Managed earnings: a closer look at pension expense.

Parker, Paula Diane

INTRODUCTION

This study scrutinizes whether or not companies use pension expense as an earnings management device to maintain a steady stream of earnings. The capital market benchmark for the current year is equal to the prior year earnings. Earlier research studies are inconsistent in providing evidence that pension expense is used as an earnings management device to manage reported bottom line earnings. This lack of convincing empirical evidence is baffling because survey evidence indicates auditors perceive pension expense as a frequently used earnings management device (Nelson et al. 2000). Most early studies are unable to consistently detect pension expense manipulation as these studies focused primarily on contracting motivators rather than on capital market reporting motivators for explaining earnings management. Another reason may be that most early studies focused on pension rate manipulation rather than directly on pension expense manipulation.

One research study (Bergstresser et al. 2006) indicates companies are more aggressive with their assumed expected long-term rate of return on pension assets when these companies are near critical earnings thresholds and this rate assumption has greater potential to impact actual reported earnings. Since changes in either the discount rate assumption or the compensation rate assumption could offset the impact on pension expense caused by the change in the assumed expected long-term rate of return on pension assets, this study extends earlier research by focusing directly on pension expense taken as a whole rather than focusing only on the affect of one of the three pension rate assumptions. Therefore, focusing directly on pension expense considers the cumulative results of all pension rate assumptions.

This study differs from earlier studies in that it examines whether or not the prior year earnings benchmark creates a motive to manipulate pension expense in a predictable economic manner. In addition, this study expands earlier capital market reporting motivator research that limited sampling to only those companies with actual earnings in a neighborhood relatively close to their capital market earnings benchmark. So the broader approach taken in this study looks at not only companies critically near their earnings threshold but companies with available data for the sample period. In essence, this study removes the sample screening process to allow for a larger sample of companies. The research findings provide practical analyses for various stakeholders, such as investors, standard setters and regulators, to carefully monitor changes in pension expense to reduce future financial reporting manipulation.

One difficulty associated with attempting to test for pension expense manipulation is that of determining what a company's pension expense would be without 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 the corridor approach. Accordingly, companies are allowed to spread pension expense over time in order to avoid the immediate recognition of wide swing market fluctuations that affect pension investments. The reason regulation allows companies to smooth (i.e., spread uniformly) pension expense is that over the long-term market fluctuations are expected to average out. Therefore, it is possible to reasonably estimate what a company's pension expense would be absent manipulation. The proxy for pension expense absent manipulation is in essence the prior year pension expense.

So based on theory, pension expense should be approximately the same from year to year unless there is a change in the number of employees, industry effects, and or time fixed effects. The research model captures the industry effects and time fixed effects with dummy variables for industry and year.

In addition, common accounting practice supports the selected proxy for current year pension expense. Managers run "what if analyses" at year-end to determine whether or not the prior year earnings benchmark amount will be achieved. In other words, are this year's earnings equal to last year's earnings? In these analyses managers substitute the prior year pension expense as the current year pension expense which allows managers the flexibility then to adjust the actual pension expense upward or downward in the direction that will move their current year reported earnings toward their desired prior year earnings amount.

Pension expense is a perfect general ledger account for manipulating. First, pension expense is one of the last general ledger accounts that can be adjusted or manipulated at year-end to meet a given earnings target. Second, there is a lack of precision in the guidelines set forth in SFAS No. 87 which allows companies great flexibility in choosing their assumed discount rate, compensation rate, and expected rate of return on pension assets. Third, it is highly probable that companies have access to and authority over superior proprietary information regarding their applicable pension plans than is readily available to other interested parties. Fourth, there is a lack of timely verification of the pension rate assumptions and estimates because these rates and assumptions cover discounted projections out in the future generally 20 plus years. The long time span in and of itself provides the ability for managers to annually tweak the numbers in the desired direction to sustain their earnings.

The behavior of pension expense is modeled in the research design by identifying the discretionary and nondiscretionary components of the expense. So that by design, any change in pension expense from year to year is considered discretionary and is the prime focus of explanation.

The benchmark (i.e., prior year earnings) test focuses importance on whether or not companies use pension expense manipulation to continue a steady stream of earnings. Barth et al. (1999) show evidence those companies with consecutive earnings increases experience higher stock prices, and when those companies encounter declines in reported earnings, the premium stock prices fall disproportionately. As a result, companies have strong motives to continue a steady stream of earnings to acquire market share and to avoid market devaluation (Matsunaga and Park 2001).

The remainder of this paper is organized into four areas. The first describes regulations and literature review. The second provides the research design, hypothesis development, sample selection and other statistical considerations. The third provides the results, interpretations, and limitations. The fourth provides the summary conclusions.

REGULATIONS AND LITERATURE REVIEW

In 1985, the FASB issued SFAS No. 87, Employers' Accounting for Pensions, which is the standard influencing financial statement measurement for defined benefit plans. In 2003, the FASB revised SFAS No. 132, Employers' Disclosures about Pensions and Other Postretirement Benefits, which is the standard influencing pension disclosure.

Pension research for the last two decades (Kwon, 1989; Blankley, 1992; Ali and Kumar, 1993; Weishar, 1997; Brown, 2001 and Bergstresser et al., 2006) focused mainly on the explanation of pension rates and how and why companies select the particular pension rates disclosed in their financial statements. Improved disclosures required by SFAS No. 132 now provide enough information to recalculate pension expense using the three pension rate assumptions. Therefore, a paradigm shift in pension research may occur where pension rates are no longer the primary focus of explanation.

Kwon's (1989) research focused on the explanation of only the discount rate. Blankley's (1992) research focused individually on the explanation of the discount rate, compensation rate, and expected long-term rate of return on plan assets. Weishar's (1997) research focused on the explanation of the simultaneous effects of the discount rate, compensation rate, and expected long-term rate of return on plan assets. Brown (2001) not only focused on explaining the three pension rates but somewhat changed the direction of research by including a market valuation model to examine the value relevance of economic factors and reporting incentive factors.

In these earlier studies, the only explanatory variable that was consistently significant in explaining pension rate assumptions was the funding ratio variable. Other variables such as leverage, unrestricted retained earnings, cash constraints, manager control, size, unionization, tax loss, and change in CEO were not consistently significant from study to study. Conceivable explanations for the inconsistent findings may be due to omitted variables, measurement error, lack of power, and or misspecified models. Therefore, it is possible these models explaining pension rates may not fully capture the impact of pension expense manipulation as it relates to financial statement reporting.

Whether managers act in self-interest or in the interest of shareholders, their performance is monitored by directors, auditors, investors, creditors, and regulators, which in turn, creates strong motives to manage earnings. For these reasons, this study expects the capital market based incentives (i.e., prior year earnings) to capture more fully the effects of pension expense manipulation on financial statement reporting than earlier pension rate studies.

Burgstahler and Dichev (1997) theorized that investors in publicly traded companies use earnings-based benchmarks, in determining company value. In addition, prospect theory was another reason for using benchmarks, whereby investors value gains and losses using a reference point rather than by an absolute level of worth. Therefore, Burgstahler and Dichev (1997) use frequency distribution as a method for demonstrating the existence of earnings management. Evidence indicated a disproportionally low incidence of companies reporting small decreases in earnings and small losses relative to a high incidence of companies reporting small increases in earnings and small positive earnings.

DeGeorge et al. (1999) used a similar research design as Burgstahler and Dichev (1997) and reported earnings are the single most value relevant item provided to investors in financial statement reporting. Earnings were used as performance measures, which in turn, provided the enticement for companies to manipulate earnings. Their research revealed how efforts to exceed thresholds, that is, to sustain recent performance, to report positive earnings, and or to meet analysts' expectations, induced particular patterns of earnings management. Clearly emerging patterns showed earnings falling just short of thresholds were managed upward. Additional evidence suggests future performances of companies just achieving thresholds were poorer than performances for control companies that were less suspect of managing earnings (DeGeorge et al. 1999).

Barth et al. (1999) depicted companies with longer strings of repeated earnings increases are priced at a premium but when these companies experience declines in earnings, the premiums fall disproportionally. Moehrle (2002) found evidence suggesting some companies record restructuring charge reversals to avoid earnings declines, to avoid reporting net losses, and to meet analysts' earnings forecasts. Parker and Sale (2007) and Parker (2009) investigated whether or not companies with actual reported earnings in a neighborhood close to their earnings benchmark (i.e., prior year earnings and analysts forecasts, respectively) use pension expense as a means to maintain a steady stream of earnings. The screened results for companies with actual reported earnings in a neighborhood very close to their earnings benchmark indicated pension expense was used to manage actual earnings when these companies would otherwise miss their capital market benchmark.

In aggregate, earlier benchmark studies suggest that companies manage earnings to avoid an earnings decline, to avoid reporting losses, and to meet analysts' earnings forecasts. Therefore, based on the logic of these earlier studies, this study extends the investigation by not limiting the sampling technique to only those companies with actual earnings in a relatively small neighborhood very near to their actual capital market benchmark.

RESEARCH DESIGN

Three basic research models are used extensively in the earnings management literature (McNichols 2000). The primary objectives of these models are to discover how companies manipulate earnings, to determine what motivates companies to manipulate earnings, and to evaluate what costs and benefits are associated with company manipulation.

The aggregate accruals model, the specific accruals model, and the earnings-based distribution model are the three models prevalent in the earnings management literature (McNichols 2000). As with all research, there are advantages, disadvantages and tradeoffs with each model.

Healy and Wahlen (1999) suggest future research contributions in earnings management will come primarily from identifying factors that limit company ability to manage earnings and from documenting the extent and magnitude of the effects of specific accruals. The specific accruals research model is based on a disaggregated concept that examines individual accounting items that are subject to substantial manager judgment and are able to significantly impact reported earnings. The most important advantage to a researcher of the specific accruals model is the ability to make directional predictions based on his or her knowledge and skill. Whereas, the disadvantage of the specific accruals research model is its inability to analyze simultaneously aggregated effects of accounting manipulation used by managers in managing earnings (McNichols 2000, Fields et al. 2000, Francis 2001).

This study uses a specific accruals research model with the explanatory variable set as an earnings-based benchmark (i.e., prior year earnings). The research model is a collection of prior research rudiments that provide discovery, better understanding, and a more complete explanation regarding whether pension expense is predictably manipulated in a logical economic manner to achieve the earnings-based benchmark.

The distinction from earlier research is determining whether or not there is an association between the change in pension expense and the amount that companies hypothetically beat or hypothetically miss their benchmark based on premanaged earnings. A distinguishing feature of the study is that it does not limit the sampling technique to companies with actual reported earnings in a neighborhood very close to their prior year actual reported earnings (i.e., earnings based benchmark).

The hypothesis presented in alternate form.

[H1.sub.A]: Pension expense is manipulated in a rational economic manner to achieve the current year earnings benchmark, which is equal to the prior year reported earnings.

The hypothesis is testing for both benchmark and smoothing behaviors. Benchmark behavior is where a company decreases actual pension expense to increase actual current year earnings in an attempt to reach their earnings benchmark (i.e., prior year earnings). Smoothing behavior exists when a company stores up reserves for meeting their earnings benchmark in future periods. Lagged assets are used to scale variables in an attempt to control for size variations in companies. The cross sectional regression model is presented below.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

[PE_diff      Is the change in pension expense equal to current year
              pension expense minus prior year pension expense all
              scaled by lagged assets.

Miss_D        Is a dummy variable that equals 1 if the continuou s
              variable, Motivate < 0, and 0 otherwise.

Motivate      Is a continuou s variable equal to pretax income absent
              manipulation minus the applicabl e benchmar k all scaled
              by lagged assets.

Interact      Is an interaction variable equal to Miss_D times
              Motivate.

[DELTA]       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.

[yrD.sub.t]   Is a dummy variable for each applicable year 1995-2001
              with the 1995 dummy effects captured in the intercept.
[indD.sub.i]  Is a dummy variable representing each applicable
              industry. The number of industries is 62.

[[alpha].     Intercept for Motivate [greater than or equal to]
sub.0]        0 where Miss_D = 0.

[[alpha]      Intercept for Motivate < 0 where Miss_D = 1.
.sub.0] +
[[alpha]
.sub. 1]

[[alpha]      Motive slope for Motivate [greater than or equal to]
sub.2]        0 where Miss_D = 0.

[[alpha]      Motive slope for Motivate < 0 where Miss_D = 1.
.sub.2] +
[[alpha]
.sub. 3]

PE diff is the measure of earnings management and the proxy for the extent of manipulation in pension expense. The proxy development is accomplished by using the feature of SFAS No. 87 whereby the prior year pension expense provides a logical approximation for the company's premanaged or premanipulated pension expense. That is assuming the number of employees remains constant from year to year and there is no yr and industry effects. PE_diff is defined as the current year pension expense minus the prior year pension expense all scaled by lagged assets.

Premanipulated actual earnings relative to the earnings benchmark (i.e., prior year earnings) represents the level of capital market motivator for earnings management. The capital market based motivator measure to manipulate earnings is represented by the continuous scaled variable, Motivate. Premanipulated actual earnings are derived by adding current year pension expense back to current year earnings to zero-out the effect of current year pension expense and then subtracting prior year pension expense. In essence, prior year pension expense is simply substituted in place of current year pension expense to calculate earnings absent pension manipulation.

Benchmark earnings, as well as premanipulated actual earnings, are reported on a pretax basis (Burgstahler and Eames 2002) rather than an after tax basis because pension expense is reported in financial statements on a pretax basis. Again, the proxy measure for pension expense absent pension manipulation is the prior year pension expense.

Because both benchmark and smoothing motivators exist, it is important to distinguish companies that hypothetically miss their benchmark from companies that hypothetically beat their benchmark. Therefore, a dummy variable (i.e., Miss_D) for hypothetically missing the benchmark is included in the analysis. Miss_D is coded zero for companies that hypothetically beat their benchmark using premanaged earnings. Whereas, Miss_D is coded one for companies that hypothetically miss their benchmark using premanaged earnings. If a [[alpha].sub.1] is significant and positive, companies missing their benchmark have a higher intercept than the other companies. If [[alpha].sub.1] is significant and negative, companies missing their benchmark have a lower intercept than the other companies. If [[alpha].sub.1] is insignificant, there is no difference between the two groups of companies.

After controlling for the change in the number of employees, industry effects, and time fixed effects, the association between PE_diff and the level of capital market motivators (i.e., Motivate) for earnings management constitutes this study's test of interest. PE_diff is expected to be positively correlated with the motivator variable, Motivate. The slope coefficient for the group of companies that hypothetically beat their benchmark is represented by a 2. The slope coefficient for the group of companies that hypothetically miss their benchmark 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.

If [[alpha].sub.2] + [[alpha].sub.3] is significant and positive, this suggests the primary companies of interest hypothetically missing their benchmark are actually decreasing pension expense to increase reported earnings to avoid missing their benchmark. If [[alpha].sub.2] + [[alpha].sub.3] is significant and negative, this suggests companies hypothetically missing their benchmark are not actually decreasing pension expense.

If [[alpha].sub.2] is significant and positive, this suggests the secondary companies of interest hypothetically beating their benchmark are actually increasing pension expense to decrease earnings to move closer to their benchmark than they would otherwise be. If a 2 is significant and negative, this suggests companies hypothetically beating their benchmark are not actually increasing pension expense.

The logic behind the predictions for [[alpha].sub.2] and [[alpha].sub.2] + [[alpha].sub.3] is that PE_diff is expected to move in the same direction as Motivate. For example, if a company has premanaged earnings equal to $.28 per share and benchmark earnings ( i.e., prior year earnings) equal to $.26 per share, the company 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., PE_diff) is expected to move $.02 in a positive direction as well. Motivate (i.e. [[alpha].sub.2]) captures the positive $.02 excess in premanaged earnings. Therefore, because PE_diff and Motivate move together in the same direction, a positive correlation is predicted.

On the other hand, if a company has premanaged earnings equal to $.26 per share and benchmark earnings (i.e., prior year earnings) equal to $.28 per share, the company is expected to decrease pension expense by $.02 to offset the $.02 negative premanaged earnings. Motivate (i.e., [[alpha].sub.2] + [[alpha].sub.3]) captures the negative $.02 deficiency in premanaged earnings. Here again, because PE_diff and Motivate move together in the same direction, a positive correlation is predicted.

So in summary, the prior year earnings (i.e., benchmark) create motivators for companies that are in opposite directions depending on their level of premanaged earnings. Therefore, companies hypothetically missing their benchmark are expected to exhibit benchmark behavior by manipulating pension expense to increase actual earnings in order to reach their benchmark earnings. On the other hand, companies hypothetically beating their prior year earnings (i.e., benchmark) are expected to exhibit smoothing behavior by manipulating pension expense to decrease actual earnings so that their actual earnings are closer to their benchmark earnings than they would otherwise be. Smoothing behavior allows cookie jar reserves to be stored up for use in future years.

[DELTA]Emp is a control variable that accounts for any variation in the dependent variable (i.e., PEdiff) caused by the change in the number of employees from year to year. [DELTA]Emp 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]Emp, should mitigate 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., PE_diff) and the change in the number of employees from year to year (i.e., [DELTA]Emp). The reasoning is plausible 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]mp.

The final sample consists of 4,203 cross-sectional company observations with applicable data for the period 1995 through 2001 which are derived from the Compustat database. The data coincides with an earlier study and is therefore very cost effective for the researcher. At the time the sample was selected, it included all years for which pension data was available from the data source. This study does not use a screening process similar to Dhaliwal et al. (2002) as the screening process looks only at companies that are more suspect of managing earnings in response to capital market motivators. Therefore possible bias from the screening process is eliminated in this study. Outlier observations are windsorized so that large and small outlier values are still large and small values within the dataset but are less likely to disrupt the mean, standard estimates, and other statistics that depend upon them. The action taken to address outlier observations should mitigate the possible influence these observations bias the overall statistical outcome.

Multicollinearity diagnostics indicate no problem exists with independent variables being highly collinear. In this study, it is important to be mindful that OLS coefficients are unbiased in the presence of heteroscedasticity. So the dollar magnitudes will not be affected even if heteroscedasticity is present. Heteroscedasticity in an OLS regression causes the true variance to be understated and causes the t-statistic to be overstated. For this reason, based on White's joint test for model misspecification and heteroscedasticity, t-statistics are reported using White's corrected t-statistics if applicable and are otherwise reported using OLS t-statistics.

The regression results reported in Table 2 use the equation (1) regression model. PE_diff, representing company manipulation, is expected to be positively correlated with the motivator variable of interest, Motivate. The motivator slope is captured in the model for the companies that hypothetically beat their benchmark by a2 and for the companies that hypothetically miss their benchmark by [[alpha].sub.2] + [[alpha].sub.2]. The slope on Motivator (i.e., [[alpha].sub.2] and [[alpha].sub.2] + [[alpha].sub.3]) represents the estimated average change in pension expense when the applicable motivator variable increases or decreases by one unit. If companies are more concerned with reaching their benchmark than smoothing, we predict that [[alpha].sub.3] > 0.

The slope coefficient (i.e., [[alpha].sub.2] > 0) for the companies that hypothetically beat their benchmark earnings is expected to be statistically significant. The slope coefficient (i.e., [[alpha].sub.2] + [[alpha].sub.3] > 0) for the companies that hypothetically miss their benchmark earnings is expected to be statistically significant as well and is the key variable of interest.

The foregoing rationale is based on the belief that pension manipulation is a function of the value of the magnitude of hypothetically missing or hypothetically beating the benchmark earnings (i.e., prior year earnings) based on premanaged earnings. The economic substance is captured in the regression model by the main effects of the motivator variable for the two distinct groups of companies. Therefore, the results on the control variables are not important for interpretation and are not reported.

Table 2 reports the results of the association test. The significant F-statistic (i.e., p-value = .0001) for the model indicates strong evidence of linearity. The R2 and Adjusted R2 are .0332 and .0166 respectively, which indicates the proportion of the change in pension expense that is explained by the combination of independent variables.

The slope on Motivate captures the average magnitude of the change in pension expense when there is a one unit change in the motivator variable for the two distinct groups of interest. As predicted, the motivator variable for both groups of companies is positive and significant. This pattern of evidence supports the notion that both groups of companies are using pension expense in a predictable rational economic manner based on the magnitude of hypothetically missing or hypothetically beating their benchmark earnings.

The results further indicate smoothing behavior is dominant. For every $1 that premanaged earnings are above the earnings benchmark (i.e., prior year pretax earnings), pension expense increases by $.04 to reduce actual reported earnings. Whereas, for every $1 that premanaged earnings are below the earnings benchmark (i.e., prior year pretax earnings), pension expense decreases by $.03 to increase actual reported earnings. One plausible explanation is that auditors may be more vigilant in constraining upward earnings manipulation (i.e., benchmark behavior) than downward earnings manipulation (i.e., smoothing behavior).

Again, it is interesting the reported results are in agreement with the findings in the Nelson et al. (2000) survey study where evidence suggests income-decreasing earnings management attempts are more likely to occur with respect to imprecise financial standards. SFAS No. 87 can be classified as an imprecise financial standard partly because of the allowed company flexibility in choosing the discount rate, the compensation rate, and the expected rate of return on plan assets. Assuming the motivation to manipulate earnings upward to meet benchmark earnings is at least equal to the motivation to manipulate earnings downward to meet benchmark earnings, the pattern of evidence from Table 2 suggests auditors are more vigilant in constraining upward earnings management to meet benchmark earnings than in downward earnings management to meet benchmark earnings. One plausible explanation is that auditors are more likely to be suited for upward earnings manipulation than for downward earnings manipulation. Here upward earnings manipulation uses future resources in the current period whereas downward earnings manipulation stores up earning reserves for use in future periods.

CONCLUSIONS

Whether acting in self-interest or in the interest of shareholders, manager performance is monitored by directors, auditors, investors, creditors, and regulators, which in turn, creates strong motives to manage earnings. Therefore managers use discretionary accounting devices to manage earnings to continue a steady stream of earnings to avoid market devaluation and to reap stock price rewards (Powell et al. 1993). As commonly known, many contracting motivators are also tied to earnings based measures which again provide strong motivators for managed earnings.

This study extends earlier research by providing additional evidence that company managers are using pension expense in a predictable economic manner to move their actual reported bottom-line earnings closer to their benchmark (i.e., prior year earnings) than they would otherwise be. The study also provides additional evidence that prior year earnings (i.e., benchmark earnings) create capital markets motivators for companies in opposite directions depending on their economic status as measured by whether or not companies will miss or beat their benchmark earnings based on premanipulated earnings. So by using "what if analyses, companies that hypothetically miss their benchmark earnings are predicted and shown to manipulate actual pension expense downward to increase actual earnings; whereas, companies that hypothetically beat their benchmark earnings are predicted and shown to manipulate actual pension expense upward to decrease actual earnings. Therefore as predicted, both groups of interest are successfully manipulating pension expense in the direction that moves their actual earnings closer to their benchmark earnings (i.e., prior year earnings) than they would otherwise be.

The results suggest that smoothing behavior is stronger than benchmark behavior. One plausible explanation is that auditors may be more vigilant in constraining efforts to manage earnings upward than in constraining efforts to manage earnings downward.

Rationale is provided that pension expense is likely the earnings management device of choice as it allows managers to manipulate earnings directionally as needed without easily being detected by interested outside parties. Furthermore, sensitivity analyses support the research findings are robust to controls for industry and time effects, as well as to the change in the number of employees.

Since capital markets and the U.S. Economy are heavily influenced by the integrity of financial statement reporting, this study should be of interest to a wide audience such as academicians, investors, directors, creditors, auditors, and regulators. It provides timely and relevant information about how managers are using pension expense to manipulate the most value relevant amount (i.e., actual reported bottom-line earnings) reported to investors. Perhaps this research will be a stimulus for the FASB to continue rethinking its current position regarding pension standards on pension measurement and reporting. Interest in pension accounting is widespread and provides many opportunities for future research.

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Bergstresser, D., Desai, M., and Rauh, J. 2006. Earnings Manipulation, Pension Assumptions and Managerial Investment Decisions. Quarterly Journal of Economic (Feb 2006) 121, no.1: 157-195.

Blankley, A. 1992. Incentives in Pension Accounting: An Empirical Investigation of Reported Rate Estimates. Dissertation, Texas A&M University, College Station, Texas.

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Burgstahler, D. and I. Dichev. 1997. Earnings Management to Avoid Earnings Decreases and Losses. Journal of Accounting and Economics 24 (1): 99-126.

Dhaliwal, D., C. Gleason, and L. Mills. 2002. Last Chance Earnings Management: Using the Tax Expense to Achieve Earnings Targets. University of Arizona, Working Paper.

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Financial Accounting Standards Board. 1998. Employers' Disclosures about Pension and Other Postretirement Benefits. Statement of Financial Accounting Standards No. 132.

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Kwon, S. 1989. Economic Determinants of the Assumed Interest Rate in Pension Accounting. Dissertation, University of Oklahoma, Norman, Oklahoma.

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Parker, P. D. 2009. Managing Pension Expense to Meet Analysts; Earnings Forecasts: Implications for New FASB Pension Standard. Parker, P. D. and M. L. Sale. 2007. Managing Pension Expense for Managed Earnings: Implications for FASB Standards.

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Paula Diane Parker, University of Southern Mississippi

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           -17,405
Firms in final sample                             4,203

Table 2: Cross Sectional Pooled Effects Estimation
With Time and Industry Fixed Effects

Variable                                               Prediction

intercept                                                  +
Miss D                                                     -
motivate                                                   +
interact                                                 + / -

[[alpha].sub.0] + [[alpha].sub.1]                          -
[[alpha].sub.2] + [[alpha].sub.3]                          +
F-statistic as p-value                    .0001
[R.sup.2]                                 .0332
Adjusted [R.sup.2]                        .0166

Variable                               Coefficient      One Tail
                                                        p-value

intercept                                -0.03305        .1318
Miss D                                    0.01415        .0925
motivate                                  0.04117        .0001
interact                                 -0.01451        .0122

[[alpha].sub.0] + [[alpha].sub.1]        -0.01890        .2596
[[alpha].sub.2] + [[alpha].sub.3]         0.02666        .0004

F-statistic as p-value
[R.sup.2]
Adjusted [R.sup.2]