A declining industry:
a motivation for CFO’s to participate in fraudulent financial reporting?
by
YASHNI BARTOL
University of Groningen Faculty of Economics and Business
ABSTRACT
This paper investigates declining industries as motivator of CFO involvement in fraudulent financial reporting (FFR), focussing on U.S. Securities and Exchange Commission (SEC) litigation releases from 2007 till March 2013 only. There were 137 cases derived for each the variables CFO involvement and industry decline were identified. Industry sector was
included as control variable. The outcome of the research was that CFO’s in declining industries are not more involved in FFR than CFO’s in non-declining industries. Ergo, a declining industry is not a motivator for CFO’s to commit FFR.
1. INTRODUCTION
Accounting scandals are found throughout modern history, increasing uncertainty in US and EU economies. The current credit crisis and financial crises in general are stimulated by and contributing to this uncertainty at the same time, influencing investors, consumers and corporations.
As the financial crisis rages on, business growth and profitability are declining rapidly and organizations are filing for bankruptcy. In order to cope with the declining markets,
businesses are reorganizing, cutting costs and the pressure to meet the earnings requirements are increasing. Do above mentioned circumstances stimulate or motivate CFO’s to report financial irregularities, thus perform fraudulent actions? Following Hawke (2007: 35)
‘‘senior executives, motivated by altruistic urges to save the company and preserve jobs, may be tempted to cook the books, which can have serious financial, legal, and reputational consequences.’’
This research focuses on how and why declining industries may act as a motivator for fraudulent financial behaviour and what role CFO’s play in this behaviour. A CFO together with a CEO is responsible for the correct representation of financial disclosures to the public and investors. The misrepresentation of these disclosures and other financial frauds originate under CFO leadership, because they are ultimately responsible for financial decisions. It is because of this unique position of the CFO, that the emphasis for this study lies on CFO’s in fraudulent financial behaviour. Especially during these economic turbulent times, it is significant to understand how and why fraudulent financial behaviour occurs and the role of the CFO in this behaviour. This could help to prevent and/or recognize fraudulent financial reporting earlier.
The test showed that CFO’s in declining industries are not more involved in fraudulent financial reporting than CFO’s in non-declining industries, but was limited by the low
amount of relevant cases (137) and the use of the SEC database only. Also, the rate of decline of industries was not taken into account.
2. RESEARCH FRAMEWORK
This section defines the concepts of “Fraudulent Financial Reporting”, “Declining Industries” and examines prior literature on fraudulent financial reporting, CFO’s and possible
motivators to engage in this behaviour.
Definition of Fraudulent Financial Reporting
A study by Karpoff, Lee & Martin (2008a) indicated that a firm can lose up to 38% of their market value when FFR is revealed to the public. Other implications of FFR will be handled further on in this paper, but because firms can lose that much market value, stock value drops and shareholders lose money. Therefore, the scope of this research is on Fraudulent Financial Reporting (FFR).
The study of Beasley, Carcello, Hermanson (1999: 11) sponsored by The Committee of Sponsoring Organizations of the Treadway Commission (COSO) defined FFR as:
‘‘Intentional material misstatement of financial statements or financial disclosures or the perpetration of an illegal act that has a material direct effect on the financial statements or financial disclosures. The term financial statement fraud is distinguished from other causes of
materially misleading financial statements, such as unintentional errors and other corporate improprieties that do not necessarily cause material inaccuracies in financial statements.”
The definition provided by Beasley et al (1999) will be used in this research, meaning fraudulent financial reporting.
Declining Industry
deepening process innovation. The emphasis of R&D herewith shifts from radical product innovations to incremental process innovations. This results in a decline in product variety and because of the process innovation, the firm’s capacity increases. Because sales do not increase per se, market share is distributed based on production capability. The firms that cannot comply leave the industry. “This mass-extinction is known as a shakeout” (Peltoniemi 2011: 354) Klepper and Miller (1995: 567) define shakeout as a “lengthy period in which there was a persistent fall in the number of firms, despite continued growth in output”. The continued growth in output is due to sustaining firm’s increasing capacity.
Following Klepper (1997: 168) ‘‘output growth tends to decline over time’’. In the first three stages, the output growth declines until it stabilizes. After that, the output starts to decline. It is in this phase that industries are declining. This is a ‘natural’ process that occurs at all industries. In order to remain profitable in declining industries, companies must reduce their capacity. Hence, companies tend to disinvest in declining industries. (Ghemewat & Nalebuff, 1990).
As declining industries comprise of firms exiting the industry and the remaining companies reduce their capacity, the output of the declining industry stabilizes and starts to decline. Therefore, declining industries will be identified by their gross output. Gross output consists of goods and services produced by an industry as defined in the “Guide to the Interactive GDP-by-Industry Account Tables” by the U.S. Department of Commerce: Bureau of
Economic Analysis (BEA). For this research changes in chain-type quantity indexes for gross output by industry are used to identify the declining industries. This index reflects inflation-adjusted quantities of gross output for the specific industry. Ergo, the effect of price changes is not taken into account. The year-to-year percent changes in the chain-type quantity indexes of an industry’s gross output shows the real value-added (to GDP) growth rate of that
particular industry.
Responsibilities of a CFO
To understand why CFO’s might be motivated to commit FFR, it is useful to know a CFO’s responsibilities. A study by Ernst & Young (2010: 14), interviewing 669 CFO’s, resulted in that: ‘the CFO’s contribution is broad, from developing to enabling to executing strategy’. Ernst & Young (2010) divided the CFO’s responsibilities in six segments:
1. Ensuring business decisions are grounded in solid financial criteria.
2. Providing insight and analysis to support the CEO and other senior managers 3. Leading key initiates in finance that support overall strategic goals.
4. Funding, enabling and executing the strategy set by the CEO. 5. Developing and defining the overall strategy for the organization.
6. And, representing the organization’s progress on strategic goals to external stakeholders.
Corson & Miyagawa (2012: 23) found that CFO’s must increasingly provide expert advice to support boardroom decisions and that current CFO’s see stakeholder communications as a joint CEO/CFO responsibility; ‘‘a full 69 percent of CFO’s worldwide believe they are better placed than CEO’s to provide stakeholders with accurate guidance on the organization’s financial performance’’. Although CFO’s believe themselves to be better qualified than a CEO, the question remains whether CFO’s are capable of performing such a task. As Corson & Miyagawa (2012) denoted: ‘communicating with, and influencing, both internal and external stakeholders is not easy for a large proportion of CFO’s, whose traditional skill sets have historically been grounded in finance’.
- Oversees preparation of financial reports and serves as the point person for external communication of financial strategy.
- Bears the ultimate responsibility for activities related to raising capital.
- Could be asked to play a role in the development and execution of corporate strategy. - Is primary responsible for the management of the financial system.
Mian (2001) examines the more traditional financial activities of CFO’s, while the studies of Ernst & Young (2010) and Corson & Miyawaga (2012) indicate that the CFO function is taking a more strategic position, involving strategy development and communicating with external stakeholders. FFR occurs in the role of traditional financial activities. Because the CFO is responsible for communication of the organization’s progress on strategic goals to external stakeholders and for the financial system of the organization and execution of it, this is where there is an incentive for a CFO to engage in FFR. Shareholders will hold the CFO accountable for not achieving financial targets. If the company fails to meet the targets, a CFO might deceive the stakeholders through FFR to uphold his reputation or avoid being sacked.
CFO Involvement in FFR
Loebeckke, Eining & Willingham (1989) showed in their research that in 40,2% of the cases examined , CFO’s participated in financial irregularities, while CEO’s were found to be participating in 43,9 %
Beasley, Carcello, Hermanson & Neal (2010) revealed in their study that in 65% of the financial statement fraud cases, the CFO was implicated, against a 72% of CEO’s and less than 40% for any other type of employee. Beasley et al (1999) earlier study revealed a similar high CEO and CFO involvement where in 83% of the cases, a CEO and/or CFO was
Motivators for CFO’s to Participate in FFR
Following Feng, Ge, Luo & Shevlin (2011) CFO’s do not appear to be participating in accounting schemes for direct personal gain, while CEO involvement is driven by their compensation incentives. Feng et al (2011: 21) concludes that: ‘‘CFO’s are involved in material accounting manipulations because they succumb to pressures from CEO’s.’’ Murhpy & Dacin (2011), argue that an individual placed in a situation in which an authority figure instructs him/her to participate in some type of fraud, the individual may simply comply and perceive that he or she is being a loyal subordinate without consideration of legality or ethicality of the actions. This study could support the study of Feng et al (2011), in which CFO’s succumb to pressures from CEO’s when committing FFR. The study of
Murphy & Dacin (2011) however argues than an individual might simply comply with fraudulent instructions. The author finds it rather unlikely that someone in the position of CFO would simply comply with instructions without consideration of legality or ethicality. The study does show however that individuals differ in their obedience to authority.
Following Ashforth and Anand (2003), corruption becomes normalized in an organization via three phases. At first, an initial corrupt decision is made by a leader within the organization, within a permissive ethical climate. From this point, corrupt processes begin to form through leader instructions to subordinates and the use of rationalizations to justify the behaviour. In the final phase, the corruption becomes routinized and mindlessly followed by all members of the organization. This is an interesting theory underpinning the role of the organizational culture with regard to FFR. Which can also be linked with the conditions of the management fraud assessment model of Loebeckke et al (1989) and opportunity aspect of the Fraud Triangle Theory of Cressey (1950).
Cressey (1950) identified pressure, rationalization and opportunity as the three key ingredients for fraud. Feng et al (2011) focuses on the pressure of the CEO on CFO’s in forcing the latter to commit FFR. Opportunity is identified as whether a control weakness is present and that the likelihood of being caught is remote. Rationalization involves individuals who commit fraud desire to remain within their moral comfort zone, requiring the fraudster to justify the fraudulent action before the act.
Loebeckke et al (1989) developed a risk of material management fraud assessment model. This model focuses on the combination of three aspects: conditions, motivations and
of motivation, Loebeckke et al (1989) described industry decline, inadequate profits, emphasis on earnings projections and significant contractual commitment as primary indicators to omit financial irregularities. Note that this study does not state these indicators as motivation for CFO’s specifically.
Following Ramamoorti & Olsen (2007: 54), ‘‘it is crucial to know what it is that a fraud perpetrator desires’’. Money, Status, Revenge, ‘a-catch-me-if-you-can-game’ or parity with others are amongst those desires. Also according to Ramamoorti & Olsen (2007), pressure is the trigger to fraud: pressure to achieve financial performance or meet analyst expectations and forecasts about earnings, smooth earnings and income to reduce volatility and to benefit from compensation or bonuses tied to earnings, to avoid sanctions,
Marks (2012) identified a pattern of behavioural elements that are common to ‘white-collar’ criminals: lack of moral compass, troubling friends, family and relationships, arrogance, deception, cleverness and creativity. Wang (2010) notes that FFR is motivated by pressure to perform.
Literature shows that there a lot of motivators to commit FFR. Organizational culture (Ashford & Annand, 2003) Opportunity, rationalization (Cressey, 1950) and conditions, motivations (Loebeckke et al, 1989) Attitude, behaviour (Loebeckke et al, 1989: Marks, 2012). In general, literature supports the idea of pressure to be the trigger in fraud (Cressey, 1950; Feng et al, 2011; Loebeckke et al, 1989; Ramamoorti & Olsen, 2007; Wang, 2010). And as Feng et al found, CFO’s are more likely to participate in FFR through pressure, than for personal gains. Organizational culture could create the conditions and opportunities to commit FFR. . Finally, behavioural aspects of the individual influence the decisions to
commit FFR. It must be noted however that there is little research on motivators for CFO’s in particular.
The assumption for this research is that a declining industry works as an motivation, hence, it is an incentive to commit FFR. This leads to the question whether CFO’s in declining
Implications of FFR
Karpoff et al (2008a: 581) found that participating in FFR can have serious consequences for the company: ‘For each dollar that a firm misleadingly inflates its market value, on average, it loses this dollar when its misconduct is revealed, plus an additional $3.08.’ And, firms lose 38% of their market value on average when financial misrepresentations are made public. Following Beasley et al (1999), fraud firms were much more likely than similar no-fraud firms to declare bankruptcy, be involuntarily delisted, or have material asset sales in the wake of the fraud.
Another study by Karpoff, Lee & Martin (2008b), examined the consequences for the managers that ‘cooked the books’. 93% lost their jobs, of which most were explicitly fired. Following Karpoff et al (2008b), there is a positive correlation between the chance of being fired and the costs of the misconduct to the shareholders and quality of firm’s governance. Further financial losses include restrictions on future employment, shareholdings in the firm and SEC fines. 28% faced criminal charges and penalties, jail sentences with an average of 4.3 years included. Karpoff et al (2008b) concluded that ‘these results indicate that the individual perpetrators of financial misconduct face significant disciplinary action’ Wang (2010) mentions that detection of FFR results in market investors questioning the firm’s ability to provide utility, which challenges the legitimacy of the firm. Wang (2010) further mentions that to face this challenge, a firm can initiate a restructuring to disassociate itself from illegitimate business operations.
3. RESEARCH DESIGN
The objective of this research is to determine whether a declining industry stimulates fraudulent behaviour of CFO’s. The research question therefore is:
“What effect does a declining industry have on fraudulent financial behaviour of a CFO?
To following hypotheses will be tested:
H0: “CFO’s in declining industries are not more involved in the commitment of financial
statement fraud as CFO’s in non-declining industries.”
H1: “CFO’s in declining industries are more involved in the commitment of financial
statement fraud.”
Archival data for this research is derived from the U.S. Securities & Exchange Commission (SEC) database consisting of accounting and auditing enforcement releases (AAER) and litigation releases. The AAER and litigation releases in the period of 2007 – March 2013 (‘relevant period’) are used, but the accounting fraud period might have occurred at any point given in time. Only the cases related to FFR as defined in the theory section (p. 3) are
included in the analyses.
The cases addressed by the SEC are US specific cases only and therefore this research is limited to companies of the US. Concerning the reliability of the data source, one could argue about the objectivity of SEC. SEC mainly focuses on prestigious fraud cases and therefore smaller or ‘lighter’ cases receive no priority and are easily neglected. Also, SEC might have failed to identify FFR. (Macey, 2010).
However, following Macey (2010: 641) “SEC is staffed by highly capable, extremely well-qualified professionals, most of whom are lawyers, and many of whom come from or move on to extremely successfully careers in the most rigorously competitive parts of the private sector (primarily law, bus also investment banking” and “there is little corruption at the
From each relevant fraud case, the fraud period is derived. The most significant period is that period in which the FFR began and the year(s) prior to the FFR period. For each fraud case, the Standard Industrial Classification (SIC) code and the related industry type is derived via the SEC’s search engine “EDGAR’. By filling in the company’s name in the engine, the database shows the SIC code and the name of the related industry.
After deriving the SIC codes and the industry type from the EDGAR search engine, the industry type is matched with the database of BEA for the case period. In the interactive ‘GDP-by-Industry’ chart , the ‘percent changes in chain-type quantity indexes for gross output by industry’ is used to determine whether the relevant industry was declining in the relevant time period.
The data tables can be found in the Appendix and there is a link provided in the reference list where one can examine the data online. The online database offers the opportunity for a visual presentation of the percentage changes by ways of line and bar charts. You can here select any specific industry at any point in recent history and the information will be presented. Alas, to include line charts and bar charts for every specific industry in the appendix would be too extensive.
The ‘chain-type quantity indexes for gross output by industry’ shows the growth rate (in its contribution to GDP) for each industry. For extensive information about the GDP-by-Industry interactive table, the reader can refer to the ‘Guide to the Interactive GDP-by-Industry
accounts tables’, for which a link can be found in the reference list. Based on this table, a declining industry is identified. The fraud case is marked as operating in a declining industry if:
- Year(s) prior to fraud period have a negative percentage change - The year in which the fraud started has a negative percentage change
Because this research focuses on whether a declining industry is a motivator for CFO
commitment to FFR, it is only relevant whether the company operated a declining industry or not. Therefore, when the CFO committed FFR in a declining industry, the fraud case will receive a score of 1, and if the CFO did not commit FFR in a declining industry, the case receives a score of 0. Hereby creating categories of ‘declining industry: yes or no’. Because I want to test to whether a declining industry is a motivator for CFO’s to engage in FFR, also the cases in which CFO’s are not involved in the relevant period are analysed. For these cases, exactly the same steps as previously described are followed, ending again with a distinction is between declining industries and non-declining industries.
Now, the test consists of two nominal variables, which makes it a bivariate analysis. The variables are categorized as: ‘CFO involvement, yes or no “ and “declining industry, yes or no”. The independent variable (X) is “declining industry” and the dependent variable (Y) is “CFO involvement”. The relationship to be tested:
Figure 1: Conceptual model
This test entails an asymmetric bivariate analysis between two nominal variables. Hence, a cross table with Chi-square test of independence can show whether a relationship exists. Further, a control variable (CV), industry sector, is included.
The specific industries derived from the fraud cases are divided into 9 sectors:
- Financial sector: financing, trusts and insurance companies.
- Motor vehicles & related: motor vehicles, bodies, trailers and parts.
- Health care & related: ambulatory health care services, hospitals, nursing, residential care facilities, medical equipment and social assistance.
- Electronics & related: computer & electronic products, electrical equipment, appliances and components, and computer systems design and related services. - Services: legal services, management services, educational services, administrative
services and waste management services.
- Others: construction, real estate and food products
- Chemicals & related: chemical products, plastics, cleaning agents. - Oil, Gas & Metals: oil, gas & metal mining, petroleum products, pipeline
transportation and supporting activities. - Retail/Wholesale
4. RESULTS
Declining industry? Yes No
CFO involved? Yes 39 40,6% 57 59,4%
No 14 34,1 % 27 65,9%
Table 1: Number of cases
The test consisted of 137 cases. From these cases, 40.6 % of the CFO’s involved in FFR were operating in a declining industry versus 59,4 % in a non-declining industry. Further, in cases that CFO’s were not involved in FFR, 65,9 % were operating in a non-declining industry, versus 34,1 % operated in a declining industry.
Minimum Maximum Mean Std. Deviation
CFO involved? 0 1 0.701 0.460
Declining industry? 0 1 0.390 0.489
Table 2: Descriptive statistics
Table 2 describes the values that the categories can take. Both categories range from 0 to 1. Because the categories are nominal (1= yes, 0=no) the mean tells us what situation occurred most in the fraud cases. The mean for CFO involvement is 0.701. This entails that of the 137 cases, CFO’s are more involved in fraud cases than they are not. For declining industry, the mean is 0.390, which tells that there are more non-declining industries within the 137 cases than there are declining industries.
To analyse whether CFO’s in declining industries are more involved in FFR than CFO’s in non-declining industries, a cross table with Chi-square with CFO involvement, declining industry (1= yes, 0=no) and the control variable industry sector (1= Financial sector, 2= Motor vehicles & related, 3= Health care & related, 4= Electronics & related, 5= Services, 6= Others, 7= Chemicals & related, 8= Oil, Gas & Metal and 9= Retail/Wholesale) was
performed.
Recall, that the following hypotheses are being tested:
H0: “CFO’s in declining industries are not more involved in the commitment of financial
H1: “CFO’s in declining industries are more involved in the commitment of financial
statement fraud.”
Table 3 below shows the Chi-square test results.
Pearson Chi-Square P-value
0.508 0.476
Table 3: Chi-square test result
The Chi-square test was not significant, Chi-square (total) = 0.508 and p = 0.476 which is greater than α = 0.05. In table 4, the statistics for the control variable can be found. (Note: detailed SPSS output can be found in the appendix.)
Industry sector Pearson Chi-Square P-value
Financial sector 0.019 0.891
Motor vehicles & related 0.240 0.624
Health care & related 0.536 0.464
Electronics & related 1.044 0.307
Services 1.170 0.279
Others 3.938 0.047
Chemicals & related 0.476 0.490
Oil, Gas & Metal 0.225 0.635
Retail/Wholesale 0.110 0.740
Table 4: Chi-square results per industry sector
All p-values, except ‘others’ are greater than α = 0.05 and are therefore insignificant. The industry ‘others’, seems to be significant, as p < 0.05. If the industry sector ‘others’ is decomposed however, then two industries (Real estate and Food products) become constant variables and therefore cannot be tested. Hence, the significance of the industry sector ‘others’ can be neglected, as it does not represent a valid industry sector.
5. DISCUSSION
The research showed that there was no significant relationship between CFO involvement in FFR and declining industries. The method used in this research was to identify a declining industry and link that to whether a CFO was involved or not.
This research did not take into account the rate of decline of industries, which might contribute to commit FFR. I believe that a higher rate of decline in an industry might put higher pressure on CFO’s to perform. And as Feng et al (2011) shows in their study, CFO’s are more likely to participate in FFR through pressure. The scope for this research however was on whether declining industries influence CFO involvement, not the rate of decline.
The category ‘others’ of the control variable showed significance, but this was only because there were some constant variables included. Because the category others only comprised of 7 cases, the category would not have changed the outcome of this test. A larger data set, comprising of more industries could have made it possible to decompose the category
‘others’ into different sectors that could have been tested. However, because the scope of this research was limited to the relevant period and the SEC was the only data source used, there was no opportunity to access more fraud cases.
6. CONCLUSION
“What effect does a declining industry have on fraudulent financial behaviour of a CFO?
By analysing AAER’s and litigation releases from the SEC database, 137 cases were derived. In these cases a distinction between declining and non-declining industries and CFO involvement in FFR was made. The test results showed that the CFO’s are not more involved in FFR in declining industries, than in non-declining industries. Ergo, a declining industry on its own does not create the incentive to engage in fraudulent financial behaviour of CFO’s.
A declining industry does add pressure to the job of a CFO as reaching financial targets and creating shareholder value tend to become harder to achieve. And, as literature supports, pressure is an important trigger to FFR. So, a declining industry adds pressure to the CFO’s job, which in turn could be a trigger to commit FFR. In practice, this implies that CFO’s in declining industries might be triggered more easily to engage in FFR. Therefore, companies that operate in declining industries should be watched more closely, as pressure to perform increases. Again, it must be noted that this research does imply that a declining industry as only factor is not enough for CFO’s commit FFR
Limitations
This research was limited to the use of SEC database information exclusively. Therefore, only fraud cases listed in this database in the period of 2007 – March 2013, originating from the US were derived, resulting in 137 relevant fraud cases. From these cases, only 39 cases involved CFO committing FFR in a declining industry. This study further does not focus on the rate of decline in industries, but limits itself to whether an industry declined or not.
Recommendations future research
7. REFERENCES
Ashforth, B.E., & Anand, V. 2003. The normalization of corruption in organizations.
Research in Organizational Behavior: 25, 1-52.
Beasley, M.S., Carcello, J.V., & Hermanson, D.R 1999. Fraudulent financial reporting: 1987-1997, An analysis of U.S. public companies. Committee of Sponsoring Organizations of the
Treadway Commission, March 1999.
Beasley, M.S., Carcello, J.V., Hermanson, D.R., & Neal, T.L. 2010. Fraudulent financial reporting: 1998-2007, An analysis of U.S. public companies. Committee of Sponsoring
Organizations of the Treadway Commission, May 2010.
Breschi, S., Malerba, F., & Orsenigo, L. 2000. Technological Regimes and Schumpeterian Patterns of Innovation. Economic Journal, 110: 388-410.
Cohen, W.M., & Klepper, S. 1996. A reprise of size and R&D. Economic Journal, 106: 925-951.
Corson, M., & Miyagawa, T. 2012. Global CFO: from scorekeeper to strategist. Financial
Executive, November.
Cressey, D.R. 1950. The criminal violation of financial trust. American Sociological Review, 15(6): 738-743.
Dorminey, J., Fleming, A.S., Kranacher, M.J., Riley, R.A. 2012. The evolution of fraud theory. Issues in Accounting Education, 27(2): 555-579.
Ernst & Young. 2010. The DNA of the CFO: A study of what makes a chief financial officer.
http://www.ey.com/Publication/vwLUAssets/The-DNA-of-the-CFO-2010/$FILE/The-DNA-of-the-CFO-2010.pdf. June 3, 2013.
Geiger, M.A., & North, D.S. 2006. Does hiring a new CFO change things? An investigation of changes in discretionary accruals. The Accounting Review, 81(4): 781 – 809.
Ghemawat, P., & Nalebuff, B. 1990. The devolution of declining industries. The Quarterly
Journal of Economics, 105(1): 167-186.
Hawke, F. 2009. Fraud in hard times. China Business Review, 36(5): 34-37
Jones, G.R. 2010. Organizational Theory, Design and Change. New Jersey: Pearson Education Inc. 391-392.
Karpoff, J.M., Lee, S., & Martin, G.S. 2008a. The cost to firms of cooking the books.
Journal of Financial and Quantitative Analysis, 43(3): 581-612.
Karpoff, J.M., Lee, S., & Martin, G.S. 2008b. The consequences to managers for financial misrepresentation. Journal of Financial Economics, 88(2): 193-215.
Klepper, S. 1997. Industry life cycles. Industrial and Corporate Change, 6(1): 145-182
Klepper, S., & Miller, J.H. 1995. Entry, exit, and shakeouts in the United States in new manufactured products. International Journal of Industrial Organization, 13: 567-591.
Loebeckke, J.K., Eining, M.M., & Willingham, J.J. 1989. Auditors’ experience with material irregularities: frequency, nature and detectability. Auditing: A Journal of Practice & Theory, 9(1): 1-28
Macey, J.R. 2010. The distorting incentives facing the U.S. Securities and Exchange Commission. Harvard Journal of Law & Public Policy, 33(2), 639-670.
Marks, J.T. 2012. A Matter of Ethics: understanding the mind of a white-collar criminal.
Mian, S. 2001. On the choice and replacement of chief financial officers. Journal of
Financial Economics, 60(1): 143-175.
Murhpy, P.R., & Dacin, M.T. 2011. Psychological pathways to fraud: understanding and preventing fraud in organizations. Journal of Business Ethics, 101: 601-618.
Peltoniemi, M. 2011. Reviewing industry life-cycle theory: avenues for future research.
International Journal of Management Reviews, 13: 349-375.
Rammamoorti, S., & Olsen, W. 2007. Fraud: the human factor. Financial Executive, July/August.
U.S. Bureau of Economic Analysis. Percent Changes in Chain-Type Quantity Indexes for
Gross Output by Industry. 2013a.
http://www.bea.gov/iTable/iTable.cfm?ReqID=5&step=1#reqid=5&step=4&isuri=1&402=17 &403=1. June 3.
U.S. Bureau of Economic Analysis. Guide to the Interactive GDP-by-Industry Accounts
Tables. 2013b.
http://bea.gov/industry/guide_to_the_interactive_gdp_by_industry_accounts_tables.htm. June 3.
U.S. Securities & Exchange Commission. Litigation releases. 2013a
http://www.sec.gov/litigation/litreleases.shtml. June 3.
U.S. Securities & Exchange Commission. Edgar Search Engine. 2013b.
http://www.sec.gov/edgar/searchedgar/webusers.htm. June 3.
Vernon, R. 1966. Product life-cycle.
http://teaching.ust.hk/~mgto650p/meyer/readings/2/08_Vernon1979.pdf. March 11, 2013.
Wang, P. 2010. Restructuring to repair legitimacy – A contingency perspective. Corporate
APPENDIX
SPSS OUTPUT
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance
CFO_involved? 137 1,00 ,00 1,00 ,7007 ,45962 ,211
Declining Industry? 137 1 0 1 ,39 ,489 ,239
Percent Changes in Chain-Type Quantity Indexes for Gross Output by Industry (2003 – 2011)
Bureau of Economic Analysis Release Date: November 13, 2012
2003 2004 2005 2006 2007 2008 2009 2010 2011
All industries 2 3,3 3,7 2,3 2 -1,7 -5,5 2,5 1,6
Private industries 2,1 3,5 4 2,5 2,1 -2,1 -6,6 2,7 2,1
Agriculture, forestry, fishing, and hunting 3,5 2,3 0,9 -0,2 -0,3 -1 3,9 -1,4 -4,6
Farms 4,2 1,6 0,5 0,2 1,4 -0,2 5,2 -1,8 -5,6
Forestry, fishing, and related activities -0,3 6,7 3,1 -2,4 -9,8 -6,4 -5,9 1,6 4,3
Mining -1,4 3,5 1,7 6 3,3 2,3 -8,2 5,2 8,6
Oil and gas extraction -6,7 -2,2 -4,9 0,3 3,3 -0,1 10,5 0,6 5,1
Mining, except oil and gas -1,7 3,6 4,2 3 -3,1 -2,9 -14,8 4,6 1,8
Support activities for mining 19,6 24,6 20,6 23 7,9 10,5 -34,6 16,6 22,7
Utilities -7,6 -4,5 4,7 -6,1 3,3 4,2 -15,2 5,6 -4,7
Construction 2,4 1 3,2 -1 -5,2 -7,2 -12,4 -8,4 -4,7
Manufacturing 0,6 2,4 3,6 1,1 3 -5,9 -12,7 6 3,2
Durable goods 1,9 2,7 4,6 2,7 4,3 -5,6 -20 11 7,1
Wood products 0,2 2,2 6,8 1,5 -6,8 -13,8 -22,3 4,3 -1,6
Nonmetallic mineral products 1,5 2,9 3,5 2,1 -0,9 -12,4 -24,4 3,8 3
Primary metals -3,8 10,4 0,2 -1,5 3,3 0,3 -26,9 22,4 18,2
Fabricated metal products -1,1 -0,2 4,6 5,1 3,9 -3,5 -22 5,7 4,2
Machinery 0,9 3,7 7,5 4,6 3,5 -2,3 -21,5 10,9 9,9
Computer and electronic products 8 8,9 5,9 8,6 9 2,2 -13,7 9,2 2,6
Electrical equipment, appliances, and components -2 1,3 1,8 0,1 3,4 -4,3 -20,5 4,5 1,4
Motor vehicles, bodies and trailers, and parts 4,9 -0,4 0,7 0 1,2 -19,8 -27,2 31,9 15,5
Other transportation equipment -4,2 -1,7 12,2 1,1 24,7 0,2 -9,2 -0,3 -1,6
Furniture and related products -2,4 2,7 3,6 -1,8 -5,4 -9,2 -27,3 -2,1 2,7
Miscellaneous manufacturing 3,3 -0,5 7,3 2,6 -2,6 1,5 -7,8 3,6 3,1
Nondurable goods -0,9 2 2,5 -0,5 1,8 -6,2 -5,5 1,8 -0,2
Food and beverage and tobacco products 1,9 -0,1 3,9 0,2 0,6 -1,5 0,7 -0,7 0,9
Textile mills and textile product mills -3,5 -1,5 0,7 -9,8 -12,3 -12,4 -21,2 7,1 -3,7
Paper products -2,8 0,2 -0,3 -1,2 1,1 -4,6 -10,2 1,4 -3,3
Printing and related support activities -3,7 0,6 -0,2 -1,3 2,4 -6,2 -15,6 -0,4 -2,3
Petroleum and coal products -5,3 8,4 5,8 -1,3 2,7 -4,2 -0,6 -1,9 1,6
Chemical products -0,4 4,1 1 1,2 6,6 -10,9 -9,8 6,2 -2,2
Plastics and rubber products -0,2 1,1 1,1 -0,6 -1,6 -10 -15,8 8,7 0,9
Wholesale trade 2,9 6,7 2,7 3,5 2,4 0,8 -20,1 13,7 7
Retail trade 3,1 3,9 0,8 3,2 0,8 -5,6 -7,2 10,8 1,4
Transportation and warehousing 2,7 4 2,2 2,8 1,9 -1,9 -11,4 4,8 3,7
Air transportation 13,7 2,5 -1,1 1,3 4,2 -4 -10,3 2,8 1
Rail transportation 2,1 5,6 6 5,1 -0,4 1,3 -15,4 14,5 4,8
Water transportation -3,3 7 -2,6 1 12,5 1,1 9,1 -3,6 4,8
Truck transportation -0,2 5,9 4,8 3 0,6 -5 -14,9 5,6 4,8
Transit and ground passenger transportation -6,3 3,1 -0,9 2,3 -1,8 -1,3 -8,4 -4,3 -2,2
Pipeline transportation -1,5 -0,2 -4,4 -0,2 1,7 7,3 -21,5 -0,6 3,6
Other transportation and support activities 1,7 3,3 2,7 2,3 0,9 -0,1 -10,1 4,5 3,3
Warehousing and storage 6,1 -0,7 -0,1 7,5 3,9 3,5 -3,8 9,2 7,6
Information 0,5 4,8 5,6 4,1 4,1 2,5 -3,1 3,1 4,9
Publishing industries (includes software) -0,4 5,9 3,6 2 5,7 0,3 -6,3 1,4 6,4
Motion picture and sound recording industries 3,6 2,1 0,4 2,1 -0,7 -1,2 -5,6 4,6 0,7
Broadcasting and telecommunications 0,5 4 7,8 4,4 3,7 3,5 -1,5 3,1 3,4
Information and data processing services 0,2 8,1 4,7 9,7 6,6 6,2 -0,9 5,6 11,5
Finance, insurance, real estate, rental, and leasing 3,7 5,1 7,4 4,2 2 -2,6 0,7 -1,6 -0,9
Finance and insurance 4,9 4,8 7,9 7,8 3,2 -4,6 5,2 -4,7 -2,2
Federal Reserve banks, credit intermediation, and related activities 5,4 2 8,3 5,4 2 1,1 7,5 -10,9 -3,4 Securities, commodity contracts, and investments 7,6 8,8 13,5 21,4 2,4 -20,3 26,4 0 -4,9
Insurance carriers and related activities 3,9 5,4 4,2 1,9 4,2 -0,5 -8,5 0,8 0,6
Funds, trusts, and other financial vehicles -2,2 10,5 4,8 6,6 11,7 3,8 -14,9 0 4,2
Real estate and rental and leasing 2,7 5,4 6,9 1,2 1 -0,9 -3,1 1,2 0,4
Real estate 2,3 5,8 7 1 0,9 -1,3 -1,7 0,8 0,2
Rental and leasing services and lessors of intangible assets 6,5 2,1 6,6 3,3 2,2 2,8 -13,7 4,8 1,2
Professional and business services 2,8 4,3 4,7 3,1 4,1 2 -4,9 2,1 3,4
Professional, scientific, and technical services 2,4 4,2 4,5 3,2 4,8 2,6 -5,2 2,1 3,2
Legal services 6,2 -1,3 -0,6 0,1 -0,7 -3 -5,4 -2 -3,2
Computer systems design and related services 3 5,5 8,8 6,4 15,2 7,1 -1 10,3 6,5
Miscellaneous professional, scientific, and technical services 1,1 5,7 5,1 3,4 4,1 3,1 -6,1 1,1 4
Administrative and support services 5,4 3,6 7 4,1 3,7 1,7 -6,1 3 5,8
Waste management and remediation services 4,6 5,4 5,3 6,7 2,3 0,9 -9,6 9,2 -2
Educational services, health care, and social assistance 3,1 2,5 2,9 2,5 2,4 3,2 1,4 2,3 3,5
Educational services -0,4 0,7 1,2 1,4 2,3 1,9 -1,3 3,4 3,8
Health care and social assistance 3,6 2,7 3,1 2,7 2,4 3,3 1,8 2,2 3,5
Ambulatory health care services 4,8 4,5 3,4 2,7 2,3 3,2 0,1 2,3 2,9
Hospitals and nursing and residential care facilities 2,5 1,7 2,7 2,8 2,2 3,3 3,4 2,2 4,4
Social assistance 3 -2,1 3,7 1,6 4,1 4,7 2 1,5 1,9
Arts, entertainment, recreation, accommodation, and food services 3,7 4,3 2,8 4,4 2,3 -1,5 -5,5 1,9 4,3
Arts, entertainment, and recreation 2,3 2,3 0,4 5,9 4,8 0 -4,8 -0,2 2,6
Performing arts, spectator sports, museums, and related activities 3,8 1,6 1,1 6,3 6 1,5 -1,6 -0,6 2,9
Amusements, gambling, and recreation industries 0,6 3,2 -0,5 5,3 3,4 -1,9 -9,1 0,5 2,2
Accommodation and food services 4,1 4,9 3,5 3,9 1,5 -1,9 -5,7 2,6 4,7
Accommodation 6,1 8,6 5,9 5,9 2,6 -0,1 -8,5 4,4 5,7
Food services and drinking places 3,4 3,6 2,6 3,2 1,1 -2,7 -4,6 1,9 4,4
Other services, except government -0,5 0,6 -1,1 1,8 -0,3 -1 -7 -0,3 1,4
Government 1,8 1,4 0,7 1 1,3 1,5 3,5 0,7 -1,6
Federal 5,8 3,5 1,1 1 0,4 5,2 4,8 3,7 -2
General government 6,6 3,8 1,2 1,2 0,6 6,2 6,2 4,2 -2,2
Government enterprises 0,3 1 0,6 -1 -1,5 -3,6 -7,9 -2,6 -0,1
State and local 0 0,4 0,5 1 1,7 -0,3 2,8 -0,9 -1,4
General government -0,3 0,2 0,4 0,8 1,7 -0,2 2,7 -1 -1,7
Government enterprises 2,3 1,9 1,3 2,8 2 -1,8 3,7 -0,3 1,3
Addenda:
Private goods-producing industries [1] 1 2,2 3,3 0,9 1,3 -5,3 -11,5 3 1,9
Private services-producing industries [2] 2,6 4,1 4,4 3,2 2,4 -0,5 -4,4 2,6 2,1
Information-communications-technology-producing industries [3] 3,6 7,3 5,6 6,3 9 3,2 -7 6,7 5,9
Legend / Footnotes:
1. Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
Percent Changes in Chain-Type Quantity Indexes for Gross Output by Industry (1995 – 2002)
Bureau of Economic Analysis Release Date: November 13, 2012
1995 1996 1997 1998 1999 2000 2001 2002
All industries 3,5 4,2 5,1 5,6 5,1 4,5 0,2 0,6
Private industries 3,9 4,7 5,5 6 5,3 4,7 -0,1 0,2
Agriculture, forestry, fishing, and hunting -3,4 1,4 5 2,1 3,5 -1,5 -0,5 0,2
Farms -4,1 2 7 0,6 2,3 1,3 -1,4 -0,4
Forestry, fishing, and related activities 0,4 -1,6 -5,2 10,9 9,6 -13,8 5 3,5
Mining -2 1,8 4,4 0,2 -5,2 1,3 5,2 -3,1
Oil and gas extraction -2,2 -1,1 1,4 1,9 -8 -5,3 1,4 1,9
Mining, except oil and gas -0,5 3,1 3 3 -0,9 -0,6 -2,2 -2,3
Support activities for mining -6,3 12,8 22,9 -10,1 -5,6 39,4 36,4 -18,9
Utilities 0,6 1,3 0,7 2,8 17,8 10,2 7,9 -23,2
Construction 1,3 7 4 7,9 1,5 3,4 -0,1 -3
Manufacturing 4,9 3,4 7 5 4,3 1,9 -4,8 -0,1
Durable goods 7,8 6,5 9,4 8 6,8 3,6 -6,5 -0,9
Wood products 2,8 2,7 2,8 5,8 3,5 -0,7 -5,9 2,2
Nonmetallic mineral products 3,1 6,7 3,6 5,7 1,4 0 -3,1 -0,1
Primary metals 1,1 3,1 3,2 3,7 -0,7 -4,6 -8,4 2
Fabricated metal products 5,4 3,6 4,7 3,6 1 3,5 -6,5 -2,4
Machinery 8,2 2,4 4,6 2,8 -1,7 5,6 -11 -6
Computer and electronic products 30,3 23 22,5 17,9 21,7 24,3 -6,4 -10,6
Electrical equipment, appliances, and components 2,7 2,9 3,9 4 1,9 4,8 -10 -8,1
Motor vehicles, bodies and trailers, and parts 2,9 0,3 9,2 5,1 13,5 -6,1 -9,5 11,7
Other transportation equipment -6,4 5 13,2 18,1 -3,9 -12,9 8,2 -5,5
Furniture and related products 1,9 1 10,8 7,3 3,2 1,6 -6 4,6
Miscellaneous manufacturing 4,3 4,4 3,1 4,3 2,6 5,2 -2,2 6,8
Nondurable goods 1,4 -0,2 4,1 1,3 0,9 -0,2 -2,7 0,8
Food and beverage and tobacco products 3,1 -1,6 3,1 3,6 -0,9 1,2 -0,1 -1,4
Textile mills and textile product mills -1 -1,8 3,4 -1,2 0,8 -2,4 -9,6 -1,1
Apparel and leather and allied products -1,5 -2,2 1,4 -6,3 -3,6 -5,5 -15,1 -17,7
Paper products -0,3 -2,4 1,8 0,2 1,3 -2,8 -5 0,5
Chemical products 0,9 1,2 6,6 0,8 1,7 -0,3 -2,7 5,6
Plastics and rubber products 2,1 3,4 6 4 5,1 0,9 -5,1 1,9
Wholesale trade 5,1 4,5 7,6 9,9 7,6 6,4 1,3 4,1
Retail trade 4,6 6,1 4,9 4 5,8 3,2 -1 4,6
Transportation and warehousing 3,7 4,7 4,1 3,6 2,5 2 -3,4 -0,4
Air transportation 5,6 7,4 5,4 -1,6 1,8 3 -10,8 -3,4
Rail transportation 4,8 1,5 0,7 -0,5 -0,8 -0,8 -0,1 -0,3
Water transportation 5,1 7,2 2,5 -2,3 -3,9 -2,3 -3,1 -6,4
Truck transportation 2,7 5,4 4,6 5,4 4,1 2,3 -2,3 0,5
Transit and ground passenger transportation 2,2 -0 4,6 8 -2,2 -4,9 0,9 1
Pipeline transportation 0,5 2,9 0,4 3,5 0,5 -9,3 -4,5 -2,6
Other transportation and support activities 1,9 3 3 8,3 4,4 5,7 -1,9 -0,5
Warehousing and storage 8,8 4,7 10,4 7,4 5,7 6,6 4,8 7,7
Information 7,9 8,9 9,6 10,4 12,2 10,4 2,3 1,6
Publishing industries (includes software) 8,3 10,2 13,5 9,9 9,3 5,9 0,2 0,7
Motion picture and sound recording industries 5,7 5,8 0,5 5,8 3,5 2,3 -1,1 5,2
Broadcasting and telecommunications 8,1 9,8 10,9 11,4 13,3 11,7 3,9 1,1
Information and data processing services 7,7 1,4 -2,1 9,6 25,4 24,8 2,7 3,6
Finance, insurance, real estate, rental, and leasing 2,7 5 4,1 6,8 7,6 9 2,6 0,7
Finance and insurance 3,7 5,7 6,1 9,7 12,3 11,9 2,5 0,7
Federal Reserve banks, credit intermediation, and related activities 2,6 5,7 4,5 5,2 8,8 4,2 8 6,3 Securities, commodity contracts, and investments 25,1 26,7 21,9 28,4 35,2 34,4 -3,9 -7,7
Insurance carriers and related activities -3,3 -3,8 -1,4 4,7 3,4 5,5 2,1 0,9
Funds, trusts, and other financial vehicles 14,8 14,8 17,6 11,9 9,9 16,8 -6,4 -9,6
Real estate and rental and leasing 1,9 4,5 2,7 4,7 3,9 6,6 2,8 0,7
Real estate 1,6 3,7 2,4 3,3 3,3 6,6 3,3 1,2
Rental and leasing services and lessors of intangible assets 5,2 11 5,6 16 9 6,6 -0,9 -4,2
Professional and business services 5,6 8,2 8,2 9,1 5,5 4,7 0,3 -0,4
Professional, scientific, and technical services 4,8 8,6 8,5 8,9 5,8 5,6 2 0,5
Legal services -1,9 4 2,6 5,2 3,7 0,3 2,3 1,3
Computer systems design and related services 9,8 17,6 23,8 28,1 18,7 9,9 -0,8 -3,5 Miscellaneous professional, scientific, and technical services 6,4 8,7 7,6 6,2 3,4 6,1 2,7 1,3
Management of companies and enterprises 2,4 5,3 6,1 7,2 3,2 1,6 1,2 0,2
Administrative and waste management services 9,3 9 8,6 10,7 6 4,3 -3,7 -2,8
Administrative and support services 10,4 9,9 9,6 10,8 6,1 4,9 -3,9 -2,8
Waste management and remediation services 3,2 3 1,7 9,6 5,5 0 -2,1 -2,9
Educational services, health care, and social assistance 2,8 2,8 2,8 3,6 2,5 3,4 4,6 4,2
Educational services 3,7 4 1,1 3,9 3,1 4 3,7 -2,5
Ambulatory health care services 3,5 3 2,6 3,4 2,2 3,7 5,3 5,8 Hospitals and nursing and residential care facilities 1,6 2,4 2,2 4 1,8 2,1 3,4 5,1
Social assistance 3,8 2,2 11,4 2,3 7,3 7,8 9,2 2,9
Arts, entertainment, recreation, accommodation, and food services 3,5 3,4 3,1 3,3 2,6 4,8 -0,6 2,7
Arts, entertainment, and recreation 8,6 5,4 4,2 3,8 0,4 1,9 1,5 3,9
Performing arts, spectator sports, museums, and related activities 7,5 5,8 2,2 6,6 1,8 5 5,8 6,7 Amusements, gambling, and recreation industries 9,5 5,2 5,9 1,4 -0,8 -0,9 -2,8 0,8
Accommodation and food services 2,1 2,9 2,8 3,2 3,3 5,6 -1,1 2,4
Accommodation 4,5 5,3 0,4 2,6 5,2 8,4 -7,3 1,7
Food services and drinking places 1,2 2 3,7 3,4 2,5 4,5 1,3 2,6
Other services, except government 4,8 3,1 1 5,9 1,9 2,3 -0,3 0,2
Government 0,6 0,9 1,9 2,2 2,7 2,1 3,4 3,6
Federal -3,2 -1,1 0 -0,9 1 1,3 3 5,2
General government -3,4 -1,7 -0,4 -1,8 0,9 1,2 4 6,5
Government enterprises -1,8 2,9 2,9 4,8 1,9 2,2 -3,6 -3,6
State and local 2,7 1,9 2,8 3,8 3,4 2,5 3,5 3
General government 2,5 1,8 2,9 4 3,4 2,7 3,8 2,9
Government enterprises 3,7 2,3 2,1 2,7 3,8 0,4 1,3 3,8
Addenda:
Private goods-producing industries [1] 3,7 3,8 6,4 5,2 3,5 2 -3,5 -0,7
Private services-producing industries [2] 4 5,2 5 6,5 6,3 6,2 1,6 0,7
Information-communications-technology-producing industries [3] 21 17,9 18,8 16,8 18,3 17 -2,9 -4,8
Legend / Footnotes:
1. Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
2. Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing; professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services; and other services, except government.
Legend / Footnotes:
1. Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
2. Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing; professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services; and other services, except government.
