As argued in section <IP_ADDRESS>.5, adverse legislation and other regulation is frequently directed towards firms with high, allegedly monopolistic earnings. Therefore, "politically visible" firms have incentive to take write-offs in order to decrease earnings and firm size, reducing political visibility and associated costs. Political visibility is assumed to relate to firm size, 1176 market share, industry concentration, and sharp increases in ac-

1172 See Watts and Zimmerman (I 986, p. 216). Dichev and Skinner (2002, pp. 1101-4) find that net worth, or equity, is also frequently used in debt covenants. In line with this finding, Beatty and Weber (2004, p. 11) predict that SF AS 142 write-off behavior is affected by the presence of net worth-based covenants. See also Mohrman ( I 996, p. **86).** However, since this variable is highly correlated with the DTE ratio, it is not explicitly considered here.

1173 See Bernstein (1993, pp. 619-20). As discussed in sections <IP_ADDRESS> .4 and <IP_ADDRESS>.2 above, similar measures are used, for the same reason, in private lending agreements between a firm and its debtholders. See also White, Sondhi and Fried (2003, pp. 130-3). This assumption is supported by the common observation that managers attempt to minimize the level of debt by keeping debt off the balance sheet. S&P (2003, p. 22) reports that, for many firms, "debt leverage ( calculated without any adjustments to reported figures) is the only focal point of[financial] policy considerations." Apparently, it is quite common to strive for a debt-to-capital ratio of 35%.

1174 For example, one of the components of Altman's (1968) Z score, a popular predictor of financial distress, is the DTE ratio. See also White, Sondhi and Fried (2003, pp. 652-3).

1175See also Moehrle (2002, p. 410), who uses the difference between the sample firm's debt-to-assets ratio and its weighted average in the firm's (four-digit SIC code) industry.

1176Since firm size potentially is a proxy for various economic and non-economic effects, it is considered as a control variable, not as a political costs proxy. Refer to section 5.2.4 below.

counting perfonnance. 1177 However, managers might likewise assume that regulators are concerned with post-transition earnings rather than earnings "distorted" by SFAS 142 adoption, in which case there would be incentive to avoid transitional write-offs, storing up future write-off potential. Due to this uncertainty, the following open hypotheses are posited.1178

#### **H~R 3a:**

Ceteris paribus, the write-off decision is associated with the finn 's market share.

#### **H~R 3b:**

Ceteris paribus, the write-off decision is associated with measures of concentration in the finn's industry.

#### **H~R 3c:**

Ceteris paribus, the write-off decision is associated with the finn's pre-write-off increase in ROA.

#### **<IP_ADDRESS> Capital market-related incentives**

#### *<IP_ADDRESS>.1 Target accounting*

#### 5 .2.3 .2.1.1 General decision model

Based on the capital market-related considerations discussed in section <IP_ADDRESS> above, transitional SF AS 142 goodwill write-offs are also expected to reflect the incentive to influence stock price favorably, *inter a/ia* by achieving earnings targets. To be relevant in this context, these thresholds must be affected by any transitional SFAS 142 goodwill write-off.1179 Because measures of operating earnings exclude accounting changes, bottom-line net income is presumed to be the relevant earnings number. 1180

1177 Refer to section <IP_ADDRESS>.5, especially Figure 8. Membership in certain industries is also argued to give rise to political exposure; however, this notion is not explored here in a regression setting. Refer instead the descriptive information presented in Table A. 2 and Table A. 4.

1178Because these variables can be thought of as dummy variables capturing whether or not a firm is politically visible, no linear relation to the write-off *amount* is expected.

11 79 Refer to section <IP_ADDRESS>.8 for a discussion of this critical assumption.

1180 The seminal paper, Burgstahler and Dichev (1997, pp. 101 -2), also adopts this approach. According to S&P (2001), COMPUSTAT Industrial Annual item DATA18 (income before extraordinary items), another target figure used by Burgstahler and Dichev (1997), excludes transitional SFAS 142 goodwill write-offs and would therefore be unaffected by them. This is because COMPUST AT Industrial Annual item DATA192 (extraordinary items) explicitly includes the cumulative effect of accounting changes. See S&P (2001, ch. 4, p. 130).

Figure 14 contains a simple model of earnings management behavior expected to occur when management is concerned about reaching a simple earnings target, T.1181 Following prior literature, 1182 two earnings targets are considered: First, prior-year annual earnings capture the incentive to avoid earnings declines and, second, zero earnings are relevant where management attempts to avoid losses. 1183With respect to the first target, it is assumed that annual 2002 earnings are managed towards a target defined by annual 2001 earnings. 1184

**Figure 14: Earnings management relative to a simple earnings target**

1184 In the special setting considered here, adoption of SF AS 142, the relevant measurement date for both actual earnings and earnings targets is not entirely self-evident. On the one hand, any goodwill impairment is to be measured as of the date of initial application (i.e. as of January I, 2002, for calendaryear firms) and disclosed via a restatement of the first quarterly report of the fiscal year. In that respect, the relevant earnings number would be (restated) first-quarter earnings, and the relevant target would be the prior period's first-quarter earnings. However, because firms are given up to 12 months to detect and measure any goodwill impairment, more than nine months may elapse before investors would learn whether the first-quarter earnings target has been met. It is not reasonable to assume that investors attach much relevance to a restated earnings figure for a past time period. Since the year-end of fiscal 2002 is the date by which firms are required to complete their initial goodwill impairment tests, information on the outcome of the test was not available on a broad scale before the time by which annual reports for 2002 were published.

1181Similar models have been developed by Abarbanell and Lehavy (2000, figures I and 2). See also Kinney and Trezevant ( 1997, p. 45).

1182  Again, refer to section <IP_ADDRESS>.5 above.

I 183 Below, these targets are collectively referred to as "the earnings target", T. A third target, analysts' (consensus) earnings forecasts, is not explicitly considered here due to lacking data availability as well as uncertainties regarding analysts' treatment of transitional SFAS 142 goodwill write-offs. For example, Philbrick and Ricks (1991, p. 401) state that "IBES provides no specific instructions to individual analysts about the treatment of extraordinary items. While analysts typically forecast *EPS* before extraordinary items, 'extraordinary' is undefined." They report that *"!BES* refers to extraordinary items as 'write downs which are at the discretion of management,' while according to *GAAP,* not all discretionary write-downs qualify as extraordinary items" (emphasis in original). Therefore, although transitional SFAS 142 goodwill write-offs are not extraordinary items under APB Opinion 30, they might or might not nonetheless be adjusted out of 1/8/E/S forecasts. Similarly, Degeorge, Patel and Zeckhauser (1999, p. 16) state that, according to 1/B/E/S, 1/B/E/S forecasts exclude unusual or nonrecurring charges. For more detail on this issue, refer to section <IP_ADDRESS>.8 above.

If pre-managed earnings, PME, is *above* the target, management is expected to take action **O,** i.e. engage in earnings management that decreases earnings to a level equal to or just above the target. This behavior has been referred to as "cookie jar reserving", 1185 and is based on the rationale that reserves are valuable and, by decreasing earnings, management reserves a portion of current earnings to balance potential future earnings shortfalls. Applied to the SF AS 142 setting, action **O** would imply that management has incentive to take a goodwill write-off equal to or less than the amount by which pre-managed earnings is above the target, (PME - T). Alternatively, if managers of "target beaters" on average expect to beat targets again in future periods, these firms can be expected to avoid transitional SFAS 142 goodwill write-offs altogether, storing up write-off potential for future "cookie-jar reserving" purposes. 1186 Since the literature reviewed in section <IP_ADDRESS>.5.1 indicates that (PME - T) is usually very small, 1187 small or no write-offs would be expected in both scenarios.

On the other hand, if PME is already *below* the target, management's expected behavior depends on the amount of earnings management reserves, R, available to offset the deficit by which PME falls short of T. If reserves are sufficient to achieve T by incomeincreasing earnings management, i.e. PME lies between T and (T - R), action **e** is expected. In this case, management attempts to achieve earnings in the amount of T by income-increasing earnings management, using up all or part of R in the process. In the partial analysis focusing on SFAS 142 goodwill write-offs, R is necessarily zero, since negative write-offs (i.e. "write-ups") are not permitted under the standard. 1188 Consequently, alternative **e** is not available to management in this setting.

Finally, if R is insufficient to balance the deficit (T - PME), managers are expected to maximize future reserves by taking action **O.** As a second-best solution when achieving T is impossible, earnings are decreased by the largest possible amount in order to anticipate future write-offs in the current period. This behavior is referred to as the "big bath"

1185 See Levitt (1998) and Abarbanell and Lehavy (2000, p. 8).

1186 Duvall et al. (1992) suggest that investors find it difficult to unravel goodwill-related financial statement effects in later periods, implying that regular goodwill write-offs are likely to be less "visible" than transitional ones.

1187 See, for example, Burgstahler and Dichev (1997), who indicate that firms frequently employ earnings management to push earnings just beyond the target. In such cases, it would be unreasonable to assume that transitional SFAS 142 write-offs would be used to manage earnings in the opposite direction.

1188 Reversals of prior impairment losses are explicitly prohibited by SFAS 142.20. If goodwill could be written up ( or goodwill write-offs reversed), a more comprehensive test of the model could be conducted. For example, Moehrle (2002) analyzes whether restructuring charge reversals are used to meet or beat earnings targets.

strategy. 1189Applied to the SFAS 142 setting, this rationale implies that managers have incentive to write off the highest possible amount of goodwill. 1190This is especially true to the extent that managers expect transitional SF AS 142 goodwill write-offs to be ignored by investors due to their (perceived) transitory nature. 1191 Taken together, the write-off amounts are expected to be significantly different where PME ~ T versus PME < T, with larger write-offs expected for the second group.

#### **H~R 4:**

Ceteris paribus, a negative relation exists between the write-off decision and the firms' pre-write-off earnings meeting earnings targets.

#### <IP_ADDRESS>.1.2 Firms exceeding earnings targets

Consistent with the considerations above, firms that, before the transitional SF AS 142 goodwill write-off decision is made, exceed their target earnings are assumed to either avoid write-offs altogether or engage in "cookie-jar reserving", writing off no more than the amount by which pre-write-off earnings exceed the target.

### **H?' 5:**

Ceteris paribus, for firms with pre-write-off earnings in excess of targets, a positive relation exists between the write-off amount and the amount of the excess.

#### <IP_ADDRESS>.1.3 Firms falling short of earnings targets

Where the above model predicts action **O** ("big bath"), it is not reasonable to assume that the available goodwill book value will be written off in full in all cases. Generally, management will increase the write-off amount as long as the marginal benefits of this behavior exceed the marginal costs. Certain factors are expected to limit the extent to which management's net benefits increase in the amount of goodwill written off. 1192In general, where a firm expects that a higher write-off will cause some accounting-based heuristic used by (potential) investors to exceed (or fall short of, respectively) some cutoff point, the amount written off will be limited by the lower amount of goodwill book value and the number relevant to such cutoff point.

1189As has been discussed in section <IP_ADDRESS>.6 above, the capital market penalties associated with large shortfalls have been found to be proportionately less than those following relatively small shortfalls. In other words, managers might consider the benefits of"big bath" accounting to outweigh its cost.

1190See also Hitz and Kuhner (2002, p. 285) and KUting, Weber and Wirth (2001, p. 192).

1191See also Eberle (2002b, p. 190), Pellens, Sellhom and Weinreis (2002), and Segal (2003, p. 21). Refer to section <IP_ADDRESS>.8.

1192Among these factors are the contracting considerations discussed in the previous section.

Extant empirical research surrounding the debate on goodwill accounting and the purchase/pooling decision mainly focuses on the impact of these procedures on firms' income statements and assets. 1193It might also be important how a firm's equity and leverage are influenced by financial reporting decisions related to goodwill. In the simplest case, the write-off amount will be limited by the amount of equity on the firm's books, since managers have incentive to avoid negative equity. However, this restriction will only apply where goodwill book value is large enough to, where written off completely, jeopardize the amount of equity.

### H~ 6:

Ceteris paribus, for firms that fall short of earnings targets, a positive relation exists between the write-off amount and the smaller amount of goodwill and equity.

While firms that fall short of earnings targets are expected to practice "big bath", the observation of frequent, large write-offs in that group is not sufficient to conclude that these write-offs result from earnings management. This conclusion can be strengthened when write-offs are observed to coincide with other indicators of a "big bath" strategy. According to the considerations discussed in section <IP_ADDRESS>.6 above, "big bath" can be expected to be most successful when the signal conveyed by management is credibly associated with good news. Such circumstances include recent changes in top management, where a new CEO re-evaluates corporate strategy and abandons unprofitable operations. 1194 In these situations, goodwill write-offs frequently coincide with other restructuring-type charges, which have found to be associated with positive stock market reactions. 1195 Likewise, the assumption that write-offs are recorded as part of a "big bath" strategy would be further supported if other income-decreasing earnings management measures were observed concurrently. 1196 The focus is placed on coinciding special items, because this specific accrual has been documented in prior research to be a low-cost instrument of earnings management that might have special relevance in a setting where management

1193 Refer to section <IP_ADDRESS> above.

1194 For a similar hypothesis, refer to Beatty and Weber (2004, p. 12).

1195 See, for example, Brickley and Van Drunen (I 990). Bartov, Lindahl and Ricks (I 998, p. 328) distinguish write-offs that purely reflect declines in asset values from those that result from the decision to change operations. Consistent with the notion that the latter category is assumed to convey ( at least in part) good news to investors, the authors find positive, albeit insignificant, market reactions to these write-offs. For similar evidence, refer to sections <IP_ADDRESS>.6 and <IP_ADDRESS> above.

1196 In contrast to this notion, Rees, Gill and Gore (1996, p. 168) argue that "documented abnonnal accruals in the write-down year ... imply that managers are responding to changes in economic circumstances as opposed to acting opportunistically."

is concerned about achieving earnings targets. 1197 Overall, it must be noted that the simultaneous occurrence of goodwill write-offs, restructuring-type charges, and management changes does not necessarily imply earnings management, because it might indicate that an actual restructuring is under way. 1198

### **H~** 7a:

Ceteris paribus, for finns that fall short of earnings targets, a positive relation exists between the write-off decision and a recent CEO change.

#### **H~R 7b:**

Ceteris paribus, for finns that fall short of earnings targets, a positive relation exists between the write-off decision and concurrent restructuring charges.

#### **H~R 7c:**

Ceteris paribus, for finns that fall short of earnings targets, a positive relation exists between the write-off decision and concurrent special items.

#### *<IP_ADDRESS>.2 Rating*

As discussed in section <IP_ADDRESS>.7, it is proposed that a manager's financial reporting decision is influenced by its expected repercussions on rating agencies' assessments of the finn and potentially resulting changes in the cost of capital. The discussion in that section outlines the rating process and the role of financial ratios in that process. Measures of financial leverage are crucial elements in arriving at a rating. Therefore, it is likely that managers are concerned about the effect that transitional SFAS 142 goodwill write-offs might have on measures of financial leverage.

S&P is concerned about a lack of balance sheet comparability due to internationally different accounting treatments of goodwill. 1199 With specific regard to SF AS 142, S&P recommends in its proposal of a "core earnings" measure that goodwill impainnent charges be excluded from core earnings. 1200 However, no statement is made to the effect

1197 See Marquardt and Wiedman (2002, pp. 29-30). In a related study, Kinney and Trezevant (1997) report that firms incur negative special items to achieve "big bath" results. See also Pourciau (1993, p. 330). Although special items are by definition expected to average out to zero under normal circumstances, both studies report negative means for special items.

1198 It is the coincidence of all those factors combined in firms that have a powerful "big bath" incentive (i.e. falling short of a target) that provides a strong indication that earnings management is behind the write-off.

1199 See S&P (2003, p. 31).

1200See S&P (2002). Charges due to other asset write-offs are also added back to earnings, which affects earnings-based ratios. See S&P (2003, p. 53).

that write-offs are adjusted for in the balance sheet. On the contrary, an S&P analyst states that, "in general [the way in which a finn's SFAS 142 goodwill write-off impacts its credit rating] is not a yes or no answer and the impact on rating, if any, will be assessed based on numerous qualitative as well as quantitative attributes associated with the writedown, and the reasons thereof, as well as any other potential consequences such as violation of debt covenants, minimum regulatory capital requirements, etc." 1201 This statement suggests that S&P treats SFAS 142 write-offs similar to direct deductions of goodwill from equity. Therefore, transitional SFAS 142 write-offs have the potential to alter leverage ratios via their effect on stockholders' equity. Managers, then, have incentive to limit the write-offs effect on financial leverage ratios.1202

Elliott and Hanna (1996) report that bond ratings, as a measure of financial risk, decline as finns record discretionary asset write-offs, especially where such write-offs occur repeatedly in consecutive years. 1203 Knowing this, managers might have incentive to avoid such write-offs. 1204 Therefore, it is assumed here that finns with low bond ratings are more reluctant than others to further jeopardize their rating by recording a write-off.1205

#### 5.2.4 Control variables

Because transitional SF AS 142 goodwill write-off behavior is expected to be influenced by factors that are not explicitly modeled, the following control variables are considered:

1201 Email correspondence dated June 23, 2003, with Neri Bukspan, credit analyst with Standard & Poor's.

1202 It must be noted that other rating-relevant ratios are affected *positively* by SFAS 142: Even if writeoffs are not simply backed out of earnings but replaced by some amortization charge, ratios such as "return on capital" **(S&P** (2003, p. 55) will increase due to lower equity. However, it is not plausible that firms are rewarded for decreased equity and impaired assets.

1203 See Elliott and Hanna ( 1996, p. 152).

1204 On the other hand, causality is not clear: Write-offs and rating downgrades are likely to be jointly determined by adverse changes in the firm's future prospects. To that extent, it is difficult to distinguish empirically between economic and incentive factors influencing write-off behavior. Refer to section <IP_ADDRESS>.6.

1205 More accurately, this reluctance should increase in the probability of a downgrade and the amount of costs anticipated to follow from it. Measuring these more subtle criteria is beyond the scope of this study.

1206 As explained in Table 6 below, the bond rating is coded as an integer descending in the firm's rating. Therefore, the lower the rating, the higher the rating variable.

First, the amount, economic characteristics, and fair value of the goodwill asset are expected to vary across industries. Therefore, industry membership is expected to be an explanatory factor behind write-off behavior, although no specific relation is predicted. 1207

Second, finn size is possibly associated with a multitude of factors, including leverage, infonnation production costs, competitive advantage, management ability, quality of accounting advice, and risk (income variability). 1208 Further, it might be a proxy for bargaining power, implying that smaller finns are forced to pay the full net present value for their acquisitions and incur larger goodwills that tend to fall below fair value more quickly when adverse economic developments occur. 1209 Also, prior research indicates that smaller finns are to a lesser extent followed by analysts, possibly implying that capital market-related incentives are less strong for them. 1210

Third, finns adopting SFAS 142 as of January l, 2002, possibly face different economic and incentive environments than do early or late adopters. Therefore, a fiscal year-end indicator variable is included to capture any difference.

Finally, the write-off amount (deflated by total assets in order to eliminate size effects) might be associated with the relative significance that overall goodwill book value has for the firm. For example, firms for which goodwill represents a substantial portion of their asset base might be reluctant to record large write-offs, which would significantly diminish their earnings and size. Also, an association of the two variables is expected simply because the write-off amount is limited by the absolute amount of goodwill. 1211 For these reasons, goodwill book value, also deflated by total assets, is considered as a control variable.

1207 Industry membership is explicitly considered in the descriptive analysis below (Table A. 2 and Table A. 4). But since there is no economic reason to assume a linear relation between the write-off amount and the firm's SIC code, it is not included as an explanatory variable in the regression analysis.

1208 Refer to Moses (1987, p. 363 fn. 6). See also Ball and Foster (1982, p. 184 fn . 36). Bujaki and Richardson (1997) provide a review of the uses of firm size in accounting research.

1209 See Clinch (1997, p. 344).

1210 See Foster, Olsen and Shevlin (1984).

1211 Furthermore, firms with larger goodwill (scaled by total assets) will, ceteris paribus, record higher goodwill write-offs (scaled by total assets).

### *5* .3. I Variable definition

Since transitional SF AS 142 goodwill write-offs are presented as a first-quarter accounting change in the adoption year,1212 COMPUSTAT quarterly item DATAI 17 (accounting changes - cumulative effect) is used to capture the transitional write-off amount. 1213 This variable is subject to potential measurement error because accounting changes other than the adoption of SFAS 142 might be included in COMPUSTAT quarterly item DATA117. In an attempt to minimize this potential measurement error, recent FASB statements are examined in order to ascertain the extent to which their adoption has possibly resulted in accounting changes being disclosed within COMPUSTAT quarterly item DAT Al 17. This analysis suggests that such effects are limited at most: First, SFAS 140, *Accounting for Transfers and Servicing of Financial Assets and Extinguishments of Liabilities -A replacement of FASB Statement 125,* issued in September 2000, was to be applied to transfers and servicing of financial assets and extinguishments of liabilities occurring after March 31, 2001 (SFAS 140.19-25). Therefore, no effect on the Ql/2002 accounting change is expected.

Second, According to SF AS 141.62, any unamortized ,,negative goodwill" balances from pre-SF AS 141 periods were to be written off and recognized as the effect of a change in accounting principle. This accounting change was, as a rule, to be recognized at the adoption date of SF AS 142. Before SFAS 141 was issued, "negative goodwill" was accounted for under APB Opinion 16. APB Opinion 16.91 requires that "negative goodwill" first be allocated to reduce proportionately the values assigned to non-current assets, and that any remainder be classified as a deferred credit and amortized systematically to income over no more than 40 years. This implies that accounting changes disclosed in the first quarter of 2002 potentially include write-offs of such unamortized ,,negative goodwill" balances. However, according to economic reasoning, such remaining deferred credits should be observed only in very exceptional circumstances, because acquirers will in most cases have to pay an acquisition price that is at least equal to the book value of net assets. Therefore, it is assumed here that any measurement error in the transitional SFAS 142 goodwill write-off variable that is introduced by this SF AS 141 effect is immaterial.

1212Refer to section 4.7.3.

1213COMPUST AT annual items DAT A368 and DAT A369 (goodwill impairment pre-tax and after tax, respectively) are not adequate in this respect, since they only capture write-offs taken *subsequent to*  the transitional impairment test.

Third, although SF AS 143, *Accounting for Asset Retirement Obligations,* issued in June 2001, is only effective for fiscal years beginning after June 15, 2002 (SFAS 143.24), early adopters of this statement also present its adoption effect as a Q 1/2002 accounting change (SFAS 143.24, 26). Therefore, firms with asset retirement obligations that also choose to adopt SF AS 143 early as of the beginning of fiscal 2002 might distort the transitional SF AS 142 goodwill write-off variable. However, due to the infrequent concurrence of these two conditions, any measurement error is assumed to be immaterial.

Finally, SFAS 144, *Accounting for the Impairment or Disposal of Long-Lived Assets,*  issued in August 2001, takes effect at the same time as SFAS 142 (SFAS 144.49). However, its initial application does not result in Ql/2002 accounting changes. All other F ASB statements issued subsequent to SF AS 144 were effective after the first quarter of 2002 and did, even where applied early, in most cases not involve disclosure as a firstquarter change in accounting principle. In addition, some of these statements involved only minor changes to existing GAAP. Consistent with the considerations explained above, any measurement error in the transitional SF AS 142 goodwill write-off variable potentially introduced by (early) adoption of other FASB statements is assumed to be immaterial.

Table *5* provides further reassurance on this issue; documenting that negative DATAl 17 entries are observed relatively frequently in goodwill firms (13.40%), whereas they hardly occur at all (0.71 %) in firms having no goodwill at the end of fiscal 2001. This finding further corroborates that negative effects of accounting changes in the goodwill sample are the result of transitional SFAS 142 goodwill write-offs.

#### **Table 5: Potential measurement error in the dependent variable**

In addition, since transitional SFAS 142 goodwill write-offs are negative items, a small number of firms that reported a positive number in the Q 1/2002 COMPUST AT quarterly

1214For the remaining firms, no goodwill data are available.

item DAT A 117 were excluded from the sample. Likewise, since the write-off cannot be higher than goodwill book value, all firms that wrote off more than that amount were also not considered.

The write-off amount is defined as a positive number. 1215Deflators encountered in the literature include total assets, 1216 the market value of equity, and the number of shares.1217 Another possibility would be the book value of goodwill itself; however, this deflator has the disadvantage that even enormous write-offs, relative to goodwill book value, might still be immaterial to both financial statement preparers and users where goodwill book value itself is immaterial. In contrast, measuring the goodwill write-off as a percentage of total assets ensures that the materiality of the resulting financial statement effect is adequately captured.

Table 6 describes all the variables used below.

1215 For non-write-off firms, the write-off amount is entered as zero.

1216 Francis, Hanna and Vincent (1996, p. 125) and Segal (2003, p. 17).

1217 Heflin and Warfield (1997, pp. 24, 26-8) use both of these deflators.

1218 The items listed denote COMPUSTAT Industrial Annual data items, unless otherwise stated, with suffixes representing the respective fiscal years. With the excep- tion of stock price (DATA199; measured in dollars and cents) and common shares outstanding (DATA25; measured in millions), all data are measured in millions of dollars. Stock price data is not adjusted for dividends and stock splits.

Thorsten Sellhorn - 978-3-631-75498-6 Downloaded from PubFactory at 01/11/2019 04:09:55AM via free access

N N --.I

**Table 6: Definition and explanation of variables**

N N '°

### 5.3.2 Sample selection

Previous studies of SFAS 142 goodwill write-offs analyze firms that publicly announce their write-off decision in addition to publishing their financial statements. 1219 By announcing the write-off without being required to do so, firms self-select into a sample not strictly comprised of write-off firms but of firms that, having written off their goodwill, chose to disclose this fact. Arguably, this group is different from write-off firms that choose not to announce the write-off. 1220 To mitigate potential problems stemming from this sample selection procedure, a broad sample of publicly listed U. S. firms is used. These firms are subjected to certain requirements. Table 7 describes the sample selection procedure.

1219 Refer to Henning, Shaw and Stock (2002) and Segal (2003).

122° Francis, Hanna and Vincent (1996, p. 119) acknowledge this problem, stating that their "analyses of the causes and shareholder wealth effects of write-offs are joint tests of managements' decisions to write off assets and to disclose in press releases that they have done so." They document that a substantial portion of write-off firms do not specifically disclose the write-off. The authors also point to other studies potentially suffering from this same shortcoming.

1221 Smaller sub-samples are later formed for specific regressions. Data required for these regressions may not be available for the full sample.

1222 11 firms were excluded due to *posWve* effects of a change in accounting principle (DAT A 117) in the first quarter of 2002. *5* more firms were excluded because DATA! 17 is larger in absolute value than goodwill itself.

#### **Table** 7: **Sample selection**

The starting point is the population of the COMPUST AT Industrial Annual tape. As of the retrieval date, 22,078 firms were included in the database. 1225 Since annual as well as quarterly data are required for my analysis, 4,193 firms are deleted that are not included on the COMPUST AT Industrial Quarterly tape. 1226 Next, 4,087 banks and financial institutions are excluded because the financial statement structures of these firms are widely different from those of most other firms and therefore would be likely to distort results.1227 Subsequently, it is required that firms have available the data required to calculate the variables summarized in Table 6 above.1228 This requirement represents the most substantial cut, resulting in 11 ,120 observations being dropped. Finally, 459 firms with negative equity in fiscal 2001 or 2002 are excluded, because not only are these firms' financial ratios difficult to interpret, they are also likely to operate in radically different economic and incentive environments than the rest of the sample. This procedure (Panel A) results in a basic sample of2,219 firms.

Panel B reconciles this basic sample to a sample of firms that are potential candidates for transitional SFAS 142 goodwill write-offs. These firms meet two additional data requirements: First, they are adopters of SF AS 142 in the year of transition. According to SFAS 142.48, that is the case when a firm has positive goodwill on its balance sheet as of

1223 Thereof firms with *regular* (post-transitional) SFAS 142 goodwill write-offs in 2002: 116 (11 .3% of the non-write-off sample).

1224 Thereof firms with *regular* (post-transitional) SFAS 142 goodwill write-offs in 2002: 27 (12.1% of the write-off sample).

1225 Annual data was retrieved on June 4, 2003, i.e. before the annual update for 2003 was available. Repeating the analysis subsequent to the annual update would result in far fewer firms being eliminated from the sample due to missing data.

1226 Quarterly data was retrieved on July 22, 2003.

1227 Like in Riedl (2002, pp. 20, 36), firms falling within the SIC 6 category are excluded.

1228 However, since data items required for computing some of the capital market-related incentives variables (e.g. change in the CEO position for 2002) are not available for the majority of firms, the sample size would be unduly reduced if firms lacking those data were *a priori* excluded from the sample. Therefore, tests relying on these data are restricted to a smaller sample size, as will be indicated where applicable.

the beginning of the fiscal year. For 955 finns, goodwill (COMPOST AT item DAT A204) is zero. Second, only those finns are included for which a potential transitional SFAS 142 goodwill write-off can be ascertained with reasonable certainty. Therefore, 11 £inns are eliminated from the sample because they report positive accounting changes (DAT A 117) in the first quarter of 2002, and 5 more £inns are excluded because DATA117 is larger in absolute amount than goodwill itself. Consequently, the basic sample disaggregates into a non-goodwill sample of 955 and a goodwill sample of 1,248 usable observations.

Finally, Panel C distinguishes two sub-samples of the goodwill sample. The 1,024 finns in the non-write-off sample did not write off their goodwill as a result of the transitional impainnent test upon adoption of SFAS 142. Conversely, the 224 £inns included in the write-off sample did write off their goodwill upon adoption, 1229 which amounts to 17.9% of the whole goodwill sample.

### 5.3.3 Descriptive statistics and comparative analyses

In this section, descriptive statistics for the four samples are provided and preliminary comparative tests undertaken. First, the four samples are compared based on the most important variables defined in Table 6 above, and equality tests for the median and mean are provided. Second, the significance of goodwill write-offs is assessed for the £inns in the goodwill sample. Third, preliminary evidence on some of the hypotheses posited in section 5.2 is presented.

Table 8 provides descriptive comparisons of the two groups comprising the basic sample: the non-goodwill finns and the goodwill finns (see Panel B of Table 7).

1229A total of 143 regular (post-transition) SFAS 142 goodwill write-offs were reported by the firms in the goodwill sample during fiscal 2002.

**Panel A:** Non-goodwill sample (N = 955)

**Panel B:** Goodwill sample (N = 1,248)

••••••• , , . Differences in mean (t-test) and median (adjusted x2-test), respectively, significant at the 10, *5,* or 1% levels. Test statistics and p-values are reported in Table A. 1 in the appendix.

0MV, TA, and SAL represent mean values (fiscal 2001 and 2002) for the market value of equity, total assets, and sales, respectively.

For other variable definitions, refer to Table 6. Monetary amounts are in million \$.

#### **Table 8: Comparison of the non-goodwill and goodwill samples**

The comparative analysis suggests that the two sub-samples are significantly different in several respects, albeit widely similar in others. The median MTB ratios are roughly equal, while the mean in the non-goodwill sample is substantially larger due to outlying observations. Accounting performance measured in terms of ROA change during 2001 is also comparable, whereas goodwill firms experienced a significantly (at the 5% level) higher median stock return during 2000 and 2001. Most strikingly, goodwill firms are vastly larger and more highly in debt, whereas the respective portions of assets-in-place are similar on average. Furthermore, goodwill firms seem to have higher market shares in their industries, which are also more concentrated. Overall, the goodwill sample appears to consist of larger, more established firms than the non-goodwill sample.

Structurally equivalent to Table 8, Table 9 provides comparative evidence for the nonwrite-off and write-off samples. Goodwill-related information is also reported for these firms.

**Panel B:** Write-off sample (N = 224)

••••••• Differences in mean (t-test) and median (adjusted )()-test), respectively, significant at the ' ' . I 0, *5,* or I% levels. Test statistics and p-values are reported in Table A. I in the appendix.

0 MV, TA, and SAL represent mean values (fiscal 2001 and 2002) for the market value of equity, total assets, and sales, respectively.

For other variable definitions, refer to Table 6. Monetary amounts are in million \$. Goodwill figures are scaled by beginning-of-period balance sheet numbers.

#### **Table 9: Comparison of the non-write-off and write-off samples**

The two subgroups of the goodwill sample exhibit significant differences. The median goodwill book value in the pre-adoption period, expressed in absolute and relative terms, is much larger in the write-off sample. At the end of 200 I, median goodwill was more than five times larger in the write-off sample (\$ I 39 million) than in the non-write-off sample (\$27 million). Due to substantial goodwill write-offs in 2002, these differences tend to disappear in 2002, making the subgroups much more homogenous. Consistent with economic factors hypothesis I, the MTB ratios of write-off firms are, on average, significantly lower. These companies also underperform non-write-off firms in terms of stock return, albeit not on change in ROA. Moreover, write-off firms have significantly higher exposure to debt and operate with a higher proportion of assets-in-place. They are also significantly larger and more dominant in their respective markets. This preliminary evidence on economic differences between the subgroups is consistent with the assumptions expressed in the economic factors hypotheses: Write-offs occur in large firms that are viewed by market participants as undergoing a phase of economic difficulty.

Table A. 2 in the Appendix contains a classification of firms by industry membership. 25 two-digit SIC categories, each with a membership of more than 1 % of all 13. 798 available firms, are reported explicitly, while the rest, including the category "non-classifiable establishments" (two-digit SIC category 99), is grouped into the "all other" category. The analysis suggests that industry representation differs among the sub-samples. While the majority of industries are represented almost equally in the non-goodwill and goodwill samples, goodwill firms seem to be distinctly over-represented in the industrial machinery/equipment, transportation equipment, fabricated metal products, printing/publishing, and paper industries, perhaps suggesting that these industries underwent significant consolidation in recent years. A reverse relation exists in the chemicals, oil/gas extraction, and metal mining industries, where goodwill firms are much less frequently represented than are non-goodwill firms.

A comparison of the non-write-off and write-off samples indicates that, on average, write-offs occur more frequently in the durable goods wholesale, amusement/recreation, and paper industries. Conversely, firms in the electronics, instruments, oil/gas extraction, food, and nondurable goods wholesale industries apparently are less susceptible to recording transitional SF AS 142 goodwill write-offs, given that they have any goodwill. Notably, the fact that oil/gas firms record write-offs less frequently provides preliminary corroboration to the notion brought up in section <IP_ADDRESS>.5, according to which "politically visible" firms could expected to avoid transitional SF AS 142 goodwill write-offs.

Table 10 describes transitional SF AS 142 goodwill write-offs in the write-off sample.'230

Goodwill figures are scaled by beginning-of-period balance sheet numbers and end-of-period sales. These data comprise write-offs taken by the 224 firms in the write-off sample.

#### **Table 10: Descriptive analysis of transitional write-offs**

1230 It would be interesting to compare transitional SFAS 142 write-off amounts to prior-period goodwill amortization charges. However, goodwill amortization is rarely disclosed separately, so data availability on this item is insufficient. See, for example, Duvall et al. (1992), who document the problem of extracting goodwill-related information from published financial statements. Furthermore, the amounts of amortization and write-off do not necessarily compare, because the write-off may relate to only part of entity-level goodwill, whereas amortization represents an arbitrary charge based a weighted average of the individual useful lives of all the components of entity-level goodwill.

The analysis indicates that the write-offs represent substantial accounting events for the affected finns. The median amount is \$28 million, whereas the considerably higher mean (\$149 million) as well as the large standard deviation suggest that several much higher write-offs distort the distribution. The maximum of the distribution is at nearly \$3 billion. 1231 The median finn wrote off 4. 7% of total assets and almost 13% of equity upon transition, with mean amounts, again, being much higher. Transitional write-offs slashed about a third of goodwill book value and represented more than 9% of the market value of equity. Finally, the median transitional SFAS 142 goodwill write-off amounted to 4% of sales.

Further descriptive information on the SF AS 142 goodwill write-off is given in the Appendix: Table A. 3 mirrors Table 10, reporting descriptive infonnation on *regular* SFAS 142 goodwill write-offs taken by the finns in the goodwill sample during 2002. These charges are smaller, on average (median = \$7 million), than the transitional ones, and are also less significant in relative tenns. The fact that they also represent, on average, about one third of goodwill book value (median= 29. l %), reflects the circumstance that these write-offs are mainly taken by finns in the transitional non-write-off sample. These finns, as shown in Table 9, have smaller goodwills than their write-off counterparts.

Furthennore, Table A. 4 in the Appendix additionally breaks down the infonnation provided in Table IO by industry. The analysis indicates that the most substantial write-offs (in absolute dollars) are recorded in the communications industry (median= \$393 million), followed by the miscellaneous manufacturing (\$246 million) and special trade contracting (\$243 million) industries. Write-offs in the latter group also represented the largest percentages of total assets (23.7%) and of the book value of equity (44.1%). However, these industries represent only a small fraction of the write-off sample. Large writeoffs were also taken by the 18 finns in the chemicals industry (median= \$45 million) and by the 16 finns in the industrial machinery/equipment industry (median = \$50 million). However, because finns in these industries are typically large, these write-offs are minor in relative tenns. The highest percentages of goodwill were written off in the eating/drinking places (median = 62.6%), rubber/plastics (60.0%), and amusement/recreation ( 56. 7%) industries.

1231 Extreme amounts such as the one recorded by AOL Time Warner are not represented here because these firms were eliminated from the sample during the sample selection procedure.

Table 11 reports the distribution of fiscal year-ends among the firms in the four samples in order to assess whether calendar-year firm are more or less likely than other firms to record an SFAS 142 goodwill write-off upon transition.

#### Total firms available represent those firms for which information on the fiscal year end 2001 is observed.

#### **Table 11: Distribution of fiscal year-ends**

This table shows that a higher proportion of the write-off firms have December year ends (79.5%) than would have been expected based on the percentages of calendar-year firms in the other sub-samples. This finding might indicate that firms with deviating year ends were reluctant to record transitional write-offs because they were unsure whether investors would be aware of their transitory nature. Speculating further, management of calendar-year firms observed (neutral and or positive) market responses to early adopters' write-offs and thus might have overcome any reluctance to incur these charges.

Table 12 provides information on whether firms in the four samples differ systematically based on whether their MTB ratios at the end of 200 I are higher or lower than one. Of special interest here is a comparison of the write-off and non-write-off samples because economic factors hypothesis I implies that firms with MTB ratios below one are more likely than others to have impaired goodwill.

Consistent with what the impainnent test procedure in SFAS 142 would suggest, finns in the write-off sample more frequently have MTB ratios below one than do non-write-off finns. This difference is highly significant, 1232 which further corroborates the notion that transitional SFAS 142 goodwill write-offs more clearly reflect economic developments rather than financial reporting incentives.

Table A. 5 in the Appendix explores this notion further. In addition to significant differences in MTB ratios, the MTB~l finns appear to be significantly smaller, on average, most notably in tenns of the market value of equity. They are also more reliant on debt financing than their MTB> l counterparts, and underperfonn them significantly in tenns of stock return. These findings suggest that write-offs in the MTB~l group reflect economic problems experienced by smaller finns. As of the transition date, the MTB> l finns, on average, had MTB ratios (median= 1.78) almost as high as those in the nonwrite-off sample (l.88) and outperfonned them in tenns of stock return. This suggests that economic causes for write-offs recorded by MTB> l finns are not so readily apparent. Possibly, these large, successful finns, concerned about the potentially adverse effects of regular write-offs becoming necessary in future periods, take the opportunity of accelerating write-offs into the transition year. Regarding the development of the MTB ratio over time (not tabulated), the median MTB ratio for the first group declined from 1.78 in 2001 to 1.66 in 2002, while that of the second group rose from 0.57 in 2001 to 0.72 in 2002. This diametrical development suggests that write-offs in the first group were perceived negatively, possibly as indicative of future problems. Conversely, those reported by the second group might have been viewed as part of a restructuring effort, promising future improvement, or simply as reflections of past problems that had already been impounded in stock price.

Financial reporting incentives hypothesis 4 predicts that managers' desire to reach earnings targets influences finns' transitional SFAS 142 goodwill write-off behavior. In Table 13, the write-off and non-write-off samples are compared based on the frequency with which sample finns' earnings figures *before* the transitional SFAS 142 goodwill write-off are above or below different earnings targets. Two earnings targets are considered: Panel A reports on the frequency with which finns in the four samples meet priorperiod net income, while Panel B contains equivalent infonnation for an earnings target embodied by positive net income.

1232 An AN OVA F-test on equality of means yields an F-statistic of 49.853 (p-value = 0.000).

#### **Table 13: Pre-write-off earnings performance relative to earnings targets**

The results provide weak evidence that write-off behavior is influenced by the desire to avoid losses (Panel B). Write-off firms avoid pre-write-off net losses less frequently than do non-write-off firms, although the difference between the two groups is not strictly significant. 1233 Significant results are also not found where prior-period net income is considered as the relevant target (p-value = 0.340).

In Table 14, further evidence is provided on financial reporting incentives hypothesis 4. The transitional write-off amounts (as a percentage of total assets) are compared between subgroups of the write-off sample that are defined based on whether a firm meets or falls short of a simple earnings target.

#### **Table 14: Comparison of write-off amounts depending on earnings performance**

A pattern similar to that observed in Table 13 above emerges: Where firms are unsuccessful in meeting their net income-based earnings targets, they appear, on average, to record large write-offs suggestive of a "big bath" strategy. Write-offs are significantly larger for net loss firms than they are for firms that meet or beat this specific target (Panel

,m An ANOVA F-test on equality of means yields an F-statistic of2.324 (p-value = 0.128).

B). 1234 This relation, albeit insignificant, is also found where prior-period net income is used as the relevant target (p-value = 0.224).

Table 15 provides information on whether non-write-off firms and write-off firms differ systematically depending on whether they experienced CEO changes in the years 2001 or 2002 (financial reporting incentives hypothesis 7a).

#### **Table 15: Frequency of CEO changes**

It appears that CEO changes, on average were more frequent in write-off firms than they were in non-write-off firms during both 2001 and 2002. Due to small sample size, these differences are not found to be statistically significant. 1235 This is also true when the occurrence of CEO changes in *either* year is considered (p-value = 0.325). The stable finding in prior literature that "big bath" behavior frequently coincides with top management turnover has mostly been documented using samples of firms where (routine or nonroutine) executive changes had been observed, ensuring that sufficiently large samples of such firms were available. 1236

Financial reporting incentives hypothesis 8 suggests that firms with low credit rating might be reluctant to record transitional SFAS 142 goodwill write-offs out of concern about the resulting financial statement effects and any negative repercussions on credit rating and the cost of capital. Therefore, it would be expected that highly rated firms are more frequent in the write-off sample, and vice versa. Table 16 reports the distribution of rating categories at the end of 2001.

1234 The adjusted x2-test of equality of medians generates a test statistic of 4.229 (p-value = 0.040).

*1235* For 2001 (2002), an ANO VA F-test of equality of means yields an F-statistic of0.006 (0.017). The pvalues are 0.936 and 0.898, respectively.

1236 Refer to section <IP_ADDRESS>.6 above.

#### **Table 16: Distribution of rating categories**

Since rating categories represents ordinal (rank) numbers, the conditions for calculating the mean and standard deviation are not given. 1238 With this caveat in mind, the standard deviation is reported as a crude proxy for the distribution of the rating categories. Based on the median and on visual inspection of the distributions, no support for financial reporting incentives hypothesis 8 is found. 1239

1237 Codes no. I, 3, 22, and 25 are unassigned. Code no. 28 is labeled "not meaningful". Refer to S&P (2001). The rating categories C (24) and CI (26) are not represented in the data.

1238 See, for example, Bamberg and Baur (2002, pp. 17, 21 ).

1239 The summary figures must be interpreted with caution because of the lacking categories.

### *5. 4 Regression analysis*

### 5.4.1 Methods employed in studies of write-off determinants

Most of the studies related to the determinants of discretionary asset write-offs 1240 employ regression analysis, the specific variant of which depends on whether or not the dependent variable is observable on a continuous and unrestricted scale. While the fundamentals of regression analysis are not explicitly discussed here, 1241 specific issues frequently encountered in discretionary write-off studies and relevant to the regression analysis below are the subject of this section.

Regression analysis is appropriate when analyzing hypothesized functional relations between a dependent variable 1242 and one or several independent variables. 1243 For example, researchers interested in the causes of discretionary asset write-offs hope to find a statistically significant relation between the existence and/or amount of a write-off (the dependent variable) and certain explanatory variables that represent factors hypothesized to have caused that write-off and/or influenced its amount (independent variables).1244 Conditional on the nature of the dependent variable, the ordinary least squares (OLS) estimator might or might not be the optimal estimator.1245 Below, specific problems stemming from the dependent variable being, first, a dichotomous variable (i.e. the decision whether or not to write off at all) or, second, a censored variable (i.e. the amount written oft) are discussed.

1240 Refer to section 3.3 above.

1241 For excellent textbook expositions of regression analysis, refer to Backhaus et al. (2003, ch. I), Bamberg and Baur (2002, pp. 42-51), Greene (2003), Gujarati (2003), Kennedy (1998), von Auer (1999), and Winker(l997, ch. 7).

1242 Common synonyms include explained variable, predictand, regressand, response, and endogenous variable. See Gujarati (2003, p. 24). However, the term "response" is inadequate in this context because it suggests a causal relation, which cannot be readily ascertained in discretionary asset write-off studies.

1243 The terms explanatory variable, predictor, regressor, stimulus, and exogenous variable are frequently used synonyms. See Gujarati (2003, p. 24). The term "stimulus" should be avoided for the same reason given in connection with the term response.

1244 Similarly, authors analyzing the effects of discretionary asset write-offs attempt to ascertain a statistically significant relation between a market-based metric (the dependent variable) and certain explanatory variables related to write-off behavior (independent variables).

1245 For an overview, refer to Greene (2003, ch. 2), Gujarati (2003, ch. 3), and Kennedy (1998, ch. 2).

#### **<IP_ADDRESS> Qualitative dependent variables: The write-off decision**

If the researcher intends to distinguish statistically between two separate groups, the dependent variable represents qualitative as opposed to quantitative characteristics. In discretionary asset write-off studies, for example, the data consists of firms belonging to two groups: Write-off firms and non-write-off firms. The researcher intends to relate the probability of observing a write-off, regardless of the specific amount, to potential explanatory factors. The dependent variable is a *binary* or dichotomous variable, referred to as WO\_ D, which assumes a value of one where a firm decides to record a write-off, and zero otherwise. This indicator variable partitions the sample into two subgroups: Writeoff firms, i.e. firms that decided to record a non-zero write-off (WO\_D = **l),** and nonwrite-off firms, i.e. firms that have not recorded any write-off (WO\_ D = 0).

In this case, special estimating problems make applying OLS methodology inappropriate.1246 If WO \_D is regressed on the explanatory variables, the predicted values should fall within the 0-1 interval because, for each firm, the predicted value would be interpreted as the likelihood that the firm, given its characteristics represented by the explanatory variables, is a write-off firm. However, the estimated OLS line ( or, in a multivariate context, the multidimensional regression hyper space) will also yield estimated probabilities that are outside the 0-1 range. 1247 Under this *linear probability model,* predicted values for WO\_D that are larger than one would converted to one, and those that are smaller than zero, to zero. 1248

However, this conversion implies that there is no difference between those firms for which the probability of them being a write-off firm was estimated at l and those for which the probability was estimated at, say, 2.5. Also, estimates such as zero and one suggest that outcomes, for which certainty does not exist, are predicted with certainty. To alleviate this concern, a function is required that "squeezes" the probability estimates asymptotically inside the 0-1 range, without creating estimates that are exactly zero or exactly one. In other words, the 0-1 observations on the dependent variable are interpreted as dichotomous realizations of a "true" *latent* variable. In the context of discretionary asset write-off studies, this latent variable could be interpreted as the amount that

1246 For much of the following, refer to Kennedy (I 998, pp. 233-5).

1247 For a graphical depiction of this problem, see Kennedy (1998, p. 234) and Maddala (2001, p. 320).

1248 See Maddala (2001, pp. 318-20) and Maddala (1991, p. 789).

the firm would "desire" to write off, which is coded as one, if positive, and as zero, if negative. 1249

Two methods are frequently used: In the *Probit* model, the probability of obtaining the dependent variable is written as a cumulative normal function of the explanatory variables, while in the *Logit* model, this probability is written as a logistic function of the explanatory variables. Estimation itself is undertaken using a maximum likelihood (ML) procedure. 1250 Maddala (2001) notes that the cumulative normal and the logistic distributions are so close to each other that the two methods are likely to generate similar results\_ 1251

Although Probit and Logit are frequently used in classificatory accounting research, 1252 few discretionary asset write-off studies actually apply this methodology, most being interested in the write-off *amount* or other quantitative (in most cases, continuous) variables. Examples include Chen and Lee (1995), Kim and Kwon (2001), Loh and Tan (2002), and Strong and Meyer (l 987). 1253 Discriminant analysis is occasionally used as an alternative methodological approach. 1254 Comparisons of the OLS and Probit/Logit approaches in accounting choice studies are provided, respectively, by Noreen (l 988) and Stone and Rasp (1991).

#### **<IP_ADDRESS> Limited dependent variables: The write-off amount**

In several of the studies presented in section 3.3 above, it is tested whether the amount written off can be statistically linked to economic factors and/or variables that stand for financial reporting incentives. In these studies, the write-off amount, commonly scaled by a size proxy such as total assets and expressed as a positive number (hereafter, WO\_A), is the dependent variable in a regression on one or several explanatory variables. WO\_ A does not have an unlimited range. Because most of the studies analyze a

1249 See Maddala (200 I, p. 322).

1250 See Greene (2003, p. 670). For an in-depth explanation of this topic area as well as a discussion comparing the relative merits of the Logit and Prob it specifications, refer to Backhaus et al. (2003, ch. 3), Kennedy (1998, pp. 237-40), Maddala (1983, esp. pp. 22-7), and Maddala (1991, pp. 788-94).

1251 See Maddala (2001, p. 323).

1252 See Noreen (1988, p. 119 fu. I).

1253 See Chen and Lee (1995, p. 108-9), Kim and Kwon (2001 , pp. 10-3), Loh and Tan (2002, pp. 142-8), and Strong and Meyer ( 1987, p. 651 fu. 18).

1254 See, for example, Strong and Meyer (1987, p. 649-51). Maddala (1991 , pp. 788-94) contrasts Logit and Probit analysis with discriminant analysis.

setting in which upward revaluations are prohibited, 1255 WO\_ A is either positive or zero. In this context, the OLS estimator is not appropriate because the dependent variable is limited in its range, causing OLS estimates to be biased as well as inconsistent, i.e. the estimates are biased even asymptotically. 1256

A censored regression model is applicable when the data on some observations on the dependent variable is *censored* or unobserved, meaning that for some known values of the independent variable(s), the corresponding exact values of the dependent variable are not observable for some reason. It is only known whether they are above or below a certain threshold. 1257 However, these observations remain included in the sample, and the unobservable values of the dependent variable are coded to assume some limit value, frequently zero. 1258 In other words, the known observations on the dependent variable are a function of a "true" *latent* variable, 1259 which is partially unobservable. The observable dependent variable, y, assumes the value of this latent variable, y\*, where the latter is above (below) the censoring limit, but is censored to assume the value of the censoring limit where the latent variable is below (above) the censoring limit. 1260 In the case of the censoring limit being zero, the probability of observing y = 0 is equal to the probability ofobserving y\* ~ 0.

Applied to the transitional SF AS 142 setting, the write-off amount actually observed, TGWO\_A, is either zero or positive. 1261 However, TGWO\_A values smaller than zero may theoretically exist in cases where the impairment testing procedure shows that not a decline, but rather an appreciation in the value of goodwill has occurred. The unob-

1255 Riedl (2002) analyzes write-offs under SFAS 121 and under the pre-SFAS 121 accounting regime. SFAS 121.11 explicitly prohibits "(r]estoration of previously recognized impairment losses." Previously, the general historical cost framework of GAAP also provided no basis for asset write-ups over and above historical cost.

1256 More technically, the coefficient estimates are biased because the expected value of the disturbances is not zero. See Gujarati (2003, p. 616), Kennedy (1998, p. 114), and Maddala (I 991, p. 795). According to Greene (2003, p. 762), "(c]onventional regression methods fail to account for the qualitative difference between *limit* (zero) observations and *nonlimil* (continuous) observations" (emphasis in original). For much of the following, refer also to Kennedy ( 1998, ch. 16).

1257 Refer to Maddala (I 983, pp. 3-6). The problems associated with censored data are also discussed in Greene (2003, pp. 761-80), Gujarati (2003, pp. 616-9), and Kennedy (1998, ch. 16).

1258 In the words of Greene (2003, p. 761), "values in a certain range [here: below zero] are all transformed to (or reported as) a single value (here: zero]."

1259 See Greene (2003, p. 764).

1260 Stated technically, where the censoring limit is zero, y = y\* ify\* > 0 and y = 0 otherwise. See Maddata (1983, p. 149) and Greene (2003, p. 764).

1261 This implies that, as is custom in the literature and also the chosen approach in this study, write-offs are measured as positive numbers.

served, latent variable, then, is the amount of change in goodwill value. SF AS 142, prohibiting upward revaluations such as write-off reversals (i.e., negative values of TGWO\_A), in effect censors TGWO\_A to exclude values below zero. It is worth noting that, in this example, the threshold or cut-off point of zero for TGWO \_A is known and identical for all observations. 1262 Therefore, negative values of TGWO \_ A cannot be observed although firms exist that would have recorded upward asset revaluations, had it not been for the restriction imposed by SFAS 142. The explanatory variables for these firms are available. The data is censored at zero.

The applicable procedure for regression analysis using censored data was developed by Tobin (1958) and is called the Tobit model. 1263 The Tobit model is a censored normal regression model. 1264 Here, estimation is undertaken employing an ML estimator. 1265 The parameter estimates generated by an ML estimator are those for which the probability of having obtained the particular sample used is greatest. 1266 Specifically, the canonical Tobit model is appropriate in this setting, since the dependent variable is a censored variable with a left limit value of zero and no right limit value. 1267

Studies also applying this methodology include Riedl (2002), Francis, Hanna and Vincent (1996), and Loh and Tan (2002). Riedl (2002) examines the determinants of the amounts of long-lived asset write-offs. 1268 He assumes that his data is censored, arguing that "the explanatory .. variables are available for all the observations, but the explained .. variable is equivalent to O because it falls below some .. threshold value .. for a subset of observations". 1269 However, he documents robustness of his findings to different distribu-

1262 For settings in which the threshold is either unknown, different for each observation, or both, refer to Maddala (1983, pp. 3-6).

1263 See Greene (2003, pp. 764-6), Kennedy (1998, pp. 250-2, 255-6), Maddala (1983, pp. 151-162), and Maddala (1991, pp. 794-7).

1264 SeeMaddala(l983,p. 151).

1265 Characterizing it as "rather involved", Gujarati (2003, p. 617) considers ML estimation beyond the scope of his "Basic econometrics" textbook.

1266 See, for example, Greene (2003, ch. 17), Gujarati (2003, pp. 114-7), and Kennedy (1998, pp. 21-2). For a more technical definition, see Kennedy ( I 998, pp. 30-1 ).

1267 Regression analysis using ML estimation requires specifying the distribution of the error term. In the context of this study, no specific distribution of the errors is known. Since, according to the central limit theorem, large numbers tend to be approximately normally distributed, the normal distribution is assumed to describe the error terms. See Kennedy (1998, p. 30). This corresponds to the assumption used in the Tobit model. See Maddala ( 1983, p. 151 ).

1268 His variable WOT Ai" denoting the net of tax amount of the asset write-off recorded by firm i in period t, divided by beginning-of-period total assets, is largely equivalent to WO\_ A defined above.

1269 Riedl (2002, p. 8).

tional assumptions, *inter a/ia* an OLS model. 1270 Arguing against the applicability of the Tobit specification in his setting, he maintains that, "because GAAP during [the] sample period does not allow firms to record .. increases in asset values, the distribution is not censored", 1271 implying that the data is *truncated* instead.1272 In the case of truncated data, calling for a truncated regression model, 1273 an observation is entirely omitted from the sample when the value of the dependent variable falls above (or below) the threshold discussed above. Applied to the write-off setting, this would be a case where only writeoff firms are considered and non-write-off firms are excluded from the sample. In this context, the Tobit model is not applicable and a different ML technique should be used.1274 Under the Tobit or censored regression model, in contrast, non-write-off firms are included in the study, but the write-off amount is censored to zero.

While researchers are prudent to conduct sensitivity analyses by using different specifications, Riedl's latter line of reasoning with respect to the non-applicability of the Tobit model does not appear to be valid. As noted above, asset write-offs are supposed to reflect economic value declines of assets. Riedl assumes that economic value increases do not occur in the sample just because the accounting regime happens to have determined an arbitrary cutoff point at zero, beyond which economic value fluctuations cannot become reflected in accounting numbers. In reality, write-offs may range above (fair value < book value) or below (fair value> book value) zero, implying that a data set that limits observations on WO \_A to assume values of zero or above is censored. 1275 The fact that Francis, Hanna and Vincent (1996) also apply a Tobit procedure without explicitly expounding the problem whether or not their data is censored might be interpreted as confirming this notion. 1276 However, Maddala (1991) cautions researchers about using the Tobit model inappropriately. Because it is "tempting to use the tobit model every time

1270 Among the studies using an OLS specification are Cotter, Stokes and Wyatt (1998) and Segal (2003), who do not, however, explicitly discuss the relative merits of the OLS and Tobit specifications.

127 1 Riedl (2002, p. 25). Results for both specifications are reported in table 3 panel A and B, respectively.

1272 The distinction between censored and truncated data is discussed in Kennedy (1998, pp. 250-1), Maddala (I 983, pp. 1-3, 149-51), and Maddala (1991 , pp. 794-7).

1273Refer to Maddala (1983, pp. 1-3, 165-70).

1274 Refer to Kennedy (1998, pp. 251, 257-8).

1275 As examples of data where use of the Tobit model is inappropriate due to the data being truncated, not censored, Maddala (1991 , pp. 795-6) refers to individual choices, e.g. automobile expenditures and hours worked, for which there is no way that the data can be negative. In this case, however, "negative" discretionary asset write-offs would be most likely be observed if standard setters did not arbitrarily prohibit write-ups and write-off reversals. For a slightly different take, refer to Gujarati (2003, p. 616), who interprets data on housing expenditures as censored.

1276 See also Riedl (2002, p. 25). Francis, Hanna and Vincent's (1996, p. 125) variable WRITE-OFF; denotes the write-off recorded by firm i, deflated by total assets.

one has a bunch of zero (or other limit) observations on [the dependent variable] ... there are many more examples of the inappropriate use of the tobit model than of its correct use."1211

### 5.4.2 Summary of hypotheses

Table 17 provides a summary of the hypotheses developed in section 5.2 above, indicating that the majority of predictions are relevant to both the write-off decision *and* the write-off amount, while some relate to only one of those aspects.

#### **Table 17: Summary of hypotheses**

In the following section, hypotheses pertaining to the dichotomous write-off *decision* are tested using primarily Probit regression methodology, whereas hypotheses about the write-off *amount* are subjected to Tobit regression analysis. While the economic factors hypotheses as well as the financial reporting incentives hypotheses 1-4 are tested on the whole goodwill sample, sub-groups of that sample are relevant for financial reporting incentives hypotheses 5-8.

1277Maddala (1991, p. 795).

### 5.4.3 Analysis of the write-off decision

As discussed in section <IP_ADDRESS> above, ML estimation using the Probit model is appropriate where determinants of the write-off *decision,* expressed as the dichotomous dependent variable TGWO\_D, are analyzed. Three multivariate models are considered in tum: Model D 1 represents explanatory variables derived from the economic factors hypotheses as well as the financial reporting incentives hypotheses 1-4. It is tested using all firms in the goodwill sample. Model D2, considering only those firms in the goodwill sample that incur a pre-write-off loss in 2002 (TARGET\_Z = 0), contains the "big bath" variables suggested by financial reporting incentives hypothesis 7. Model D3 is a variant of model D1 with RA TING as an added explanatory factor. Consequently, only those (highprofile) firms for which S&P debt ratings are available are included in regression tests of model D3. The control variables defined in section 5.2.4 are included in all models.

#### **<IP_ADDRESS> Full-sample test**

The correlation matrix for model D1 (reported in Table A. 6 in the Appendix) indicates that, on the whole, most of the variables are largely uncorrelated with each other. Notable positive correlation exists, *inter a/ia,* between TAR GET \_PP and ~PWOROA (both reflect the change in accounting earnings over the prior year), DTE and MTB (highly leveraged firms apparently trade at a premium), HERF and MSH (market share per firm is higher, on average, in concentrated industries), and SIZE and MSH (larger firms have higher market share).

Probit regression results for model D1 are reported in Table 18 below.

**Variable definitions** (refer to Table 6 for further details):

**Table 18: Probit regression results for model Dl (full sample)**

The likelihood ratio **(LR)** test statistic of 122.04 indicates that the overall model contributes significantly to explaining variation in the dependent variable. 1278 The McFadden R2, an analog to the R2 in the OLS regression context, is l 0.4%.1279 The majority of coefficient estimates carry the expected signs. Both MTB and RET have a highly significant (p-values = 0.016 and 0.002, respectively), negative association with the probability of observing a transitional SFAS 142 goodwill write-off. For the change in ROA over the pre-adoption period, however, this relation is insignificant. 1280 Also according to expectations, the proportion of assets-in-place is positively associated with write-off probability (p-value = 0.072). To the extent that high assets-in-place stand for the existence of an accounting-based compensation plan, this result suggests that management operating under such plans are more likely to record a (potentially compensation-neutral) transitional write-off, possibly to decrease the likelihood of having to record future, compensation-decreasing write-offs. On the other hand, it must be noted that a pure earningsmanagement explanation might be premature since assets-in-place potentially proxy for other factors, such as industry membership. The coefficient estimate on DTE neither is significant nor does it have the predicted sign, suggesting that the extent to which a firm's DTE ratio is below the industry median does not influence the likelihood of observing a write-off. 1281 All three political visibility proxies, for which no signs were predicted, are found to be positively associated with write-off probability, albeit only MSH significantly so. A possible earnings-management interpretation is that dominant, highprofile firms, ceteris paribus, accelerate write-offs in order to decrease bottom-line income and firm size, reducing potential exposure to regulatory scrutiny. However, alternative economic interpretations include the notion that such firms to a large extent had to

1278In the Probit and Logit models, the LR test is used to assess whether all the slope coefficients are zero. See Greene (2003, p. 678). LR tests for all of the independent variables individually (not tabulated) produce results largely identical to those indicated by the corresponding z-statistics. The zstatistic is the equivalent to the OLS I-statistic in estimating situations where normality holds only asymptotically, as is the case in the Probit, Logit, and Tobit models. Z values follow a standard normal distribution. See Gujarati (2003, p. 619). They are obtained by dividing the coefficient estimate by its standard error.

1279This measure is selected from a multitude of goodness-of-fit measures applicable in binary models. It assumes values between zero and one and increases as the fit of the model increases. See Greene (2003, pp. 683-6), Maddala (2001 , pp. 327-9), and Veall and Zimmermann (1996).

1280Substituting the change in sales from 2000 to 2001 for ~ROA as an alternative accounting-based perfonnance measure (not tabulated) yields a significantly negative coefficient estimate (p-value = 0.008). The other coefficients are largely unchanged. However, this variable is likely to capture changes in firm size rather than firm profitability, and is therefore difficult to interpret. Another substitute, the change in income before extraordinary items from 2000 to 200 I, generates results equivalent to ~ROA (not tabulated).

1281When the pre-write-off DTE ratio at the end of2002 is used alternatively, the coefficient estimate has the expected negative sign but remains insignificant (p-value = 0.363).

rely on external growth, forcing them to overpay in acquisitions.1282 Regarding the target variables, both coefficient estimates carry the expected signs, which might indicate that especially prior-period net income (p-value = 0.061) is an earnings target that management deems important to obtain. The control variable FYR exhibits a significantly positive relation with the likelihood of observing a write-off. Calendar-year firms are more likely to record transitional write-offs, although the reasons for this finding can only be speculated upon. 1283 Finally, the significant positive coefficient estimate on GW indicates that a write-off is more likely to occur where goodwill is a significant portion of the firm's overall asset base. On the whole, consistent with the tenor of the descriptive evidence presented in section 5.3.3 above, this finding suggests that economic factors strongly contribute to explaining variation in write-off probability, with write-offs being more frequent in firms that are viewed unfavorably by the market. The effects of financial reporting incentives, on the other hand, are open to several explanations.

Since economic factors hypothesis I suggests that firms for which MTB> I have no economic inducement to record a write-off,1284 model DI is re-estimated using only this subsample (not tabulated). The number ofobservations drops to 921 firms, only 124 (13.5%) of which record a transitional SF AS 142 goodwill write-off. As expected, the economic factors lose their explanatory power in this sub-sample. From among the other explanatory factors, AIP (p-value = 0.075) and TAR GET \_PP (0.032) yield coefficient estimates that are significant and carry the expected signs. MSH is also highly explanatory (p-value = 0.000), suggesting that dominant firms are more likely to report write-offs. Overall, these results are consistent with the notion that economically successful firms' write-offs are motivated by factors other than goodwill impairment.

1282 In any event, MSH to some extent seems to capture size effects, which is also indicated by the fact that the SIZE variable itself is insignificant. The insignificant, positive coefficient estimate on the SIZE variable is stable to alternative measures such as total assets (p-value = 0.168) and sales (0.475). However, the estimates are significantly positive when the logarithmized variants SIZE\_LOG are used (not tabulated). In that event, the other inferences remain unchanged. Since the coefficient estimate on MSH also retains its significance, it can be concluded with some confidence that MSH captures earnings-management motivations.

1283 Refer to the interpretation of Table 11 above.

1284 See also the descriptive evidence presented in Table 12 and Table A. *5.* Again, it must be cautioned that the entity-level MTB ratio is a potentially noisy proxy for goodwill impairment in diversified, multi-segment firms.

#### **<IP_ADDRESS> Tests for robustness**

Applying a Logit specification to model DI yields widely similar results (not tabulated). 1285 The only exception is that the coefficient estimate on AIP is insignificant in this setting (p-value = 0.235). These similarities are consistent with Maddala's (2001) statement that the Probit and Logit models generate comparable estimates. 1286

In order to restrain the influence that outlying observations on the regressors have on the inferences, model DI is re-estimated using a truncated sample (not tabulated). Truncation is achieved by excluding those firms representing the bottom 5% and the top 5% of the respective distributions. As a result of truncation, the sample size drops to 687 firms, I 06 (15.4%) of which record a transitional SFAS 142 goodwill write-off. Apparently, writeoff firms are overrepresented in the outlying firms (21.0%). Applying the Probit estimator to model DI based on this truncated sample yields results that are somewhat different from the initial estimate reported in Table 18. While the coefficient estimate on MTB remains highly significant with the expected negative sign (p-value = 0.000), RET and ~ROA lose the predicted sign and are insignificant. This latter fact might indicate that economic developments resulting in the write-off had not yet been captured by previous performance measures. AIP, while retaining the predicted sign, is also no longer found to have significant explanatory power. Again, the size effect is captured by SIZE\_LOG (pvalue = 0.025), rendering the coefficient estimate on MSH insignificantly positive (0.537). Both target variables are insignificant with the expected sign. Overall, the writeoff decision in the outlier-adjusted sample still appears to be predominantly associated with the MTB ratio, suggesting that financial reporting motivations play only a minor role in "average" firms' write-off decisions.

#### **<IP_ADDRESS> Sub-sample tests**

Probit regression results for model D2 are reported in Table 19 below. 1287 Because SPEC is only observed for a small minority of firms, this variable is omitted from the regression equation. 1288

1285Despite the theoretical shortcomings of OLS regression in the context of dichotomous dependent variables, Noreen **(1988)** documents empirically that, for certain kinds of data and sample sizes commonly encountered in classificatory accounting studies, OLS performs as good as the theoretically superior Probit model. However, because it is not certain that the write-off decision setting at hand corresponds to those investigated by Noreen ( 1988), OLS methodology is not applied here.

1286See Maddala (2001, p. 323).

1287The correlation matrix is reported in Table A. 7 in the Appendix.

**Variable definitions** (refer to Table 6 for further details):

### **Table 19: Probit regression results for model D2 (partial sample of firms missing earnings targets)**

The analysis indicates that financial reporting incentives hypothesis 7 does not appear to predict the write-off decisions of finns with pre-write-off net losses. While the overall fit of the model is significant (p-value = 0.000), LR tests for the explanatory factors individually (not tabulated) yield insignificant results. These findings are stable in a Logit specification (not tabulated). The explanatory power that the model does exhibit can be traced back to the control variables SIZE and GW. This finding suggests that there is no significant association of "big bath"-type incentives and/or restructuring situations with the write-off decision in finns that fail to avoid net losses in the adoption year of SF AS 142.

Finally, the variable RA TING is added to the set of explanatory variables in model DI, yielding model D3. As shown in Table A. 8 in the Appendix, this variable is highly correlated with several of the other regressors. Most notably, it is negatively associated with SIZE and MSH, suggesting that larger, more dominant finns are viewed more favorably

1288 Its inclusion truncates the sample to 51 observations (TGWO\_D = 0: 32; TGWO\_D = 1: 19). Probit regression analysis of this smaller sample (not tabulated) yields no significant coefficient estimates.

by S&P .1289 Conversely, positive correlation is found between AIP and DTE. Probit regression results for model D3 are reported in Table 20.

1289 Recall that high scores on the RATING variable represent low rating categories.

#### **Table 20: Probit regression results for model D3 (partial sample of firms rated by S&P)**

Restricting the analysis to a set of goodwill firms for which S&P domestic issuer credit ratings (RA TING) are available yields a completely different sample. Only the largest, most visible firms are represented, which decreases the number of observations to a total of 447.1290 The write-off firms (114) represent a larger portion of the sample (25.5%) than is the case in the overall goodwill sample (17.9%). Interestingly, economic factors no longer significantly explain the write-off decision in these firms. The formerly significantly negative coefficient estimates on both MTB and RET are insignificant, while still negative. While most of the other coefficients are roughly unchanged, avoiding a prewrite-off net loss (TAR GET\_ Z) appears to be a powerful incentive for these high-profile firms (p-value = 0.031 ).1291 The RA TING variable itself, however, does not appear to be associated with write-off probability, indicating that the costs of recording a transitional write-off are not a linear function of the rating category. 1292 Consistent with the findings

1290For example, these firms are, on average, more than six times larger in terms of the market value of equity (median= \$1,747 million) than are firms in the overall goodwill sample. They also have significantly higher market shares (median= 1.3%). Their median MTB ratio is 2.00.

1291Recall that, in model DI, the coefficient estimate on this variable is insignificant.

1292Possibly, downgrades are more costly for firms in the higher rating categories because such events potentially trigger large increases in financing cost. Likewise, these costs might be especially high around certain cutoff points, e.g. where a firm is in jeopardy of losing its investment grade rating.

discussed above, financial reporting incentives appear to play a more important role in large, high-profile firms than they do in the overall sample.

Re-estimating model D3 using only firms for which MTB> 1 generates results similar to those discussed in the context of model D 1 above (not tabulated). 1293 In this setting, economic factors largely lose their explanatory power, whereas TARGET\_Z (p-value = 0.029) yields a highly significant coefficient estimate that carries the expected sign. The findings are consistent with the interpretation that these high-profile firms avoid writeoffs where in perceived jeopardy of recording a net loss. The RA TING variable, again insignificant, appears to proxy for the high profiles and capital market following of the firms in this sub-sample.

### 5.4.4 Analysis of the write-off amount

The determinants of the write-off *amount,* expressed as the left-censored dependent variable TGWO\_A, are analyzed using the Tobit model. 1294 Four separate models are estimated: Model A 1 contains explanatory variables derived from the economic factors hypotheses as well as financial reporting incentives hypothesis 2. It is tested using all firms in the goodwill sample. Model A2 is used to investigate financial reporting incentives hypothesis 5, considering only those firms in the goodwill sample that meet the zero prewrite-off net income target in 2002 (TARGET\_Z = 1). Model A3, a test of financial reporting incentives hypothesis 6, is applied to the other half of the goodwill sample (TARGET\_Z = 0) in an attempt to ascertain whether "big bath"-type write-offs are limited by the available amount of equity. 1295 Finally, model A4 is an analog to Probit model D3, where RA TING is added as an explanatory factor to the initial model, A 1.1296

1293 This sample comprises 368 observations, of which 79 firms (21.5%) record a write-off.

1294 Tobit regression coefficients must be interpreted somewhat differently from those obtained in OLS estimation. Refer to Kennedy (1998, pp. 255-6) and McDonald and Moffitt (1980, pp. 318-9). The Tobit slope coefficient indicates the degree of change in the independent variable necessary to bring about a one-unit change in the *desired* transitional SFAS 142 goodwill write-off. The expected *actual*  change in write-off is comprised of two components, owing to the fact that the write-off amount is left-censored at zero. Due to the censoring limit, observing any change in write-off at all for a given firm is conditional on the probability with which that firm is a non-limit observation, i.e. a write-off firm. Consequently, the components are, first, the change in expected write-off of above-limit observations times the probability of being above the limit, plus, second, the expected write-off of abovelimit observations weighted by the change in probability of being above the limit

129s To exclude firms for which the amount of equity is not a limiting factor, this test is restricted to firms where goodwill > equity. The restriction of positive equity imposed during the sample selection procedure (see Panel A of Table 7) is removed to allow for write-offs to cause negative equity.

1296 Again, only those (large) firms for which S&P debt rating data is available are included in this model.

#### **<IP_ADDRESS> Full-sample test**

Tobit regression results for model Al are reported in Table 21 below.

**Table 21: Tobit regression results for model Al (full sample)**

The results yielded in the Tobit specification correspond to a large extent to those found for model DI in the dichotomous setting: The write-off amount is significantly higher when price-based performance measures such as MTB and RET are lower, whereas for .!1ROA, the accounting-based performance variable, such association is not found. 1297 Also, DTE lacks explanatory power. 1298 This finding suggests that economic factors, more significantly than do variables associated with financial reporting incentives, ex-

1297 As in model DI above, substituting the change in sales from 2000 to 2001 for ~ROA as an alternative accounting-based performance measure (not tabulated) yields a significantly negative coefficient estimate (p-value = 0.042), while the other coefficients remain largely unchanged. Likewise similarly, the change in income before extraordinary items from 2000 to 200 I generates results equivalent to ~ROA (not tabulated).

1298 As in model DI, when the pre-write-off DTE ratio at the end of2002 is used alternatively, the coefficient estimate has the expected negative sign but remains insignificant (p-value = 0.304).

plain not only the write-off decision itself, but to some extent also the amount written off. Again, the control variables FYR and GW significantly account for the overall fit of model A 1, 1299 which is evidenced by the high LR test statistic. 1300

Parallel to the procedure adopted in section <IP_ADDRESS> above, model Al is re-estimated including only those firms for which MTB> 1 (not tabulated). In contrast to the dichotomous setting, the economic factors variables MTB (p-value = 0.064) and RET (0.063) retain significant explanatory power for the write-off *amount.* Apparently, write-offs are lower where price-based performance is higher. Furthermore, the SIZE variable exhibits a significantly positive association with the write-off amount (p-value = 0.072), especially when logarithmized (0.001), suggesting that larger firms, on average, record larger (in relative terms) write-offs.

#### **<IP_ADDRESS> Tests for robustness**

A robustness check of the preceding findings is undertaken by re-estimating model Al using an OLS estimator (not tabulated). With the exception of the SIZE variable, all of the coefficient estimates retain their signs from the Tobit setting. 1301 However, the coefficients on all variables but GW become insignificant, indicating that the adjusted R2 of 11.5% is largely attributable to that variable. Considering the methodological discussion in section <IP_ADDRESS> above, Tobit rather than OLS estimation, is appropriate to this censored dependent variable situation. Therefore, the results presented in Table 21 are regarded as relevant.

Corresponding to the procedure used for the binary model D1 in section <IP_ADDRESS> above, the Tobit model Al is re-estimated for the same truncated sample. 1302 The results are very similar to those yielded and explained above. Most notably, MTB remains highly

1299 Parallel to model DI, firm size only contributes significantly to explaining variation in the write-off amount when the logarithmized variant SIZE\_LOG is used (p-value = 0.060).

1300 As in the binary setting, the LR test is an overall significance test for Tobit models as well. See, for example, Kennedy (1998, pp. 60-1, 67-8, 70-1). The LR test statistic has an unknown small-sample distribution, but is asymptotically distributed as a x2. See also Quantitative Micro Software (2002, pp. 451-2). It indicates whether specified (groups of) independent variables contribute to the fit of the model or must be classified as redundant.

1301 As a general rule, the OLS coefficient estimates are approximately obtained by multiplying the Tobit estimates with the proportion of non-limit (i.e. non-write-off) observations in the goodwill sample. See Kennedy (1998, p. 255).

1302 As indicated above, truncation is achieved by excluding those firms representing the bottom 5% and the top 5% of the respective distributions.

significant (p-value = 0.000), consistent with the fact that economic factors are highly associated with the write-off amount even after adjusting for outlying observations.

#### **<IP_ADDRESS> Sub-sample tests**

Table 22 contains the Tobit regression results for model A2. 1303

### **Table 22: Tobit regression results for model A2 (partial sample of firms meeting earnings targets)**

Estimating model A2 yields mixed evidence on financial reporting incentives hypothesis 5, according to which firms that obtain net income-based earnings targets will tend to record transitional write-offs in the amount of the "slack", i.e. the difference between actual pre-write-off earnings and the relevant target. While the results shown in Table 22 suggest such behavior where prior-period earnings are the target (p-value = 0.016), the negative coefficient estimate on DIF \_Z calls for a different interpretation: Apparently, the amount written off decreases in pre-write-off net income, which again indicates an economic, rather than earnings-management explanation for the write-off amount. This

1303 The correlation matrix for this model is shown in Table A. 9 in the Appendix.

notion is further corroborated by the fact that write-off firms are underrepresented in this sub-sample (16.7%). 1304

Results for the firms that fall short of earnings targets are presented in Table 23. 1305

### **Table 23: Tobit regression results for model A3 (partial sample of firms missing earnings targets)**

Goodwill book value, deflated by total assets, measured at the end of 2001.

The coefficient estimate on the EQ variable has the expected sign and is significant at the 5% level. From these results, it appears that financial reporting incentives hypothesis 6 is supported by the data. 1306 Where firms record "big bath" write-offs, the amount of equity seems to represent a behavioral threshold that is difficult to explain in economic terms.

GW

1304 Because DIF \_PP and DIF \_Z are highly correlated (Pearson correlation coefficient= 0.342), the regressions are repeated by including each of these regressors individually (not tabulated). This procedure yields an insignificantly positive coefficient estimate on DIF \_PP (p-value = 0.523), suggesting that the amount by which prior-period net income is beaten, in itself, does not explain variation in the transitional write-off amount. However, the significantly negative coefficient estimate on DIF \_Z is almost unchanged (p-value = 0.000), which validates the initial interpretation.

1305 The correlation matrix for this model is shown in Table A. IO in the Appendix.

1306 Further analysis shows that only five firms recorded transitional SF AS 142 goodwill write-offs that caused their equity to become negative.

Finally, similar to the procedure followed in the Probit setting above, the initial model A I is re-estimated after adding RA TING as a supplementary regressor. The results are shown in Table 24.

TGWO\_Ai =a +P1MTBi +P2RETi +P36ROAi +P4DTEi +P5RATING

**Variable definitions** (refer to Table 6 for further details):

#### **Table 24: Tobit regression results for model A4 (partial sample of firms rated by S&P)**

As in the Probit setting, the economic factors variables lose their initial explanatory power compared to the initial model, while the coefficient estimate on the RA TING variable itself turns out insignificant. Again, this effect arguably relates to the dramatically altered sample composition achieved by focusing on firms rated by S&P.

### *5. 5 Inferences*

### 5.5.1 Summary of findings

Guided by the hypotheses derived in section 5.2 above, the preceding analysis of determinants of transitional SFAS 142 goodwill write-offs comprised three steps: First, descriptive evidence and comparative significance tests were presented in order to portray the sample, deriving preliminary conclusions about differences between goodwill and non-goodwill firms and, primarily, write-off and non-write-off firms. Second, multivariate Probit regression analysis was conducted to assess the aggregate explanatory power of a set of potential write-off determinants for the dichotomous write-off decision. Third, multivariate Tobit regression analysis was employed to explain the transitional SF AS 142 goodwill write-off amount. A cautious interpretation of the results follows. 1307

Transitional SF AS 142 goodwill write-offs, on average, represent significant accounting events in terms of their effects on write-off firms' financial statements. The analysis strongly indicates that write-off behavior predominantly reflects economic circumstances. In the overall goodwill sample, Probit regressions indicate that write-offs are less likely where MTB ratios and stock performance are higher. Using Tobit regression methodology, the same relation is found for the amount written off, which decreases, on average, in firms' price-based performance. On average, write-off firms are also larger and more highly in debt than their non-write-off counterparts. The findings are further consistent with the notion that managers are concerned with avoiding losses and earnings declines. Also, calendar-year firms are more susceptible to write-offs, possibly because they used their opportunity to observe capital market reactions to early adopters' writeoff announcements and judged that transitional write-offs are not penalized by investors. 1308

These findings, especially regarding the MTB ratio, are largely stable to alternative estimation procedures, sub-sample analyses, and the truncation of outlying observations. Despite the enormous discretion that characterizes SF AS 142 especially upon adoption, the impairment-only approach is apparently successful at capturing goodwill impairment at least to some extent, inducing weak performers to record write-offs. 1309 The fact that

1307 Hong, Kaplan and Mandelker (1978, p. 44) accurately point out that "[n]o single study is ever completely convincing on settling a controversy. Problems in sample selection, financial and statistical models, and interpretation ofresults are inherent in all empirical work."

1308 A similar conclusion is reached by Henning, Shaw and Stock (2002, p. 26).

1309 Segal's (2003, p. 29) conclusion is somewhat less optimistic; his comparison of SF AS 121 and SFAS 142 "raises questions with respect to the new rule's ability to deliver."

transitional SF AS 142 goodwill write-offs apparently represent the accounting "echo" of economic declines occurring as long as two years previously is consistent with the assumption that such transitional write-offs partly "catch up" impairments that the weaker SF AS 121 trigger was unable to detect.

A prominent pattern emerging in the sub-sample analysis indicates that the transitional SF AS 142 goodwill write-off behavior of large, dominant firms is clearly different from that of smaller, less "visible" companies. Several proxies of "high profile", including size, market share, and the availability of S&P rating data, are associated with large write-offs even where goodwill impairment is not necessarily apparent *a priori.* In these firms, write-off behavior seems to reflect financial reporting incentives rather than economic developments. Apparently, the transition period is perceived to be a window of opportunity, in which goodwill, the "Sword of Damocles" hanging above many firms' financial statements, can be discarded at low cost. Write-offs are accelerated into the transition period, possibly in order to decrease political visibility, shelter future management compensation, and avoid future hits to earnings that might be viewed more negatively by capital market participants. Nevertheless, this finding is likewise open to different interpretations, including conservatism.

Most of the economic-consequences hypotheses do not appear to be descriptive, possibly due to data availability constraints and the resulting reliance on inappropriate proxy variables. In addition, among the hypothesis not corroborated by the data are those related to "cookie-jar reserving" and, in part, "big bath" behavior. There is only weak evidence that write-offs coincide with CEO changes or restructuring efforts in firms falling short of earnings targets. Likewise, firms beating earnings targets do not appear to coordinate write-off amounts with the available "slack", possibly because financial reporting decisions other than the write-off amount are made after the latter is ultimately determined. However, the data indicates that the amount of equity presents a strong limiting factor for the amount written off.

Overall, the stable core findings suggest that write-off behavior differs with the degree of firms' "visibility" in the capital markets. In smaller, less exposed firms, write-offs seem to mainly reflect difficult economic circumstances, whereas larger, more dominant firms appear to more actively coordinate their write-off behavior with management's financial reporting strategies. However, even where the findings appear to be consistent with earnings management behavior, the availability of alternative, economic explanations rules out premature conclusions. Further, while financial reporting objectives might play a significant role in explaining most firms' *overall* financial reporting behavior in the

SF AS 142 adoption period, it is not necessarily the transitional write-off that is used to achieve them. This fact might in part be explained by the notion that outstanding, nonrecurring income statement items, especially those met with substantial prior attention, are too costly to be suited for certain subtle types of earnings management, such as target accounting. Possibly, other financial reporting incentives that are not captured by my hypotheses explain part of the variation in write-off behavior.

### 5.5.2 Limitations

A possible limitation of this study relates to potential measurement error in the dependent variable. While every attempt was made to ascertain that TGWO reflected nothing but the transitional SFAS 142 goodwill write-off, disclosed as a change in accounting principle, the discussion in section 5.3. l above indicates that other effects might be captured by that variable. To the extent that such measurement error does not introduce systematic bias, it is incorporated in the disturbance term and the resulting coefficient estimates are unaffected. 1310

Further, the results might, at least to some extent, be influenced by measurement error in some of the independent variables. As has been discussed at length in sections <IP_ADDRESS> and 5.2.3.l above, this study, like much of the preceding earnings management literature, had to rely on potentially imprecise proxy variables, which might have decreased the power of the regression tests. 1311 For example, recent research has increasingly relied on actual contract data to circumvent these problems.1312 However, due to restrictions in data availability, no superior surrogate variables could be devised at this point. To the extent that the proxies used measure the underlying variables with error, the resulting coefficient estimates might be biased and also inconsistent. 1313 Measurement error might also plague the economic-factors variables because these are based on firm-level as op-

1310 See, for example, Kennedy ( 1998, p. I 40). However, the estimates might have larger variances than would be the case in the absence of measurement error. See Gujarati (2003, pp. 524-5).

1311See, for example, Huijgen (1996, pp. 48-50). Watts and Zimmerman (1990, pp. 143-6) also discuss research method issues typical to empirical tests of positive accounting theory.

1312See, for example, Dichev and Skinner (2002, p. 1101) for actual accounting-based debt covenant restrictions. Some of the most frequent ratios used (debt to cash flow, interest coverage, fixed charge coverage, tangible net worth) are not influenced by transitional SFAS 142 goodwill write-offs. Also, AOL Time Warner, in its annual report on form 10-K/ A for the period ended on December 31 , 2001, states that its expected \$54 billion goodwill write-off "will not result in a violation of any of the Company's covenants."

131 3Refer to Gujarati (2003, pp. 526-8) and Kennedy (1998, pp. 140-3). The fact that measurement error might hamper the conclusions from goodwill write-off studies is also discussed by Wilson (1996, p. 173).

posed to reporting unit-level data. The analysis could be further refined by using segment data where more detailed information is unattainable. 1314 Overall, a possibility remains that, while the hypotheses accurately described the data, the testing methods and variables lacked the power necessary to find at least some of the expected relations. Inference problems might also stem from the fact that management expects financial statement users to respond differently to transitional SF AS 142 goodwill write-offs than what is predicted here.

In addition, it has not been explicitly tested whether all the conditions required for using limited dependent variable models are fulfilled in the data. 1315 For example, since finn size appears to be a significant explanatory factor, heteroskedasticity is a potential problem. On the other hand, autocorrelation should not be of concern because cross-sectional data is used. While these methodological concerns might preclude exact, quantitative inferences, the findings appear to be sufficiently stable to allow general, qualitative conclusions to be drawn.

Finally, caution must be exercised with regard to generalization of the results found here. First, the transition to the SFAS 142 goodwill impairment-only approach represents a unique setting in which to study write-off determinants. 1316Future, "regular" write-offs might be associated with vastly different determinants, one possible reason being that any "catching-up" effect is likely to be out of the way. The way in which future SF AS 142 write-offs will be considered in accounting-based contracts, in the political arena, and, perhaps most importantly, by capital-market participants, is likely to be far less uncertain to management. 1317 Second, since the sample selection and truncation procedures resulted in significant trimming of the available observations, some of the findings do not necessarily generalize to the entirety to of publicly listed U. S. corporations, let alone to firms domiciled in other economies.

1314See also Riedl (2002, p. 3).

1315 Refer to Greene (2003, ch. 21, 22), Maddala (2001, ch. 8), and Maddala (1983, ch. 2, 6).

1316For a similar "disclaimer", refer to Segal (2003, p. 30).

1317 Henning, Shaw and Stock (2002, p. 2) provide evidence suggesting that ''the market will view .. transition write-offs differently from [pre-SF AS 142) write-offs of goodwill."

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### **6 Conclusion**

Published in 2001, SFAS 142, *Goodwill and Other Intangible Assets,* preliminarily concludes decades of heated debate about the appropriate accounting procedures for acquired goodwill. The impairment-only approach, which replaced goodwill amortization, met with a largely critical echo from the financial community. While the theoretical, conceptual soundness of this method is largely acknowledged, constituents are concerned about the tremendous degree of subjectivity that the goodwill impairment test involves. The predecessor standard, SFAS 121, also relied heavily on management's judgment, but the goodwill amortization requirement assured that this dubious asset was charged to earnings over a predictable, if arbitrary, period of time. Under the new rules, however, goodwill can be carried on a firm's books indefinitely under certain circumstances.

Firms applying U.S. GAAP adopted SFAS 142 in the fiscal year 2002. Due to the standard's immense discretionary freedom and the uncertainty regarding its perception by financial statement users, firms had the opportunity to harmonize transitional goodwill write-off behavior with their financial reporting incentives and objectives. In this setting, the study at hand analyzes whether managers of a large sample of publicly traded U. S. firms took advantage of this opportunity or whether firms' economic circumstances explain transitional write-off behavior.

In order to generate hypotheses regarding managers' expected write-off behavior, two distinct areas of research are analyzed in detail. In **chapter 2,** a review of empirical evidence on the goodwill debate suggests that managers are concerned about the financial statement effects of goodwill accounting. Apparently, the assumption is widespread among managers that financial statement users take accounting information at face value, which induces them to avoid and/or delay goodwill-related charges where possible. In contrast to that, tests of investors' perceptions indicate that management's "cosmetic" earnings management is not rewarded because market participants, on average, appear to see through it to the firms' underlying economic characteristics.

In **chapter 3,** it is argued that a transitional SF AS 142 goodwill charge can be characterized as a discretionary asset write-off. These accounting events, large and growing in economic significance, are governed by accounting guidance involving a high degree of flexibility and judgment. Because they are also extremely visible due to separate disclosure, these charges have been thoroughly investigated. Based on the assumption that write-off behavior is largely at the discretion of management, empirical researchers are interested in the determinants of that decision and in its assessment by investors. This research is reviewed extensively in order to draw inferences about the extent to which

management's past write-off behavior is applicable to the transitional SFAS 142 setting and to lay the theoretical groundwork for hypothesis development. Studies of write-off determinants suggest that these charges can be explained to some extent by impairment of the underlying assets, but frequently to a greater degree by managers' contract-based as well as capital market-related financial reporting incentives. Perhaps the most stable finding is that discretionary asset write-offs are taken when a new CEO adopts a "big bath" strategy, signaling to investors that past problems are being aggressively dealt with. With regard to investors' perception of write-offs, empirical findings indicate that these charges are frequently anticipated and that the market reaction depends on the kind of information conveyed.

Predicting earnings management explanations behind transitional SFAS 142 goodwill write-offs requires that the new standard actually provides sufficient subjective elements for being applied at management's discretion. Therefore, much of **chapter 4** is devoted to exposing the manifold discretionary parameters in the impairment-only approach. The analysis shows that management's subjectivity and judgment is called upon at all levels, especially during the period in which SF AS 142 is applied for the first time.

Finally, **chapter 5** contains an empirical study of transitional SFAS 142 goodwill writeoffbehavior exhibited by a large sample of publicly traded U.S. firms. Based on theory and empirical findings presented in chapters 2 and 3, hypotheses are derived about the determinants of write-off behavior. These predictions are grouped into three main categories: First, since the impairment-only approach is designed to detect declines in goodwill value (impairment), variables representing economic downturns affecting the sample firms are expected to be associated with write-off behavior. Second, to the extent that management expects write-offs to affect the firm's exposure to contractual consequences, write-off behavior is predicted to relate to proxies for these consequences. Third, because firms are expected to gear write-off behavior towards favorably influencing investors' perceptions, variables representing such incentives are assumed to explain write-offs.

Descriptive analyses, univariate significance tests, and multivariate Probit and Tobit regressions were used to examine these hypotheses. A persistent pattern emerging from the data suggests that write-off behavior, on average, reflects the economic situation of sample firms. In full sample tests, Probit regressions indicate that write-off probability is lower where MTB ratios and price-based performance are higher. Tobit regression tests reveal a similar relation of these variables to the write-off amount. In addition, write-off firms are larger and more highly in debt than their non-write-off counterparts. Further, sample firms are concerned about achieving simple earnings targets and maintaining

positive equity. They are also more likely to record a transitional write-off when their fiscal year is the calendar year. These core results are largely stable to robustness checks and alternative specifications, suggesting that the highly discretionary impairment-only approach does capture goodwill impairment at least to some extent. Possibly, transitional SF AS 142 write-offs in part represent a "catching up" of value declines that the weaker SF AS 121 procedure was unable to reveal.

Comprehensive sub-sample analyses indicate that transitional SF AS 142 goodwill writeoff behavior is not homogenous throughout the goodwill sample. Large, high-profile firms apparently record write-offs even where goodwill impairment is not apparent from observation of economic conditions. Rather, write-off behavior seems to be consistent with financial reporting incentives, suggesting that "visible" firms use the window of opportunity presented by the adoption of SFAS 142 to inexpensively discard goodwill, the "Sword of Damocles" hanging above their financial statements. Such accelerating of write-offs is consistent with political cost considerations and a desire to shelter future earnings and compensation.

Overall, the qualitatively established core findings indicate that write-off behavior differs with the degree of sample firms' "visibility" in the capital markets. On average and especially in smaller firms, write-offs appear to be economically induced, while high-profile firms apparently harmonize their write-off behavior with management's financial reporting objectives to a higher degree. However, premature conclusions about earnings management causes for the observed behavior are unwarranted due to the availability of alternative, economic explanations.

The impairment-only approach is likely to remain a fruitful area of research for some time to come, and the research approach chosen here can be extended into several promising directions. Perhaps the most natural complementary approach to this study would be an investigation of market reactions to transitional SFAS 142 goodwill write-offs in order to ascertain investors' perception of the "quality" of these charges. However, researchers must be mindful of several methodological problems associated with this approach. For example, market reactions can only be studied where write-offs are voluntarily announced in a timely manner. The fact that not all write-offs fulfill this criterion is likely to introduce selection bias into the sample. Further, an analysis of market responses usually takes the form of an event study. This research design involves specific difficulties, which include determining the exact event date, modeling the expected market development absent the write-off, estimating the amount of write-off expected by investors at the write-off date, and taking into account the effects of any concurrent

events affecting the write-off finn. These problems invariably imply that two or more joint hypotheses are tested, which enonnously complicates the drawing of inferences.

Another viable avenue would be to compare goodwill write-off determinants and/or market responses between SFAS 142 and its predecessor regimes. However, due to changed economic and institutional circumstances, such comparison might be hard to interpret and conclusions regarding the superiority of one set of rules over the other difficult to justify. In order to keep as many factors as possible constant, it might be rewarding to analyze management's goodwill-related write-off behavior post-adoption, where financial statement users' interpretation of these "regular" write-offs is likely to be much more predictable to management.

Finally, future research could investigate what other concurrent indicators of earnings management, e.g. aggregate discretionary accruals or individual discretionary accounts, suggest about management's financial reporting strategy. As noted above, the highly visible transitional SF AS 142 goodwill write-off might not have been the earnings management instrument of choice for some firms, and the extent to which firms preferred other instruments might vary cross-sectionally. A small-sample or case study approach might be a worthwhile methodological alternative to using large samples and relying on crude proxy variables construed from commercially available data. Further progress could also result from studies of auditors' role in the context of discretionary financial reporting decisions.

Currently, the FASB and the IASB are contemplating another radical change in the accounting procedures for business combinations: In acquisitions of less than l 00% of the acquiree's equity, any resulting goodwill will be grossed up to reflect the entire difference between the acquiree's overall fair value and the fair value of its net assets. The resulting goodwill, to be allocated in part to non-controlling shareholders' remaining interest in the acquiree's equity, can no longer be termed "purchased" goodwill in its entirety. This "full goodwill method" introduces new discretionary elements into the accounting for goodwill.1318 Exploring management's use of this flexibility will be another interesting research opportunity.

1318See, for example, Pellens, Basche and Sellhom (2003).

### **Appendix**

0 MV, TA, and SAL represent mean values (fiscal 2001 and 2002) for the market value of equity, total assets, and sales, respectively.

For other variaole definitions, refer to Table 6. Monetary amounts are in million \$.

The following 2-digit SIC categories are not represented (reasons given in parentheses): 3-6, 11, 18, 19, 66, 68, 69, 71, 74, 77, 85, 90-98 (unassigned), 43 (U.S. Postal Service), 60-65 and 67 (depository institutions, eliminated during sample selection), 84 (cultural institutions), and 88 (private households).

0 Includes category 99 (non-classifiable establishments, 1.4% of"total firms available") and all other categories representing less than I% of"total firms available".

> **Table A. 2: Industry breakdown of the four samples**

RGWO\_A is the regular SFAS 142 goodwill write-off. Goodwill figures are scaled by beginning-ofperiod balance sheet numbers and end-of-period sales. These data comprise write-offs taken during fiscal 2002 by 143 firms in the goodwill sample.

**Table A. 4: Industry breakdown of transitional write-offs**

 --..J --..J

**Panel A:** WO sample with MTB>I (N = 124)

•, ••, \*\*\*: Differences in mean (t-test) and median (adjusted x;2-test) significant at IO, 5, or 1%.

0 MV, TA, and SAL represent mean values (fiscal 2001 and 2002) for the market value of equity, total assets, and sales, respectively. For other variable definitions, refer to Table 6.

Monetary amounts in million \$. Goodwill figures scaled by beginning-of-period balance sheet numbers.

#### **Table A. 5: Comparison ofwrite-offfinns with MTB>l versus MTBSl**

**Table A. 6: Correlation matrix for model DI**

**Table A. 7: Correlation matrix for model D2**

**Table A. 8: Correlation matrix for model D3**

**Table A. 9: Correlation matrix for model A2**

**Table A. 10: Correlation matrix for model A3**

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### **List of references**

### **List of accounting pronouncements and legal provisions**

#### **APB Opinions**

(Printed in: F ASB, Original pronouncements, 2002/2003 edition - Accounting Standards as of June 1, 2002, Vol. III, New York et al.: John Wiley & Sons, Inc., 2002.)

APB Opinion 16, Business Combinations, August 1970.

APB Opinion 17, Intangible Assets, August 1970.

#### **IAS**

(Printed in: IASB, International Financial Reporting Standards 2003 incorporating International Accounting Standards and Interpretations - The full text of all International Financial Reporting Standards extant at 1 January 2003, London: International Accounting Standards Committee Foundation, 2003.)

IAS 22, Business Combinations, July 1998.

#### **SFAC**

(Printed in: F ASB, Original pronouncements, 2002/2003 edition - Accounting Standards as of June 1, 2002, Vol. III, New York et al.: John Wiley & Sons, Inc., 2002.)

#### **SFAS**

(Printed in: F ASB, Original pronouncements, 2002/2003 edition - Accounting Standards as of June 1, 2002, Vol. I and II, New York et al.: John Wiley & Sons, Inc., 2002. Available for download at http://www.fasb.org/st/.)

#### **Other regulation**

Aktiengesetz (German Stock Corporation Act) of 6 September 1965. (Status: 1 October, 2002.)

#### **Bochumer Beitrage zur Unternehmungsfuhrung und Unternehmensforschung**

#### Herausgegeben vom Direktorium des lnstituts tor Unternehmungstohrung und Unternehmensforschung der Ruhr-Universitat Bochum

#### **Band 1** • **55 erschlenen beim Gabler Verlag Wiesbaden**

www .peterlang.de

lngo Tschach

# **The Theory of Development Finance**

### **How Microcredit Programmes Alleviate Credit and Labour Market Segmentation**

Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien, 2002. XIII, 160 pp., 64 fig., 3 tab. Development and Finance.

Edited by Manfred Nitsch, Reinhard H. Schmidt and Claus Peter Zeitinger. Vol. 11 ISBN 3-631-39203-6 / US-ISBN 0-8204-5493-1 · pb. € 39.00\*