end of the fiscal year. Share price is reported in pounds sterling; market value, sales, assets, book value of equity, earnings, and R&D expense are measured in millions of pounds sterling. Age is the number of years for which Datastream has data on the firm. Leverage is measured as total debt divided by total assets.

22

Table 2 Percentage of Firms Cutting Their R&D Expenditurea

Panel A: Earnings Goal is Positive Earnings Levelb

C1 C2 C3 Expensers Number of Observations 444 156 2647 Percentage Cut R&D 0.40 0.47 0.32 Capitalizers Number of Observations 126 16 349 Percentage Cut R&D 0.43 0.38 0.34

Panel B: Earnings Goal is Positive Earnings Changec

C1 C2 C3 Expensers Number of Observations 920 379 1948 Percentage Cut R&D 0.41 0.38 0.30 Capitalizers Number of Observations 196 33 262 Percentage Cut R&D 0.39 0.36 0.34

a This table reports the percentage of firm-year observations which have cut their R&D expenditure in year t relative to year t-1. b In Panel A firm-year observations are classified based on an earnings target of positive pre-tax pre-R&D earnings, as follows: C1 - if the firm reported negative pre-tax pre-R&D earnings in year t; C2 - if the firm reported positive pre-tax pre-R&D earnings in year t, but less than R&D expense in year t-1; C3 - if the firm reported positive pre-tax pre- R&D earnings in year t, and greater than R&D expense in year t-1. c In Panel B firm-year observations are classified based on an earnings target equal to the prior year’s pre-tax pre-R&D earnings, as follows: C1 - if the firm reported a negative change in pre-tax pre-R&D earnings in year t, and the absolute value of the change is greater than R&D expense in year t-1; C2 - if the firm reported a negative change in pre-tax pre-R&D earnings in year t, and the absolute value of the change is less than R&D expense in year t-1; C3 - if the firm reported a positive change in pre-tax pre-R&D earnings.

23

Table 3 Logit Regression of Indicator for Cut in R&D Expendituresa

Expensers | Capitalizers Levels Changes | Levels Changes | Intercept -0.13 0.15 | -0.48 -0.40 | -0.51 -0.34 | -0.56 -0.76 (0.42) (0.44) | (0.01) (0.01) | (0.32) (0.55) | (0.12) (0.08) C1 -0.28 -0.53 | 0.10 0.02 | 0.22 0.18 | 0.10 0.27 (0.13) (0.01) | (0.40) (0.87) | (0.68) (0.76) | (0.79) (0.53) C3 -0.63 -0.84 | -0.39 -0.29 | -0.17 -0.16 | -0.11 0.51 (0.01) (0.01) | (0.01) (0.02) | (0.74) (0.78) | (0.78) (0.22) PCRD 0.00 | 0.00 | 6.56 | 7.36 (0.85) | (0.75) | (0.01) | (0.00) CIRD -0.06 | -0.06 | -0.10 | -0.09 (0.10) | (0.11) | (0.21) | (0.28) CCAPX -0.35 | -0.36 | -0.05 | -0.06 (0.01) | (0.01) | (0.72) | (0.69) CSALES -0.75 | -0.67 | -0.87 | -1.01 (0.01) | (0.01) | (0.01) | (0.01) TOBQ -0.08 | -0.06 | -0.23 | -0.24 (0.01) | (0.01) | (0.01) | (0.01) SIZE -0.01 | -0.02 | 0.02 | 0.02 (0.63) | (0.27) | (0.77) | (0.76) LEV 0.89 | 0.78 | 0.56 | 0.64 (0.01) | (0.01) | (0.28) | (0.22) FCF 0.16 | 0.08 | -0.27 | -0.42 (0.17) | (0.45) | (0.24) | (0.21) DIST 0.00 | 0.00 | 0.01 | 0.00 (0.65) | (0.15) | (0.20) | (0.32) | | | C1 v C3 0.01 0.02 | 0.01 0.01 | 0.06 0.07 | 0.21 0.27 | | | # Obs: | | | C1 444 444 | 920 920 | 126 126 | 196 196 C2 156 156 | 379 379 | 16 16 | 33 33 C3 2647 2647 | 1948 1948 | 349 349 | 265 265

24

Table 3 – Continued Logit Regression of Indicator for Cut in R&D Expenditures

aThis table reports the results (coefficients and p-values) of the estimating the following logistic regression of cutting R&D expenditures: CutRDt = β0 + β1C1t + β2C3t + β3PCRDt + β4CIRDt + β5CCAPEXt + β6CSALESt

+ β7TOBQt + β8SIZEt + β9LEVt + β10FCFt + β11DISTt + ε CutRDt = indicator variable equal to one if the firm cut its R&D expense in year t, and zero otherwise; PCRDt = prior change in R&D expenditures (R&D expenditures in year t-1 minus R&D expenditures in year t-2 all divided by current R&D expenditures); CIRDt = change in industry R&D intensity, excluding the firm (log of industry R&D intensity (R&D expenditure divided by sales) in year t minus log of industry R&D intensity in year t-1); CCAPEXt = change in capital expenditures (log of capital expenditures in year t minus log ofcapital expenditures in year t-1); CSALESt = change in sales (log of sales in year t minus log of sales in year t-1); TOBQt = Tobin’s q in year t (market value of equity plus preferred stock plus total debt all divided by total assets); SIZEt = log of market value of equity measured at fiscal year end of year t; LEVt = leverage in year t (total debt divided by total assets). FCF is free cash flows in year t (measured as cash flow from operations minus average capital expenditure over the prior two years all divided by current assets); DIST is the distance from the earnings goal; in the ‘Levels’ column it is measured as pre-tax pre-R&D earnings divided by lagged R&D expense all minus one; in the ‘Changes’ column it is measured as the change in pre-tax pre-R&D earnings from year t-1 to year t all divided by lagged R&D expense. In the ‘Levels’ column firm-year observations are classified based on an earnings target of positive pre-tax pre- R&D earnings, as follows: C1 - if the firm reported negative pre-tax pre-R&D earnings in year t; C2 - if the firm reported positive pre-tax pre-R&D earnings in year t, but less than R&D expense in year t-1; C3 - if the firm reported positive pre-tax pre-R&D earnings in year t, and greater than R&D expense in year t-1. In the ‘Changes’ column firm-year observations are classified based on an earnings target equal to the prior year’s pre-tax pre-R&D earnings, as follows: C1 - if the firm reported a negative change in pre-tax pre-R&D earnings in year t, and the absolute value of the change is greater than R&D expense in year t-1; C2 - if the firm reported a negative change in pre-tax pre-R&D earnings in year t, and the absolute value of the change is less than R&D expense in year t-1; C3 - if the firm reported a positive change in pre-tax pre-R&D earnings. The row labelled ‘C1 v C3’ reports the p-value for testing whether the coefficient on C1 equals the coefficient on C3.

25

Table 4 Percentage of Capitalizers Cutting Their R&D Expensea

C1 C2 C3 Levels Number of Observations 126 16 349 Percentage Cut R&D 0.23 0.50 0.22 Changes Number of Observations 196 33 262 Percentage Cut R&D 0.24 0.48 0.19

a This table reports the percentage of capitalizer firm-year observations which have cut their R&D expense in year t relative to year t-1. See Table 2 for a description of the categories C1, C2 and C3.

26

Table 5 Logit Regression of Indicator for Cut in R&D Expense By Capitalizersa

Levels Changes Intercept 0.00 0.06 | -0.06 -0.23 (0.97) (0.91) | (0.86) (0.62) C1 -1.21 -1.02 | -1.09 -0.95 (0.03) (0.08) | (0.01) (0.02) C3 -1.28 -1.37 | -1.38 -1.00 (0.01) (0.01) | (0.01) (0.01) PCRD 1.12 | 0.91 (0.36) | (0.48) CIRD -0.13 | -0.12 (0.19) | (0.22) CCAPX -0.17 | -0.17 (0.26) | (0.27) CSALES -1.17 | -1.10 (0.01) | (0.01) TOBQ -0.23 | -0.21 (0.02) | (0.03) SIZE 0.16 | 0.14 (0.02) | (0.03) LEV -0.96 | -0.87 (0.17) | (0.21) FCF 0.00 | 0.00 (0.94) | (0.95) DIST 0.02 | 0.00 (0.04) | (0.21) | C1 v C3 0.77 0.25 | 0.21 0.85 | # Obs: | C1 126 126 | 196 196 C2 16 16 | 33 33 C3 349 349 | 265 265

a See Table 3 for a description of the test and variable definitions. The dependent variable is equal to one if the firm cut their R&D expense in year t relative to year t-1, and zero otherwise.

27

28

Table 6 Capitalization Rates of R&D Expenditures

Panel A: Percentage of R&D Expenditures Capitalizeda

Lag Cap% Cap % % Increase Levels C1 0.622 0.599 0.234 C2 0.148 0.259 0.500 C3 0.526 0.547 0.326 Changes C1 0.593 0.596 0.289 C2 0.194 0.227 0.393 C3 0.546 0.561 0.313

Panel B: Regression of the Increase in the Capitalization Percentageb

Logit | OLS Levels Changes | Levels Changes | Intercept -0.00 -0.43 | 0.11 0.03 (0.99) (0.23) | (0.03) (0.35) C1 -1.19 -0.47 | -0.13 -0.05 (0.03) (0.24) | (0.02) (0.21) C3 -0.73 -0.36 | -0.11 -0.03 (0.16) (0.35) | (0.03) (0.44) | C1 v C3e 0.10 0.63 | 0.50 0.37

a This panel reports the mean percentage of R&D expenditures capitalized in the current year (Cap%), lagged year (Lag Cap %), and the percentage of firms increasing their capitalization percentage (%Increase). b This panel reports the results (coefficients and p-values) of estimating the following regressions: (Logit column) INC-CAP% = β0 + β1C1+ β2 C3 + ε, where INC-CAP% equals one if the firm increased its capitalization percentage from year t-1 to year t; and (OLS column) ∆CAP% = β0 + β1C1+ β2 C3 + ε, where ∆CAP% equals the percentage change in capitalization percentage. See Table 2 for a description of the categories C1, C2 and C3.