Inventory Earnings Managment

8/6/2019 Inventory Earnings Managment

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Managing Earnings by Manipulating Production:

The Effects of Timing, Tax, Compensation, and Governance Considerations

Kirsten A. Cook

Mays Post-Doctoral Fellow

Accounting Department

Mays Business School

Texas A&M University

449Q Wehner, 4353 TAMU

College Station, Texas 77843-4353

(979) 845-0542

[email protected]

January 22, 2008

This paper originates from the third chapter of my dissertation. I thank my advisory committee

members (Mike Kinney, Tom Omer, Tom Wehrly, Mike Wilkins, and Chris Wolfe) and

workshop participants at Texas A&M for helpful comments. I also thank John Graham for

sharing simulated marginal tax rate data and I/B/E/S International, Inc. for sharing forecasted

and actual earnings data. I gratefully acknowledge financial support from the Mays Post-

Doctoral Fellowship at Texas A&M.


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Managing Earnings by Manipulating Production:

The Effects of Timing, Tax, Compensation, and Governance Considerations

INTRODUCTION

Firms that meet or beat their consensus analyst forecasts enjoy positive stock price

reactions (Kasznik and McNichols 2002, Lopez and Rees 2002, Bartov, Givoly, and Hahn 2002).

Conversely, the market punishes companies that miss these income targets (Kinney, Burgstahler,

and Martin 2002), and these penalties are larger in magnitude than the rewards to reaching or

exceeding earnings goals (Conrad, Cornell, and Landsman 2002, Lopez and Rees 2002, Skinner

and Sloan 2002). To capitalize on this reward structure, firms that would otherwise fall short of

these benchmarks face incentives to manage their book income upward, while companies that

would otherwise exceed these targets possess motives to create cookie jar reserves for use in

future periods. Given these incentives, prior studies examine the distribution of reported

earnings and document a high frequency of zero and small positive earnings surprises (that is, the

difference between firms actual earnings and their consensus analyst forecasts) and a low

frequency of small negative earnings surprises, prima facie evidence of income management

(Degeorge, Patel, and Zeckhauser 1999, Burgstahler and Eames 2006).

In addition to these distributional tests, which ignore both the magnitude and methods of

earnings management (Healy and Wahlen 1999), previous research has investigated firms use of

accrual manipulation to achieve income objectives.1

For example, Payne and Robb (2000)

provide evidence that, when premanaged earnings fall below (exceed) analyst forecasts, firms

use their discretion over accounting accruals to increase (decrease) reported income. Past studies

also reveal that considerations other than financial reporting incentives impact companies use of

discretionary accruals to manage earnings. Stringent financial statement audits in the fourth


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quarter relative to more lenient review procedures in the first three quarters motivate firms to

decrease earnings at fiscal yearend (Brown and Pinello 2007). High tax rates (and the associated

tax liabilities) encourage companies to manage income downward (Manzon 1992). As the

percentage of their executives compensation linked to reported earnings increases, firms use

discretionary accruals to generate additional income (Healy 1985, Bergstresser and Philippon

2006). Strong corporate governance practices constrain accrual-based earnings management

strategies, resulting in truer reported income numbers (Klein 2002).

In this study, I examine an earnings management strategy that is unique to manufacturing

firms. Specifically, companies that miss their earnings benchmarks can produce inventory in

excess of sales, thereby shifting fixed manufacturing costs from cost of goods sold (COGS) to

inventory accounts, increasing income, and potentially reaching their earnings targets. In

contrast, firms that beat their income goals can underproduce relative to sales, transferring fixed

costs from asset to expense accounts, reducing profits, and building cookie jar reserves for use

in future periods. The purpose of this paper is to extend the earnings management literature by

investigating whether timing, tax, compensation, and governance considerations influence

manufacturing companies discretionary production decisions in the presence of financial

reporting incentives.

Timing considerations contain both audit and tax components. Firms face motivations to

lower taxable income in the fourth quarter, relative to the first three quarters, for purposes of (1)

complying with rigorous, yearend audit procedures and averting a qualified audit opinion and (2)

minimizing the current years tax liability or maximizing the refund, avoiding an underpayment

penalty, and lowering the subsequent years estimated tax payments. Tax considerations reflect

that, as their marginal tax rates (MTRs) rise, firms incur larger tax liabilities for each dollar


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increase (or enjoy greater tax savings for each dollar decrease) in taxable income; thus, high-tax

companies hold a greater incentive to reduce their taxable earnings than low-tax firms possess.

Compensation considerations imply that, as the proportion of executives total pay stemming

directly (cash bonuses) and/or indirectly (appreciation in the value of stock holdings and options)

from reported earnings increases, these top managers may add to their compensation by making

profit-maximizing discretionary inventory changes. Effective corporate governance limits

executives discretion, perhaps restricting firms use of production manipulation to manage

earnings either upward or downward.

For firms that exceed their earnings benchmarks absent production manipulation, timing

and tax considerations align with financial reporting incentives. Thus, I predict that these

companies make discretionary inventory decreases in the current period (which permit

overproduction in a later period) and that these cuts to production are larger (1) in the fourth

quarter relative to the first three quarters and (2) as MTRs rise. Conversely, these companies

face friction between compensation/governance considerations and financial reporting

incentives. Therefore, I expect that discretionary inventory decreases made by these firms are

smaller (1) as the percentage of executive compensation relating to accounting income and/or

stock price increases and (2) as the percentage of independent board members rises. I find

results consistent with these hypotheses.

For firms that miss their income targets without modifying inventory, timing, tax, and

governance considerations oppose financial reporting incentives; therefore, I expect that these

companies accelerate production (relative to sales) to improve their book income and potentially

reach their consensus analyst forecasts but that these discretionary inventory increases are

smaller (1) in the fourth quarter compared to the other three quarters, (2) as MTRs climb, and (3)


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as board independence increases. In contrast, because their compensation considerations and

financial reporting incentives complement one another, I hypothesize that these companies

stockpile excess inventory as the remunerative benefits to their executives of this earnings

management strategy increase. My findings support these expectations. This sub-sample of

firms also provides an opportunity to study whether financial reporting considerations dominate

other factors when companies make discretionary production decisions. I discover that higher

MTRs do not prevent these firms from augmenting inventory to increase income. However, in

isolation, both timing and governance considerations attenuate this earnings management

strategy.

Relative to the accrual literature, the research stream examining earnings management

perpetrated by production manipulation is nascent. My study contributes to this burgeoning area

of inquiry by demonstrating that timing, tax, compensation, and governance considerations

impact firms discretionary inventory changes just as these factors have influenced discretionary

accruals in prior studies. Thus, I provide evidence that these incentives exert a consistent

pressure on firms earnings management behavior, regardless of the specific methods that their

managers use to achieve income goals. I contend that future studies investigating production

manipulation should control for these four factors in their multivariate models.

The remainder of this paper proceeds as follows. In the next section, I review five

streams of literature relevant to my research topic and formalize my hypotheses. Then, I detail

the data and two-stage regression methodology that I use to test these hypotheses. In the

subsequent section, I provide the results of these tests. Finally, I conclude.

MOTIVATION AND HYPOTHESES

Financial Reporting Considerations


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Graham, Harvey, and Rajgopal (2005) survey more than 400 financial executives,

question whether these managers would engage in actions with real economic consequences (real

activities management) to meet earnings benchmarks, and report results consistent with this

behavior. For example, 79.9 percent of respondents either agreed or strongly agreed that they

would decrease discretionary spending on items such as research and development or advertising

to hit earnings targets.

One form of real activities management that researchers have empirically investigated is

production manipulation. Generally Accepted Accounting Principles (GAAP) mandate that

manufacturing firms use absorption costing to value their inventories for financial reporting

purposes. Under absorption costing, fixed manufacturing costs (such as depreciation expense

related to manufacturing facilities and equipment) attach to products. These fixed manufacturing

costs reside in inventory until firms sell their products, at which point these costs flow to COGS.

Thus, holding sales constant, as manufacturing firms increase production, the amount of fixed

costs included in inventory rises, the amount included in COGS falls, and, consequently, income

increases. This technique also functions in reverse: as production slows, the value of inventory

falls, COGS rises, and earnings decline. Accordingly, manufacturing firms can manage earnings

by manipulating production.

Jiambalvo, Noreen, and Shevlin (1997) investigate the relation between security returns

and the earnings component generated by overproduction and find that (1) the market generally

views overproduction as good news (that is, a leading indicator of strong future performance)

but (2) the market discounts this good news for firms that overproduce to manage earnings.

Thomas and Zhang (2002) also proffer earnings management as an explanation for their

observed negative correlation between current inventory changes and future returns.


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More recent studies have directly investigated the use of production manipulation to

manage earnings. Roychowdhury (2006) finds that manufacturing firms report higher

unexpected production costs than companies in other industries during years in which earnings

management is suspected (that is, years in which income narrowly exceeds target levels). In

their studies investigating how firms coordinate various earnings management techniques

(within-GAAP versus non-GAAP, accrual manipulation versus real activities management),

Badertscher (2007) and Zang (2006) also provide evidence that firms overproduce inventory to

lower COGS and augment reported earnings. Cook, Huston, and Kinney (2007) document that

(1) a systematic association exists between firms inventory changes and their financial reporting

incentives and (2) this association varies with the proportions of fixed manufacturing costs in

these companies cost structures and their choices of inventory valuation methods.

My first task is to document that, on a quarterly basis, manufacturing firms do manipulate

production to manage earnings. Specifically, companies with premanaged income above their

target levels (beat firms) may use discretionary inventory decreases to reduce their profits and

build cookie jar reserves.2 In contrast, firms with premanaged earnings below their target

levels (miss firms) may use discretionary inventory increases to boost their income and

potentially reach their goals. Hypothesis 1 formalizes these expectations:3, 4

H1: Beat (Miss) firms make discretionary inventory decreases (increases) to lower (raise)

reported earnings.

Timing Considerations

Relative to firms annual reports, auditors have less involvement with companies

quarterly financial statements (Frankel, Johnson, and Nelson 2002). Specifically, annual reports

are subject to thorough audits, whereas interim quarterly reports undergo less comprehensive


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review procedures (Mendenhall and Nichols 1988, Brown and Pinello 2007). This differential in

audit stringency between interim and annual financial reports has motivated researchers to

examine whether firms willingness to manage earnings varies across quarters. Elliot and Shaw

(1988) find that 63 percent of discretionary asset write-offs occur during the fourth quarter,

implying that firms delay these income-decreasing special items until auditors mandate their

recognition at yearend. Brown and Pinello (2007) report that the likelihood of income-increasing

earnings management (proxied by positive discretionary accruals) is lower in the fourth quarter

relative to the first three quarters. These authors also provide evidence that the rigorous audit

process at yearend motivates firms to manage expectations downward rather than manage

reported income upward to reach their earnings benchmarks in the fourth quarter. Das, Shroff,

and Zhang (2007) discover that approximately 25 percent of companies with positive earnings

changes for the year report income-decreasing discretionary accruals in the fourth quarter. This

finding implies that auditors tolerate downward earnings management but object to income-

increasing manipulations in the fourth quarter and complements St. Pierre and Anderson (1984)

and Becker, DeFond, Jiambalvo, and Subramanyam (1998), who provide evidence that auditors

face litigation for permitting clients to overstate earnings but not for allowing companies to

understate income. The increased rigor of yearend audit procedures relative to those in interim

quarters may limit firms use of production manipulation to generate profits just as prior

literature has demonstrated this effect on accruals-based strategies.

Tax considerations may also impact the timing of firms production decisions.

Companies face incentives to reduce taxable income in the fourth quarter, relative to the first

three quarters of the year, for purposes of determining the current years tax liability (or refund)

and the subsequent years estimated tax payments. With respect to the current years tax burden,


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firms that have overproduced (relative to sales) to hit their earnings targets in the first three

quarters of the year may limit or reverse this strategy in the fourth quarter in anticipation of filing

IRS Form 1120, thereby reducing the liability (or increasing the refund) reported on these tax

returns. With respect to the subsequent years estimated tax payments, companies that meet four

criteria (prior tax year was 12 months, filed a tax return in the prior year, did not report a tax

liability in the prior year, and had less than $1 million of taxable income in each of the prior

three years) do not incur underpayment penalties in the subsequent year if the sum of their

estimated tax payments in that year equals or exceeds their total tax in the current year. For

firms that fail to meet these conditions, the IRS will not assess underpayment penalties if the sum

of estimated tax payments in the current year equals or exceeds that years total tax. Under

either scenario, corporations face stronger incentives in the fourth quarter to reduce their taxable

income than they confront in the first three quarters.5

For firms that exceed their income goals without manipulating production, the timing

incentive suggests more downward earnings management in the fourth quarter. However, for

firms that miss their earnings targets absent discretionary income changes, this timing motivation

implies less upward earnings management in the fourth quarter. For miss firms, determining

whether timing concerns overshadow financial reporting considerations is an empirical question.

Hypothesis 2 articulates my expectations (assuming that timing impacts companies

discretionary production decisions):

H2: Beat (Miss) firms make larger (smaller) discretionary inventory decreases (increases) in

the fourth quarter relative to the first three quarters.

Tax Considerations

Effective tax planning and tax minimization are not synonymous (Scholes, Wolfson,


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Erickson, Maydew, and Shevlin 2005). Given the positive correlation between income for tax

and financial reporting purposes, strategies that minimize taxes may lead to reductions in book

earnings and associated declines in firm value.6 Conversely, the incursion of additional taxes

may constrain firms willingness to manage their income upward. Many studies have examined

this tradeoff.7 Some researchers discover that tax factors eclipse financial reporting

considerations. For example, Manzon (1992) finds that, to minimize the cost of the alternative

minimum tax (AMT), firms facing high MTRs engaged in downward earnings management

using accruals to a greater extent than companies with low MTRs, and this result is robust to the

inclusion of controls for financial reporting incentives.

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Guenther, Maydew, and Nutter (1997)

report that, in response to mandatory adoption of the accrual basis for taxable income

determination, former cash-basis taxpayers deferred book income to preserve tax savings.

Monem (2003) studies the years preceding the introduction of the Australian Gold Tax in 1991

and provides evidence that gold-mining firms exploited income-decreasing discretionary

accruals to minimize the political costs associated with soaring profits.

Other studies provide evidence that financial reporting concerns dominate tax savings.

For instance, Maydew, Schipper, and Vincent (1999) show that, in structuring divestitures of

assets as either taxable sales or tax-free spin-offs, companies willingly incur taxes to increase

book income and cash flows. Erickson, Hanlon, and Maydew (2004) demonstrate that their

sample of restatement firms reported overstated book income, included these inflated profits on

their tax returns, and thus overpaid their taxes to preserve financial reporting benefits.

Still other papers establish that tax and financial reporting factors jointly impact firms

business decisions. Guenther (1994) reports that the corporate tax rate reduction included in the

Tax Reform Act of 1986 (TRA1986) motivated firms to defer income to lower-tax years, but


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firms facing higher financial reporting costs (proxied by the ratio of long-term debt to total

assets) deferred less income. Maydew (1997) also uses TRA86 as a natural experimental setting

and provides evidence that (1) firms with net operating loss (NOL) carrybacks deferred income

and accelerated expenses to enlarge tax refunds in high-tax years and (2) this intertemporal

income shifting was increasing in the associated tax benefits and decreasing in the related

financial reporting costs.

Overall, the extant literature examining tax and financial reporting tradeoffs reveals that

both factors impact firms business decisions and that neither incentive consistently prevails. In

this study, I examine this tradeoff for firms using discretionary inventory changes to alter their

earnings. Firms managing book income downward by decelerating production (relative to sales)

receive a tax benefit from this strategy by simultaneously decreasing their taxable income. For

these companies, no tradeoff exists between tax and financial reporting factors; rather, these

incentives align. In contrast, one cost of managing income upward by accelerating production

(relative to sales) is the tax liability that the Internal Revenue Service (IRS) levies on these

additional profits. Thus, as their tax burdens increase, manufacturing firms that miss their

income goals absent overproduction may limit their use of this earnings enhancement strategy.

These companies must weigh the tax cost and financial reporting benefit (that is, the possibility

of reaching their earnings targets) of this technique, and which consideration dominates in this

scenario is ripe for empirical investigation. Since discretionary production decisions affect

firms earnings at the margin, I use MTRs to proxy for the tax costs and benefits associated with

under- or overproduction. Hypothesis 3 expresses my predictions (presuming that taxes matter):

H3: Beat (Miss) firms make larger (smaller) discretionary inventory decreases (increases) as

their MTRs increase.


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Compensation Considerations

The vast majority of U.S. corporations use bonus plans based on accounting earnings to

remunerate top managers (Fox 1980, Murphy 1999). Since firms reported earnings directly

impact their executives bonus pay, compensation considerations encourage top managers to

engage in strategies that boost income, and prior research provides evidence of this practice.

Healy (1985) documents that managers incentives to manipulate their employers earnings

depend on the terms of their compensation contracts. Specifically, companies rewarding their

executives with unconstrained, earnings-based bonuses report less negative total accruals than

firms in which additional profits would not provide a monetary incentive for managers to inflate

income (because these companies earnings absent accrual manipulation are either above or too

far below the upper and lower limits, respectively, of their bonus contracts).9

Gaver, Gaver, and

Austin (1995) extend Healy (1985) by examining a more recent sample period and using

discretionary (rather than total) accruals as their measure of earnings management; in contrast to

Healy (1985), these authors find that firms with earnings absent accrual manipulation below the

lower bounds of their bonus contracts report income-increasing discretionary accruals.

Holthausen, Larcker, and Sloan (1995) find similar results to Gaver et al. (1995) for this sub-

sample of companies with pre-accrual-managed earnings below the minimum levels specified in

their executives bonus contracts. Balsam (1998) reports that, as the correlation between CEOs

cash compensation and their employers reported earnings increases, these firms discretionary

accruals also rise. Matsunaga and Park (2001) show that CEOs earn smaller bonuses when their

firms reported earnings miss their quarterly income goals.

Hall and Liebman (1998) document that, between 1982 and 1994, the mean value of

CEOs stock and option holdings increased by more than 350 percent, far outstripping the 90-


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percent rise in their cash compensation.10 As the link between executives wealth and the prices

of their employers stocktightens, these top managers face incentives to inflate equity values.

According to a theoretical model developed by Stein (1989), because investors use current

earnings to predict future earnings when valuing securities, compensation considerations

motivate executives to use their accounting discretion to generate profits. Recent empirical

studies provide evidence consistent with this managerial opportunism. Cheng and Warfield

(2005) find that firms with higher managerial equity incentives report a higher incidence of

meeting or beating their consensus analyst forecasts than companies whose executives lack these

inducements; they also provide evidence of accrual manipulation as the tactic for hitting these

benchmarks. Further, these authors document that CEOs with high equity incentives sell more

shares in periods immediately after their firms reach or exceed their earnings targets than after

missing these income goals, implying that these top managers inflate profits for personal gain.

Bergstresser and Philippon (2006) show that, as the sensitivity of CEOs compensation to their

employers stock prices rises, these firms use of discretionary accruals to enhance income also

increases. Similar to Cheng and Warfield (2005), these authors discover that, in periods when

discretionary accruals constitute a large portion of companies reported earnings, CEOs exercise

options and sell shares, presumably to capitalize on the positive returns associated with strong

earnings.

Prior literature demonstrates that firms whose managers benefit from increased earnings

directly (through cash bonuses tied to accounting numbers) and/or indirectly (through stock

holdings and options that appreciate in value when the present value of expected future earnings

grows) manipulate accruals to reach their benchmarks. I investigate whether firms also modify

production in response to these compensation considerations. Unlike timing and tax factors,


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which I hypothesize to enhance beat firms use of production manipulation to decrease earnings

(and constrain miss firms application of this strategy to increase income), I expect that

compensation considerations encourage managers to augment reported profits by stockpiling

inventory. Thus, as the percentage of executive compensation relating to accounting income

and/or stock price increases, a conflict exists for beat firms between the desire to limit production

in the current period (which permits overproduction in a subsequent period) and the incentive to

overproduce in the current period to maximize the value of bonuses, stock holdings, and stock

options. On the other hand, for miss firms, financial reporting and compensation motives

complement one another. Hypothesis 4 states my predictions (assuming that executives seek to

maximize their pay):

H4: Beat (Miss) firms make smaller (larger) discretionary inventory decreases (increases) as

the percentage of executive compensation relating to accounting income and/or stock price

increases.

Governance Considerations

Former Securities and Exchange Commission Chairman Arthur Levitt has referred to

earnings management as a game that, if not addressed soon, will have adverse consequences for

Americas financial reporting system (Levitt 1998). He has mentioned the zeal to satisfy

consensus earnings estimates and firms that stash accruals in cookie jars during the good times

and reach into them when needed in bad times as pervasive problems that erode the quality of

reported earnings and has suggested enhanced oversight of the financial reporting process by

those entrusted as the shareholders guardians to curtail this behavior. In this study, I use the

percentage of independent directors (which the New York Stock Exchange defines as board

members who are not current or former employees of the firm and are not linked or affiliated


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with the firm beyond their role as directors) on each sample firms board to investigate

governance. Consistent with Levitts call to action, prior research demonstrates that more

independent boards result in both less fraudulent financial reporting and reduced manipulation of

reported earnings. Beasley (1996) documents that, as the percentage of outside (that is, non-

employee) directors on the board increases, the likelihood of financial statement fraud decreases.

Chtourou, Bdard, and Courteau (2001), Klein (2002), Xie, Davidson, and DaDalt (2003), and

Kao and Chen (2004) all examine board independence and provide evidence that higher levels of

this variable are associated with reductions in the magnitudes offirms discretionary accruals.

If one goal of effective corporate governance is a reduction in earnings management,

firms with greater board independence should exhibit less evidence of income distortion. In the

case of production manipulation, the magnitudes of discretionary inventory changes for

companies with more independent boards should be smaller than those of firms with less

independent boards. Stated differently, legitimate business purposes (rather than earnings

management) should induce a larger percentage of total inventory changes for companies with

stronger board oversight. For beat firms that possess financial reporting motivations to build

earnings reserves by depleting inventory, the presence of more independent boards should

constrain managers use of production manipulation to reduce income. In contrast, for miss

firms facing incentives to produce inventory in excess of current sales and future demand,

greater board independence should limit upward earnings management perpetrated by

discretionary inventory increases. Hypothesis 5 presents my expectations (presuming that board

independence is a relevant factor in curtailing production manipulation):

H5: Beat (Miss) firms make smaller discretionary inventory decreases (increases) as the

percentage of independent directors on their boards increases.


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DATA AND METHODOLOGY

Data

My initial sample contains 46,630 manufacturing firm-quarter observations (SIC codes

between 2000 and 3999) with complete data from the Compustat Industrial Quarterly database

for the years 1988 through 2005. I begin my sample period in 1988, following the last

significant restructuring of the Internal Revenue Code (TRA86), so that the tax structure is

relatively stable during the sample period.11

I remove 27,757 observations that report no

inventories, fail to report inventory valuation methods, or use methods other than first-in, first-

out (FIFO) to value their inventories.

12

I eliminate non-FIFO firms because I cannot anticipate

how alternate inventory valuation methods, such as last-in, first-out (LIFO), impact companies

incentives to manage earnings through discretionary inventory changes.13

I also discard 10,827

observations that lack forecasted and actual earnings data from I/B/E/S. These cuts yield a

sample of 8,046 firm-quarter observations representing 896 unique firms, which I use to examine

my timing hypothesis. The mean and median quarters of data per firm are 8.98 and 6,

respectively. Due to missing data in John Grahams MTR website

(http://faculty.fuqua.duke.edu/~jgraham/), the Compustat Executive Compensation (Execucomp)

database, and the Investor Responsibility Research Center (IRRC) database, the samples that I

use to investigate my tax, compensation, and governance hypotheses contain 6,675, 3,238 and

2,446 observations, respectively.14

Panel A of Table 1 presents the screening process that I use

to determine these sample sizes, and Panel B displays the industry composition of my sample.

----- Insert Table 1 here -----

Methodology

Discretionary Inventory Changes


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To test my hypotheses, I use a two-stage regression procedure. In the first stage, I

develop Model (1) to purge firms total quarterly inventory changes of the non-discretionary

component associated with current sales, future demand, and firm- and year-specific factors.

The residuals from this model represent firms quarterly discretionary inventory changes, which

I presume relate in part to financial reporting incentives. I provide Compustat Industrial

Quarterly variable numbers in parentheses:

Inv_chi,q = + 1 Sal_chi,q + 2 Fut_demi,q+1 + 3 Fut_demi,q+2 + 4 Fut_demi,q+3 + 5-

899 Firm Indicatorsi + 900-915 Year Indicatorsy + i,q, where (1)

Inv_chi,q = inventoriesi,q (data38)inventoriesi,q-1

Sal_chi,q = salesi,q (data2)salesi,q-1

Fut_demi,q+1 = salesi,q+1salesi,q

Fut_demi,q+2 = salesi,q+2salesi,q+1

Fut_demi,q+3 = salesi,q+3salesi,q+2

The response variable,Inv_ch, measures sample firms total inventory changes during the current

quarter.15

Sal_ch captures sales changes occurring contemporaneously withInv_ch, and

Fut_dem represents sales changes occurring in the three subsequent quarters (proxies for

managers expectations of future demand for their firms products). If companies strive to

maintain a constant ratio of inventory to sales in order to avoid excess carrying costs and stock

outages, a positive relation should exist betweenInv_ch and Sal_ch. However, if increases in

sales draw down inventory (or decreases in sales build up inventory) without offsetting

adjustments to production, a negative association should manifest betweenInv_ch and Sal_ch.

Thus, I place no directional prediction on this variable. In contrast, assuming that firms modify

their production in anticipation of future changes in demand, I expect a direct association


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betweenInv_ch and Fut_dem. I include in Model (1) proxies for future demand in the next three

quarters because companies production decisions in the current quarter may relate to anticipated

sales over various time horizons. For example, a calendar-year toy manufacturer may produce

action figures during the first quarter in anticipation of holiday sales in the fourth quarter of the

same year. A calendar-year automobile manufacturer may build cars in the fourth quarter in

anticipation of summer sales in the second and third quarters of the following year.

I winsorize all variables in Model (1) at the 1st

and 99th

percentiles to reduce the influence

of outlying observations and employ a two-way fixed effects approach that controls for firm- and

year-specific factors that influence firms inventory changes but may not relate to financial

reporting considerations.16, 17 I label the residuals from this estimationDisc_inv_ch, a measure

offirms discretionary inventory changes. Table 2 provides definitions of all variables that I use

in this study.


Earnings Effects of Discretionary Inventory Changes

For a firm that uses the FIFO inventory valuation method, the following equation

captures the earnings effect of a discretionary inventory change:

Earn_effi,q = Disc_inv_chi,q FCRi,q, where

Disc_inv_chi,q = the residual from Model (1)

FCRi,q = property, plant, and equipmenti,q (data42) / assetsi,q (data44)

This equation demonstrates that, as a FIFO firm alters its level of production (for a constant level

of sales), a corresponding change in income occurs. As Cook et al. (2007) demonstrate, the

potency of this earnings management strategy depends on the firms cost structure. As the

proportion of fixed manufacturing costs in the firms cost structure (the fixed-cost ratio, FCR)


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increases, the earnings effect of a discretionary inventory change also increases.18

In the second stage, I construct Model (2) to evaluate whether financial reporting

incentives influence the earnings effects of firms discretionary inventory changes and to what

extent timing, tax, compensation, and governance considerations impact this earnings

management strategy:

Earn_eff_assi,q = + 1 Missi,q + 2 X + 3 Missi,q X + i,q, where (2)

Earn_eff_assi,q = Earn_effi,q / assetsi,q

Missi,q = an indicator variable coded 1 if firm i misses its income target in quarter q

absent the earnings effect of its discretionary inventory change, and 0

otherwise

X = the timing, tax, compensation, or governance variable under investigation

The response variable,Earn_eff_ass, measures the earnings effect of sample firms discretionary

inventory changes, scaled by total assets to control for firm size.19

Financial Reporting Considerations

The indicator variableMiss captures firms financial reporting incentives. An appendix

details the calculation ofMiss. This variable assumes that companies with premanaged earnings

(that is, actual earnings less the earnings effect of discretionary inventory changes) that fail to

reach their consensus analyst forecasts possess an incentive to boost their income. Graham et al.

(2005) report that 73.5 percent of executives in their survey list the consensus analyst forecast as

an important earnings benchmark. Brown and Caylor (2005) find that, in recent years,

stockholders value companies abilities to meet or beat theirconsensus analyst forecasts more

than they reward these firms for avoiding losses and negative quarterly earnings surprises. In

keeping with H1, I expect a negative coefficient for the intercept ( ), indicating that beat firms


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decrease production, thereby decreasing their reported financial earnings and creating cookie

jar reserves. I predict a positive sum of the intercept and the coefficient for theMiss main effect

( + 1), implying that miss firms increase production, thereby raising their reported financial

earnings and potentially reaching their income goals.

Timing Considerations

The indicator variable Timecaptures firms audit and tax timing incentives:

Timeq = an indicator variable coded 1 if quarter q is the fourth quarter of the year,

and 0 otherwise

In keeping with H2, I expect a negative coefficient for the Time main effect ( 2), indicating that

beat firms make larger discretionary inventory decreases in the fourth quarter relative to the first

three quarters. I anticipate a positive coefficient for the interaction ofMiss with Time ( 3)

because miss firms possess financial reporting considerations that oppose audit and tax timing

incentives, whereas these motivations coordinate for beat companies. I predict a negative sum of

the coefficients for Time and the interaction ofMiss with Time ( 2+ 3), implying that miss firms

make smaller discretionary inventory increases in the fourth quarter relative to the first three

quarters. If the sum of 2 and 3 does not differ significantly from zero, this finding indicates

that audit (comply with rigorous audit procedures and avert a qualified opinion) and tax

(minimize the current years tax liability or maximize the refund, avoid an underpayment

penalty, and minimize the subsequent years estimated tax payments) timing incentives do not

overwhelm the desire to improve earnings in the fourth quarter.

Tax Considerations

The continuous variableMTRcaptures firms tax rate incentives:

MTRi,y-1 = marginal tax ratei,y-1 based on income after deducting interest expense


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Since the earnings effects of firms discretionary inventory changes indirectly influence their

MTRs by modifying their taxable income, an endogenous relationship exists between

contemporaneous measures ofEarn_eff_ass andMTR; thus, I use one-year lagged values ofMTR

in Model (2).20

Consistent with H3, I expect a negative coefficient for theMTR main effect ( 2),

indicating that beat firms make larger discretionary inventory decreases as their MTRs rise. I

anticipate a positive coefficient for the interaction ofMiss withMTR ( 3) because, unlike beat

firms, miss firms face friction between their tax rate and financial reporting incentives. I predict

a negative sum of the coefficients forMTR and the interaction ofMiss withMTR ( 2+ 3),

implying that miss firms make smaller discretionary inventory increases as their MTRs rise.

However, if the sum of 2 and 3 does not differ significantly from zero, this result provides

evidence that higher tax rates do not deter miss firms from overproducing to increase their book

income.

Compensation Considerations

The continuous variableIRcaptures firms compensation incentives. To calculate this

variable, I modify the methodology from Bergstresser and Philippon (2006). In that paper, the

authors use a two-stage compensation incentive measure. In the first stage, they calculate the

value to a CEO of a one-percent increase in stock price:

Onepcti,y = 0.01 Pricei,y (Sharesi,y + Optionsi,y)

In the second stage, they calculate the share of the CEOs total compensation that results from

this increase:

Incentive_Ratioi,y = Onepcti,y / (Onepcti,y + Salaryi,y + Bonusi,y)

In this paper, I refine this measure as follows: In the first stage, (1) rather than including all

shares held by the CEO, I subtract the number of restricted shares, and (2) rather than including


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all options held by the CEO, I use only unexercised, vested options.21 In the second stage, since

the purpose of this measure is to capture the sensitivity of compensation to both accounting

income and stock price, I include both OnepctandBonus in the numerator. I provide

Execucomp variable numbers in parentheses:

Onepcti,y = 0.01 fiscal year-end close pricei,y (prccf) * [shares ownedi,y (shrown)

restricted stock holdingsi,y (rstkhld) + unexercised exercisable optionsi,y

(uexnumex)]

IRi,y = [Onepcti,y + Bonusi,y (bonus)] / [Onepcti,y + Salaryi,y (salary) + Bonusi,y]:

In keeping with H4, I expect a positive coefficient for theIR main effect ( 2), indicating that beat

firms make smaller discretionary inventory decreases as the percentage of their CEOs

compensation that relates to accounting income and/or stock price rises. I anticipate a positive

coefficient for the interaction ofMiss withIR ( 3) because, as beat firms decelerate production to

decrease earnings, their executives also reduce the value of their bonuses, stock holdings, and

stock options; however, for miss firms, compensation considerations complement financial

reporting goals, thus encouraging top managers of miss firms to make larger discretionary

inventory changes than their counterparts at beat companies. I predict a positive sum of the

coefficients forIR and the interaction ofMiss withIR ( 2+ 3), implying that miss firms make

larger discretionary inventory increases as the proportion of their total pay derived directly

(earnings-based bonuses) and/or indirectly (appreciation in the value of stock holdings and

options) from accounting income rises.

Governance Considerations

The continuous variableInd_percaptures firms governance incentives:

Ind_peri,y = the percentage of independent board members


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Fama and Jensen (1983) argue that independent directors are less likely to cooperate with

managers to expropriate shareholder wealth. Chtourou et al. (2001) demonstrate that, relative to

firms with high positive and high negative levels of discretionary accruals, companies with low

levels have a higher percentage of independent board members; similarly, Klein (2002) discovers

a negative relation between board independence and the magnitude of abnormal accruals. Xie et

al. (2003) report that the percentage of outside directors is negatively related to discretionary

current accruals, and Kao and Chen (2004) find that both current and total accruals are

decreasing in the count of independent board members for their sample of Taiwanese companies.

If board independence inhibits earnings management (as studies in the accrual literature

reveal), less production manipulation should occur among firms whose boards contain larger

percentages of independent directors. In keeping with H5, I expect a positive coefficient for the

Ind_permain effect ( 2), indicating that beat firms make smaller discretionary inventory

decreases as board independence rises. I predict a negative sum of the coefficients forInd_per

and the interaction ofMiss withInd_per( 2+ 3), implying that miss firms with more

independent boards make smaller discretionary inventory increases.

Simultaneous Study of Timing, Tax, Compensation, and Governance Considerations

While investigating the distinct influences of timing, tax, compensation, and governance

considerations by modeling them separately is informative, these incentives do not exist in

isolation. Thus, I formulate Model (3) to explore whether the results of Model (2) persist in a

multivariate setting in which I regress the earnings effects of companies discretionary inventory

changes on all four considerations simultaneously:

Earn_eff_assi,q = + 1 Missi,q + 2 Timeq + 3 Missi,q Timeq + 4 MTRi,y-1 + 5 Missi,q

MTRi,y-1 + 6 IRi,y + 7 Missi,q IRi,y + 8 Ind_peri,y + 9 Missi,q


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Ind_peri,y + i,q (3)

As in Model (1), I winsorize the continuous variables in Models (2) and (3) at the 1st and

99th

percentiles to limit the impact of outliers.22

RESULTS

Model (1)

Table 3, Panels A, B, and C present descriptive statistics, correlation coefficients, and

regression coefficients related to Model (1). In Panel B, the correlation betweenInv_ch and

Sal_ch is negative and significant, indicating that firms allow their inventories to shrink as sales

increase (and swell as sales decrease) rather than sustaining a stable ratio between these

variables. The strongest association in this panel exists betweenInv_ch and one-quarter-ahead

Fut_dem, implying that companies inventory levels in the current quarter are primarily

influenced by anticipated demand in the near term. However, the correlations betweenInv_ch

and two- and three-quarter-ahead Fut_dem are also positive and significant, indicating that

longer-term product demand also affects managers current production decisions.


In Panel C, I report the results of estimating Model (1). The coefficient on Sal_ch is

positive but only marginally significant. However, the coefficients on Fut_dem in each of the

subsequent three quarters are positive and highly significant, suggesting that inventory levels in

the current quarter reflect managers expectations of customers demand for their products in the

next three quarters. The condition index for Model (1) is 1.51; thus, multicollinearity does not

appear to impact these regression results. The R2 statistic for Model (1) is 25.91 percent,

meaning that the independent variables in the model explain more than a quarter of the variation


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inInv_ch. An F test (untabulated) also reveals that the firm and year indicator variables

contribute significant explanatory power to the model.

Model (2)

Table 4, Panels A, B, and C present descriptive statistics, correlation coefficients, and

regression coefficients related to Model (2). Given that my expectations differ depending on

whether firms miss or beat their quarterly earnings benchmarks, I partition Panels A and B

according to the indicator variableMiss and display separate results for each sub-sample of firm-

quarter observations. For my sample, 5,111 observations (63.52 percent) report premanaged

earnings equal to or in excess of their consensus analyst forecasts, and 2,935 observations (36.48

percent) fail to reach their income targets absent the earnings effect of discretionary inventory

changes. In Panel A, both the mean and median values forEarn_eff_ass are negative for beat

firms (Miss=0) and positive for miss firms (Miss=1). These statistics provide univariate support

for H1; on average, manufacturing firms that exceed their quarterly income targets absent

discretionary inventory changes cut production (relative to sales) and lower their reported

financial earnings, while companies that miss their income goals without manipulating

production add to their inventories and boost their profits.


In Panel B, the correlations betweenEarn_eff_ass and both Time andMTR are negative

and significant for the beat sub-sample, providing initial support for H2 and H3. Consistent with

H4 and H5, the relationships betweenEarn_eff_ass and bothIR andInd_perare positive and

significant for beat firms. Thus, beat firms appear to make larger (that is, more negative)

discretionary inventory decreases (1) in the fourth quarter relative to the first three quarters and

(2) as their MTRs increase, and these companies seem to make smaller (that is, less negative)


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discretionary inventory decreases (1) as the percentage of executive compensation relating to

accounting income and/or stock price increases and (2) as the percentage of independent board

members rises.

For miss firms, the correlation betweenEarn_eff_ass and Time is negative and

significant, bolstering H2; however, the association betweenEarn_eff_ass andMTR is

insignificant, lending no support for this aspect of H3. Note that the association between

Earn_eff_ass and Time is smaller in magnitude for the miss sub-sample relative to the beat sub-

sample. These bivariate correlation coefficients demonstrate that timing and tax considerations

align with financial reporting incentives for beat firms but create friction for miss firms.

Consistent with H4 and H5, the correlation betweenEarn_eff_ass andIR is positive and

significant for miss firms, while the association betweenEarn_eff_ass andInd_peris negative

and significant for this sub-sample. Thus, compensation considerations complement financial

reporting incentives and encourage miss firms to make larger increases to production, but

governance considerations mitigate this earnings management strategy for these companies.

The regression results in Panel C provide additional support for the conclusions drawn

from the univariate descriptive statistics in Panel A and the bivariate correlations in Panel B.

Specifically, the negative coefficient for the intercept ( ) and the positive sum of the coefficients

for the intercept andMiss ( + 1) in all six regressions imply that beat firms use inventory

decreases to manage their earnings downward and create cookie jar reserves, while miss firms

manage their earnings upward and potentially hit their income targets; these findings support H1.

The condition indices for all six estimations of Model (2) are less than 15, indicating that

multicollinearity does not severely influence these results.


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In the first and second estimations of Model (2) in Panel C, the negative coefficients for

Time andMTR indicate that, for beat firms, timing and tax considerations work in unison with

financial reporting incentives, encouraging these companies to underproduce inventory (relative

to sales) to reduce both tax and book income and avoid censure from their auditors; these results

bolster H2 and H3. In contrast, the positive coefficients for the interactions ofMiss with both

Time andMTR suggest that, for miss firms, tension exists between timing/tax motives and

financial reporting factors and that, relative to beat companies, the negative influences of timing

and tax on the earnings effects of discretionary inventory changes are suppressed. By adding the

coefficients on (1) Time andMiss Time and (2)MTR andMissMTR, I test the importance of

timing and tax incentives for miss firms. Consistent with H2, the sum ofTime andMiss Time is

negative and significant, revealing that miss firms sacrifice book income to lower taxable income

and comply with stringent audit procedures in the fourth quarter. However, the sum ofMTR and

MissMTR is insignificant, implying that, contrary to H3, higher tax rates (and the associated

tax burdens) imposed on the additional income generated by overproducing inventory do not

discourage miss firms from manipulating production to enhance their income and potentially

reach their earnings benchmarks.

Also in Panel C, the positive coefficient forIR and the positive sum of the coefficients for

IR andMissIR provide evidence that, for both beat and miss firms, as the percentage of CEOs

compensation derived directly (via bonuses) and indirectly (via appreciation in the value of stock

holdings and options) from reported earnings increases, compensation considerations motivate

these top managers to overproduce and generate additional profits as H4 predicts. Stated

differently, asIR rises, beat firms report less negative discretionary inventory decreases, and

miss companies make more positive discretionary inventory increases. The marginally positive


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coefficient for the interaction ofMiss andIR demonstrates that the influence of compensation

considerations on miss firms discretionary production decisions is amplified (relative to beat

companies) because incentives related to financial reporting and pay align for the miss sub-

sample.

The final estimation of Model (2) in Panel C investigates the impact of governance

considerations on firms discretionary production decisions. ForInd_per, the coefficient on the

main effect is positive and significant. Thus, beat firms report smaller discretionary inventory

decreases as the percentage of independent directors on their boards rises. In contrast, the

negative sum of the coefficients onInd_perand the interaction ofMiss withInd_perindicates

that miss firms report smaller discretionary inventory increases as the fraction of non-employee,

non-linked board members rises. Thus, consistent with H5, higher percentages of board

independence lead companies to report actual income numbers that are closer in value to

earnings absent production manipulation for both the beat and miss sub-samples.

Model (3)

In Table 5, I report the results of estimating Model (3), which allows timing, tax,

compensation, and governance considerations to simultaneously influence firms discretionary

inventory changes. While my sample size for this estimation falls to 1,993 firm-quarter

observations with complete data for all variables, most of the conclusions that I draw from Panel

C of Table 4 continue to obtain. The notable exception is that the sum on the coefficients on

Time and the interaction ofMiss with Time is insignificant in Model (3). This finding implies

that miss firms do not reduce their fourth-quarter discretionary inventory increases despite the

audit and tax timing incentives that exist at yearend. According to Belsley, Kuh, and Welsch


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(1980), the condition index of 25.92 for Model (3) indicates that linear dependencies among the

explanatory variables may adversely affect these coefficient estimates.


CONCLUSION

This study demonstrates that manufacturing firms manipulate production to manage

earnings on a quarterly basis and examines whether timing, tax, compensation, and governance

incentives magnify or temper this strategy. Companies that exceed their quarterly consensus

analyst forecasts absent the earnings effects of discretionary inventory changes cut production

(relative to sales), creating earnings cookie jars for future quarters. For this sub-sample,

timing and tax considerations align with financial reporting incentives; thus, these companies

make larger discretionary inventory decreases (1) in the fourth quarter relative to the first three

quarters and (2) as their MTRs rise. In contrast, these beat firms face friction between

compensation/governance considerations and financial reporting incentives, and I discover that

the discretionary inventory decreases made by these firms are smaller (1) as the percentage of

executive compensation relating to accounting income and/or stock price increases and (2) as

board independence rises.

The sub-sample of firms that miss their income goals without manipulating production

use discretionary inventory increases to enhance their earnings and potentially reach their

quarterly benchmarks. For these companies, timing, tax, and governance considerations oppose

financial reporting incentives. Results indicate that higher tax rates do not impede miss firms

from managing their earnings upward; however, timing and governance considerations dissuade

these companies from opportunistically manipulating production. I also document that these


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companies stockpile more inventory as the compensation benefits to their executives of this

earnings management strategy increase.


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NOTES

1. Companies manipulate accruals for reasons other than reaching external earnings

benchmarks and creating income reserves for use in future periods. Jones (1991) finds that

firms use income-decreasing discretionary accruals to manage their earnings downward

during import relief investigations. Erickson and Wang (1999) provide evidence that, in

anticipation of stock-for-stock mergers, acquiring companies employ income-increasing

unexpected accruals to augment profits and raise their equity values (thereby lowering the

cost of buying target firms). Kasznik (1999) documents that companies whose managers

issue earnings forecasts use income-increasing discretionary accruals to avoid the legal and

reputational penalties associated with missing these benchmarks.

2. In this context, the creation of cookie jar reserves implies that underproduction relative to

demand in the current period permits overproduction relative to demand in a future period,

granting the firm the associated earnings improvement in that future period.

3. This strategy involves costs. If a manufacturing firm increases production to boost income,

the company incurs carrying costs (storage, insurance, property taxes, etc.) for the unsold

units and may suffer inventory obsolescence. In contrast, if a manufacturing firm decreases

production to reduce income, the business may experience stock outages and order

backlogs (and the associated reputational effects). Similarly, a company operating near its

production capacity may be unable to increase inventory, and curtailing production may not

be feasible for a firm using a just-in-time inventory management system. If firms engage

in this form of earnings management only to the extent that the financial reporting benefits

exceed these costs, the presence of such costs biases against discovering my hypothesized

results.


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4. I assume that managers recognize that their firms earnings will either beat or miss their

income goals with sufficient lead time to modify production accordingly. For companies

overproducing inventory to enhance their profits, fixed costs attach to products as they

enter and flow through the manufacturing process, not when they exit as finished goods.

Thus, even firms with long production cycles can utilize this strategy.

5. The IRS also permits firms that fail to meet the four conditions to base their estimated tax

payments on annualized income; that is, they may determine the amount of their quarterly

payments according to the taxable income earned through the month before the month in

which the payment is due (for example, firms use taxable income earned through May to

calculate the estimated tax payment due on June 15). For these companies, the tax costs

and benefits of manipulating production occur uniformly throughout the four quarters. The

presence of firms in my sample that use the annualization approach biases against

discovering my hypothesized results related to timing.

6. Kothari (2001) and Holthausen and Watts (2001) provide thorough reviews of the literature

examining the relationship between accounting earnings and stock price.

7. Shackelford and Shevlin (2001, 326-338) provide a thorough review of the literature

examining the tradeoffs between tax and financial reporting.

8. Boynton, Dobbins, and Plesko (1992) examine a related research question. They discover

that firms subject to the AMT reported unusual income-decreasing discretionary accruals in

1987 but did not manipulate their earnings in 1986 in anticipation of the AMTs inception.

9. Guidry, Leone, and Rock (1999) find comparable results when they investigate a sample of

business-unit managers.

10. Growth in stock- and option-based compensation during this period may reflect firms


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attempts to improve corporate governance. Jensen and Meckling (1976) suggest

managerial ownership as a mechanism to align the incentives of executives with

shareholders and curtail the undesirable consequences associated with the separation of

ownership and control within firms, and Core and Guay (1999) advocate the use of stock

and options in executives compensation packages to amplify managerial ownership.

11. TRA86 reduced the top statutory tax rate for corporations from 46 percent in 1986 to 40

percent in 1987 and again to 34 percent in 1988. The Revenue Reconciliation Tax Act of

1993 raised this rate slightly to 35 percent.

12. For some firms, Compustat reports multiple-digit values for data59, signifying that these

companies use more than one method to value their inventories. For these firms,

Compustat lists the methods in the order of the relative amount of inventory valued by each

method. To reduce noise, I include in my sample only firms with single-digit values for

data59.

13. I exclude LIFO firms from my sample for three reasons. First, in 2005 (the most recent

year in my sample period), the Compustat universe includes only 66 firms that use LIFO as

their exclusive inventory valuation method (data59=2); of these 66 companies, 41 engage

in manufacturing. In contrast, 2,037 firms apply the FIFO method (data59=1) in 2005, and

1,357 of these companies report manufacturing SIC codes. Thus, LIFO firms constitute a

small percentage of my potential sample of manufacturing firms. Second, as Cook et al.

(2007) note, LIFO firms face a tradeoff between overproduction and the liquidation of

LIFO layers in managing their earnings upward; this friction does not affect FIFO firms.

Third, for LIFO firms, examining the impact of financial reporting incentives on

production decisions requires quarterly LIFO reserve data because a change in the LIFO


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reserve leads to a corresponding change in COGS, and these data are not readily available

on a quarterly basis.

14. One reason for the large reductions in sample size for tests of compensation and

governance hypotheses is that the Execucomp and IRRC databases do not contain data for

the early years of my sample period. Execucomp and IRRC provide data for years

beginning with 1992 and 1996, respectively.

15. Ideally, I would define my inventory measure as the sum of work-in-process and finished-

goods inventories because raw-materials inventory does not absorb fixed manufacturing

costs and thus is irrelevant to earnings management perpetrated by production

manipulation. However, Compustat does not report these three components of total

inventory on a quarterly basis. Cook et al. (2007) find that the results of their annual model

are qualitatively unaltered if they use total inventory or subtract the raw-materials

component and use the sum of work-in-process and finished-goods inventories.

16. The results of estimating Models (1) and (2) are qualitatively unaltered if I replace firm

indicator variables in Model (1) with industry indicator variables based on two-digit SIC

codes.

17. To capture the impact of time-specific factors on firms quarterly inventory changes, year-

quarter indicators (that is, the interaction of year indicators with quarter indicators) would

be more appropriate than year indicators. However, incorporating quarter indicators into

Model (1) would usurp the predictive power of the Time variable in Model (2), precluding

me from investigating the influence of timing considerations on companies use of

production manipulation to manage earnings.

18. Firms fixed costs ratios are unobservable. I use the ratio of property, plant, and equipment


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to assets as my proxy because this measure is likely correlated with the largest components

of fixed manufacturing costs, including depreciation expense, property taxes and insurance,

maintenance costs, and the fixed portion of utilities expense.

19. The results of estimating Model (2) are qualitatively unaltered if I useEarn_eff(unscaled)

as the response variable and include the natural logarithm of assets as an explanatory

variable to control for firm size.

20. During my sample period, the average change in MTR from one year to the next was less

than one percent for the majority of firms in John Grahams MTR database. Given that

MTRs are relatively stable across time, I assume that firms actual MTRs in the previous

year represent their anticipated MTRs in the current year. Since John Graham provides

MTR data on an annual basis, I apply the one-year laggedMTR values to all quarters in the

current year.

21. If managers manipulate income in the current period to capitalize on positive stock returns

coincident with the release of earnings news to the market (by exercising options and

selling shares) as Cheng and Warfield (2005) and Bergstresser and Philippon (2006)

contend, the calculation ofOnepctshould incorporate only unrestricted stock and vested,

exercisable options.

22. The results of estimating Model (2) are qualitatively unaltered if I include firm (or

industry) and year indicator variables. However, I specifically exclude these explanatory

variables from Model (2) because Model (1) theoretically purges these effects from firms

total inventory changes.


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APPENDIX

Calculation ofMiss

ETRi,q = income taxesi,q (data6) / pretax incomei,q (data23)

Forecasti,q = the last I/B/E/S consensus forecast estimatei,q (meanest) common shares used

to calculate EPSi,q (data15) / (1ETRi,q)

Actuali,q = I/B/E/S actual EPSi,q(value) common shares used to calculate EPSi,q / (1

ETRi,q)

Premani,q = Actuali,qEarn_effi,q

IfForecastexceeds Preman, I codeMiss as 1. IfPreman exceeds or equals Forecast, I

codeMiss as 0.


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TABLE 1

Sample Selection and Industry Distribution

Panel A. Sample Selection

Initial manufacturing firm-quarter observations from the Compustat Industrial Quarterly database for years 1988-2005 (2000


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TABLE 1 (continued)

Sample Selection and Industry Distribution

Panel B. Industry Distribution

Observations Firms

SIC Code Industry Description Count Percent Count Percent

2000 - 2099 Food 417 5.18% 42 4.69%

2100 - 2199 Tobacco 0 0.00% 0 0.00%

2200 - 2299 Textile Mill 76 0.94% 11 1.23%

2300 - 2399 Apparel 250 3.11% 24 2.68%

2400 - 2499 Lumber and Wood 86 1.07% 10 1.12%

2500 - 2599 Furniture and Fixtures 177 2.20% 16 1.79%

2600 - 2699 Paper 90 1.12% 8 0.89%

2700 - 2799 Printing and Publishing 281 3.49% 29 3.24%

2800 - 2899 Chemicals 972 12.08% 86 9.60%

2900 - 2999 Petroleum Refining 9 0.11% 3 0.33%

3000 - 3099 Rubber and Plastics 219 2.72% 23 2.57%

3100 - 3199 Leather 150 1.86% 8 0.89%

3200 - 3299 Stone, Clay, Glass, and Concrete 58 0.72% 8 0.89%

3300 - 3399 Primary Metal 101 1.26% 14 1.56%

3400 - 3499 Fabricated Metal 187 2.32% 22 2.46%

3500 - 3599 Industrial and Commercial Machinery 1075 13.36% 154 17.19%

3600 - 3699 Electronic and Electrical Equipment 1560 19.39% 188 20.98%

3700 - 3799 Transportation Equipment 546 6.79% 49 5.47%

3800 - 3899 Measuring and Analyzing Instruments 1578 19.61% 178 19.87%

3900 - 3999 Miscellaneous Manufacturing 214 2.66% 23 2.57%

8,046 100% 896 100%

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TABLE 2

Variable Definitions

Variable Definition

Inv_chi,q total inventory change: inventoriesi,q (data38) - inventoriesi,q-1

Sal_chi,q sales change: salesi,q (data2) - salesi,q-1

Fut_demi,q+1 future demand one quarter ahead: sales i,q+1 - salesi,q

Fut_demi,q+2 future demand two quarters ahead: salesi,q+2 - salesi,q+1

Fut_demi,q+3 future demand three quarters ahead: salesi,q+3 - salesi,q+2

Disc_inv_chi,q discretionary inventory change: the residual from Model (1)

FCRi,q fixed cost ratio: property, plant, and equipment i,q (data42) / assetsi,q (data44)

Earn_effi,q earnings effect of discretionary inventory change: Disc_inv_ch i,q FCRi,q

Earn_eff_assi,q Earn_effi,q / assetsi,q

ETRiq effective tax rate: income taxesi,q (data6) / pretax incomei,q (data23)

Forecasti,q forecasted earnings: the last I/B/E/S consensus forecast estimatei,q (meanest) * common shares used to calculate EPSi,q (data54) / (1 - ETRi,q)

Actuali,q actual earnings: I/B/E/S actual EPSi,q (value) * common shares used to calculate EPSi,q / (1 - ETRi,q)

Premani,q pre-managed earnings: Actuali,q - Earn_effi,q

Missi,q an indicator variable coded 1 if Forecasti,q exceeds Premani,q, and 0 otherwise

Timeq an indicator variable coded 1 if quarter q is the fourth quarter of the year, and 0 otherwise

MTRi,y-1 marginal tax rate for firm i in year y-1 based on income after deducting interest expense

IRi,y incentive ratio: the percentage of CEO compensation of firm i in year y that relates to accounting income and/or stock price

Ind_peri,y independence percentage: the percentage of independent board members of firm i in year y

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TABLE 3

Descriptive Statistics, Correlation Coefficients, and Regression Coefficients for Model (1)

Panel A. Descriptive Statistics

Variable N Mean Std. Dev. Minimum Q1 Median Q3 Maximum

Inv_chi,q 8,046 2.9379 15.9738 -54.3030 -0.6550 0.6280 3.4650 83.0000

Sal_chi,q 8,046 7.9648 53.0791 -196.0000 -1.1180 1.4550 7.5320 295.0000

Fut_demi,q+1 8,046 9.4185 53.6729 -170.8320 -1.1640 1.5585 8.0300 316.0000

Fut_demi,q+2 8,046 10.4278 57.8821 -176.9710 -1.2120 1.6860 8.4000 342.1060

Fut_demi,q+3 8,046 9.2527 59.3173 -196.0000 -1.5350 1.5685 8.2460 343.3780

I winsorize all variables at the 1st

and 99th

percentiles.

(continued)

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TABLE 3 (continued)

Panel B. Correlation Coefficients with Inv_chi,q

Variable Prediction Correlation Coefficient

Sal_chi,q ? -0.0374 ***

Fut_demi,q+1 + 0.4185 ***

Fut_demi,q+2 + 0.0525 ***

Fut_demi,q+3 + 0.0607 ***

*** The correlation coefficient is significant at the 0.01 level.

** The correlation coefficient is significant at the 0.05 level.

* The correlation coefficient is significant at the 0.1 level.

(continued)

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TABLE 3 (continued)

Model (1): Inv_chi,q = + 1 Sal_chi,q + 2 Fut_demi,q+1 + 3 Fut_demi,q+2 +

4 Fut_demi,q+3 + 5-899 Firm Indicatorsi +

900-915 Year Indicatorsy + i,q

Panel C. Model (1) Regression Coefficients

Variable Prediction Regression Coefficient

Intercept ? 5.9113

Sal_chi,q ? 0.0067 *

Fut_demi,q+1 + 0.1178 ***

Fut_demi,q+2 + 0.0206 ***

Fut_demi,q+3 + 0.0095 ***

Observations 8,046

Condition Index 1.51

R2 25.91%

I estimate these regression coefficients using a two-way fixed effects model.

I omit firm and year indicator variables from the table for concision.

*** The regression coefficient is significant at the 0.01 level.

** The regression coefficient is significant at the 0.05 level.

* The regression coefficient is significant at the 0.1 level.

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TABLE 4

Descriptive Statistics, Correlation Coefficients, and Regression Coefficients for Model (2)

Panel A. Descriptive Statistics

Missi,q=0


Earn_eff_assi,q 5,111 -0.0020 0.0046 -0.0200 -0.0032 -0.0008 0.0002 0.0173

Timeq 5,111 0.2690 0.4435 0 0 0 1 1

MTRi,y-1 4,161 0.2736 0.1330 0.0000 0.2898 0.3400 0.3500 0.3900

IRi,y 2,119 0.6163 0.1978 0.0035 0.4994 0.6337 0.7558 0.9990

Ind_peri,y 1,611 0.6097 0.1762 0.1538 0.5000 0.6250 0.7500 0.9167

Missi,q=1


Earn_eff_assi,q 2,935 0.0029 0.0050 -0.0200 0.0001 0.0017 0.0046 0.0173

Timeq 2,935 0.2269 0.4189 0 0 0 0 1

MTRi,y-1 2,514 0.2879 0.1222 0.0000 0.3254 0.3400 0.3500 0.3900

IRi,y 1,119 0.5650 0.2178 0.0035 0.4349 0.5799 0.7297 0.9990

Ind_peri,y 860 0.5954 0.1787 0.1538 0.5000 0.6125 0.7273 0.9091

I winsorize all continuous variables at the 1st

and 99th

percentiles.

(continued)

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TABLE 4 (continued)

Panel B. Correlation Coefficients with Earn_eff_assi,q

Missi,q=0

Variable Prediction Correlation Coefficient

Timeq - -0.1003 ***

MTRi,y-1 - -0.0627 ***

IRi,y

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