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ADVANCES IN ACCOUNTING
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ADVANCES IN ACCOUNTING Series Editor: Philip M. J. Reckers Recent Volumes: 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Volumes 13–18: edited by Philip M. J. Reckers
ADVANCES IN ACCOUNTING VOLUME 19
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EDITED BY
PHILIP M. J. RECKERS Arizona State University, Tempe, USA ASSOCIATE EDITORS
LOREN MARGHEIM University of San Diego, California, USA
RICHARD MORTON Florida State University, Florida, USA
LYNN REES Texas A&M University, Texas, USA
STACEY WHITECOTTON Arizona State University, Arizona, USA
2002
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CONTENTS
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LIST OF CONTRIBUTORS
vii
EDITORIAL BOARD
ix
EMPLOYEE STOCK OPTIONS AND PRO FORMA EARNINGS MANAGEMENT Terry A. Baker, Dale R. Martin and Austin L. Reitenga
1
A NOTE ON TESTING A MODEL OF COGNITIVE BUDGETARY PARTICIPATION PROCESSES USING A STRUCTURAL EQUATION MODELING APPROACH Vincent K. Chong
27
AN EXPERIMENTAL MARKET ANALYSIS OF AUDITOR WORK-LEVEL REDUCTION DECISIONS Maribeth Coller, Julia L. Higgs and Stephen Wheeler
53
FIXED COST ALLOCATION AND THE CONSTRAINED PRODUCT MIX DECISION Susan Haka, Fred Jacobs and Ronald Marshall
71
DO INITIAL PUBLIC OFFERING FIRMS UNDERSTATE THE ALLOWANCE FOR BAD DEBTS? Scott B. Jackson, William E. Wilcox and Joel M. Strong
89
COMMON UNCERTAINTY EFFECTS ON THE USE OF RELATIVE PERFORMANCE EVALUATION FOR CORPORATE CHIEF EXECUTIVES Leslie Kren v
119
vi
THE EFFECTS OF PROCEDURAL JUSTICE AND EVALUATIVE STYLES ON THE RELATIONSHIP BETWEEN BUDGETARY PARTICIPATION AND PERFORMANCE Chong M. Lau and Edmond W. Lim 139
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AN ANALYSIS OF THE ACCURACY OF LONG-TERM EARNINGS PREDICTIONS Kenneth S. Lorek and G. Lee Willinger
161
A PRELIMINARY FRAMEWORK IN EXAMINING THE INFLUENCE OF OUTCOME INFORMATION ON EVALUATIONS OF AUDITOR DECISIONS D. Jordan Lowe and Philip M. J. Reckers
177
INCOME LEVEL AND INCOME TYPE AS DETERMINANTS OF TAX RETURN PREPARATION FEES: AN EMPIRICAL INVESTIGATION D. Shawn Mauldin, Philip A. Brown, Morris H. Stocks and Robert L. Braun 189 PRODUCT DECISIONS IN PRACTICE Jack W. Paul and Samuel C. Weaver
215
EVOLVING RESEARCH BENCHMARKS Peter M. Johnson, Philip M. J. Reckers and Lanny Solomon
235
Chapter Title
vii
LIST OF CONTRIBUTORS
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Terry A. Baker
Wake Forest University, USA
Robert L. Braun
Southeastern Louisiana University, USA
Philip A. Brown
Harding University, USA
Vincent K. Chong
University of Western Australia
Maribeth Coller
University of South Carolina, USA
Susan Haka
Michigan State University, USA
Julia L. Higgs
Florida Atlantic University, USA
Fred Jacobs
Michigan State University, USA
Scott B. Jackson
University of Texas at San Antonio, USA
Peter M. Johnson
Arizona State University, USA
Leslie Kren
University of Wisconsin – Milwaukee, USA
Chong M. Lau
University of Western Australia
Edmond W. Lim
Singapore Standard Chartered Bank
Kenneth S. Lorek
Northern Arizona University, USA
D. Jordan Lowe
University of Nevada, Las Vegas, USA
Ronald Marshall
Michigan State University, USA
Dale R. Martin
Wake Forest University, USA vii
viii
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LIST OF CONTRIBUTORS
D. Shawn Mauldin
Southeastern Louisiana University, USA
Jack W. Paul
Lehigh University, USA
Philip M. J. Reckers
Arizona State University, USA
Austin L. Reitenga
University of Texas at San Antonio, USA
Lanny Solomon
University of Missouri – Kansas City, USA
Morris H. Stocks
University of Mississippi, USA
Joel M. Strong
St. Cloud State University, USA
Samuel C. Weaver
Lehigh University, USA
Stephen Wheeler
University of the Pacific, USA
William E. Wilcox
Bradley University, USA
G. Lee Willinger
University of Oklahoma, USA
Chapter Title
ix
EDITORIAL BOARD
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M. J. Abdolmohammadi Bently College
Anthony H. Catanach, Jr. Villanova University
Urton L. Anderson University of Texas at Austin
C.S. Agnes Cheng University of Houston
Vairam Arunachalam University of Missouri – Columbia
Alan Cherry Loyola Marymount University
Frances L. Ayres University of Oklahoma
Eugene C. Chewning, Jr. University of South Carolina
Steve Baginski Indiana University
C. Bryan Cloyd University of Illinois
Charles Bailey University of Central Florida
Jeffrey Cohen Boston College
Alan Bathke Florida State University
Maribeth Coller University of South Carolina
Bruce Behn University of Tennessee
James W. Deitrick University of Texas at Austin
James Boatsman Arizona State University
William Dilla Iowa State University
Bruce Branson North Carolina State University
Gordon Leon Duke University of Minnesota
Timothy D. Cairney Florida Atlantic University
Peggy Dwyer University of Central Florida ix
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EDITORIAL BOARD
Martha M. Eining University of Utah
William Hillison Florida State University
Pieter Elgers University of Massachusetts
Karen Hooks Florida Atlantic University
Richard File University of Nebraska-Omaha
Jill Hopper Middle Tennessee State University
Don W. Finn Louisiana State University
Eric Johnson University of Texas at Arlington
Timothy J. Fogarty Case Western Reserve University
Khondkar E. Karim Rochester Institute of Technology
Thomas A. Gavin University of Tennessee at Chattanooga
Tim Kelley University of San Diego
James T. Godfrey George Mason University
Inder K. Khurana University of Missouri
Michael H. Granof University of Texas
Thomas E. Kida University of Massachusetts
Robert Greenberg Washington State University
Thomas King Southern Illinois University
Thomas Hall University of Texas-Arlington
Jayanthi Krishnan Temple University
Kenneth Harmon Middle Tennessee State University
George Krull Wheaton, Illinois
Bart P. Hartman Saint Joseph's University
Tanya Lee University of North Texas
John M. Hassell Indiana University
Steve Lim Texas Christian University
Charlene Henderson University of Texas at Austin
Tom Linsmeier Michigan State University
Editorial Board Title
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xi
Chao-Shin Liu University of Notre Dame
William Pasewark University of Houston
Thomas Lopez Georgia State University
John W. Paul Lehigh University
Kenneth Lorek Northern Arizona University
Jamie Pratt Indiana University
Marty Loudder Texas A&M University
K. Raghunandan University of Massachusetts – Dartmouth
D. Jordan Lowe University of Nevada, Las Vegas Loren Margheim University of San Diego James Martin University of South Florida H. Fred Mittelstaedt University of Notre Dame Richard Morton Florida State University
Robert Ramsay University of Kentucky William J. Read Bentley College James Rebele Lehigh University Phil Regier Arizona State University Robert Roussey University of Southern California
Dennis Murray University of Colorado at Denver
H. Sami Temple University
Kaye Newberry University of Arizona
Arnold Schneider Georgia Institute of Technology
Carl Pacini Florida Gulf Coast University
Richard Schroeder University of North Carolina – Charlotte
Don Pagach North Carolina State University
Ken Schwartz Boston College
Kurt Pany Arizona State University
Gerry Searfoss University of Utah xi
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EDITORIAL BOARD
David B. Smith Iowa State University
Sam Tiras University of Oregon
Rajendra P. Srivastava University of Kansas
Mary Jeanne Welsh La Salle University
Jerry Strawser Texas A&M University
Stephen W. Wheeler University of the Pacific
Thomas L. Strober University of Notre Dame Steve G. Sutton Texas Tech University Mike Tearney University of Kentucky Paula Thomas Middle Tennessee State University
David Williams Ohio State University Lee Willinger University of Oklahoma Bernard Wong-On-Wing Washington State University Awni Zebda Texas A&M University – Corpus Christi
EMPLOYEE STOCK OPTIONS AND PRO FORMA EARNINGS MANAGEMENT 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Terry A. Baker, Dale R. Martin and Austin L. Reitenga
ABSTRACT Effective in 1996, FAS 123 established new financial reporting requirements for employee stock options. This study extends previous research that suggests that opposition to FAS 123 was politically motivated and that options have been used to manage earnings. We find strong evidence that reported option values under FAS 123 are influenced by various financial reporting costs of the firm, including political costs related to executive pay. The results appear to be consistent with previous research regarding the effects of political considerations and earnings management strategies on the use and disclosure of employee options.
1. INTRODUCTION Current U.S. financial reporting standards require firms to disclose information on employee stock options in two separate reports. Since 1992, the SEC has mandated that details of individual awards to executive officers be reported in the firm’s proxy statement. Effective in 1996 with the adoption of FAS 123,
Advances in Accounting, Volume 19, pages 1–26. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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firms must also disclose aggregate information in their financial statements on awards made to all employees, including the pro forma effect of options compensation expense on net income. As has been well documented, FAS 123 was an extremely controversial standard (see for example Dechow et al., 1996; Fraser et al., 1998). Its implementation has prompted concerns in the financial press about issues such as a growing corporate reliance on options and the long-term impact of options on corporate earnings, and not just for small firms or technology sectors. For example, a study by consulting firm William M. Mercer found that annual option grants at 350 Fortune 500-type firms increased by more than 20% from 1993 to 1995 (reported in Jereski, 1997). More recently, an analyst’s study of the 200 largest public firms found that option awards made in 1999 represented over 2% of outstanding shares on average, double the award level five years earlier. When combined with previous awards, the average total options outstanding rose to about 14% per firm (reported in Leonhardt, 2000). In an attempt to quantify this development, analysts and investors appear to adjust for what is known as “overhang,” meaning the potential dilution of earnings and shareholder wealth from the firm’s use of employee options (Fox, 2001; Leonhardt, 2000). Academic research seems to corroborate anecdotal evidence of a dilution effect (Aboody, 1996). Regarding this apparent trend, Federal Reserve Chairman Alan Greenspan, among others, has expressed concerns that the proliferation of options has impaired investors’ ability to judge current and future earnings and has led to recurring overstatement of corporate profitability (Leonhardt, 2000; Lowenstein, 1997; Jereski, 1997; MacDonald & McGough, 1999). Since the adoption of FAS 123 in 1996, financial analysts have regularly examined the impact of options on corporate earnings. Typical reported estimates range from 1–5% of net income on average for large industrial firms to 25–50% on average for certain technology sectors (WSJ, 2000; MacDonald & McGough, 1999; MacDonald, 1998; Lowenstein, 1997). Previous research provides evidence that disclosures on employee options have value-relevance for investors, implying that there could be incentives to manage such disclosures (Aboody, 1996). One stream of research has focused on proxy statement disclosure of awards to CEOs. This stream provides consistent evidence that proxy disclosure is influenced by political costs related to excessive executive pay (Murphy, 1996; Yermack, 1998; Baker, 1999). Given the vigorous opposition to FAS 123, and the reporting discretion that the standard allows, the issue arises whether the disclosure of firm-wide option awards in financial statements (as mandated by FAS 123) is similarly influenced and whether such influence results in pro forma earnings management.
Employee Stock Options and Pro Forma Earnings Management
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3
While numerous studies have examined aspects of FAS 123 such as theoretical problems of option valuation and value-relevance of disclosures, there does not appear to be any scholarly evidence on the potential for earnings management upon implementation. Our study focuses on this aspect of the standard. Further research on this issue could offer an alternate view of the public policy debate over options disclosure, including why firms attempted to influence the standardsetting process and what the economic consequences of this influence might be. To investigate this issue, we examined footnote disclosures on employee options in financial statements issued for 1996, the first year affected by FAS 123. Specifically, we analyzed the process by which firms estimated the fair value of their options, including a comparison of the reported assumptions and fair values to our independent benchmarks. Next, we tested whether the reported values appear to be systematically related to factors such as managerial compensation levels, earnings pressure, accounting policies, and dilution of share value. Our general assertion is that firms tend to report lower (more aggressive) values for their option awards, thereby lessening the charge to pro forma net income, as the financial reporting costs of implementation increase. As a result of our tests, we address the contention in previous research that financial statement disclosure under FAS 123, because it is done on a firm-wide aggregate basis rather than on individual awards, is less likely to be affected by political costs of managers’ pay (Yermack, 1998). Our results offer several insights on options reporting. First, we find that the impact of FAS 123 adoption, measured as the mean of pro forma options expense, was about 3% of 1996 net income for our sample of large firms. Our result is comparable to studies of FAS 123 prepared by independent financial analysts (noted above). Second, consistent with previous research on the reporting of options in proxy statements, we find that aggregate option values under FAS 123 are generally much lower than independent benchmarks. On average, sample firms reported aggregate fair values that were 30% less than our benchmarks. In our analysis of the Black-Scholes model assumptions used to estimate option values, we find that firms often selected assumptions that are value-decreasing relative to historical levels, potentially lessening the impact on pro forma net income. Third, we generally find evidence supporting our assertions that disclosure under FAS 123 is influenced by various financial reporting costs. For example, we measure the potential for dilution through both the number and value of options awarded by the firm (Aboody, 1996) and find mixed evidence that as potential dilution increases firms appear to report option values more aggressively. Aggressive reporting also appears to be related to the political costs of managers’ compensation. We find that CEO pay – both absolute and relative to firm performance – appears to affect the firm’s reporting 3
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choice. This result appears to be consistent with evidence that political considerations affect the disclosure of options in proxy statements (Murphy, 1996; Yermack, 1998; Baker, 1999) and motivated firms’ lobbying behavior against FAS 123 (Dechow et al., 1996). However, it contradicts the assertion in Yermack (1998) that the reporting of aggregate option awards to all employees under FAS 123, as opposed to individual award values in proxy statements, is not likely to be similarly influenced. In the following section, we discuss related research in more detail and develop our hypotheses. Sections 3 and 4 present our sample selection and data analysis, and Section 5 provides our conclusions and discusses the relevance of the findings.
2. PREVIOUS RESEARCH AND HYPOTHESIS DEVELOPMENT The changes in options reporting imposed by the SEC and FASB have inspired several streams of research. One has examined theoretical problems of estimating fair value. Because employee options are private, non-transferable contracts, their true values are not observable. A main point of contention in developing reporting standards was how to obtain reliable estimates (FASB, 1995). Numerous studies have examined estimation issues, including the impact on fair value of the unique features of employee options and the appropriateness of the Black-Scholes (1973) model for financial reporting (Rubinstein, 1995; Kulatilaka & Marcus, 1994; Huddart & Lang, 1996; Foster et al., 1991; Hemmer et al., 1994, 1996, 1998, among others). General results of this research are that fair values can be materially sensitive to estimation procedures and that the range of acceptable estimates under current reporting requirements appears to be broad. So, firms appear to have considerable latitude in reporting fair values for their options. Even Dennis Beresford, FASB Chairman at the time of FAS 123, conceded that on this issue firms will be depending largely on “their consciences and the limited guidance we give them” (Lowenstein, 1995, p. C1). A second stream of research has examined how firms have complied with the SEC’s 1992 change in proxy disclosure rules regarding awards to senior executives (SEC, 1992, 1993). Reporting entities expressed strong opposition to this new requirement, which suggests that it imposed significant financial reporting costs (Murphy, 1996). Consistent with this notion, early studies by Lewellen et al. (1995) and Murphy (1996) provided evidence that firms were systematically choosing among allowable procedures to report option values. Murphy hypothesized and found confirming evidence that firms’ reporting choices could be influenced by political costs related to investors’ perceptions of executive pay. Subsequent research appears to corroborate the political cost
Employee Stock Options and Pro Forma Earnings Management
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hypothesis (Yermack, 1998; Baker, 1999). The evidence from these studies suggests that firms whose CEOs receive higher pay relative to firm performance have incentives to report lower fair values for their CEOs’ option awards. The main implication from this line of research is that political pressure regarding the reasonableness of executive pay, which is one form of financial reporting cost, seems to influence options reporting. However, Yermack (1998) notes important differences in the reporting of option awards under FAS 123. First, the standard only requires disclosure of awards to all employees of the firm on an aggregate basis. For example, the reported option value is the weighted-average fair value of options awarded to all employees during the year. Details of individual awards are not presented in the financial statements as they are in the firm’s proxy statement. Second, there is no explicit coordination of valuation procedures between the FASB and SEC rules on option disclosure. In other words, the assumptions used to value firm-wide awards under FAS 123 are not required to be the same as those used for individual awards to executives in the proxy statement. Yermack therefore argues that the political costs of managerial compensation, which appear to influence the reporting of individual awards in the proxy statement, are not likely to be relevant to the firm’s reporting decision under FAS 123. Presumably, aggregate disclosure inhibits investors’ ability to assess managerial pay relative to performance. We believe that this argument is reasonable. However, an empirical study by Dechow et al. (1996) could offer evidence to the contrary. Dechow et al. examined responses to the FASB’s exposure draft on options (FASB, 1993) and tested arguments offered by critics that it would depress stock prices and increase firms’ cost of capital. The authors did not find systematic support for these arguments. Instead, the evidence indicated that firms submitting opposing comment letters were characterized by higher levels of executive pay and larger executive option awards. This result suggests that managers perceived that expense recognition and disclosure under FAS 123, even in the aggregate, could be politically costly. The authors concluded that the cost of capital argument was “a politically palatable excuse to disguise top executives’ self-interested behavior,” especially “concerns with public scrutiny of their compensation” (pp. 18–19). Contrary to Yermack, the implication from Dechow et al. is that the political cost hypothesis could be relevant to options reporting under FAS 123, which suggests that there could be a link between the assumptions used to value executive option awards in the proxy statement and those used to value aggregate awards under FAS 123. We attempt to address this apparent contradiction with a test of our first hypothesis. Given the findings in Dechow 5
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et al. and in previous proxy statement disclosure studies indicating that political costs are relevant, we express this hypothesis in its alternative form. H1: As political costs related to executive pay levels increase, firms will report lower option values to mitigate the financial reporting costs of implementation.
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We expect that firms with greater political costs of implementation will report lower option values. As measures of relevant political costs, we follow previous research and consider both the absolute level of executive pay and the level of pay relative to firm performance in our test of hypothesis 1 (Dechow et al., 1996; Yermack, 1998; Baker, 1999). Other research has focused on the way firms use options in their managerial pay packages, specifically substitutions between options and cash compensation. Before FAS 123, financial accounting standards generally did not require expense recognition for options that were granted at-the-money (exercise price equal to stock price). So, unlike other forms of compensation, option awards typically had no effect on income. Matsunaga (1995) asserted that because of this differential treatment, firms could adjust the quantity of options awarded over time as an “income management strategy” (p. 5). The study examined awards made prior to FAS 123 and found evidence that options were being used to manage earnings. The relevant result was that firms that exhibited a general tendency to adopt income-increasing accounting methods also tended to grant more options, especially during years in which income fell below historical levels. The adoption of FAS 123 could reduce the incentive to favor options over other forms of compensation to manage earnings. However, the question that arises is whether firms’ disclosure of option values will be influenced similarly by the impact of options on pro forma net income. We argue that, as an implication of the Matsunaga study, the reported fair values under FAS 123 could also be influenced by firms’ earnings management strategies. We make the following related predictions about factors that could affect reporting. H2a: As earnings pressure from poor performance increases, firms will report lower option values to mitigate the financial reporting costs of implementation. H2b: Firms that are generally more aggressive in their accounting policies will report lower option values to mitigate the financial reporting costs of implementation.
Employee Stock Options and Pro Forma Earnings Management
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Like Matsunaga, we measure earnings pressure as the firm’s recent performance relative to historical levels, and we use discretionary accruals to proxy for aggressiveness of the firm’s accounting policies. Our expectation is that firms that are under greater earnings pressure will report lower option values to improve pro forma results. Similarly, we expect firms that are generally more aggressive in their financial reporting practices to report lower values when they implement FAS 123. Elsewhere, research by Aboody (1996) provides evidence that stock prices reflect dilution of value from employee options. We argue that dilution of share value is another potential financial reporting cost of options disclosure. Disclosures under FAS 123 could provide incremental information about the magnitude of dilution. Based on results in Aboody, we make the following prediction about this effect. H3: Firms that are heavier users of employee options will report lower option values to mitigate the financial reporting costs of implementation. Following Aboody, we measure the use of options in two ways: the number of options awarded and their total fair value. We expect both measures to be associated with more aggressive reporting under FAS 123. Our procedures to test our hypotheses are described next.
3. SAMPLE SELECTION Our overall objective in sample selection was to identify firms that were subject to implementation of FAS 123. The standard was effective for years ending after December 15, 1996, but early adoption was permitted. We therefore focused on annual reports issued for 1996, examining firms with December year-ends that were required to adopt as well as firms with earlier year-ends that could have elected to adopt early. Because our analysis requires detailed data on executive compensation, we obtained our sample from firms included in the Wall Street Journal/William M. Mercer 1996 CEO Compensation Survey (WSJ, 1997), which reports details of CEO compensation annually. In studies of executive pay, obtaining estimates of the value of individual option awards is problematic. An advantage of using this survey is that it provides independent estimates of the value of options awarded to CEOs, which we believe improves data reliability. However, the survey tends to follow large, established entities that are typical of the Fortune 500. So, our sample does not include small firms that are purportedly heavier users of options. We consider this potential limitation in our concluding section.1 7
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The survey consists of 350 publicly traded U.S. firms with diverse industry membership. Forty-nine different 2-digit SIC classes were represented, with the largest concentration occurring in manufacturing sectors. We collected data from the survey on each firm’s CEO compensation package for 1996, including cash salary and bonus, and restricted stock and option awards. The survey data was checked for accuracy against executive compensation disclosures in the firm’s proxy statement for the same year. Using the annual reports issued for 1996 by the same firms, we collected data by hand on the use of employee options. Disclosure under FAS 123 is provided in a footnote for stock-based compensation plans. Among the 350 firms examined, 80 firms with non-calendar year-ends were not required to implement the standard, and all chose not to. Of the remaining 270 firms, 149 were omitted because they did not report sufficiently detailed data on their use of options, most often because the firm claimed that the impact was immaterial. The final sample of usable observations consists of 121 firms. Summary statistics are presented in Table 1. As noted earlier, nearly all sample firms are members of the Fortune 500. Median assets at year-end 1996 were $6.1 billion and median revenues for 1996 were $5.7 billion. Median net income was $330 million. Statistics are also presented on firms’ option awards for 1996 as reported under FAS 123. As shown, the mean number of options awarded to employees in total was about 3.3 million per firm or 1.47% of year-end common shares outstanding. The number of options awarded ranged from 18,000 (Genuine Parts) to over 51 million (Pepsico). The mean reported total value of options awarded during the year was $36.9 million, or 0.38% of total revenues, with the maximum value at over $450 million (Pepsico), or almost 2% of revenues. Table 1 also presents statistics on the reported pro forma impact of FAS 123 on 1996 net income.2 Mean pro forma compensation expense for 1996 (the difference between reported and pro forma net income) was $13.4 million or 3.13% of net income.3 Employing the same materiality threshold used in an analysis of employee options by Foster et al. (1991), we find that the pro forma impact of implementation exceeded 3% of net income for 32 of 121 sample firms. So, about one in four sample firms faced a potentially material reduction in net income. Our results for the use of options and pro form impact on net income appear to be consistent with financial analysts’ studies of large firms (WSJ, 2000; MacDonald & McGough, 1999; MacDonald, 1998; Lowenstein, 1997). Summary statistics are also shown in Table 1 for compensation to CEOs. Note that option awards were the dominant component of CEO pay, averaging over $2 million or 41% of the total pay package for 1996. Distribution by industry membership is also provided in the table. Firms in 32 different 2-digit SIC codes
Employee Stock Options and Pro Forma Earnings Management
Table 1.
9
Summary Statistics on Sample Firms (n = 121). mean
median
minimum
maximum
18,271 8,937 703
6,105 5,713 330
823 958 (327)
148,431 52,184 5,908
3,260 (1.47) 36,955 (0.38) 13,412 (3.13)
1,642 (1.25) 15,435 (0.27) 6,000 (1.77)
18 (0.01) 114 (0.002) -0(0.00)
51,305 (5.41) 456,102 (1.88) 98,000 (66.77)
826 1,112 277 2,161 (41.2) 4,376
797 800 0 1,335 (41.3) 3,218
431 0 0 0 (0.00) 750
2,000 9,387 4,385 17,930 (89.9) 20,111
Financial statement data for 1996 ($millions):1 Total assets at year-end Total revenue Net income Employee option awards for 1996:2
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Total number awarded (thousands) (as % of common shares outstanding) Total reported value ($thousands) (as % of total revenue) Pro forma compensation expense ($thousands)4 (as % absolute value of reported net income) CEO compensation for 1996 ($thousands):3 Salary Bonus Restricted stock Options (as % of total compensation) Total compensation 1
Source: annual report. Source: FAS 123 footnote to annual report. 3 Source: WSJ Survey/proxy statement. 4 Reported net income less pro forma net income. 2
number of firms Industry membership (2-digit SIC): food and kindred products (20) paper and allied products (26) chemicals and allied products (28) primary metal industries (33) industrial and commercial machinery and computer equipment (35) transportation equipment (37) communications (48) insurance carriers (63) holding and other investment offices (67) 23 other categories
5 6 16 6 7 6 7 10 7 51
total sample
121
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are represented, with the largest concentrations in manufacturing, communications, insurance, and financial services industries. Our analysis of data is described next.
4. DATA ANALYSIS 4.1 Reported Option Fair Values and Assumptions 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Using data collected by hand from the option footnote, we examined the procedures used by firms to estimate fair value, including their choice of optionpricing model (nearly always Black-Scholes) and their input assumptions. Standard option-pricing models require assumptions about four factors: interest rate, dividend yield, option life, and volatility of the underlying stock. Under the traditional Black-Scholes approach, fair value is positively related to interest rate, life, and volatility, and negatively related to dividend yield. Employee options have relatively long contractual terms, typically 10 years. As a result, estimates of fair value can be sensitive to changes in these assumptions, especially regarding expected option life and stock volatility. The new standard suggests factors that firms should consider in developing assumptions, but does not require precise methods. As a result, managers appear to have considerable discretion in valuing their options as noted earlier.4 In this section, we examine firms’ reported option values and input assumptions relative to independent benchmarks. Our intent is to investigate whether the assumptions and resulting option values reported by sample firms exhibit a pattern relative to an effect on pro forma net income. We start with an overall assessment of reported option values. As a means of comparison, we use procedures similar to Yermack (1998) and Aboody (1996) to develop a benchmark option fair value for each sample firm based on the Black-Scholes model. We use independent assumptions for interest rate, volatility, and dividend yield. For the interest rate assumption, we use year-end 1996 zero-coupon U.S. Treasury yields with maturities matching the firm’s reported expected life assumption. We obtain volatility and dividend yield estimates using 1996 historical data. We also use the weighted-average grant date stock price and exercise price reported by the firm and assume a life equal to the option’s contractual term. For each firm, we compare our benchmark fair value to the reported value and compute the variable DISCOUNT, defined as the proportionate difference between the benchmark fair value and the firm’s reported fair value relative to the benchmark.5 Therefore, as a firm chooses potentially more aggressive assumptions, the value of DISCOUNT increases. The distribution of DISCOUNT, illustrated in Fig. 1, reveals that firms’ reported fair values are
Employee Stock Options and Pro Forma Earnings Management
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often considerably less than the benchmark. The mean benchmark fair value per share is $16.50 versus a mean reported value of $11.35 (difference in means t-statistic = 14.7, p-value < 0.001). The mean value of DISCOUNT is 29.8%. So, on average, the fair value reported by firms is more than one-fourth lower than the benchmark. Furthermore, one in four firms discounted its option value by over 40% relative to the benchmark. Based on the sample mean for the pro forma impact of options on net income (3.13%), reducing option fair value by 29.8% would have the effect of increasing pro forma net income by over 1%. Given that reported option values generally appear to be significantly lower than our benchmark values, we next examine firms’ reported input assumptions used to value their options. As a test for systematic tendencies, we compare the distributions of reported volatility, dividend yield, and interest rate assumptions with various independent benchmarks. For these three factors,
Fig. 1. Distribution of DISCOUNT. Figure 1 shows the frequency distribution of the variable DISCOUNT, defined as [benchmark option fair value – reported option fair value] / benchmark option fair value. The benchmark was computed using independent assumptions for interest rate (zero-coupon U.S. Treasury yields at year-end), dividend yield (1996 annual dividend divided by year-end stock price), and volatility (standard variation of one-year daily returns for 1996). The weighted-average exercise price reported by the firm was used and was assumed to be equal to the grant date stock price. Option life was assumed to be equal to the option’s contractual term. The mean of DISCOUNT is 29.8% and is significantly different from zero (t-statistic = 20.7).
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Table 2 shows the mean assumptions reported by sample firms along with means of comparable historical benchmarks. For example, the mean volatility assumption reported by sample firms was 23.75%, which is less than the mean one-year historical level of 25.59% computed from CRSP daily return data for 1996. (Recall that fair value is positively related to volatility.) A t-test indicates that the difference in means between the reported and benchmark assumptions is significant at better than the 1% level. Table 2 also shows the number and proportion of firms that chose an assumption lower than the one-year volatility benchmark mean. As shown, 72% of the sample chose a volatility assumption that would have decreased fair value relative to the one-year benchmark. With one exception (three-year dividend yield), we observe consistent results on other benchmark comparisons for volatility, dividend yield, and interest rate assumptions. In general, results for these variables indicate that the assumptions used by firms to value their options tended to be value-reducing relative to our benchmarks. In the final comparison, we examine the assumption regarding the option’s expected life. For all but one sample firm, the contractual terms were reported to be 10 years, resulting in a sample mean contractual term of 9.96 years (see Table 3). However, the expected lives reported by firms were generally much shorter (5.56 years at the mean), thereby reducing option fair value. All but 7 of 121 sample firms assumed option lives that were shorter than the contractual term of the option – 43% shorter at the mean. However, evaluating the reasonableness of the expected life assumption is problematic. Exercise data on company-wide option plans are not publicly available. Therefore, no historical benchmark for expected life can be constructed. As an alternative, we employed Garman’s (1989) algorithm to compute the expected life of an option under the following assumptions: (1) the option holder is indifferent to risk, (2) the option is exercisable any time after vesting, and (3) any dividends on the underlying stock are paid quarterly. Using a standard binomial option-pricing model (Cox et al., 1979), this algorithm predicts the expected life of an option based on optimal risk-neutral exercise decisions in the presence of dividends. In Table 3, firms’ reported expected lives are sorted into deciles and compared to the computed risk-neutral expected lives of the same options. Percentage differences between the two estimates are given in the far right column. Several points are worth noting from Table 3. First, there is considerable variation in firms’ expected life assumptions, with the decile mean estimates ranging from 3.33 to 8.88 years. This result suggests that there could be significant differences in exercise patterns across firms, perhaps because of differences in firm-specific risk levels, rates of forfeiture, and employees’ ability to diversify. Second, the risk-neutral expected life that we compute is relatively constant
Employee Stock Options and Pro Forma Earnings Management
Table 2.
13
Comparison of Reported Assumptions with Benchmark Assumptions.
Assumption
(t-statistic)1
mean
firms choosing value-reducing assumptions2 N=
% of sample
3
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Stock volatility: (n = 120) Reported by firm Benchmarks: 1-year historical (1996) 3-year historical (1994–1996) Dividend yield: (n = 121)4 Reported by firm Benchmarks: 1-year historical (1996) 3-year historical (1994–1996) Interest rate: (n = 121) Reported by firm Benchmarks: Year-end zero-coupon rates (1996) Monthly average for year (1996) Option life (years): (n = 121) Reported by firm Benchmarks: Contractual term Risk-neutral life (see Table 3)
23.75%
––
––
––
25.59% 24.89%
3.74* 2.90*
87 75
72%† 62%†
2.216%
––
––
––
1.983% 2.221%
4.33* 0.10
76 58
63%† 48%
6.015%
––
––
––
6.244% 6.219%
5.96* 5.31*
79 78
65%† 64%†
5.68
––
––
––
9.96 9.48
29.8* 25.1*
114 114
94%† 94%†
Data definitions – Statistics are shown for assumptions reported by the firm and for our comparable benchmark assumptions. Benchmark volatility is computed as the standard deviation of daily returns obtained from CRSP for the one-year and three-year period ending with the firm’s 1996 fiscal year-end. Benchmark one-year dividend yield is based on the annual dividend for 1996 divided by the year-end stock price, and the three-year dividend yield is the average yield over the 1994–1996 period. Benchmark interest rates were obtained from zero-coupon U.S. Treasury yields with remaining maturities corresponding to the reported expected option life. Benchmarks for option life consist of the contractual term of the option and the risk-neutral expected life computed using Garman’s (1989) algorithm for simulating expected exercise patterns for an American option. 1 t-test of difference in means between reported assumptions and benchmark assumptions. * indicates that result is significant at 1% level or better, two-tailed test. 2 Number and percentage of sample firms reporting fair value-reducing assumptions relative to the benchmark estimate. † indicates that equality of proportions test for p=50% is significant at 1% level. 3 Historical volatility data was not available for one firm that merged in 1996 thereby reducing the number of volatility observations to 120. 4 Two firms in the sample reported zero dividend yield assumptions, even though their 3-year historical yields exceeded 5%. As disclosed in their annual reports, these firms provided dividend equivalent reimbursements on their employee options, resulting in an effective yield of zero for purposes of valuing their options. Assuming a yield of zero in the presence of dividend equivalent reimbursement is consistent with FAS 123. Therefore, we have also set the yields in our benchmarks to zero for these two firms.
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Table 3.
Comparison of Option Expected Life: Reported vs. Risk-neutral.
Note: Data for all columns are sorted by reported expected life (first column.) Statistic (n = 121) decile means:
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Reported
Risk-neutral
Difference1
years
years
%
1st (largest) 2nd 3rd 4th 5th 6th 7th 8th 9th 10th (smallest)
8.88 7.09 6.66 5.89 5.64 5.08 5.01 4.99 4.32 3.33
9.35 9.39 9.50 9.22 9.48 9.40 9.68 9.92 9.53 9.34
12.1 25.0 30.3 36.6 41.0 46.2 48.4 49.8 55.4 65.3
overall mean standard deviation
5.68 1.62
9.48 0.60
45.0 ––
1
Difference = [risk-neutral expected life – reported expected life]/risk-neutral expected life.
across the sample. The overall mean risk-neutral life of 9.48 years is only about 5% less than the overall mean contractual term of 9.96 years. Theoretically, the only reason a risk-neutral option holder would rationally exercise prior to maturity is to capture dividends paid on the underlying stock. So, this result suggests that, by itself, the overall dividend effect on the tendency to exercise early is small for sample firms, shortening the theoretical life of the option by about six months on average. Third, when computing fair values, firms shorten the expected lives of their options considerably from even their risk-neutral expected lives. On average, firms’ reported expected lives are 45% shorter than our computed risk-neutral lives, with the extreme cases as much as 65% shorter. As noted above, there is unfortunately no observable reference for assessing the reasonableness of the expected life assumptions. Choosing a reduced life can be justified empirically and theoretically. For example, employees have been observed to forfeit unexercised options in systematic patterns (Huddart & Lang, 1996), and aversion to risk can rationally lead to early exercise in models of employee options (Rubinstein, 1995; Kulatilaka & Marcus, 1994). However, only two firms in the sample provided specific justifications for reducing option values for such factors. So, assessing a pattern relative to a benchmark remains an open question. In the computation of our overall benchmark for fair value, discussed earlier, we chose to follow previous research and used the option’s contractual term as the assumption for expected life. In testing our hypotheses,
Employee Stock Options and Pro Forma Earnings Management
15
discussed next, we try to control for firm-specific differences in risk and related factors that could lead to early exercise. 4.2 The Influence of Political and Financial Reporting Costs
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The above results tentatively suggest that firms’ choices of input assumptions could systematically lead to lower option values. We test this notion further by exploring cross-sectional variation in reported values relative to our benchmark. Recall that our hypotheses are that firms will be more aggressive in their reported estimates of fair value given: (1) greater political costs related to executive pay, (2) more earnings pressure and more aggressive accounting policies overall, and (3) greater potential dilution of share value. As our measure of aggressive reporting, we use the variable DISCOUNT, which we computed earlier as the proportionate difference between the firm’s reported option value and our benchmark (see Section 4.1). In our initial model of reporting, we include the following explanatory variables. As a measure of potential dilution, we construct the variable AWARD$, defined as the number of options awarded in 1996 multiplied by our benchmark Black-Scholes fair value per option, with the result scaled to total revenue for 1996. In other words, AWARD$ is an estimate of the total value of options awarded by the firm during the year relative to firm size (Aboody, 1996). Based on H3, we expect AWARD$ to be positively related to the dependent variable DISCOUNT. Following Matsunaga (1995), we include the variable INCOME-GAP to measure earnings performance relative to an historical target. INCOME-GAP is defined for firm i and time period t (=1996) as follows: INCOME-GAPi,t = IBEi,t – TARGET-IBEi,t where IBEi,t = income before extraordinary items, and TARGET-IBEi,t = IBEi,t⫺1 + (IBEi,t⫺1 – IBEi,t⫺5)/5 if IBEi,t⫺1 > IBEi,t⫺5 and IBEi,t⫺1 otherwise. Therefore, for firms with above-target earnings performance, INCOME-GAP is the excess of 1996 income (before extraordinary items) over previous year’s income plus a five-year average growth increment. For firms below target, INCOME-GAP is 1996 income less previous year’s income. Like Matsunaga, we use INCOME-GAP to represent pressure on management to report improved firm performance. We expect INCOME-GAP to be negatively related to 15
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DISCOUNT. In other words, we predict that a lower income relative to target will be associated with a larger value of DISCOUNT (H2a). We also include the variable ACCRUALS, which is intended to proxy for aggressive accounting procedures in response to earnings pressure. ACCRUALS is an estimate of the discretionary component of total accounting accruals included in current year’s net income and is obtained from a variation of the Modified Jones Model (see Dechow et al., 1995; Collins et al., 1999).6 We interpret larger values of ACCRUALS to represent more aggressive accounting policies and therefore expect that ACCRUALS will be positively related to DISCOUNT (H2b). To test the effect of political costs related to compensation (H1), we include a measure of managerial pay relative to performance. Following Yermack (1998), we estimate a model of CEO compensation as a function of firm size (natural log of total revenue), market-adjusted shareholder return (net of S&P 500), and age of the CEO as follows: COMPi = 0 + 1(firm size)i + 2(market-adjusted return)i + 3(CEO age)i + The dependent variable COMP is the natural log of total compensation for 1996, consisting of the sum of salary, bonus, restricted stock, and options. We estimate this model over the entire sample of CEOs in the Wall Street Journal 1996 compensation survey for whom we have complete data (n = 346).7 The residuals from the model estimation are interpreted as the amount of pay not explained by firm performance and productivity of the CEO and are used as a proxy for potential political costs of managerial pay (Yermack, 1998). The residuals are then scaled by total compensation to obtain a measure of unexplained pay relative to the CEO’s level of pay (Baker, 1999). Consistent with H1, we expect the variable UNEXPLAINED-PAY to be positively related to DISCOUNT. Recall that in our analysis of reported assumptions we observed that firms generally used lives that were significantly shorter than the contractual terms of the options thereby reducing option value. While testing our main explanatory variables, we try to control for the portion of DISCOUNT justifiably attributable to shortened expected option lives. As discussed earlier, Huddart and Lang (1996) found that the tendency to exercise early appeared to be related to organizational rank. Lower level employees, perhaps because they are more averse to risk, were generally observed to have exercised earlier. Therefore, it is possible that firms that awarded options more broadly were justified in reducing their estimates of fair value for factors such as the likelihood of early exercise or forfeiture. As a control for such factors, we include a measure of the distribution of awards within the firm. We define the variable CONCENTRATION as
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the number of options awarded to the top five officers, as reported in the firm’s proxy statement, divided by the total number of awards to all employees for the year. For sample firms, the value of CONCENTRATION exhibits large variability, ranging from 0 to 95%, with a mean of 19%. Theoretically, the more concentrated option awards are among senior executives the less their fair value should be discounted for factors such as early exercise or forfeiture. A negative coefficient estimate on this variable would be consistent with our expectation and with the empirical finding in Huddart and Lang (1996). Finally, to control for size effects, we include log-transformed total revenues for 1996 in our model. To summarize, our initial model is specified as follows (expected signs in parentheses): DISCOUNTi = 0 + 1AWARD$i + 2INCOME-GAPi + 3ACCRUALSi (+) (⫺) (+) + 4UNEXPLAINED-PAYi + 5CONCENTRATIONi + 6FIRM-SIZEi + (+) (⫺) (?) Results from our regression on 105 firms with complete data are shown in Table 4 as model 1.8,9 Overall, the model explains about 27% of the cross-sectional variation in DISCOUNT.10 The coefficient estimates on the main variables of interest appear to support most of our hypotheses. Consistent with H3, AWARD$ is positive and significant at the 5% level (p-value = 0.03).11 This result suggests that the disclosure of option value under FAS 123 could be perceived as a signal of dilution. As predicted in H2b, the result on the ACCRUALS variable is positive and significant at the 10% level (p-value = 0.052), indicating that firms relying more on income-increasing accruals to boost earnings are also more aggressive in their options reporting decision. We test the sensitivity of this result by substituting estimates of accruals from the original form of the Modified Jones Model (see appendix). Our results are qualitatively unchanged. Consistent with H1, UNEXPLAINED-PAY is positive and significant at the 1% level (p-value < 0.01). This result appears to be contrary to the assertion in Yermack (1998) and suggests that the reporting of options, even in the aggregate, is influenced by reporting costs related to managerial pay. The CONCENTRATION control variable is negatively signed as expected (p-value < 0.01). The relatively large magnitude of this coefficient estimate is consistent with the notion that option values are heavily discounted for early exercise and forfeiture related to organizational rank as documented in Huddart and Lang (1996). Contrary to our expectation, the sign of the INCOME-GAP variable is positive, though not significant at conventional levels (p-value = 0.40, two-tailed 17
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Table 4.
Options Reporting Models.
Model 1: DISCOUNT = 0 + 1AWARD$ + 2INCOME-GAP + 3ACCRUALS + 4UNEXPLAINED-PAY + 5CONCENTRATION + 6FIRM-SIZE + OLS estimates (n = 105) model 1
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explanatory variable (expected sign)
coefficient estimate
AWARD$ (+) AWARD% (+) INCOME-GAP (⫺) ACCRUALS (+) UNEXPLAINED-PAY (+) CONCENTRATION (⫺) FIRM-SIZE (?)
0.0054 –– 0.3217 0.4497 0.0366 ⫺0.3122 0.0018
Model F-statistic R2 statistic
6.69 27.2%
model 2
t-statistic1 1.89** –– 0.85 1.64* 3.03*** 2.82*** 0.13
coefficient estimate
t-statistic1
–– 2.521 0.3747 0.4898 0.0440 ⫺0.3762 0.0073
–– 2.51*** 1.00 1.82** 3.88*** 4.32*** 0.52
8.16 27.6%
Variable definitions – The dependent variable, DISCOUNT, is defined as (benchmark fair value – reported fair value)/benchmark fair value. AWARD$ is the benchmark Black-Scholes option fair value times the number of options awarded in 1996 divided by total revenue for 1996. AWARD% is the number of options awarded in 1996 relative to common shares outstanding at year-end. INCOME-GAP is income before extraordinary items (IBE) less target IBE (see Section 4.2). ACCRUALS is the amount of discretionary accruals included in income and is estimated using the Modified Jones Model (see Section 4.2 and appendix). UNEXPLAINED-PAY is the residual obtained from a compensation model predicting CEO pay as a function of firm sales, shareholder return, and CEO age (see Section 4.2). The residuals are scaled to total compensation. CONCENTRATION is the ratio of options awarded to the firm’s senior officers in 1996 to the total awarded to all employees during the year. FIRM-SIZE is total revenue for 1996 in billions. 1
Absolute value of statistic based on White (1980) heteroscedastic-robust standard errors. * Significant at 10% level, ** significant at 5% level, *** significant at 1% level, all as one-tailed tests.
test). As a sensitivity test on the INCOME-GAP result, we substituted other measures of earnings trend including changes in EPS and ROE, but still obtain similar results on these variables. Therefore, hypothesis 2a is not supported by the results. So, except for the INCOME-GAP variable, our results from model 1 appear to be consistent with previous research and our hypotheses. We next consider another form of the reporting model. Following Aboody (1996), we use the number of options awarded, as opposed to total value, as an alternate measure of the potential dilution effect. The variable AWARD% is the total number of options awarded during 1996 relative to common shares outstanding. We
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substitute AWARD% for AWARD$ in the regression and report results as model 2 in Table 4. The estimate on AWARD% is positive as expected and significant (p-value < 0.01), which we interpret as additional evidence that potential dilution appears to affect the reporting decision (H3). All other results in model 2 are similar to those reported for model 1. Next, we investigate the sensitivity or our results to the UNEXPLAINEDPAY variable (H1). We re-estimate models 1 and 2 after substituting the absolute level of CEO pay as our measure of political costs. The variable TOTAL-PAY is defined as the natural log of the sum of salary, cash bonus, options, and restricted stock. Results are reported as models 3 and 4 in Table 5. In both models, the estimate on TOTAL-PAY continues to be positive and significant at the 1% level. Results on all other variables of interest are consistent with those reported previously in Table 4. As an additional test, we use the unscaled residuals from our compensation model (see above) as our measure of political costs and obtain similar results (not reported). So, results related to H1 appear to be robust to various measures of this construct.12 Table 5.
Options Reporting Models.
Model 3: DISCOUNT = 0 + 1AWARD$ + 2INCOME-GAP + 3ACCRUALS + 4TOTAL-PAY + 5CONCENTRATION + 6FIRM-SIZE + OLS estimates (n = 105) model 3 explanatory variable (expected sign)
coefficient estimate
AWARD$ (+) AWARD% (+) INCOME-GAP (⫺) ACCRUALS (+) TOTAL-PAY (+) CONCENTRATION (⫺) FIRM-SIZE (?)
0.0051 –– 0.2505 0.4718 0.0642 ⫺0.2783 ⫺0.0384
Model F-statistic R2 statistic
model 4
t-statistic1 1.68** –– 0.67 1.61* 2.37*** 2.45*** 1.81†
6.10 26.5%
coefficient estimate –– 2.132 0.2870 0.5075 0.0764 ⫺0.3337 ⫺0.0412
t-statistic1 –– 1.94** 0.77 1.75** 3.23*** 3.59*** 2.01††
7.31 26.4%
Variable definitions – TOTAL-PAY is equal to the natural log of total CEO compensation (salary, cash bonus, options, and restricted stock) for 1996. All other variables are defined in Table 5. 1
Absolute value of statistic based on White (1980) heteroscedastic-robust standard errors. * Significant at 10% level; ** significant at 5% level; *** significant at 1% level, all as one-tailed tests. †
Significant at 10% level;
††
significant at 5% level, all as two-tailed tests.
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Finally, we test the overall sensitivity of results to our dependent variable measure. Recall that in the construction of DISCOUNT we chose to follow previous research to develop an independent benchmark for option value (Yermack, 1998; Abbody, 1996). These studies have relied on more recent history as a basis for model assumptions. Consequently, our original benchmark was computed using the following assumptions: one-year historical volatility, one-year historical dividend yield, year-end zero-coupon rates, and an option life equal to contractual term (see Table 2). To construct alternate measures of DISCOUNT, we develop three other benchmarks as follows. We use two other combinations of the assumptions shown in Table 2, substituting the three-year historical levels for dividend yield and volatility and the 1996 monthly average of zero-coupon interest rates. We also use a benchmark equal to the CEO’s per share option value as reported in the Wall Street Journal survey, which was our primary source for compensation data (see Section 3). The models were re-estimated using these alternate measures of the dependent variable (results not reported). We find that the results are robust with respect to UNEXPLAINED-PAY (H1), ACCRUALS (H 2b), and the control variable CONCENTRATION. However, we observe mixed results on the dilution variables AWARD$ and AWARD%. So, results on hypothesis 3 appear to be sensitive to specification of the dependent variable.
5. CONCLUSIONS Effective in 1996, firms faced new financial statement disclosure requirements regarding their use of employee stock options. Previous research indicates that the reporting of individual option awards to CEOs is systematically influenced by political costs related to managerial pay and that opposition to FAS 123 was primarily motivated by related concerns. We have extended this line of research by testing for a similar influence on the reporting of firm-wide awards under FAS 123. We have also extended other research documenting that prior to FAS 123 options were used to manage earnings and that option disclosures have value-relevance. Based on our examination of option footnote disclosure in financial statements issued for 1996, we documented several observations about options reporting under FAS 123. First, relative to our benchmark, sample firms reported significantly lower values for their option awards – on average about 30% lower. Consequently, we examined the reported assumptions for expected volatility, dividend yield, interest rate, and life used by firms to value their options. Compared to independent benchmarks, it appears that firms tended to select assumptions that would have decreased the reported value of their options
Employee Stock Options and Pro Forma Earnings Management
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and, potentially, the resulting impact on pro forma net income and share dilution. Second, we observed that firms generally seemed reluctant to disclose fully as encouraged in the new standard. For example, of the 350 financial statements that we examined to form our sample, we found that 80 firms had year-ends prior to the effective date of FAS 123. None of these firms adopted early. Of the remaining 270 firms that were required to implement the standard in 1996, none adopted the expense recognition provision strongly recommended by the FASB. All reported the pro forma effect on net income in a footnote to the financial statements instead. In the context of previous research, we interpreted these observations as indirect evidence that the financial reporting costs of implementing FAS 123 are significant. More formally, we hypothesized that the firm’s option reporting decision is influenced by: (1) political costs related to managerial pay, (2) earnings pressure and overall accounting policies, and (3) potential dilution of share value from employee options. We found strong evidence that reporting is influenced by executive pay relative to firm performance. Firms that are aggressive in their use of discretionary accruals also appear to report lower option values when adopting FAS 123. Our results also suggest that disclosure of the number and value of options awarded is perceived as relevant to share value through dilution, although our results on these dilution measures are sensitive to specification of the dependent variable. In general, we interpret our results as consistent with previous research. In particular, the results regarding executive pay appear to corroborate the findings in Dechow et al. (1996) that lobbying behavior toward the exposure draft on options was politically motivated. However, the results appear to contradict the assertion in previous research that the factors that influence the reporting of executive option awards in proxy statements are not likely to apply to aggregate disclosure under FAS 123 (Yermack, 1998). We can think of two possible explanations for our result. One is that managers are concerned about potential political costs related to option compensation even when the information is presented in aggregate form. This explanation would be consistent with firms’ vigorous opposition to the proposed new standard. An alternative is simply that firms use the same set of assumptions to estimate the fair value of options for reporting in proxy statements and financial statements. So, aggressive reporting in financial statements could merely be a residual effect of the way that political costs appear to influence disclosure in proxy statements. Overall, we interpret the evidence in our study as consistent with the explanation that firms’ opposition to the exposure draft and their apparent reluctance to disclose under FAS 123 were motivated by financial reporting 21
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costs, including political costs related to managerial pay. We offer this research as a means of better understanding the public debate over options disclosure and the political influences that shape the outcomes of the standard-setting process generally. We note that our study is subject to a potential limitation regarding our sample of firms. As discussed earlier, sample firms were large, established entities which are representative of the Fortune 500. We did not focus on subjects such as biotechnology, e-commerce, or information technology firms that purportedly use options more intensively. How these firms report their use of options under FAS 123 and the extent to which their disclosures are influenced by reporting costs are issues for further research.
NOTES 1. The only scholarly evidence we can find relevant to our sample selection procedure is in empirical studies of FAS 123 lobbying behavior by Dechow et al. (1996) and Fraser et al. (1998). Surprisingly, the evidence does not support the notion that small firms had more at stake with the proposed standard. Dechow et al. found that firm size was positively related to the likelihood of a firm submitting an opposing comment letter to the FASB, meaning that larger firms were more likely to publicly oppose the exposure draft. Dechow et al also examined stock returns for a sample of biotechnology firms and found no evidence of abnormal return reactions to event dates during the development of the standard. Similarly, Fraser et al. generally found evidence of abnormal negative returns on event dates for large firms, but no evidence of abnormal returns for small firms. 2. As a result of compromise in the standard-setting process, the new standard encourages, but does not require, firms to recognize compensation expense in the income statement. In the alternative, any firm not adopting expense recognition must provide footnote disclosure of pro forma net income, as if the firm had recognized the expense (FASB, 1995). Of the 270 firms in the initial sample that were required to implement the standard for 1996, none adopted the expense recognition provision. All reported only the pro forma impact on net income instead. 3. Six sample firms reported a net loss for 1996. For these firms, the pro forma options expense ranged from 1.4% to 19.4% of their net loss. In Table 1, the statistics on pro forma compensation expense as a percentage of net income are transformed to their absolute value. The transformation affects only these six firms. Without the transformation to absolute value, the mean percentage pro forma expense is 2.38%. 4. Under certain conditions, FAS 123 allows firms to choose more aggressive (valuereducing) assumptions. The standard provides that if an input assumption can be estimated only within a range, and if the estimates within the range are equally likely, then the firm can choose an estimate that results in the lowest impact on earnings (FASB, 1995, paragraph 275). However, none of the sample firms disclosed that it used such an approach or that it considered the sensitivity of the results to a range of assumptions. Furthermore, ex ante, our null hypothesis would be that a firm’s choice of value-reducing assumptions within the guidelines of the standard would not be correlated with financial reporting costs.
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5. Historical volatility data was not available for one firm. Therefore, a benchmark fair value could not be computed for that firm, reducing the number of DISCOUNT observations to 120. 6. Our procedures to estimate discretionary accruals are detailed in the appendix. 7. Our compensation model estimation results are as follows: 1 = 0.355 (t = 7.16), 2 = 0.961 (t = 3.64), 3 = ⫺0.010 (t = 0.98), R2 = 20%, model F-statistic = 25.8, n = 346. Yermack reports identical coefficient signs, similar coefficient significance levels and a comparable R2 of 16%. 8. We were not able to collect data for INCOME-GAP and ACCRUALS for 15 sample firms, thereby reducing our model observations to 105. We tested the sensitivity of our results by omitting these two variables from the model and estimating it on the full sample of 120 firms. Results are qualitatively unchanged from those reported in Table 4. 9. Regression diagnostics indicate heteroscedasticity in some of our estimation results. Consequently, we report t-statistics based on White (1980) robust standard errors throughout. We also performed tests for problems with extreme observations, including omitting and limiting (winsorizing) outliers, but found that our results do not appear to be affected. Furthermore, tests for multicollinearity do not indicate bias in the results. 10. The explanatory power of our reporting choice models appears to be comparable to or higher than results reported elsewhere. R2 statistics on option discounting models in Baker (1999) range from 25 to 43%. Yermack (1998) reports R2 statistics of 3% on models with fewer variables. 11. Based on our hypotheses, all significance levels and p-values are reported as one-tailed tests unless otherwise noted. 12. Unlike in models 1 and 2, the FIRM-SIZE control variable is negative and significant in models 3 and 4 in the presence of the absolute level of CEO pay (TOTAL-PAY). However, in sensitivity testing, we do not find that FIRM-SIZE is significant in other specifications of the reporting models. Our results generally do not indicate that firm size is a factor in the reporting decision.
ACKNOWLEDGMENTS The authors are grateful for comments from seminar participants at the American Accounting Association 2001 annual meeting, especially those of discussant Thomas Carnes. We also appreciate comments received from the associate editor and two anonymous reviewers.
REFERENCES Aboody, D. (1996). Market Valuation of Employee Stock Options. Journal of Accounting and Economics, 22, 357–391. Baker, T. (1999). Options Reporting and the Political Costs of CEO Pay. Journal of Accounting, Auditing and Finance, 14, 125–145. Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81, 637–654.
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Collins, D., Hribar, W., & Tippie, H. T. (1999). Errors in Estimating Accruals: Implications for Empirical Research. Unpublished manuscript, University of Iowa, Iowa City. Cox, J., Ross, S., & Rubinstein, M. (1979). Option Pricing: A Simplified Approach. Journal of Financial Economics, 7, 229–263. Dechow, P., Sloan, R., & Sweeney, A. (1995). Detecting Earnings Management. The Accounting Review, 70, 193–225. Dechow, P., Hutton, A., & Sloan, R. (1996). Economic Consequences of Accounting for Stockbased Compensation. Journal of Accounting Research, 34, 1–20. Financial Accounting Standards Board (FASB) (1993). Proposed Statement of Financial Accounting Standards: Accounting for Stock-Based Compensation. Norwalk, CT: FASB. Financial Accounting Standards Board (FASB) (1995). Statement of Financial Accounting Standards No. 123: Accounting for Stock-Based Compensation. Norwalk, CT: FASB. Foster, T., Koogler, P., & Vickrey, D. (1991). Valuation of Executive Stock Options and the FASB Proposal. The Accounting Review, 66, 595–610. Fox, J. (2001). The Amazing Stock Option Sleight of Hand. Fortune, (June 25), 86–92. Fraser, D., Lee, D., Reising, J., & Wallace, W. (1998). Political Costs and the Fate of the FASB Proposal to Recognize the Costs of Employee Stock Options. Journal of Financial Statement Analysis, 3, 67–79. Garman, M. (1989). Semper Tempus Fugit. RISK, 2, 34–35. Hemmer, T., Matsunaga, S., & Shevlin, T. (1994). Estimating the ‘Fair Value’ of Employee Stock Options with Expected Early Exercise. Accounting Horizons, 8, 23–42. Hemmer, T. (1996). The Influence of Risk Diversification on the Early Exercise of Employee Stock Options by Executive Officers. Journal of Accounting and Economics, 21, 45–68. Hemmer, T. (1998). Cost of Granting Employee Stock Options with a Reload Provision. Journal of Accounting Research, 36, 231–256. Huddart, S., & Lang, M. (1996). Employee Stock Option Exercises: An Empirical Analysis. Journal of Accounting and Economics, 21, 5–43. Jereski, L. (1997). Share the Wealth: As Options Proliferate, Investors Question Effect on Bottom Line. Wall Street Journal, January 14, A1. Kulatilaka, N., & Marcus, A. (1994). Valuing Employee Stock Options. Financial Analysts Journal, 50, 46–56. Leonhardt, D. (2000). Will Today’s Huge Rewards Devour Tomorrow’s Earnings? New York Times, April 2, 3.1. Lewellen, W., Park, T., & Ro, B. (1995). Executive Stock Option Compensation: The Corporate Reporting Decision. Managerial and Decision Economics, 16, 633–647. Lowenstein, R. (1995). Intrinsic Value: The Cost of Employee Stock Options, Now Hidden, Might Earn a Footnote. Wall Street Journal, July 6, C1. Lowenstein, R. (1997). Intrinsic Value: Coming Clean on Company Stock Options. Wall Street Journal, June 26, C1, C3. MacDonald, E. (1998). Options’ Effect on Earnings Sparks Debate. Wall Street Journal, May 13, C1. MacDonald, E., & McGough, R.. (1999). Stock Options Take Hidden Toll on Profit. Wall Street Journal, May 24, C1. Matsunaga, S. (1995). The Effects of Financial Reporting Costs on the Use of Employee Stock Options. The Accounting Review, 70, 1–26. Murphy, K. (1996). Reporting Choice and the 1992 Proxy Disclosure Rules. Journal of Accounting, Auditing and Finance, 11, 497–515. Rubinstein, M. (1995). On the Accounting Valuation of Employee Stock Options. The Journal of Derivatives, 3, 8–24.
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U.S. Securities and Exchange Commission (SEC) (1992). Release No. 33-6962 (October 16, 1992), as corrected in Release No. 33-6966 (November 9, 1992). U.S. Securities and Exchange Commission (SEC) (1993). Release No. 33-7009 (August 6, 1993). Wall Street Journal (WSJ) (1997). The Wall Street Journal/William M. Mercer 1996 CEO Compensation Survey, April 10, R1–R20. Wall Street Journal (WSJ) (2000). Stock-option Grants Cut Earnings by 6% for S&P 500 Firms August 25, B2. White, H. (1980). A Heteroskedasticity-consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48, 817–838. Yermack, D. (1998). Companies’ Modest Claims about the Value of CEO Stock Option Awards. Review of Quantitative Finance and Accounting, 10, 207–226.
APPENDIX We obtain estimates of the amount of discretionary accruals included in income using the Modified Jones Model. In the model, an estimate of total accruals is regressed on fixed assets and the change in receivables. An estimate of the discretionary component of total accruals is obtained from the model estimation residuals. The original form of the model is shown below (see Dechow et al., 1995). TAit ␣ = + 1 Ait⫺1 Ait⫺1
(⌬REVit ⫺ ⌬ARit) Ait⫺1
冉
PPEit Ait⫺1
冊 冉 冊 + 2
+ it
where, TAit Ait⫺1 ␣ ⌬REVit ⌬ARit PPEit it
= = = = = = =
total accruals for firm i in year t, net total assets for firm i in year t⫺1, intercept, change in revenue for firm i from year t⫺1 to year t, change in accounts receivable for firm i from year t⫺1 to year t, gross property plant and equipment for firm i in year t, and error term for firm i in year t.
Total accruals (TA) are measured using the formula below: TA = ⌬Current Assets ⫺ ⌬Current Liabilities ⫺ ⌬Cash + ⌬Current Maturities of Long-Term Debt ⫺ Depreciation and Amortization Expense A variation of the Modified Jones Model using a cash flow approach to total accruals has been shown to be a more robust measure of discretionary accruals in Collins et al. (1999). In this variation, the formula for total accruals is as shown below. 25
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TA = Income before extraordinary items ⫺ Operating cash flows The estimates for the variable ACCRUALS reported in Tables 4 and 5 were obtained from the model using the Collins et al cash flow approach. As reported in Section 4.2, we tested the sensitivity of results to the method of obtaining discretionary accruals. We obtained qualitatively similar results using the original form of the Modified Jones Model as well. 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
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A NOTE ON TESTING A MODEL OF COGNITIVE BUDGETARY PARTICIPATION PROCESSES USING A STRUCTURAL EQUATION MODELING APPROACH Vincent K. Chong
ABSTRACT This paper reports the results of a study which re-examines Chenhall and Brownell (Accounting, Organizations and Society, pp. 225–233, 1988). In that study, it was hypothesized that role ambiguity acted as an intervening variable in the association between budgetary participation and outcome criteria. Although the results were supportive of the hypotheses, a number of limitations may be observed. This paper incorporates variations in sampling and finds results, which provide strong support for Chenhall and Brownell’s hypotheses when tested using a structural equation modeling (SEM) technique.
INTRODUCTION Subordinate’s participation in the budget-setting process is thought to have both attitudinal and behavioral consequences. Participation in the budget-setting process has been studied extensively for the past four decades. However, the Advances in Accounting, Volume 19, pages 27–51. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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results of early empirical studies have been equivocal.1 Prior studies have examined the motivational and cognitive mechanism by which budgetary participation might be related to subordinates’ job satisfaction and performance (Brownell & McInnes, 1986; Chenhall & Brownell, 1988; Kren, 1992; see also Kren & Liao, 1988; Murray, 1990 for a comprehensive discussion).2 In general, it is argued that the motivational role budgetary participation enhances “a subordinate’s trust, sense of control, and ego-involvement with the organization, which then jointly cause less resistance to change and more acceptance of, and commitment to, the budget decisions, in turn causing improved performance” (Shields & Shields, 1998, p. 59). The cognitive mechanism of budgetary participation provides subordinates the opportunities to gather, exchange and disseminate job-relevant information to facilitate their decisionmaking process, which in turn enhance their performance (Kren, 1992; Shields & Shields, 1998; Chong & Chong, 2000). In an attempt to bring some closure to the inconsistent results of prior studies on the relationship between budgetary participation and managerial attitudes and behaviors, Chenhall and Brownell (1988) examined the cognitive mechanism by which budgetary participation might be related to employees’ job satisfaction and performance. Specifically, relying on role theory (Kahn et al., 1964), Chenhall and Brownell used role ambiguity as an important cognitive factor in explaining the relationship between budgetary participation and job satisfaction and performance. The cognitive budgetary participation processes model proposed by Chenhall and Brownell is shown in Fig. 1. Specifically, they found that budgetary participation was negatively associated with role ambiguity, which in turn was negatively associated performance and job satisfaction. Chenhall and Brownell attributed their results to the fact that budgetary participation provides subordinates the opportunities to gather job-relevant information that clarifies their role expectations, methods of fulfilling role expectations, or the consequences of role performance. Nevertheless, two issues merit further investigation. First, Chenhall and Brownell’s study was based on a small sample (n = 33) of managers drawn from a single organization. Numerous studies (e.g. Kendall et al., 1987; Aldag & Stearns, 1988; Brownell & Dunk, 1991; Dunk, 1993; Lal et al., 1996) have criticized research findings that used sample drawn from one organization. These studies argued that research based on a single organization will put at risk the external validity of its results and will suffer from the generalizability of its findings. Dunk (1993, p. 576), for example, argued that “the use of single organizational samples may confound the results of studies because of the possible impact of firm effect(s)”. To overcome these potential limitations,
Testing a Model of Cognitive Budgetary Participation Processes
Budgetary Participation
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Role Ambiguity
29
Job Satisfaction or Performance
Fig. 1. Cognitive Budgetary Participation Processes Model.
the use of a larger randomly selected sample would be more appropriate (see Brownell & Dunk, 1991; Dunk, 1993; Lal et al., 1996). Second, Chenhall and Brownell’s study has tested a model of cognitive budgetary participation processes, relying on path analysis technique, by summing the items composing each perceptual scale as a single indicator of the underlying latent variable of the scale. Such single indicator approach to path analysis has been criticized for assuming that there is no random measurement error in the scale items (see Bagozzi et al., 1991). Structural equation modeling (SEM) technique can be used to resolve the problem of single indicator approach and their measurement error in path analysis. SEM is a technique that has been used for instrument validation and model testing.3 It is considered as a second-generation multivariate analysis (Fornell, 1982, 1987). According to Hair et al. (1998, p. 584), “SEM techniques are distinguished by two characteristics: (1) estimation of multiple and interrelated dependence relationships, and (2) the ability to represent unobserved concepts in these relationships and account for measurement error in the estimation process”. The purposes of this paper are: (1) to re-examine a model of cognitive budgetary participation processes using a structural equation modeling approach relying on a cross-sectional and larger random sample, and (2) to offer an example of how SEM technique can be used to validate and modify instrument for better psychometric properties. This paper is structured as follows. The next section discusses the theory underlying the study. Subsequent sections present the research method employed, results and discussions. 29
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PRIOR LITERATURE AND THEORY UNDERPINNING
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Previous literature suggests that budgetary participation and role ambiguity are associated with one another (Chenhall & Brownell, 1988; O’Connor, 1995; Chong & Bateman, 2000; see also Tosi & Tosi, 1970; Schuler, 1980). Budgetary participation is defined in this study as a process whereby subordinates are given the opportunities to get involved in and have influence on, the budget setting process (Brownell, 1982a); while role ambiguity is concerned with the lack of clear information regarding expectations, methods and consequences of the role (Kahn et al., 1964). Specifically, it is argued that budgetary participation is inversely related to role ambiguity. The support for a negative association between budgetary participation and role ambiguity is based on role theory (see Kahn et al., 1964) and existing empirical evidence (e.g. Chenhall & Brownell, 1988; O’Connor, 1995). For example, Chenhall and Brownell found that there was a negative association between budgetary participation and role ambiguity. O’Connor (1995, p. 388) argued that “. . . budget participation is seen as useful in reducing role ambiguity”. In addition, prior non-accounting studies (Schuler, 1980; Jackson & Schuler, 1985) found that high level of budgetary participation leads to lower role ambiguity. For example, Schuler (1980) found that participation in decision-making and role ambiguity was negatively associated in both manufacturing and public utility firms. Jackson and Schuler (1985) found that high level of participation leads to lower role ambiguity. With respect to the relationships between role ambiguity and managerial attitudes and behaviors, the extant literature (e.g. Chenhall & Brownell, 1988; Rebele & Michaels, 1990; Fogarty et al., 2000) supports the view that role ambiguity is negatively associated with performance and job satisfaction. For example, Chenhall and Brownell (1988) found that budgetary participation was negatively associated with role ambiguity, which in turn was negatively associated with performance and job satisfaction. They attributed their results to the fact that budgetary participation facilitates the clarification of the information in the three areas (i.e. expectations, methods and consequences). They argued that the expectations of the role will become clearer when goals or budgets are set. By participating, various methods of achieving role expectations can be examined to consider how the expectations can be obtained. Furthermore, the consequences of performance in the role can be clarified by participating in the planning and evaluation stage of the budget setting process. Rebele and Michaels (1990) and Fogarty et al. (2000) found that higher level of role ambiguity was associated with lower level of performance and job satisfaction. Numerous other studies (e.g. Senatra, 1980; Ameen et al., 1995) found that role ambiguity was negative and significantly associated with job satisfaction.
Testing a Model of Cognitive Budgetary Participation Processes
31
In summary, existing theory and prior empirical evidence suggest that budgetary participation is negatively associated with role ambiguity, and role ambiguity is negatively associated with performance and job satisfaction.
METHOD Sample Selection 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
To address the issue of using non-random sampling in Chenhall and Brownell (1988), this study randomly selected 80 manufacturing firms located in Sydney, Australia from the Kompass Australia (1998).4 From these firms, 160 middlelevel managers were identified. Two managers were drawn from each firm from different functional areas (such as accounting, production and marketing). The purpose is to ensure that the selected sample was representative of a variety of managerial background and experience. A questionnaire with a covering letter stating the objectives of this study together with a reply-paid self-addressed envelope for its return, were mailed to each manager to ask them to provide data to four variables: budgetary participation, role ambiguity, job satisfaction and performance. The response rate to the mail-out was 101 (63%). Of the 101 responses, four were unusable due to improper completion; thus the final sample for testing was 97 (61%).5 The respondents had held their current positions for an average of 6 years and had been employed by their respective companies for an average of 9 years. The average length of experience in their areas of management was 11 years and the average number of employees in their areas of responsibility was 37. The average number of employees in the sample firms was 217 employees. Variables Measurement Budgetary participation. Consistent with Chenhall and Brownell (1988), budgetary participation was measured by a six-item, seven-point Likert-type scale instrument developed by Milani’s (1975). This instrument has been tested and used extensively in other accounting studies (e.g. Lau et al., 1995; O’Connor, 1995; Chong & Bateman, 2000). Role ambiguity. Role ambiguity was measured by a six-item, seven-point Likerttype scale developed by Rizzo et al. (1970). This instrument was used by Chenhall and Brownell (1988) and other accounting researchers (e.g. O’Connor, 1995; Chong & Bateman, 2000). 31
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Job satisfaction. Consistent with Chenhall and Brownell (1988), job satisfaction was measured by a twenty-item instrument developed by the Weiss et al. (1967). The Weiss et al. (1967) instrument required respondents to indicate on a fivepoint Likert-type scale (where 1 = “very dissatisfied” and 5 = “very satisfied”) how satisfied or dissatisfied they were with the various dimensions of their job experience. This instrument has been tested and used extensively in other accounting studies (Choo & Tan, 1997; Chong & Bateman, 2000). 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Performance. Consistent with Chenhall and Brownell (1988), performance was measured by a single-item which asked respondents to evaluate their overall performance on a seven-point Likert-type scale, where 1 = “barely satisfactory” and 7 = “extremely good”.
RESULTS To test the cognitive budgetary participation processes model (see Fig. 1) proposed by Chenhall and Brownell (1988), a structural equation modeling technique was used. Specifically, this study has chosen to use the computer software programme EQS (Bentler, 1995) to analyze data.6 The first step is to test the measurement model. The measurement model was evaluated by confirmatory factor analysis. Based on the results of the measurement model analysis, necessary modifications were incorporated in the structural model, which was then tested with the study data. The evaluation of model fit in this study was based on the recommended goodness-of-fit measures such as the Chi-square statistics, the normed fit index (NFI), the non-normed fit index (NNFI); the comparative fit index (CFI); the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), average off-diagonal standardized residual (AOSR) and root mean square error of approximation (RMSEA) (Bentler & Bonnet, 1980; Bollen & Long, 1992). Table 1 presents the recommended values of various goodness-of-fit measures. Analysis of the Measurement Models Budgetary Participation The measurement model of BP (budgetary participation) was evaluated first. Figure 2 depicts the measurement model of BP and the summary of goodnessof-fit measures observed for the model. As can be seen in Fig. 2, the measurement model does not provide good fit to the data. In addition, the EQS output indicated that there was a large average off-diagonal value (0.62) for items BP1 and BP3. This result suggests that there was a small degree of misfit
Testing a Model of Cognitive Budgetary Participation Processes
Table 1.
33
Recommended Values of Goodness-of-Fit Measures.
Model Fit Measures
Recommended Value
Chi-square p-value
The Extent of Value Indication
≥ 0.05
Fit Indices:
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Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
≥ ≥ ≥ ≥ ≥
0.90 0.90 0.90 0.90 0.90
Higher Higher Higher Higher Higher
values values values values values
indicate indicate indicate indicate indicate
better better better better better
fit. fit. fit. fit. fit.
Residual Analysis: Average Off-Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
≤ 0.05
Lower values indicate better fit.
≤ 0.10
Lower values indicate better fit.
related to items BP1 and BP3. Therefore, there is a need to separate these two items from other items in the construct and to form an additional factor. This view is confirmed by an exploratory factor analysis. The results of the exploratory factor analysis revealed that there were two factors, which accounted for 84.73% of the total variance explained. The results of the factor analysis (see Table 2) showed that four items were loaded on the first factor (Factor I) and accounted for 65.12% of the total variance. Factor I revealed influence dimension of budgetary participation. In addition, the remaining two items were loaded on the second factor (Factor II) and accounted for 19.61% of the total variance. Factor II revealed involvement dimension of budgetary participation. Based on the above, the measurement model of BP was re-specified to be of a two-factor structure, and was re-estimated. The results of the re-estimated measurement model of BP and the summary of goodness-of-fit measures for the model are shown in Fig. 3. As can be seen in Fig. 3, the re-specified measurement model of BP provided a very good fit to the data. All goodness-of-fit measures of the modified model surpassed the recommended levels. The Cronbach alpha coefficients (Cronbach, 1951) were 0.91 for influence dimension of budgetary participation and 0.92 for involvement dimension of budgetary participation, which indicates very satisfactory internal reliability for the scales (Nunnally, 1967). The Cronbach alpha statistic for the composite score (i.e. six items) of budgetary participation was 0.89 indicating very satisfactory internal reliability for the scale (Nunnally, 1967). 33
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E1
E2 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
E3
E4
E5
E6 Fig. 2. The Measurement Model of BP (Budgetary Participation) and Summary of Goodness-of-Fit Measures. Goodness-of-Fit Measures: Statistical Tests: Chi-square d.f. p-value Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI) Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
Result 114.15 9 0.001 0.52 0.80 0.79 0.77 0.64 0.07 0.35
Testing a Model of Cognitive Budgetary Participation Processes
Table 2.
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Results of Exploratory Factor Analysis for Budgetary Participation (Sorted Rotated Factor Matrix).
Item
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Factor Loading I
II
BP4. BP6. BP5. BP2.
How much influence. Frequency of opinions sought. Importance of contribution. Reasoning of budget revisions.
0.927 0.915 0.903 0.703
0.190 0.263 0.192 0.272
BP1. BP3.
Involved in setting budget. Frequency of suggestions.
0.251 0.234
0.930 0.937
3.907 65.12%
1.177 19.61%
Eigenvalues Total variance explained
Role Ambiguity The measurement model of RA (role ambiguity) was evaluated next. Figure 4 shows the measurement model of RA and the summary of goodness-of-fit measures observed for the model. As shown in Fig. 4, the measurement model provided a reasonably good fit to the data. However, a closer examination of Fig. 4 reveals that the Chi-square statistic, adjusted goodness-of-fit index (AGFI), and the results of the residual analysis did not meet the recommended levels. The EQS output shows that there was a large average off-diagonal value (0.162) for items RA5 and RA6. This result suggests that there was a model misfit associated with items RA5 and RA6. Taken together, these results suggest that although the global fit of the RA measurement model is fairly good, there is a small degree of misfit related to at least two items (i.e. RA5 and RA6). This represents a correlated error among items of the same measurement instrument, which is a common finding with attitude scales in general (e.g. Newcomb et al., 1986; Tanaka & Huba, 1984; Byrne, 1991, 1993). Consequently, the error covariance should be re-specified as freely estimated parameters. Figure 5 presents the re-specified measurement model of RA and the summary of goodness-of-fit measures for the model. As can be seen in Fig. 5, all goodness-of-fit measures of the re-specified model achieved the recommended values. The Cronbach alpha coefficient (Cronbach, 1951) was 0.92 for role ambiguity, which indicates very satisfactory internal reliability for the scale (Nunnally, 1967). 35
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VINCENT K. CHONG
Fig. 3. The Re-Specified Measurement Model of BP and Summary of Goodness-of-Fit Measures. Goodness-of-Fit Measures: Statistical Tests: Chi-square d.f. p-value
Result 5.92 8 0.66
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.95 0.98 1.00 0.99 1.01
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
0.03 0.00
Testing a Model of Cognitive Budgetary Participation Processes
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Fig. 4. The Measurement Model of RA (Role Ambiguity) and Summary of Goodness-of-Fit Measures. Goodness-of-Fit Measures: Statistical Tests:
Result
Chi-square d.f. p-value
23.21 9 0.01
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.83 0.93 0.97 0.95 0.94
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
0.03 0.13
37
37
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Fig. 5. The Re-specified Measurement Model of RA and Summary of Goodness-of-Fit Measures. Goodness-of-Fit Measures Statistical Tests: Chi-square d.f. p-value
Result 3.24 8 0.92
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.97 0.99 1.00 0.99 1.02
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
0.01 0.00
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Job Satisfaction The last measurement model assessed was for the construct JS (job satisfaction). Due to the large number of indicants used (20 items) for JS, a composite or a single indicator of the items is required (see Bagozzi, 1980a, b; Gaski, 1986; Howell, 1987; Poznanski & Bline, 1997). Initially, exploratory factor analysis was used to ascertain which item to include as part of a factor. An item is included as part of a factor when it is loaded at 0.40 or higher, and did not load at 0.30 or higher on any other factors (Nunnally, 1967; Smith et al., 1993).7 Table 3 presents the results of exploratory factor analysis. As can be seen from Table 3, five factors emerged with numerous items having relatively high cross-loadings (i.e. loaded at > 0.30 on any other factors). The high cross-loading items were deleted and five composite measures (i.e. COMP-JS1, COMP-JS2, COMP-JS3, COMP-JS4 and COMP-JS5) were formed based on the remaining items.8 The evaluation of the measurement model of JS was based on the five composite measures. Figure 6 shows the measurement model of JS and the summary of goodness-of-fit measures for the model. As shown in Fig. 6, the measurement model provides a good fit to the data. However, a closer examination of the results revealed that COMP-JS5 has a very low item reliability (R2 = 0.03) and insignificant standardized loading (0.18). Furthermore, based on the output of Wald test, the statistically insignificant parameter (i.e. COMP-JS5) can be dropped from the measurement model (see Bentler, 1995). This is confirmed by the results of the Pearson correlation matrix of the five composite measures as shown in Table 4. As can be seen in Table 4, the correlations between COMP-JS1 and COMP-JS2 (r = 0.348, p < 0.01), COMP-JS1 and COMP-JS3 (r = 0.334, p < 0.01), COMP-JS1 and COMP-JS4 (r = 0.361, p < 0.01), COMP-JS2 and COMP-JS3 (r = 0.252, p < 0.05), COMP-JS2 and COMP-JS4 (r = 0.242, p < 0.05), and COMP-JS3 and COMP-JS4 (r = 0.415, p < 0.01) were positive and significant, as expected, with the exception of the correlations between COMP-JS1 and COMP-JS5, COMP-JS2 and COMP-JS5, COMP-JS3 and COMP-JS5, and COMP-JS4 and COMP-JS5. Thus, re-specification of the measurement model was necessary. Figure 7 presents the re-specified measurement model of JS and the summary of goodness-of-fit measures for the model. As can be seen in Fig. 7, all goodness-of-fit measures of the re-specified model achieved the recommended values. The Cronbach alpha coefficient (Cronbach, 1951) was 0.82 for job satisfaction, which indicates satisfactory internal reliability for the scales (Nunnally, 1967). 39
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Table 3.
Results of Exploratory Factor Analysis for Job Satisfaction (Sorted Rotated Factor Matrix).
Item
Factor Loading I
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II
III
IV
V
JS6. JS5. JS12. JS17. JS19.# JS13.# JS14.#
0.826 0.812 0.700 0.666 0.635 0.565 0.479
0.170 0.168 0.233 0.166 0.086 0.266 0.300*
0.201 0.243 0.265 0.106 0.014 0.047 0.221
⫺0.040 0.009 0.104 0.170 0.399* 0.408* 0.322*
0.250 0.074 0.245 ⫺0.205 ⫺0.139 ⫺0.017 ⫺0.378*
JS3. JS16.# JS15.# JS2.#
0.161 0.241 0.418* ⫺0.059
0.756 0.751 0.734 0.695
0.123 0.309* 0.132 0.030
0.089 0.196 0.073 ⫺0.026
⫺0.034 ⫺0.139 ⫺0.195 0.522*
JS11. JS18.# JS8.# JS7.#
0.057 0.162 0.328* 0.323*
0.233 0.047 0.108 0.305*
0.782 0.682 0.615 0.521
0.260 ⫺0.259 0.183 0.121
0.057 ⫺0.430* 0.131 0.098
JS4. JS1.# JS9.# JS20.#
0.214 0.075 0.057 0.421* 0.142 ⫺0.035 0.405* 0.455*
JS10.
0.124 7.215 19.884
Eigenvalues Total variance explained #
0.286 ⫺0.075 0.557* 0.234
0.767 0.589 0.561 0.456
⫺0.049 0.289 0.199 ⫺0.184
⫺0.088
0.118
0.066
0.788
1.793 15.118
1.642 12.767
1.454 10.842
1.279 8.300
Cross-loading items; * Factor loading > 0.30.
Table 4. Variable COMP-JS1 COMP-JS2 COMP-JS3 COMP-JS4 COMP-JS5
COMP-JS1 –– 0.348** 0.334** 0.361** 0.169
The Pearson Correlation Matrix. COMP-JS2
–– 0.252* 0.242* ⫺0.049
COMP-JS3
–– 0.415** 0.135
* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).
COMP-JS4
COMP-JS5
–– 0.102
––
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Fig. 6.
The Measurement Model of JS (Job Satisfaction) and Summary of Goodness-of-Fit Measures.
Goodness-of-Fit Measures Statistical Tests:
Result
Chi-square d.f. p-value
5.16 5 0.40
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.91 0.98 1.00 0.99 0.91
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
41
0.03 0.02
41
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Fig. 7.
The Re-specified Measurement Model of JS and Summary of Goodness-of-Fit Measures.
Goodness-of-Fit Measures Statistical Tests:
Result
Chi-square d.f. p-value
2.03 2 0.36
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.95 0.99 1.00 1.00 0.96
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
0.04 0.02
Analysis of Structural Model The results shown in Fig. 8 were based on the global analysis, which used the composite score of budgetary participation as an independent variable. As can be seen in Fig. 8, budgetary participation was negative and statistically significant (path coefficient = ⫺0.29, p < 0.05) associated with role ambiguity.
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Further, the results show that role ambiguity was negative and statistically significant associated with job performance (path coefficient = ⫺0.27, p < 0.05) and job satisfaction (path coefficient = ⫺0.22, p < 0.05). These results were consistent with those of Chenhall and Brownell (1988). In addition, as can be seen in Fig. 8, all goodness-of-fit measures of the structural model achieved the recommended values. Recall that the budgetary participation (Milani, 1975) scale consists of two dimensions (i.e. influence and involvement dimensions), additional analysis relies on each of the two dimensions, in turn, as the independent variables were undertaken. The results shown in Fig. 9 were based on the dimensions analysis, which used the influence and involvement dimensions of budgetary participation as the independent variables. As can be seen in Fig. 9, all goodness-of-fit
Fig. 8. Cognitive Budgetary Participation Processes Model – Global Analysis. Goodness-of-Fit Measures Statistical Tests:
Result
Chi-square d.f. p-value
0.69 2 0.71
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.98 1.00 1.00 0.97 1.26
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
43
0.02 0.00
44
VINCENT K. CHONG
measures of the structural model achieved the recommended values. The influence and involvement dimensions of budgetary participation were, as expected, correlated significantly (r = 0.49, p < 0.05). Further, the standardized parameter estimate between the influence dimension of budgetary participation and role ambiguity was negative and statistically significant (path coefficient = ⫺0.27, p < 0.05). However, the standardized parameter estimate between the involvement dimension of budgetary participation and role ambiguity was negative but not statistically significant (path coefficient = ⫺0.04, n.s.). 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Fig. 9. Cognitive Budgetary Participation Processes Model – Dimensions Analysis. Goodness-of-Fit Measures Statistical Tests:
Chi-square d.f. p-value
Result
1.18 4 0.88
Fit Indices: Adjusted Goodness-of-Fit Index (AGFI) Goodness-of-Fit Index (GFI) Comparative Fit Index (CFI) Normed Fit Index (NFI) Non-Normed Fit Index (NNFI)
0.98 1.00 1.00 0.98 1.18
Residual Analysis: Average Off Diagonal Standardized Residual (AOSR) Root Mean Square Error of Approximation (RMSEA)
0.02 0.00
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Furthermore, the standardized parameter estimates between role ambiguity and performance and job satisfaction were negative and statistically significant (path coefficient = ⫺0.27, p < 0.05 and path coefficient = ⫺0.22, p < 0.05, respectively). Taken together, these results suggest the intervening role of role ambiguity on the relationships between budgetary participation and performance and job satisfaction, were driven mainly by the influence dimension of budgetary participation. 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
DISCUSSIONS AND CONCLUSION Our study contributes to the accounting literature by offering an example of how SEM technique can be used to: (1) test instrument validation and modify instrument for better psychometric properties, and (2) reconstruct the research model for better model fit. The illustration is made through a re-examination of a model of cognitive budgetary participation processes proposed by Chenhall and Brownell (1988). Overall, the results of this study are consistent with those of Chenhall and Brownell (1988), except that it is clear evidence that it is the influence dimension of budgetary participation, which is principally responsible for the results. In addition, the results of this study are in line with those of Libby (1999) who found that the performance level of the subordinates are improved when they are given the opportunity to get involved in and have influence in the budget setting before it is finalized. In summary, it can be argued that involvement with influence in the budgeting process enhance subordinates’ performance while involvement without influence in the budgeting process can have de-motivating effects on subordinates, as they may perceive that the budgeting process is pseudo-participative (see Argyris, 1952; Pasewark & Welker, 1990). Several limitations of this study should be noted. First, the sample for this study was selected from the manufacturing industries. Therefore, generalization of the results of this study to other industries (such as financial institutions and services industries) should be done cautiously. Further research involving the financial institutions and services industries would be worthwhile. Note that the results of this study are consistent with prior studies that have been undertaken in low power distance nations (e.g. Tosi & Tosi, 1970; O’Connor, 1995; Chong & Bateman, 2000), further research can systematically extend Chenhall and Brownell model to high power distance nations.9 Second, the use of a self-rating scale to measure performance and job satisfaction are likely to have higher leniency error (higher mean values) and lower variability error (a restrictive range) in the observed score than a superior rating (see Prien & Liske, 1962; Thornton, 1968; Lau et al., 1995). Brownell (1995), for example, argued 45
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that a self-rated performance scale is less susceptible to halo error. Halo error refers to the tendency of raters to make global assessment of the variable and to be unable to differentiate their assessments on the various dimensions (Brownell, 1995, p. 45). It is further argued that self-assessment instruments can produce more reliable and uninhibited responses from respondents when they are assured of anonymity and/or confidentiality. Third, this study focuses on role ambiguity as an intervening variable. Other intervening variables such as role conflict (Chong & Bateman, 2000), budget goal commitment (Chong & Chong, 2000), job-relevant information (Kren, 1992; Magner et al., 1996; Chong & Chong, 2000) and organizational commitment and budget adequacy (Nouri & Parker, 1998) can add to the explanation of the indirect paths between budgetary participation and performance and job satisfaction. Finally, the survey method allows for the examination of statistical associations at one point in time, and the statements about the direction of relationships can only be made in terms of consistency of results with the effects proposed in the theoretical discussion. Future research can employ different research methods (e.g. longitudinal field studies) to systematically investigate the theoretical causal relationships proposed in this study.
NOTES 1. Early empirical studies used a universalistic approach to examine the direct effects of budgetary participation on managerial attitudes and behaviors. For example, some studies (e.g. Bass & Leavitt, 1963; Brownell, 1982b) have found a strong positive relationship between budgetary participation and performance. Other studies (e.g. Stedry, 1960; Bryan & Locke, 1967; Chenhall & Brownell, 1988) have found that budgetary participation did not improve performance. Few studies (e.g. Milani, 1975; Kenis, 1979) have found that the relationship between budgetary participation and performance was insignificant. These inconsistent findings have prompted accounting researchers to attempt to reconcile the conflicting results by adopting a contingency approach (see e.g. Brownell, 1985; Govindarajan, 1986; Mia, 1989; Gul et al., 1995). Other accounting researchers have relied on an intervening variable model (see e.g. Brownell & McInnes, 1986; Chenhall & Brownell, 1988; Magner et al., 1996; Nouri & Parker, 1998; Chong & Chong, 2000). 2. Prior studies that examined the motivational role of budget participation have relied on expectancy theory (e.g. Brownell & McInnes, 1986) and goal-setting theory (Chong & Chong, 2000); while prior studies that examined the informational role of budget participation have relied on contingency theory (e.g. Gul et al., 1995), role theory (e.g. Chenhall & Brownell, 1988; Chong & Bateman, 2000) and information processing theory (e.g. Kren, 1992; Magner et al., 1996; Nouri & Parker, 1998; Chong & Chong, 2000). 3. The importance of instrument validation has been highlighted in a study by Kwok and Sharp (1998). For a detailed discussion, see Kwok and Sharp (1998).
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4. This sampling approach enables each of the listed companies in the Kompass Australia (1998) an equal chance of being selected to ensure as far as possible that the sample was a representative of the population of manufacturing companies (Kerlinger, 1986; Lal et al., 1996). A wide range of industries was represented in this sample. These industries included chemicals, clothing and footwear, commercial machinery, consumer durable products, electrical and electronics products, furniture, medical and health-care products, rubber and plastic products, petroleum refining, printing and publishing, steel and metal products, transportation equipment, tobacco products, wire and cable. The main reasons to select only firms from the manufacturing industries were: first, we could compare our results with that of Chenhall and Brownell (1988), and second, it has been suggested that by broadening the sample to include non-manufacturing firms may introduce “noise” into the data (see Welker & Johnson, 1999). 5. A test for non-response bias was conducted by the approach suggested by Oppenheim (1966, p. 34). The results revealed that there were no statistically significant differences in the mean scores between the early and late responses. 6. Other popular computer software programmes include LISREL (Jöreskog & Sörbom, 1996) and AMOS (Arbuckle, 1995a, b). 7. Nunnally (1967) argued that an item loaded at 0.30 or higher can be considered statistically significant. Consequently, by including items which loaded at 0.30 or higher on two factors might confound meaningful interpretation of each factor. 8. Poznanski and Bline (1997, p. 160) argued that “if single-item measures are used, it is not possible to empirically estimate the reliabilities of the measurement model. This necessitates using more than one composite measure.” 9. Power distance refers to the way in which societies handle the problem of human inequality (see Hofstede, 1980). It is argued that in a low power distance culture (such as Australia), budgetary participation is useful in reducing role ambiguity while in a high power distance culture (such as Singapore), budgetary participation is not expected to facilitate a large reduction in role ambiguity (O’Connor, 1995, p. 388).
ACKNOWLEDGMENTS Thanks are due to the Associate Editor and two anonymous referees for their helpful comments and suggestions on earlier drafts of this paper.
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Senatra, P. T. (1980). Role Conflict, Role Ambiguity, and Organizational Climate in Public Accounting Firm. The Accounting Review, 594–603. Smith, K. J., Everly, G. S., & Johns, T. R. (1993). The Role of Stress Arousal in the Dynamic of the Stressor-to-Illness Process among Accountants. Contemporary Accounting Research, 9, 432–449. Shields, J. F., & Shields, M. D. (1998). Antecedents of Participative Budgeting. Accounting, Organizations and Society, 49–76. Stedry, A. C. (1960). Budget Control and Cost Behavior. Englewood, Cliffs, N. J.: Prentice-Hall. Tanaka, J. S., & Huba, G. J. (1984). Confirmatory Hierarchical Factor Analyses of Psychological Distress Measure. Journal of Personality and Social Psychology, 46, 621–635. Thornton, G. C. (1968). The Relationship Between Supervisory and Self-Appraisals of Executive Performance. Personnel Psychology, 441–456. Tosi, H., & Tosi, D. (1970). Some Correlates of Role Conflict and Role Ambiguity Among Public School Teachers. Human Relations, 1068–1075. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Minnesota studies in vocational rehabilitation. Manual for the Minnesota Satisfaction Questionnaire: 22, Minneapolis. Welker, K., & Johnson, E. (1999). The Effects of a Budget-Based Incentive Compensation Scheme on the Budgeting Behavior of Managers and Subordinates. Journal of Management Accounting Research, 11, 1–28.
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AN EXPERIMENTAL MARKET ANALYSIS OF AUDITOR WORK-LEVEL REDUCTION DECISIONS Maribeth Coller, Julia L. Higgs and Stephen Wheeler
ABSTRACT In this paper, we use an experimental market to test the link between competitive fee pressure and its alleged downstream consequences: time pressure and audit quality reduction. To induce fee pressure, transaction (auditor switching) costs, which have been found to be associated with fee lowballing in prior research, are manipulated in a competitive bidding environment. Our results show that (after controlling for subject skill level) the presence of transactions costs induces lower audit fees in first year engagements which result in significantly lower time budget levels selected and then, in turn, significantly higher levels of audit work-level reductions. Also, confirming prior survey results on the causes of audit quality reduction acts, we find that subjects encountering lower perceived misstatement rates select significantly lower time budgets and then, in turn, evidence significantly higher audit work-level reductions. These findings lend support to regulators’ assertions about the link between fee competition and the potential for reductions in audit quality.
Advances in Accounting, Volume 19, pages 53–70. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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INTRODUCTION
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Factors that affect audit quality have long been of significant interest to practitioners, academicians, regulators and investors. For example, the Commission on Auditors’ Responsibilities (Cohen Commission, 1978, pp. 110–115) asserted that excessive competition creates pressure to set “unrealistic and unnecessary deadlines for completion of audits” possibly leading to substandard auditing. Excessive time pressure, the Commission concluded, is one of the most pervasive causes of audit failures. Similarly, the National Commission on Fraudulent Financial Reporting (Treadway Commission, 1987) cited institutional and individual pressures as having the potential to compromise CPAs’ professional skepticism and integrity. Along with fee and budget pressures, tight reporting deadlines were presumed to encourage auditors to reduce audit quality. Audit quality reduction acts take several forms, including premature sign-off on an audit program step, reducing the amount of work performed on an audit step below a reasonable level, failing to research an accounting principle, making superficial reviews of client records, and accepting weak client explanations (Kelley & Margheim, 1987, 1990). Under-reporting of time may also contribute to reduced audit quality because current year audit budgets often are based on prior year amounts. When current budgets are unrealistic, auditors on these jobs may have an incentive to engage in other audit quality reduction acts (Kelley & Margheim, 1990). In another line of audit research, DeAngelo (1981) develops a model of audit fee “lowballing” (offering services below cost) as a rational competitive response to obtain new clients. In this model, the presence of “transactions costs” associated with new audits makes lowballing possible. That is, auditors incur a fixed start-up cost (e.g. initial internal control evaluation, designing a new audit plan, etc.) and clients incur a fixed switching cost (e.g. bidding costs, retraining personnel). The model holds that auditors attract clients by charging a fee that is less than their total cost in the initial period and then recoup the loss in future periods by charging a higher fee that is just less than their ongoing audit costs plus the client switching cost. Our experiments build on these prior lines of research in an attempt to link them. First, we include transaction costs as a way to induce lowballing. Second, we provide a rich experimental setting to measure potential audit quality reduction in the form of work-level reduction decisions. Our analysis then looks at the empirical relationships among these and other hypothesized variables to ultimately examine the logical link between fee pressure and audit quality reduction.
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THEORETICAL BACKGROUND Audit Quality
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Given the institutional features that have arisen to encourage auditors to report breaches in manager/owner contracts and avoid them in auditor/client contracts, one might expect audit quality reduction acts to be rare. While the extent of breaches cannot be measured directly, some indirect evidence exists. For instance, the Big-6 CPA firms disclose in a letter to the AICPA membership that they spent $477 million on legal matters in 1991, and in 1990, the seventh largest firm (Laventhol & Co.) declared bankruptcy (Cook et al., 1992). Although much of this expense may be due to other causes, the magnitude of the legal expense suggests that audit firms find it necessary to defend themselves against accusations of breaches (St. Pierre & Anderson, 1982, 1984; Graham, 1985; Palmrose, 1991). More directly, several surveys have established that premature sign-offs do occur. For example, Rhode (1978), in a survey sponsored by the Cohen Commission, reports that 47% of the respondents admitted signing-off on an audit step without completing the work or noting the omission. Other surveys (Alderman & Deitrick, 1982; Raghunathan, 1991) confirm the Rhode results. Two additional studies, (Buchman & Tracy, 1982; Reckers, Wheeler & Wong-on-Wing, 1997) use techniques designed to provide more anonymity for sensitive questions and find even higher rates. Reckers et al. (1997) report that 79% of the auditors in their sample admitted to signing-off prematurely at least once in the year preceding their study. Premature sign-off also has been found to extend to government auditors (Berry et al., 1987) and to internal auditors (Buchman, 1983). Hypothesized Causes of Reduced Audit Quality Time pressure and low risk assessment are commonly cited precursors to audit quality reduction acts in several prior studies. In the Rhode report (1978), the primary motivation cited for omitting a required audit step is time budget pressure (p. 180). Similarly, Lightner et al. (1982) and Lightner et al. (1983) conduct surveys establishing the extent and causes of under-reporting behavior and find that pressure to meet infeasible budgets is cited as the primary cause. A variety of other studies have examined specific ways in which time pressure can affect audit quality. See for example, Margheim and Pany (1986); Kermis and Mahapatra (1985); Kelley and Margheim (1990, 1987); McDaniel (1990); Ashton (1990); Marxen (1990); Waggoner and Cashell (1991); and Margheim and Kelly (1992). 55
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In a different survey designed to determine the reasons for premature signoffs in particular, Raghunathan (1991) finds low risk to be the most cited reason. The experiments conducted here are designed to address the empirical question of how low risk assessments and time constraints affect audit quality. The evidence from prior research indicates that each of these factors may affect audit quality directly. Alternatively, perceptions of risk may affect audit quality through its effect on time budgets. In suggesting these alternatives, we note that auditors must assess client inherent and control risks before determining the extent of substantive testing to be done. As such, reducing testing due to a low risk assessment is completely proper and does not necessarily reduce audit quality. However, the behavior we are interested in occurs when audit procedures are represented as having been done when in fact they have not. The question is thus whether a low risk assessment leads to setting a time budget that is relatively tighter for a given audit procedure (and thereby leads to audit quality reduction), or whether a low risk assessment has some direct effect on audit quality other than through inducing time pressure. The Cohen and the Treadway Commissions also cite excessive audit competition as a potential cause of reduced audit quality. Although firms compete on several dimensions, price is one of the most important. Since DeAngelo’s (1981) original model of lowballing, several studies have been conducted to determine if firms reduce audit fees in response to competition. On a macro scale, Maher et al. (1992) demonstrate that audit fees decreased for sample firms during the period 1977 to 1981, a period cited as having an increased level of competition. Simon and Francis (1988) compare the fee structure of companies depending on whether or not an auditor change has occurred. They find that fees for firms that change auditors are 24% below fees reported for ongoing engagements. Similarly, Rubin (1988) finds that auditor tenure is a significant (positively related) factor in determining fees for municipal audits. These findings are consistent with the DeAngelo (1981) lowballing model. Related Experimental Studies Schatzberg (1990) tests DeAngelo’s (1981) model using a laboratory market methodology. He hypothesizes that in the presence of positive start-up and switching costs, the fee negotiated in the first period will be equal to the cost of conducting the audit less the client switching costs. The fee negotiated in the second period is hypothesized to be equal to the audit cost plus auditor start-up costs and client switching costs. The results indicate that the presence of transaction costs generally results in lowballing, while, in the absence of transaction costs, lowballing does not occur.
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Schatzberg (1994) extends this literature by developing a model that allows for differences in audit opinions (i.e. there are instances where auditors may differ in their judgments about the appropriate audit report for a given client). Experimental market tests of the model thus include outcomes where different auditor types vary in their opinion on the resolution. The results are consistent with the concept of “opinion shopping” in the audit market and demonstrate that such heterogeneity in auditor reporting behavior can also result in lowballing, even without positive transactions costs. Schatzberg (1994) does not investigate the possibility of related reductions in audit quality. Calegari et al. (1998) do investigate reductions in audit quality in an experimental setting, but such reductions take the form of misrepresenting the audit results. In these markets, the auditor observes one of two possible outcomes and then reports (either honestly or dishonestly). Although these experiments are a more complete test of the price-independence relationship, the opportunity to reduce audit quality is not present. Dopuch and King (1996) conduct experiments where reductions in audit quality take the form of shirking on the amount of investigation performed in the audit. Their setting is somewhat similar to ours in that they include transactions costs in a market where all auditors have common costs and audit-quality choices. As opposed to the other studies discussed, Dopuch and King (1996) do not treat audit costs as fixed. Instead, the auditors may choose between a costly high-quality audit (which reveals the value of the client’s asset) or a costless low-quality audit (which does not reveal the value of the asset). The audit decision is thus dichotomous and can be depicted as the choice between a complete investigation and no investigation. In the event that the costless (no investigation) audit is chosen, the auditor reports the asset value as he chooses (either high or low) and thereby misrepresents the extent of his audit. Dopuch and King (1996) find that lowballing occurs in their markets, but find little evidence of reductions in audit quality. They were able to induce some reductions in quality, but only in markets where a high degree of lowballing was imposed in the absence of competition.
HYPOTHESES This study addresses whether a competitive market environment contributes to reduced audit quality as suggested by the Cohen and Treadway Commissions. This involves linking several intervening variables suggested in prior research. The hypothesized linkage is pictured in Fig. 1. DeAngelo’s (1981) model of audit competition asserts that lowballing is a rational strategy to obtain new clients. Schatzberg’s (1990) empirical results 57
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Fig. 1. Hypothesized Relationships.
show lowballing to be contingent upon the presence of transactions costs. Thus, the first hypothesis addresses whether lowballing will occur when it is costly to switch auditors. As such, this hypothesis is tested to ensure that we have induced downward pressure on audit fees for this experiment. H1: In the presence of transactions costs, audit fee lowballing will occur. To then test the linkage between lowballing and time pressure, the selected time budgets in markets with positive transactions costs are compared with those in markets without transactions costs. The results provide insight into the supposition by the Cohen Commission that the competitive environment leads to increased time pressure. Accordingly, the second hypothesis is: H2: In the presence of transactions costs, tighter total budgets will be selected. The third hypothesis addresses the link between time pressure and audit quality reduction as follows: H3: Higher audit work level reductions will be positively associated with tighter total budgets. We next address the suggestions from the Raghunathan (1991) survey regarding risk levels and attempt to determine if the perceived risk level causes a higher degree of time pressure or if the risk level directly affects work level decisions in addition to its effect on time pressure. The fourth and fifth hypotheses are as follows: H4: Tighter total budgets will be selected when the expected error rate is low.
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H5: Higher audit work-level reductions will occur when the expected error rate is low, after controlling for time budget selected.
EXPERIMENTAL TASK
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The laboratory market experiments conducted here involve the selling of services and the performance of an attest–type task. The basic design includes seven rounds, with each round consisting of two attest periods. In the first period of each round, each subject submits a private offer for the performance of an attestation task to each computer-simulated client. Each market includes six auditors and five clients. Engagements are awarded based upon the “best” offer and no auditor can be awarded more than one job in any period. All auditors who are awarded jobs then select a time budget, perform the audit task, and finally render an opinion on the accuracy of thirty calculations. The contracting and attest process is then repeated in the second round. Experimental manipulations incorporate the presence/absence of transaction costs (where transaction costs are used to induce lowballing) and differential perceptions of audit risk (set at two levels). Both are manipulated between audit markets, resulting in four treatments. Each treatment is conducted twice, resulting in a total of eight experiments. In each experiment, subjects are first provided with written instructions.1 After everyone finishes reading these, the experimenter summarizes the sequence of events and answers any questions. A one-period practice session is then conducted during which the experimenter explains the computer screens and talks the subjects through the mechanics of the task. Subjects are next provided with an initial endowment of $5.00, and the experiment begins. The first two complete rounds are treated as practice rounds. This is done in order to allow subjects to practice the entire sequence of events without financial risk. During these rounds, subjects have ample opportunity to ask questions between each period. At the end of the second round, earnings are reset to zero, and the initial endowment of $5.00 is restored. In hypothesis testing, only data from rounds three through seven are used. Subjects are informed that the maximum value of the job to the client is $2.00. Providing the value of the audit gives an indication of an upper bound for offer starting points that should encourage convergence of audit prices. The bidding process begins with each subject submitting offers to conduct an audit to each client. In the two treatments where transactions costs are present, all subjects who are awarded jobs are charged a $0.30 start-up cost in period one. In period two, only those subjects who contract with a different client than in 59
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period one (i.e. an auditor switch) are charged the start-up cost. The computersimulated client faces a switching cost of $0.25 in the second period only if the auditor used in period one is not retained for period two. The remaining two treatments are conducted in the same way, with the exception that there are no start-up or switching costs present. Jobs are awarded based on the highest non-negative value to the client. This is not always the lowest offer since, when the client faces transactions costs related to auditor switching, the lowest offer may not result in the highest value to the client. In the event of a tie, the winner is decided based on the first subject to submit the tying offer. Because there are more auditors (six) than clients (five), at least one auditor will not be awarded a job in each period. This is done in order to provide a competitive environment and encourage convergence of audit prices to equilibrium. Any subject who is not awarded a job is allowed the option of conducting a “practice” audit, involving only the attest task, where no costs are assessed and no fee is earned. This procedure ensures that everyone has the same number of periods to conduct an audit. To the extent that task-related skills may develop with repetition, this is a necessary control. In addition, this procedure ensures that no one might feel embarrassed by having to sit idle while the other subjects conduct audits. After the contracts are awarded, subjects also are given information about the winning prices for each job. Subjects are then asked to choose a time budget. Five budget levels (2, 2.5, 3, 3.5, and 4 minutes) are available. These times were determined through pre-tests to represent options ranging from a virtually impossible time budget to a very easy one. At this point, the cost of time is $0.25 for the first two minutes with each additional one-half minute costing an additional $0.25. Once the budget is selected, the subject/auditor is asked to verify 30 multiplication problems. Two levels of errors are seeded in the problems. Half of the experiments have an error rate of 5% (the probability of an error in at least one of the 30 problems is 5%, and the probability that there are no errors is 95%). The remaining experiments use a 40% error rate (i.e. there is a 60% probability that all 30 problems are correct). In general terms, subjects are told what the error rate is. The known error rates are somewhat analogous to an auditor assessing client inherent and control risk and having expectations about the degree of error. While not completely analogous, for the experiment’s purposes, it is simply the perception of high or low error rates that is necessary to accomplish the treatment effect. Each of the thirty multiplication problems consists of two three-digit numbers. Subjects are asked to enter the equation in blanks on a computer worksheet. The computer multiplies the typed numbers, and the subject compares the product to the previously provided answer. If the subject finds an error, he/she marks a
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decision box to indicate which equation is incorrect. Otherwise, no mark is made other than the typed equation. An on-screen clock, ticking down in ten second increments, displays the time remaining based on the budget selected. At the end of the budget period, the subject is asked if he/she wishes to opine on the correctness of the equations or to buy more time to work on the task. If the subject buys additional time, the cost is $0.40 for each additional one-half minute. Because additional time can only be purchased at premium rates (analogous to overtime pay and opportunity costs to audit firms) at this point, the auditor has an incentive to select an initial time budget that is considered realistic. When the additional time (if any) expires, the subject is again given the opine/additional time option, continuing until the subject issues an opinion (either “no errors present” or “one or more errors present”). The computer then checks the errors in the equations against the decision boxes marked. If the subject does not identify an equation that is in error, a penalty of $1.50 is assessed (a flat penalty, regardless of how many errors the subject fails to identify). Conversely, if the subject incorrectly identifies an equation as being in error, a penalty of $0.50 is assessed for each equation incorrectly marked, up to a maximum of three. These penalties are included to parallel the type I/type II errors possible in an audit and the differential severity of the errors to the auditor (type II errors, missing a material error, are considered more severe due to litigation possibilities). Subjects must correctly identify all errors in the thirty multiplications to avoid the $1.50 penalty. Therefore, the option to quit checking after one error is found and issue an opinion of “one or more errors present” could still result in a penalty. For analysis purposes, the computer also keeps track of how many equations the subject checks. This information is not used to determine subject earnings, only to measure the audit work-level selected. At the completion of the period, subjects are provided with their results and credited with the contract amount, reduced by costs for time, additional time, start-up costs, and penalties. The process is identical for the second period of each round, except for the additional possibility of switching auditors (and hence start-up and switching costs). To measure and control for differences in subjects’ typing skills, the experiment concludes by asking the subjects to perform a verification task containing 100 equations with three minutes to verify as many equations as possible. For each equation typed correctly, the subject earns an additional $0.10; incorrectly entered equations reduce earnings by $0.25, but not below the amount earned before the verification task begins. This measure, number of equations correct, is used as a covariate in the subsequent analysis. Finally, subjects are informed of their total earnings and paid accordingly. Figure 2 depicts the entire experimental process. 61
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Fig. 2.
Overall, forty-eight student subjects participated in eight separate markets. All subjects had taken at least one computer course. They ranged in age from 20 to 43 (an average of 24), and in class level from junior standing to graduate level. Because students were required to have a sophomore level computer class, basic computer literacy was assumed. Beyond this, no special skills were required. Pre-tests (including group discussion with pre-test subjects) indicated that this task was seen as non-trivial, requiring some amount of effort, but was not so difficult that subjects did not want to participate.
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The average time to implement one market was just under two hours. Earnings ranged from $11.25 to $25.75, with an average of $19.19. Open-ended debriefing responses indicated that subjects understood the task, and casual analysis of the data showed little evidence of unusual heuristical approaches. Specifically, subjects demonstrated differing strategies in deciding which calculations to check or not to check. Most applied a sequential approach, but some employed an apparently random selection strategy. Beyond speculation, no reasons could be determined as to the selection of such strategies. To address possible end-of-game strategies, the data analyses were also run excluding round seven data. No substantial differences in results occurred. Also, results were substantially consistent between the different administrations of the experiments.
RESULTS To test the first hypothesis regarding lowballing, we make several analyses. First we compare the average fees in period one to fees in period two across rounds three through seven. In markets where transactions costs are present, the average fee in period one is $1.35, while the average fee in period two is $1.40. The p-value for a paired comparison t-test of the difference is 0.02. By comparison, in comparable markets without transactions costs, the first period fees average $1.47 compared to a $1.46 average across all second periods (p = 0.54 for the t-test). Second, we compare first period fees in markets with transactions costs to first period fees in markets without transactions costs. The first period fees in the presence of transactions costs ($1.35) are significantly lower than first period fees ($1.47) where no transactions costs are present (t = 2.56, p ≤ 0.01). These combined comparisons provide evidence of fee pressures in the experiment and indicate that subjects did lowball in the presence of transactions costs. To address Hypotheses 2 and 4, regarding time pressure, an ANCOVA analysis is conducted using the time budget selected as the dependent variable, with transactions costs and expected error rate as independent variables. Keyboarding skill (as measured by the results of subjects’ post-experiment verification task) is included as a covariate. Because the design of the experiment includes as many as ten sequential observations from each of six subjects (fewer if the subject is not awarded a job in all periods), round number is also included as a blocking factor. Table 1 presents cell means for the budgeted time selected by treatment in Panel A, and ANCOVA results in Panel B. Both treatment variables are highly significant and are in the directions predicted by H2 and H4. Specifically, we find that: (1) in the presence of transactions costs, subjects choose tighter time 63
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budgets, and (2) subjects choose tighter time budgets when the expected error rate is lower. The interaction term Error Rate x Transaction Costs was significant (F = 9.42, p < 0.0023). Multiple comparison (Scheffe) tests and examination of cell means in Panel A reveal that the significance of these two treatments was driven by the low error rate/ transaction costs present cell. Thus the effect of the transaction costs variable (on time budget selected) manifested itself more when perceived error rates were low. Similarly, the effects of error rate perceptions (on time budget selected) were more pronounced when transaction costs were present. The significance of the round number attests to a learning effect across time. That is, as subjects’ task familiarity increased, they tended to perceive a need for less time to accomplish the task. Hypotheses 3 and 5 predict that work-level reductions will be higher when tighter time budgets are selected and when error rate perceptions are low. To Table 1.
Analysis of Time Budget Selected.
Panel A: Cell Means by Treatment Transactions Costs Error Rate
Absent
Present
Total
High (0.40) Low (0.05) Total
2.57 2.40 2.49
2.40 2.08 2.24
2.49 2.24
Panel B: ANCOVA Results; dependent variable = total time budget selected Variable
df
F
p-value
Transactions Costs Error Rate Subject Skill Level Round Number
1 1 1 4
132.25 125.44 90.75 38.43
0.0001 0.0001 0.0001 0.0001
Model R2 = 76.9 Model F = 22.61 (p ≤ 0.0001) Variable Definitions: Time Budget Selected: Total time purchased for audit task. Transactions Costs: Presence of start-up and switching costs, Yes/No. Error Rate: High (the probability of at least one error in the thirty problems is 60%) or Low (the probability of at least one error in the thirty problems is 5%). Subject Skill Level: Number of calculations typed correctly during final verification task. Round Number: Round Number, 3–7.
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test these hypotheses, we conduct a second analysis of variance with the number of calculations not verified as the dependent variable and time budget selected, transaction costs and error rates as independent variables. As previously discussed, keyboarding skill is included as a covariate and round number is also included. The results, presented in Table 2, indicate support for Hypothesis 3. The selected time budget is highly significant in the ANCOVA and is negatively related to the number of calculations not verified (the correlation is ⫺0.49 and is significant at a level of less than 0.0001). Again, the significance of the round number variable apparently indicates that subjects required more evidence to opine as task familiarity increased. Interestingly, the two treatment variables, transactions costs and error rate, are not significant in directly explaining audit work-level reduction. Thus, these results do not support Hypothesis 5; instead they suggest that any effect of low Table 2.
Analysis of Number of Calculations Not Verified.
Panel A: Cell Means by Treatment Transactions Costs Error Rate
Absent
Present
Total
High (0.40) Low (0.05) Total
3.85 3.52 3.68
4.24 9.67 6.95
4.05 6.59
Panel B: ANCOVA Results; dependent variable = number of calculations not verified Variable
df
Time Budget Selected Transactions Costs Error Rate Subject Skill Level Round Number
1 1 1 1 4
F 429.94 1.85 0.41 498.20 9.45
p-value 0.0001 0.1744 0.5209 0.0001 0.0001
Model R2 = 84.9 Model F = 37.51 (p ≤ 0.0001) Variable Definitions: Time Budget Selected: Total time purchased for audit task. Transactions Costs: Presence of start-up and switching costs, Yes/No. Error Rate: High (the probability of at least one error in the thirty problems is 60%) or Low (the probability of at least one error in the thirty problems is 5%). Subject Skill Level: Number of calculations typed correctly during final verification task. Round Number: Round Number, 3–7.
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risk on audit work-level choice is likely to be manifested through the intervening time budget selection variable. As an overall check on the causal relationships implied by the individual ANCOVA analyses, we conduct a path analysis. This analysis is performed using the SAS System’s CALIS procedure which uses maximum likelihood estimated coefficients that produce an estimated covariance matrix that is as close as possible to the sample covariance structure. The relationships analyzed appear in Figure 3. As in the previous analyses, typing skill and round number are included to provide the best specification of the overall model. The specified model appears to fit the data quite well. The chi-square statistic is 0.7343, indicating an inability (p = 0.69) to reject the null hypothesis that the reproduced covariance matrix has the specified model structure. The model explains 41.9% of the variation in the number of calculations verified and 33.0% of the variation in the time budget selected. Individual coefficient results and significance levels, also reported in Fig. 3, are consistent with the ANCOVA results. All coefficients are significant (at less than the 0.01 level) and in the predicted direction. The path analysis tests reveal that the presence of transaction costs has a significant negative effect on the time budget selected, while perceived error rate has a significant positive effect on the time budget selected. In turn, the lower the time budget selected, the greater the number of equations not checked.
Fig. 3. Path Analysis Results Standardized Coefficients.
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A path analysis of an alternative model indicates that significant causal relationships are present neither from transactions costs directly to the number of equations not checked nor directly from error rate to number of equations not verified (Neither estimated coefficient is significant at conventional levels (p > 0.10)). The significance of the paths directly from skill level and round number to number not checked provides further assurance about the treatment effects, even after partitioning subject skills and learning effects. Taken together, our path analysis results suggest that fee pressure is present in the markets with transaction costs. In these markets, and also in markets where low levels of risk are present, subjects select lower time budgets. The reduced time budget then leads to greater levels of work-level reduction. No direct relationship is found between fee pressure or risk level and the extent of audit work-level reduction.
DISCUSSION In this paper we examine the potential causes of premature sign-off in an experimental market setting. Regulators have long asserted that competition in the market for audit services, which causes downward pressure on audit fees, induces time pressures and may be responsible for decreased audit quality. Surveys have cited time pressure and perception of low risk as primary causes of quality reduction acts. The contribution of this study lies in extending the linkage of the prior literature on lowballing with survey results citing time pressure as a cause of audit quality reduction. While prior laboratory research has demonstrated the competition-lowballing link, prior experimental research has found little evidence of the audit quality reduction acts that may occur. In this study, we provide an experimental setting with a much richer time budget selection and attest-type task than prior research has used, and find evidence of subsequent audit work-level reduction. A second contribution lies in clarifying how perceptions of low audit risk affect audit quality reduction acts. We find that, after controlling for time pressure, risk assessment does not significantly explain audit work-level reduction as suggested in prior survey research. That is, holding time pressure constant, our subjects were not any more likely to reduce work loads on a low risk task than on a high risk task. Instead, the effect of risk assessment is manifested through its effect on time budget selection. Therefore, absent time budget constraints, the tendency to reduce audit quality is not necessarily inherent. Further, our results are consistent with the Cohen Commission’s original take on the issue: fee pressures create budgeting constraints, which then potentially affect audit quality. 67
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MARIBETH COLLER, JULIA L. HIGGS AND STEPHEN WHEELER
Our findings are limited by certain factors. First, the use of student subjects, although appropriate for this generic attest-type task, makes generalization of the results to auditor behavior more difficult. Second, subjects were told in advance about the actual error rates in the populations. While successful in achieving the desired risk-perception-treatment effects, the correspondence with actual audit procedures (where auditors have only perceptions about what error rates might be) is less than perfect. Third, while our results show significant reductions in audit work levels, the question of whether such reductions constitute premature sign-off acts cannot be answered without a defined minimum level. This question is left for future research.
NOTE 1. Copies of the instructions and computer screen examples are available from the authors upon request.
ACKNOWLEDGMENTS We gratefully acknowledge the helpful comments of Rob Bloomfield, Gene Chewning, Al Leitch, Barbara Pierce, Brad Ruffle, Jeff Schatzberg, Vernon Smith, John Wermert, and participants at workshops at the Economics Science Association Conference, the American Accounting Association National meeting, and the Western Decision Sciences meeting. Bonnie Glasberg and Alex Maitland provided invaluable programming assistance.
REFERENCES American Institute of Certified Public Accountants (1978). The commission on auditors’ responsibilities: report, conclusions, and recommendations. The Commission on Auditor’s Responsibilities, M. F. Cohen (Chair). Alderman, C. W., & Deitrick, J. W. (1982). Auditors’ perceptions of time budget pressures and premature sign-offs: a replication and extension. Auditing: A Journal of Practice and Theory, 1(2), 54–68. Ashton, R. H. (1990). Pressure and performance in accounting decision settings: paradoxical effects of incentives, feedback and justification. Journal of Accounting Research, 28(Suppl.), 148–180. Berry, L. E., Harwood, G. B., & Katz, J. L. (1987). Performance of auditing procedures by governmental auditors: some preliminary evidence. The Accounting Review, 62(January), 14–28. Buchman, T. A (1983). The reliability of internal auditors’ working papers. Auditing: A Journal of Practice and Theory, 3(1), 92–103. Buchman, T. A., & Tracy, J. A. (1982). Obtaining responses to sensitive questions: conventional questionnaire versus randomized response technique. Journal of Accounting Research, 20(1), 263–271.
Market Analyis of Auditor Work-Level Reduction Decisions
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Calegari, M. J., Schatzberg, J. W., & Sevcik, G. R. (1998). Experimental evidence of differential auditor pricing and reporting strategies. The Accounting Review, 73(2), 255–275. Cook, J. M., Freedman, E. M., Groves, R. J., Madonna, J. C., O’Malley, S. F., & Weinbach, L. A. (1992). The liability crisis in the United States: impact on the accounting profession. Journal of Accountancy, 174(November), 18–23. DeAngelo, L. E. (1981). Auditor independence, ‘lowballing’, and disclosure regulation. Journal of Accounting and Economics, 3(2), 113–127. Dopuch, N., & King, R. (1996). The effects of lowballing on audit quality: An experimental markets study. Journal of Accounting, Auditing, and Finance, 11(1), 45–68. Graham, L. E. (1985). Audit risk – part V. CPA Journal, 55(12), 26–35. Kelley, T., & Margheim, L. (1987). The effect of audit billing arrangement on under-reporting of time and audit quality reduction acts. Advances in Accounting, 5, 221–233. Kelley, T., & Margheim, L. (1990). The impact of time budget pressure, personality, and leadership variables on dysfunctional auditor behavior. Auditing: A Journal of Practice and Theory, 9(2), 21–42. Kermis, G. F., & Mahapatra, S. (1985). An empirical study of the effects of time pressure on audit time allocations. Advances in Accounting, 2, 261–274. Lightner, S. M., Leisenring, J. J., & Winters, A. J. (1983). Under-reporting chargeable time. Journal of Accountancy, 155(1), 52–57. Lightner, S. M., Adams, S. J., & Lightner, K. M. (1982). The influence of situational, ethical, and expectancy theory variables on accountants’ under-reporting behavior. Auditing: A Journal of Practice and Theory, 2(1), 1–12. Maher, M. W., Tiessen, P., Colson, R., & Broman, A. J. (1992). Competition and audit fees. The Accounting Review, 67(1), 199–211. Margheim, L., & Pany, K. (1986). Quality control, premature sign-off, and under-reporting of time: some empirical findings. Auditing: A Journal of Practice and Theory, 5(2), 50–63. Margheim, L., & Kelley, T. (1992). The perceived effects of fixed fee audit billing arrangements. Accounting Horizons, 6(4), 62–75. Marxen, D. E. (1990). A behavioral investigation of time budget preparation in a competitive audit environment. Accounting Horizons, 4(2), 47–57. McDaniel, L. S. (1990). The effects of time pressure and audit program structure on audit performance. Journal of Accounting Research, 28(2), 267–285. National Commission on Fraudulent Financial Reporting (1987). Report of the national commission on fraudulent financial reporting. J. C. Treadway, Jr. (Chair). Palmrose, Z. (1991). An analysis of auditor litigation disclosures. Auditing: A Journal of Practice and Theory, 10(Suppl.), 54–71. Raghunathan, B. (1991). Premature signing-off of audit procedures: an analysis. Accounting Horizons, 5(2), 71–79. Reckers, P., Wheeler, S., & Wong-On-Wing, B. (1997). An examination of the reliability of response elicitation techniques used in investigating auditor premature sign-off. Auditing: A Journal of Practice and Theory, 16(Spring), 69–78. Rhode, J. G. (1978). The independent auditor’s work environment: a survey, commission on auditors’ responsibilities research study no. 4 (New York: AICPA). Rubin, M. A. (1988). Municipal audit fee determinants. The Accounting Review, 63(2), 219–236. Schatzberg, J. W. (1990). A laboratory market investigation of low balling in audit pricing. The Accounting Review, 65(2), 337–362. Schatzberg, J. W. (1994). A new examination of auditor Alow ball@ pricing: theoretical model and experimental evidence. Auditing: A Journal of Practice and Theory, 13(Suppl.), 33–55.
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Simon, D. T., & Francis, J. R. (1988). The effects of auditor change on audit fees: tests of price cutting and price recovery. The Accounting Review, 63(2), 255–269. St. Pierre, K., & Anderson, J. (1982). An analysis of audit failures based on documented legal cases. Journal of Accounting, Auditing, and Finance, 1981–1982, 5(3), 229–247. St. Pierre, K., & Anderson, J. (1984). An analysis of the factors associated with lawsuits against public accountants. The Accounting Review, 59(2), 242–262. Waggoner, J., & Cashell, J. D. (1991). The impact of time pressure on auditors’ performance. The Ohio CPA Journal, (Jan.–Feb./Mar.–Apr.), 27–32.
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FIXED COST ALLOCATION AND THE CONSTRAINED PRODUCT MIX DECISION 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Susan Haka, Fred Jacobs and Ronald Marshall
ABSTRACT This paper focuses on the benefits of fixed cost allocation in product mix decisions. We show that in a constrained production environment where at least one factor of production is fixed and in short supply, oligopoly firms can earn higher profits by allocating the costs of these fixed factors. The higher profits occur because the use of full absorption product costs leads firms closer to mix decisions that would be made if they were able to collude. A duopoly example is presented to illustrate these profit effects, and the necessary conditions for higher absorption costing profits are developed and explained.
INTRODUCTION Accountants are divided on the benefits of allocating fixed costs to products when making capacity- related operating decisions. On one side is the traditional microeconomic argument that fixed costs are not incremental with respect to alternative uses of a firm’s production capacity and are therefore irrelevant. Moreover, allocations are potentially misleading and can result in sub-optimal decisions. A common response is that allocated fixed costs can be used to approximate the opportunity costs of alternative capacity use and must Advances in Accounting, Volume 19, pages 71–88. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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therefore be considered. In addition, fixed cost allocations are mandated for external reporting by accounting standard-setting bodies and tax authorities, and as a result, all publicly traded companies have in place absorption cost accounting systems that automatically provide fully allocated product costs. This availability of full cost data from externally oriented product costing systems makes it relatively easy for firms to use allocated fixed costs in internal decision making; for many firms, additional effort and cost is required to obtain variable cost data alone. This is confirmed by surveys of internal management accounting practices reporting that absorption costing systems serve as the primary, if not the only source of product cost data for internal decision-making use (Ask & Ax, 1992; Fremgen & Liao, 1981; Inoue, 1988; Wijewardena & DeZoysa, 1999). In this paper we examine the potential economic benefits of allocating fixed costs to products to make constrained product-mix decisions. We assume an oligopoly market setting where firms have an opportunity to act strategically in response to actions of their competitors. In this setting, we show that oligopoly firms have an economic incentive to use a costing system that allocates the costs of the fixed factor inputs. By using fully allocated product costs rather than variable costs alone, firms can achieve higher profits. This result is due to the fact that, under certain conditions, full cost data from an absorption costing system can provide oligopoly firms with a mechanism that allows them to reach an equilibrium output closer to the output level they would produce if they were permitted to coordinate their mix decisions under some collusive arrangement. The paper is organized as follows. In the next section, we review the relevant research. The following section describes the basic setting and related model formulations. Then an example is introduced to illustrate how use of an absorption costing system can produce higher profits. In the results section, we identify the conditions that will produce higher profits and provide an intuitive explanation. Finally, the equilibrium nature of the solution is discussed, and the paper concludes with a brief summary.
RELEVANT LITERATURE There is a significant body of research that addresses the allocation of common fixed costs from several perspectives. The set of papers summarized here are relevant to this study because they focus on the role of fixed cost allocation in decision-making and/or assume a market/firm environment that is similar to the one described later. Kaplan and Thompson (1971) and Kaplan and Welam (1974) devise a set of fixed cost allocation procedures that are neutral with
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respect to optimal product mix decisions. Their goal is to identify methods for allocating fixed overhead that result in identical product mix decisions both before and after allocation. The procedures themselves are based on the dual variables from linear and non-linear programming formulations that maximize firm contribution margin (revenue minus variable costs). The authors do not defend fixed cost allocation for decision-making but offer their procedures to managers that want to allocate fixed costs for other unspecified reasons (e.g. financial reporting). Karmarkar and Pitbladdo (1994) also use the decision-neutral approach but in a setting where fixed cost allocation serves a specific decision-making purpose. They identify a fixed cost allocation rule that has no impact on the optimal product mix decision while facilitating the product-line decision (i.e. produce or don’t produce). They assume a market of Cournot competition in which firms produce multiple products with independent demands and independent, linear operating costs. Constrained and unconstrained capacity cases are both examined. They demonstrate that allocating fixed costs in proportion to individual product contribution margins achieves optimal productline decisions without adversely affecting profit-maximizing production quantities. The benchmark quantities are the Cournot equilibrium quantities derived from a traditional, micro-economic product mix formulation that maximizes firm contribution margin. Balakrishnan and Sivaramakrishnan (1996) examine the role of fixed cost allocation in the product mix decision for a firm in a perfectly competitive market that faces deterministic demand over two periods. They also use a contribution margin-based product mix formulation to show that when demand is the same in both periods, allocated fixed resource acquisition costs are identical to opportunity costs and can therefore be used to assess product profitability. They also show that when demand is different each period, full product costs misstate product profitability and result in sub-optimal production levels. Banker and Hughes (1994) similarly examine the usefulness of fixed cost allocations in the product mix problem, but they assume a monopoly firm that faces uncertain demand. The firm has committed resources with associated costs, called normal costs, that are incurred even if actual usage is below the amount available. But for an incremental cost above normal costs, the supply of these resources can be increased in response to unexpected high demand. The authors show that in this setting, the firm will achieve optimal profits if it uses variable costs plus allocated normal costs to make its product mix decision. Karmarkar and Pitbladdo (1993) add decentralization to the firm and market settings in their 1994 paper above to examine whether fixed cost allocation can be effectively used to allocate central resources to profit centers. The measure 73
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of effectiveness is again the impact of the allocation on the firm’s optimal production levels. They show that allocated fixed costs generally do not approximate the opportunity costs of scarce resources. The optimal internal prices must be based instead on the size of capacity increments and on the competitive market environment, and as such, require direct coordination of production plans across products/departments. Furthermore, including allocations of fixed costs overcharges profit centers for their use of constrained, scarce resources. Alles and Datar (1998) and Göx (2000) examine optimal transfer pricing strategies for two decentralized firms that engage in price competition and produce differentiated products. Both papers identify conditions under which the optimal transfer price exceeds the marginal cost of the transferred product. Göx requires that the transfer prices be observable or that both firms use an absorption costing system; Alles and Datar do not require observability. Their results help to explain why we see firms continue to use full cost-based transfer prices instead of the classical “transfer price equals marginal cost” rule derived by Hirshleifer (1956). Finally, Gal-Or (1993) examines how fixed cost allocation facilitates decision making in a setting where decentralized firms produce one product in an oligopolistic market and another in a perfectly competitive market. Her stylized model includes differentiated products, congestion costs, and uncertainty, but excludes capacity constraints. The derived Cournot equilibrium production quantities imply a fixed cost allocation rule with two components, one favoring products that are relatively more profitable, and the other favoring products that are sold in markets where strategic considerations are relatively more important. Interestingly, Göx and Gal-Or express skepticism about the decisionfacilitating role of fixed cost allocation in centralized settings: However, the new equilibrium can only be achieved in the decentralized setting . . . any deviation from the Nash strategy [marginal cost transfer pricing] in the centralized setting would not be credible because it would not be profit maximizing (Göx, 2000, p. 335). It is crucial to point out that the main assumption that drives our result is that top management finds it optimal to delegate production decisions to the departments’ managers. In the absence of such delegation, there are no strategic considerations to take into account. The issue of cost allocation is entirely irrelevant if ‘quantity forcing’ is selected by top management (Gal-Or, 1993, p. 397).
In summarizing the literature, these comments seem justified. It has been shown that at best, it may be possible to allocate fixed costs in a way that does not adversely affect optimal product mix decisions; at worst, fixed cost allocation can distort product profitability and contribute to sub-optimal firm profits.
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THE MODEL Consider an oligopoly market in which r symmetric firms produce n products with the following price function, pj, for product j (j = 1, . . . , n): pj = ␣j ⫺ jQj, (␣j, j > 0),
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where Q j denotes total market output for product j across all firms. Although our results can be generalized to asymmetrical firms, the symmetry assumption simplifies the analysis. The cost function for an individual firm, say Firm X, is ⌺jvjxj + FC, where vj is the variable cost per unit of product j, xj is the amount of product j produced by Firm X, and FC are total fixed costs. The profit function for Firm X, denoted , is therefore = ⌺j(␣j ⫺ jQj)xj ⫺ ⌺jvjxj ⫺ FC. We assume m factors of production, with bi denoting the supply of factor i (i = 1, . . . ,m) and aij denoting its per-unit demand by product j. We also assume that the supply of one fixed factor, denoted factor g, is insufficient to meet its demand. In the absence of strategic considerations, Firm X will select the product mix, x, that maximizes profits subject to the constraints on factor supply: maxx{(x) = ⌺j(␣j ⫺ jQj)xj ⫺ ⌺jvjxj ⫺ FC} s.t.
⌺jaijxj ≤ bi xj ≥ 0
(i = 1, . . . , m), (j = 1, . . . , n).
[P1]
We call the Cournot solution to [P1] the Cournot mix and denote it xcn. Firm profits with the Cournot mix are called Cournot profits and denoted (xcn). Note that common factors of production, i.e. factors whose consumption cannot be traced to individual products, are not represented by constraints, although the costs of these factors are included in FC. Note also that the solution to [P1] and to [P2] and [P3] to follow are stated explicitly in the appendix as (A1), (A2), and (A3), respectively. We will later show that Firm X, acting strategically, can earn profits in excess of Cournot profits by using an absorption costing (AC) profit function in its product mix formulation. The focus is on a traditional AC accounting system that allocates FC by combining them into one cost pool and allocating the pool 75
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with a single factor of production, or cost driver. If factor k is selected as the cost driver, the allocation rate is denoted ƒ k = FC/bk, and the fixed cost allocated to product j is ƒ kakj. The AC profit function alters product profitability because the full unit cost of product j inflates its marginal cost by the amount of the fixed cost allocation ƒ kakj. Using this AC profit function, denoted ac, the firm’s profit maximization problem is: maxx{ac(x) = ⌺j(␣j ⫺ jQj)xj ⫺ ⌺jvjxj ⫺ ⌺jƒ kakjxj} 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
s.t.
⌺jaijxj ≤ bi xj ≥ 0
(i = 1, . . . , m), (j = 1, . . . , n).
[P2]
The Cournot solution to [P2] is called the AC mix, denoted xac, and the resulting profits, denoted (xac), are called AC profits. Note that the AC profit function is used in [P2] to determine the firm’s product mix, but firm profits resulting from that mix are measured using . This is due to the fact that the costs of the firm’s fixed factors, FC, are incurred regardless of production levels. To help demonstrate how the AC profit function can result in firm profits above Cournot profits and, therefore, closer to profits that firms would earn if they colluded, we provide the collusion formulation. Colluding firms would jointly determine the profit maximizing market mix and each firm’s share of that mix. With symmetric firms, output would be shared equally, so that each firm could determine its own product mix by solving [P1] with Qj = rxj. Firm X’s profit maximization problem under collusion can therefore be written: maxx{co(x) = ⌺j(␣j ⫺ jrxj)xj ⫺ ⌺jvjxj ⫺ FC} s.t.
⌺jaijxj ≤ bi xj ≥ 0
(i = 1, . . . , m), (j = 1, . . . , n).
[P3]
co
is called the collusion profit function, the solution to [P3] is called the collusion mix, xco, and the resulting firm profits, (xco), are called collusion profits. Note that with the restriction that Qj = rxj, the collusion profit function is a special case of the profit function in [P1], with the result that firms can solve [P3] without making conjectures about competitor behavior.
A NUMERICAL EXAMPLE Before presenting the conditions for profit improvement, we introduce an example. Consider a duopoly market with symmetric Firms X and Y producing and selling the same two products, 1 and 2. The prices of the two products
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depend on the combined output of both firms according to the following price functions: p1 = $35 ⫺ 0.1(x1 + y1) p2 = $75 ⫺ 0.2(x2 + y2), where x1, x2, y1, and y2 are the outputs of the two products for Firms X and Y, respectively. The cost function for Firm X is: 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
$16x1 + $12x2 + $1,800. Each firm is limited to 275 labor hours and 300 machine hours. A unit of Product 1 requires 1 labor hour and 3 machine hours, and a unit of Product 2 requires 2 labor hours and 2 machine hours. That is, there are two constraints: x1 + 2x2 ≤ 275 and 3x1 + 2x2 ≤ 300. We assume that both firms use labor hours as the single driver in the AC system. The fixed cost allocation rate is therefore $6.55 per hour (= $1,800 ⫼ 275 hours), $6.55 is assigned to each unit of Product 1 (= $6.55 ⫻ 1 hour), and $13.10 is assigned to each unit of Product 2 (= $6.55 ⫻ 2 hours). The solutions to [P1]–[P3] for both firms are summarized: Product 1 Product 2 Cournot [P1] 36.1 95.9 absorption costing [P2] 41.5 83.2 collusion [P3] 47.5 78.8
LH 228 208 205
MH 300 291 300
Profits () $988 $1,117 $1,132
Note that the AC profit function moves firm profits above Cournot profits and closer to collusion profits. Note also that as the firm moves from Cournot profits to collusion profits, production of Product 1 increases while production of Product 2 decreases. The three mixes are depicted in Fig. 1. The Cournot and collusion mixes lie on the machine hour constraint line, using the maximum 300 machine hours that are available; the AC mix is an interior solution. The curve in the figure is the iso-collusion profit curve, the locus of all product mixes that would provide colluding firms with Cournot profits. Part of the iso-collusion profit curve is also part of the border for the shaded area that we call the profit improvement region. This region contains product mixes that generate profits for each firm that are greater than Cournot profits, mixes that place each firm on a higher isocollusion profit curve than does the Cournot mix. Since firm profits with the AC mix exceed Cournot profits, the AC mix falls within this profit improvement region. 77
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SUSAN HAKA, FRED JACOBS AND RONALD MARSHALL Labor Factor 1x1 + 2x2 = 275
{x | co(x) = co(xcn)} • xcn (36.1, 95.0)
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• xac (41.5, 83.2) • xco (47.5, 78.8)
Profit Improvement Region
Machine Factor 3x1 + 2x2 = 300
X1 Fig. 1. Duopoly Example Solution Graph. xcn – Cournot mix xac – absorption costing mix xco – collusion mix {x | co(x) = co(xcn)} – mixes providing colluding firms with profits equal to Cournot profits
RESULTS AC Allocation Effects Although both firms in the example earn higher profits by using an AC system, higher profits do not always occur. In order for an AC system to move the firm towards profits in excess of Cournot profits, the factor of production selected
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as the cost driver must satisfy the following necessary condition. The amount of the factor required to produce the Cournot mix must exceed the amount that would be required to produce the collusion mix; notationally, with k as the selected cost driver, ⌺jakjxjcn > ⌺jakjxjco. A factor that satisfies this necessary condition will subsequently be referred to as an over-consumed factor. With factor k over-consumed, higher profits are then guaranteed whenever ƒk ≤ ƒ䉫, where ƒ䉫 satisfies the following condition: 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
ƒ䉫 = {⌬⌺j[⭸co(xj䉫)Ⲑ⭸xj][agj Ⲑj]}Ⲑ{⌺j[⭸co(x䉫j)Ⲑ⭸xj][akj Ⲑj]}. In this expression for ƒ 䉫, ⌬ = gcn ⫺ gac, where cn and gac are the shadow g prices for factor g in the Cournot and AC solutions, respectively, factor g is the constraining factor in the Cournot solution, and x䉫j is product j’s output in the AC mix when ƒ k = ƒ䉫. There is actually a range for ƒ k above ƒ䉫 for which profits are still above Cournot profits. However, there is no convenient way to characterize this rate, so we instead use ƒ䉫 to establish, analytically, a sufficient condition for profit improvement. It is shown in the appendix that with the over-consumption and upper bound conditions satisfied, profits at the AC mix will necessarily be higher than at the Cournot mix. The following proposition summarizes the profit improvement result: Proposition: For symmetric, oligopoly firms making constrained product mix decisions and using the same costing system, profits under an AC system will be greater than Cournot profits if and only if the factor selected as the cost driver is over-consumed and the fixed cost allocation rate is less than or equal to ƒ䉫. As noted earlier, the symmetry assumption has been invoked to simplify the example, the discussion, and the proof, but the proposition still holds for asymmetrical firms. The assumption that all firms use the same costing system is an important one that is discussed later when equilibrium issues are considered. Intuition The two conditions in the proposition permit a firm to achieve higher profits with an AC system because the AC product mix is closer to the mix that oligopoly firms would produce if they were able to collude. Reformulating the AC profit function will help to explain why this is so. First, the profit function in [P1] is rearranged and substituted in the AC profit function in [P2], yielding ac = + FC ⫺ ⌺jƒkakjxj. 79
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Then, ƒkbk is substituted for FC: ac = + ƒkbk ⫺ ƒk ⌺jakjxj = + ƒk(bk ⫺ ⌺jakjxj),
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where bk ⫺ ⌺jakjxj is the slack or unused portion of factor k when x is selected as the product mix. In this reformulated version, the AC profit function has two components, firm profits and a slack term related to the single driver. Since the terms together must be maximized, the AC profit function directs the firm to trade off one for the other, sacrificing firm profits for increased slack, or decreased factor use. Now consider product mixes that increase AC profits above the level at xcn by using less of factor k (i.e. mixes for which ⌺jakjxjcn > ⌺jakjxj). If factor k satisfies the two cost driver conditions, ⌺jakjxjcn > ⌺jakjxjco and ƒk ≤ ƒ䉫, these mixes will consume an amount of factor k that is closer to what xco requires. The result will be an AC mix that is further from the Cournot mix and closer to the collusion mix, with firm profits that are higher than Cournot profits. Alternatively, consider what happens if factor k is not over-consumed (i.e. ⌺jakjxjcn ≤ ⌺jakjxjco). Mixes that use less of the factor will consume an amount that is less than that required by both xcn and xco. The AC mix will be further from these mixes and will result in profits that are lower than Cournot profits. To illustrate, reconsider the duopoly example. The labor factor used as the single driver in the AC system meets the over-consumption condition since 228 labor hours are required to produce the Cournot mix while only 205 hours would be required to produce the collusion mix. It also meets the fixed rate upper bound requirement, since the fixed rate is $6.55 and the upper bound ƒ䉫 in this example is $7.95. Because these conditions are satisfied, the AC mix uses fewer labor hours than are required by the Cournot mix (208 versus 228) and closer to the hours required by the collusion mix (205). The result is AC profits in excess of Cournot profits ($1,117 versus $988). The machine hour factor, on the other hand is not over-consumed, since 300 machine hours are required to produce both the Cournot mix and the collusion mix. As a result, if machine hours were used as the single driver in the AC system, the AC mix would use fewer than required by both the Cournot and collusion mixes, and the result would be AC profits below Cournot profits ($726 versus $988). The intuition can be seen with Fig. 1. With labor hours as the cost driver, the AC mix when ƒ = 0 is equal to the Cournot mix. As the fixed allocation rate increases, the corresponding increase in the slack component of ac exceeds the marginal loss in the profit component , and the AC mix gradually moves southeasterly away from the Cournot mix and labor hour constraint, along the
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machine hour constraint. Eventually the mix moves southwesterly away from the machine hour constraint, finally stopping in the profit-increasing region when ƒ = 7.95. In contrast, with machine hours as the cost driver, any positive fixed allocation rate would immediately pull the AC mix away from the machine hour constraint and the Cournot mix. But here the movement would be in a southwesterly direction away from the collusion mix, missing the profitincreasing region entirely. 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
EQUILIBIUM DISCUSSION In a single-period, non-cooperative setting, the higher profits that occur under the proposition are, in fact, not attainable since there is an incentive for firms to unilaterally deviate from the AC solution. In a single encounter, the only equilibrium outcome is the one in which firms do not allocate FC and therefore earn Cournot profits. The example again illustrates the point. If Firm X uses Cournot and Firm Y uses AC, the following solution results: Firm X 132.1 2101.8 $1,221
Units of Product Units of Product Profits
Firm Y 41.5 83.2 $846
If the roles are reversed, Firm Y enjoys the higher profits. The resulting payoff table depicts the familiar prisoners’ dilemma game: Firm Y AC AC
Cournot
$1117, $1117
$846, $1221
$1221, $846
$988, $988
Firm X Cournot
If both firms use AC, they will earn $1,117 each. But because Firm X can earn $1,221 by using Cournot when Firm Y uses AC, and vice versa, neither firm can trust the other. Although both firms are together better off using AC, each firm is individually better off using Cournot, and in this single-period game, the only sustainable equilibrium is the one in which neither firm allocates fixed costs and both earn $988. 81
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A different picture emerges, however, if we assume that firms in the market maintain an ongoing relationship in which they make output decisions repeatedly over time and maximize the present value of future profits. Specifically, assume the following sequence of events in a repeated-play setting. At the beginning of the first period, the firms simultaneously select a costing system and then use the resulting product costs to make the product mix decision. During each period, market prices are established on the basis of all firms’ combined levels of production. At the end of each period, the firms observe the market prices and again simultaneously select a costing system and a product mix for the next period. The distinguishing feature of this environment is that firms can observe and react to the behavior of their rivals. This allows them to design a retaliation strategy that will insure the AC solution and the corresponding higher profits unattainable in the single-play setting. One such strategy is that each firm uses AC as long as all other firms do but uses Cournot forever if another firm is ever observed using Cournot. To illustrate, assume our duopolists adopt this strategy. One possible scenario is that each firm uses AC every period, thereby avoiding retaliation by the other and always earning $1,117. Assuming a discount rate of 10%, the present value of this alternative is $11,170 (= $1117 ⫼ 0.1). Alternatively, while one firm uses AC, the other might use Cournot and earn the higher profits of $1,221. But when the AC firm observes this defection, it retaliates with Cournot, and both firms earn lower profits of $988 in every period that follows. The present value of this alternative for the defecting firm is $10,092: 1,221 + (988 ⫼ 0.1) . 1.1 Thus, if the objective of the two firms is to maximize the present value of future profits, both will use AC every period. The retaliation strategy makes it too costly to defect. The one-time higher profits available to either firm in any given period from unilaterally defecting and using Cournot are outweighed by the loss in profits in all subsequent periods. Neither firm has an incentive to deviate from AC, and it becomes the equilibrium solution. In general, with this strategy and sufficient weight given to future profits, AC profits can always be assured. This result is an application of the folk theorem, so named because it belongs to the folk literature of game theory (Kreps, 1990). In this example, the specified strategy is robust to the discount rate choice. Only if the discount rate is above 124% will the lower, future profits be so unimportant that firms will have an incentive to defect from the AC solution to achieve the early, one-period, higher profits. This rate is determined by solving the following equation for i:
Fixed Cost Allocation and the Constrained Product Mix Decision
1,117 i
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=
1,221 + (988⫼ i)
83
.
1+i
Note that there can be problems associated with this retaliation strategy, such as noisy signals and incomplete and/or imperfect information about competitors’ production parameters. However, there are ways to address these problems that although costly, preserve the basic equilibrium results (Tirole, 1988). It is also important to note that in a repeated-play game, the kind of retaliation strategy just described, in which cooperation is rewarded and competition discouraged, can be used to sustain multiple equilibria, including that of tacit collusion. That is, the equilibrium implied by the proposition is not unique. We return again to the duopoly example, where both firms could earn one-period profits of $1,132 if they colluded. As with the Cournot/AC single-period game, however, unilateral defection from collusion to Cournot is rewarded, in this example with one-period profits of $1,293. As a result, both firms will defect and earn the lower Cournot equilibrium profits of $988. But in a repeated-play setting, it can be shown that the two firms can earn the collusion profits of $1,132 every period if they adopt the retaliation strategy described above and if the discount rate is below 89.4%. So why wouldn’t firms follow this strategy and collude? After all, there appears to be no incentive for firms to allocate fixed costs and earn AC profits if they can instead independently solve the collusion problem and earn higher profits. The difficulty is that without explicit and illegal pre-play communication, oligopoly firms may not know exactly how to collude. That is, they may not be able to determine how much to produce and how to share profits. In the duopoly example, the symmetry assumption makes equal division of output and profits an obvious way for firms to play the collusion game. If one removes the symmetry assumption, however, it is much more uncertain how firms might resolve the sharing of outputs and profits. Kreps (p. 529) emphasizes this point, “For two symmetric firms, the ‘half-the-monopoly-quantity’ equilibrium seems a rather strong focal point, but in cases of asymmetric firms, we have no particular theory about which equilibrium will arise, if any.” In such circumstances, an equilibrium outcome is often attributed to a particular convention or, as mentioned above, a focal point (Schelling, 1960). The classic illustration of focal point theory is a non-economic one, where subjects are asked to pick a time and a place in New York City to meet a complete stranger. Although the possibilities are endless, virtually all respondents choose noon and the majority choose Grand Central Station. These two choices stand out among the rest; they are focal points. In general, focal points may owe their distinction to analogy, symmetry, precedent, aesthetics, or 83
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accident of arrangements, but they all share the one property of apparent uniqueness. They provide a mechanism for coordinating behavior without the need for explicit communication. In economics, focal point theory has been used to explain price lining, round number discounting, and identical competitive bidding (Scherer & Ross, 1990). In our setting, AC provides the natural mechanism or focal point that all firms, symmetrical or not, can use to compete/cooperate. Since firms must use AC for external reporting, use of AC in internal decision-making is a natural extension. And with the product mix decision examined here, a traditional AC system that allocates fixed costs according to the conditions in the proposition enables each firm to independently solve the AC problem and achieve higher profits.
SUMMARY In a setting where constraints are imposed by factor inputs that are fixed and in short supply, allocating the costs of these fixed inputs to products inflates their marginal costs. Except in certain non-oligopoly market settings or decentralized firms, previous research has not shown that firms can benefit from fixed cost allocations when making internal capacity-related decisions. This paper establishes such an economic incentive in a repeated-play, oligopoly environment – by using full product costs from a traditional, single-driver absorption costing accounting system that satisfies certain conditions, oligopoly firms can make product mix decisions that more closely resemble those under collusion, and earn profits in excess of profits resulting from a contribution margin approach.
REFERENCES Alles, M., & Datar, S. (1998). Strategic Transfer Pricing. Management Science, 44, 451–461. Ask, U., & Ax, C. (1992). Trends in the Development of Product Costing Practices and Techniques – A survey of the Swedish Manufacturing Industry. Working Paper, Gothenburg School of Economics. Balakrishnan, R., & Sivaramakrishnan, K. (1996). Is Assigning Capacity Costs to Individual Products Necessary for Capacity Planning. Accounting Horizons, 10, 1–11. Banker, R., & Hughes, J. (1994). Product Costing and Pricing. The Accounting Review, 69, 479–494. Fremgen, J., & Liao, S. (1981). The Allocation of Corporate Indirect Costs. New York: National Association of Accountants. Gal-Or, E. (1993). Strategic Cost Allocation. The Journal of Industrial Economics, 41, 387–402. Göx, R. (2000). Strategic Transfer Pricing, Absorption Costing, and Observability. Management Accounting Research, 11, 327–348. Hirshleifer, J. (1956). On the Economics of Transfer Pricing. Journal of Business, 172–184.
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Inoue, S. (1988). A Comparative Study of Recent Development of Cost Management Problems in USA, U.K., Canada, and Japan. Kagawa University Economic Review, (June). Kaplan, R., & Thompson, G. (1971). Overhead Allocation via Mathematical Programming Models. The Accounting Review, 46, 352–364. Kaplan, R., & Welam, U. (1974). Overhead Allocation with Imperfect Markets and Non-linear Technology. The Accounting Review, 49, 477–484. Karmarkar, U., & Pitbladdo, R. (1993). Internal Pricing and Cost Allocation in a Model of Multiproduct Competition with Finite Capacity Increments. Management Science, 39, 1039–1053. Karmarkar, U., & Pitbladdo, R. (1994). Product-line Selection, Production Decisions and Allocation of Common Fixed Costs. International Journal of Production Economics, 34, 17–33. Kreps, D. (1990). A Course in Microeconomic Theory. Princeton: Princeton University Press. Schelling, T. (1960). The Strategy of Conflict. Cambridge: Harvard University Press. Scherer, F., & Ross, D. (1990). Industrial Market Structure and Economic Performance. Boston: Houghton Mifflin Company. Tirole, J. (1988). The Theory of Industrial Organization. Cambridge: MIT Press. Wijewardena, H., & De Zoysa, A. (1999). A Comparative Analysis of Management Accounting Practices in Australia and Japan: An Empirical Investigation. International Journal of Accounting, 34, 49–70.
APPENDIX For the problem formulation in [P1], the Lagrangian is L = ⌺j [(␣j ⫺ jQj)xj ⫺ ⌺jvjxj ⫺ FC + ⌺icn (bi ⫺ ⌺jaijxj)], i where cn is the shadow price for factor i under . Noting that ⭸Qj/⭸xj = 1, the i relevant first-order conditions are ⭸L/⭸xj = ␣j ⫺ jQj ⫺ jxj ⫺ vj ⫺ ⌺icn a =0 i ij
(j = 1, . . . ,n).
Because of symmetry, Qj = rxj, and with factor g constraining, the Cournot solution to [P1] is xcn = [␣j ⫺ vj ⫺ gcnagj]/(r + 1)j, j
(A1)
where gcn is the shadow price for factor g under . The Lagrangian and the solution to [P2], with factor g constraining and factor k the single driver, are L xac j
= [⌺j (␣j ⫺ jQj)xj ⫺ ⌺jvjxj ⫺ ⌺jƒkakjxj + ⌺iac (bi ⫺ ⌺jaijxj)], and i ac k = [␣j ⫺ vj ⫺ g agj ⫺ ƒ akj]/(r + 1)j, (A2)
where gac is the shadow price for factor g under ac. The Lagrangian and the solution to [P3], with factor g constraining, are 85
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L = ⌺j[(␣j ⫺ jrxj)xj ⫺ ⌺jvjxj ⫺ FC + ⌺iico(bi ⫺ ⌺jaijxj), and xco = [␣j ⫺ vj ⫺ gcoagj] ⫺ 2rj, j
(A3)
where gco is the shadow price for factor g under co. Finally, from (A1) and (A2), ⌬xac = [⌬agj ⫺ ƒkakj]/(r + 1)j, j 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
(A4)
where ⌬xj = xac ⫺ xcn and ⌬ = cn ⫺ gac is the difference in the respective j j g shadow prices for factor g under the profit functions and ac. Definition: A profit increasing mix x is a mix for which co(x) > co(xcn), where co is the collusion profit function in [P3]. Lemma: For any profit increasing mix x, product j’s output in x is greater (less) than its output in xcn if and only if product j’s output in xco is also greater (less) than its output in xcn. That is, ⌬xj > 0 ⇔ ⌬xco > 0 for all j, j co where ⌬xj = xj ⫺ xcn and ⌬x = xco ⫺ xjcn. j j j Proof of Lemma: For any profit increasing mix x = [xj], the output of each product j can be written as a weighted sum of xcn and xco , using positive j j weights j: xj = (1 ⫺ j)xcn + jxco j j xj = xcn ⫺ jxcn + jxco . j j j Therefore, xj ⫺ xcn = j(xco ⫺ xcn ) or ⌬xj = j⌬xco , and ⌬xj > 0 ⇔ ⌬xco > 0 for all j. j j j j j Proposition: For symmetric oligopoly firms making constrained product mix decisions and using the same costing system, profits under an AC system will be greater than Cournot profits if and only if the factor selected as the cost driver is over-consumed and the fixed allocation rate is less than or equal to ƒ 䉫. Proof of the Proposition: (i) Suppose xac is a profit increasing mix. Show that the selected factor k must be over-consumed (⌺jakjxjcn > ⌺jakjxjco). = xac ⫺ xjcn. From the Lemma, ⌬xac > 0 ⇔ ⌬xco > 0 for all j, where ⌬xac j j j j Then, ⌬xac ⌬xco > 0 for all j. j j Substituting ⌬xac from (A4) gives j
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[(⌬agj ⫺ ƒ kakj)/(r + 1)j]⌬xco > 0 for all j j ⇒ [⌬agj ⫺ ƒ kakj]⌬xco > 0 for all j. j Summing over j yields ⌺j[⌬agj ⫺ ƒ kakj]⌬xco >0 j ⇒ ⌬⌺jagj⌬xco ⫺ ƒ k⌺jakj⌬xco >0 j j 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
冘 a ⌬x
⇒ ⌬
j gj
co j
> ƒ k⌺jakj⌬xco . j
We know that xco will never use more of factor g than xcn (compare (A1) and (A3) and note that because collusion restricts competition, collusion output is always less than Cournot output in the unconstrained case). Thus, ⌺jagj⌬xco = ⌺jagj(xco ⫺ xcn ) ≤ 0, j j j which, in turn, implies that ƒ k⌺jakj⌬xco < 0. Hence, j ⌺jakj⌬xco = ⌺jakj(xco ⫺ xcn ) < 0, or ⌺jakjxcn > ⌺jakjxco , j j j j j i.e. factor k is over-consumed. (ii) Now suppose that factor k is over-consumed (⌺jakjxcn > ⌺jakjxco j ). Show that j ac k 䉫 x is a profit increasing mix whenever ƒ ≤ ƒ where ƒ 䉫 satisfies the following condition: ƒ 䉫 = {⌬⌺j[⭸co(x䉫j )/⭸xj][agj/j]}/{⌺j[⭸co(x䉫j )/⭸xj][akj/j]}. with x䉫j denoting product j’s output in the AC mix when ƒ k = ƒ 䉫. Then for ƒk ≤ ƒ䉫 ƒ k ≤ {⌬⌺j[⭸co(xac )/⭸xj][agj/j]}/{⌺j[⭸co(xac )/⭸xj][akj/j]} j j Rearranging the inequality yields ⌬⌺j[⭸co(xac )/⭸xj][agj/j] ⫺ ƒ k⌺j[⭸co(xac )/⭸xj][akj/j] ≥ 0. j j Factoring out ⭸co(xac )/⭸xj and 1/j then gives j ⌺j[⭸co(xac )/⭸xj][(⌬agj ⫺ ƒkakj)/j] ≥ 0 j ⇒ ⌺j[⭸co(xac )/⭸xj][(⌬agj ⫺ ƒkakj)/(r + 1)j] ≥ 0 [dividing by (r+1)] j ac ⇒ ⌺j[⭸co(xac )/⭸x ]⌬x ≥ 0 [substituting ⌬xjac from (A4)] j j j co ac ac ⇒ ⭸ (x )/⭸⌬x ≥ 0, where ⭸co(xac)/⭸⌬xac is the directional derivative of co measuring marginal collusion profits at xac in the direction of the change (⌬xac) in the VC mix 87
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xvc implied by xac. Let x␦ = ␦xac + (1 ⫺ ␦)xvc for ␦ ≥ 0. In particular, x␦=0 = xvc and x␦ = 1 = xac, so that ⭸co(xac)/⭸⌬xac = ⭸co(x␦ = 1)(⭸⌬xac ≥ 0. Because co is continuous and strictly concave in x, co is strictly increasing with ␦ over the interval (0,1). Therefore, co(xac) = co(x␦ = 1) > co(x␦ = 0) = co(xvc), 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
implying xac is a profit increasing mix.
DO INITIAL PUBLIC OFFERING FIRMS UNDERSTATE THE ALLOWANCE FOR BAD DEBTS? 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Scott B. Jackson, William E. Wilcox and Joel M. Strong
ABSTRACT In this study, we investigate whether initial public offering (IPO) firms understate the allowance for bad debts in the two annual periods adjacent to their IPOs. The evidence suggests that IPO firms understate the allowance for bad debts in both periods, and that high quality auditors have little effect on the extent to which the allowance for bad debts is understated. The evidence also indicates that the magnitude of the understatement is economically significant in relation to the recorded balance in the allowance account. It is estimated that the mean (median) understatement of the allowance for bad debts by IPO firms is approximately 40% (75%) and 35% (60%) of its recorded balance in the year before and year after IPO, respectively.
1. INTRODUCTION AND MOTIVATION Accounting information is provided to the investing public by firm managers who have situation specific incentives to alter the profile of that information. Although generally accepted accounting principles (GAAP) and external Advances in Accounting, Volume 19, pages 89–118. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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auditors reduce the amount of discretion exercised by firm managers, the accrual accounting system mandated by GAAP nonetheless affords them a substantial amount of accounting discretion. The purpose of this study is to explore whether managers of initial public offering (IPO) firms use the flexibility inherent in GAAP to systematically understate the allowance for bad debts in the two annual periods adjacent to IPOs in order to bolster earnings and assets.1 This study is distinguished from prior research on earnings management by IPO firms in the following ways. First, most prior studies focus on total accruals (Aharony et al., 1993; Friedlan, 1994; Teoh et al., 1998b), while this study focuses on a single accrual account. As discussed below, there are several advantages associated with focusing on a single accrual account rather than total accruals. Second, while Teoh et al. (1998a) examine the allowance for bad debts of IPO firms, they do so in the context of a broad analysis of earnings management by IPO firms and perform simple univariate tests on the allowance account. In contrast, our study is dedicated exclusively to analyzing the allowance for bad debts and we perform a focused, in-depth analysis of discretionary behavior with respect to this accrual account. For example, unlike Teoh et al. (1998a), we develop empirical models to estimate the discretionary component of the allowance for bad debts which remove the portion of the allowance that is dictated by accounts receivable and sales and the year-to-year change in those accounts. The univariate tests conducted by Teoh et al. (1998a) do not take these variables into account. Third, unlike Teoh et al. (1998a), we provide estimates of the economic significance of the discretionary component of the allowance for bad debts, and examine whether high quality auditors mitigate any tendency of IPO firms to understate this accrual account. Finally, we motivate our analysis of the allowance for bad debts by highlighting why this account is a likely target for manipulation.2 This study focuses on the allowance for bad debts of IPO firms for two primary reasons. First, the allowance for bad debts is likely to be a singularly material accrual account. Initial public offering firms usually experience unprecedented increases in both sales and accounts receivable in the periods surrounding IPOs so discretion over the allowance for bad debts could have a material impact on earnings and assets. In addition, anecdotal evidence in the financial press indicates that part of the increase in accounts receivable in the periods surrounding IPOs may be the result of managers accepting low quality credit sales. Hall and Renner (1988) suggest that firms may “cut a few corners” to register sales just before IPOs, and Khalaf (1992) suggests that IPO firms may try to “dress up their (accounting) numbers” before IPOs, particularly those related to sales. More generally, these and other articles (Browning, 1998; Schroeder, 1994) allege that IPO firms use the leeway in GAAP to inflate earnings and assets just before stock offerings.
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Second, the allowance for bad debts requires professional judgment to determine its balance, suggesting that managers have a substantial amount of discretion over this accrual account. While auditors likely scrutinize financial statement accounts involving managers’ subjective judgment, it is unlikely that they fully counteract intentional or unintentional bias in those accounts. Rather, auditors are likely to develop a range of acceptable values for judgmentally determined accounts and cannot insist upon a particular point estimate within that range (Arens & Loebbecke, 1996; Pany & Whittington, 1997; Robertson, 1996). If managers of IPO firms establish allowances that tend toward the lower end of that range, intentional or unintentional bias is likely to persist despite auditors’ efforts to counteract it. The results of this study are summarized as follows. In the two periods adjacent to IPOs, the evidence reveals that the discretionary component of the allowance for bad debts is negative, suggesting that the allowance is understated. Not only is the understatement statistically significant, but it is economically significant in relation to the recorded balance in the allowance account. It is estimated that the mean (median) IPO firm understates the allowance for bad debts by approximately 40% (75%) and 35% (60%) of its recorded balance in the year before and year after IPO, respectively. This finding is consistent with the claim that managers of IPO firms use the flexibility afforded by GAAP to bolster earnings and assets, and that auditors of IPO firms do not fully counterbalance intentional or unintentional bias in the allowance for bad debts. In addition, the evidence does not suggest that high quality auditors mitigate the general tendency of IPO firms to understate the allowance for bad debts. This finding suggests that the intensive monitoring provided by high quality auditors has little incremental effect on the accounts of IPO firms that involve highly subjective evidence. Furthermore, supplemental analyses reveal that the results are robust with respect to model specification, industry fineness, and the financial profile of existing public companies used to develop proxies for the non-discretionary component of the allowance for bad debts of IPO firms. Readers should note that economic significance is measured relative to the recorded balance in the allowance account. Economic significance could also be measured relative to total assets, in which case the understatement of the allowance account would be about one-half of 1% of total assets (see Panel A of Table 4, DALL2).3 This observation does not mean that the results reported in this paper are uninformative about whether earnings and assets of IPO firms are manipulated. Indeed, when the evidence reported in this study is considered in conjunction with the evidence reported in related studies, it is reasonable to conclude that earnings and assets are manipulated and that the allowance for 91
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bad debts is one instrument through which managers achieve their financial reporting objectives. Nonetheless, readers should be cautious about concluding that managers’ discretion over the allowance account alone has a material effect on earnings and assets. The remainder of this study is organized as follows. Section 2 reviews relevant prior research on earnings management. Section 3 discusses the motivations to manage accruals in the periods adjacent to IPOs and develops the hypotheses that are tested in this study. Section 4 provides the models used to estimate the discretionary component of the allowance for bad debts. Section 5 reports sample selection procedures, descriptive statistics, primary results, and supplemental analyses. Section 6 contains concluding remarks and discusses some limitations of this study.
2. PRIOR RESEARCH ON EARNINGS MANAGEMENT BY IPO FIRMS Studies on earnings management by IPO firms include Friedlan (1994), Teoh et al. (1998a, b), and Aharony et al. (1993). The results of Friedlan (1994) suggest that IPO firms record positive discretionary accruals in the period (interim or annual, whichever was the last reported) immediately preceding IPOs, and the results of Teoh et al. (1998a) suggest that IPO firms record positive discretionary accruals in the annual period following IPOs. Teoh et al. (1998a) also document an inverse relation between discretionary accruals in the period following IPOs and subsequent earnings, which suggests that discretionary accruals are opportunistic. Teoh et al. (1998b) find that the IPO firms which most aggressively record discretionary accruals in the period following IPOs are the ones that experience the most severe post-IPO stock price underperformance. Finally, Aharony et al. (1993) find weak evidence of earnings management by IPO firms in the annual period immediately preceding IPOs. Several studies on earnings management have also focused on a single accrual account. McNichols and Wilson (1988) focus on the provision for bad debts of industrial firms in three industries. Beaver and Engel (1996) focus on the allowance for loan losses in the banking industry. As part of a larger study on accruals, Teoh et al. (1998a) perform a limited analysis of the allowance for bad debts of IPO firms.4 Guidry et al. (1999) focus on the inventory reserve account in their study on earnings management by business unit managers. All of these studies find evidence of discretionary behavior with respect to the account examined.
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3. HYPOTHESES DEVELOPMENT 3.1. Motivations to Manage Accounting Information
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The primary source of information about an IPO firm is its offering prospectus. This document contains audited financial statements which are consistently mentioned as being useful in IPO pricing decisions (Richardson, 1976; Perez, 1984; Bartlett, 1988; Weiss, 1988; Bloch, 1989; Blowers et al., 1995). Under the plausible assumptions that: (1) managers are able to manipulate accounting information in the periods preceding stock offerings,5 (2) IPOs are valued, in part, by reference to information contained in the financial statements, and (3) underwriters and other investors do not fully adjust accounting numbers for bias or manipulation,6 it seems reasonable that accounting choices could have an effect on the offering proceeds received by the firm and its entrepreneurs. Thus, a primary motivation for firms to manage accounting information in the last annual period preceding IPOs (referred to as year 0 in the remainder of this paper) is to influence the perceptions of investors about firm value in order to obtain higher offering proceeds. Figure 1 illustrates the periods examined in this study and the relation between those periods and the IPO date. With respect to the first annual period after IPOs are consummated (referred to as year 1 in the remainder of this paper), Teoh et al. (1998a) discuss three reasons why firms might manage earnings. First, managers of IPO firms may be under pressure from underwriters and investors to meet verbal earnings forecasts (which are perhaps optimistic) made when marketing new issues. By meeting these forecasts, managers of IPO firms develop reputations with investors for reliability and potentially avoid lawsuits by disgruntled shareholders. Second, because there is a lock-up period for 180 days or longer after the offering date during which entrepreneurs agree not to sell their shares, managers may try to report higher earnings and asset values until the lock-up period expires to enhance their personal wealth. Third, underwriters practice
Fig. 1. Periods Examined in this Study. 93
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what is called “price stabilization” whereby they purchase stock of the IPO firm in the open market, often at the original issue price, to prevent or retard a decline in its open market price (Hanley et al., 1993). Since this activity is costly, underwriters might pressure IPO firms to manage accounting information after the IPO is consummated to support the stock price. 3.2. Understatement of the Allowance for Bad Debts 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
When credit is extended to a firm’s customers, there is uncertainty about whether collection will occur in the future. If uncollectible receivables are both probable and estimable, an allowance for bad debts must be recognized in accordance with Statement of Financial Accounting Standards (SFAS) No. 5 and the matching principle. Unfortunately, there is a substantial amount of inexactness and ambiguity inherent in the allowance for bad debts. As a result, this account poses special problems for both management and auditors because subjective evidence is used to establish its balance. Management is responsible for making the accounting estimates reflected in the financial statements and auditors are responsible for evaluating the reasonableness of those estimates. Auditors may gain satisfaction that the allowance for bad debts is adequate by, among other things, confirming accounts, evaluating the client’s credit policies, examining customer credit files, analyzing write-off experience, and examining cash collections. Despite a variety of auditing techniques and procedures, auditors must nonetheless rely to some extent on managements’ representations because the allowance for bad debts involves subjective evidence. As a result, auditors cannot entirely eliminate discretion in accounting estimates because subjective evidence is difficult or impossible to verify. In addition, Kreutzfeldt and Wallace (1986) document that accounts receivable is prone to error and that judgmental errors are more prevalent in this account than in other current asset accounts. Judgmental errors are consistent with attempts by management to manipulate earnings (DeFond & Jiambalvo, 1994). Moreover, auditors can only develop a range of acceptable values for an accounting estimate and usually cannot insist upon a particular point estimate within that range (Arens & Loebbecke, 1996; Pany & Whittington, 1997; Robertson, 1996). Indeed, auditors are likely to identify unreasonable accounting estimates but may not isolate instances where management introduces intentional or unintentional bias into accounting estimates that is not of an egregious or erroneous nature. For example, management may shade the allowance for bad debts towards the lower bound of a range of reasonable values. In such instances, auditors may find it difficult to support the position that the allowance
Do Initial Public Offering Firms Understate the Allowance for Bad Debts?
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for bad debts is materially understated. Moreover, auditors are not expected to substitute their judgment for that of management when auditing accounts involving subjective evidence. Auditing standards recognize the subjectivity inherent in many accounting estimates and the risks associated with them. For example, Statement on Auditing Standards (SAS) No. 57 suggests that the risk that management would or could misstate an account balance increases with the subjectivity involved in determining that balance. When accounting numbers involve judgment and subjective evidence, reasonable individuals can come to justifiably different conclusions given identical information. In addition, IPO firms pose special problems because they commonly have short operating histories and large increases in sales and accounts receivable, which exacerbate the conventional problems associated with auditing the allowance for bad debts. As a result, intentional or unintentional bias in the allowance for bad debts is likely to persist despite auditors’ efforts to counteract it. Collectively, the discussion in this section indicates that managers of IPO firms have the incentives, opportunity, and ability to manage the allowance for bad debts. This discussion gives rise to the following testable hypothesis (stated in alternative form): Hypothesis 1: The allowance for bad debts of IPO firms is understated (i.e. low relative to a benchmark for what the allowance for bad debts should be absent managerial discretion) in years 0 and 1. 3.3. Understatement of the Allowance and High Quality Auditors Researchers often argue that Big Five accounting firms provide higher quality audits than their non-Big Five counterparts, because Big Five firms have incentives to protect their investments in reputation capital (DeAngelo, 1981; Francis & Wilson, 1988). Consistent with the desire to protect their reputations, Big Five accounting firms are more likely to: (1) use audit tests and procedures capable of identifying understatements of the allowance for bad debts, (2) develop a narrower range of acceptable values for the allowance for bad debts, thereby reducing intentional or unintentional bias, and (3) report disagreements if the client fails to make necessary adjustments to the allowance for bad debts. Thus, although hypothesis 1 predicts that the allowance for bad debts will be understated in years 0 and 1, we expect that the understatement is less for firms audited by Big Five accounting firms than for firms audited by non-Big Five accounting firms.7 This discussion gives rise to the following testable hypothesis (stated in alternative form): 95
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Hypothesis 2: The understatement of the allowance for bad debts of IPO firms is less negative in years 0 and 1 for firms audited by Big Five auditors than for firms audited by non-Big Five auditors.
4. RESEARCH DESIGN 4.1. Measurement of the Discretionary Component of the Allowance 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Conceptually, the allowance for bad debts (ALL) can be partitioned into a discretionary (DALL) and non-discretionary (NALL) component: ALL = DALL + NALL.8
(1)
Because both DALL and NALL are unobservable, an estimate of one of the two components is needed. We develop two expectation models for the nondiscretionary component of the allowance for bad debts (NALL1 and NALL2) and use them along with ALL to derive the corresponding estimates of the discretionary component of the allowance for bad debts (DALL1 and DALL2). In developing the empirical models to estimate the discretionary component of the allowance for bad debts, we adopt a balance sheet emphasis because prior research (McNichols & Wilson, 1988) and auditing texts (Arens & Loebbecke, 1996; Pany & Whittington, 1997; Robertson, 1996) indicate that managers and auditors are more concerned with proper balance sheet valuation than with matching the provision to current revenues. However, as discussed in Section 5.4, we perform additional tests which incorporate income statement information into the models to assess the robustness of the results. The first expectation model assumes that the non-discretionary component of the allowance for bad debts of IPO firm i in year t is equal to the mean allowance for bad debts (stated as a percentage of gross trade accounts receivable (AR)) of existing public companies in the same industry (same two-digit SIC code), but excluding the IPO firm and all other firms that went public in the previous five years.9,10 The first expectation model is expressed as follows: NALL1it = mean(ALLjt/ARjt),
(2)
where j is a firm index for the industry estimation sample. Two-digit SIC code matching is used if the number of firms in the industry estimation sample is six or greater, otherwise one-digit SIC code matching is used.11 Prediction errors represent the discretionary component of the allowance for bad debts, DALL1, and are defined as follows: DALL1it = ALLit/ARit – NALL1it.
(3)
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The second expectation model assumes that the non-discretionary component of the allowance for bad debts of IPO firm i in year t is a linear function of gross trade accounts receivable and the year-to-year change in gross trade accounts receivable (⌬AR). To estimate this relationship, we use existing public companies in the same industry (same two-digit SIC code) as the IPO firm, but exclude the IPO firm and all other firms that went public in the previous five years, to estimate the following cross-sectional regression: 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
ALLjt/TAjt = ␣0t(1/TAjt) + ␣1t(ARjt/TAjt) + ␣2t(⌬ARjt/TAjt) + jt,
(4)
where TA is total assets.12 Ordinary least squares is used to obtain estimates a0t, a1t, and a2t of ␣0t, ␣1t, and ␣2t, respectively. Equation (4) is scaled by contemporaneous total assets in an attempt to reduce heteroskedasticity.13 Two-digit SIC code matching is used if the number of firms in the industry estimation sample is 15 or greater, otherwise one-digit SIC code matching is used.14 The non-discretionary component of the allowance for bad debts for IPO firm i in year t is defined as follows: NALL2it = a0t(1/TAit) + a1t(ARit/TAit) + a2t(⌬ARit/TAit).
(5)
Prediction errors represent the discretionary component of the allowance for bad debts, DALL2, and are defined as follows: DALL2it = ALLit/TAit ⫺ NALL2it.
(6)
This model assumes that the fitted allowance for bad debts is the amount necessary to state gross trade accounts receivable at its net realizable value and that the prediction errors primarily reflect managerial accounting discretion. In Eq. (4) the coefficient on AR is expected to be positive because a larger balance in gross trade accounts receivable should require a larger balance in the allowance for bad debts. The coefficient on ⌬AR is expected to be negative. Our intuition is that managers may not fully and immediately adjust the allowance for bad debts in response to year-to-year changes in accounts receivable. In other words, increments and decrements to accounts receivable are expected to have a smaller impact on the allowance for bad debts than the beginning balance in accounts receivable. This expectation is supported by research in psychology related to the anchor and adjustment heuristic (Tversky & Kahneman, 1974). Specifically, the anchor is the prior year allowance for bad debts and the adjustment is the increment or decrement to the opening balance of the allowance account. Research suggests that the anchor takes on great psychological importance in many decision contexts and that the adjustment to the anchor is often inadequate (Tversky & Kahneman, 1974), which supports our expectation that the yearto-year change in accounts receivable will have a negative coefficient. 97
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By including both AR and ⌬AR in Eq. (4), we attempt to remove the nondiscretionary component of the allowance for bad debts and isolate that portion of the allowance which reflects managerial discretion. As discussed later, the regression results support our expectations concerning the signs on AR and ⌬AR.15 4.2. Statistical Tests of the Discretionary Component of the Allowance 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
Tests of significance are computed using standardized (DALL2) and unstandardized (DALL1 and DALL2) prediction errors and percentage prediction errors from Eqs (3) and (6). Following Defond and Jiambalvo (1994), standardized prediction errors are computed as Vit = DALL2it/s(ejt),
(7)
where s(ejt) is the standard deviation of the error term from the cross-sectional regression estimated using existing public companies in the same industry as IPO firm i in year t. Parametric significance tests of the standardized prediction errors are computed as Zvt = ⌺Vit/[(⌺Ii ⫺ k)/(Ii ⫺ (k + 2))]1/2,
(8)
where Ii is the number of firms in the estimation portfolio for IPO firm i and k is the number of parameter estimates in the model. Both parametric and nonparametric tests of standardized and unstandardized prediction errors are reported. The unstandardized prediction errors measure the discretionary component of the allowance for bad debts as a percentage of either gross trade accounts receivable (DALL1) or total assets (DALL2). Percentage prediction errors measure the discretionary component of the allowance for bad debts as a percentage of the recorded balance in that account. They are defined for model 1 (PER1) and model 2 (PER2) as follows: PER1it = (ALLit/ARit ⫺ NALL1it)/ALLit/ARit,
(9)
PER2it = (ALLit/TAit ⫺ NALL2it)/ALLit/TAit.16
(10)
Percentage prediction errors provide a convenient way to assess the economic significance of the discretionary component of the allowance for bad debts because they express the magnitude of the understatement in relation to the recorded balance in the allowance for bad debts.17
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5. SAMPLE SELECTION, DESCRIPTIVE STATISTICS, AND RESULTS 5.1. Sample Selection
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A list of all IPOs occurring from 1980 through 1984 (n = 2,396) was obtained from Jay Ritter’s IPO database.18 Firms on this list had to be covered by Compustat in either year 0 or year 1 to be included in this study. In order to conduct the analyses relating to model 2 in year 0, we had to obtain financial statement information related to year –1. As Compustat begins coverage of IPO firms in year 0, data had to be manually collected from prospectuses. Microfiche copies of prospectuses were obtained from other researchers and the University of Texas-Austin library. Table 1 summarizes the sample selection procedures and the resulting sample sizes for the statistical tests. Table 1.
Description of Sample Selection. Model 1a b
Model 2
Year 0
Year 1
Year 0
Year 1
Number of IPOs occurring from 1980 through 1984 listed in Ritter’s IPO database
2,394
2,394
2,394
2,394
Less: Number of firms either not covered by Compustat or not reporting required data items
1,827
1,692
1,871
1,842
Less: Number of firms for which microfiche copies of prospectuses could not be obtained
––
––
223c
––
Number of firms used to test hypothesis 1
567
702
305
552
8
10
––
8
559
692
305
544
Less: Number of firms for which the auditor could not be determinedd Number of firms used to test hypothesis 2 a
Expectation models 1 and 2 are described in Section 4.1 of the text. Year 0 is the last annual accounting period before an IPO occurs and year 1 is the first annual accounting period after an IPO is consummated. c In order to conduct the analyses in year 0 for model 2, financial statement data for year –1 is required. Since Compustat does not begin coverage of IPO firms until year 0, microfiche copies of prospectuses had to be obtained in order to obtain the required data items. d Auditor type (Big Five/non-Big Five) was obtained from Ritter’s IPO database. Some firms had missing data for this variable so it was manually collected from microfiche copies of available prospectuses. Auditor type could not be determined for some sample firms because prospectuses were not available. b
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5.2. Descriptive Statistics
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Table 2 reports descriptive statistics for receivables-related variables of sample firms. The information in Table 2 is partitioned by empirical model (model 1 or model 2) and year (year 0 or 1), since a different number of firms is used for each model year. The mean (median) ratio of allowance for bad debts to trade accounts receivable is approximately 4% (2.4%) and 4.8% (2.7%) in years 0 and 1, respectively, for both models. The mean (median) allowance for bad debts as a percentage of total assets ranges from a low of 1.03% (0.53%) to a high of 1.24% (0.72%), depending on the year and model. The mean (median) ratio of trade accounts receivable to total assets in year 0 for both models is approximately 32% (29%) and the mean (median) ratio of trade accounts receivable to total assets in year 1 for both models is approximately 24% (21%). With respect to the latter two ratios, the cause of their decline between years 0 and 1 is the inclusion of IPO proceeds in total assets in year 1. Table 3 reports summary statistics for Eqs (2) and (4). With respect to mean (ALL/AR) (Eq. (2)), it is estimated using all firms in the same two-digit SIC code as the IPO firms. There are 567 and 702 sample firms in years 0 and 1 (see Table Table 2.
Descriptive Statistics for Receivables-Related Variables of Sample Firms. Model 1a
Allowance/Receivablesc Mean Median Standard deviation Allowance/Assets Mean Median Standard deviation Receivables/Assets Mean Median Standard deviation Number of observations a
Model 2
Year 0b
Year 1
Year 0
Year 1
0.0401 0.0242 0.0577
0.0478 0.0273 0.0752
0.0409 0.0265 0.0543
0.0470 0.0281 0.0657
0.0124 0.0063 0.0238
0.0103 0.0053 0.0182
0.0117 0.0072 0.0164
0.0108 0.0056 0.0192
0.3200 0.2934 0.2320 567
0.2361 0.2120 0.1597 702
0.3208 0.2952 0.1766 305
0.2449 0.2184 0.1611 552
Expectation models 1 and 2 are described in Section 4.1 of the text. Year 0 is the last annual accounting period before an IPO occurs and year 1 is the first annual accounting period after an IPO is consummated. c Components of these ratios are defined as follows: allowance is the allowance for bad debts; receivables is gross trade accounts receivable; assets is total assets. b
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1), respectively, resulting in 1,269 industry observations.19 Panel A reveals that the mean (median) fraction of trade accounts receivable reserved at the two-digit SIC code level is 5.55% (4.64%). With respect to matched(ALL/AR), it is estimated for each IPO firm by averaging ALL/AR of the two four-digit SIC code matches. The motivation for computing matched(ALL/AR) and using it to estimate the discretionary component of the allowance for bad debts of IPO firms is discussed in Section 5.4 (supplemental analyses). Panel A reveals that the mean (median) fraction of trade accounts receivable reserved at the four-digit SIC code level for industry matches is 6.04% (4.02%). A comparison of the descriptive statistics reported in Tables 2 and 3 reveals that IPO firms reserve a smaller fraction of their gross trade accounts receivable than their publicly-owned counterparts. Table 3.
Summary Statistics for Models 1 and 2. Quartiles
Variable
a
Mean
Std. Dev.
1st
2nd
3rd
Panel A: Summary Statistics for Model 1 (Eq. (2)) and Modified Model 1b Mean(ALLjt/ARjt) Number of observations Matched(ALLjt/ARjt)b Number of observations
0.0555 1,269 0.0604 1,269
0.0264
0.0395
0.0464
0.0625
0.0644
0.0243
0.0402
0.0693
⫺0.0001 0.01 0.0337 4.95 ⫺0.0532 ⫺3.36 0.48 34
0.0070 1.29 0.0412 7.45 ⫺0.0296 ⫺1.78 0.60 64
0.0209 3.07 0.0607 10.24 ⫺0.0059 ⫺0.30 0.70 131
Panel B: Summary Statistics for Model 2 (Eq. (4))c ␣0t (1/TAjt) t-statistic ␣1t (ARjt /TAjt) t-statistic ␣2t (⌬ARjt /TAjt) t-statistic Adjusted R2 Number of observations
0.0156 2.26 0.0513 7.85 ⫺0.0317 ⫺2.22 0.59 93
0.0357 6.82 0.0339 3.91 0.1056 3.31 0.17 74
a
Variables are defined as follows: ALL is the allowance for bad debts; AR is gross trade accounts receivable; TA is total assets; ⌬AR is year-to-year change in AR; j is a firm index for the industry estimation sample; t is a time index. b Mean (ALL/AR) and matched (ALL/AR) represent the mean non-discretionary component of the allowance for bad debts of all IPO firms in years 0 and 1 combined. With respect to mean (ALL/AR) for a particular IPO firm, it is estimated using all firms in the same two-digit SIC code as the IPO firm. See Section 4.1 of the text. With respect to matched (ALL/AR) for a particular IPO firm, it is estimated by averaging two four-digit SIC code matched firms. See Section 5.4 of the text. c Amounts are obtained by averaging regression statistics from industry-by-industry regressions in years 0 and 1. The number of observations represents the average number of firms used to estimate the industry-by-industry regressions.
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Panel B of Table 3 also reports summary regression statistics related to Eq. (4). These cross-sectional regression statistics were calculated by averaging relevant Eq. (4) amounts across industries represented in the sample. As expected, the average coefficient on AR/TA is positive and highly significant (average t-statistic 7.85). Also as expected, the average coefficient on ⌬AR/TA is negative and significant (average t-statistic –2.22). The independent variables in Eq. (4) explain much of the variation in the allowance for bad debts, as revealed by the average R2 of 59%. The strength of these results suggest that the independent variables in Eq. (4) control for factors driving the allowance for bad debts and that the difference between the observed allowance for bad debts of IPO firms and their fitted allowance for bad debts approximately isolates the discretionary component of the allowance for bad debts. 5.3. Primary Results Hypothesis 1 predicts that the allowance for bad debts of IPO firms is understated (i.e. the discretionary component of the allowance for bad debts is negative) in years 0 and 1. Prediction errors from model 1 (DALL1) and model 2 (DALL2) proxy for the discretionary component of the allowance for bad debts. Table 4 contains an analysis of prediction errors (Panel A) and percentage prediction errors (Panel B) relating to models 1 and 2 in years 0 and 1. The first two columns report prediction errors for model 1, the middle 2 columns report unstandardized prediction errors for model 2, and the last two columns report standardized prediction errors for model 2. The bottom two rows in each panel of Table 4 report parametric (t-test) and non-parametric (Wilcoxon test) p-values (one-tailed) for tests of whether the prediction errors are significantly negative. The results reported in Panel A of Table 4 reveal that the mean prediction errors are significantly negative in years 0 (–0.0155, p < 0.0001) and 1 (–0.0076, p < 0.0001) for model 1. Panel A also reveals that the mean unstandardized prediction errors are significantly negative in years 0 (–0.0083, p < 0.0006) and 1 (–0.0039, p < 0.0001) for model 2. Similar results are reported for mean standardized prediction errors in Panel A. Notice that the minimum and maximum values reported in Panel A are of a sufficient magnitude that they could have an undue influence on mean prediction errors. As a result, the nonparametric Wilcoxon test is used to assess whether median prediction errors are significantly negative. This test reveals that median prediction errors are significantly negative in years 0 (–0.0245, p < 0.0001) and 1 (–0.0227, p < 0.0001) for model 1. Panel A of Table 4 also reveals that median unstandardized prediction errors are significantly negative in years 0 (–0.0042,
Do Initial Public Offering Firms Understate the Allowance for Bad Debts?
Table 4.
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Analysis of Prediction Errors From Models 1 and 2 in Years 0 and 1. Prediction Errorsa Model 2 (DALL2, Unstandardized) Year 0 Year 1
Model 1 (DALL1) Year 0b Year 1
Model 2 (DALL2, Standardized)c Year 0 Year 1
Panel A: Prediction Errors
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Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valued Non-parametric p-valuee
⫺0.0155 ⫺0.0245 0.0604 ⫺0.4247 0.5775 143 424 0.0001 0.0001
⫺0.0076 ⫺0.0227 0.0765 ⫺0.2148 0.8858 186 516 0.0044 0.0001
⫺0.0083 ⫺0.0042 0.0437 ⫺0.6464 0.1136 88 217 0.0006 0.0001
⫺0.0039 ⫺0.0026 0.0221 ⫺0.1691 0.2216 191 361 0.0001 0.0001
⫺0.4330 ⫺0.7699 0.6642 ⫺1.0000 0.9500 88 217 0.0001 0.0001
⫺0.3561 ⫺0.6078 0.6976 ⫺1.0000 1.0000 191 361 0.0001 0.0001
⫺0.3619 ⫺0.3526 2.0265 ⫺17.5441 10.3299 88 217 0.0001 0.0001
⫺0.0969 ⫺0.1878 1.5101 ⫺8.8983 17.2969 191 361 0.0125 0.0001
Panel B: Percentage Prediction Errorsf Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valued Non-parametric p-valuee
⫺0.5197 ⫺0.8800 0.5983 ⫺1.0000 0.9545 143 424 0.0001 0.0001
⫺0.4812 ⫺0.8216 0.6210 ⫺1.0000 0.9628 186 516 0.0001 0.0001
a
⫺ ⫺ ⫺ ⫺ ⫺ ⫺ ⫺ ⫺ ⫺
⫺ ⫺ ⫺ ⫺ ⫺ ⫺ ⫺ ⫺ ⫺
Prediction errors are computed using the procedures described in Section 4.1 of the text. Standardized prediction errors and percentage prediction errors are computed as described in Section 4.2 of the text. b Year 0 is the last annual accounting period before an IPO occurs and year 1 is the first annual accounting period after an IPO is consummated. c Percentage prediction errors are not computed for these values because there is no obvious interpretation of the percentages. d The parametric p-values for unstandardized prediction errors and percentage prediction errors are one-tailed t-tests. The parametric p-values for standardized prediction errors are also one-tailed and are derived as described in Section 4.2 of the text. e The non-parametric p-values are obtained from one-tailed Wilcoxon tests. f Negative prediction errors and negative percentage prediction errors suggest that the allowance for bad debts is understated. Prediction errors and percentage prediction errors are defined in Sections 4.1 and 4.2 of the text, respectively.
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p < 0.0001) and 1 (–0.0026, p < 0.0001). Similar results are reported for median standardized prediction errors. Consistent with expectations, the results reported in Panel A of Table 4 strongly support the view that the allowance for bad debts is understated in years 0 and 1. While the discretionary component of the allowance for bad debts is statistically significant, it may not be economically significant. To assess economic significance, Panel B of Table 4 provides percentage prediction errors. These values express the discretionary component of the allowance for bad debts as a percentage of its recorded ending balance. The mean (median) percentage prediction error for model 1 is approximately ⫺52% (⫺88%) and ⫺48% (⫺82%) in years 0 and 1, respectively. Similarly, the mean (median) percentage prediction error for model 2 is approximately ⫺43% (⫺77%) and ⫺36% (⫺61%) in years 0 and 1, respectively. The mean and median percentage prediction errors are significantly negative ( p < 0.0001) for both models in both years.20 It is reassuring to note that both models yield plausible and similar estimates of the percentage by which the allowance for bad debts is understated. However, it is probably inappropriate to view percentage prediction errors as precise estimates. In addition, the percentage estimates from model 2 are arguably more reliable than those from model 1 because model 2 better controls for nondiscretionary factors that determine the allowance for bad debts. With these considerations in mind, we estimate that the mean (median) IPO firm understates the allowance for bad debts by approximately 40% (75%) and 35% (60%) of its recorded balance in the year before and year after the IPO, respectively. This discussion supports the contention that the allowance for bad debts of IPO firms is materially understated in the periods adjacent to IPOs. Hypothesis 2 predicts that the understatement of the allowance for bad debts of IPO firms is less negative for firms audited by Big Five auditors than for firms audited by non-Big Five auditors. To test this hypothesis, the analysis reported in Table 5 was conducted. The first two columns of Table 5 report and analyze prediction errors in years 0 and 1 of IPO firms audited by Big Five auditors, and the second two columns of Table 5 report and analyze prediction errors in years 0 and 1 of IPO firms audited by non-Big Five auditors. The last two columns of Table 5 report p-values for differences between mean and median prediction errors of IPO firms audited by Big Five and non-Big Five firms in years 0 and 1. Panels A and B of Table 5 report the tests of hypothesis 2 using prediction errors and percentage prediction errors, respectively, from model 1 (DALL1). In Panel A, the difference between means is insignificant in years 0 ( p = 0.7429) and 1 (p = 0.9851). The difference between medians is marginally significant
Do Initial Public Offering Firms Understate the Allowance for Bad Debts?
105
in year 0 (p = 0.0771) and insignificant in year 1 (p = 0.7594). Similar results hold for differences in percentage prediction errors reported in Panel B. Panel C and D test hypothesis 2 using prediction errors and percentage prediction errors, respectively, from model 2. In Panel C, the difference between means Table 5.
Analysis of Prediction Errors From Models 1 and 2 in Years 0 and 1 Partitioned by Auditor Type Prediction Errorsa
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Big Five Year 0b Year 1
Non-Big Five Year 0 Year 1
P-value for Diff. in Means/Medianse Year 0 Year 1
Panel A: Model 1 Prediction Errors (DALL1) Partitioned by Auditor Type Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valuec Non-parametric p-valued
⫺0.0168 ⫺0.0226 0.0515 ⫺0.4245 0.2448 104 297 0.0001 0.0001
⫺0.0138 ⫺0.0231 0.0505 ⫺0.1969 0.2205 123 359 0.0001 0.0001
⫺0.0123 ⫺0.0296 0.0791 ⫺0.1015 0.5775 35 123 0.0259 0.0001
0.0036 ⫺0.0220 0.1102 ⫺0.2148 0.8858 58 152 0.6358 0.0001
0.7429 0.0771
0.9851 0.7594
Panel B: Model 1 Percentage Prediction Errors (DALL1) Partitioned by Auditor Type f Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valuec Non-parametric p-valued
⫺0.5087 ⫺0.8003 0.5918 ⫺1.0000 0.8103 104 297 0.0001 0.0001
⫺0.5042 ⫺0.8306 0.6008 ⫺1.0000 0.8327 123 359 0.0001 0.0001
⫺0.5575 ⫺0.9056 0.6089 ⫺1.0000 0.9450 35 123 0.0001 0.0001
⫺0.4444 ⫺0.7911 0.6547 ⫺1.0000 0.9628 58 152 0.0001 0.0001
0.1923 0.0881
0.8789 0.7753
Panel C: Model 2 Prediction Errors (DALL2, Unstandardized) Partitioned by Auditor Type Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valuec Non-parametric p-valued
⫺0.0081 ⫺0.0041 0.0482 ⫺0.6464 0.1136 73 166 0.0051 0.0001
⫺0.0032 ⫺0.0026 0.0207 ⫺0.1001 0.2216 141 249 0.0013 0.0001
⫺0.0090 ⫺0.0043 0.0207 ⫺0.0930 0.0268 15 51 0.0004 0.0001
105
⫺0.0061 ⫺0.0028 0.0256 ⫺0.1691 0.1107 46 108 0.0019 0.0001
0.4157 0.2412
0.1065 0.1771
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Table 5.
Continued
Prediction Errorsa Big Five Year 0b Year 1
Non-Big Five Year 0 Year 1
P-value for Diff. in Means/Medianse Year 0 Year 1
Panel D: Model 2 Percentage Prediction Errors (DALL2, Unstandardized) Partitioned by Auditor Typef
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Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valuec Non-parametric p-valued
⫺0.4168 ⫺0.7001 0.6727 ⫺1.0000 1.0000 73 166 0.0001 0.0001
⫺0.3402 ⫺0.4665 0.6791 ⫺1.0000 1.0000 141 249 0.0001 0.0001
⫺0.4919 ⫺0.9035 0.6336 ⫺1.0000 1.0000 15 51 0.0001 0.0001
⫺0.4169 ⫺0.8083 0.6966 ⫺1.0000 1.0000 46 108 0.0001 0.0001
0.2084 0.2236
0.1241 0.1183
Panel E: Model 2 Prediction Errors (DALL2, Standardized) Partitioned by Auditor Type Mean ⫺0.2567 Median ⫺0.3315 Standard deviation 1.8971 Minimum ⫺14.8246 Maximum 10.3299 Number positive 73 Number negative 166 Parametric p-valuec 0.0001 Non-parametric p-valued 0.0001 a
⫺0.0305 ⫺0.7431 ⫺0.1655 ⫺0.4415 1.5926 2.4164 ⫺4.2643 ⫺17.5441 17.2969 3.8903 141 15 249 51 0.2776 0.0001 0.0001 0.0001
⫺0.2788 ⫺0.2282 1.3055 ⫺8.8983 3.9637 46 108 0.0003 0.0001
0.0671 0.1140
0.0310 0.0793
Prediction errors are computed using the procedures described in Section 4.1 of the text. Standardized prediction errors and percentage prediction errors are computed as described in Section 4.2 of the text. b Year 0 is the last annual accounting period before an IPO occurs and year 1 is the first annual accounting period after an IPO is consummated. c The parametric p-values for unstandardized prediction errors are derived using one-tailed t-tests. The parametric p-values for standardized prediction errors are one-tailed and are derived using the procedures described in Section 4.2 of the text. d The non-parametric p-values are obtained from one-tailed Wilcoxon tests. e The p-values for differences between means are obtained using one-tailed t-tests. The p-values for differences between medians are obtained using one-tailed Wilcoxon tests. f Negative prediction errors and negative percentage prediction errors suggest that the allowance for bad debts is understated. Prediction errors and percentage prediction errors are defined in Sections 4.1 and 4.2 of the text, respectively.
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is insignificant in years 0 (p = 0.4157) and 1 (p = 0.1065). The difference between medians is also insignificant in years 0 (p = 0.2412) and 1 (p = 0.1771). Qualitatively similar results hold for percentage prediction errors reported in Panel D. Finally, Panel E of Table 5 tests hypothesis 2 using standardized prediction errors from model 2. The difference between means is marginally significant in year 0 (p = 0.0671) and significant in year 1 (p = 0.0310). The difference between medians is insignificant in year 0 (p = 0.1140) and marginally significant in year 1 (p = 0.0793). Taken together, the evidence reported in all panels of Table 5 provides little support for the hypothesis that Big Five auditors mitigate the general tendency of IPO firms to understate the allowance for bad debts. 5.4. Supplemental Analyses The primary analyses reported in this paper are based on the two models developed in Section 4.1. This section discusses two important assumptions of those models and analyzes whether the inferences of this study are sensitive to them. First, we assume that industry can be accurately defined at the two-digit SIC code level, although there is significant diversity across firms within that industry definition. Second, we assume that Eq. (4) is properly specified, although the allowance for bad debts may be jointly determined by both accounts receivable and sales. Having highlighted these assumptions, the remainder of this section is devoted to analyzing whether the main results of this study are sensitive to them. We begin the analysis by selecting two industry matched firms for each IPO firm. These firms are chosen by identifying two existing public companies in the same four-digit SIC code as the IPO firm that have the closest ratio of trade accounts receivable to total assets (referred to as the AR ratio hereafter). The first public firm in these sets has an AR ratio just above the IPO firm’s AR ratio and the second public firm has an AR ratio just below the IPO firm’s AR ratio.21 This matching procedure is implemented in both years 0 and 1 and is implemented without replacement. In addition, unlike the main analysis which requires that existing public companies be at least five years old, the industry matched firms must only be at least two years old. This should help mitigate problems associated with using relatively mature public firms to estimate the non-discretionary component of the allowance for bad debts of IPO firms. With respect to the first expectation model, the non-discretionary (NALL1, see Eq. (2)) and discretionary (DALL1, see Eq. (3)) components of the allowance for bad debts were re-computed using the sets of industry matched firms rather than industry means. The benefits of this analysis are twofold. First, because we 107
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define industry at the four-digit level instead of the two-digit level, there is greater comparability between IPO firms and existing public companies. Second, because we match on the AR ratio, only existing public companies with financial profiles similar to that of IPO firms are used to estimate the nondiscretionary component of the allowance for bad debts of IPO firms. The results of the above analysis are reported in Table 6. Panel A analyzes model 1 prediction errors in years 0 and 1. The mean (median) prediction error is ⫺0.0185 (⫺0.0134) and ⫺0.0140 (⫺0.0135) in years 0 and 1, respectively. The mean (median) percentage prediction error is ⫺40.76% (⫺70.16%) and ⫺35.88% (⫺61.55%) in years 0 and 1, respectively. Not only are the prediction errors and percentage prediction errors in Table 6 significant in all cases (p < 0.0001), but they are comparable to those reported in Table 4. With respect to the main results discussed in Section 5.3 and reported in Table 4, we therefore conclude that they are unaffected by industry fineness and the financial profile of existing public companies used to estimate the nondiscretionary component of the allowance for bad debts. With respect to the second expectation model, we estimate several crosssectional regressions similar to Eq. (4) to evaluate whether the main results are sensitive to alternative model specifications. These regressions contain both IPO firms and the sets of industry matched firms selected according to the procedures described above. As shown in Table 7, these regressions include different combinations of trade accounts receivable and net sales and yearto-year changes in those accounts. The regressions also contain time fixed-effects (not reported) and a test variable (IPOD) which is coded as 1 for IPO firms and 0 for industry matched firms. The coefficient on IPOD is expected to be negative since IPO firms are expected to have lower fractions of the allowance for bad debts reserved than their publicly-owned counterparts. Panels A and B of Table 7 report the results of pooled cross-sectional regressions for years 0 and 1, respectively. Regressions in both panels are highly significant (p < 0.0001) and explain a substantial amount of the variation in the dependent variable. As expected, the coefficient on IPOD is significantly negative (p < 0.01, one-tailed) in year 0 for all specifications. Similar results are reported in Panel B of Table 7, although the coefficient on IPOD in the second and sixth regressions is only significant at about the 0.06 level (one-tailed). The coefficient on IPOD in the remaining regressions is significant at the 0.025 level (one-tailed) or better. These results suggest that alternative specifications of Eq. (4) are unlikely to yield results that are materially different from those reported in Table 4. Finally, we examine whether firm and offering characteristics of IPO firms are associated with the discretionary component of the allowance for bad debts
Do Initial Public Offering Firms Understate the Allowance for Bad Debts?
Table 6.
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Analysis of Model 1 (DALL1) Prediction Errors Using Industry Matched Firms.a Year 0b
Year 1
Panel A: Prediction Errors
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Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valuec Non-parametric p-valued
⫺0.0185 ⫺0.0134 0.0761 ⫺0.5398 0.5817 186 381 0.0001 0.0001
⫺0.0140 ⫺0.0135 0.0954 ⫺0.4962 0.8534 246 456 0.0001 0.0001
⫺0.4076 ⫺0.7016 0.6556 ⫺1.0000 0.9481 186 381 0.0001 0.0001
⫺0.3588 ⫺0.6155 0.6857 ⫺1.0000 0.9837 246 456 0.0001 0.0001
Panel B: Percentage Prediction Errorse Mean Median Standard deviation Minimum Maximum Number positive Number negative Parametric p-valuec Non-parametric p-valued
a Industry matched firms are chosen by identifying two existing public companies in the same fourdigit SIC code as the IPO firm that have the closest ratio of trade accounts receivables to total assets (referred to as the AR ratio). The first public firm in these sets has an AR ratio just above the IPO firm’s AR ratio and the second public firm has an AR ratio just below the IPO firm’s ratio. Using these sets of industry matched firms, mean (ALL/AR) is calculated for each IPO firm, which represents the non-discretionary component of the allowance for bad debts. See Section 5.3 of the text. b Year 0 is the last annual accounting period before an IPO occurs and year 1 is the first annual accounting period after an IPO is consummated. c The parametric p-values for prediction errors are derived using one-tailed t-tests. d The non-parametric p-values are obtained from one-tailed Wilcoxon tests. e Negative prediction errors and negative percentage prediction errors suggest that the allowance for bad debts is understated. Prediction errors and percentage prediction errors are defined in Sections 4.1 and 4.2 of the text, respectively.
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Table 7.
Regression Results for Alternative Model 2 Specificationsa,b Regression Coefficientsc ␣0
␣1
␣2
␣3
␣4
␣5
Adj. R2
F-Stat.
0.35
63.59
0.35
56.83
0.26
42.21
0.29
42.16
0.37
60.99
Panel A: Regression Results for Year 0 (n = 915)d ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2IPODit + it
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Coefficient t-statistic
0.0075 1.26
0.0054 ⫺0.0070 4.44 ⫺2.66
ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2(⌬ARit/TAit) + ␣3IPODit + it Coefficient t-statistic
0.0075 1.59
0.0570 ⫺0.0090⫺0.0063 4.59 ⫺1.30 ⫺2.42
ALLit/TAit = ␣0(1/TAit) + ␣1(SALEit/TAit) + ␣2IPODit + it Coefficient t-statistic
0.0082 ⫺0.0001 ⫺0.0047 5.77 ⫺0.98 ⫺2.74
ALLit/TAit = ␣0(1/TAit) + ␣1(SALEit/TAit) + ␣2(⌬SALEit/TAit) + ␣3IPODit + it Coefficient t-statistic
0.0074 0.0001 5.27 ⫺0.81
0.0075 ⫺0.0089 5.54 ⫺4.78
ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2(⌬SALEit/TAit) + ␣3IPODit + it Coefficient t-statistic
0.0068 1.30
0.0527 4.72
0.0065 ⫺0.0105 1.71 ⫺2.50
ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2(⌬ARit/TAit) + ␣3(SALEit/TAit) + ␣4(⌬SALEit/TAit) + ␣5IPODit + it Coefficient t-statistic
0.0069 0.0520 5.25 10.01
0.0020 ⫺0.0001 0.0067 ⫺0.0107 0.31 ⫺0.64 4.88 ⫺5.58
0.37
49.87
0.34
108.29
0.35
101.62
0.23
63.21
Panel B: Regression Results for Year 1 (n = 1,656)d ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2IPODit + it Coefficient t-statistic
0.0085 2.80
0.0583 ⫺0.0042 5.12 ⫺3.28
ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2(⌬ARit/TAit) + ␣3IPODit + it Coefficient t-statistic
0.0074 2.32
0.0657 ⫺0.0312⫺0.0022 5.61 ⫺2.31 ⫺1.50
ALLit/TAit = ␣0(1/TAit) + ␣1(SALEit/TAit) + ␣2IPODit + it Coefficient t-statistic
0.0105 6.81
0.0010 ⫺0.0025 2.82 ⫺2.01
Do Initial Public Offering Firms Understate the Allowance for Bad Debts?
Table 7.
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Continued
Regression Coefficientsc ␣0
␣1
␣2
␣3
␣4
␣5
Adj. R2
F-Stat.
0.26
64.00
0.36
106.39
Panel B: Continued ALLit/TAit = ␣0(1/TAit) + ␣1(SALEit/TAit) + ␣2(⌬SALEit/TAit) + ␣3IPODit + it
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Coefficient t-statistic
0.0097 6.35
0.0011 ⫺0.0001 ⫺0.0027 3.13 ⫺7.39 ⫺2.20
ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2(⌬SALEit/TAit) + ␣3IPODit + it Coefficient t-statistic
0.0078 2.75
0.0582 ⫺0.0001 ⫺0.0044 5.10 ⫺30.78 ⫺3.49
ALLit/TAit = ␣0(1/TAit) + ␣1(ARit/TAit) + ␣2(⌬ARit/TAit) + ␣3(SALEit/TAit) + ␣4(⌬SALEit/TAit) + ␣3IPODit + it Coefficient t-statistic
0.0066 2.25
0.0693 ⫺0.0339 ⫺0.0010⫺0.0001 ⫺0.0022 5.23 ⫺2.48 ⫺1.09 ⫺30.11 ⫺1.54
0.38
93.27
a
Regressions are estimated using IPO firms and their industry matches. Industry matches are chosen by identifying two existing public companies in the same four-digit SIC code as the IPO firm that have the closest ratio of trade accounts receivables to total assets (referred to as the AR ratio). The first public firm in these sets has an AR ratio just above the IPO firm’s AR ratio and the second public firm has an AR ratio just below the IPO firm’s ratio. See Section 5.4 of the text. b Regression coefficients are estimated using ordinary least squares. When the null hypothesis of homoskedasticity is rejected (p < 0.10), t-statistics are computed using the heteroskedasticityconsistent covariance matrix (White, 1980). Variables are defined as follows: ALL is the allowance for bad debts; TA is total assets; AR is gross trade accounts receivable; IPOD is a dummy variable coded as 1 for IPO firms and 0 otherwise; SALE is net sales; ⌬AR is the year-to-year change in AR; ⌬SALE is the year-to-year change in SALE; i is a firm index; t is a time index. c Regressions also include time fixed-effects (not reported). The coefficients on the year dummy variables are jointly significant in the third and fourth regressions of both panels A and B (p < 0.01). d Year 0 is the last annual accounting period before an IPO occurs and year 1 is the first annual accounting period after an IPO is consummated.
(DALL1 and DALL2). The firm and offering characteristics considered are retained ownership, age, risk, and offering size. Retained ownership is the percentage of equity retained after the offering by all previous shareholders. Age is the number of months from the date of incorporation to the offering date. Risk is the number of risk factors listed in the prospectus. Offering size is the ratio of total IPO proceeds to the firm’s book value. Retained ownership is expected to have a positive relationship with DALL1 and DALL2 because managers that sell larger fractions of their ownership concurrent with the IPO may be more likely to make accounting choices that 111
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bolster earnings at the time of IPO. Age is expected to have a positive relation with DALL1 and DALL2 because younger firms have short operating histories, making it more difficult for auditors to determine an appropriate balance for the allowance account. Risk is expected to have a negative relation with DALL1 and DALL2 because higher risk firms may be concerned with portraying strong financial performance to counterbalance investors’ perceptions of risk. Offering size is expected to have a negative relation with DALL1 and DALL2 because firms making relatively large offerings may be more reliant on the offering proceeds and may feel greater pressure to make incomeincreasing accounting choices. To examine whether firm and offering characteristics of IPO firms are associated with the discretionary component of the allowance for bad debts, we regressed retained ownership, age, risk, and offering size on DALL1 and DALL2 in years 0 and 1. The only variable that was significantly associated with DALL1 and DALL2 in any of the regressions was risk (p = 0.05). This finding suggests that the allowance for bad debts of risky IPO firms may be understated to a greater extent than IPO firms in general.
6. CONCLUDING REMARKS AND LIMITATIONS This study adds to a growing body of empirical accounting research which indicates that managers of firms respond to external stimuli (i.e. bonus plans, debt covenant violations, political scrutiny) by exercising discretion over reported accounting numbers. The results of this study are consistent with the view that managers respond to the incentives arising in connection with IPOs by making accounting choices that bolster earnings and assets. This study also documents that managers’ accounting response is not only statistically significant, but that it is economically significant in relation to the account examined. Interestingly, despite the incremental monitoring role commonly ascribed to high quality auditors, the evidence does not indicate that Big Five auditors mitigate the general tendency of IPO firms to understate the allowance for bad debts. Prior studies on earnings management by IPO firms have generally concluded that firm managers exercise discretion over accounting information reported to the investing public. These studies, however, must be interpreted with some caution because their methodologies are subject to some important limitations. The methodology used in this study, however, differs from those used in prior studies in that it focuses on a single accrual account of IPO firms rather than total accruals or accounting method choices. Importantly, the accrual account examined in this study was chosen because it is inherently subjective and is arguably representative of managers’ accounting responses to stock offerings.
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The results of this study are reassuring in that they are consistent with prior research, yet they are based on a methodology that is significantly different from that used in prior research. One benefit of focusing on one accrual account rather than total accruals is that researchers can specifically identify accounts over which managers exercise accounting discretion. As a result, this study should be of interest to practicing auditors since it provides evidence that IPO firms tend to understate the allowance for bad debts. Such knowledge could influence how auditors allocate their audit effort when examining the financial statements of IPO firms.22 This study should also be of interest to standard setters because it could help them assess the “. . . pervasiveness of earnings management and the overall integrity of financial reporting” (Healy & Wahlen, 1999). Further, the results of this study bring indirect evidence to bear on the merits of discretion versus uniformity. Should the accounting profession continue to afford managers substantial discretion over determining the appropriate balance in the allowance for bad debts or should it establish more stringent guidelines that prescribe to some extent how the allowance should be determined? Finally, the results of this study should be interpreted with the following limitations in mind. First, managers of IPO firms may initiate stock offerings during periods in which their firms are performing particularly well, suggesting that the allowance account of IPO firms is justifiably below that of their industry peers. On the other hand, Boyajian (1994) suggests that the allowance for doubtful accounts of IPO firms should be somewhat comparable to that of their industry peers. Further, anecdotal evidence in the financial press suggests that IPO firms may relax their credit policies prior to stock offerings in an effort to bolster sales (Hall & Renner, 1988; Khalaf, 1992). If the latter contention is correct, the allowance of IPO firms should actually exceed that of their industry peers, which biases our tests against finding that IPO firms understate the allowance. Nonetheless, the results of this study should be interpreted with some caution because we cannot disentangle the “firm performance” explanation for our findings from the “earnings management” explanation. Second, since the allowance for bad debts is only one accrual account over which managers have discretion, earnings management by IPO firms might exist but be targeted at other accrual accounts. DeAngelo (1988) points out that one could observe no unusual patterns in the discretionary component of the provision for bad debts when in reality earnings manipulation has taken place via total accruals. Conversely, one could observe unusual patterns in the discretionary component of the provision for bad debts and erroneously conclude that earnings manipulation has taken place because the discretionary component of other accrual accounts were not incorporated into the research design.23 113
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However, when the results of this study are considered along with the results of related studies on earnings management by IPO firms, this concern does not seem to be particularly compelling.
NOTES
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1. Throughout this paper, the term “allowance for bad debts” refers to the contraaccounts receivable account in the balance sheet. The term “provision for bad debts” refers to the expense account included in the income statement. Accounting textbooks use a variety of synonymous names for the allowance for bad debts and provision for bad debts such as “allowance for uncollectible accounts” and “uncollectible accounts expense,” respectively. 2. It should be noted that Teoh et al. (1998a) directed most of their attention towards examining total accruals and secondarily examined the allowance for bad debts. It was not their intention to perform an in-depth analysis like the one performed in this study. 3. Economic significance could also be measured relative to earnings. However, many IPO firms report negative earnings or earnings that are around zero making it difficult to interpret economic significance when the understatement is evaluated relative to earnings. 4. Section 1 discusses the analyses performed by Teoh et al. (1998a) and describes how this study extends and is distinguished from Teoh et al. (1998a). 5. The results and discussion contained in Teoh et al. (1998a and 1998b) strongly support this assumption. In particular, see Appendix B of Teoh et al. (1998b) for a detailed description of how companies can manage earnings. 6. The assumption that outsiders do not adjust accounting numbers for bias or manipulation does not seem unreasonable. For example, the results of Dechow et al. (1996) indicate that investors may not see through even the most aggressive forms of earnings management. In their study of firms subject to SEC scrutiny, the market initially valued the earnings of sample firms normally and only recognized aggressive reporting when the SEC pointed out the overstatement of earnings. 7. We do not suggest that Big Five accounting firms counteract all bias in the allowance for bad debts. Rather, we suggest that they mitigate a general tendency of IPO firms to understate this accrual account. 8. Variables related to Eq. (1) are discussed here in their unscaled form for convenience. As discussed later, these variables are scaled by either accounts receivable (expectation model 1) or total assets (expectation model 2). 9. Firms that went public within five years preceding the formation of industry estimation samples were excluded to ensure that they had no incentives to understate the allowance for bad debts in connection with IPOs. 10. Both models developed in this section are cross-sectional. This is because IPO firms do not report a sufficient number of yearly observations to estimate time-series models. Most IPO firms report two years of balance sheet information and three years of income statement information. 11. Admittedly, the requirement that at least six firms be included in every two-digit SIC code is somewhat arbitrary. The rationale for imposing this minimum requirement is that an outlying firm in a particular industry could have an undue influence on NALL1 if there is a small number of firms included in that industry.
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12. To evaluate the predictive ability of this model, we performed the following analyses. First, we selected the five industries (28XX, 35XX, 36XX, 38XX, 73XX) with the greatest representation in our IPO sample (IPO firms are not involved in the analyses). Second, we partitioned firms in each industry into an estimation sample and a holdout sample. The holdout sample consisted of every fifth firm in an industry while the estimation sample consisted of the remaining firms. Third, we estimated Eq. (4) on an industry-by-industry basis using firms in the estimation samples. Fourth, we computed and analyzed the prediction errors, as defined in Eq. (6), of the holdout sample. Our central concern is whether Eq. (4) yields unbiased prediction errors for the holdout sample. If Eq. (4) is unbiased, prediction errors for a sample of existing public companies will have mean/median values that do not differ from zero. For each of the five industries, parametric and non-parametric tests indicate that the prediction errors have mean and median values that do not differ significantly from zero (all p-values exceed 0.18). This analysis provides evidence that the results reported in this study are not induced by model misspecification. In addition to evaluating whether the prediction errors are unbiased, we also evaluated the absolute percentage prediction errors, as defined in Section 4.2. The mean and median absolute percentage prediction errors are approximately 40%, which is consistent with some diversity across firms in their credit and collection policies. Given evidence that the prediction errors are unbiased, the size of the absolute prediction errors means that we may not detect manipulation when it actually exists. In order to increase confidence in the results, we performed a variety of supplemental analyses as described in Section 5.4 which rely upon complementary methodology. All analyses yielded similar conclusions. 13. Two potential econometric problems arise in these cross-sectional regressions. The problems are biased standard error estimates resulting from heteroskedasticity and autoregressive error terms associated with using observations that are clustered on time and industry. We use the parameter estimates for predictive purposes rather than testing for statistical significance so these problems are not a major concern. As noted in Kmenta (1986), parameter estimates are unbiased in the presence of both of these econometric problems. 14. The rationale for the requirement that at least 15 firms be included in every twodigit SIC code is that using fewer than 15 observations could result in erratic regression coefficients. In addition, this requirement is consistent with closely related studies on earnings management. 15. There are two untested assumptions in the expectation models. First, they assume that the allowance for bad debts is influenced by industry factors. Second, they assume that the relationship between accounts receivable and the allowance for bad debts varies intertemporally. To test these assumptions, we performed the following analysis. A pooled cross-sectional and time-series regression with the allowance for bad debts (scaled by contemporaneous total assets) as the dependent variable and dummy variables for industry (two-digit SIC code) and time (year) as the independent variables was estimated. Both the industry (F-statistic = 5.79) and time (F-statistic = 23.61) dummy variables were significant at the 0.0001 level, thus supporting our assumptions. 16. In cases where ALL/AR (ALL/TA) is zero, PER1 (PER2) is set equal to 1 to avoid division by zero. Also, when PER1 or PER2 is greater than 1, they are set equal to 1 to avoid allowing extreme values (caused by division by a relatively small number) to have an undue influence on the results. 115
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17. The use of percentage prediction errors in statistical tests also overcomes a problem associated with using the prediction error metrics defined in Eqs (3) and (6). This problem is illustrated by the following example. Assume that firm A has a predicted scaled allowance of 0.095 and an actual scaled allowance of 0.085, and firm B has a predicted scaled allowance of 0.035 and an actual scaled allowance of 0.025. Based on the prediction error metrics defined in Eqs (3) and (6), the understatement of the allowance for bad debts of firm A is the same as that of firm B (⫺0.01 in both cases), despite obvious differences in the relative magnitude of the prediction errors. However, using Eqs (9) and (10), the percentage prediction error for firm A is ⫺12% while the percentage prediction error for firm B is ⫺40%. Because the error metrics defined in Eqs (3) and (6) and those defined in Eqs (9) and (10) are complementary, we use both error metrics to test the hypotheses. 18. There have been no changes in GAAP related to accounting for bad debts for industrial companies over the past 20 years. As a result, we believe that the results reported in this study are also representative of more recent time periods. 19. Note that there are 1,269 firm years in our IPO sample related to model 1 (Eq. (2)). Thus, there are 1,269 industry level observations used to determine NALL1 in Eq. (2) for the 1,269 IPO firms. 20. Percentage prediction errors (see Eqs (9) and (10)) are computed by dividing prediction errors by the observed balance in the allowance account. An alternative way to define percentage prediction errors is to divide prediction errors by the expected balance in the allowance account (NALL1 or NALL2). Because the results suggest that IPO firms understate the allowance, dividing prediction errors by the expected balance in the allowance account rather than the actual balance will result in smaller percentage prediction errors. The following schedule shows what the percentage prediction errors would have been if the alternative definition were used. Percentage prediction errors computed using the alternative definition are all significant at p < 0.0001.
Mean (as reported in Table 4) Mean (using alternative definition) Median (as reported in Table 4) Median (using alternative definition)
Model 1 (DALL1) Year 0 Year 1
Model 2 (DALL2) Year 0 Year 1
⫺0.5197 ⫺0.3385 ⫺0.8800 ⫺0.5196
⫺0.4330 ⫺0.2536 ⫺0.7699 ⫺0.4652
⫺0.4812 ⫺0.2805 ⫺0.8216 ⫺0.4796
⫺0.3561 ⫺0.1721 ⫺0.6078 ⫺0.3780
21. In cases where four-digit matches could not be found, firms were matched at successively broader industry definitions. In excess of 75% of sample firms were matched at the four-digit level. For approximately 20% of the sample firms we could not identify one match above and one match below the AR ratio for IPO firms. In such circumstances, both matches were either above or below the IPO firms’ AR ratio. 22. It should be pointed out that the way we measure economic significance may not correspond to the way auditors measure audit significance. We measure economic significance by reference to the recorded balance in the allowance account, while auditors probably measure audit significance by reference to net income or total assets. Thus, it is possible that auditors are aware that IPO firms tend to understate the allowance for bad debts, but do not require them to adjust the allowance because the understatement is not material in relation to total assets or net income.
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23. DeAngelo’s (1988) comments concern the provision for bad debts (an income statement account), while the focus of this study is the allowance for bad debts (a balance sheet account). Her comments apply with equal force to this study since the provision for bad debts has a direct impact on the allowance for bad debts. Note the following relationship: Allowance for bad debtst = Allowance for bad debtst–1 + Provision for bad debtst – Write-offs of accounts receivablet.
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We thank Jay Ritter for giving us access to his IPO database and John Friedlan, Chris James, William Megginson, and Kathleen Weiss Hanley for giving us access to some of the prospectuses used in this study. We also thank two anonymous reviewers, John Barrick, Cheryl Fulkerson, Jim Groff, Elaine Mauldin, Siva Nathan, Marshall Pitman, Jeff Quirin, Robin Radtke, and workshop participants at the University of Nebraska-Lincoln and the 1999 AAA Southeast Region Meeting for providing helpful comments.
REFERENCES Aharony, J., Lin, C., & Loeb, M. P. (1993). Initial public offerings, accounting choices, and earnings management. Contemporary Accounting Research, 10, 61–81. American Institute of Certified Public Accountants (AICPA) (1988). Auditing Accounting Estimates. Statement on Auditing Standards No. 57. New York: AICPA. Arens, A. A., & Loebbecke, J. K. (1996). Auditing: An Integrated Approach (7th ed.). Englewood Cliffs, NJ: Prentice-Hall, Inc. Bartlett, J. W. (1988). Venture Capital: Law, Business, Strategies, and Investment Planning. New York, NY: John Wiley and Sons, Inc. Beaver, W. H., & Engel, E. E. (1996). Discretionary behavior with respect to allowances for loan losses and the behavior of security prices. Journal of Accounting and Economics, 22, 177–206. Bloch, E. (1989). Inside Investment Banking. Homewood, IL: Dow Jones-Irwin. Blowers, S. C., Ericksen, G. K., & Milan, T. L. (1995). The Ernst and Young Guide to Taking Your Company Public. New York, NY: John Wiley and Sons, Inc. Boyajian, V. H. (1994). Early planning for your initial public offering. Securities Regulation Law Journal, 22, 67–77. Browning, E. S. (1998). IPOs often come dressed up with best figures, studies say. Wall Street Journal, (March 10), C1–C2. DeAngelo, L. E. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3, 183–199. DeAngelo, L. E. (1988). Discussion of evidence of earnings management from the provision for bad debts. Journal of Accounting Research, 26, 32–40. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70, 193–225. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13, 1–36.
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DeFond, M. L., & Jiambalvo, J. (1994). Debt covenant violation and manipulation of accruals. Journal of Accounting and Economics, 17, 145–176. Financial Accounting Standards Board (FASB) (1975). Accounting for Contingencies. Statement of Financial Accounting Standards No. 5. Stamford, CT: FASB. Francis, J. R., & Wilson, E. R. (1988). Auditor changes: A joint test of theories relating to agency costs and auditor differentiation. The Accounting Review, 63, 663–682. Friedlan, J. M. (1994). Accounting choices of issuers of initial public offerings. Contemporary Accounting Research, 11, 1–31. Guidry, F., Leone, A., & Rock, S. (1999). Earnings-based bonus plans and earnings management by business unit managers. Journal of Accounting and Economics, 26, 113–142. Hall, W., & Renner, A. (1988). Lessons that auditors ignore at their own risk. Journal of Accountancy, 166, 50–59. Hanley, K. W., Kumar, A. A., & Seguin, P. J. (1993). Price stabilization in the market for new issues. Journal of Financial Economics, 34, 177–197. Healy, P. M., & Wahlen, J. M. (1999). A review of the earnings management literature and its implications for standard setting. Accounting Horizons, 13, 365–383. Jones, J. J. (1991). Earnings management during import relief investigation. Journal of Accounting Research, 29, 193–228. Khalaf, R. (1992). Buyer, do thy homework. Forbes, (April 13), 47–48. Kmenta, J. (1986). Elements of Econometrics (2nd ed.). New York, NY: Macmillan. Kreutzfeldt, R. W., & Wallace, W. A. (1986). Error characteristics in audit populations: Their profile and relationship to environmental factors. Auditing: A Journal of Practice and Theory, 6, 20–43. McNichols, M., & Wilson, G. P. (1988). Evidence of earnings management from the provision for bad debts. Journal of Accounting Research, 26, 1–31. Pany, K., & Whittington, R. (1997). Auditing (2nd ed.). New York, NY: McGraw Hill, Inc. Perez, R. C. (1984). Inside Investment Banking. New York, NY: Praeger Publishers. Richardson, M. R. (1976). Going Public. London: Business Books. Robertson, J. C. (1996). Auditing (8th ed.). New York, NY: McGraw Hill, Inc. Schipper, K. (1989). Commentary on earnings management. Accounting Horizons, 3, 91–102. Schroeder, M. (1994). The Sherlock Holmes of accounting. BusinessWeek, (September 5), 48–52. Teoh, S. H., Wong, T. J., & Rao, G. R. (1998a). Are accruals during initial public offerings opportunistic? Review of Accounting Studies, 3, 175–208. Teoh, S. H., Welch, I., & Wong, T. J. (1998b). Earnings management and the long-run market performance of initial public offerings. The Journal of Finance, 53, 1935–1974. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131. Weiss, M. (1988). Going Public: How to Make Your Initial Stock Offering Successful. Blue Ridge Summit, PA: Liberty House. White, H. (1980). A heteroskedasticity-consistent covariance estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.
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COMMON UNCERTAINTY EFFECTS ON THE USE OF RELATIVE PERFORMANCE EVALUATION FOR CORPORATE CHIEF EXECUTIVES Leslie Kren
ABSTRACT Inter-manager comparisons should be more useful when manager-specific uncertainty and peer-group uncertainty are highly correlated because then the evaluator is better able to isolate the performance effects of the manager’s effort from the effects of common uncertainty (affecting all managers). The hypothesis proposed in this paper is that the degree of emphasis on relative performance should be related to the level of common uncertainty. The results provide support for the hypothesis but are sensitive to the measure of common uncertainty.
INTRODUCTION Corporate CEOs are often evaluated and paid on their performance relative to the performance of their competitors. Agency theory suggests that relative performance evaluation (RPE) can improve compensation contract efficiency by partially eliminating the effects of common uncertainty from an agent’s performance outcome. The performance effects of the agent’s effort can then be separated from the effects of external factors affecting the performance of Advances in Accounting, Volume 19, pages 119–138. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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all agents. Common uncertainty refers to the uncertainty affecting all managers in a reference group. For example, exchange rate risk is a common uncertainty for managers engaging in transactions denominated in a foreign currency. Intermanager comparisons of managers facing similar exchange rate filters out the effect of exchange rate fluctuations on the outcomes associated with a particular manager. Prior research has provided only mixed evidence about the descriptive validity of RPE theory. However, no prior attempt has been made to determine if RPE occurs more frequently when it is more useful for performance evaluation. Intuitively, inter-manager comparisons should be more useful when managerspecific uncertainty and peer-group uncertainty are highly correlated (Choudhury, 1986). In this case, the evaluator is better able to isolate the performance effects of the manager’s effort from the effects of common uncertainty (affecting all managers). In other words, when common uncertainty is high, the penalty imposed on a manager’s inability to match peer-group performance should be larger than when common uncertainty is low. The objective of this paper is to examine the descriptive validity of RPE across levels of common uncertainty. The hypothesis developed below suggests that RPE use should be positively related to common uncertainty because common uncertainty affects the informativeness of peer-group performance about a particular agent’s behavior (Janakiraman et al., 1992). Thus, the degree of emphasis on relative performance measures for executive compensation should be related to the degree of common uncertainty. The results, based on a longitudinal analysis of 241 firms over a ten-year period provide support for the hypothesis, but are sensitive to the operational definition of common uncertainty. The next section discusses the effects of common uncertainty on the usefulness of RPE and presents the hypothesis. Subsequent sections contain a description of the methodology and the results, and the final section contains a summary and conclusions.
DEVELOPMENT OF HYPOTHESIS In an agency setting where the agent’s behavior is unobservable, the principal obtains protection from moral hazard by basing compensation on realized output. RPE can improve the efficiency of an output-based contract because information about the performance of other agents provides incremental information about an agent’s unobservable behavior by revealing the effects of external factors that affect the performance of both managers (i.e. common uncertainty) (Dye, 1992; Holmstrom, 1982).
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To test the validity of RPE, researchers have examined whether CEO compensation is adjusted for industry performance. The expectation has been that CEO compensation and dismissal decisions will be based on performance relative to other firms in the CEO’s industry. Thus, in a regression of CEO compensation on firm and industry performance, the coefficient estimate on industry performance should be negative (holding firm performance constant). As noted by Gibbons and Murphy (1990), it seems reasonable to expect that industry or market shocks, such as the 1987 stock market crash, are filtered out of CEO compensation decisions. Research based on cross-sectional or pooled time-series analysis has provided support for RPE. Gibbons and Murphy (1990) analyzed executive cash compensation in a pooled regression of over 1,000 firms over a 13-year period. They interpreted their evidence as strongly in support of RPE theory. In another cross-sectional study, Morck et al. (1989) reported that replacement of the top management team was more likely in firms that under-performed their industries. They concluded that boards of directors faced with poor performance relative to industry encouraged friendly acquisitions. However, observers have questioned the validity of cross-sectional analysis of data that is pooled into one regression. Research evidence strongly indicates that regression coefficients are not statistically identical across firms so it is likely that specification errors will arise in cross-sectional or pooled regressions of executive compensation.1 Thus, it is probably inappropriate to pool CEO compensation samples into a cross-sectional regression (Janakiraman et al., 1992, note 2). The RPE question has also been addressed using longitudinal, within-firm analyses. In contrast, to the results of cross-sectional studies, longitudinal research has provided little support for RPE. Antle and Smith (1986) in a longitudinal analysis of executive compensation in 39 large firms found only weak evidence consistent with RPE theory in some of the firms in their sample. Similarly, Janakiraman et al. (1992) concluded that there was no evidence of RPE in a longitudinal analysis of CEO cash compensation in over 600 firms with an average of 15 years of compensation and performance data. This study extends prior longitudinal research by examining the effects of common uncertainty, which should affect the usefulness of RPE, on the prevalence of RPE. Given that uncertain environmental (state) factors and managerial effort jointly determine outcome performance, relative performance information will be informative only if agents face common uncertainty, because only then can one agent’s output provide information about another agent’s state uncertainty. In fact, as argued by Frederickson (1992), the informativeness of relative performance information should be directly related to the degree of common 121
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uncertainty (see also Holmstrom, 1979). This is because the informativeness of relative performance information must be reflected in the weight placed on the signal about peer group performance in the aggregated performance measure used to evaluate the agent. The weight placed on peer group performance should be greater when the informativeness of relative performance information is greater (Banker & Datar, 1989). Otherwise, if the contract parameters are held constant, the principal will not capture the improved risk-sharing benefits which will instead accrue to the agent. This is the primary hypothesis that will be examined in this paper: As the common uncertainty between firm and industry performance increases, industry performance will receive more emphasis in executive compensation.
METHODOLOGY Sample Selection The empirical analysis was based on CEO compensation data over the ten-year period from 1985 through 1994. Companies were retained in the sample if CEO compensation data was available from the annual Forbes salary survey and if required financial data, as described below, was available from the Compustat data file. These procedures produced a sample of 241 firms and 2,410 firm-year observations, representing a wide range of industries.2 Measurement of Industry For measures of industry performance and common uncertainty, industry is defined as all other firms listed on Compustat in the same 4-digit SIC as each sample firm. A sample firm was excluded in the calculation of its corresponding industry measure. One methodological problem with previous research has been obtaining historic SIC codes for sample firms and industry groupings. Previous research has generally relied on the Compustat database, which provides only the most recent SIC code. This problem affects classification of sample firms, as well as formulation of industry groupings. For this study, historic SIC codes were separately developed for each sample and industry firm using segmentlevel information contained on Compustat, which is reported in accordance with financial reporting requirements (AICPA, 1976). Standard and Poors assigns a primary SIC code to each reported segment of each firm listed in the Compustat database. For this study, the SIC code for the largest segment (in terms of sales) in which each firm operated in each year of the ten-year sample period was used as the primary SIC code for each sample and industry firm.3 Thus, both
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sample firms and industry groupings were readjusted each year to reflect industry changes.4 Measurement of Firm and Industry Performance
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Theory or evidence to identify explicit performance measures used to evaluate a particular CEO is not well developed. Agency theory provides little guidance on appropriate measures of performance, suggesting only that additional information about a manager’s reference group will incrementally improve performance evaluation (Holmstrom, 1979, 1982). Prior research has focused on a variety of accounting and stock market-based performance measures. Since performance measures based on accounting results frequently appear in proxy statements and are widely reported as measures of management’s’ performance, return on average assets (ROA), defined as net income divided by average total assets, was used in this study. This measure has been often used in previous research and has also been shown to be highly correlated with other accounting measures such as return on equity (Antle & Smith, 1986). Industry performance was similarly measured as the mean ROA for all other firms in each sample firm’s 4-digit SIC industry group.5 Measurement of Common Uncertainty Following prior accounting research, uncertainty is defined as change or variability in the organization’s external environment (Kren, 1992). Thus, the objective in developing a proxy for common uncertainty was to measure the relation between a sample firm’s external environment and the external environment faced by the firm’s peer group (presumably the industry group). Two variables were used to measure common uncertainty in this study. The first variable was constructed in two steps. In the first step, a measure of environmental uncertainty, based on a series of accounting variables used in prior literature, was constructed for each sample firm and its industry . Next, the correlation between firm-specific and industry uncertainty was calculated as the final measure of common uncertainty. Tosi et al. (1973) operationalized uncertainty using accounting variables along three dimensions: (1) market volatility, the coefficient of variation of net sales; (2) technological volatility, the coefficient of variation of the sum of research and development and capital expenditures divided by total assets; and (3) income volatility, the coefficient of variation of profits before taxes (used as a composite measure to capture other sources of volatility).6 The coefficient of variation (the variance standardized by the mean) is used because it allows comparisons across 123
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firms of different size. Tosi et al. (1973) argued that more stable patterns in these measures indicate more stable environments. In a later study, Bourgeois (1985) suggested using first differences of the Tosi et al. measure.7 Thus, for this study, the coefficient of variation of first differences of the Tosi et al. measure of market, technological, and income uncertainty were used to measure environmental uncertainty. The three variables were standardized and summed for each sample firm and for all other firms in each sample firm’s 4-digit SIC industry.8 The final measure of common uncertainty (the relation between the uncertainty faced by a sample firm and its industry) was the Spearman rank correlation between each sample firm’s uncertainty measure and the mean of the industry measure over the sample period. The second measure of common uncertainty was the Spearman rank correlation between each sample firm’s monthly common stock return and the mean of the industry monthly common stock return over the ten-year sample period. As noted above, industry was defined as all other firms listed on Compustat in the same four-digit SIC as each sample firm. Measurement of CEO Compensation Two different measures of CEO compensation are used in the subsequent analyses. Both measures are based on the Forbes salary survey. First, cash compensation is defined as salary, cash bonus, and deferred cash. Deferred cash is earned but not paid in the current period. The second measure, denoted ‘total compensation’, includes cash compensation plus restricted stock awards, stock options, payouts on long-term incentive plans, and thrift plan contributions.9 Empirical Procedures While new SEC requirements have increased proxy statement disclosures in recent years, the functional form of the performance-compensation relation is not disclosed. Moreover, compensation decisions are subject to undisclosed, ad-hoc adjustments by board of director compensation committees. Thus, explicit evidence about the use of RPE in CEO compensation decisions is not available and must be inferred from statistical analysis. The empirical procedure to evaluate the hypothesis that common uncertainty affects the relation between CEO pay and industry performance was conducted in two steps. In step 1, the relation between 4-digit SIC industry performance and CEO compensation (the pay-performance sensitivity) was estimated longitudinally for each firm in the sample. In step 2, additional analysis was conducted to determine whether variations in the relation between CEO compensation and
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industry performance (magnitude of the coefficient estimate) were explained by common uncertainty. A longitudinal analysis of pay-performance sensitivity (step 1 of the empirical procedure) was conducted separately for each firm because, as noted above, evidence indicates that coefficients of the pay-performance relation are not constant across firms and cross-sectional regressions are likely to be mis-specified (Janakiraman et al., 1992). Pay-performance sensitivity was estimated separately for each firm using regression (1). 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
CEO compensationijt
= 0 + 1i(ROAit) + 2i(industry ROAit) + ⌺ijEXECj + ijt,
(1)
This model is based on analysis in the literature showing that the optimal RPE contract is based on a linear combination of agent and peer-group performance (Banker & Datar, 1989; Janakiraman et al., 1992; Lambert & Larcker, 1987). The regression was run separately with each CEO compensation measure as the dependent variable. ROA and industry ROA were defined above. The variable EXEC was set to 1 if individual j is CEO of firm i and 0 otherwise. This specification allows the pay-performance relation (1i, 2i) to vary across firms and the intercept to vary across firms and executives. Thus, the slope is separately estimated for each firm, but the intercept can vary across CEOs within each firm-specific regression.10 This is intended to control for compensation level differences across CEOs within a firm, particularly when CEOs are changed.11 In this regression, 1 > 0 indicates that the CEO is rewarded for increasing ROA and RPE implies 2 < 0 because the CEO’s compensation should be negatively related to industry ROA, holding firm ROA constant.12 In step 2 of the analysis, 2i (the sensitivity of CEO pay to industry performance), was regressed on the measure of common uncertainty and other control variables using the following regression,13 2i = ␣0 + ␣1CUi + ⌺␣jCj + ij
(2)
Where CUi is the measure of common uncertainty and Cj represents a vector of control variables, described below. RPE theory implies that 2i < 0, and since CUi > 0, support for the hypothesis implies that ␣1 < 0. Control variables are included in the analysis because the CEO is able to make resource allocation decisions that shift the firm into lines of business in which he has a competitive advantage over potential candidates for his position. Thus, industry (or reference group) is not exogenously determined but can be changed by CEO actions. Dye (1992) argues that the value of RPE is inversely related to a manager’s ability to change his reference group (industry). The control 125
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variables are intended to proxy for the CEO’s ability to change the reference group to which his performance is compared. These control variables are financial leverage, free cash flow, firm growth, market share, CEO tenure, and industry. Both financial leverage and free cash flow proxy for the CEO’s ability to deploy resources to product lines that increase his job security. Stulz (1990), for example, proposed that debt pre-commits managers so it reduces managerial discretion (over cash flows), including investments in negative net present value (NPV) projects with high personal value to managers, such as projects to move the firm into industries in which the CEO has a competitive advantage. Similarly, Jensen (1986) suggests that free cash flow, defined as the excess available after funding of all positive NPV investments, increases managerial discretion. Leverage is measured as the book value of long-term debt divided by the market value of equity (see Natarajan, 1996). Consistent with Lehn and Paulsen (1989), free cash flow is measured as operating income before depreciation minus dividends divided by total assets, which is intended to measure operating cash flows not committed to claimants (Lang et al., 1991). Growth and market share are also included to proxy for the CEO’s ability to influence industry membership. High growth firms and those with larger market share are better able to drive out other firms in the industry and to limit new entry. Janakiraman et al. (1992), for example, suggest that managers in oligopolist settings have incentive to raise entry barriers, altering the nature of the RPE contract. Growth is measured as the growth rate in sales (⌬ln) over the ten-year sample period and market share is measured as the time series mean over the ten-year sample period of annual firm sales divided by total industry sales. CEO tenure is included since opportunities to influence industry membership increase with tenure. Tenure is measured using an indicator variable set to one if the CEO held the position over the entire ten-year sample period and zero otherwise. Thirty-one percent of sample firms had only one CEO over the ten-year sample period. Finally, industry membership is included to capture any remaining clustering of RPE by industry. Industry is measured as a series of indicator variables for the major industry groups in the SIC categorization. These are: natural resources, construction, manufacturing, transportation and utilities, wholesale trade, retail trade, financial services, and business services (see also note 2). Of the control variables, free cash flow, growth, market share, and tenure should be positively related to CEO discretion (i.e. the CEO’s ability to move the firm into industries where he has a competitive advantage) and leverage should be negatively related. One could argue that CEOs with higher discretion and thus greater control over their firm’s industry membership are more responsible for their ability to match industry performance and thus more weight should be placed on industry performance. On the other hand, it is also
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reasonable to assert that CEOs with greater discretion will move their firms into industries that better fit their management skills, reducing the information value about the focal CEO’s skill and effort in the measure of industry performance and reducing the weight placed on industry performance in the compensation contract. Overall, the sign of these control variables in regression (2) is an empirical question.
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Table 1 shows descriptive statistics for both measures of CEO compensation, sales (size), firm and 4-digit SIC industry ROA, and both measures of common uncertainty. The variables shown are for the last year in the observation period, although time-series means, averaged across firms and years, have similar distributions. For this table and all subsequent analyses, financial variables and CEO compensation are adjusted to 1994-constant dollars using the year-end Consumer Price Index. At the mean (median), cash compensation represents Table 1. Descriptive Statistics for CEO Compensation, Sales, Return on Assets, Industry Return on Assets, and Common Uncertaintya.
cash compensation (000s)b total compensation (000s)c sales ($B) ROA (%)d industry ROA (%)d common uncertainty based on stock pricee common uncertainty based on accounting variablesf
mean
median
sd
1,338 2,201 9.35 4.99 0.17
1,119 1,602 4.15 4.23 2.20
962 2,130 17.4 3.92 10.8
759 935 2.24 2.21 ⫺0.05
0.55
0.57
0.17
0.45
0.66
0.26
0.31
0.43
⫺0.03
0.59
a
Q1
Q3 1,610 2,558 9.28 6.96 3.73
All variables are adjusted to constant dollars using the Consumer Price Index. Salary plus bonus. c Cash compensation plus cash compensation plus restricted stock awards, stock options, payouts on long-term incentive plans, and thrift plan contributions. d Net income divided by average total assets. Industry ROA is the mean ROA for all other firms in each sample firm’s 4-digit SIC industry group e The Spearman rank correlation between each sample firm’s common stock return and the mean 4-digit SIC industry common stock return over the sample period. f The Spearman rank correlation between each sample firm’s accounting variable-based measure of uncertainty and the corresponding mean 4-digit SIC industry measure over the sample period. The accounting variables were market volatility (net sales), technological volatility (research and development plus capital expenditures divided by total assets), and income volatility (profits before taxes). b
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61% (70%) of total compensation.14 The larger magnitude of common uncertainty based on stock price compared to common uncertainty based on accounting measures suggests that mean industry stock price is much more closely related to firm stock price than industry accounting-based measures of uncertainty are related to firm accounting-based measures of uncertainty. Regression Analysis of the Pay-Performance Relation 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
The longitudinal regressions of the relation between CEO compensation and firm and industry performance were estimated separately for each firm, with separate intercepts for each CEO within each firm. Summary statistics for these regressions for the full sample are shown in Table 2. As expected, the mean Table 2. Descriptive Statistics for Firm-Specific Pay-Performance Regressions over 1985–1994a Modelb: CEO compensationijt = 0 + 1i(ROAit) + 2i (industry ROAit) + ⌺ijEXECj + ijt cash compensationc 1i 2i mean median  estimates > 0 first quartile third quartile Z-statistice independent observations required at p < 0.05f mean/median adj. R-square mean/median F-statistic
65.3 15.2 160 (66.4%) ⫺9.81 59.2 9.66*** 24 0.24/0.25 4.04/1.98
total compensationd 1i 2i
⫺5.19 ⫺0.10 119 (49.4%) ⫺25.9 20.5 ⫺0.36
3.81 3.51 152 (63.4%) ⫺4.14 16.8 4.65***
—
6
⫺7.11 0.28 124 (51.5%) ⫺14.3 4.88 ⫺1.29 — 0.16/0.14 4.47/1.46
a
Estimates shown are OLS, although consistent results were obtained estimating the regressions using the two-step full-transform method to control serial correlation (Harvey, 1981). b ROA is net income divided by average total assets; industry ROA is the mean ROA for all other firms in each sample firm’s 4-digit SIC industry group; the variable EXEC is set to 1 if individual j is CEO of firm i and 0 otherwise. c Salary plus bonus. d Cash compensation plus cash compensation plus restricted stock awards, stock options, payouts on long-term incentive plans, and thrift plan contributions. e
f
冘 N
Z = (1/冑N)
i=1
ti/[冑(ki/(ki ⫺ 2))].
Where ti is the t-statistic for firm i for 1 or 2, ki is degrees of freedom, and N is the number of sample firms.
Calculated as (Z/t)2, where t is 1.96 (p < 0.05). *** p < 0.01.
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and median 1 > 0 are statistically significant for both measures of CEO compensation (p < 0.01) indicating that CEOs were rewarded for increasing ROA. In addition, more than 60% of the firm-specific regressions for both compensation measures exhibited positive estimates for 1.15 RPE would predict that the 2 coefficient estimate would be negative, indicating that CEO compensation was negatively related to industry ROA, holding firm ROA constant. This is the result that has been reported in prior crosssectional research. However, the 2 estimate was not significantly negative for either compensation measure (p > 0.10) in Table 2. Moreover, approximately half of the firm-specific regressions exhibited positive estimates for 2.16 Overall, these results are not consistent with prior cross-sectional tests of RPE theory, but are consistent with prior longitudinal research which has failed to confirm a negative coefficient for 2. Failure to find a negative coefficient on 2 for the full sample is not inconsistent with the hypothesis in this study, however, which proposes that the magnitude of 2 is related to the level of common uncertainty. Thus, the hypothesis predicts that a significant negative coefficient on industry performance will be found only when common uncertainty is high because CEOs whose firms exhibit greater common uncertainty should be penalized more for their inability to match peer-group performance. The next step in the analysis is to examine the hypothesis of this paper that the magnitude of 2 is related to the level of common uncertainty. Common Uncertainty Effects on the Relation Between CEO Pay and Industry ROA The results of regression estimates of the effects of common uncertainty on the relation between CEO pay and industry performance [regression (2)] are reported in Table 3. Under RPE theory, 2 < 0 and since CU > 0, the hypothesis predicts ␣1 < 0. For common uncertainty based on stock price (models 2 and 3), the results reported in Table 3 are consistent with the hypothesis for both measures of CEO compensation. In model 2, ␣1 is significantly negative and remains negative after addition of the control variables (model 3). This is consistent with the hypothesis which suggests that the penalty imposed on the CEO’s inability to match industry performance will be larger when common uncertainty is high than when common uncertainty is low. The results reported in Table 3 are not consistent with the hypothesis when common uncertainty is measured using accounting metrics, although the point estimate is negative.17 These results indicate that as common uncertainty (measured using stock price) increases, the weight on 2 in the compensation function increases 129
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Table 3. Cross-Sectional Determinants of the Relation Between CEO Compensation and Industry Performance over the Period, 1985-1994 (asymptotic t-statistics in parenthesesa). Modelb: 2i = ␣0 + ␣1CUi + ⌺␣j(control variablesj) + i, where 2i is based on: CEO compensationijt = 0 + 1i(ROAit) + 2i(industry ROAit) + ⌺ijEXECj + ijt cash compensationc model 1
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intercept CU – common uncertainty based on stock price CU – common uncertainty based on accounting vars
model 2
model 3
model 1
model 2
model 3
43.2 40.7 ⫺53.2 121.6 251.7 (3.13***) (1.65) (⫺1.85) (2.16**) (1.76*) ⫺88.6 -60.7 ⫺312.7 ⫺216.4 (⫺2.97***) (⫺2.04**) (⫺2.72***) (⫺1.86*) ⫺16.7 1.26 ⫺18.7 30.3 (1.19) (0.11) (⫺0.38) (0.69) 1.59 (0.23)
control variables sales (size) financial leverage free cash flow growth market share CEO tenure industry dummies natural resources (SIC 0-1499) construction (SIC 1500-1800) manufacturing (SIC 1800-3999) transp/utilities (SIC 4000-4999) wholesale trade (SIC 5000-5199) retail trade (SIC 5200-5999) finance (SIC 6000-6799) F-stat. adj. R-square
total compensationd
1.50 0.00
6.95*** 0.02
0.00 (0.47) ⫺10.6 (⫺1.12) ⫺17.3 (⫺0.14) 12.5 (1.09) ⫺49.8 (-0.69) ⫺16.0 (⫺1.38)
⫺0.00 (⫺0.35) ⫺10.0 (⫺0.13) ⫺704.0 (⫺1.40) 36.5 (0.71) ⫺558.5 (⫺1.39) ⫺61.3 (1.16)
2.99 (0.013) 26.1 (1.03) ⫺5.37 (⫺0.43) ⫺52.7 (-3.02***) ⫺21.8 (⫺1.76*) 1.94 (0.11) 33.5 (1.73*)
⫺20.6 (⫺0.21) 242.5 (1.58) ⫺52.3 (0.93) ⫺241.6 (3.32***) ⫺91.2 (1.30) 52.9 (0.84) ⫺52.2 (0.42)
2.28*** 0.08
0.11 0.00
4.52*** 0.02
1.28 0.02
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Table 3.
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Continued.
a
Significance tests are based on the corrected covariance matrix according to White (1980). CU based on stock price is the Spearman rank correlation between each sample firm’s common stock return and the mean 4-digit SIC industry common stock return over the sample period; CU based on accounting measures is the Spearman rank correlation between each sample firm’s accounting variable-based measure of uncertainty and the corresponding mean 4-digit SIC industry measure over the sample period. The accounting variables were market volatility (net sales), technological volatility (research and development plus capital expenditures divided by total assets), and income volatility (profits before taxes); control variables are leverage (the book value of longterm debt divided by the market value of equity); free cash flow (operating income before depreciation minus dividends divided by total assets); growth (ln in sales over the ten-year sample period); market share (time series mean over the ten-year sample period of annual firm sales divided by total 4-digit SIC industry sales); CEO tenure (an indicator variable set to one if the CEO held the position over the entire ten-year sample period and zero otherwise); ROA is net income divided by average total assets; industry ROA is the mean ROA for all other firms in each sample firm’s 4-digit SIC industry group; the variable EXEC was set to 1 if individual j is CEO of firm i and 0 otherwise. c Salary plus bonus. d Cash compensation plus cash compensation plus restricted stock awards, stock options, payouts on long-term incentive plans, and thrift plan contributions. * p < 0.10; ** p < 0.05; *** p < 0.01. b
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(becomes more negative). Thus, RPE theory better represents the compensation function as common uncertainty increases when common uncertainty is measured as the correlation between firm and industry stock price. This can perhaps be more readily seen in Table 4 which shows the distribution of 2 estimates across sub-samples split at the median for common uncertainty measured using stock price.18 Only the 2 estimate for cash compensation is included in Table 4. When common uncertainty is high (above the median), the estimate of 2 is significantly negative (z-stat = ⫺2.48; p < 0.05) and over 60% of 2 < 0, consistent with predictions of the hypothesis. When common uncertainty is low (below the median), the mean/median point estimate of 2 is positive and marginally significantly (z-stat = 1.97; p < 0.10). These results suggest that compensation system designers focus on common uncertainty based on stock price more than accounting-based common uncertainty in determining the usefulness of RPE. This may indicate that the former encompasses a broader range of factors that affect all firms in an industry group and better reflects the underlying economics of common uncertainty than the latter. Thus, variations in industry stock price appear to provide more information about common shocks experienced by CEOs across an industry. An extreme example is the October, 1987 stock market crash, the effects of which could have been readily measured using industry stock price and included easily 131
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Table 4. Descriptive Statistics for Firm-Specific Pay-Performance Regressions over 1985-1994 for sub-samples split at the median of common uncertainty based on stock pricea Modelb: CEO compensationijt = 0 + 1i(ROAit) + 2i(industry ROAit) + ⌺ijEXECj + ijt
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common uncertainty above the medianc (n = 120) 1i 2i mean median  estimates > 0 first quartile third quartile Z-statisticd independent observations required at p < 0.05e
96.1 22.0 82 (68.3%) ⫺8.99 73.8 7.13*** 13
mean/median adj. R-square mean/median F-statistic
⫺12.8 ⫺9.05 46 (38.3%) ⫺48.0 15.5 ⫺2.48** 2
0.26/0.26 0.455/2.12
common uncertainty below the medianc (n = 121) 1i 2i 33.5 13.1 88 (72.7%) ⫺10.6 57.5 6.53*** 11
2.56 3.83 75 (62.0%) ⫺10.0 21.7 1.97* —
0.22/0.20 3.70/1.76
a
Estimates shown are OLS, although consistent results were obtained estimating the regressions using the two-step full-transform method to control serial correlation (Harvey, 1981). b Only the regressions for CEO cash compensation are shown in the table. ROA is net income divided by average total assets; industry ROA is the mean ROA for all other firms in each sample firm’s 4-digit SIC industry group; the variable EXEC is set to 1 if individual j is CEO of firm i and 0 otherwise. c The Spearman rank correlation between each sample firm’s common stock return and the mean 4-digit SIC industry common stock return over the sample period.
冘 N
d
Z = (1/冑N)
i=1
ti/[冑(ki/(ki ⫺ 2))].
Where ti is the t-statistic for firm i for 1 or 2, ki is degrees of freedom, and N is the number of sample firms.
e Calculated as (Z/t)2, where t is 1.96 (p < 0.05). * p < 0.10; ** p < 0.05; *** p < 0.01.
into CEO compensation contracts. Moreover, common uncertainty based on stock price may be more often used in contracting since industry stock price is easily measured, at minimal cost, with little potential for collusion by executives.19 The adjusted R-square statistics in Table 3 are small, calling into question the economic significance of these results. Table 5 provides some insight into the economic significance for CEO compensation. Cell entries in Table 5 were
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calculated following two steps. First, Table 3 estimates were used to calculate 2 at the first and third quartiles of common uncertainty (using Eq. (2), 2i = ␣0 + ␣1CUi + ⌺␣jCj + ij). Next, Table 2 estimates were used to calculate CEO compensation at the first and third quartiles of industry ROA (using Eq. (1), CEO compensationijt = 0 + 1i(ROAit) + 2i(industry ROAit) + ⌺ijEXECj + ijt). RPE theory predicts that as industry ROA increases, holding firm ROA constant, CEO compensation will decrease, reflecting a penalty imposed on the CEO’s inability to match better industry performance. Consistent with the hypothesis, Table 5 shows that this penalty is larger when common uncertainty is high than when common uncertainty is low. When common uncertainty is high, a CEO whose industry ROA was at the third quartile (holding firm ROA constant) was penalized $59,900 (6.3%) compared to a CEO whose industry ROA was at the first quartile. However, when common uncertainty is low, industry ROA has a much smaller effect on CEO compensation (an increase of $16,800; 0.12%).20
Table 5. Estimated Effect on CEO Cash Compensation of Common Uncertainty Implied by the Estimated Regression Coefficients Reported in Tables 2 and 3 (dollar amounts in 000s) high common uncertainty (Q3 = 0.67)
low common uncertainty (Q1 = 0.45)
high industry ROA (Q3 = 3.69%)
947.1
1,018.5
low industry ROA (Q1 = ⫺1.07%)
1,007.0
1,006.0
marginal effect on CEO compensation of a change in industry ROA from Q1 to Q3 (holding firm ROA constant)
⫺59.9 (6.3%)
16.8 (0.12%)
Cell entries were calculated using Table 3 coefficient estimates for cash compensation to calculate the 2 coefficient at the first and third quartiles of common uncertainty for the Table 2 model which is then used to calculate CEO cash compensation at the first and third quartiles of 4-digit SIC industry ROA. Firm ROA is set to the median.
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SUMMARY AND CONCLUSION
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This study extends previous research by identifying conditions that determine the descriptive validity of RPE theory. The results provide support for the hypothesis that the emphasis on relative performance measures for executive compensation is related to the degree of common uncertainty. Regression results indicated that the coefficient on industry performance in firm-specific CEO compensation functions varies with the degree of common uncertainty when common uncertainty is measured using the correlation between firm stock price and industry stock price. Thus, when common uncertainty is high, CEOs are penalized to a greater extent for failing to match peer-group performance than when common uncertainty is low. Results did not hold when common uncertainty was measured as the correlation between firm-level accounting measures of uncertainty and the corresponding industry uncertainty measures.
NOTES 1. The validity of cross-sectional analysis depends upon a homogeneity assumption across firms about several factors that affect compensation contract design, including personal attributes of the CEO, characteristics of the managerial labor market, and the firm’s production function (Lambert & Larcker, 1987). Even though these factors are unobservable, specification errors will arise in the estimates of cross-sectional regression models if they are omitted. A longitudinal within-firm analysis is appropriate to the extent that these factors are constant for a given firm over time but vary across firms at any point in time. Previous evidence indicates that coefficients of the pay-performance relation are not constant across firms (Antle & Smith, 1986; Defoe et al., 1989; Lambert & Larcker, 1987). In fact, the results of this study, discussed later, strongly supports assertions that regression coefficients are not statistically identical across firms. Thus, it would be inappropriate to pool the sample into a cross-sectional regression. 2. Two firms were in natural resources (sic 0-1499), 1 is in construction (sic 15001799), 122 are in manufacturing (sic 1800-3999), 47 are in transportation and utilities (sic 4000-4999), four are in wholesale trade (sic 5000-5199), 15 are in retail trade (sic 5200-5999), 44 are in financial services (sic 6000-6799), and six are in business services (sic 6800-9999). 3. Since an edition of the Compustat database only contains six years of segment data, archived editions of the database were obtained. 4. As a practical matter, this adjustment seemed to have little effect on the results. There were 183 firms in the sample that retained the same SIC code throughout the sample period. The subsequent analyses were repeated on this subset of firms with virtually identical results to those reported. 5. To check for the problem of heterogeneity within a four-digit SIC code, alternate analysis was also conducted using weighted means for each 2-digit industry reference group. Weights were based on the correlation of each sample firm’s ROA with each industry firm’s ROA. Thus, industry firms whose ROA was more ‘closely related’ to
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(more highly correlated with) a sample firm’s ROA received greater weight in calculation of mean industry ROA than industry firms whose ROA was not ‘closely related’ to (less highly correlated with) a sample firm’s ROA (Antle & Smith, 1986). The results using weighted means for industry were consistent with the reported results. 6. These measures were further verified by Snyder and Glueck (1982). 7 Bourgeois argued that a high but constant, and thus predictable, rate of change could produce a high coefficient of variation. However, it is not only the rate of change that creates uncertainty, but also the unpredictability of the change (see also Downey & Slocum, 1975; Milliken, 1987). Consequently, Bourgeois argued that the coefficient of variation of first differences provides a better measure of discontinuities. 8. A factor analysis of the time-series means (as well as the annual means) of the three resulting variables over the ten-year sample period for the entire Compustat data file revealed only one factor with an eigenvalue greater than one, suggesting that only one construct was measured so it seemed appropriate to sum the standardized variables. 9. The definition of total compensation used in the Fortune survey varies slightly over the years but consistently includes the components noted in the text. For stock awards and stock options where dates or share prices are not reported in the proxy statement, compensation amounts are calculated by Fortune based on the average share price during the fiscal year. 10. For this sample, 75 firms had one CEO over the sample period, 119 had 2 CEOs, 40 had 3 CEOs, six had 4 CEOs, and one firm had 5 CEOs. 11. Ordinary least squares estimates are used in the reported results, although reestimating regression (1) incorporating the two-step full-transform method to control serial correlation in the error term provided consistent results. The two-step fulltransform method is based on Harvey (1981): CEO compensationijt = 0 + 1i(ROAit) + 2i(industry ROAit) + ⌺ij(EXECj) + ijt, where ijt = ijt ⫺ ␣ijt⫺1. 12. The statistical significance of the regression parameters (1 and 2) was tested using the following Z-statistic described in Healy et al. (1987).
冘 N
Z = (1/冑N)
i=1
ti/[冑ki/(ki ⫺ 2)].
Where ti is the t-statistic for firm i associated with 1 or 2, ki is the degrees of freedom in the regression for firm i, and N is the number of sample firms. Under the null hypothesis that a parameter equals 0, this Z-statistic is a standard normal variate (Healy et al., 1987). This test assumes cross-sectional independence of coefficient estimates, however, so the Z-statistic will be overstated to the extent that there are unknown time-period effects causing correlation across firms. To evaluate the validity of the results, therefore, the minimum number of independent observations required to reject the null can be calculated as (Z/t)2, where t is the mean t-statistic from the firm-specific estimates of the relevant  coefficient (see Clinch & Magliolo, 1993). 13. For regression (2), asymptotic standard errors were calculated using the heteroskedasticity adjustment described in White (1980) for all significance tests, although the results are qualitatively similar without the White adjustment. 14. These proportions are consistent with prior research. For example, Murphy (1985) reported that cash represented about 70% to 80% of total compensation. In a later study Murphy (1998) finds that 70% of executive compensation is paid in cash. A Conference 135
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Board report (Peck, 1996) indicates that the prevalence of incentive compensation is increasing but that 90% of companies continue to pay incentives in cash. Finally, a CEO survey in the Chief Executive similarly finds that cash compensation represents the largest component of CEO pay (Weinberg, 1999). 15. A negative coefficient estimate for 1, however, does necessarily indicate contracting inefficiencies. As noted by Sloan (1993), a negative weight on a performance measure in a compensation function may merely indicate that the measure is sensitive to the noise in another performance measure and is included with a negative weight to shield the manager from the noise in the second measure. 16. When the regressions were run with industry ROA measured using weighted ROA (see note 5), the estimate for 2 was negative and statistically significant (z = ⫺3.30; p < 0.01). This may indicate that weighted mean industry ROA is a better measure of the reference group than unweighted mean ROA for the full sample. 17. Using weighted industry ROA (see note 5) yielded similar results to those reported in Table 3. None of the control variables yielded statistically significant coefficient estimates. 18. In the analysis reported in this table, four outliers were removed. These were CEOs that received extraordinary, one-time compensation awards that would confound examination of the hypothesis. For example, the CEO of Disney received a $202 million dollar stock award during one year of the sample period. 19. As argued by Choudhury (1986), executives who value personal relationships may not wish to compete, defeating the effectiveness of RPE (see also Dye, 1992). This is less likely to be a problem in the case of CEO compensation (Gibbons & Murphy. 1990). 20. The analysis was also repeated using weighted industry ROA (see note 5). Using weighted industry ROA, the estimated penalty incurred by a CEO in the high common uncertainty sub-sample was much larger than for a CEO in the low common uncertainty sub-sample. When common uncertainty was low, a CEO whose industry ROA was at the third quartile (holding firm ROA constant) was penalized $49,000 (5.0%) compared to a CEO whose industry ROA was at the first quartile. However, when common uncertainty is high, a CEO whose industry ROA is at the third quartile suffers a significantly larger penalty ($214,000; 19.9%), compared to a CEO whose industry ROA is at the first quartile.
ACKNOWLEDGMENTS The author gratefully acknowledges helpful comments by Paul Kimmel, Michael Schadewald, and the Accounting Research Workshop at the University of Wisconsin-Milwaukee on earlier drafts of this paper.
REFERENCES AICPA (1976). Financial Reporting for Segments of a Business Enterprise. FASB no. 14. Antle, R., & Smith, A. (1986). An empirical investigation of the relative performance evaluation of corporate executives. Journal of Accounting Research, 24(Spring), 1–39. Banker, R., & Datar, S. (1989). Sensitivity, precision, and linear aggregation of signals for performance evaluation. Journal of Accounting Research, 27, 21–39.
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Bourgeois, L. J. (1985). Strategic goals, perceived uncertainty, and economic performance in volatile environments. Academy of Management Journal, 28(September), 548–573. Choudhury, N. (1986). Responsibility accounting and controllability. Accounting and Business Research, 17(Summer), 189–198. Clinch, G., & Magliolo, J. (1993). CEO compensation and components of earnings in bank holding companies. Journal of Accounting and Economics, 16, 241–272. Defoe, V. J., Lambert, R. A., & Larcker, D. F. (1989). The executive compensation effects of equity-for-debt swaps. The Accounting Review, 64(1), 201–227. Downey, H. K., & Slocum, J. W. (1975). Uncertainty: Measures, research, and sources of variation. Academy of Management Journal, 18(September), 562–578. Dye. R. A. (1992). Relative performance evaluation and project selection. Journal of Accounting Research, 30, 27–52. Frederickson, J. (1992). Relative performance information: The effects of common uncertainty and contract type of agent effort. The Accounting Review, 67(4), 647–669. Gibbons, R., & Murphy, K. M. (1990). Relative performance evaluation for chief executive officers. Industrial and Labor Relations Review, 43(special issue), 30s–51s. Harvey, A. C. (1981). The econometric analysis of time series. New York, NY: John Wiley and Sons. Healy, P. M., Kang, S. H., & Palepu, K. G. (1987). The effect of accounting procedure changes on CEOs cash salary and bonus compensation. Journal of Accounting and Economics, 9, 7–34. Holmstrom, B. (1979). Moral hazard and observability. Bell Journal of Economics, 10(1), 74–91. Holmstrom, B. (1982). Moral hazard in teams. Bell Journal of Economics, 13(2), 324–340. Janakiraman, S. N., Lambert, R. A., & Larcker, D. F. (1992). An empirical investigation of the relative performance evaluation hypothesis. Journal of Accounting Research, 30(Spring), 53–69. Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and the market for takeovers. The American Economic Review, 76, 323–329. Kren, L., (1992). Budgetary participation and managerial performance: The impact of information and environmental volatility. The Accounting Review, 67(3), 511–526. Lambert, R. A., & Larcker, D. F. (1987). An analysis of the use of accounting and market measures of performance in executive compensation contracts. Journal of Accounting Research, 25(supplement), 85–125. Lang, L. H. P., Stulz, R. M., & Walking, R. A. (1991). A test of the free cash flow hypothesis. Journal of Financial Economics, 29, 315–335. Lehn, K., & Paulsen, A. (1989). Free cash flow and stockholder gains in going private transactions. Journal of Financial Economics, 24, 197–154. Milliken, F. J. (1987). Three types of perceived uncertainty about the environment: State, effect, and response uncertainty. Academy of Management Review, 12(January):133–143. Morck, R., Schleifer, A., & Vishny, R. W. (1989). Alternative mechanisms for corporate control. American Economic Review, 79, 842–852. Murphy, K. J. (1985). Corporate performance and managerial remuneration: an empirical examination. Journal of Accounting and Economics, 7, 11–42. Murphy, K. J. (1998). Executive compensation. Working paper. University of Pennsylvania. Natarajan, R. (1996). Stewardship value of earnings components: Additional evidence on the determinants of executive compensation. The Accounting Review, 71(1), 1–22. Sloan, R. G. (1993). Accounting earnings and top executive compensation. Journal of Accounting and Economics, 16, 55–100.
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Snyder, N. H., & Glueck, W. F. (1982). Can environmental volatility be measured objectively? Academy of Management Journal, 25(March), 185–192. Stulz, R. M. (1990). Managerial discretion and optimal financing policies. Journal of Financial Economics, 26, 3–27. Tosi, H., Aldag, R., & Storey, R. (1973). On the measurement of the environment: An assessment of the Lawrence and Lorsch environmental uncertainty subscale. Administrative Science Quarterly, 18(March), 27–36. United Shareholders Assoc. (1991). Executive compensation in corporate America in ’92. Weinberg, C. R. (1999). CEO compensation: Greed or glory?. Chief Executive, 147, 44–59. White, H. (1980). A heteroskedasticity consistent covariance matrix estimator and a direct test of heteroskedasticity. Econometrica, 48, 817–838.
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THE EFFECTS OF PROCEDURAL JUSTICE AND EVALUATIVE STYLES ON THE RELATIONSHIP BETWEEN BUDGETARY PARTICIPATION AND PERFORMANCE Chong M. Lau and Edmond W. Lim
ABSTRACT Prior studies suggest that budgetary participation is important to those subordinates who are evaluated by a high budget emphasis evaluative style. It enables them to influence their budget targets. This study, however, proposes that budgetary participation is needed only if subordinates perceive their organizations’ performance evaluation and reward allocation systems as unfair. In such situations, budgetary participation may be useful for communicating grievances and for rectifying unfairness. This suggests that budgetary participation may be more effective in enhancing managerial performance when procedural justice is low than when it is high. These expectations are supported by the results of the study.
INTRODUCTION Much research has been undertaken on the relationships between supervisory evaluative style (budget emphasis) and subordinates’ behavior and performance Advances in Accounting, Volume 19, pages 139–160. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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(Kren & Liao, 1988; Briers & Hirst, 1990; Lindsay & Ehrenberg, 1993; Otley & Fakiolas, 2000). The continuing stream of studies in this area and the related area of budgetary participation has resulted in what Brownell and Dunk (1991, p. 703) regard as “the only organized critical mass of empirical work in management accounting”, and what Lindsay and Ehrenberg (1993, p. 223) considered as “one of the relatively few areas in management accounting research where there has been any sequence of repeated studies”. Continuous research in these two related areas is not surprising, considering the importance of budgets and budgetary participation in the planning, control and performance evaluation systems of most contemporary organizations (Otley, 1999). Hence, there is strong justification to continue to extend the research in these areas. Prior studies on evaluative styles suggest that if subordinates are evaluated by a high budget emphasis evaluative style, they should be allowed high budgetary participation before improvement in performance is attainable (Brownell, 1982; Brownell & Dunk, 1991; Lau et al., 1995). This study proposes that procedural justice may affect this relationship among budget emphasis, participation and managerial performance. Specifically, it examines if the positive effect of budgetary participation on managerial performance in a high budget emphasis situation is conditional upon the extent of procedural justice. Additionally, it investigates if these moderating effects are found only in the high budget emphasis situation and not in the low budget emphasis situation. This suggests the existence of a significant three-way interaction among budget emphasis, participation and procedural justice affecting managerial performance. This research is consistent with the stream of research which indicates that the effects of supervisory evaluative style (budget emphasis) on subordinates’ behavior and performance are contingent upon a number of moderating variables. These include budgetary participation (Brownell, 1982), task uncertainty (Hirst, 1981, 1983), environment uncertainty (Govindarajan, 1984), business strategy (Govindarajan & Gupta, 1985), participation and task uncertainty (Brownell & Hirst, 1986), participation and task difficulty (Brownell & Dunk, 1991), locus of control (Frucot & Shearon, 1991), national culture (Harrison, 1992), personality (Harrison, 1993) and interpersonal trust (Ross, 1994). However, omitted from these studies is the moderating effect of procedural justice. Folger and Konovsky (1989) defined procedural justice as the perceived fairness of the means used to determine the amounts of reward the employees receive. It encompasses the employees’ perceptions of the fairness of all aspects of the organization’s process used by their superiors to evaluate their performance, communicate performance feedback and determine their rewards such as promotions and pay increases. The theoretical framework for research
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on procedural justice is based on prior research in the legal and psychology disciplines. The early work of Thibaut and Walker (1975), which focused on process control and outcome as the key variables affecting procedural justice, led to much research on the impact of these two factors on procedural justice judgments in legal settings. However, this seminal work of Thibaut and Walker (1975) was challenged by Leventhal (Leventhal, 1980; Leventhal et al., 1980) as being too restrictive in defining fairness criteria. According to him, apart from process control, there are other fairness criteria such as consistency, bias suppression, accuracy of information, correctability and ethicality. Greenberg and Folger (1983) also found the relationship between procedural justice and participation to be complex. Additional subsequent research also found both procedural justice and participation to be important moderating variables (e.g. McFarlin & Sweeney, 1992). With respect to management accounting, Lindquist (1995) found that process control (vote or voice) interacted with the fairness of budget (attainable or unattainable) to affect task and budget satisfaction. Libby (1999) found a significant interaction between participation (voice) and explanation affecting performance. These results, together with those of other studies, have led Lind and Tyler (1988, p. 175) to conclude that “procedural justice does affect performance, sometimes in a straightforward fashion and sometimes not”. In particular, they suggest that the organization’s performance evaluation systems, participatory programs and the subordinates’ perceptions of procedural justice are intricately linked to affect the subordinates’ behavior and performance. This provides the motivation and theoretical justification for the present study to examine if procedural justice interacts with budgetary participation and budget emphasis to affect the subordinates’ performance. Specifically, it investigates if the effects of budgetary participation on the subordinates’ performance are stronger when procedural justice is low than when it is high. It also investigates if these effects are confined only to the high budget emphasis situation. This suggests a threeway interaction among budgetary participation, procedural justice and budget emphasis affecting managerial performance. To-date, these relationships have not been previously explored. Figure 1 presents the model used to investigate these relationships.
HYPOTHESIS DEVELOPMENT Concept of Procedural Justice Early research on procedural justice was based primarily on dispute resolution in law. Based on such research, Thibaut and Walker (1978) generated a theory 141
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Fig. 1. Three-Way Interaction Among Participation, Procedural Justice and Budget Emphasis Affecting Performance.
which advocated that, for disputes involving strong conflicting interests, procedures which are in accordance with societal definitions of fairness, rather than objective criteria of fairness, should be used. This theory is important because it acknowledges that there are different criteria of fairness and that different procedures are needed to settle different types of dispute. But because the theory was based on the early research findings in legal setting, and since such research was primarily preoccupied with the effects of voice in dispute resolution, it had relatively restricted standard of fairness. Leventhal’s theory (Leventhal, 1980; Leventhal et al., 1980) extended the theory of Thibaut and Walker by suggesting that there are other standards of fairness besides outcome. These are consistency, bias suppression, accuracy of information, correctability, ethicality and representativeness. Consistency refers to the consistency in the application of procedures across persons and across time. Bias suppression refers to suppression of prior beliefs and doctrines in the application of procedures. Accuracy of information suggests that procedures should lead to decisions which are based on accurate information. Correctability means that there are avenues for correcting bad decisions. Ethicality suggests that procedures should conform to some standards of ethics and morality. Finally, representativeness suggests that the interests of subgroups should be considered. Leventhal’s theory is important because it does not restrict procedural justice to only the possible effects of participation and outcome. Participation is only one of the myriad of organizational factors which could influence the
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subordinates’ perceptions of procedural justice. Many other procedural justice criteria (e.g. consistency of application, accuracy of information) and the structural components of the organizational procedures (e.g. selecting agents to gather information, setting ground rules) may also influence procedural justice. Subsequent research also indicated that the weights assigned to different fairness criteria might vary across situations (Lind & Tyler, 1988). This means that procedural justice could be high even if participation is low, if other features of the procedures induce perception of high procedural justice. Similarly, procedural justice may be perceived as low even if participation is high if subordinates regard other justice criteria as more important than participation. For instance, Fry and Leventhal (1979) found that inconsistency in setting ground rules was regarded as a much more serious violation of procedural justice than representativeness (participation). Hence, in situations where the consistency criterion is violated, subordinates may perceive procedural justice as low even if participation is high. Procedural justice is therefore a different and a much broader concept and construct than participation. Participation, Budget Emphasis and the Role of Procedural Justice Hopwood (1972) suggested that a high reliance on accounting performance measures such as budget targets by superiors to evaluate their subordinates (high budget emphasis) may be associated with high job-related tension, dysfunctional behavior and poor job performance. Brownell (1982), however, suggested that budgetary participation might moderate the relationship between budget emphasis and managerial performance. Based on the principle of operant conditioning and balance theory, he theorized and found that a match between high (low) budget emphasis and high (low) budgetary participation is crucial for beneficial behavioral outcomes to occur. Hence, the organization’s interest is best served if subordinates, who are evaluated with a high budget emphasis evaluative style, are allowed high budgetary participation, whilst subordinates evaluated with a low budget emphasis evaluative style are allowed only low budgetary participation. A number of subsequent studies extended the research of Brownell (1982) by the inclusion of other moderating variables or other dependent variables. These include Hirst (1983) who studied the interaction between task uncertainty and budget emphasis; Brownell and Hirst (1986) who hypothesized and found a significant three-way interaction among budget emphasis, participation and task uncertainty affecting job-related tension; and Mia (1989) who addressed the interaction between task difficulty and budgetary participation. Several subsequent studies also provide further extensions (e.g. Dunk, 1989, 1990, 1993; 143
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Brownell & Dunk, 1991; Frucot & Shearon, 1991; Harrison, 1992, 1993; Lau et al., 1995; Lindquist, 1995). These studies, together with those related to procedural justice mentioned previously, provide the basis for the present study to propose that procedural justice is also likely to affect the interaction between budgetary participation and budget emphasis affecting managerial performance.
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High Budget Emphasis Situations The proposition that high budgetary participation is needed in high budget emphasis situations before favorable behavioral outcomes are possible (Brownell, 1982) implies that subordinates may feel disadvantaged if they are not allowed to participate in the budget target setting process. Subordinates may feel disadvantaged because they are deprived of the opportunities offered by budgetary participation to communicate their opinions and arguments to their superiors to ensure favorable outcomes. These assumptions may not always be true because the subordinates’ need for high budgetary participation in high budget emphasis situations is likely to be conditional upon their perceptions of procedural justice. As discussed previously, procedural justice literature suggests that, apart from budgetary participation, many other organizational factors could also influence the subordinates’ perception of procedural justice (Leventhal, 1980; Lind & Tyler, 1988). Hence, procedural justice could be high on account of factors such as consistency of application, bias suppression, accuracy of information, correctability and ethicality. If subordinates perceive procedural justice as high, meaning that they regard the process of allocation and determining their rewards as fair, budgetary participation may be unimportant because it is not needed to voice resentment or to rectify the process of allocation since it is already perceived to be fair. Second, it is also not needed to clarify the evaluative style and criteria because evaluative styles based on accounting information are relatively objective and unambiguous. Hopwood (1972, p. 173) found that subordinates have greater trust in evaluative styles based on accounting based criteria than nonaccounting styles. This means that in high budget emphasis situations, budgetary participation is likely to have little or no effects on performance when procedural justice is high. In contrast, if subordinates perceive procedural justice as low, there is likely to be a greater need for budgetary participation to ameliorate discontent and to remedy the unfair process. Budgetary participation provides the opportunity for the subordinates and their superior to communicate with each other, for the subordinates to voice their concerns regarding the unjustness of the procedures (Lindquist, 1995) and for the superior to provide explanation for the unjust process (Libby, 1999). This may help to appease and alleviate the
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resentment associated with low procedural justice and assist in aligning the subordinates’ interest with that of the organization. Libby (1999) found that consultative participation enhanced performance even if ultimately the superiors imposed unfair and unattainable budget targets, provided explanation for the unfair and imposed budgets are given to the subordinates. Similarly, Lindquist (1995) found that consultative participation enhanced budget satisfaction even if ultimately unfair and unattainable budget targets were set by the superiors and the subordinates’ preferences were not acceded to. Finally, Thibaut and Walker (1975) suggest that allowing individuals to voice their opinions enhances their satisfaction even though the ultimate outcomes may not be in the individuals’ favor. The above therefore suggests that budgetary participation is important to the subordinates if procedural justice is perceived by them to be low. Based on the above discussion, it is possible to conclude that budgetary participation is likely to have a stronger impact on performance when procedural justice is low than when procedural justice is high. This means that, when budget emphasis is high, there is likely to be a significant two-way interaction between budgetary participation and procedural justice affecting managerial performance. Since the impact of participation on managerial performance is expected to be high for low procedural justice and low for high procedural justice, a negative interaction is expected. The following hypothesis is therefore tested: H1: In high budget emphasis situations, there is a significant and negative two-way interaction between budgetary participation and procedural justice affecting managerial performance. Budgetary participation is more effective in enhancing managerial performance in low procedural justice situations than in high procedural justice situations. Low Budget Emphasis Situations In a low budget emphasis situation, subordinates are likely to be evaluated by multiple nonaccounting criteria, such as concern with quality, ability to get along with superiors and ability to handle the work force. Even though the recent interest in nonfinancial performance indicators (Kaplan, 1983; Kaplan & Norton, 1996) have led to the development of quantifiable nonfinancial performance criteria, these criteria are still likely to be much more subjective and ambiguous than accounting-based performance measures because nonfinancial criteria are generally much harder to quantify. In addition, with nonaccounting criteria, subordinates are usually required to satisfy more than one criterion (Kaplan & Norton, 1996), which therefore involves arbitrary assignment of 145
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weights to the different criteria. Thus, there is likely to be a higher degree of subjectivity and ambiguity associated with a low budget emphasis evaluative style than a high budget emphasis evaluative style. Hopwood (1972, p. 174) considered a nonaccounting evaluative style as ambiguous and characterized it as “rather vague . . . surrounded by a great deal of uncertainty . . . is difficult to clearly specify what constitutes good and bad performance, and . . . difficult to determine when improvement occurs.” Similarly, Ross (1994, p. 630) described a low budget emphasis evaluative style as “somewhat subjective . . . ambiguous and difficult to measure” and are subject to “a superior’s biases and idiosyncrasies”. In view of the subjectivity and ambiguity associated with a low budget emphasis evaluative style, budgetary participation may serve as an important avenue for the subordinates to seek information and clarification on performance evaluation criteria. This means that budgetary participation is likely to be important in low budget emphasis situation, regardless of the levels of procedural justice. Consequently, the significant two-way interaction between procedural justice and participation affecting managerial performance, predicted above for the high budget emphasis situation, is unlikely to be found in the low budget emphasis situation. The following hypothesis is therefore tested: H2: In low budget emphasis situations, budgetary participation and procedural justice do not interact to affect managerial performance.
METHOD The data collection involved a survey questionnaire. Seventy manufacturing companies, each employing more than 100 employees, were randomly chosen from the list of manufacturing companies published in Kompass Australia (1997). Questionnaires were mailed to 200 functional heads. A follow-up letter was sent to each manager who had not responded after three weeks. Seventeen managers indicated that their companies were no longer involved in manufacturing and hence were removed from the sample. Of the remaining 183 questionnaires, a total of 85 were returned. Two responses were excluded from the study due to the failure of the respondents to complete the whole questionnaire. Hence, the remaining 83 useable responses constitute a response rate of 45.4%. In order to ascertain whether a non-response bias existed, a t-test was undertaken for each of the variables used in this study by splitting the sample into two halves, the first half comprising the earliest 50% responses and the
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second half comprising the latest 50% responses received. Oppenheim (1966) suggested the possibility that similarity exists between respondents who reply late and non-respondents. If later responses differ significantly from earlier ones, it can be concluded that non-response bias may be present. As no significant differences were found for any of the variables in this study, it can be concluded that the sample was not biased by non-responses. The mean age of the respondents was 46.2 years and the respondents had held their current positions for an average of 5.7 years. On average, the respondents had 14.5 years of experience in their area of responsibility and were responsible for 112 employees. Sixty one percent of them had either tertiary or professional qualifications. These data indicate that the respondents were highly experienced, held highly responsible positions and generally highly educated and qualified. Table 1 presents the descriptive statistics for the independent variables of budget emphasis, budgetary participation and procedural justice, and the dependent variable of managerial performance. The Pearson Correlation Matrix of budgetary participation, procedural justice, budget emphasis and managerial performance is presented in Table 2. Table 1. Variables Budget emphasis Budgetary participation Procedural justice Performance
Table 2.
Descriptive Statistics.
Mean
Standard deviation
12.0964 33.9036 14.9518 5.9036
1.9731 7.2509 3.1231 0.7261
Theoretical range Min Max 2 6 4 1
14 42 20 7
Actual range Min Max 5 6 4 4
14 42 20 7
Correlation Matrix Among Independent and Dependent Variables. Budget Emphasis
Budgetary Participation Procedural Justice Managerial Performance
0.225* 0.254* 0.126
* p < 0.05 ** p < 0.01.
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Budgetary Participation
Procedural Justice
0.297** 0.445**
0.267*
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MEASUREMENT INSTRUMENTS
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Budgetary participation. This variable was measured using a six-item, sevenpoint Likert-type scale instrument developed by Milani (1975). This construct assesses the degree of subordinates’ participation in the budget and includes several aspects of participation – involvement, superior’s explanation for changes, frequency, influence, and importance of subordinates’ inputs. This instrument had been used extensively in studies on budgetary participation (e.g. Brownell, 1982; Brownell & Hirst, 1986; Chenhall & Brownell, 1988; Mia 1989; Brownell & Dunk, 1991; Harrison, 1992, 1993) and they had consistently reported high Cronbach alpha values. The Cronbach alpha for this study was 0.90, indicating high internal reliability. All six items also loaded satisfactory on a single factor (Eigenvalue = 4.086; Total variance explained = 68.10%). Procedural justice. The four-item instrument developed by McFarlin and Sweeney (1992) was employed to measure the subordinates’ perceptions of procedural justice. Respondents were requested to rate the fairness of the procedures used to evaluate their performance, communicate performance feedback, and determine their pay increases and promotion, on a five-point Likert scale. An overall measure of procedural justice was obtained by summing up responses to the four individual items. A Cronbach alpha of 0.89 was obtained for this study, which is comparable to the 0.88 obtained by McFarlin and Sweeny (1992). The factor analysis extracted only one factor with an eigenvalue greater of than one (Eigenvalue = 3.024; Total variance explained = 75.60%). This supports the unidimension of this instrument. Budget emphasis. In assessing budget emphasis, Hopwood’s (1972) instrument was used. This instrument has been widely employed in this research area (Otley, 1978; Brownell, 1982; Brownell & Hirst, 1986; Brownell & Dunk, 1991; Harrison, 1992; Lau & Buckland, 2000). Only the two accounting based items of “concern with costs” and “meeting the budget” were combined to obtain the overall score for budget emphasis. The use of the rating form and the summing of these two items permit budget emphasis to be operationalized “along a continuum from accounting to non-accounting style of evaluation” (Brownell & Dunk, 1991, p. 702). A high combined score indicates high budget emphasis while a low combined score represents low budget emphasis. Brownell (1985) argued that it is justifiable to sum these two items if they are highly correlated. For this study, these two items have a relatively high coefficient of correlation of 0.56 (p < 0.01).
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Managerial performance. Managerial performance was evaluated by the ninedimensional Mahoney et al. (1963, 1965) self-rating measure used extensively by many prior studies (e.g. Brownell, 1982; Brownell & Hirst, 1986; Brownell & McInnes, 1986; Brownell & Dunk, 1991; Kren, 1992; Lau et al., 1995; Lau & Tan, 1998). This measure comprises eight dimensions of performance and a single overall performance rating. Brownell (1982, pp. 17–18) argued that “the nine-dimensional structure of the measure clearly captures the multidimensional nature of performance without introducing the problem of excessive dimensionality”. In line with prior studies, the overall performance rating was used as a measure of managerial performance. In their developmental work, Mahoney et al. (1963, 1965) concluded that the eight dimensions of the performance measure should be independent and explain about 55% of the variance in the overall rating, while the remaining 45% was attributed to job-specific factors. To test for this criterion, the overall rating is regressed on the ratings of the eight dimensions. The regression provided a coefficient of determination of 0.52 (p = 0.001) which is close to the 0.55 suggested by Mahoney et al. (1963, 1965). Consequently, the overall measure was used in the analysis as the measure of managerial performance.
RESULTS AND DISCUSSION Test of Hypotheses H1: High Budget Emphasis Situations Recall that hypothesis H1 relates to the high budget emphasis situations and, hypothesis H2 relates to the low budget emphasis situations. In order to test these two hypotheses, budget emphasis was dichotomized at its mean to obtain a high budget emphasis subsample of 41 respondents and a low budget emphasis subsample of 42 respondents. The approach of dichotomizing the sample with the mean is consistent with a number of prior studies (e.g. Dunk, 1993; Lau et al., 1995; Nouri & Parker, 1996). Moreover, as the means of the variables in this study are very close to the medians, the results of this study based on the means are also similar to those based on the medians. Regression models were used to test the hypotheses. To analyze the main effects and the two-way interactive effect between participation and procedural justice in each of the low and high budget emphasis situations, the following regression models were used: Yi = c0 + c1Pi + c2Ji + ei
(1)
Yi = c0 + c1Pi + c2Ji + c3PiJi + ei
(2)
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where Yi = managerial performance, Pi = budgetary participation, Ji = procedural justice and ei = error term. Equations (1) and (2) were used to analyze the main effects and the two-way interaction effect, respectively. Tests undertaken indicate that the inherent assumptions of the regression models used in this study were satisfied. The results of the main effects Eq. (1) and the two-way interaction Eq. (2) between participation and procedural justice for the high budget emphasis subsample are presented in Table 3. As expected, the results indicate that the coefficient c3 for the two-way interaction between participation and procedural justice on managerial performance is significant (est. = ⫺0.015; p < 0.022). The R2 of the interactive model is 28.1%, which is considerably higher than that of the additive model (19.7%). A comparison of Eq. (1) and Eq. (2) shows that the introduction of the interaction term causes the adjusted R2 to increase by 6.9% from 15.4% to 22.3%. These results provide support for hypothesis H1, that participation interacts with procedural justice to affect managerial performance in high budget emphasis situations. In addition, these results are also consistent with the expectation that high procedural justice is associated with improved managerial performance in high budget emphasis situations. This is evidenced from coefficient c2 for procedural justice, which is positive and significant (est. = 0.064; p < 0.028; Eq. (1)). The results in Eq. (2) do not indicate whether participation contributes positively to managerial performance across all level of procedural justice as “merely inspecting the signs and magnitudes of regression coefficients is Table 3. Results of Regression of Managerial Performance on Budgetary Participation and Procedural Justice: High Budget Emphasis Subsample (n = 41).
Variable Constant Budgetary participation (P) Procedural justice (J) P⫻J
Coeff. c0 c1 c2 c3
Equation (1) (main) Est. p 3.699 0.037 0.064
0.001 0.025 0.028 Equation (1)
R2 Adjusted R2 F value P< Adjusted R2 explained by interaction term =
0.197 0.154 4.653 0.008
Equation (2) (2-way) Est. p ⫺3.961 0.258 0.570 ⫺0.015
0.149 0.011 0.013 0.022 Equation (2) 0.281 0.223 4.820 0.003 6.9%
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insufficient analysis for contingency hypotheses” (Schoonhoven, 1981, p. 2). To ascertain whether non-monotonicity effect exists, the test for monotonicity suggested by Schoonhoven (1981) was employed. The partial derivative of Eq. (2) was computed as: dYi / dPi = c1 + c3Ji Eq. (3). If dYi / dPi is always positive or negative across the entire range of procedural justice, the relationship between managerial performance and participation is considered monotonic. This can be examined by calculating the point of inflexion by equating Eq. (3) to zero. Hence, the point of inflexion is calculated as –c1 / c3 (⫺0.258 / ⫺0.015) which is 17.2. As the observed range of procedural justice scores is from 4 to 20 (see Table 1), this point of inflexion lies within the observed range and is close to the mean score of 14.95. Hence, it can be concluded that participation has a nonmonotonic effect on managerial performance across the entire observed range of procedural justice. This implies that the effect of budgetary participation on managerial performance is positive for procedural justice scores below 17.2 and negative for scores above 17.2. In other words, the lower the level of procedural justice, the stronger the positive impact of budgetary participation on managerial performance. A graphical representation of the results is presented in Fig. 2. These results provide further support for hypothesis H1. In conclusion, hypothesis H1, which states that in high budget emphasis situations, there is a significant and negative two-way interaction between budgetary participation and procedural justice affecting managerial performance, is supported. The negative coefficient c3 for the
Fig. 2. Relationship of Budgetary Participation, Procedural Justice and Managerial Performance: High Budget Emphasis Subsample. 151
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two-way interaction term also provides support for the expectation that budgetary participation is more effective in enhancing managerial performance in low procedural justice situations than in high procedural justice situations. Test of Hypotheses H2: Low Budget Emphasis Situations
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Table 4 presents the results of the main effects Eq. (1) and the two-way interaction Eq. (2) between participation and procedural justice for the low budget emphasis subsample. Hypothesis H2 states that in low budget emphasis situations, budgetary participation and procedural justice do not interact to affect managerial performance. This is supported by the non-significant result for coefficient c3 (est. = ⫺0.001; p < 0.463) and the value of R2 obtained for both the main and the interaction models, which is 22.9% for both models. Furthermore, the introduction of the interaction term in Eq. (2) led to a decrease in the adjusted R2 of 2.1%. Thus, hypothesis H2 is supported. Further Analysis of the Two-Way Interaction Model To assist in the interpretation of the results presented in Tables 3 and 4 for Eq. (2), graphs of regression lines were plotted and presented in Figure 3 for the Table 4. Results of Regression of Managerial Performance on Budgetary Participation and Procedural Justice: Low Budget Emphasis Subsample (n = 42).
Variable Constant Budgetary participation (P) Procedural justice (J) P⫻J
R2 Adjusted R2 F value P< Adjusted R2 explained by interaction term =
Coeff. c0 c1 c2 c3
Equation (1) (main) Est. p 4.386 0.044 ⫺0.007
0.000 0.002 0.492
Equation (2) (2-way) Est. p 4.188 0.050 0.015 ⫺0.001
0.033 0.225 0.466 0.463
Equation (1)
Equation (2)
0.229 0.189 5.789 0.003
0.229 0.168 3.764 0.009
⫺2.1%
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high budget emphasis subsample, and Fig. 4 for the low budget emphasis subsample. Each graph presents two regression lines, one for high procedural justice, based on the regression equation for high procedural justice, and another for low procedural justice, based on the regression equation for low procedural justice. It indicates graphically how the relationship between budgetary participation and managerial performance differs between high procedural justice and low procedural justice. 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 Fig. 3. Two-way Interaction Between Participation and Procedural Justice Affecting Performance (High Budget Emphasis Subsample; N = 41). 153
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Fig. 4. Two-way Interaction Between Participation and Procedural Justice Affecting Performance (Low Budget Emphasis Subsample; n = 42).
High Budget Emphasis Subsample For the high budget emphasis subsample, Table 3 indicates that coefficient c3 for the two-way interaction between participation and procedural justice is significant (p < 0.022). Since this coefficient is significant only if the slopes of the two regression lines are significantly different (Cohen & Cohen, 1983, p. 16), the significant result found in Table 3 for coefficient c3 (p < 0.022) provides the statistical support that the slopes of the two regression lines in Fig. 3 are
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significantly different. This is supported by additional computation which indicates that the slopes of the two regression lines are significantly different (p < 0.06). They also indicate that for the high procedural justice subsample, the effect of participation on managerial performance is not significant (p < 0.215). In contract, for the low procedural justice subsample, the effect of participation on managerial performance is highly significant (p < 0.008). Figure 3 also indicates that the relationship between budgetary participation and managerial performance is less positive for high procedural justice than for low procedural justice. This indicates the existence of a negative interaction and is consistent with the results in Table 3, which indicates that the two-way interaction coefficient c3 is both significant and negative (est = ⫺0.015). Overall, these results provide support for Hypothesis H1, which states that budgetary participation is more effective in enhancing managerial performance in low procedural justice situation than in high procedural justice situation. Low Budget Emphasis Situations Figure 4 presents graphically the results for the low budget emphasis subsample. It indicates that the slope of the high procedural justice regression line is not statistically different from that for the low procedural justice regression line (p < 0.363). This is consistent with the results in Table 4, which indicate that coefficient c3 for the two way interaction between budgetary participation and procedural justice is not significant (p < 0.463). This means that the relationship between budgetary participation and managerial performance does not differ significantly between high procedural justice and low procedural justice in low budget emphasis situations. Hypothesis H2, which states that in low budget emphasis situations, there is no significant interaction between budgetary participation and procedural justice affecting managerial performance, is therefore supported. Three-way Interaction Test To provide further statistical support for the expectation that the results for the high budget emphasis situation is significantly different from those for the low budget emphasis situation, the following model, involving a three-way interaction among budget emphasis, participation and procedural justice affecting managerial performance, was used: Yi = b0 + b1Bi + b2Pi + b3Ji + b4BiPi + b5BiJi + b6PiJi + b7BiPiJi + ei
(4)
where Yi = managerial performance, Bi = budget emphasis, Pi = budgetary participation, Ji = procedural justice and ei = error term. 155
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Table 5. Results of Regression of Managerial Performance on Budget Emphasis, Budgetary Participation and Procedural Justice (n = 83). Equation (4) Variable
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Constant Budget emphasis (B) Participation (P) Procedural justice (J) B⫻P B⫻J P⫻J B⫻P⫻J
Coeff
Est.
p
b0 b1 b2 b3 b4 b5 b6 b7
31.994 ⫺2.489 ⫺0.696 ⫺2.306 0.066 0.204 0.063 ⫺0.005
0.010 0.018 0.048 0.011 0.034 0.009 0.020 0.016 Equation (4)
R2 Adjusted R2 F value p
0 & 0 & MC***
14
7
4 9
22
13 0 3 NMC > MC***
1
10
16
1
17 1 0 NMC > MC***
11
15 2 NMC > MC***
1
* The volume coefficient indicates the amount of change in costs for a 1% change in production volume. For example, a value of 0.50 indicates that a 1% change in volume would cause costs to change by 0.50%. ** The order coefficient indicates the amount of change in costs for a 1% change in the number of customer orders. For example, a value of 0.50 indicates that a 1% change in the number of orders would cause costs to change by 0.50%. *** These numbers indicate the number of cases in which the coefficient for non-manufacturing costs exceeds that for manufacturing costs.
cost decrease, 18 indicated some decrease in cost but less than the decrease in volume, and four indicated that the decrease in cost is greater than the volume decrease. Volume decreases also correlate with changes in non-manufacturing costs, but the movements were less dramatic than the changes associated with manufacturing costs. For only one observation did the response coefficient for nonmanufacturing costs exceed the response coefficient for manufacturing costs. Analysis and Recommendations For our subjects, changes in production volume have a greater impact on costs than do changes in the number of orders. Moreover, manufacturing costs respond more quickly to changes in both production volume and the number of orders than do non-manufacturing costs. As non-manufacturing costs appear to be more “sticky” than manufacturing costs, the companies in our study should examine their procedures for assuring that non-manufacturing resources can be rapidly augmented when production and orders are rising. These companies should develop contingency plans for adding the necessary non-manufacturing
Product Decisions in Practice
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resources and talents. For example, lists of vendors and personnel agencies should be maintained so that resources can be acquired when needed. These participants should also have procedures for downsizing non-manufacturing resources when production and orders are falling and when it appears that these decreases are other than temporary. Along these same lines, these companies should be reducing non-manufacturing costs when products are abandoned. Did our participants eliminate the support costs associated with a product once that product was dropped? If so, how long did it take to get rid of these costs? In five of the nine companies, management specifically follows up to assure that activities related to abandoned products are eliminated. Managers in these companies indicated that it took three to six months for the costs associated with these activities to be eliminated. Two of the other four companies use budget mechanisms to monitor costs associated with discontinued products. One manager remarked, “It’s hard to tell what the support costs are much less whether the costs were eliminated.” This individual did concede that most of the facility’s drops relate to small volume items. Nevertheless, the lesson here is that monitoring mechanisms should be in place to assure that activities and their related costs are eliminated once products are abandoned. Accordingly, budgets should reflect the necessary reductions, while follow-up procedures should be employed to assure that the resources are in fact eliminated.
DIRECTIONS FOR FUTURE RESEARCH This study is exploratory; additional work remains to better understand how companies make add/drop and pricing decisions. Offering an exploratory overview of the processes and practices associated with these decisions, this study raises the following questions as avenues for future research: • Do companies view product decisions in isolation as opposed to a series of interrelated events and what is the impact of doing so? • What are the important qualitative factors that play a role in product add/drop decisions and how are these factors weighted in the decision process? • Does the product add/drop decision process need to be improved to make it more effective and efficient? • Which pricing models take into account the time horizon of the decision and are easy to implement and practical in their application? • To the extent that manufacturing efficiencies are used to subsidize nonmanufacturing activities, what is the impact on profits? • Are non-manufacturing costs responsive to changes in production volume and customer orders? If not, how can these costs be made to be more responsive? 231
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SUMMARY AND CONCLUSIONS Our study of diverse manufacturing facilities at nine companies reveals a variety of product decision practices. Six of the nine participants indicated the use of relevant costs for product add/drop decisions. The three that use a total product costing approach for product add/drop decisions do so because total costs, in their words: 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
• • • • •
Provide a direct link to financial accounting reports Keep fixed costs highly “visualized” Determine an accurate margin Reflect a capital cost and Initiate a more involved review process.
Two participants described “rules of thumb” for product abandonment and add decisions. In one such example, if sales fall below $5,000 for a given product, that product is eliminated. Another indicated that the hurdle for new products is a gross profit rate exceeding 30%–50%. The three ABC participants (C, D, H) recognize the cost interdependence of their product add decisions. Closely related to the add/drop decision is product pricing. Eight of our nine survey companies are in highly competitive markets. For product pricing, four base pricing decisions on total product costs, two on manufacturing costs, and three use market-based pricing exclusively. One manager commented that his company maintains, “a balance between what the market can bear and the profitability level shown by the [fully absorbed cost] analysis.” For the facilities in our study, both manufacturing and non-manufacturing costs are more sensitive to changes in volume than to changes in the number of customer orders. However, manufacturing costs are more sensitive to changes in both volume and the number of orders than are non-manufacturing costs. From all these data and the processes outlined by our contacts, both shortcomings and effective practices were revealed. These include the following: Shortcomings • The use of total product costs with little or no recognition of this technique’s limitations • Lack of established procedures for add/drop decisions requiring significant additional analysis
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• Almost half of the companies view product decisions in isolation • Processes related to add/drops and pricing decisions require improvement to be considered efficient and/or effective • Participants often do not distinguish between short- and long-term pricing objectives and procedures Effective Practices 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111 1 2 3 4 5 6 7 8 9 0111
• In seven companies, some form of monitoring is in place to assure management that when a product is eliminated, its associated costs are also eliminated • With respect to add/drop decisions: the use of pricing committees • A standardized process for developing new products that specifically takes into account those costs within the value chain • A noteworthy practice involves the use of a Product Review Committee (PRC), a multi-functional committee that includes representatives from manufacturing, marketing, sales, accounting, and top management. Although these procedures are very helpful, we found that in our subject companies the cost accounting systems often lack the ability to provide the information that management needs to make informed product decisions. These systems need to be refined to provide the necessary information.
NOTES 1. This instrument is available upon request. 2. This is apparently an example of using upwardly biased costs to achieve a particular result, in this case to keep managers aware of the magnitude of the fixed costs (Merchant & Shields, 1993). 3. PACE is a standardized product development methodology. 4. This manufacturing division was located in Great Britain. Payback appears to be a more prevalent capital budgeting technique in Europe than in the United States. 5. Note that for changes in production volume there are 27 responses (nine participants with three response levels each) related to increases and 27 responses related to decreases. Likewise, for customer orders there are 27 responses related to increases and 27 related to decreases.
ACKNOWLEDGMENT The authors would like to express their appreciation to James A. Largay of Lehigh University for his helpful comments. 233
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REFERENCES
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Boltin, G., & Gorneau, S. (1998). Why You Must Crop Your Products. Financial Executive, (July/Aug.), 18–22. Boyd, L. H. (1997). The Use of Cost Information For Making Operating Decisions. Journal of Cost Management, (May/June), 42–47. Cooper, R. (1990). Cost Classification in Unit-Based and Activity-Based Manufacturing Cost Systems. Journal of Cost Management, (Fall), 4–14. Cooper, R., & Kaplan, R. S. (1988). Measure Costs Right: Make the Right Decisions. Harvard Business Review, (September–October), 96–103. Cooper, R., & Kaplan, R. S. (1992). Activity-Based Systems: Measuring the Cost of Resource Usage. Accounting Horizons, (September), 1–13. Drury, C., Braund, S., Osborne, P., & Tayles, M. (1993). A Survey of Management Accounting Practices in U.K. Manufacturing Companies, Chartered Association of Certified Accountants. Drury, C., & Tayles, M. (1995). Issues Arising From Surveys of Management Accounting Practice. Management Accounting Research, 6, 267–280. Hirsch, Jr., M. L., & Nibbelin, M. C. (1992). Cost Management Concepts and Principles. Journal of Cost Management, 6, 39–47. Kaplan, R. S., Shank, J. K., Horngren, C. T., Boer, G. B., Ferrara, W. L., & Robinson, M. A. (Eds) (1990). Contribution Margin Analysis: No Longer Relevant/Strategic Cost Management: The New Paradigm. Journal of Management Accounting Research, 2, 1–32. Kennedy, A. (1995). Activity-Based Management and Short-Term Relevant Cost: Clash or Complement? – 2. Management Accounting – London, 73, 28–30, 51. Merchant, K. A., & Shields, M. D. (1993). When And Why to Measure Costs Less Accurately to Improve Decision Making. Accounting Horizons, 7, 76–81. Mitchell, F. (1994). A Commentary on the Application of Activity-Based Costing. Management Accounting Research, 5, 261–277. Scott, P., & Morrow, M. (1991). Activity-Based Costing and Make-Or-Buy Decisions. International Cost Management, (Winter), 48–51. Shim, E., & Sudit, E. F. (1995). How Manufacturers Price Products. Management Accounting, (February), 37–39.
EVOLVING RESEARCH BENCHMARKS
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Peter M. Johnson, Philip M. J. Reckers and Lanny Solomon
1. INTRODUCTION There are few events in an academic professional’s life that are more stressful than application for promotion and tenure. The integrity of the accompanying performance-review process is extremely critical to the success of an institution’s research mission. Recruitment, professional development, and retention of highly productive faculty are fundamental to success. The integrity of assessment processes resides with the identification of appropriate standards of performance and their fair and equitable application. Identification of appropriate standards, however, is not easy, as standards are dynamic over time. Numerous studies have discussed this topic in the past. Some studies have addressed the number of published articles necessary for tenure and/or promotion. Examples are Campbell and Morgan (1987), Zivney et al. (1995), Hasselback and Reinstein (1995), Read et al. (1998), and Hasselback et al. (2000a). Other studies have focused on standards of research quality by ranking or weighing the perceived quality of various research journals. Examples are Hull and Wright (1990), Hall and Ross (1991), Street and Baril (1994) and Brown and Huefner (1994). Past research does not meet current benchmarking needs because of several factors, including: (a) Much of the current benchmark data is global (aggregate) in nature, not having been partitioned to meet the needs of different types of institutions with different espoused missions. Schultz et al. (1989), Campbell and Advances in Accounting, Volume 19, pages 235–243. Copyright © 2002 by Elsevier Science Ltd. All rights of reproduction in any form reserved. ISBN: 0-7623-0871-0
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Morgan (1987) and Hull and Wright (1990) assert standards differ across types of institutions. (b) Much of the available benchmark data is old data and/or data aggregated over too lengthy of a time period to reflect recent changes in education, such as enhanced competition for faculty time by curricular issues. (c) Much of the currently available data fails to reflect the enhanced recent emphasis on specialty journals (e.g. AAA section journals), and/or changes in the editorial policies or the evolving quality perceptions of journals over time. (d) Much extant research fails to accommodate a simultaneous analysis of both quantity and quality dimensions. Research productivity is a prerequisite for promotion and tenure at most universities (Hull & Wright, 1990). Accounting department, business college and university administrators, and faculty involved in the promotion and tenure process seek reliable benchmarks of research quantity and quality. This is the case although all such benchmarks will possess inherent limitations and the institution must ultimately take responsibility for determination of their unique standards for tenure and promotion. The objective of this paper is to provide current and disaggregated benchmarks.
2. METHODS AND RESULTS Questionnaires were sent to all administrators of accounting programs identified in the Prentice Hall Faculty Directory: 2000–2001 (Hasselback, 2000). Administrators were deemed to be appropriate participant groups because they have a direct stake in and actual experiences with tenure and promotion decisions and they routinely provide counsel to faculty on such matters. A response rate of 25% was achieved. Ninety-six responses were received from members of the Accounting Programs Leadership Group (APLG), and sixty-six responses were obtained from non-APLG participants. The 162 responses represented: 39 Ph.D. Granting Institutions 91 Comprehensive Institutions (bachelors and masters programs) 32 Undergraduate Only Accounting Programs. Participants were asked to address the following three questions/issues: (1) What is the minimum number of Class A (Tier #1) publications necessary for promotion to associate professor (with tenure) at your school? (2) In addition to the minimum number of Class A publications, how many other Class B (i.e. less than Class A) publications are necessary for promotion to associate professor (with tenure) at your school?
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(3) From the list of journals provided, indicate Class A journals by marking an “A,” and Class B journals by marking a “B.”
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Participants were given a list of 33 journals in which accounting faculty publish frequently. While more journals certainly exist, the list provided did include all journals that had been cited as Class A journals in other prior research and many journals of recent vintage (e.g. AAA section journals and selected information systems journals). Responses were coded such that journals designated as Class A were assigned three points, journals designated as Class B were assigned two points and “other” journals were assigned one point. Essentially, all journals designated were ranked by participants as either Class A or Class B. (Participants were also given the option of responding “NA” for journals with which they were not familiar). Given that most journals were identified as either Class A or Class B, a composite average score of 2.50 indicates about 50% of participants ranked that journal as a Class A journal, a composite score of 2.75 indicates about 75% of participants ranked that journal as a Class A journal, etc. Thus, in Table 1, we identify journals as Class A journals (with shading) when they were so perceived by more than 50% of respondents. Note that only six journals surpass the 2.50 composite threshold. Also, in aggregate, respondents noted 1.53 Class A and 3.82 Class B articles are required for promotion. Does Table 1 provide a consensual list and a reliable benchmark to guide internal decision making across institutions? Hardly! The authors anticipated many readers would take exception to this type of composite ranking, which reflects the varied interests, needs and perceptions of the respondents and possible biases developing in favor of that category of school with the most respondents (Ph.D. granting (39), Comprehensive (91) or Undergraduate Only (32)). Accordingly, Table 2 partitions data by type of institution. Marked changes standout. Respondents from Ph.D. granting schools identify 7 Class A journals, not 6. Moreover, two journals fall from this list of Class A journals (Auditing and JATA) and three journals now make the list, which on a composite basis did not qualify as Class A: namely, CAR, Decision Sciences and MIS Quarterly. Respondents from Ph.D. granting institutions note that about 3 Class A and 4 Class B articles are necessary for promotion at their institutions. Respondents from Comprehensive schools identify 11 journals as belonging in the Class A category and that number swells to 18 journals for Undergraduate Only Institutions. Surprising was the very low score assigned to MIS Quarterly by non-Ph.D. granting institutions and the widely varied perceptions of the Journal of Accounting, Auditing, and Finance, among others (e.g. National Tax Journal and Journal of Public Economics). 237
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Table 1.
Aggregated Results.
Panel A: Publications Needed for Promotion and Tenure Overall Responses Class A Publications Needed
1.53
Other Publications Needed besides Class A
3.82
Panel B: Overall Journal Rankings
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1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33.
Accounting Review Journal of Accounting Research Journal of Accounting & Economics Accounting, Organizations, & Society Auditing: A Journal of Practice & Theory Journal of American Taxation Association Accounting Horizon Contemporary Accounting Research Journal of Accounting, Auditing & Finance Issues in Accounting Education Behavioral Research in Accounting Decision Science Advances in Accounting Journal of Accounting Literature Journal of Taxation Journal of Management Accounting Research Advances in Taxation National Tax Journal Journal of Accounting Education Accounting & Business Research Critical Perspectives in Accounting Journal of Business Finance & Accounting Tax Advisor MIS Quarterly Tax Notes Review of Accounting Studies Information Systems Research Communications of the ACM Journal of Computer Information Systems IEEE Transactions Journal of Public Economics Journal of Strategic Information Systems Journal of Economic Psychology
TAR JAR JAE AOS APT JATA AHR CAR JAF IAE BRA DSC AIA JAL JOT JMAR AIT NTJ JED ABR CPA JBF TXA MISQ TXN RAS ISR ACM JCIS IET JPE JSIS JEP
3.00 2.97 2.85 2.72 2.56 2.51 2.49 2.47 2.40 2.34 2.33 2.31 2.23 2.22 2.20 2.19 2.17 2.16 2.14 2.12 2.05 2.04 1.88 1.86 1.83 1.69 1.53 1.50 1.48 1.44 1.41 1.27 1.27
Evolving Research Benchmarks
Table 2.
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Disaggregated Results.
Panel A: Publications Needed for Promotion and Tenure
Class A Publications Needed Other Publications Needed besides Class A
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Ph.D. Granting Institution
Comprehensive Institution
Undergraduate only
2.97
1.19
0.815
3.96
4.18
2.69
Panel B: Journal Ranking by the Various Types of Academic Institutions Journal Rankings
Ph.D. Granting Institutions
Accounting Review Journal of Accounting Research Journal of Accounting & Economics Contemporary Accounting Research Accounting, Organizations, & Society Decision Science Auditing: A Journal of Practice & Theory Behavioral Research in Accounting Accounting Horizon Journal of American Taxation Association Information Systems Research Journal of Economic Psychology Journal of Public Economics Journal of Computer Information Systems Advances in Accounting Communications of the ACM Journal of Management Accounting Research Journal of Strategic Information Systems MIS Quarterly Journal of Accounting, Auditing & Finance Issues in Accounting Education Accounting & Business Research Advances in Taxation Journal of Business Finance & Accounting Journal of Taxation National Tax Journal Review of Accounting Studies IEEE Transactions Journal of Accounting Literature Journal of Accounting Education Critical Perspectives in Accounting Tax Advisor Tax Notes
1 2 3 4 5 6 9 13 16 11 14 31 17 30 20 8 18 32 7 15 23 19 26 27 21 12 10 25 22 29 24 28 33
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3.000 3.000 2.971 2.686 2.657 2.571 2.400 2.355 2.281 2.382 2.333 2.000 2.250 2.000 2.179 2.417 2.226 2.000 2.565 2.333 2.138 2.207 2.074 2.071 2.174 2.375 2.400 2.133 2.156 2.000 2.136 2.045 2.000
Comprehensive Institutions 1 2 3 8 4 7 5 10 11 6 21 24 28 30 22 20 13 31 23 9 18 16 19 26 14 17 27 25 12 29 15 32 33
3.000 2.968 2.897 2.620 2.803 2.644 2.690 2.558 2.557 2.684 2.333 2.300 2.235 2.143 2.321 2.353 2.460 2.091 2.320 2.576 2.393 2.415 2.365 2.267 2.449 2.400 2.263 2.286 2.490 2.217 2.444 2.064 2.045
Undergraduate Only 1 2 3 10 4 16 5 14 9 11 6 7 8 12 13 15 17 18 26 27 19 20 21 22 23 24 25 28 29 30 31 32 33
3.000 3.000 2.857 2.643 2.733 2.500 2.733 2.500 2.647 2.625 2.667 2.667 2.667 2.571 2.500 2.500 2.500 2.500 2.364 2.357 2.471 2.467 2.467 2.462 2.400 2.385 2.375 2.333 2.333 2.286 2.273 2.231 2.231
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Comprehensive Institutions set threshold promotion benchmarks at 1.19 Class A and 4.18 Class B articles while Undergraduate Only Institutions weighed in with thresholds set at .815 Class A and 2.69 Class B articles. Thus, across institutions not only is there marked difference of opinion as to what qualifies as Class A research, there also exists significant differences of the number of required publications in each category. Had one relied on the aggregate data from Table 1 (which is similar to the type of aggregate data found in prior research), an incorrect conclusion could have been advanced that 1.53 Class A publications would be adequate at a Ph.D. Granting Institution, whereas the disaggregated data suggest the bar is nearly twice as high (at 2.97 Class A publications). Similar misinterpretations of aggregate data apply to Comprehensive and Undergraduate Only institutions; a requirement of 1.53 articles from a list of only 6 Class A journals (derived from aggregate data) is significantly beyond the true requirements of either type institution. The partitioned data of Table 2 indeed is more consistent with the recent data provided by James Hasselback (2000b). Hasselback reports that from 1967 to 1996, 53% of faculty had no publications in 21 identified accounting journals, and 74% had two or fewer articles during this period.
3. ANCILLARY COMMENTS REGARDING TECHNOLOGY Advances in technology over the last decade have brought fundamental changes to how business is conducted, globally. Kevin Kelly, author of New Rules for the New Economy, observes: The new economy represents a tectonic upheaval. Technology that once progress at the periphery of culture now engulfs our minds as well as our lives. Is it any wonder that technology triggers such intense fascination, fear and rage? Those who play by the new rules will prosper, while those who ignore them will not (Kelly, 1998 p. 1).
Arguably, accounting educators have failed to seize the moment (i.e. to reflect adequate responsiveness to demands for change in business in recent years). Albrecht and Sack (2000) in Accounting Education: Charting the Course through a Perilous Future document reasons for the precipitous decline in accounting programs’ enrollment. The Report of the AAA 2000–2001 Information Technology Interaction Committee (AAA, 2001) discusses the marked deficiencies, and their root causes, in the area of technology infusion within accounting programs.
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The AICPA conducted a series of visioning conferences to help educators in strategic planning processes. The FSA (Federation of Schools of Accountancy) and APLG (Accounting Programs Leadership Group) have prominently included these topics on their national agendas. And, yet has progress been made in these areas that relates to the topic discussed in this paper? A pertinent question is whether the decisions of decision-makers within the tenure and promotion process reflect awareness of the new economy realities. Do decision makers exhibit familiarity with the new technology oriented academic journals in which accounting information systems faculty can be expected to publish (or aspire to publish) their works. To address this question, seeded within the list of journals that accounting administrators were asked to evaluate/rank were 6 journals in which AIS faculty might publish. These journals are listed in Table 3 with their rankings and mean quality scores based on recent surveys published in two major information systems research journals. As you can see, included were four journals consistently ranked as Class A journals in the information system related field; MIS Quarterly, Information Systems Research (ISR), Communications of the ACM (CACM), and Decision Sciences. IEEE Transactions is consistently ranked as a Class B journal while Journal of Computer Information Systems is significantly ranked lower than the other system related journals. In this study, the following questions were addressed pertaining to system journals relative to traditional accounting journals:
Table 3.
Journal Rankings of System Related Journals. ISR Surveya Rank
MIS Quarterly Information Systems Research Communications of the ACM Decision Sciences IEEE Transactions Journal of Computer Information Systems
CACM Survey b
Mean Score
Rank
Mean Score
1 4 2 5 9
4.57 4.13 4.37 4.10 3.79
1 2 4 6 12
3.72 3.71 3.49 3.28 3.02
21
3.20
27
2.58
a
Whitman, Hendrickson and Townsend (1999). Academic Rewards for Teaching, Research, and Service: Data and Discourse. Information Systems Research, 10(2)(June 1999), pp. 99–109. b Hardgrave and Walstrom. (1997). Forums for MIS Scholars. Communications of the ACM, 40, (11)(November) 1997, pp. 119–124.
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• Will accounting administrators recognize Class A systems journals as Class A research journals relative to traditional accounting research journals? • Can accounting administrators differentiate systems journals of different quality? (Has adequate investment of effort been made by accounting administrators to support integration of AIS faculty within their units?)
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Looking back to Table 2, among Ph.D. Granting Institutions, Decision Sciences and MIS Quarterly achieve a score greater than 2.50. (indicating that more than 50% of respondents surveyed classified these journals as Class A journals. However, they barely make the Class A designation with scores of 2.57 and 2.56, respectively.) Communications of the ACM and ISR fail to achieve Class A status with scores of 2.41 and 2.33, respectively. ISR was ranked 14th among traditional accounting journals by Ph.D. Granting Institutions. Among Comprehensive Institutions, matters degrade further with only Decision Sciences recognized as a Class A journal. CACM is ranked 20th; ISR, 21st; and MIS Quarterly, 23rd. These perceptions (or lack of investment by their department chairpersons to better understand the systems field) will certainly discourage AIS faculty. Accounting administrators at Undergraduate Only Institutions similarly fail to reflect an understanding of the relative quality of these journals with CACM ranked 15th, Decision Sciences ranked 16th, and MIS Quarterly ranked 26th. Apparently, some greater investment of time will be required if accounting programs are to enthusiastically embrace more technology in their programs and support faculty delivering that technology to the curriculum. The second question addressed was perception of relative quality among systems journals. Operationally, the question is whether IEEE was ranked below the previously discussed Class A journals and whether the Journal of Computer Information Systems was ranked lower still. Ph.D. Granting Institutions did indeed order the journals consistent with the cited surveys in the systems field (although ISR and IEEE would undoubtedly be disappointed in their absolute numbers and ranks). Comprehensive Institutions similarly ordered the Class A journals higher than IEEE and IEEE higher than Journal of Computer Information Systems. However, this was achieved by ranking all except Decision Sciences within the 20–30 ranges of ranks. These findings fail to reflect the quality differences assessed by knowledgeable researchers in the field. Respondents from Undergraduate Only Institutions failed the mark badly ranking the Journal of Computer Information Systems second only to ISR, whereas it arguably should rank last among this list. From the findings related to the seeded systems journals, it appears safe to say that the degree that accounting administrators are currently familiar with the seeded systems journals is modest.
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REFERENCES
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American Accounting Association (2001). The Report of the AAA 2000-2001 Information Technology Interaction Committee. Unpublished report; Sarasota, FL. Albrecht, S. W., & Sack, R. J. (2000). Accounting Education: Charting the Coursethrough a Perilous Future. A Joint Project of the AAA, AICPA, IMA, and the Big Five Accounting Firms. Accounting Education Series, 16. Brown, L. D., & Huefner, R. J. (1994). The Familiarity with and Perceived Quality of Accounting Journals: Views of Senior Accounting Faculty in Leading U.S. MBA Programs. Contemporary Accounting Research, (Summer), 223–250. Campbell, D. R., & Morgan, R. G. (1987). Publication Activity of Promoted Accounting Faculty. Issues in Accounting Education, (Spring), 28–43. Hall, T. W., & Ross, W. R. (1991). Contextual Effect in Measuring Accounting Faculty Perceptions of Accounting Journals: An Empirical Test and Updated Journal Rankings. Advances in Accounting, 9, 161–182. Hardgrave, B. C., & Walstrom, K. A. (1997). Forums for MIS Scholars. Communications of the ACM, 40(11), 119–124. Hasselback, J. R. (2000). Accounting Faculty Directory. New Jersey: Prentice Hall. Hasselback, J. R., & Reinstein, A. (1995). A Proposal for Measuring Scholarly Productivity of Accounting Faculty. Issues in Accounting Education, (Fall), 269–306. Hasselback, J. R., Reinstein, A., & Schwan, E. S. (2000a). Benchmarks for Evaluating the Research Productivity of Accounting Faculty. Journal of Accounting Education, 18, 79–97. Hasselback, J. R., Reinstein, A., & Schwan, E. S. (2000b). Prolific Authors of Accounting Literature. Working paper at Florida State University. Hull, R. P., & Wright, G. B. (1990). Faculty Perceptions of Journal Quality: An update. Accounting Horizons, (March), 77–98. Kelly, K. (1998). New Rules for the New Economy. New York, New York: Viking Penguin of Penguin Putnam Inc. Read, W. J., Rama, D. V., & Raghunandan, K. (1998). Are Publication Requirements for Accounting Faculty Promotions Still Increasing? Issues in Accounting Education, 13(2), 327–339. Schultz, J. J., Meade, J., & Khurana, I. (1989). The Changing Roles of Teaching, Research, and Service in the Tenure and Promotion Decisions for Accounting Faculty. Issues in Accounting Education, (Fall), 109–119. Street, D. L., & Baril, C. P. (1994). Scholarly Accomplishments in Promotion and Tenure Decisions of Accounting Faculty. Journal of Accounting Education, (Spring), 121–139. Whitman, M. E., Hendrickson, A. R., & Townsend, A. M. (1999). Academic Rewards for Teaching, Research, and Service: Data and Discourse. Information Systems Research, 10(2), 99–109. Zivney, T. L., Bertin, W. J., & Gavin, T. A. (1995). Publish or Perish: What is the Competition Really Doing? Issues in Accounting Education, (Spring), 1–25.
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