Managing pension expense to meet analysts' earnings forecasts: implications for new FASB pension standard.

Parker, Paula Diane

INTRODUCTION

This research study focuses on whether or not managers manipulate pension expense to meet analysts' earnings forecasts. The primary motivation for this study is the integrity of financial statement reporting.

Various stakeholders, such as investors, creditors, directors, auditors, regulators, and standard setters rely heavily on the integrity of financial statement information in assessing firm value and in making a wide range of business decisions. Therefore, when the true economic condition of a firm is distorted by financial statement manipulation the ultimate outcome is poor decisions based on flawed information. Capital markets are weakened and public confidence in the accounting profession is impaired as a result of financial statement manipulation. For these reasons, this study based on the directional change in pension expense to meet analysts' earnings forecasts is relevant to decision makers in today's business environment and makes an important contribution to the accounting literature.

This study differs from most prior studies in that it examines whether or not analysts' earnings forecasts create incentives for managers to use pension expense as an earnings management vehicle for financial statement manipulation. The research design raises public awareness and provides important information about the predicted directional change in pension expense that is indispensable in detecting and preventing future earnings management of this kind. This study provides basic information and practical analyses for stakeholders, particularly standard setters, to more carefully monitor the changes in pension expense to reduce future financial statement manipulation.

One problem associated with attempting to identify financial statement manipulation is that of determining what a firm's financial statements would report absent the manipulation. The Statement of Financial Accounting Standards No. 87, Employers' Accounting for Pensions (SFAS No. 87), provides a unique measure of what pension expense should be from year to year based on its built-in smoothing (1) technique. Firms are allowed to smooth pension expense to avoid the immediate recognition of wide swing market fluctuations that affect pension investments. The logic behind the allowed smoothing of pension expense is a long-term perspective where market fluctuations are expected to average out over the long-term. The problem is overcome of reasonably estimating what a firm's pension expense would be absent the manipulation because of the transparency of the allowed smoothing technique (Parker and Sale 2007).

A basic characteristic of the research design is modeling the behavior of pension expense to identify its discretionary and nondiscretionary components. This study builds on an approach similar to the random walk approach whereby the prior year's pension expense is assumed to be the most relevant and reliable approximation for predicting the current year pension expense. So theoretically, pension expense is expected to be the same from year to year. Therefore by design, any change in pension expense from year to year is considered discretionary and is the primary focus of explanation in the present study. In addition, the specific accruals research design is used because it is more powerful in detecting earnings management than the aggregate accruals research design as the explanatory factors for the discretionary portion of pension expense can be tested directly.

An earlier study by Powall et al. (1993) finds evidence that earnings forecasts are value relevant, and thus, establishes their importance in capital markets. Investors often use analysts' earnings forecasts in assessing firm value rather than using more costly and complex valuation tools. According to Collinwood (2001), firms convey good news by meeting analysts' earnings forecasts and firms convey bad news by missing analysts' earnings forecasts. Roen et al. (2003), in studying the effect of preliminary voluntary disclosure and preemptive preannouncement on the slope of the regression of returns on earnings surprise, find when firms manage earnings by attempting to inflate them; the response to negative earnings surprise is stronger than the response to positive earnings surprise. Accordingly, managers are motivated to meet analysts' earnings forecasts to avoid stock price penalties and to receive stock price rewards.

Most prior studies are unable to provide convincing evidence that pension expense is used as an earnings management vehicle. This lack of empirical evidence is surprising because auditors as well as many others perceive pension expense as being a frequently used earnings management vehicle. Parker and Sale (2007) suggest that most prior studies are unable to detect earnings management via pension accounting for two fundamental reasons. The first reason is that most prior studies focus on contracting incentives rather than on capital market incentives for explaining earnings management. The second reason is that most prior studies focus on the manipulation of pension rates rather than on the direct manipulation of the pension expense amount. So following Parker and Sale (2007) this study focuses directly on the manipulation of pension expense in response to capital markets incentives.

GAAP REGULATIONS AND PRIOR LITERATURE

In 1966, shortly after 4,000 auto workers lost their promised retirement benefits (2), the Accounting Principles Board (APB) issued APB Opinion No. 8, Accounting for the Cost of Pension Plans. This opinion was issued to avoid possible government intervention in the financial reporting and disclosure process as well as to address public demands for pension reform.

In 1980, the Financial Accounting Standards Board (FASB) issued Statement of Financial Accounting Standards (SFAS) No. 35, Accounting and Reporting by Defined Benefit Pension Plans, for the purpose of providing additional pension information to help interested parties determine whether pension plans were funded in a manner adequate to provide for payments of retirement benefits when due. In 1985, the FASB issued SFAS No. 87, Employers' Accounting for Pensions, which remains the primary standard influencing pension expense measurement for defined benefit pension plans. In 1998, the FASB issued SFAS No. 132, Employers' Disclosures about Pensions and Other Postretirement Benefits, which was intended to make pension disclosures more informative.

Then again in 2006, the FASB issued SFAS No. 158, Employers' Accounting for Defined Benefit Pension and Other Postretirement Plans, which improves financial reporting by requiring an employer to recognize the overfunded or underfunded status of a defined benefit plan as an asset or liability in its statement of financial position and to recognize changes in that funded status in the year in which the changes occur through comprehensive income of a business entity or changes in unrestricted net assets of a not-for-profit organization. Although SFAS No. 158 is an amendment of SFAS No. 87, 88, 106, and 132 (R), SFAS No. 87 is not amended for the calculation of pension expense. The changes in SFAS No. 158 represent Phase 1 of the Board's planned two-phase project to reconsider the accounting for pensions and other postretirement benefits. The second phase is expected to be a multi-year, comprehensive review of the fundamental issues underlying SFAS No. 87 and 106, including measurement of liabilities and the determination of pension expense. As a result, the public can expect more pension accounting changes to be implemented in the not so distant future.

VanDerhei and Joanette (1988) show earnings management incentives are correlated with the permitted actuarial cost method choices made by sponsors in the pre-SFAS No. 87 era. The findings lend credibility to the FASB's decision in SFAS No. 87 to mandate a standardized actuarial cost method for the purpose of averting sponsors from manipulating pension expense through the strategic choice of different actuarial cost methods.

Kwon (1989) focuses on the explanation of the discount rate. The results provide evidence that managers use the assumed discount rate to manipulate financial statements. The finding highlights policy implications in connection with the two opposing schools of thought on strict FASB guidelines. One school asserts the assumed discount rate should be elastic in order to reflect the characteristics of different pension plans. The other school advocates strict FASB guidelines in establishing specific benchmark rates for all pension plans in order to stop rate manipulation by managers.

Blankley (1992) investigates incentives for managerial selection of pension rate estimates by incorporating two distinct paradigms, efficient and opportunistic behavior, (3) rather than assume one or the other applies to accounting choice. A learning effect is discovered, whereby as managers get more familiar with SFAS No. 87 opportunistic incentives play a greater role in the choice of pension rates.

Weishar (1997) focuses on the explanation of the simultaneous effects of the three pension rates and finds pension rates are not changed independent of each other. Brown (2001) not only focuses on explaining the three pension rates but changes the direction of research by using a market valuation model.

In an auditing survey paper, Nelson et al. (2000) find twenty-three potential areas where managers attempt earnings management along with several factors that affect the frequency of decisions of managers and auditors with respect to earnings management. Pensions are included as one of the twenty-three potential areas where managers attempt earnings management. Results indicate managers attempt earnings management to increase earnings, however, forty percent of the determinable current year income effects are income decreasing. Evidence supports income-decreasing earnings management attempts are more likely to occur with respect to imprecise financial standards such as SFAS No. 87.

Parker and Sale (2007) use a specific accrual model to investigate whether or not firms use pension expense as an earnings management tool to maintain a steady stream of earnings. The results indicate that pension expense is an active tool used by firms to manage actual earnings when the firm would otherwise miss achieving its current year earnings target that is equal to its prior year earnings.

The post-SFAS No. 87 research primarily uses contracting variables in attempting to explain pension rate assumptions. A paradigm shift where pension rates are no longer the primary focus of explanation is expected because of SFAS No. 132 and 158.

Whether managers act in self-interest or in the interest of shareholders, their performance is monitored by directors, investors, creditors, and regulators, which in turn, creates strong incentives to manage earnings. The capital markets based incentive known as analysts' earnings forecasts is expected to capture financial statement manipulation as it relates to pension expense. This approach is conceptually similar to that used by Parker and Sale (2007) where the change in pension expense is explained by the capital market incentive known as prior year earnings.

Burgstahler and Dichev (1997) theorize that investors in publicly traded firms use simple low-cost Heuristics (4), more specifically earnings-based benchmarks, in determining firm value. Burgstahler and Dichev (1997) use frequency distribution as a method for demonstrating the existence of earnings management. Evidence indicates a disproportionally low incidence of firms reporting small decreases in earnings and small losses relative to a high incidence of firms reporting small increases in earnings and small positive earnings.

DeGeorge et al. (1999) use a similar research design as Burgstahler and Dichev (1997) and report earnings are the single most value relevant item provided to investors in financial statements. Earnings are used as performance measures that provide the enticement for managers to manipulate earnings. Empirical evidence reveals how efforts to exceed thresholds, that is, to sustain recent performance, to report positive earnings, and or to meet analysts' expectations, induce particular patterns of earnings management. Clearly emerging patterns show earnings falling just short of thresholds are managed upward. Whereas earnings falling far from thresholds, regardless of the direction, call for the thresholds to be adjusted for future ease of attainment.

In summary, a number of relatively recent studies provide evidence firms are managing earnings to continue a steady stream of earnings (Burgstahler and Dichev 1997, Barth et al. 1999, DeGeorge et al. 1999, Moehrle 2002), to avoid reporting a loss (Burgstahler and Dichev 1997; DeGeorge et al. 1999), and or to meet analysts' earnings forecasts (DeGeorge et al. 1999, Brown 2001). In addition, Matsunaga and Park (2001) show evidence of manager compensation-based incentives to avoid earnings declines and to meet analysts' earnings forecasts.

Based on the logic of prior findings, this study examines whether firms use the discretionary portion of pension expense as a vehicle to accomplish earnings management to meet analysts' earnings forecasts.

RESEARCH DESIGN

The aggregate accruals method, the specific accruals method, and the earnings-based distribution method are the three research designs prevalent in the earnings management literature (McNichols 2000). Each particular research design has its own advantages, disadvantages, and tradeoffs. The common themes of these designs are the discovery of how managers manipulate earnings, what motivates managers to manipulate earnings, and the costs and benefits associated with earnings management.

The aggregate accruals research method considers the aggregated outcome of the multiple vehicles used by managers in managing earnings. However, the disadvantages of this research method include the limitations of its models to detect manipulation, as well as its inability to identify specific accounting vehicle used by managers in managing earnings (Francis 2001, Fields et al. 2001).

The specific accruals research method is a disaggregated or piece-meal approach. This approach advocates the examination of individual accounting items that are subject to substantial manager judgment and are able to significantly impact reported earnings. One advantage of this research method is the specification for yielding directional predictions based on researcher knowledge, skill, and scrutiny of individual accounting vehicles used by managers in managing earnings. However, this research method lacks the ability to analyze simultaneously aggregated effects of accounting vehicles used by managers in managing earnings (McNichols 2000, Fields et al. 2000, Francis 2001, Parker and Sale 2007).

A relatively new stream of earnings management literature is a result of the seminal work by Burgstahler and Dichev (1997) in the area of earnings-based distributions. The advantage of this research method is that it provides the ability for strong predictions about the frequency of earnings realizations that are unlikely to be due to nondiscretionary components of earnings. McNichols (2000) uses the frequency of earnings realizations in the vicinity above and below benchmark earnings to analyze whether the number of companies reporting that level of earnings is more or less than expected. One disadvantage of this research method is its inability to identify specific accounting vehicles used by managers in managing earnings. In essence, there is not adequate information provided by this method to prevent future earnings management (Parker and Sale 2007).

According to Healy and Wahlen (1999), future research contributions in the earnings management area are expected from documenting the extent and magnitude of the effects of specific accruals and from identifying factors that limit the ability of managers to manage earnings. So following Parker and Sale (2007), this study uses a specific accruals research model with earnings-based benchmarks as the explanatory variables. The distinction from prior research is determining whether or not there is an association between the change in pension expense and the amount by which firms would otherwise miss or beat their targeted analysts' earnings forecasts.

The methodology for this study is a newly revised model that investigates the impact of a particular capital market incentive (i.e., analysts' earnings forecasts) instead of focusing only on contracting incentives. Most prior pension studies focus on contracting variables in attempting to explain pension manipulation. However Parker and Sale (2007), use a capital market incentive model to investigate whether or not firms use pension expense as an earnings management tool to maintain a steady stream of earnings. Parker and Sale (2007) make clear the argument for investigating capital market incentives. Therefore this study follows the premise established in Parker and Sale (2007) and investigates one more capital incentive (i.e., analysts' earnings forecasts).

The theoretical concepts discussed above are formalized in alternate form in the following hypothesis.

[H1.sub.A]: Pension expense is managed to meet analysts' earnings forecasts.

The estimated cross-sectional regression model is presented below.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

* PEchg is the change in pension expense equal to current year pension expense minus prior year pension expense all scaled by lagged assets.

* Miss_UE_Dummy is a dummy variable that equals 1 if the continuous variable, UE < 0, and 0 otherwise.

* UE is a continuous variable equal to pretax income absent manipulation minus the applicable benchmark all scaled by lagged assets.

* Interact is an interaction variable equal to Miss_UE_Dummy times UE.

* [Delta]Employ is a control variable equal to the number of employees for the current year minus the number of employees for the prior year all scaled by lagged assets.

* yr[D.sub.t] is a dummy variable for each applicable year 1995-2001 with the 1995 dummy effects captured in the intercept.

* ind[D.sub.i] is a dummy variable representing 55 industries.

* [[alpha].sub.0] is the intercept for UE [greater than or equal to] 0 where Miss_UE_Dummy = 0.

* [[alpha].sub.0] + [[alpha].sub.1] is the intercept for UE < 0 where Miss_UE_Dummy = 1.

* [[alpha].sub.2] incentive slope for UE [greater than or equal to] 0 where Miss_UE_Dummy = 0.

* [[alpha].sub.2] + [[alpha].sub.3] incentive slope for UE < 0 where Miss_UE_Dummy = 1.

* PI is pretax income.

* PIAM is pretax income absent manipulation. The basic calculation is PI + ([PE.sub.t] - [sub.Pet-1]).

* [A.sub.t-1] is assets lagged one period.

* BM is or target earnings. The applicable benchmark is analysts' earnings forecasts on a pretax basis.

* PE is pension expense.

As is the case in all earnings management studies, a reasonable proxy for earnings management is developed. The regression analysis incorporates PEchg as the earnings management proxy which is the dependent variable. The proxy development is accomplished by using the unique smoothing feature of SFAS No. 87 whereby the prior year pension expense provides a logical approximation for the firm's premanaged pension expense. Assuming the number of employees remains unchanged, current pension expense should be approximately the same as the prior year pension expense. PEchg is defined as the current year pension expense minus the prior year pension expense all scaled by lagged assets. Thus, PEchg is a proxy for the extent of manipulation in pension expense after controlling for the change in the number of employees. So that, earnings management is measured by PEchg.

Premanipulation earnings relative to analysts' earnings forecasts represent the level of capital markets incentives for earnings management. The capital markets based incentive measure to manipulate earnings is represented by the variable called UE. The independent variable, UE, is a continuous scaled variable and is calculated as the difference between pretax earnings absent pension manipulation (i.e., PIAM) and the analysts' earnings forecasts.

Following Burgstahler and Eames (2002), a benchmark representing target earnings is necessary. The benchmark for target earnings is pretax analysts' earnings forecasts. Pretax analysts' earnings forecasts are used for consistency because pension expense is reported in the financial statements on a pretax basis. Earnings absent pension manipulation are constructed using pretax income adjusted for the change in pension expense and is called PIAM. The measure for pension expense absent pension management is, therefore, the prior year pension expense.

A dummy variable (i.e., Miss_UE_Dummy) for hypothetically missing analysts' earnings forecasts is included in the analysis. Miss_UE_Dummy is coded zero for firms that hypothetically beat their analysts earnings forecasts using premanaged earnings. Whereas, Miss_UE_Dummy is coded one for firms that hypothetically miss their analysts' earnings forecasts using premanaged earnings. If [[alpha].sub.1] is significant and positive, firms missing their analysts' earnings forecasts have a higher intercept than the other firms. If [[alpha].sub.1] is significant and negative, firms missing their analysts' earnings forecasts have a lower intercept than the other firms. If [[alpha].sub.1] is insignificant, there is no difference between the two groups of firms.

After controlling for the change in the number of employees, the association between PEchg and the level of capital markets incentive (i.e., UE) for earnings management constitutes this study's test of interest. Because both smoothing and benchmark incentives exist and may not be equally important, the slope coefficient on UE is allowed to vary with the prediction on Interact (i.e., [[alpha].sub.3]) being nondirectional.

The dependent variable, PEchg, is expected to be positively correlated with the incentive variable UE. The slope coefficient for the group of firms that hypothetically beat their analysts' earnings forecasts is represented by [[alpha].sub.2]. The slope coefficient for the group of firms that hypothetically miss their analysts' earnings forecasts is represented by [[alpha].sub.2] + [[alpha].sub.3]. Thus, I predict that [[alpha].sub.2] > 0, and that [[alpha].sub.2] + [[alpha].sub.3] is > 0.

The logic behind the predictions for [[alpha].sub.2] and [[alpha].sub.2] + [[alpha].sub.3] is that the dependent variable, PEchg, is expected to move in the same direction as the independent incentive variable, UE. For example, if a firm has premanaged earnings equal to $.20 per share and forecasted earnings equal to $.18 per share, the firm is expected to manipulate actual earnings by increasing pension expense by $.02 in order to offset the $.02 excess in premanaged earnings. In this situation, there is a positive $.02 excess in premanaged earnings and the change in pension expense (i.e., PEchg) is expected to move $.02 in a positive direction as well. The variable UE (i.e. 2) captures the positive $.02 excess in premanaged earnings. Therefore, because PEchg and UE move together in the same direction, a positive correlation is predicted.

On the other hand, if a firm has premanaged earnings equal to $.18 per share and forecasted earnings equal to $.20 per share, the firm is expected to decrease pension expense by $.02 to offset the $.02 negative premanaged earnings. The variable UE (i.e., [[alpha].sub.2] + [[alpha].sub.3]) captures the negative $.02 deficiency in premanaged earnings. Here again, because PEchg and UE move together in the same direction, a positive correlation is predicted.

Since the coefficient on Interact (i.e., [[alpha].sub.3]) is predicted as nondirectional, it will be interpreted as follows. If [[alpha].sub.3] is positive, this will indicate that firms hypothetically missing their analysts' earnings forecasts are actually decreasing pension expense (i.e., increasing earnings) more, to avoid missing their analysts' earnings forecasts, than firms hypothetically beating their analysts' earnings forecasts are actually increasing pension expense (i.e., decreasing earnings) to smooth income downward in the direction of their analysts' earnings forecasts. On the other hand, if [[alpha].sub.3] is negative, this will indicate that firms hypothetically missing their analysts' earnings forecasts are decreasing pension expense (i.e., increasing earnings) less, to avoid missing their analysts' earnings forecasts, than firms hypothetically beating their analysts' earnings forecasts are actually increasing pension expense (i.e., decreasing earnings) to smooth income downward in the direction of their analysts' earnings forecasts.

In other words, if [[alpha].sub.3] is significant and positive, firms missing their analysts' earnings forecasts have a steeper slope than the other firms. Whereas, if [[alpha].sub.3] is significant and negative, firms missing their analysts' earnings forecasts have a flatter slope than the other firms. However, if [[alpha].sub.3] is insignificant, then both groups of firms have the same slope.

In summary, analysts' earnings forecasts create incentives for firms that are in opposite directions depending on the level of premanaged earnings relative to their earnings targets. So that, if firms hypothetically miss their analysts' earnings forecasts they are expected to exhibit benchmark behavior by manipulating pension expense to increase actual earnings in order to reach their benchmark. On the other hand, if firms hypothetically beat their analysts' earnings forecasts they are expected to exhibit smoothing behavior by manipulating pension expense to decrease actual earnings so that their actual earnings are closer to their analysts' earnings forecasts than they would otherwise be (Parker and Sale 2007).

Big bath behavior is another consideration. However, because the research design uses a sample screening process this behavior is not expected to cause confounding effects. The screening process eliminates firms whose performance is not close to their analysts' earnings forecasts. The logic is that firms closer to their analysts' earnings forecasts are more likely to exhibit sensitivity to earnings management incentives such as benchmark behavior (5) and smoothing behavior (6), whereas, firms missing their analysts' earnings forecasts by a large amount are expected to exhibit big bath behavior (Parker and Sale 2007).

[Delta]Employ is a control variable to account for any variation in the dependent variable (i.e., PEchg) caused by the change in the number of employees from year to year. [Delta]Employ is calculated as the current year number of employees minus the prior year number of employees all scaled by lagged assets. In addition, the inclusion of the control variable, [Delta]Employ, should lessen confounding results attributable to changes in organizational structure such as mergers and acquisitions. A positive relationship is expected between the change in pension expense (i.e., PEchg) and the change in the number of employees from year to year (i.e., [Delta]Employ). The reasoning is likely because an increase in the number of employees is expected to result in an increase in pension expense, whereas a decrease in the number of employees is expected to result in a decrease in pension expense. Therefore, a positive slope coefficient is predicted for [Delta]Employ.

On the other hand, if an economy of scale exists, then a negative slope may occur for [Delta]Employ. For example, when a higher paid employee is replaced by two new lesser paid employees and the overall pension expense is less for the two new employees than it was for the one higher paid employee, an economy of scale occurs. In this situation, the addition of one new employee (2 - 1 = 1) actually decreases pension expense; whereas, adding an additional employee would normally be expected to increase pension expense.

A merger or acquisition may also cause an economy of scale for [Delta]Employ. Another possible scenario is where the actuarial assumptions are different for the acquiring firm's pension plan and the purged plan automatically becomes overfunded as a result of using the acquiring firm's actuarial assumptions. Two additional control variables (indDi and yrDt) are included in the model. These are intended to control for industry and time fixed effects.

Recent studies (Schwartz 2001, Dhaliwal et al. 2002) indicate managers may attempt to guide analysts' earnings forecasts in order to then meet the analysts' forecasts. Therefore, if managers do not manage pension expense or do effectively guide analysts' earnings forecasts, there should be no association between the change in pension expense (i.e., PEchg) and the amount that firms hypothetically miss or hypothetically beat their analysts' earnings forecasts (Dhaliwal et al. 2002, Parker and Sale 2007).

RESULTS AND INTERPRETATIONS

The sample begins with the total number of firms with defined benefit pension plans and no missing data from the Compustat files for the period 1995-2001. Following the rationale used by Dhaliwal et al. (2002) a twelve cent earnings per share screening process is applied. Afterwards there are 968 firm observations and 55 industries in the final sample. Table 1 summarizes these results.

Table 2 reports the results or the regression analysis. The rationale for explaining Table 2 results is based on the belief that pension expense manipulation is a function of the value of the magnitude of hypothetically missing or hypothetically beating the benchmark based on premanaged earnings. Therefore, the economic substance is captured by the regression main effects of the incentive variable for the two distinct groups of firms. For simplicity, the results of the control variables are not reported because they are not important for interpretation.

PEchg, representing firm manipulation, is expected to be positively correlated with UE, the incentive variable of interest. The incentive slope is captured in the model for the firms that hypothetically beat their benchmark by [[alpha].sub.2] and for the firms that hypothetically miss their benchmark by [[alpha].sub.2] + [[alpha].sub.3]. The slope on UE (i.e., [[alpha].sub.2] and [[alpha].sub.2] + [[alpha].sub.3]) represents the estimated average change in pension expense when the applicable incentive variable increases or decreases by one unit. If managers are more concerned with reaching their benchmark than smoothing, then the prediction is that [[alpha].sub.3] > 0.

The slope coefficient (i.e., [[alpha].sub.3] > 0) for the firms that hypothetically beat their benchmark is expected to be statistically significant and is tested with a t-test. The slope coefficient (i.e., [[alpha].sub.2] + [[alpha].sub.3]) for the firms that hypothetically miss their benchmark is expected to be statistically significant and is tested with an F-test.

The results of the association test using the twelve cent pretax earnings per share screen are reported in Table 2. The significant F-statistics (i.e., p-value = .0001) indicates strong evidence that the linear relationship between the change in pension expense (i.e., PEchg) and the independent explanatory variables does, in fact, exist as expected. The R2 and adjusted R2 are .2391 and .1852 respectively, which indicate a high proportion of the change in pension expense is explained by the combination of independent variables.

The slope on UE captures the average magnitude of change in pension expense (PEchg) when there is a one unit change in the incentive variable for the two distinct groups of interest. The incentive for the group of firms that hypothetically miss their analysts' earnings forecasts is not statistically significant. The predicted sign, however, is in the right direction indicating firms are using pension expense in a predictable manner. The overall inference is that the change in pension expense (i.e., PEchg) is not significantly explained by the amount firms hypothetically miss their analysts' earnings forecasts. It is important to mention the dollar impact of this behavior on financial reporting. As for every $1 that premanaged earnings are below the earnings benchmark (i.e., analysts' earnings forecasts) pension expense decreases $.37.

Since [[alpha].sub.2] > 0 is statistically significant, firms' smoothing behavior is stronger than their benchmark behavior. The inference here is that the change in pension expense can be significantly explained by the amount firms hypothetically beat their analysts' earnings forecasts. Again the predicted sign on [[alpha].sub.2] is in the right direction indicating firms are using pension expense in a predictable manner. The dollar impact is greater than for the benchmark behavior. For every $1 premanaged earnings are above the analysts' earnings forecasts pension expense increases by $.54.

The findings in the Nelson et al. (2000) survey study suggests income-decreasing earnings management attempts are more likely to occur with respect to imprecise financial standards. The results in this study support that more actual manipulation is occurring in financial statement reporting in the direction of income decreasing earnings management through pension expense. So that more earnings manipulation attempts in this direction appears to lead to more actual manipulation in this direction. Again assuming the incentive to manipulate earnings upward to meet the benchmark is at least equal to the incentive to manipulate earnings downward to meet the benchmark, the pattern of evidence suggests auditors are less vigilant in constraining downward earnings management than upward earnings management.

Sensitivity analyses are conducted using screening criteria slightly different than those reported with essentially the same findings. Sensitivity analyses also support the research findings are not driven by a few influential outlier observations.

SUMMARY CONCLUSIONS

Managers have strong incentives to manage earnings to achieve analysts' earnings forecasts in order to reap stock price advantage and to avoid market devaluation. In addition, many contracting incentives are tied directly or indirectly to earnings based measures which also provide strong incentives for earnings management.

This research study contributes to the literature by providing evidence that managers are, in fact, using pension expense to manipulate reported earnings in a predictable rational economic manner. The research provides evidence that analysts' earnings forecasts create capital market incentives in opposite directions depending on the economic status as measured by whether or not firms will miss or beat their analysts' earnings forecasts based on premanaged earnings.

By using "what if" analyses, firms that hypothetically miss their analysts' earnings forecasts are shown to manipulate actual pension expense downward to increase actual reported earnings; whereas firms that hypothetically beat their analysts' earnings forecasts are shown to manipulate actual pension expense upward to decrease actual reported earnings. As predicted, both groups of interest are successfully manipulating pension expense in the direction that moves their actual reported earnings closer to their analysts' earnings forecasts than they would be otherwise. The results suggest that smoothing behavior is stronger than benchmark behavior. One reason may be that auditors are more cautious in constraining effort to manage earnings upward than in constraining earnings downward.

This research is timely as it has relevant implications in support of FASB's planned upcoming Project Phase 2 to again comprehensively review the determination of pension expense. As a result of the recently completed Project's Phase 1, FASB issued SFAS No. 158 addressing pension reform exclusive of pension expense. Since the research findings indicate both groups of firms use pension expense in managing their actual reported earnings, FASB will again want to consider more stringent rules for measuring pension expense to mitigate predictable earnings management in future financial statements through the use of pension expense.

Capital markets and the U.S. economy are heavily influenced by the integrity of financial statement reporting. Thus, this research should be of interest to investors, directors, creditors, auditors, regulators, and standard setters.

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ENDNOTES

(1.) The term smoothing is used in this paper in two different contexts. In this instance, smoothing indicates spreading over time. Later, the term smoothing is used in another context as a means for identifying firm behavior.

(2.) The Financial and Estate Center published this information at www.worldtraffice.com in All About Pension Plans.

(3.) Efficient behavior proxies for the three pension rates are (1) the Pension Benefit Guaranty Corporation's (i.e., PBGC's) published discount rate, (2) the industry average compensation rate, and (3) the firm's actual rate of return on plan assets. Opportunistic behavior proxies for the three pension rates are (1) the firm's discount rate adjusted for the PBGC's published discount rate, (2) the firm's compensation rate adjusted for the industry average compensation rate, and the firm's expected rate of return on plan assets adjusted for the actual rate of return on plan assets. The theory is that firms are simultaneously influenced by both efficient and opportunistic behavior. Therefore, Blankley's study controls for efficient behavior and attempts to explain opportunistic behavior in terms of the independent variables which are cash constraints, debt-covenant constraints, monitoring by union concentration, tax management incentives, and the number of analysts covering the firm.

(4.) When it is expensive for investors to retrieve and process detailed information about earnings, it is conjectured that investors use information processing heuristic cutoffs, i.e., zero changes in earnings or zero earnings, to assess firm value.

(5.) Benchmark behavior is where a firm decreases pension expense to increase actual earnings in an attempt to reach their target performance.

(6.) Smoothing behavior is where a firm increases pension expense to decrease actual earnings in an attempt to store up reserves and be closer to their target performance than they would otherwise be. TABLE 1: Sample Selection Firms in original sample covering 1995-2001 21,608 Firms that do not have defined benefit plans and firms with missing observations -18,704 Firms eliminated in the $.12 screening process -1,936 Firms in the final sample 968 Table 2: Cross Sectional Pooled Effects Estimation Using $12 Screen with Time and Industry Fixed Effects Variable Prediction intercept + miss_ue_dummy - ue + interact + / - [[alpha].sub.0] + [[alpha].sub.1] - [[alpha].sub.2] + [[alpha].sub.3] + F-statistic as p-value .0001 [R.sup.2] .2391 Adjusted [R.sup.2] .1852 Variable Coefficient One Tail p-value intercept 0.0003 .4602 miss_ue_dummy 0.0017 .8158 ue 0.5466 .0377 interact -0.1726 .0016 [[alpha].sub.0] + [[alpha].sub.1] 0.0020 .7028 [[alpha].sub.2] + [[alpha].sub.3] 0.3740 .1912 F-statistic as p-value [R.sup.2] Adjusted [R.sup.2]