rasmussen 2013

Accounting Horizons American Accounting AssociationVol. 27, No. 1 DOI: 10.2308/acch-502912013pp. 91112

Revenue Recognition, Earnings Management,and Earnings Informativeness in the

Semiconductor Industry

Stephanie J. Rasmussen

SYNOPSIS: Manufacturers that sell products to distributors experience product returnand pricing adjustment uncertainties until the products are resold to end-customers.Such manufacturers recognize revenue when products are delivered to distributors (sell-in), when distributors resell products (sell-through), or under some combination of thesemethods (sell-in for some distributor sales and sell-through for others). This studyexamines the implications of these revenue recognition methods for a sample ofsemiconductor firms during 20012008. Semiconductor firms face rapid productobsolescence, declining prices over product life cycles, and unexpected industrydownturns, which naturally lead to product return and pricing adjustment uncertainties. Ifind that sell-through and combination firms are less likely to manage earnings comparedto sell-in firms. I also find that earnings are more informative for sell-through firmscompared to both sell-in and combination firms. These findings suggest thatmanufacturers that sell products through the distribution channel should defer revenuerecognition until product return and pricing adjustment uncertainties are resolved.

Keywords: revenue recognition; earnings management; earnings informativeness;distributors; manufacturers.

JEL Classications: M41.

Data Availability: Data are available from the sources identied in the text.

Stephanie J. Rasmussen is an Assistant Professor at The University of Texas at Arlington.

I gratefully acknowledge helpful comments and suggestions offered by Terry Shevlin (editor), two anonymous referees,Anwer Ahmed (dissertation chair), Kris Allee, Cory Cassell, Mike Drake, Jap Efendi, Rebecca Files, Tom Omer, DudleyPoston, Jaime Schmidt, Mary Stanford, Senyo Tse, Connie Weaver, and workshop participants at Texas A&MUniversity, University of Houston, The University of Texas at Arlington, and the 2009 AAA Annual Meeting. I amindebted to the following practitioners for willingly sharing insights from their own experiences with revenue recognitionin the semiconductor industry: Sanjoy Chatterji, Wendy Clancy, Bernard Gutmann, Linda King, Carl Mangine, andLaurie Martens. I thank Linying Zhou for research assistance. Financial support from Texas A&Ms Mays BusinessSchool and The University of Texas at Arlington is greatly appreciated.

This paper is based, in part, on my dissertation completed at Texas A&M University.

Submitted: October 2011Accepted: June 2012

Published Online: September 2012Corresponding author: Stephanie J. Rasmussen

Email: [email protected]

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INTRODUCTION

Revenue is one of the most important earnings components, usually the largest item on the

income statement, and a strong indicator of firm performance (e.g., Turner 2001). Revenue

is also very complex as evidenced by more than 200 publications (statements, opinions,

bulletins) that provide revenue recognition guidance under U.S. GAAP (FASB 2005). Given the

importance and complexity of revenue, it is imperative for financial statement users to have a strong

understanding of revenue recognition and its implications for evaluating firm performance and

valuation.

Standard-setters and academic researchers note that trade-offs exist between the relevance and

reliability of different accounting practices (see e.g., FASB 1980; Schipper 2003). Prior research

examines the implications of revenue recognition when uncertainties exist related to product

delivery (Altamuro et al. 2005; Zhang 2005) and the pricing of undelivered contract elements

(Srivastava 2011). These studies find that earnings are more informative, yet more likely to be

managed, when revenue recognition occurs before the uncertainties are resolved. I extend these

studies by examining the implications of revenue recognition for manufacturers that sell their

products to distributors. Such manufacturers face product return and pricing adjustment

uncertainties until the distributors resell products to end-customers. In addition, these manufacturers

have opportunities for real earnings management through channel stuffing. Channel stuffing

refers to either pulling in distributor orders from a future period or shipping large, unusual orders to

distributors in order to boost revenue (Penman 2007). Therefore, it is not clear whether prior

studies findings with respect to the implications of revenue recognition will hold for firms with

product return and pricing adjustment uncertainties.

Three revenue recognition methods exist for manufacturers sales to distributors. Under the

sell-in method, manufacturers recognize revenue upon delivery of product to the distributor (i.e.,sales into the distribution channel) and maintain product return and pricing adjustment accrualsuntil distributor rights have lapsed at resale. Under the sell-through method, manufacturersrecognize revenue when the distributor resells product to an end-customer and all uncertainties have

been resolved (i.e., sales through the distribution channel). Manufacturers exclusively use one ofthese methods to recognize revenue for sales to all distributors, or they use the sell-in method for

some sales to distributors and the sell-through method for other sales (hereafter, the combinationmethod).

Consistent with prior research (Altamuro et al. 2005; Srivastava 2011), I examine the trade-offs

between managerial discretion and earnings informativeness for differing revenue recognition

methods. Since the sell-in method recognizes revenue before product return and pricing adjustment

uncertainties are resolved, managers must estimate the likelihood of those events. Managers at

sell-in firms also have opportunities to intentionally manipulate their estimates or stuff the

distribution channel in order to meet or beat earnings benchmarks (Glass, Lewis & Co. 2004;

Greenberg 2006; Schilit and Perler 2010). In contrast, combination firms only have opportunities to

exercise discretion for a portion of all distributor revenue, and managerial discretion does not exist

for sell-through firms that defer revenue recognition until product resale by distributors. Due to the

differing opportunities for managers to exercise discretion, I expect that the incidence of earnings

management is less likely for sell-through and combination firms compared to sell-in firms.

While I expect earnings management differences among the revenue recognition methods for

sales to distributors, it is not clear whether the informativeness of manufacturers earnings differs

among the methods. On one hand, the sell-in method provides financial statement users with more

timely information than the combination and sell-through methods about the business transactions

between manufacturers and distributors. On the other hand, estimates of product returns and pricing

adjustments required under the sell-in method are susceptible to both intentional and unintentional

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Accounting HorizonsMarch 2013

estimation errors. In addition, sell-in firms receive a greater benefit from channel stuffing compared

to combination and sell-through firms. Thus, the sell-in method would not reflect a firms actual

revenue-generating performance as well as the combination or sell-through method if these issues

prevail.

I examine revenue recognition methods for sales to distributors and their trade-offs using a

sample of 1,572 firm-quarters for 80 semiconductor manufacturers during 20012008. Thirty-two

percent of the sample uses the sell-in method, 20 percent of the sample uses the sell-through

method, and 48 percent of the sample uses a combination of the two revenue recognition methods. I

limit the sample to semiconductor firms because this industry experiences rapid product

obsolescence, declining prices over product life cycles, and unexpected industry downturns. These

issues contribute to channel stuffing, product return, and pricing adjustment uncertainties for

manufacturers with distributor customers. Semiconductor firms are also more likely to disclose

information about their relationships with distributors and less likely to have significant service- or

retail-related revenue (which could add noise to empirical analyses) than other firms that sell to

distributors. I begin the sample period in 2001 because revenue recognition principles have been

consistent since that year (first under Staff Accounting Bulletin [SAB] 101 and later under SAB

104).

I first test whether earnings management is less likely for sell-through and combination firms

compared to sell-in firms. Consistent with prior research (e.g., Barton and Simko 2002; Cheng and

Warfield 2005), I use the incidence of meeting or beating analysts consensus quarterly earnings

forecast as a proxy for earnings management to a benchmark. I find that sell-through and

combination firms are significantly less likely to meet or beat analysts consensus quarterly earnings

forecast compared to sell-in firms after controlling for other determinants of meeting or beating that

have been identified by prior work. The likelihood of meeting or beating analysts consensus

earnings forecast does not differ between sell-through and combination firms. These findings

suggest that earnings management is more likely for firms that recognize all revenue before product

return and pricing adjustment uncertainties are resolved.

Next, I test for earnings informativeness differences among sell-in, sell-through, and

combination firms. Specifically, I examine whether the earnings response coefficient, an indicator

of earnings informativeness, varies for firms with different revenue recognition methods (e.g.,

Altamuro et al. 2005; Srivastava 2011). I define unexpected earnings as the difference between

actual earnings per share and analysts last consensus earnings forecast prior to the quarterly

earnings announcement (scaled by stock price at quarter-end), and unexpected returns are

cumulative market-adjusted stock returns for two trading days beginning on the quarterly earnings

announcement date. I also control for many determinants of earnings response coefficients

identified by prior research. I find that the earnings response coefficient (the coefficient on

unexpected earnings) is significantly higher for sell-through firms compared to both sell-in and

combination firms. This finding suggests that earnings are more informative for firms that defer

revenue recognition until all product return and pricing adjustment uncertainties are resolved.

Collectively, this study suggests that manufacturers with product return and pricing adjustment

uncertainties should recognize revenue from sales to distributors after the uncertainties are resolved.Although this conclusion differs from prior studies, which suggest that earnings are more

informative if firms recognize revenue before uncertainties are resolved (Altamuro et al. 2005;Srivastava 2011), important differences exist between my setting and those examined in prior work.

First, the revenue recognition settings examined by Altamuro et al. (2005), and Srivastava (2011)

allow for manager manipulation of accounting estimates while my setting allows for both

manipulation of accounting estimates and real earnings management. Channel stuffing is a seriousconcern of regulators, forensic accountants, and others (Glass, Lewis & Co. 2004; Greenberg 2006;

Schilit and Perler 2010), and egregious channel stuffing schemes have resulted in Securities and

Revenue Recognition, Earnings Management, and Earnings Informativeness 93


Exchange Commission (SEC) enforcement actions. It is likely that public awareness of channel

stuffing in my setting contributes to the finding that earnings are more informative when revenue

recognition is deferred until distributors have resold product to end-customers.

Second, I examine sample firms that consistently use the same revenue recognition method

throughout the sample period while Altamuro et al. (2005) and Srivastava (2011) do not. Forester

(2008) suggests that the cumulative-effect adjustment of an accounting change impacts earnings

informativeness in the transitional period following firms adoption of a new revenue recognition

method. When he excludes the transitional period from his analysis of Altamuro et al.s (2005)

setting, he finds that earnings are more informative for firms deferring revenue recognition under

SAB 101 compared to firms that accelerated revenue recognition prior to SAB 101. This result is

comparable to what I find with respect to the earnings informativeness of sell-through and sell-in

firms with distributor customers. Srivastavas (2011) results would not be affected by a transitional

period because firms in his setting were not required to report a cumulative-effect adjustment when

changing revenue recognition methods.

I expect that my study will interest students, managers, and other financial statement users

because it contributes to a growing literature that examines revenue recognition in specific

industries (Bowen et al. 2002; Zhang 2005; Srivastava 2011). Differences in revenue recognition

practices often make it difficult for financial statement users to compare revenue and earnings

among entities and industries (Schipper et al. 2009), and it is important to examine many settings in

order to improve our understanding of the implications of revenue recognition for firms. This study

is also potentially useful to investors, analysts, auditors, and regulators who monitor semiconductor

and other high-technology manufacturers that sell to distributors. Since the results suggest that

earnings management concerns about the sell-in method are warranted and earnings informative-

ness differs based on firms revenue recognition methods, these factors should be considered when

interpreting the financial statements and stock returns of manufacturers that sell to distributors and

comparing them to other firms.

The next section discusses background and develops hypotheses. I then discuss the empirical

models and describe the sample in the third section. Empirical evidence is presented the fourth

section and the final section summarizes and concludes.

BACKGROUND AND HYPOTHESES

Background

Distributors purchase products from manufacturers and later resell the products. This

arrangement benefits manufacturers because distributors (1) act as an additional sales force, (2)aggregate and service small orders that manufacturers are otherwise unwilling to fulfill, and (3)

reduce manufacturers collection risk (Credit Suisse 2007). Many manufacturers rely heavily on

distributors. For example, research suggests that distributors service more than 25 percent of global

semiconductor/electronic component sales (Credit Suisse 2007), and some semiconductor

manufacturers indicate that at least 50 percent of their sales are to distributors (e.g., Cypress

Semiconductor 2006 10-K filing; Fairchild Semiconductor 2008 10-K filing).

In order for manufacturers to recognize revenue from sales to distributors, SAB 101 and later

SAB 104, both require that (1) persuasive evidence of an arrangement exists, (2) delivery has

occurred, (3) the final selling price is fixed or determinable, and (4) collectability is reasonably

assured (SEC 1999, 2003). Although manufacturers sales to distributors easily meet the

arrangement, delivery, and collectability requirements, the manufacturer must decide if the final

selling price is fixed or determinable. A conservative interpretation of the revenue recognition

standard suggests that the final selling price is indeterminable for sales subject to pricing

adjustments or rights-of-return. However, interpretive guidance suggests that a selling price is

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determinable if product returns and pricing adjustments can be reasonably estimated.1 Thus, the

revenue recognition standard allows managers some discretion to communicate private, value-

relevant information to financial statement users but there is also the risk that managers will use the

discretion to manipulate earnings (Healy and Wahlen 1999).

Depending on the fixed or determinable nature of the final selling price, a manufacturer will

recognize revenue from sales to distributors under one of three revenue recognition methods: sell-

in, sell-through, or combination. Under the sell-in method, the manufacturer records accounts

receivable, reduces inventory, and recognizes both revenue and cost of goods sold when product

is delivered to the distributor. This accounting method provides a timely reflection of product

transfer between the two parties. Since revenue is recognized at delivery, the manufacturer

maintains product return and pricing adjustment accruals for limited return rights on regular

purchases2 and pricing adjustments intended to compensate for falling market prices or

incentivize sales of certain products (Lee et al. 2000; Credit Suisse 2007). These accrual estimates

are typically based on historical distributor return and pricing adjustment data. Manufacturers

revenue recognition disclosures suggest that the sell-in method is used when (1) distributors do

not have product return and pricing adjustment rights (i.e., selling prices are fixed at the time of

sale), or (2) distributors product returns and pricing adjustments can be reasonably and reliably

estimated (see Appendix A).

Under the sell-through method, the manufacturer reduces inventory and records accounts

receivable, deferred revenue, and deferred cost of goods sold when product is delivered to the

distributor. The manufacturer recognizes revenue and cost of goods sold once notification is

received from the distributor that the product has been resold.3 This method more accurately reflects

end-customer demand, and product return and pricing adjustment accruals are not needed since

revenue is deferred until distributor rights have lapsed. Although distributors provide inventory and

resale data to manufacturers, challenges exist regarding data reliability and format. Chipalkatti et al.

(2007) note that once data are received from multiple distributors, manufacturers must remove

errors, validate the data, and convert the data into one consistent format.4 In order to address these

issues, sell-through revenue recognition requires additional internal controls beyond those needed

for other sales. Revenue recognition disclosures suggest that manufacturers typically use the sell-

through method to recognize revenue when they believe they are unable to accurately estimate

distributors product returns and pricing adjustments (see Appendix A).

A combination of the two methods occurs when a manufacturer recognizes revenue under the

sell-in method for some distributors and under the sell-through method for other distributors. Under

this method, product return and pricing adjustment accruals are maintained only for those sales to

distributors that are recognized under the sell-in method. Manufacturers disclosures suggest that

they use a combination of the sell-in and sell-through methods if the firm is able to reasonably and

1 SAB 104 interpretive guidance refers to Statement 48, }6 and 8, which state that revenue cannot be recognized if afirm is unable to make a reasonable estimate of product returns (FASB 1981). SAB 104 also directs users to SOP97-2, }26 and 30-33, which state that prices on products sold to distributors are not fixed and determinable if theseller is unable to make reasonable estimates of pricing adjustments (AICPA 1997).

2 Manufacturers often give distributors the right to return a certain percentage of their inventory or exchange oldinventory for new inventory (i.e., stock rotation) (Chipalkatti et al. 2007). In addition, distribution agreementstypically include clauses that allow distributors to return any product on hand if the relationship with themanufacturer is terminated (e.g., Arrow Electronics 2008 10-K filing; Ingram Micro 2008 10-K filing).

3 Practitioners indicated that, regardless of the revenue recognition method, manufacturers regularly receive resaleand inventory data from distributors. These data are used to understand end-customer demand and planproduction.

4 Texas Instruments cites lack of confidence in distributor data as one reason it uses the sell-in method (Greenberg2006). KPMG LLP (2006) finds that at least 20 percent of resale reports from channel partners contain errors. Dataissues also affect accrual estimation under the sell-in method.



reliably estimate product return and pricing adjustment accruals for some distributors but not for

others. Estimation abilities can differ among distributors based on (1) the availability of historical

data needed to predict future returns and pricing adjustments, or (2) differing inventory levels that

could influence distributor power over the manufacturer. Alternatively, manufacturers disclosures

suggest that some firms use a combination of the two methods if product return and pricing

adjustment rights are only given to some distributors.5

Manufacturers in many industries exhibit some use of the sell-in and sell-through methods

for sales to distributors (Glass, Lewis & Co. 2004; Greenberg 2006; Chipalkatti et al. 2007). I

focus my study on firms in the semiconductor industry for a variety of reasons. First, the

semiconductor industry experiences rapid product obsolescence, declining prices over product

life cycles, and unexpected industry downturns. These issues all contribute to product return and

pricing adjustment uncertainties for sales to distributors. Second, my review of SEC filings

indicates that semiconductor firms are more likely than firms in other industries to disclose

information about their distributor relationships. Specifically, more semiconductor firms disclose

the percentage of revenue attributable to all or some of their distributor customers, which

indicates firm reliance on the distribution channel. Third, I find that semiconductor firms generate

most of their revenue from product sales, while manufacturers selling to distributors in other

industries often have significant service-related revenue. Examining firms with a significant

amount of service revenue could add noise to my empirical analyses if revenue recognition

methods differ for products and services. Fourth, semiconductor firms are less likely than other

manufacturers to sell to retailers because semiconductor products are typically components used

in the assembly of a product. Sales to retailers could also add noise to my empirical analyses.

Finally, only examining the semiconductor industry allows me to focus on a set of manufacturers

with relatively homogeneous characteristics.

Hypotheses

The sell-in method offers opportunities for managers to manipulate earnings using both real

activities and accounting accruals. For example, managers can ship excess product to distributors at

the end of an accounting period in order to increase earnings (i.e., channel stuffing) (Penman 2007).

Channel stuffing boosts revenue of sell-in firms because revenue recognition occurs when product

is delivered to distributors. The SEC has investigated channel stuffing and brought enforcement

actions against firms with egregious channel stuffing activities (e.g., Vitesse Semiconductor). Lynn

Turner, former SEC chief accountant, summarizes concerns about these activities as follows: Ifound nothing good about revenue recognition upon sell-in. Sooner or later, the urge to stuff the

channel, especially when things are not going well and numbers for the next quarter are short, is

very tempting (Greenberg 2006). In contrast, managers using the sell-through method are lesslikely to stuff the distribution channel because revenue recognition is deferred until distributors

resell products to end-customers.

Managers at sell-in firms can also manage earnings through accrual manipulations. As

discussed earlier, sell-in firms maintain product return and pricing adjustment accruals until

distributor rights have lapsed. Estimation of these accruals is subject to managerial discretion, and

extensive accounting research suggests that managers use accruals to manage earnings (see Healy

5 Changes in a manufacturers ability to estimate product returns and pricing adjustments or contractual rightsoffered to a distributor over time could lead the manufacturer to change the revenue recognition method for thedistributor in question. Such a change in the revenue recognition method necessitates that the firm report theaccounting change and a cumulative-effect adjustment, which should discourage opportunistic revenuerecognition changes.

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and Wahlen 1999; Beneish 2001; Fields et al. 2001 for surveys).6 In contrast, sell-through firms do

not have the opportunity to manipulate product return and pricing adjustment accruals for

distributor customers since these accruals are not maintained.

Prior research suggests that earnings management occurs through both real activities

manipulation and accrual manipulation (e.g., Healy and Wahlen 1999; Fields et al. 2001; Graham

et al. 2005; Roychowdhury 2006).7 Sell-in firms have opportunities to manage earnings from

distributor sales using both types of manipulation while sell-through firms do not. In addition, sell-

in firms have more manipulation opportunities than combination firms since the combination

method recognizes revenue at product delivery for only a portion of a firms sales to distributors.

My first hypotheses (stated in the alternative form) are as follows:

H1a: The incidence of earnings management is less likely for sell-through firms compared to

sell-in firms.

H1b: The incidence of earnings management is less likely for combination firms compared to

sell-in firms.

Earnings informativeness could also differ based on a firms revenue recognition method for

sales to distributors. Earnings that provide new information to the market about expectations of

future cash flows, as evidenced by changes in stock prices, are considered to be informative

(Kothari 2001). On one hand, the sell-in method provides a timely reflection of product transfer

between manufacturers and distributors. New accounting information is more quickly incorporated

into the financial statements under this method compared to the sell-through and combination

methods and should be useful to the market assuming that accrual estimates are accurate and

manufacturers do not stuff the distribution channel. When sell-in revenue recognition results in

accurate and reliable financial statements, earnings should be more informative for sell-in firms

compared to sell-through and combination firms.

On the other hand, if sell-in firms have intentional performance manipulations (i.e., channel

stuffing, accrual manipulation), earnings reported by these firms are not earned and earnings

informativeness should suffer. In addition, unintentional estimation errors can reduce the

informativeness of earnings. Marketing theory suggests that powerful distributors have the ability

to heavily influence trade terms with manufacturers (e.g., Tsay 2002). Since manufacturers often

sell a large amount of product to distributors and 75 percent of semiconductor/electronic component

distributors purchases represent speculation and forecasts of future end-customer orders (Credit

Suisse 2007), distributors have leverage to pressure manufacturers into accepting special return or

pricing adjustment requests if end-customer demand does not materialize. For example, BCD

Semiconductor responded to distributor requests following a recent industry downturn by allowing

distributors to return nearly four times more product than what was required under the companys

standard return rights (BCD Semiconductor 2008 Registration Statement). Distributors are also

likely to request special returns for product that was previously stuffed into the channel. Thus, if

6 Use of product return and pricing adjustment accruals under the sell-in method also increases the risk ofunintentional accrual estimation errors by management.

7 Earnings management opportunities also exist for sales to non-distributor customers. Revenue recognitiondisclosures for sample firms suggest that manufacturers typically recognize revenue from non-distributorcustomers at the time of product delivery and sales returns are only allowed for defective products. Thus,managers have opportunities to stuff non-distributor channels and manipulate warranty accruals. Since sell-in,sell-through, and combination firms all have the opportunity to stuff the non-distribution channel and manipulatenon-distributor sales accruals, I attempt to control for these activities in my empirical test of earningsmanagement.



sell-in revenue recognition results in inaccurate earnings or product returns estimates, earnings

should be less informative for sell-in firms compared to sell-through and combination firms.

Because it is unclear whether earnings informativeness differs based on a manufacturers

revenue recognition method for sales to distributors, my final hypothesis (stated in the null form) is

as follows:

H2: Earnings informativeness does not differ among sell-in, sell-through, and combinationfirms.

METHOD

Revenue Recognition and Earnings Management

I use the following logistic regression model to examine if sell-through and combination firms

manage earnings less than sell-in firms (H1a and H1b):

MeetBeatit a0 a1SellThroughit a2Comboit a3Sales Growthit a4Rank of MTBit a5NOAit a6Rank of Sharesit a7Rank of Sizeit a8Reportit a9Report3Avg Disty Revenueit a10Bonus%it a11Options%it a12Analystsit a13CVAFit a14Revise Downit axTime Indicators e: 1

Degeorge et al. (1999) find that a disproportionate number of firms meet or beat analysts earnings

forecasts. Consistent with prior literature (e.g., Barton and Simko 2002; Cheng and Warfield 2005),

I use the incidence of meeting or beating analysts consensus earnings forecast as a proxy for

managing earnings to a benchmark. MeetBeat is an indicator variable that equals 1 if the firm meetsthe last I/B/E/S consensus earnings forecast prior to the quarterly earnings announcement or beats it

by any amount, and 0 otherwise. I examine the incidence of meeting or beating analysts consensus

earnings forecast and not analysts consensus revenue forecast because (1) revenue recognitionmethods for sales to distributors affect both revenues and cost of goods sold, and (2) executives

report that earnings is a more important performance metric than revenues (Graham et al. 2005).

The main variables of interest in Model 1 are Sell-Through and Combo, which are indicators equalto 1 if the firm uses the sell-through or combination revenue recognition method for sales to

distributors, and 0 otherwise. Consistent with H1a and H1b, I expect negative coefficients on Sell-Through and Combo, respectively, indicating that earnings management is less likely under the sell-through and combination methods compared to the sell-in method.

Model 1 includes a variety of control variables that prior research suggests are associated with

meeting or beating analysts consensus earnings forecast. Consistent with prior research (e.g., Das

et al. 1998; Barton and Simko 2002; Cheng and Warfield 2005), I control for the following firm

characteristics: growth opportunities (Sales Growth, Rank of MTB), constraints on earningsmanagement (net operating assets [NOA], shares outstanding [Rank of Shares]), and firm size (Rankof Size). NOA is an accrual-based measure of net assets, and Barton and Simko (2002) find thatprior abnormal accruals are positively associated with NOA. High NOA suggests overstated assetsand previous earnings management through abnormal accruals, which should make it more difficult

for managers to manipulate earnings in the current quarter.

I control for the percentage of revenue attributable to distributor customers8 because reliance

on the distribution channel likely influences firms revenue recognition practices and opportunities

8 Revenue attributable to distributor customers (if reported) is disclosed in the 10-K filing. However, not all firmsfollow consistent reporting practices. For instance, some firms report revenue attributable to all distributorcustomers while other firms only report revenue attributable to their top one or two distributor customers. Inaddition, some firms do not report revenue attributable to distributor customers every year while other firms neverreport this information.

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to stuff non-distributor channels and/or manipulate non-distributor sales accruals.9 Report controlsfor the fact that not all firms report how much of their revenue is attributable to distributor

customers. This variable is an indicator set to 1 for those firms reporting revenue attributable to

distributor customers at least once during the sample period and 0 for those firms never reporting

this information. Report3 Avg Disty Revenue is the interaction between Report and the average ofall annual revenue attributable to distributor customers reported by the firm during the sample

period. This variable is set to 0 for firms that never report revenue attributable to distributor

customers. I control for the percentage of CEO compensation attributable to cash-based incentives

(Bonus%) and stock options (Options%) because prior research suggests that management incentivecompensation is associated with earnings management activities (e.g., Cheng and Warfield 2005;

Bergstresser and Philippon 2006; Cornett et al. 2008) and accounting choices (e.g., Aboody et al.

2000; Aboody et al. 2004; Efendi et al. 2007).

Consistent with prior research (e.g., Johnson 1999; Payne and Robb 2000; Barton and Simko

2002; Cheng and Warfield 2005), I also control for characteristics of the analysts forecasts

including the number of analysts following a firm (Analysts), variation among analysts forecasts(CVAF), and recent downward revisions in analysts forecasts (Revise Down). Finally, Model 1includes calendar-quarter fixed effects and clusters standard errors by firm (Petersen 2009). All

variables included in Model 1 are defined in either Table 1 or Table 2.

Revenue Recognition and Earnings Informativeness

I use the following regression model to examine if earnings informativeness differs among sell-

in, sell-through, and combination firms (H2):

URit b0 b1UEit b2SellThroughit b3Comboit b4UEit3 SellThroughit b5UEit3Comboit bxControlsit byUEit3Controlsit bzTime Indicators e:

2Prior accounting research models stock price as a function of future dividends (which are assumed

to be related to future earnings), and derivations of this model lead to an association between

unexpected stock returns and unexpected earnings (see e.g., Collins and Kothari 1989; Lev 1989;

Kothari 2001). The coefficient on unexpected earnings (i.e., the earnings response coefficient) is

considered to be an indicator of earnings informativeness (e.g., Francis et al. 2006), and Model 2

examines whether the earnings response coefficient varies based on firms revenue recognition

methods for sales to distributors.

Consistent with the recommendations of Berkman and Truong (2009), I measure

unexpected returns (UR) as the cumulative market-adjusted stock return for two trading daysbeginning on the quarterly earnings announcement date. Unexpected earnings (UE) equals thedifference between actual earnings per share and analysts last consensus earnings forecast prior

to the quarterly earnings announcement (both reported by I/B/E/S), scaled by stock price at

quarter-end. This definition of UE is similar to prior studies (see e.g., Lopez and Rees 2002;Nelson et al. 2008; Wilson 2008; Chen et al. 2011) and is consistent with MeetBeat in Model1.10 Sell-Through and Combo are as previously defined.

9 NOA should help to control for non-distributor accrual manipulation. However, a measure of revenue attributableto distributor customers further controls for any managerial discretion involving non-distributor sales that affectsthe likelihood of a firm meeting or beating analysts consensus earnings forecast.

10 Prior studies calculate unexpected earnings as the difference between actual earnings and either analysts consensusearnings forecast or actual earnings from a prior period (see Lev [1989] and Kothari [2001] for surveys of the literature).However, recent research suggests that managers use earnings guidance to influence analysts forecasts (e.g., Cotter et al.2006), which could affect the UE measure used in my main test. As a sensitivity test, I use an unexpected earningsmeasure based on the earnings time series, and inferences are unchanged.



The main variables of interest in Model 2 are UE, UE3 Sell-Through, and UE3Combo. UErepresents the earnings response coefficient (ERC) for sell-in firms. UE3 Sell-Through and UE3Combo indicate the incremental ERC for sell-through and combination firms, respectively,compared to sell-in firms. Positive and significant coefficients for UE 3 Sell-Through and UE 3Combo will indicate that earnings are more informative (i.e., ERCs are higher) for sell-through andcombination firms compared to sell-in firms. Negative and significant coefficients for the

interactions will indicate that earnings are less informative for sell-through and combination firms

compared to sell-in firms.

Model 2 also controls for factors that are expected to affect the relationship between unexpected

earnings and unexpected returns. Prior research finds that ERCs are affected by firm size (Collins and

Kothari 1989), the market-to-book ratio (Collins and Kothari 1989), earnings persistence (Kormendi

and Lipe 1987), equity beta (Collins and Kothari 1989; Easton and Zmijewski 1989), incidence of a

net loss (Hayn 1995), fourth-quarter earnings announcements (Salamon and Stober 1994), and the

number of analysts following the firm (Shores 1990). Prior research also suggests that managers are

incentivized to exercise accounting discretion when their incentive compensation is based on earnings

performance (e.g., Bowen et al. 2003). Measures of executive compensation can control for

managerial discretion (other than that exercised under the revenue recognition method) that affects

ERCs. I also expect that ERCs are affected by a manufacturers over or under reliance on distributors

compared to other customers. Thus, I include the following control variables in the model: Rank ofSize, Rank of MTB, Persist, Beta, Loss, Fourth Quarter, Analysts, Bonus%, Options%, Report, andReport3 Avg Disty Revenue; and, I interact each of these control variables with UE. All variablesincluded in Model 2 are defined in Table 1, Table 2, or Table 3.

Sample

In order to develop my sample, I first obtain quarterly data for all semiconductor firms (SIC

3674) in the Compustat Fundamentals Quarterly database during 20012008. I begin the sample

period in 2001 because SAB 101, which offered additional guidance on revenue recognition

disclosures, became effective in that year. SAB 104 later rescinded guidance in SAB 101 that was

superseded by the FASBs Emerging Issues Task Force (EITF) 0021, but SAB 104 did not change

the revenue recognition principles in SAB 101 (SEC 1999, 2003). Next, I hand collect all available

annual SEC filings during 20012008 for the semiconductor firms with Compustat data. I exclude a

firm from the sample if it did not file at least one annual report (10-K or 20-F) with the SEC during

the sample period, or if the annual SEC filings indicate that the firm (1) is not a semiconductor

manufacturer, (2) does not sell products to distributors, (3) has significant consignment agreements,

(4) generates more than half of its revenue from service activities, (5) sells to retailers,11 (6) does

not offer product return privileges or pricing adjustments to distributors,12 or (7) changed revenue

recognition methods during the sample period.13 I also exclude firms lacking the Compustat, CRSP,

I/B/E/S, and ExecuComp data required for my analyses. The final sample includes 80 unique

semiconductor firms with required data for 1,572 firm-quarters.

I classify the sample firms as sell-in, sell-through, or combination based on the revenue

recognition disclosures presented in their annual SEC filings (10-K or 20-F). Sell-in firms are those

firms that use the sell-in revenue recognition method for all sales to distributors. Sell-through firms

11 I exclude firms with significant consignment agreements, service revenue, and retail sales because revenuegenerated from these activities could add noise to the empirical analyses.

12 This restriction ensures that all firms in my sample have product return and pricing adjustment uncertaintiesrelated to sales to distributors.

13 Fourteen firms appeared to switch revenue recognition methods during the sample period. Because this subsamplewas so small, I was unable to perform empirical tests that examined only these firms.

100 Rasmussen


use the sell-through revenue recognition method for all sales to distributors. Combination firms use

the sell-in revenue recognition method for some distributor sales and the sell-through revenue

recognition method for other distributor sales.

EMPIRICAL EVIDENCE

Descriptive Statistics

Table 1, Panel A presents descriptive statistics for the full sample of 1,572 firm-quarters, while

Panel B presents descriptive statistics for the sell-in, combination, and sell-through subsamples.

Twenty percent of the observations represent use of the sell-through method exclusively (Sell-Through), 48 percent of the observations represent a combination of both the sell-in and sell-through methods (Combo), and 32 percent of the observations represent use of the sell-in methodexclusively. As expected, deferred revenue is smallest for firms recognizing revenue for all

distributors when products are delivered (sell-in) and largest for firms deferring revenue recognition

for all distributors until the products have been resold (sell-through). Mean (median) current

deferred revenue scaled by total assets at quarter-end (Deferred Revenue) is 0.00 (0.00), 0.01 (0.00),and 0.02 (0.01) for sell-in, combination, and sell-through firms, respectively, and the means

(distributions) significantly differ at p , 0.000 for all three types of firms. Actual reported revenueattributable to distributor customers (Raw Disty Revenue) is available for two-thirds of the firm-quarters, and significant differences exist among the firms reporting this information. Raw DistyRevenue is highest, on average, for sell-through firms (54 percent) followed by combination firms(34 percent) and sell-in firms (30 percent). Meanwhile, 82 percent of the firm-quarters represent

firms that report revenue attributable to distributor customers at least once during the sample period

(Report). Mean Report 3 Avg Disty Revenue, the distributor revenue variable included in theempirical tests, is 29 percent for the full sample, 51 percent for sell-through firms, 25 percent for

combination firms, and 23 percent for sell-in firms. Report 3 Avg Disty Revenue is significantlyhigher for sell-through firms compared to both combination and sell-in firms.

With respect to the other variables included in the analyses, firms meet or beat analysts

consensus earnings forecast (MeetBeat) in 79 percent of the full sample quarters, 82 percent of thesell-in quarters, 77 percent of the combination quarters, and 79 percent of the sell-through firm-

quarters. Mean and median tests suggest that the incidence of meeting or beating analysts

consensus earnings forecast significantly differs only between sell-in and combination firms.

Meanwhile, mean unexpected stock returns (UR) are 0.002 for the full sample, 0.002 for sell-infirms, 0.002 for combination firms, and 0.007 for sell-through firms, but there is little evidence that

these returns significantly differ among the three types of firms. Table 1, Panel B also suggests that

many of the control variables used in the empirical analyses significantly differ among sell-in,

combination, and sell-through firms.14

Revenue Recognition and Earnings Management

Table 2 presents estimation results for Model 1, with examines the incidence of earnings

management for sell-in, sell-through, and combination firms. The sample used to estimate this

model consists of the 1,572 firm-quarters during 20012008 with required data for all analyses. The

Pseudo R2 is 10 percent and the area under the ROC curve is 72 percent, suggesting that the model

14 Recent studies examining the earnings-returns association (Hirshleifer et al. 2009; Drake et al. 2012) use decileranks of firm size and market-to-book measures in their empirical models instead of raw values. I follow thisapproach and include Rank of Size and Rank of MTB in Model 1 and Model 2. I also correct for skewness ofShares by including the decile rank of this measure (Rank of Shares) in Model 1.



TA

BL

E1

Des

crip

tiv

eS

tati

stic

s

Pa

nel

A:

Fu

llS

am

ple

Fu

llS

am

ple

(n

1,5

72

)

Mea

n2

5%

Qu

art

ile

Med

ian

75

%Q

ua

rtil

e

Sell

-Thr

ough

0.2

00

00

Com

bo0

.48

00

1

Def

erre

dR

even

ue0

.01

0.0

00

.00

0.0

1

Raw

Dis

tyR

even

ue0

.37

0.1

90

.36

0.5

3

Rep

ort

0.8

21

11

Rep

ort3

Avg

Dis

tyR

even

ue0

.29

0.1

10

.27

0.4

6

Ana

lyst

s1

25

10

17

Bet

a2

.66

1.6

92

.61

3.5

3

Bon

us%

0.1

00

.00

0.0

50

.16

CV

AF

0.0

30

.00

0.0

30

.10

Los

s0

.23

00

0

Mee

tBea

t0

.79

11

1

MT

B2

.93

1.4

32

.39

3.9

3

NO

A4

.26

2.6

33

.67

5.3

1

Opt

ions

%0

.48

0.0

70

.56

0.7

9

Per

sist

0.4

00

.16

0.4

00

.62

Rev

ise

Dow

n0

.82

11

1

Sale

sG

row

th0

.11

0.0

80

.08

0.2

6

Size

($M

)1

,82

0.4

12

28

.19

71

8.9

32

,12

2.7

1

Shar

es($

M)

18

6.9

83

0.9

68

7.2

92

55

.75

UE

0.0

00

50

.00

00

0.0

00

40

.00

20

UR

0.0

02

0.0

54

0.0

02

0.0

59

(con

tinu

edo

nn

ext

pa

ge)

102 Rasmussen


TA

BL

E1

(co

nti

nu

ed)

Pa

nel

B:

Sel

l-In

,C

om

bin

ati

on

,a

nd

Sel

l-T

hro

ug

hS

ub

sam

ple

s

Sel

l-In

(n

50

7)

Co

mb

o(n

75

0)

Sel

l-T

hro

ug

h(n

31

5)

Mea

n2

5%

Qrt

l.M

ed.

75

%Q

rtl.

Mea

n2

5%

Qrt

l.M

ed.

75

%Q

rtl.

Mea

n2

5%

Qrt

l.M

ed.

75

%Q

rtl.

Def

erre

dR

even

uea,b

,c,d

,e,f

0.0

00

.00

0.0

00

.00

0.0

10

.00

0.0

00

.02

0.0

20

.00

0.0

10

.04

Raw

Dis

tyR

even

uea,b

,c,d

,e,f

0.3

00

.15

0.2

90

.47

0.3

40

.17

0.3

20

.48

0.5

40

.30

0.5

50

.73

Rep

orta

,b,c

,d,e

,f0

.82

11

10

.75

11

10

.97

11

1

Rep

ort3

Avg

Dis

tyR

even

uec,d

,e,f

0.2

30

.03

0.2

30

.36

0.2

50

.10

0.2

50

.43

0.5

10

.32

0.5

10

.70

Ana

lyst

sc,d

,e,f

11

61

01

61

15

10

16

14

51

42

1

Bet

aa,b

,e,f

2.5

11

.53

2.4

83

.30

2.8

11

.86

2.7

53

.75

2.5

51

.48

2.5

73

.43

Bon

us%

a,b

,e,f

0.1

10

.00

0.0

70

.19

0.0

90

.00

0.0

40

.14

0.1

10

.01

0.0

70

.16

CV

AF

c,d

,e,f

0.0

10

.00

0.0

30

.09

0.0

40

.00

0.0

20

.11

0.0

70

.00

0.0

40

.09

Los

sc,d

,e,f

0.2

50

01

0.2

60

01

0.1

30

00

Mee

tBea

ta,b

0.8

21

11

0.7

71

11

0.7

91

11

MT

Ba,b

,c,d

,f2

.47

1.3

81

.93

3.1

13

.13

1.4

32

.50

4.1

63

.20

1.6

23

.29

4.4

6

NO

Aa,b

,c,d

4.5

03

.04

4.0

55

.37

4.2

22

.27

3.3

15

.56

3.9

82

.42

3.4

75

.05

Opt

ions

%a,b

,c,d

0.4

50

.12

0.5

00

.70

0.5

00

.10

0.5

60

.80

0.5

10

.00

0.6

50

.82

Per

sist

b,c

,d,e

,f0

.36

0.1

00

.33

0.5

90

.38

0.1

70

.38

0.5

80

.51

0.2

70

.49

0.7

6

Rev

ise

Dow

na,b

,e,f

0.8

41

11

0.7

91

11

0.8

61

11

Sale

sG

row

tha,e

0.0

90

.09

0.0

90

.26

0.1

30

.09

0.0

80

.30

0.0

80

.04

0.0

90

.21

Size

($M

)a,c

,d,f

2,2

85

.03

21

1.0

46

81

.85

2,0

12

.20

1,5

77

.92

20

7.9

26

48

.15

2,0

88

.11

1,6

49

.93

31

3.0

81

,45

8.7

82

,51

2.3

1

Shar

es($

M)a

,b,c

,d,f

21

5.9

63

0.6

37

2.7

51

52

.55

16

8.6

93

5.8

99

1.3

42

91

.89

18

3.8

62

9.8

61

75

.79

33

5.8

5

UE

b,d

,e0

.00

08

0.0

00

00

.00

06

0.0

02

10

.00

01

0.0

00

00

.00

03

0.0

02

10

.00

09

0.0

00

00

.00

03

0.0

01

3

UR

d0

.00

20

.06

00

.00

50

.05

90

.00

20

.05

80

.00

40

.06

10

.00

70

.03

80

.00

80

.05

1

Pan

elA

pre

sents

des

crip

tive

stat

isti

csfo

ra

sam

ple

of

1,5

72

quar

terl

yobse

rvat

ions

for

80

uniq

ue

sem

iconduct

or

firm

sduri

ng

20012008.

Pan

elB

pre

sents

des

crip

tive

stat

isti

csfo

rth

ese

ll-i

n,

com

bin

atio

n,

and

sell

-thro

ugh

subsa

mple

s.M

ean

(med

ian)

dif

fere

nce

sat

p,

0.1

0usi

ng

atw

o-t

aile

dt-

(Wil

coxon

Sum

-Ran

k)

test

are

den

ote

dby

a(b)

for

sell

-in

firm

sver

sus

com

bin

atio

nfi

rms,

c(d)

for

sell

-in

firm

sver

sus

sell

-thro

ugh

firm

s,an

de(

f)fo

rco

mbin

atio

nfi

rms

ver

sus

sell

-thro

ugh

firm

s.A

llco

nti

nuous

var

iable

sar

ew

inso

rize

dat

the

1st

and

99th

per

centi

les.

(con

tinu

edo

nn

ext

pa

ge)



TA

BL

E1

(co

nti

nu

ed)

Var

iable

Defi

nit

ions:

Sell

-Thr

ough

in

dic

ator

var

iable

equal

to1

ifth

efi

rmuse

sth

ese

ll-t

hro

ugh

reven

ue

reco

gnit

ion

met

hod

for

sale

sto

dis

trib

uto

rs,

and

0oth

erw

ise;

Com

bo

indic

ator

var

iable

equal

to1

ifth

efi

rmuse

sth

eco

mbin

atio

nre

ven

ue

reco

gnit

ion

met

hods

for

sale

sto

dis

trib

uto

rs,

and

0oth

erw

ise;

Def

erre

dR

even

ue

curr

ent

def

erre

dre

ven

ue

scal

edby

tota

las

sets

atquar

ter-

end;

Raw

Dis

tyR

even

ue

per

centa

ge

of

annual

reven

ue

attr

ibuta

ble

todis

trib

uto

rcu

stom

ers

and

isas

signed

toal

lof

the

firm

squar

terl

yobse

rvat

ions

duri

ng

the

fisc

alyea

r(t

hes

edat

aar

eav

aila

ble

for

1,0

58

of

the

sam

ple

firm

-quar

ters

);R

epor

t

indic

ator

set

var

iable

set

to1

ifth

efi

rmre

port

san

nual

reven

ue

attr

ibuta

ble

todis

trib

uto

rcu

stom

ers

atle

ast

once

duri

ng

the

sam

ple

per

iod,

and

0oth

erw

ise;

Rep

ort3

Avg

Dis

tyR

even

ue

aver

age

of

all

annual

reven

ue

attr

ibuta

ble

todis

trib

uto

rcu

stom

ers

report

edby

the

firm

duri

ng

the

sam

ple

per

iod

and

isas

signed

toal

lof

the

firm

squar

terl

yobse

rvat

ions,

or

isse

tto

0fo

rfi

rms

that

do

not

report

this

info

rmat

ion

inan

yof

the

sam

ple

yea

rs;

Ana

lyst

s

num

ber

of

anal

yst

earn

ings

fore

cast

sin

cluded

inth

ela

stI/

B/E

/Sco

nse

nsu

sfo

reca

stpri

or

toth

equar

terl

yea

rnin

gs

announce

men

t;B

eta

syst

emat

icri

skfr

om

the

mar

ket

model

for

the

twel

ve-

month

per

iod

endin

gbef

ore

the

star

tof

the

quar

ter;

Bon

us%

CE

Os

cash

-bas

edin

centi

ve

com

pen

sati

on

scal

edby

the

CE

Os

tota

lan

nual

com

pen

sati

on

and

isas

signed

toal

lof

the

firm

squar

terl

yobse

rvat

ions

duri

ng

the

fisc

alyea

r;C

VA

F

coef

fici

ent

of

var

iati

on

(the

stan

dar

ddev

iati

on

scal

edby

the

mea

n)

of

the

last

I/B

/E/S

conse

nsu

sea

rnin

gs

fore

cast

pri

or

toth

equar

terl

yea

rnin

gs

announce

men

t;L

oss

indic

ator

var

iable

equal

to1

ifac

tual

quar

terl

yea

rnin

gs

report

edby

I/B

/E/S

are

neg

ativ

e,an

d0

oth

erw

ise;

Mee

tBea

tin

dic

ator

var

iable

equal

to1

ifth

efi

rmm

eets

the

last

I/B

/E/S

conse

nsu

sea

rnin

gs

fore

cast

pri

or

toth

equar

terl

yea

rnin

gs

announce

men

tdat

eor

bea

tsit

by

any

amount,

and

0oth

erw

ise;

MT

B

mar

ket

val

ue

of

equit

yat

quar

ter-

end

scal

edby

book

val

ue

of

equit

yat

quar

ter-

end;

NO

A

net

oper

atin

gas

sets

atth

een

dof

the

pri

or

quar

ter,

scal

edby

sale

sfo

rth

epri

or

quar

ter;

Opt

ions

%

doll

arval

ue

of

new

opti

ons

gra

nte

dto

the

CE

Oduri

ng

the

yea

rsc

aled

by

the

CE

Os

tota

lan

nual

com

pen

sati

on

and

isas

signed

toal

lof

the

firm

squar

terl

yobse

rvat

ions

duri

ng

the

fisc

alyea

r.(I

foll

ow

the

appro

ach

use

dby

Chen

gan

dF

arber

[2008]

and

Efe

ndi

etal

.[2

013]

tocr

eate

this

var

iable

);P

ersi

st

firs

t-ord

erau

toco

rrel

atio

nco

effi

cien

tof

quar

terl

yea

rnin

gs

esti

mat

edover

the

pas

tfo

ur

yea

rs;

Rev

ise

Dow

n

indic

ator

var

iable

equal

to1

ifat

leas

tone

anal

yst

revis

eddow

nhis

/her

earn

ings

fore

cast

for

the

firm

duri

ng

the

per

iod

bet

wee

nth

epri

or

quar

ter

sea

rnin

gs

announce

men

tan

dth

ecu

rren

tquar

ter

sea

rnin

gs

announce

men

t,an

d0

oth

erw

ise;

Sale

sG

row

th

sale

sfo

rth

equar

ter

div

ided

by

sale

sfr

om

the

sam

equar

ter

inth

epri

or

yea

r,le

ssone;

Size

tota

las

sets

atquar

ter-

end;

Shar

es

aver

age

num

ber

of

com

mon

shar

esouts

tandin

gduri

ng

the

quar

ter;

UE

dif

fere

nce

bet

wee

nac

tual

earn

ings

per

shar

ean

dth

ela

stco

nse

nsu

sea

rnin

gs

fore

cast

pri

or

toth

equar

terl

yea

rnin

gs

announce

men

tdat

e(b

oth

report

edby

I/B

/E/S

),sc

aled

by

stock

pri

ceat

quar

ter-

end

(rep

ort

edby

CR

SP

);an

dU

R

cum

ula

tive

stock

retu

rnfo

rtw

otr

adin

gday

sbeg

innin

gon

the

quar

terl

yea

rnin

gs

announce

men

tdat

e,ad

just

edby

the

val

ue-

wei

ghte

dm

arket

index

.

104 Rasmussen


has acceptable predictive power of the incidence of meeting or beating analysts consensus earnings

forecast (Hosmer and Lemeshow 2000). Although the model controls for calendar-quarter fixed

effects, I do not tabulate the fixed effects coefficients for parsimony.

Consistent with H1a and H1b, the Sell-Through and Combo coefficients are negative andsignificant (a1 0.457, p 0.067; a2 0.493, p 0.015, respectively), suggesting that sell-through and combination firms are both less likely to meet or beat analysts consensus earnings

forecast than sell-in firms. Since combination firms have more opportunity to manage earnings

compared to sell-through firms, it is surprising to find that the Combo coefficient appears larger inmagnitude and has a stronger statistical significance compared to the Sell-Through coefficient.However, an untabulated v2 test indicates that the Sell-Through and Combo coefficients do notsignificantly differ. In order to quantify the impact of the revenue recognition method on the

likelihood of a firm meeting or beating analysts consensus earnings forecast, I calculate average

partial effects (untabulated).15 The average partial effect of the sell-through (combination) method

decreases the likelihood of a firm meeting or beating analysts consensus earnings forecast by 6.9

(7.0) percent, and this decrease is significant at the p0.041 (0.017) level.16 Since sell-through andcombination firms are less likely to meet or beat analysts consensus earnings forecast (the proxy

for earnings management), these findings suggest that sell-in firms exercise discretion under their

revenue recognition method to manage earnings. With respect to control variables, Sales Growth,Rank of MTB, Bonus%, and Analysts are positively associated with MeetBeat, while Revise Down isnegatively associated with MeetBeat.

In an untabulated test, I use the Heckman procedure (Heckman 1979; Wooldridge 2002) to

control for the possibility that determinants of the revenue recognition method are correlated with

the likelihood that a firm meets or beats analysts consensus earnings forecast. I estimate two

selection equations, each predicting use of either the sell-through or the combination method. Each

selection equation includes all control variables from Model 1 plus two instrument variables: the

decile rank of firm age and an indicator representing use of an industry specialist auditor (auditor

with the highest market share in the semiconductor industry).17 I then calculate the inverse Mills

ratios from the selection equations and include these inverse Mills ratios in the outcome equation

predicting the likelihood of meeting or beating analysts consensus earnings forecast. Sell-Throughand Combo remain negatively and significantly associated with MeetBeat in the outcome equation(results untabulated), consistent with Table 2.18

15 Average partial effects equal the sample average of marginal effects computed for each observation (Wooldridge2002, 2224). Since Sell-Through and Combo are indicator variables representing different categories of a singleunderlying variable (revenue recognition), I follow Bartus (2005) and restrict the observations used to computeaverage partial effects to the category of interest and the reference group. When calculating the average partialeffect of Sell-Through, observations are restricted to sell-through and sell-in firms. Similarly, when calculatingthe average partial effect of Combo, observations are restricted to combination and sell-in firms.

16 The average partial effect of Sell-Through (Combo) decreases the likelihood of a firm meeting or beating analystsconsensus earnings forecast from 83.3 (82.7) percent to 76.4 (75.8) percent. These likelihoods can be comparedto the 78.8 percent unconditional mean for the full sample (1,239 firm-quarter observations meeting or beatinganalysts consensus earnings forecast versus 1,572 sample observations).

17 Heckmans procedure requires at least one instrument variable in the selection model. Untabulated results of thelogistic regression selection models indicate that the decile rank of firm age is negatively and significantlyassociated with Sell-Through (p , 0.000). Use of the industry specialist auditor is positively and significantlyassociated with Combo (p , 0.000).

18 Correlation tests indicate that the inverse Mills ratios calculated from the two selection equations have a strongnegative correlation (0.575, p , 0.000). As an alternative to including both inverse Mills ratios in the outcomeequation, I estimate two outcome equations. The first outcome equation is estimated using the subsample of sell-in and sell-through firms and includes Sell-Through and the inverse Mills ratio calculated when predicting Sell-Through. The second outcome equation is estimated using the subsample of sell-in and combination firms andincludes Combo and the inverse Mills ratio calculated when predicting Combo. The coefficient of interest (Sell-Through or Combo), is negatively and significantly associated with MeetBeat in each of these outcome equations.



Collectively, the results presented in Table 2 and the additional analysis indicate that earnings

management to a benchmark does differ based on firms revenue recognition methods for sales to

distributors. Specifically, the results suggest that earnings management is more likely for firms that

recognize all revenue before product return and pricing adjustment uncertainties are resolved. This

finding implies that earnings management concerns about the sell-in method are warranted.

Revenue Recognition and Earnings Informativeness

Table 3 presents estimation results for Model 2, which examines the ERCs of firms with

different revenue recognition methods. The sample used to estimate this model consists of the 1,572

firm-quarters during 20012008 with required data for all analyses. The model specification

explains 3.5 percent of unexpected returns, which is consistent with prior studies that examine the

earnings-returns association (Lev 1989). For parsimony, I do not tabulate the coefficients for the

control variables or their interactions with UE.Unexpected earnings (UE) are positively and significantly associated with unexpected returns

(UR) (b1 4.371; p 0.003), indicating a positive ERC for sell-in firms. The main effects of Sell-Through and Combo and the UE3 Combo coefficient are insignificant. However, the UE3 Sell-Through coefficient is positive and significant (b4 2.302; p 0.065). Because UE3 Sell-Throughindicates the incremental ERC for sell-through firms compared to sell-in firms, this finding suggests

TABLE 2

The Association between Earnings Management, Revenue Recognition Methods for Sales toDistributors, and Control Variables

Variable Prediction Coeff. p-value

Intercept / 0.334 (0.559)Sell-Through 0.457 (0.067)Combo 0.493 (0.015)Sales Growth 0.459 (0.042)Rank of MTB 1.018 (0.004)NOA 0.010 (0.393)Rank of Shares / 0.055 (0.945)Rank of Size / 0.241 (0.703)Report / 0.449 (0.202)Report 3 Avg Disty Revenue / 0.624 (0.311)Bonus% 1.716 (0.024)Options% 0.075 (0.395)Analysts 0.044 (0.033)CVAF 0.043 (0.428)Revise Down / 0.754 (0.001)Calendar-quarter fixed effects Yes

n 1,572

Pseudo R2 0.10

Area under ROC curve 0.72

This table presents the results of Model 1 where the dependent variable is MeetBeat. Variables are defined in Table 1with the following exceptions. Rank of MTB, Rank of Shares, and Rank of Size are decile ranks of MTB, Shares, and Size,scaled to range between 0 and 1. All standard errors are clustered by firm (Petersen 2009). Bold coefficients and p-valuesindicate statistical significance at the 0.10 level or less. One-tailed tests are used when a direction is predicted, and two-tailed tests are used when there is no prediction.

106 Rasmussen


that the unexpected earnings of sell-through firms are more informative (have a stronger association

with unexpected stock returns) than the unexpected earnings of sell-in firms. In addition, the

insignificant UE 3 Combo coefficient indicates that sell-through firms also have a significantlyhigher ERC compared to combination firms. Untabulated coefficients for the interactions between

UE and control variables indicate that ERCs are lower during the firms fourth quarter and when the

firm reports a loss. Meanwhile, ERCs are higher as analyst following increases.

I perform a variety of untabulated tests to assess the robustness of the results presented in Table

3. First, I use the Heckman procedure (Heckman 1979; Wooldridge 2002) to control for potential

endogeneity of the revenue recognition method. The selection equations predict use of the sell-

through and combination methods and include all controls from Model 2 plus the decile rank of

firm age and an indicator representing use of the industry specialist auditor.19 Inverse Mills ratios

generated from the selection equations are included in the outcome equation.20 Second, instead of

measuring unexpected returns over a short window surrounding the earnings announcement, I

measure UR over the window starting two days after the prior quarters earnings announcement

date and ending one day after the current quarters earnings announcement date. Since many of the

sample firm-quarter return windows overlap in this untabulated test, I include calendar-quarter fixed

effects. Third, I use an alternate measure of UE based on the earnings time series. Specifically, I

calculate UE as the difference between actual earnings per share for the current quarter and actual

earnings per share for the same quarter in the prior year (both reported by I/B/E/S), scaled by stock

price at quarter-end. Untabulated results for all of these independent tests indicate that UE3 Sell-Through is positively and significantly associated with unexpected returns while the UE3 Combocoefficient is insignificantly different from zero.21

Finally, I examine the effect of the revenue recognition method on the association between

unexpected returns and unexpected gross margin. For this analysis, unexpected gross margin

(UGM) equals the difference between actual gross margin percentage and analysts last consensus

gross margin percentage forecast prior to the quarterly earnings announcement (both reported by I/

B/E/S), scaled by analysts last consensus gross margin percentage forecast.22 There are 411 firm-

quarter observations during 20062008 with data available for UGM. When UGM replaces UE in

Model 2 and is interacted with the revenue recognition indicators and control variables, the UGM3Sell-Through coefficient is positive and significant (p 0.004) and UGM 3 Combo isinsignificantly different from zero.

In sum, the results presented in Table 3 and the additional analyses suggest that unexpected

earnings are more strongly associated with unexpected returns for sell-through firms compared to

both sell-in and combination firms. Stated differently, the results suggest that earnings are more

informative for firms that defer revenue recognition until all product return and pricing adjustment

uncertainties are resolved.

19 Untabulated results of the logistic selection models indicate that the decile rank of firm age is negatively andsignificantly associated with Sell-Through (p , 0.000) and use of the industry specialist auditor is positively andsignificantly associated with Sell-Through and Combo (p 0.10 and p , 0.000, respectively).

20 As with the MeetBeat analysis, correlation tests indicate that the inverse Mills ratios calculated from the twoselection equations have a strong negative correlation (0.543, p , 0.000). Untabulated results indicate thatinferences with respect to UE3Sell-Through and UE3Combo are the same for a full-sample specification of theoutcome equation as well as separate outcome equations estimated using either the sell-through/sell-in orcombination/sell-in subsample of firms.

21 The statistical significance of the UE3 Sell-Through coefficient using a two-tailed test is marginal (0.11) for thequarterly returns test and less than 0.10 for all other untabulated tests.

22 Since gross margin forecasts are percentages, I use analysts last consensus gross margin percentage forecast asthe scalar instead of the stock price at quarter-end.



SUMMARY AND CONCLUSIONS

Although revenue is one of the most important figures reported on the income statement, little

research exists on specific revenue recognition methods and their implications for firms. This study

examines the revenue recognition methods of semiconductor firms and their implications for

earnings management and earnings informativeness. Semiconductor firms sell a significant amount

of product to distributors and face product return and pricing adjustment uncertainties until the

distributors resell the product to end-customers. I find that firms deferring revenue recognition until

product return and price adjustment uncertainties are partly or fully resolved are less likely to meet

or beat analysts consensus earnings forecast than firms immediately recognizing revenue for sales

to distributors. This finding suggests that earnings management is more likely when firms recognize

revenues before all uncertainties are resolved. I also find that earnings are more informative (the

earnings-returns association is stronger) for firms that defer revenue recognition until products are

resold to end-customers (i.e., all product return and pricing adjustment uncertainties have been

resolved).

This study extends a growing stream of research that examines the implications of revenue

recognition for firms in different industries (Bowen et al. 2002; Zhang 2005; Srivastava 2011) and

can help students, practitioners, and other financial statement users better understand revenue

recognition methods and their associations with earnings management and earnings informative-

ness. This study is also potentially useful for investors, analysts, and auditors who monitor

high-technology manufacturers because the results imply that earnings management concerns about

the sell-in method are warranted and earnings are less informative when revenue is immediately

recognized for sales to at least some distributors. This study is also potentially informative for

regulators and standard-setters because the findings suggest that manufacturers with significant

product return and pricing adjustment uncertainties should only recognize revenue from sales to

distributors once all of the uncertainties are resolved.

TABLE 3

The Association between Unexpected Returns, Unexpected Earnings, Revenue RecognitionMethods for Sales to Distributors, and Control Variables

Variable Prediction Coeff. p-value

Intercept / 0.025 (0.012)UE 4.371 (0.003)Sell-Through / 0.004 (0.639)Combo / 0.007 (0.168)UE 3 Sell-Through / 2.302 (0.065)UE 3Combo / 0.264 (0.714)Controls YesUE 3Controls Yes

n 1,572

Adjusted R2 0.035

This table presents the results of Model 2 where the dependent variable is UR. The control variables include: Rank ofSize, Rank of MTB, Persist, Beta, Loss, Fourth Qtr, Analysts, Bonus%, Options%, Report, and Report 3 Avg DistyRevenue. Variables are defined in Table 1 and Table 2 with the following exception. Fourth Qtr is an indicator variableequal to 1 if the firm is announcing fourth-quarter earnings, and 0 otherwise. Bold coefficients and p-values indicatestatistical significance at the 0.10 level or less. One-tailed tests are used when a direction is predicted, and two-tailed testsare used when there is no prediction.

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This study is subject to limitations. First, because I limit my sample to semiconductor firms, I

have not examined if the findings generalize to all industries and revenue recognition methods.

Second, although I have attempted to control for the characteristics of firms with different revenue

recognition methods in the empirical tests, it is possible that the results reflect inherent differences

among sell-in, sell-through, and combination firms. Even with these limitations, the study provides

insight into the observed effects and improves our understanding of firms with different revenue

recognition methods.

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