DBJ Discussion Paper Series, No. 1803

The Deterrent Effect of Anti-Bribery Law Enforcementon the Quality of Earnings

Pramuan Bunkanwanicha（ESCP Europe）
Olivier Greusard（ESCP Europe）

February 2019

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in order toprotect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the views of
the Institute.

The Deterrent Effect of Anti-Bribery Law Enforcementon the Quality of Earnings
Pramuan Bunkanwanicha†
Olivier Greusard‡This version: February 8, 2019

Abstract
This paper studies the effect of anti-bribery enforcement on the accrual quality of investigatedfirms and their peers. We analyze a hand-collected sample of 241 bribery cases investigatedunder the US Foreign Corruption Practices Act (FCPA) over the period 1978-2015.
Exploiting the disclosure of anti-bribery law enforcement actions, we document a positiveeffect on the accrual quality of investigated firms’ peers, but not the investigated firms
themselves. Additional tests document that this positive effect is significant only for cases
revealed before 2005, when the prosecutors developed their enforcement activity and the useof alternative resolution vehicles. Our results show a positive impact of anti-bribery law
enforcement that incentivizes peer firms to enhance their accrual quality once they
acknowledge bribing behavior in their industry.
Keywords: Corruption, Anti-bribery Laws, Accrual Quality, Deterrent EffectJEL Classification: F23, G30, G38, K42, M41, M48

We thank the Laboratory of Excellence for Financial Regulation (LabEx-ReFi) for financial support. We also
thank Edith Ginglinger, Clifford G. Holderness, Seraina C. Anagostopoulou, Shyam Sunder, Gerard Hertig,
Léah Ambler (OECD), José Martin Flores, Arthur Petit-Romec, Andrew Zylstra, PhD students from ESCP
Europe and Labex ReFi, all Professors from ESCP Europe. We finally thank 13th Annual Conference on
Empirical Legal Studies (CELS) in Ann Arbor, Michigan, at the University of Michigan Law School, 35th
Annual Conference of the French Finance Association (AFFI) in Paris at ESCP Europe, 41st Annual Congress ofEuropean Accounting Association (EAA) in Milan at Bocconi University, and all Public Authority and FinanceInternational Conference 2017 participants in Paris as well as LabEx ReFi PhD seminar participants for theircomments. Parts of this paper were written when Pramuan Bunkanwanicha was a Shimomura Fellow at
Development Bank of Japan in July-August 2018.
†- ESCP Europe – Email: pbunkanwanicha@escpeurope.eu‡- ESCP Europe – Email : olivier.greusard@edu.escpeurope.eu

1The Deterrent Effect of Anti-Bribery Law Enforcementon the Quality of Earnings

This version: February 8, 2019

Abstract
This paper studies the effect of anti-bribery enforcement on the accrual quality of investigatedfirms and their peers. We analyze a hand-collected sample of 241 bribery cases investigatedunder the US Foreign Corruption Practices Act (FCPA) over the period 1978-2015.
Exploiting the disclosure of anti-bribery law enforcement actions, we document a positiveeffect on the accrual quality of investigated firms’ peers, but not the investigated firms
themselves. Additional tests document that this positive effect is significant only for cases
revealed before 2005, when the prosecutors developed their enforcement activity and the useof alternative resolution vehicles. Our results show a positive impact of anti-bribery law
enforcement that incentivizes peer firms to enhance their accrual quality once they
acknowledge bribing behavior in their industry.
Keywords: Corruption, Anti-bribery Laws, Accrual Quality, Deterrent EffectJEL Classification: F23, G30, G38, K42, M41, M48

2

1 - Introduction
Anti-bribery law enforcement activity is becoming an important parameter for
regulators and for companies that do business abroad. Since the OECD Anti-Corruption
Convention came into force in 1999, 43 countries have ratified the document. At a national
level, several countries like the United Kingdom 1 and France 2 recently passed new laws inorder to develop anti-bribery enforcement activity. The USA was a forerunner in the field, as
the Foreign Corrupt Practices Act (FCPA), the U.S. anti-bribery law, was passed in 1977 andremained the only anti-bribery law in the world for decades. U.S. anti-bribery enforcement
activity remained quite sporadic until it became a priority for the U.S. law enforcement
agencies in the 2000s (Karpoff, Lee, & Martin, 2012). Out of 150 enforcement actions in our
sample for the period 1978-2015, 78% were enforced after the beginning of 2007. This recent
development of anti-bribery enforcement activity over the last decade creates a new field foranalysis on whether more activity by the regulator leads to a change in the behavior oftargeted firms, but also their peers, due to a deterrent effect of anti-bribery law enforcement.
In this paper, we investigate the quality of the accounting information of bribe-paying
firms and their competitors. Using a difference-in-differences approach, we test whether
FCPA enforcement generates a deterrent effect and leads to a change in the behavior of firms,
proxied by their Accrual Quality. Following the modified Dechow Dichev model, we
calculate the volatility of discretionary accruals over two three-year periods (3 years before
the acknowledgment year and 3 years after) and use it as our main measure of Accrual
Quality. We document a positive effect on the quality of the accounting information of bribepaying firms’ competitors, but not the bribe-paying firms themselves. Additional tests
document that this positive effect is stronger for cases revealed in the last decade (in or after

1
2

See U.K Bribery Act: https://www.legislation.gov.uk/ukpga/2010/23
See Loi Sapin II: https://www.legifrance.gouv.fr/eli/loi/2016/12/9/ECFM1605542L/jo/texte3

2005) when enforcement became a priority of prosecutors, symbolized by a higher number ofcases and a broader choice of resolution vehicles, with the possibility to use Non or Deferred
Prosecution Agreements (NPAs/DPAs) in addition to the already existing PAs (Plea
Agreements).
Our paper is one of the first, to our knowledge, to use the volatility of discretionary
accruals as a proxy of materiality to test for a change in the Accrual Quality of bribing firms
and their peers. Where other papers integrate different sources from Securities and Exchange
Commission (SEC) enforcement and private class actions, this paper focuses on FCPA
enforcement and de facto includes Department of Justice (DOJ) enforcement under FCPA andSEC FCPA enforcement, even though the two prosecutors tend to work more and more incollaboration on FCPA cases. This paper also uses an updated database of enforced cases up
to 2015, reflecting the increase in enforcement activity over the last decade. Our paper
contributes to the literature on the deterrent effect of the law by showing that theacknowledgment of bribing behavior that enters into the scope of FCPA leads to a significant
increase in the Accrual Quality of peers, while no significant change is observed for targetedfirms. These results suggest a positive deterrent effect of law enforcement for peers only.
The development of anti-bribery enforcement activity in the last decade has beenaccompanied by more and more publicity on the revelation date by prosecutors, the DOJ andthe SEC, and higher levels of fines at the settlement date. The Siemens AG case seems tohave been a “catalyst case” in this respect. In 2008, Siemens AG and three subsidiaries
pleaded guilty to FCPA violations for what was for a long time the highest penalty paid to the
DOJ and SEC in an FCPA case. 3 The coordinated enforcement action by the DOJ, SEC andGerman authorities resulted in $1.6 billion in fines, penalties and disgorgement of profits. The
prosecutor said in the press release: “Today’s announcement of the guilty pleas entered by3

Siemens is number one in the Top Ten List of Corporate FCPA Settlements for the period of our study (19782015)

4

Siemens AG and several of its regional companies reflects the FBI’s dedication to enforce theprovisions of the Foreign Corrupt Practices Act. Simply stated, it is a federal crime for U.S.
citizens and companies traded on U.S. markets to pay bribes in return for business.” 4
In 2009, Kellogg Brown & Root LLC (KBR) and its former parent company,
Halliburton Company, pleaded guilty and agreed to pay $579 million for their decade-longscheme to bribe Nigerian government officials to obtain engineering, procurement andconstruction contracts. The Acting Assistant Attorney General Rita M. Glavin of the Criminal
Division commented that “the successful prosecution of KBR (…) demonstrates that no one isabove the law, and that the Department is determined to seek penalties that are commensurate
with, and will deter, this kind of serious criminal misconduct”. FBI Special Agent Andrew R.
Bland III added, “this case, which represents the second largest fine ever in an FCPA
prosecution, demonstrates the FBI’s continued commitment to aggressively investigate
violations of the law. We will continue to investigate these matters (…) to ensure that
corporate executives who have been found guilty of bribing foreign officials in return forlucrative business contracts, are punished to the full extent of the law.” 5 The updated “TopTen” enforcement actions of all time, published by the FCPA blog in 2017, reveals that all of
the ten highest FCPA settlements occurred between 2008 and 2017, 6 confirming thewillingness of the two prosecutors, the DOJ and the SEC, to rise to a new level in FCPA
enforcement.
In 2002, in the aftermath of the Enron case, the collapse of Arthur Andersen caused
the DOJ to reconsider its traditional approach to resolving alleged instances of corporate
criminal liability, as criminal charges alone could be a death sentence for a businessorganization (Koehler, 2015). The DOJ implemented new vehicles in 2004 for the resolution
4

See full DOJ Press Release https://www.justice.gov/archive/opa/pr/2008/December/08-crm-1105.html
See full DOJ Press Release https://www.justice.gov/opa/pr/kellogg-brown-root-llc-pleads-guilty-foreignbribery-charges-and-agrees-pay-402-million
6
See http://www.fcpablog.com/blog/2017/9/22/telia-tops-our-new-top-ten-list-after-we-do-some-math.html
55

of enforcement, called NPAs and DPAs. The use of these new resolution vehicles as apotential alternative to PAs (Plea Agreements) also accelerated the publicity generated aroundan FCPA investigation.
The sharp rise in enforcement cases, the new scale of fines in the last decade, and theuse of new vehicles for resolution since December 2004 make the FCPA a useful framework
to analyze the impact of law enforcement on the behavior of firms. It is also a stable
framework, as the main provisions of the law have remained unchanged since 1978 and no
private action is possible, as only the DOJ and the SEC share FCPA enforcement authority. 7
In the same vein as Shleifer and Vishny (1993) and Rose Ackerman (2010), whosuggested that public policy should seek to discourage bribery because it is detrimental to
growth, the previous literature has already studied the impact of anti-bribery laws. Hines(1995) states that the passage of FCPA weakened U.S. foreign investment in the absence ofeffective legal action by other countries, while Graham (1984) finds no negative effect of
FCPA on U.S. exports. Zeume (2017) suggests that imposing unilateral anti-bribery
regulations on some firms may benefit their unregulated competitors after observing that U.K.
companies operating in high-corruption countries experienced a drop in their value following
the passage of the U.K. Bribery Act 2010. Using a sample of 107 publicly listed firms
worldwide, Cheung et al. (2012) find that firm performance, the rank of the politicians bribedas well as bribe-paying and bribe-taking characteristics affect the magnitude of the bribes andthe benefits that firms derive from them. Karpoff et al. (2012) find that the Net Present Value(NPV) of the bribe becomes negative for firms enforced under FCPA if they face comingled
charges of financial fraud because of direct and reputational costs, and suggest thatprosecutors should increase the probability of bribing firms getting caught or should obtain ahigh level of penalties to make bribery unattractive on an ex-ante basis.
7

See FCPA Resource Guide https://www.justice.gov/sites/default/files/criminalfraud/legacy/2015/01/16/guide.pdf

6The SEC has limited resources (P. M. Dechow, Ge, Larson, & Sloan, 2011) and musthope for a deterrent effect (Jennings, Kedia, & Rajgopal, 2011). The deterrence of criminal
behavior depends on the probability of detection and punishment and on the penalties
imposed by the legal system (Becker, 1968). As penalties are not known at the time the SEC
investigation first becomes public, we focus our study on the deterrent effect created by anincrease in the probability of detection consequent to the acknowledgement of theinvestigation of a bribing firm. In a corporate context, the state can deter crime by inducing
firms to take optimal prevention and policing measures (Arlen, 2012). When the DOJ and/or
the SEC announce the opening of a new FCPA investigation for a firm’s bribing behavior, it
can create a deterrent effect on the peer firms in the same industry, who revise upwards theirprobability of being the next target of the prosecutor (Jennings et al., 2011). Through theenforcement process, the SEC develops know-how about how to trace wrongdoing in a
specific industry (accounting schemes to dissimulate bribes, bribing patterns that can besimilar from one firm in the same industry to the next). As the SEC selects firms for
enforcement action where there is strong evidence of manipulation (P. M. Dechow et al.,2011), we assume that peer firms will modify, among other things, the level of their earning
manipulation when they become aware of an FCPA investigation in their industry. Firms canintervene to deter (or encourage) crime both ex ante and ex post (Arlen, 2012).
SEC enforcement activity has already been studied in the literature through its
accounting-based enforcement actions in the Accounting and Auditing Enforcement Releases(AAERs). AAERs are “designed to anticipate emerging reporting problems and to maintainthe credibility of the disclosure system” and the SEC “most often pursued overstatements ofaccounts receivable and inventories resulting from premature revenue recognition and delayed
write-off, respectively” (Feroz, Park, & Pastena, 1991). Using accruals and discretionary

7

accruals measures, Dechow et al. (1996) find a significant decrease in the accruals of SEC
AAER targets after the alleged year of manipulation compared to peer firms.
The link between deterrent effect and earning management has also been tackled in theliterature. Analyzing both SEC enforcement actions and class action lawsuits over the years
1996-2006, Jennings et al. (2011) find a significance deterrence associated with both SEC
enforcement actions and class action lawsuits in the form of a reduction in performancematched discretionary accruals (Kothari, Leone, & Wasley, 2005). Using a real earning
management (REM) approach, Huang et al. find that litigation deters REM when they
conduct a difference-in-differences test centered on class action lawsuits against firms
headquartered in the Ninth Circuit (Huang et al. 2017).
The FCPA provides a well-defined scope to analyze a potential deterrent effect of law
enforcement on the behavior of firms. Public prosecutors, the DOJ and the SEC share FCPA
enforcement authority and no private action is possible. Enforcement actions are identified
and publicly available on the SEC and DOJ websites. In addition, the number of sets ofprovisions is limited to two: anti-bribery provisions and accounting provisions.
When, in the mid-1970s, the U.S. congress became aware of a foreign corporate
payments problem revealed by the office of the Watergate Special Prosecutor, the SEC andthe Church Committee, a multi-pronged investigation was conducted, from which the FCPA
would emerge two years later. One of the investigators was the SEC. In its report onquestionable and illegal corporate payments and practices, the SEC states that the focus is“not whether the discovered domestic and foreign payments were or should be illegal, butrather whether such payments were or should be disclosed to investors” (Koehler, 2012).
FCPA integrates this informational preoccupation and addresses the problem ofinternational corruption with two types of provisions: anti-bribery provisions and accounting
provisions. While anti-bribery provisions focus on prohibiting “individuals and businesses8from bribing foreign government officials in order to obtain or to retain business” (15 U.S.C.
§ 78dd-1), accounting provisions impose certain record-keeping (15 U.S.C. § 78m(b)(2)(A))
and internal control requirements (15 U.S.C. § 78m(b)(2)(B)). Under the “books and records”
provision, companies must keep books and records that accurately reflect the entity’stransactions and dispositions of the assets of the issuer. However, no concrete standard has
been set forth to determine what information must be reported in accounting records (Shaw,
1988). Shaw suggests that the concept of materiality should be used.
In accounting and for the SEC, materiality is defined as the “magnitude of an omission
or misstatement of accounting information, that in the light of surrounding circumstances,makes it probable that the judgement of a reasonable person relying on the information would
have been changed or influenced by the omission or misstatement”. 8
As materiality is linked to the magnitude of an omission or misstatement of accounting
information, the volatility of earnings can be a proxy for the change in materiality. In the
Dechow Dichev (DD) model, Accrual Quality (AQ) is measured as the volatility of thediscretionary accruals of a firm (P. M. Dechow & Dichev, 2002). In this model, the volatilityof the discretionary accruals is the variable of interest. A higher volatility of discretionary
accruals is associated with a lower quality of earnings. We consider that AQ is a proxy formateriality and we use the Dechow Dichev model augmented by McNichols (McNichols,
2002) to test for a change in the Accrual Quality of bribing firms and their peers.
If the acknowledgment of a future investigation of a firm leads to a decrease in thevolatility of earnings, we interpret that as an increase in the quality of the earnings and thelevel of materiality. As one goal of the FCPA is to address the problem of materiality throughaccounting provisions, we can assume that an increase in the AQ of a peer is a manifestationof the deterrent effect of FCPA, as companies improve the quality of their earnings and8

FASB Concept 2 then 89therefore their materiality to fit with the purpose of the law and avoid a future investigation.
The acknowledgement of an FCPA investigation of a firm can lead a peer firm in the same
industry to improve the disclosure of its accounting and financial information to avoid being
the next target of the SEC. The improvement in accounting information would be amanifestation of the deterrent effect of law enforcement.
The rest of the paper is organized as follows. Section 2 develops the hypotheses.
Section 3 discusses the research design and the sample. Section 4 presents descriptive
statistics and reports the main results and additional results, while Section 5 provides a
supplementary analysis. Section 6 concludes.

2 – Development of Hypotheses
As we are seeking to show the deterrent effect of anti-bribery enforcement using
Accrual Quality following the modified Dechow Dichev model, we test whether the volatilityof discretionary accruals is different before and after the acknowledgment of bribing behavior
for bribing firms, but also for their peers, using a difference-in-differences design around theexogenous shock materialized by an event, namely the revelation of future FCPA
enforcement. In this formulation, higher volatility of discretionary accruals is associated withlower Accrual Quality, and a decrease in volatility after the event is associated with anincrease in Accrual Quality. We finally test whether the Accrual Quality of bribing firms andpeer firms behaves differently around the event date.
Firstly, we test whether the acknowledgement of future enforcement has an impact on
the Accrual Quality of the bribing firms. If bribing firms ex-ante window-dressed their AQ,
the trigger event could lead to a decrease in their AQ, as they do not need to show a high levelof AQ to the users of the financial statements ex-post, especially the prosecutors in our case.
Under this assumption, Accrual Quality would be higher before the event date, as observed

10

for example by Dechow et al. (2011), for whom firms that have been subject to enforcement
action by the SEC for allegedly misstating their financial statements show measures ofaccruals quality that are unusually high in misstating years relative to the broad population offirms. Conversely, future enforcement can lead companies to enhance the quality of theircompliance and accounting processes in order to avoid future bribing behavior within the firm
(especially when top management was not aware of the bribing pattern that occurred, forexample in a geographically distant subsidiary). In that case, we would observe an increase inAccrual Quality after the event date for the investigated firms where a decrease in thevolatility of discretionary accruals would be consistent with a deterrent effect of law
enforcement. Hypothesis 1a (H1a) is stated as follows:
H1a: the AQ of investigated firms increases after revelation of a new investigation.

Secondly, we test whether the acknowledgement of future potential enforcement has
an impact on the Accrual Quality of non-investigated firms. We can expect peers to react
when they become aware of the opening of an investigation of one of their peers. FCPA
enforcement is costly for firms, directly through fines, disgorgements and other financial
compensations disclosed in the settlements. It can also be indirectly costly through reputation
losses (Karpoff et al., 2012), and “observing a public SEC enforcement in its industry against
a target firm is likely to increase a peer firm’s knowledge about SEC activity and cause it torevise upward its subjective probability of attracting such an action against itself” (Jennings etal., 2011). Therefore, we can expect an increase in the AQ of peers after the revelation of
future enforcement against one firm in the same industry. The increase can be positively andnegatively stated. Following a positive assumption, i.e. an increase in AQ, peer companies
may improve their Accrual Quality because they genuinely and actively implement actions
within the firm to reduce bribing behavior. AQ is here a proxy for fair business practices. Or

11they may increase the window-dressing of their financial statements to avoid future
enforcement by showing a better image to prosecutors. AQ is here a proxy for windowdressing. Under a negative assumption, we assume that AQ would decrease after enforcement.
Peer firms could genuinely and actively implement actions within the firm to reduce windowdressing and that would lead to a decrease in AQ. Our hypothesis 1b (H1b) is stated in thenull form as follows:
H1b: the AQ of Peer firms increases after revelation of a new investigation

Finally, we investigate whether the change in AQ differs between investigated firms and peer
firms. Our design comprises multiple events. Each revelation of future enforcement occurs at
a different date and numerous different industries are involved. For each event taken
separately, investigated and peer firms face the same macro-economic conditions. To better
analyze the difference in AQ, both for investigated firms and peer firms, before and after thebribing pattern is revealed, we perform a difference-in-differences analysis. Hypothesis 1c(H1c) is as follows:
H1c: There is a significant difference in the change in AQ between Investigated firms andPeer firms after revelation of a new investigation of one firm in the industry

FCPA enforcement activity grew stronger in the mid-2000s and a broader range ofresolution vehicles became available to prosecutors. Nearly 64% of the enforcement actions
in our final sample were initiated after 2004. 9 On December 6th 2004, the DOJ used analternative vehicle for the first time in the resolution of the enforcement action against
InVision Technologies Inc and General Electric Company (Koehler, 2015). To go further in
our understanding of the change in the AQ of investigated firms and peer firms around the
9

In the final sample used in this study, 51 out of 80 cases, that is, 64% of the cases, were acknowledged after the
6th of December 200412

acknowledgment date, we split our main sample into two subsamples and check whether thetrend is different for a subsample made up of cases acknowledged before 2005 and anothersubsample made up of cases acknowledged during or after 2005. Hypotheses 2a (H2a) andH2b are as follows:
H2a: There is a significant difference in the change in AQ between Investigated firms andPeer firms after revelation of an investigation of one firm in the industry for cases that wereacknowledged before 2005
H2b: There is a significant difference in the change in AQ between Investigated firms andPeer firms after revelation of an investigation of one firm in the industry for cases that wereacknowledged in or after 2005

3 – Research Design and Sample
3.1 – Research Design
A number of papers have examined whether regulatory scrutiny increases thelikelihood of earnings management (Healy & Wahlen, 1999). For example, discretionary
accruals for firms from the cable television industry are found to be more negative during
periods of Congressional scrutiny, according to the political cost hypothesis (Key, 1997).
Moreover, high-accruals firms tend to have high discretionary accruals, have less persistentearnings, and be more subject to SEC enforcement action (P. Dechow, Ge, & Schrand, 2010).
In addition, Francis et al(Francis, LaFond, Olsson, & Schipper, 2005) remind us that

previous research has documented severe economic consequences for earnings of sufficiently
low quality as to attract SEC enforcement actions (Beneish, 1999; P. M. Dechow et al., 1996;
Feroz et al., 1991). Feroz et al. use the AAERs issued by the SEC to analyze which types ofaccounting and auditing problems motivate enforcement actions and find that, in their sample,
overstatements of accounts receivable and inventories resulting from premature revenue

13

recognition and delayed write-off represent 70% of investigations. They suggest that the SEC
is more likely to pursue alleged disclosure violations dealing with premature revenue
recognition or overstatements of current assets. As these two items are part of the accruals,
discretionary accruals seem to be the right tool to analyze the change in quality of accounting
information of the investigated and peer firms in our sample.
To estimate the discretionary accruals, we follow the Dechow Dichev accrual model
as modified in McNichols (McNichols, 2002). The original DD model (P. M. Dechow &Dichev, 2002) views the matching function of accruals to cash flows as being of primary
importance and thus models accruals as a function of current, past, and future cash flows. The
standard deviation of the residuals from the model is the proxy for earnings
quality. McNichols proposed to add the specificities of the Jones model to the DD model
because “including sales in the DD model provides a useful specification check on themagnitude of measurement error in their cash-flow variables.” The modified DD model is anextended version of the modified Jones model (P. M. Dechow, Sloan, & Sweeney, 1995) that
includes as additional independent variables the cash flow from operations (CFO) of last year,
the CFO of the current year, and the CFO of next year. McNichols argues that the change insales revenue and Property, Plant and Equipment (PPE) are important in forming expectations
about current accruals, over and above the effects of operating cash flows (McNichols, 2002).
Adding these variables to the cross-sectional DD regression significantly increases its
explanatory power, thus reducing measurement error. We believe that the use of the modified
DD model helps us to obtain a better-specified expectations model that, in turn, should lead toa better-specified stream of residuals.
We follow the Modified Dechow and Dichev model and calculate Discretionary
Accruals as the residual of the model taking into account CFO of last year, current year and14

next year and also integrate PPE and change in sales, as used in the Jones model and proposedby McNichols in the discussion paper about the DD model:
Accruals = Income Before Extraordinary Items (ib) minus Operating Cash Flows(oancf after 1988 and ib – Δact + Δ che + Δlct – Δdlc + dp before 1988)
Where ib is Income Before Extraordinary Items (Compustat item 18)
act is Total Current Assets (Item 4)che is Cash and Short-Term Investments (Item 1)
lct is Total Current Liabilities (Item 5)
dlc is Debt in Current Liabilities (Item 34)
dp is Depreciation and Amortization (Item 14)
oancf is Net Cash Flow from Operating Activities (Item 308)
We calculate the residuals from the annual cross-sectional industry regression models toestimate the discretionary accruals. Technically, we regressed our data for each sic2-year pair.
The Discretionary Accruals are the residuals of the following regressions:
Accruals t = α 0 + α 1 CFO t-1 + α 2 CFO t + α 3 CFO t+1 + α 4 ΔSales + α 5 ΔPPE t + ε t
Where:
Accruals t = Difference in Total Accruals between two years measured by Income Before
Extraordinary Items (ib) minus Operating Cash Flows (CFO)
CFO = oancf after 1988 or ib - Δact + Δche + Δlct – Δdlc +dp before 1988
CFO t-1 = Operating Cash Flows of last year
CFO t = Operating Cash Flows of the current year
CFO t+1 = Operating Cash Flows of next year
ΔSALES it = change in sales (Item 12) scaled by lagged total assets (ASSETS it-1 )PPE it = Gross property, plant and equipment (Item 7) scaled by ASSETS it-1

15

In our study, following Dechow and Dichev (2002), Kothari et al (2005) and Chaney
Faccio Parsley (2011), the variability of discretionary accruals is the primary object of interest.
In this formulation, a higher standard deviation of the discretionary accruals is associated withlower-quality earnings data. Dechow and Dichev use a rolling 10-year window to determine
the standard deviation of the residuals. These estimations yield ten firm- and year-specificresiduals that form the basis for the accrual quality metric. “Accrual Quality” equals thestandard deviation of firm j’s estimated residuals. In our study, we calculate the standard
deviation of the discretionary accruals over a 3-year period before the trigger event and thestandard deviation of the discretionary accruals over a 3-year period after the trigger event tomeasure the difference-in-differences of Accrual Quality between the two groups of interest.
The Investigated group is made up of targeted firms that are investigated by the prosecutor.
The peer group is made up of peers from the same industry, considered at the SIC3 level,
existing during the 7-year period covered by the analysis, with at least one year of foreign
sales disclosed during that period.
Our main event date, called acknowledgment date or trigger event date, is the moment
when the investigated company becomes aware of the bribing behavior. It is different fromthe moment when the information becomes public, which we call disclosure date and whichtakes the form of a conspicuous announcement related to the firm that draws the SEC’s
scrutiny, generally firm-initiated disclosures of potential problems (Karpoff, Lee, & Martin,
2008). For the bribing firm, it is logical to consider the acknowledgement date as the main
event date, as we can expect the firm to change its Accrual Quality as soon as bribing
behavior is internally acknowledged. The question is more prominent for peer firms. And if
the disclosure date is sometimes used as the main event date, it could be argued that the
SEC’s investigation may have been anticipated by peers when the problems at the target firm
first surfaced (Jennings et al., 2011). As a consequence, we assume that the acknowledgement

16

date is the moment when both bribing firms and their peers will correct their Accrual Quality.
And to finally alleviate the concern, out of the 80 cases of investigated bribing firms in our
final sample, only 7 cases show acknowledgment dates which are more than one accounting
year distant from the disclosure dates.
To identify the acknowledgement date for each case, we carried out searches in
Factiva, the Trace Compendium International database, other public FCPA databases or
specialized sites such as Shearman FCPA, FCPA.Stanford, and FCPA Professor. We also
searched for “FCPA related misbehavior”, “Bribery” and “Corruption”, among otherkeywords in the SEC 10k filings of companies in EDGAR to identify the first time the bribing
behavior was mentioned in the notes. Finally, we used Google when necessary. We took theearliest acknowledgement date we identified, which could be the earliest acknowledgment
date mentioned in the 10k filings, or the earliest acknowledgment date mentioned in a press
release, or the date of self-disclosure to the SEC/DOJ. We use a 7-year event window (three
years before the year of the trigger event, three years after the year of the trigger event). The
year of the event is considered as year 0 and is not included in the calculations of AQ.
We perform a difference-in-differences analysis to study the change in Accrual Quality ofinvestigated firms (investigated firms), but also the change in Accrual Quality of peers (peerfirms), after the event occurred. We expect the level of Accrual Quality to be significantly
higher after the event, as it would represent a positive impact of law enforcement, andtherefore a positive deterrent effect, on the behavior of firms. Additionally, the difference-indifferences design enables us to observe whether the trend is significantly different between
the investigated and peer firms. FCPA law enforcement is the treatment, and theacknowledgment of potential future FCPA enforcement is an exogenous shock that represents
the event. The event occurs at different moments in time for the different investigated firms inour sample. For each of the 80 events in our final sample, we consider one investigated firm17and build a peer group that fits with the characteristics of this investigated firm. For the peer
firm to have parallel trends and be impacted in a similar way by the event of acknowledgmentof future FCPA enforcement, allowing us to detect a potential deterrent effect of theenforcement, we consider that the peer company must belong to the same industry (same
SIC3) as the investigated firm, must have existed throughout the same 7-year timespan of theanalysis, and must have disclosed foreign sales in the segment information of Compustat atleast once during this 7-year window. Therefore, we build 80 distinct peer groups, each one
related to one of the investigated firms. This ensures that we are comparing investigated andpeer firms operating in the same industry, allowing us to “difference away” unobserved timevarying industry shocks to post-treatment trends in the variable of interest (Heider &Ljungqvist, 2015). In addition, for each of the 80 peer groups, we keep only firms that arepresent both before and after the treatment. This means that the firms before and the firms
after the treatment are identical, both for investigated and peer firms. The difference-indifferences approach enables us to control for time-invariant, firm-specific omitted variables
as well as time-varying industry trends and nationwide shocks (Mukherjee, Singh, &Žaldokas, 2017), and to obtain an appropriate counterfactual to estimate the causal effect.
Indeed, the question, as in any difference-in-differences set-up, is whether post-treatment
trends would have continued to be parallel had it not been for the disclosure of future
enforcement (Heider & Ljungqvist, 2015). We assume that, without the acknowledgement of
future FCPA enforcement, both investigated and peer companies would keep the same levelof Accrual Quality or would adjust their Accrual Quality in the same way in response to thesame industry and/or time events, keeping the difference before and the difference after equal.
Therefore, the difference-in-differences design allows us to evidence the differential change
in Accrual Quality that is linked to the exogenous shock represented by the acknowledgementof FCPA enforcement. One can argue that other exogenous events could occur during year 0.

18

It is very unlikely that these events would have an impact on the result, as year zero isdifferent from one case to the next and each case is industry-specific. The fact that the sample
is built around 80 distinct event dates dramatically lowers the risk of an aggregated significant
effect of an alternative exogenous shock. In addition, following Dechow and Dichev (2002),
we control for firm characteristics that are likely to be correlated with the standard deviationof the discretionary accruals.
In this paper, we are seeking to test whether the investigation of a company under FCPA has adeterrent effect on the investigated firm but also on its peers, even if the peer firms do notdirectly receive the treatment, i.e. an FCPA investigation. Our difference-in-differences
design must therefore analyze the differential impact of the treatment, but must also analyze
whether this differential impact is due to the change in AQ of the investigated firms, of thepeer firms, or both.

3.2 - Sample
We manually collected all the settlements linked to an FCPA enforcement action forthe period 1978-2015 on the DOJ site and on the SEC site. The prosecutor does not make allprosecution actions public. Therefore, our sample does not include some cases. We analyze
the differential impact of the revelation of an FCPA investigation on the Accrual Quality ofboth investigated firms and their industry peers; the exclusion of those cases does notconstitute a selection bias, as we consider that competitors cannot react if they are not awareof the beginning of an investigation. Some enforcement actions are carried out against
individuals and other cases are linked to companies. We gathered all the subsidiary, parent or
individual cases that were related to the same scheme under a unique core case at the parent
company level. When the individual does not work for the investigated company but is stillpart of the bribery scheme (the individual is an intermediary, for example), we tried to19identify the individual’s company. Indeed, as the company is indicated in the complaint or thesettlement, the company of the intermediary could suffer from a decrease or an increase in
AQ, in the same way as any defendant company. 241 different parent companies were linked
directly and/or through an individual to an FCPA enforcement action between 1978 and 2015.
68 companies are privately held and therefore excluded from the sample. 23
miscellaneous cases are also excluded from the sample for various reasons: for 8 anonymous
cases, no name for the company is disclosed in the settlement; 9 cases are linked toindividuals working for entities that are not companies (Tourism Authority of Thailand, US
Congress, US Army, World Bank, Central Asian American Enterprise Fund, government of
Saskatchewan, Minister of Tourism of the Government of Jamaica); and 6 cases are linked toindividuals working as an individual intermediary with no corporate structure. 150 cases
remain after these two steps.
38 cases are linked to non-U.S. companies. As we use Compustat to collect financial
information about the investigated firms in our sample, this paper focuses on U.S. cases only.
We discard the 38 non-U.S. cases from our sample. For 18 cases, we do not have financial
data for the firm, or no match on Compustat. 94 cases are left in our sample of investigatedcompanies after this step. For 14 cases, the financial data are incomplete and do not enable us
to calculate total accruals or to collect the necessary dependent or control variables. In
addition, for some cases, we do not have the necessary complete event window of 7 years to
perform the difference-in-differences analysis. As shown in Table 1, our final sample ofinvestigated firms is made up of 80 cases, representing 74 unique companies and 6 companies
for which we identified 2 distinct enforcement settlements.

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

20

To build the peer group, we firstly consider the entire universe of companies in
Compustat. Then, in Compustat we select all companies with the same SIC2 as one of theinvestigated companies; these companies existed throughout the 7 years of the trigger event
window and disclosed foreign sales at least once during the 7 years of the trigger event
window. In the next step, we keep only the peer companies with the same SIC3 as one of theinvestigated firms. In the rare cases (4 cases) for which we do not have peer companies at the
SIC3 level, we stay at the SIC2 level. After discarding all companies with a SIC3 code forwhich there is no investigated firm, we obtain a sample of 16,940 Sic3/year observations for
1,587 unique companies in the peer group and 551 sic3/year observations for 74 uniquecompanies and 80 cases in the investigated group.
We then use a propensity score matching within common support and a caliper of 0.05
to select the closest neighbors among the peer companies identified in the previous step. We
calculate the propensity matching score in year t-1, the year before the acknowledgment date.
For each investigated firm, we select neighbors among the peer companies that belong to thesame case, i.e. that existed during the same 7-year period of time, belong to the same industry(SIC3 level) and disclosed foreign sales at least once in the Compustat segment information
during the 7-year period. Indeed, FCPA is related to bribes paid to foreign officials, i.e. whencompanies try to develop their turnover abroad. When selecting nearest neighbors, it istherefore logical to keep only peer firms that have non-U.S. sales.
We use the following 2 criteria to match peer and investigated firms: Size (logarithmof market capitalization) and percentage of foreign sales. We believe that size is an importantdriver when comparing bribing firms with their peers. Companies of the same size have
similar means of action and a similar level of internal processes. We also believe that thepercentage of foreign sales enables us to identify similar peers in the context of FCPA
enforcement. Indeed, companies with a similar percentage of activity in foreign countries

21could have the same ability to potentially pay bribes and face the same conditions whendeveloping business abroad.
In sum, we match peer companies from the same industry, existing during the same 7year period, and with foreign sales disclosed at least once, using size and percentage of
foreign sales in year t-1 (year before the acknowledgment date) as matching criteria to selectthe closest neighbors within a caliper of 0.05 of the propensity score, when possible.
For four investigated firms, there are no potential neighbors at the SIC3 level. In thesefour cases, we select the neighbors at the SIC2 level within a caliper of 0.05 of the propensity
score. For three investigated firms, there are no neighbors within a caliper of 0.05 and weselect the neighbor using a caliper of 0.10 around the propensity score. In an undisclosed table,
we calculated the results without the seven cases above. Results remain unchanged. We also
disclose results using an alternative caliper of 0.10 and keeping all companies at SIC3 level.
Results remain unchanged and significant at the 1% level.
The same peer company can be used in different cases and at different moments oftime, if the peer company meets the criteria developed above. On average, 20 neighbors per
event are available using the matching method detailed above, with a minimum of 1 neighborand a maximum of 249 neighbors for 1 event. In the robustness tests, we use an alternative
matching method, keeping the 3, 5 or 7 nearest neighbors instead of all available neighbors
within a caliper of 0.05 of the propensity score. Results remained unchanged and significant.
After this propensity score matching, we obtain a sample of 1,658 peers for 1,054
unique companies in the peer group and 80 investigated companies for 74 unique companies
in the investigated group.

4 – Results
4.1 – Summary Statistics

22

Table 2 shows summary statistics for the main variables for investigated bribing(Investigated) and non-bribing (peer) companies for the 7-year period of the study, then forthe 3-year periods before and after the trigger event. The peer group with propensity scorematching includes the nearest neighbors within a caliper of 0.05, 10 selected from the peergroup built at the SIC3 level matched using the following two criteria: Size (Log of Market
Cap) and Percentage of foreign sales, both considered in year t-1, the year before theacknowledgment date. All variables are winsorized at the 1st and 99th percentiles. The valueof accruals is calculated using a direct approach with information from the cash flow
statement, and the value of discretionary accruals is equal to the residuals obtained in the
regressions using the Modified Dechow Dichev Model.
Investigated companies are bigger in size (market capitalization and total assets) but
the percentage of foreign sales between the 2 groups is similar (43% for the investigated firms
vs. 40% for the peer firms over the 7-year period). The accruals measures always remain
negative. The average value of accruals as a percentage of lagged total assets is lower for thepeer group than for the investigated group (-7.41 and -4.97 respectively). The value of
discretionary accruals calculated with the Modified Dechow Dichev Model is also higher for
the investigated group than the peer group (1.64 vs. 0.61 respectively). Two samples t-tests(undisclosed) for all the variables in Table 2 are significant, but as we are seeking to show adeterrent effect within an industry, we choose to retain a large group with different
characteristics to see the global effect of the acknowledgement of future investigation.
In an undisclosed table, we compare the summary statistics between the investigatedgroup and a peer group when applying the propensity score matching with 3, 5, and 7 nearest
neighbors. For the 3 nearest neighbors matching, the sample then contains 222 peers in thepeer group for 188 unique companies and 80 investigated firms for 74 unique companies in
10

We consider alternative peer groups (All peers at the SIC3 level or using a caliper of 0.10 instead of 0.05 onlyin the propensity score matching) in the main results of the paper.

23the investigated group. T-tests of all variables shown in Table 2 are insignificant, except forsize (logarithm of market capitalization), and the results of the main regressions remain
significant and coherent with the matching using calipers. These results suggest that thematching using a caliper of 0.05 or 0.10 is successful as, for each case, we restrict the peer
companies to companies from the same industry (SIC3), at the same period of time (7 years
around the trigger event), with foreign activity (all companies must disclose foreign sales tobe kept in the sample), and the significant difference in the main variables shown in Table 2
does not prevent us from analyzing the difference-in-differences as long as we have parallel
trends within the two groups. Again, in this paper we are also attempting to evidence a
deterrent effect of law enforcement at the industry level, and must therefore keep a large panelof peer companies that can show different characteristics from those of the related bribing
company, but can equally interpret the opening of an investigation in their industry as a realrisk of future investigation for themselves.
In sum, the matching within a caliper of 0.05 can reasonably be used for difference-indifferences analysis.

------- Insert Table 2 about here -------

4.2 – Main Result: Difference-in-Differences with Standard Deviation over a 3-year periodusing the Modified Dechow Dichev Model
We use a difference-in-differences design to analyze the change in Accrual Quality.
Our measure of Accrual Quality is the standard deviation of the discretionary accruals over 3year periods, before (year t-3 to year t-1) and after (year t+1 to year t+3) the year of thetrigger event, year 0, when the company becomes aware of the bribing behavior, following
the Modified Dechow Dichev Model (P. M. Dechow & Dichev, 2002; McNichols, 2002).

24

Table 3 shows the results of the difference-in-differences for the standard deviation of
the discretionary accruals using the modified Dechow Dichev model over a 3-year period(ModDD_3years). The Investigated group is made up of 80 firms identified in our final
sample (See Table 1). The peer group is made up of the nearest neighbors within common
support and a caliper of 0.05, from the same industry (SIC3 level), existing during the same 7year timespan, with at least one year of foreign sales disclosed during the 7-year timespan,
matched using the propensity score technique detailed in Section 3.

------- Insert Table 3 about here -------The results are quite surprising. We observe that the coefficient (i.e. the standard
deviation of the discretionary accruals over a 3-year period) for the investigated group does
not vary significantly (t-test: 1.26, pvalue: 0.211), with an insignificant increase of 0.47 (from3.09 to 3.57). This means that, counterintuitively, the investigated bribing companies do not
improve their Accrual Quality after their acknowledgement of a future FCPA enforcement
and there is no significant deterrent effect provided by FCPA law enforcement on the sample
of investigated bribing firms:
Hypothesis 1a (H1a), “the AQ of investigated firms increases after revelation of a newinvestigation”, is rejected.

On the contrary, for the peer group we observe a large and significant decrease in thestandard deviation over 3 years, of 0.93, from 7.04 to 6.11 (t-test: -4.62, pvalue: 0.000). Thismeans that the volatility of discretionary accruals decreases for the peer firms after they
acknowledge potential bribing behavior by a firm in their industry. As a consequence, the25

acknowledgement of a new investigation of a firm in their industry creates a positive deterrent
effect for peers, who significantly increase their Accrual Quality:
Hypothesis 1b (H1b), “the AQ of peer firms increases after revelation of a newinvestigation”, is confirmed at a 1% significance level.

The interaction term of the difference-in-differences is positive with a value of 1.40
and significant at a 1% level (t-test: 3.32, pvalue: 0.0001). This confirms the significant
difference in attitude between the groups after the revelation of bribing behavior. While theinvestigated firms do not significantly adjust their AQ, their peers significantly and positively
react to the event and increase their AQ when they become aware of future FCPA
enforcement in their industry. There is a positive deterrent effect of FCPA enforcement only
on peer firms, as we observe a significant decrease in the 3-year standard deviation of
discretionary accruals of peer firms compared to investigated firms in the difference-indifferences interaction term.
Hypothesis 1c (H1c), “There is a significant difference in the change in AQ between
investigated firms and peer firms after revelation of the investigation of one firm in theindustry”, is confirmed at a 1% significance level.

This means, and this is the main contribution of the paper, that the peer firms tend to
improve their Accrual Quality when they are informed that an FCPA investigation has been
opened against one of their competitors, while investigated firms do not significantly change
their Accrual Quality, and even show a slight decrease in AQ over the same timespan.
The two groups show opposite trends throughout the 7-year period under study. In linewith the quasi-experimental design of difference-in-differences analysis, there is a change inoutcome over time that is due to the treatment (acknowledgement of future FCPA

26

enforcement in our case) and usually affects the investigated group. What we observe is that
there is no intervention effect on investigated firms as the change in outcome affects only
peers, suggesting that the deterrent effect of law enforcement impacts only peer firms.
One could argue that the significance of the results in Table 3 could be mitigated by
the fact that the value of the dependent variable is significantly different before the trigger
event. However, in a difference-in-differences design, the level of the dependent variable canbe different between the two groups before the event. According to our assumption, without
the acknowledgement of future FCPA enforcement, both investigated and peer companies
would keep the same level of Accrual Quality or would adjust their Accrual Quality in thesame way in response to the same industry and/or time events, keeping the difference before
and the difference after equal. What is important is that the trend of the change in outcome isdifferent between the two groups (insignificant decrease in AQ for the investigated firms,
significant increase in AQ for the peer firms), clearly showing that the two groups do notreact in the same way to future FCPA enforcement. On the other hand, we use as our main
dependent variable the standard deviation of the discretionary accruals, which is already thevariation in the value of the dependent variable between years, and we aggregate this standard
deviation into two periods of three years: pre- and post-intervention periods. This techniquehelps to remove the time series correlation problem (Bertrand, Duflo, & Mullainathan, 2004).
In addition, the interaction term (difference-in-differences) remains significant when we use
alternative specifications in the robustness tests (propensity score matching with 3,5,7
neighbors), where the difference in the value of the dependent variable before the trigger
event is smaller between the two groups.
In conclusion, by using the standard deviation of the discretionary accruals over two 3year periods (before and after the trigger event), and by testing alternative sizes andpropensity score matching techniques for the peer group, we alleviate the concern linked to27the difference in the value of the dependent variable before the event and respect the parallel
trend assumption of the difference-in-differences design. As our aim is to show the deterrent
effect of anti-bribery law enforcement on peers, we also need to retain a peer group of peers
which is large enough to observe this deterrent effect.

4.3 - Additional Results
Table 4 shows the results for the difference-in-differences analysis when theinvestigated group and the peer group comprising companies matched using the propensity
score matching within common support and a caliper of 0.05 are split into two subsamples,
following a time criterion: companies became aware of the bribing behavior before 2005 on
the one hand, and during or after 2005 on the other hand.

------- Insert Table 4 about here -------

These additional results shed further light on the main results shown in Table 3. In
both subsamples, the ModDD_3 years increases insignificantly after the trigger event forinvestigated companies, reflecting the trend highlighted in Table 3. For the peer companies,
the trend differs between the two subsamples. For cases acknowledged before 2005, the trend
is similar to Table 3 as peers significantly reduce the standard deviation of their discretionary
accruals from 8.59 to 6.52 (t-test: -6.35, pvalue: 0.000). For cases acknowledged in or after
2005, there is a change in the trend as peer companies do not significantly correct their
Accrual Quality around the event date. The difference between the two periods in the value of
ModDD_3 years is nearly constant, with a value of 0.05 (t-test: 0.20, pvalue: 0.843).
Accordingly, the difference-in-differences value is significant only in the subsample “before
2005”, meaning that the deterrent effect is stronger for cases acknowledged before 2005 and28

still affects only peer firms with a value of 3.07 (t-test: 3.66, pvalue: 0.000). For the
subsample of cases acknowledged in or after 2005, the interaction term of the difference-indifferences shows an increase in AQ of 0.16, which is statistically insignificant (t-test: 0.35,
pvalue: 0.727).
Hypothesis 2a, “There is a significant difference in the change in AQ between investigatedfirms and peer firms after revelation of an investigation of one firm in the industry for casesthat were acknowledged before 2005”, is confirmed at a 1% level.
We interpret this additional finding in the following way. In December 2004, the prosecutor
used alternative resolution vehicles for the first time to resolve FCPA cases. These vehicles,
namely the DPA and the NPA, led to less in-depth investigations, offset by higher levels offines. We suggest that since 2005, peers did not correct their Accrual Quality after
acknowledgment of the opening of an FCPA investigation in their industry, as they revised
downwards the need to improve their Accrual Quality now that alternative resolution vehicles
could be used.
Hypothesis 2b, “There is a significant difference in the change in AQ between
investigated firms and peer firms after revelation of an investigation of one firm in theindustry only for cases that were revealed in or after 2005”, is rejected.

4.4 – Difference-in-differences with SD over a 3-year period using Modified Dechow Dichev
with Control Variables
According to Dechow Dichev (2002), five factors explain accrual quality (firm size,
cash flow variability, sales variability, length of operating cycle, and incidence of negative
earnings realizations), with three variables shown by prior research to influence one or moreof the other earnings attributes: absence of intangibles, intangibles intensity, and capitalintensity. We use the same control variables as Dechow Dichev (2002) and Chaney et al.

29

(2011), among other things to control for these factors. Ln Mkt Cap is the natural log of thecompany’s market capitalization in US dollars, to control for size. σ(CFO/TA) × 100 is the 7year standard deviation of CFO over total assets (*100). σ(Sales/AT) × 100 is the 7-year
standard deviation of cash sales over total assets (*100). Operating cycle is defined as the logof the sum of days in receivable and days in inventory, as defined in Dechow Dichev (2002).
Negative Earnings is the frequency of negative earnings realization over the 7-year period ofstudy. We also use additional control variables: Sales growth is the annual growth of sales,
Market-to-book is the ratio of market capitalization to book value of equity in t -1 andLeverage is total debt as a percentage of total assets in t -1 . Additional variables linked to thecases themselves are also included as control variables in the regressions. Court_DOJ is an
ordinal variable which considers the district court that settles the DOJ case. Court_SEC is an
ordinal variable which considers the district court that settles the SEC case. Dummy_2005 is adummy variable that takes the value of 1 if the case was acknowledged in or after 2005 andzero otherwise. First Case is a dummy variable that takes the value of 1 if the case is the firstone in the industry and zero otherwise. We finally control for industry and year fixed effects.
Observations are clustered at the company level in regressions.
The main result of the difference-in-differences using ModDD_3years with apropensity score matching within common support and a caliper of 0.05 remains significant at
a 1% level, with a differential increase of 1.3983 in the AQ of peers compared to theinvestigated bribing firm (t-test: 3.27, pvalue: 0.001) when controlling for the five factors
developed by Dechow and Dichev (2002) and the additional control variables developed
above. We also present in Table 5 the results of the OLS regression for alternative size in thegroup with no specified caliper, and all peer companies at the SIC3 level on the one hand andusing a caliper of 0.10 instead of 0.05 on the other hand. The results remain significant at a
1% level for these alternative sizes in the peer group, with an increase of 1.71 (t-test: 4.14,30

pvalue: 0.000) in the AQ of peers compared to investigated firms at the SIC3 level, and anincrease of 1.80 (t-test: 4.27, pvalue: 0.000) using a caliper of 0.10.

------- Insert Table 5 about here -------

5- Supplementary Analysis
Our main result shows that the increase in Accrual Quality around the date of antibribery enforcement is significant for peer firms only, following a deterrent effect.
We observe that the level of AQ remains nearly the same for investigated firms before
and after the trigger event. It seems logical that the level of AQ does not deteriorate after thetrigger event. Indeed, through their settlement, investigated companies have to implement
corrective actions in terms of compliance and monitoring that prevent AQ from deteriorating.
This is also the case in AAERs, where the targets’ AAER managers settle enforcement action
by consenting to an injunction that prohibits future violations of the securities laws (Feroz etal., 1991). But why does the level of AQ not increase after the trigger event for investigatedfirms? One explanation could be that investigated firms do not feel the need to improve their
AQ since they have already been caught. Another explanation could be that the ex-ante
window-dressing is replaced by a real ex-post improvement in AQ, linked to theimplementation of new compliance and monitoring systems.
According to Feroz et al (1991), through the AAERs, the SEC is trying to use one caseas a precedent and to spread the word to other companies that the SEC is able to identify (andto investigate) a particular issue, creating a deterrent effect. The SEC’s anti-bribery unit,
created in 2010, pursues the same goal and uses the same philosophy. By disclosing antibribery behaviors of firms, they create a precedent to influence the business practices ofcompanies and create a deterrent effect. In addition, Feroz et al. (1991) point out that since the31

SEC has more targets than it can practically pursue, and since formal investigations are bothcostly and highly visible, the SEC ranks targets according to the probability of success. Thus,
it is reasonable to assume that firms facing enforcement actions by the SEC have knowingly
and intentionally engaged in earnings manipulation (P. M. Dechow et al., 1996). As aconsequence, we think that peer companies may improve their AQ to convince the SEC of
their good behavior and avoid investigation. The improvement in AQ may be real or merely
window-dressing but, in both cases, it demonstrates the deterrent effect of FCPA enforcementon peer firms.
In anti-bribery enforcement, the revelation of the bribing behavior does not usuallyoccur through SEC surveillance of the AQ level, as with the AAERs, but mainly throughother factors. Worldwide, 31% of foreign bribery cases were brought to the attention of theauthorities through self-reporting, and the revelation remained unknown in 29% of cases. Law
enforcement represents only 13% of the detection pattern. 11 The situation is different for peer
companies. They have not been investigated yet. But they just became aware that a company
in the same industry is under investigation. Investigation of a company can lead to moreinvestigations of peers in the same industry. Indeed, the administration (SEC and DOJ for
anti-bribery enforcement) does not have the time or the means to investigate all companies.
When one company in a specific industry has settled, the administration develops greaterknow-how of the patterns used by the company to pay bribes and to dissimulate them. The
SEC and the DOJ can then use indicators similar to the ones used in the AAERs to target a
specific company, following a propagation effect. In that case, it makes sense that peers
would try to improve their AQ once the trigger event happens to a company in the same
industry, because the revelation will no longer be whistleblowing or self-reporting, but could
come from low AQ among the peers identified by the prosecutor.

11

OECD Foreign bribery Report, 201432

6 – Robustness Tests

6.1 – Specifications of the models used for robustness tests
Table 6 shows the results of robustness tests of the OLS regressions with peer
variables using alternative dependent variables. We use 3 alternative dependent variables. We
firstly use the standard deviation over 3 years of the discretionary accruals obtained with the
Dechow Dichev model (P. M. Dechow & Dichev, 2002). Then we use the Modified Jones
Model (P. M. Dechow et al., 1995). We finally use the Jones model (J. J. Jones, 1991).
To calculate the discretionary accruals, we follow the same two-step regression
principle as the one for our main model. In the first step, we determine the discretionary
accruals linked to the model. In the second step, we calculate the standard deviation of thediscretionary accruals found in the first step over 3 years (before and after the event) and use
them as our main dependent variable of the OLS regressions to determine the coefficient ofinterest, which is the coefficient of the interaction term of the dummy variable Investigated(INV), set equal to 1 if the company has been investigated under FCPA and is part of our final
sample of investigated companies and 0 otherwise, and the time dummy variable (AFTER),
set equal to 1 if the 3-year period is after the event and 0 otherwise.

In the first step, the discretionary accruals are determined as the residuals of theregressions, where the accruals are the dependent variables and the independent variables are
the explanatory variables, which differ from one model to the next (see details below).

Dechow Dichev model33

In the Dechow Dichev model (P. M. Dechow & Dichev, 2002), we use the same
methodology as for the modified Dechow Dichev model (McNichols, 2002) to determine thediscretionary accruals, except that we do not use the impact of the change in sales and PPE as
independent variables. The discretionary accruals are the residuals of the following regression
calculated for each sic2/year pair:Accruals t = α 0 + α 1 CFO t-1 + α 2 CFO t + α 3 CFO t+1 + ε t
Where:
Accruals t = Difference in Total Accruals between two years measured by Income Before
Extraordinary Items (ib) minus Operating Cash Flows (CFO)
CFO = oancf after 1988 or ib - Δact + Δche + Δlct – Δdlc +dp before 1988
CFO t-1 = Operating Cash Flows of last year
CFO t = Operating Cash Flows of the current year
CFO t+1 = Operating Cash Flows of next year
Once the discretionary accruals are obtained, we calculate the standard deviation of
those residuals over the 3-year periods (DD_3y) before and after the trigger event year anduse them as our dependent variable in the OLS regressions to determine the interaction termcoefficient of the difference-in-differences estimate, stated as INV*AFTER.

Modified Jones model
We also use the standard deviation of the discretionary accruals calculated with themodified Jones model (P. M. Dechow et al., 1995) as a benchmark. Total Accruals in the
Modified Jones model are calculated as follows:
TA = Change in non-cash current assets – change in current liabilities excluding thecurrent portion of long-term debt – depreciation and amortization

34

For one specific year, TA are calculated as follows in respect to data items present in
Compustat:
TA t = [Δcurrent Assets t (act) – Δcash t (che) – Δcurrent liabilities t (lct) + Δ Debt inCurrent liabilities (dlc) – Depreciation and amortization expense t (dp)] / lagged Total
Assets (at)Where Δ is the difference between t and t-1,
And items indicated parenthetically are Compustat data items.
We then use the following regression linked to the Jones model to obtain theparameters that we will use as a second step in the modified Jones model:
TA it = β 0 [1/ASSETS it-1 ] + β 1 [ΔSALES it ] + β 2i [PPE it ] + ε it
Where
ASSETS it-1 = Lagged total assets (at)TA it = total accruals as defined above
ΔSALES it = change in sales (sale) scaled by lagged total assets (ASSETS it-1 )PPE it = Gross property, plant and equipment (ppegt) scaled by ASSETS it-1

To obtain modified-Jones discretionary accruals, following Dechow et al (1995), we
use the parameters from the Jones model, but apply them to a modified sales change variable:
TA it = β 0 [1/ASSETS it-1 ] + β 1 [ΔSALES it - ΔAR it ] + β 2i [PPE it ] + ε it
Where
TA it = total accruals as defined above
ΔSALES it = change in sales (sale) scaled by lagged total assets (ASSETS it-1 )ΔAR it = change in accounts receivable (rect) scaled by lagged total assets (ASSETS it-1 )PPE it = Gross property, plant and equipment (ppegt) scaled by ASSETS it-1

35

Once the discretionary accruals are obtained, we calculate the standard deviation of
those residuals over the 3-year periods (ModJones_3y) before and after the trigger event year
and use them as our dependent variable in the OLS regressions to determine the interaction
term coefficient of the difference-in-differences estimate, stated as INV*AFTER.

Jones Model
We also use the standard deviation of the discretionary accruals calculated with theJones model (J. J. Jones, 1991). Total Accruals in the Jones model are calculated as follows:
TA = Change in non-cash current assets – change in current liabilities excluding thecurrent portion of long-term debt – depreciation and amortization
For one specific year, TA are calculated as follows in respect to data items present in
Compustat:
TA t = [Δcurrent Assets t (act) – Δcash t (che) – Δcurrent liabilities t (lct) + Δ Debt inCurrent liabities (dlc) – Depreciation and amortization expense t (dp)] / lagged Total
Assets (at)Where Δ is the difference between t and t-1,
And Items indicated parenthetically are Compustat data items.
Discretionary accruals are the residuals of the following regressions:
TA it = β 0 [1/ASSETS it-1 ] + β 1 [ΔSALES it ] + β 2i [PPE it ] + ε it
Where
TA it = total accruals as defined above
ΔSALES it = change in sales (sale) scaled by lagged total assets (ASSETS it-1 )PPE it = Gross property, plant and equipment (ppegt) scaled by ASSETS it-1
Once the discretionary accruals are obtained, we calculate the standard deviation of
those residuals over the 3-year periods (Jones_3y) before and after the trigger event year and36

use them as our dependent variable in the OLS regressions to determine the interaction termcoefficient of the difference-in-differences estimate, stated as INV*AFTER.

6.2 – Results of the robustness tests using alternative dependent variables
Table 6 shows the results of robustness tests of the OLS regressions with control
variables using alternative dependent variables. The peer group comprises companies with thesame SIC3 as the related investigated firm, in existence throughout the 7 years of the event
window, and with foreign sales disclosed at least once. For each alternative dependent
variable, we show the results for three specifications of the peer group, as done in Table 5 for
ModDD-3y: Peer companies using the propensity score matching within common support anda caliper of 0.05, 12 but also for all the peer companies available at the SIC3 level, and finallypeer companies within a caliper of 0.1.
The results using the Dechow Dichev model (DD_3y) confirm the main results inTable 5. The interaction term is positive and significant at 1% level for all the specificationsof the peer group. Using a caliper of 0.05, the differential increase in Accrual Quality is 1.15
(t-test: 3.02, pvalue:0.003), and this differential increase remains of the same nature at the
SIC3 level (1.46 with a t-test of 4.03 and a pvalue of 0.000) or using a 0.1 caliper (1.46 with at-test of 4.09 and a pvalue of 0.000) respectively. Peer companies significantly improve theiraccounting comparatively to the investigated companies after acknowledgement of the
opening of an FCPA investigation in their industry. This result confirms the robustness of themain result in Table 5, as the Dechow and Dichev-based measures have the highest
association with fraud and explanatory power for fraud beyond total accruals (K. L. Jones,
Krishnan, & Melendrez, 2008).

12

The propensity score is calculated using the same process than for the main dependent variable, i.e. using thetwo following criteria: Size (Log of Market Cap) and Percentage of foreign sales, both considered in year t-1, theyear before the acknowledgment date.

37

The results of the OLS regression variables using the Modified Jones and the Jonesmodels confirm the main trend. Results are significant at a 5% level for the Modified Jonesmodel at the SIC3 level with a differential increase in Accrual Quality of 1.05 (t-test:2.02,
pvalue:0.044) and using a caliper of 0.1 (Increase in AQ of 1.12 with a t-test of 2.10 and apvalue of 0.036). Results are significant at a 10% level for the Jones model at the SIC3 levelwith a differential increase in Accrual Quality of 0.95 (t-test:1.84, pvalue:0.066) and using acaliper of 0.1 (Increase in AQ of 1.12 with a t-test of 2.10 and a pvalue of 0.036). Results of
the regressions are less explanatory when we use total accruals instead of accrual estimation
errors, which is in line with Jones, Krishnan and Melendrez (2008). Finally, the explanatory
power of total accruals models is lower for a caliper of 0.05, when the size of the peer groupis smaller. As our aim is to show the deterrent effect of FCPA law enforcement on peers, thefact that the results are more significant when the size of the peer group is larger tends tounderline the significance of the deterrent effect at the industry level.

------- Insert Table 6 about here -------

6.3 – Results of the robustness tests using nearest neighbor matching instead of caliper
matching for ModDD_3y
To provide assurances that our results are not driven by the propensity score matchingspecifications, we repeat the difference-in-differences test using nearest neighbor matching
instead of caliper matching as the propensity score matching technique for our main
dependent variable ModDD_3years, which represents the standard deviation of theDiscretionary Accruals over a 3-year period (actually 6 years divided into 2 periods of 3 years,

38

one before the event and one after the event) following the modified Dechow Dichev model.
Table 7 shows the results of robustness tests of the OLS regressions with peer variables forthree specifications of the peer group using nearest neighbor matching with the 7 nearest
neighbors, 5 nearest neighbors, and 3 nearest neighbors respectively. The Investigated Bribing
Group is made up of 160 observations (80 before the event and 80 after the event),
representing the 80 cases (for 74 unique companies). The peer group is made up of 916
observations (for 351 unique companies) when using the 7 nearest neighbors, 684
observations (for 274 unique companies) when using the 5 nearest neighbors and 438
observations (for 186 unique companies) when using the 3 nearest neighbors. If the number ofneighbors is lower than expected for a specific event, no replacement is made. Observations
are clustered at the company level in regressions.
Results in Table 7 confirm the main results of the paper in Table 5 when using acaliper of 0.05 instead of nearest neighbor matching. Regardless of the number of neighbors
disclosed, the interaction term of the regression is positive and significant. With 7 neighbors,
Accrual Quality increases by 0.92 (t-test: 1.96, pvalue:0.050), while AQ increases by 1.05 (ttest: 2.13, pvalue:0.034) with 5 neighbors and increases by 1.02 (t-test: 1.94, pvalue:0.053)
with the 3 nearest neighbors for each investigated firm. The result in Table 7 is interesting, as
it shows that even when the size of the peer group is reduced (for 3 neighbors, the size of theinvestigated group represents 36.5% of the size of the peer group in terms of observations),
the main result is confirmed.
In sum, these results are consistent with the previous results developed in Table 3 andconfirm our main result: peer companies react to the revelation of an FCPA investigation ofcompanies in their industry by improving their Accrual Quality, while investigated firms do
not significantly correct their AQ. We observe a significant deterrent effect of FCPA law
enforcement on the Accrual Quality of peer firms, but not investigated firms.

39------- Insert Table 7 about here -------

7 – Conclusions
In this paper we analyze a hand-collected sample of 241 bribery cases investigatedunder the US Foreign Corruption Practices Act (FCPA) over the period 1978-2015. To beable to investigate the quality of the accounting information of the companies underinvestigation, we focus on the U.S. companies in our sample that existed during the 7-year
period around the acknowledgement of potential future enforcement by bribing firms, forwhich financial information is available on Compustat, and end up with a sample of 80 cases(74 unique companies).
To measure the quality of accounting information, we follow Dechow and Dichev and
Mac Nichols (2002) and use Accrual Quality, measured as the variability of discretionary
accruals as our main dependent variable. A higher standard deviation of discretionary accruals
is associated with a lower quality of earnings.
We firstly conduct a difference-in-differences analysis over a 7-year period (3 years
before acknowledgement of the bribing behavior and 3 years after) to compare the standard
deviation of the discretionary accruals between the investigated group, made up of the 80 U.S.
cases, and a peer group. We document a positive effect on the Accrual Quality of FCPA
investigated firms’ competitors, but not the FCPA investigated firms themselves (Hypothesis
1). Using the Modified Dechow Dichev model and a peer group selected thanks to apropensity score matching within common support and a caliper of 0.05 (Table 3), theinteraction term shows a difference-in-differences of 1.40 for the standard deviation
calculated over a 3-year period and is significant at the 1% level (pvalue: 0.001). While theAccrual Quality of the investigated firms does not significantly change, and even deteriorates

40

slightly (+0.47 from 3.09 to 3.57 for the volatility of the discretionary accruals), peers’
Accrual Quality significantly increases: the standard deviation of the discretionary accruals
decreases by 0.93 (from 7.04 to 6.11). In terms of economic effect, the 1.40 increase inAccrual Quality shown in the interaction term represents 45.30% of the value of the standard
deviation of the investigated firms before the trigger event (3.09). Additional results tend toshow that results are significant only for cases revealed before 2005 (Table 4), when theprosecutor began to use alternative resolution vehicles such as NPAs and DPAs to resolve
FCPA cases. Our main result remains consistent when we add control variables in the OLS
regressions of the difference-in-differences (Table 5), following the 5 main factors explaining
Accrual Quality listed in Dechow Dichev (2002) and additional control variables linked to thecases themselves. These results are robust when we use other dependent variables, from the
Dechow Dichev 3-year standard deviation to the Modified Jones or Jones 3-year standard
deviation of the discretionary accruals (Table 6). These results are also robust to alternative
choices in the specifications of our peer group in our difference-in-differences design (Table7), when we select the 7, 5 or 3 nearest neighbors instead of neighbors within a caliper of 0.05.
We put forward a deterrent effect hypothesis to explain these results. An investigationof allegedly bribing companies can deter their peers from engaging in bribery. That would
show a positive impact of financial regulation on the behavior of firms. But the explanation
may also be linked to window-dressing. Feroz et al (1991) showed that the SEC does not havethe resources to investigate all companies and must select targets with the highest probabilityof success. Analyzing the reasons why companies were investigated under AAERs, theauthors show that a large portion of AAER enforcement is linked to manipulation of current
assets. They also show that the SEC is able to calculate the Accrual Quality of firms using a
discretionary accruals approach. We posit that the SEC is seeking to capitalize on its
experience of enforcement in one specific industry to select future targets. According to the41

OECD Anti-Bribery Report (2014), enforcement under the Anti-Bribery law mainly stems
from self-revelation, unknown results or whistleblowing. The event that triggers enforcement
is not an ex-ante analysis of the Accrual Quality of firms by the SEC. However, once theprosecutor has identified patterns and developed know-how in one specific industry, we makethe assumption that the SEC will select future targets in the same industry using, among otherindicators, the level of Accrual Quality, following a propagation effect. Peers are aware of thepotential effect of propagation to other companies in the same industry of anti-bribery
investigations by the SEC and the DOJ. That is why we assume that they may window-dress
their Accrual Quality to decrease the probability of being selected as a target by theprosecutor.
This study has several limitations. The size of the sample of investigated firms is small,but cannot be extended as all the enforced actions are already included. We are not aware ofcases that have been settled without publicity between the prosecutor and the investigatedcompany. As we analyze the impact of the revelation of future enforcement on competitors,
this is not really a factor limiting the contribution of this paper. Furthermore, other methods
for propensity score matching such as Kernel matching could be tested to get a better
understanding of the deterrent effect we identified through a significant increase in the
Accrual Quality of peer companies when it becomes public that one of their competitors inthe same industry is to face an investigation by the SEC and/or the DOJ under the FCPA, theUS Anti-Bribery Law.

42

References
Arlen, J. (2012). Corporate Criminal Liability: Theory and Evidence. NYU Law andEconomics Research Paper No. 11-25.
Becker, G. S. (1968). Crime and Punishment: An Economic Approach. Journal of Political
Economy, 76(2), 169‑ 217.
Beneish, M. D. (1999). Incentives and Penalties Related to Earnings Overstatements that
Violate GAAP. The Accounting Review, 74(4), 425‑ 457.
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust DifferencesIn-Differences Estimates? The Quarterly Journal of Economics, 119(1), 249‑ 275.
Chaney, P. K., Faccio, M., & Parsley, D. (2011). The quality of accounting information inpolitically connected firms. Journal of Accounting and Economics, 51(1‑ 2), 58‑ 76.
Cheung, Y. L., Rau, P. R., & Stouraitis, A. (2012). How much do firms pay as bribes andwhat benefits do they get? Evidence from corruption cases worldwide (No. w17981).
Cambridge, MA: National Bureau of Economic Research.
Dechow, P., Ge, W., & Schrand, C. (2010). Understanding earnings quality: A review of theproxies, their determinants and their consequences. Journal of Accounting and Economics,50(2‑ 3), 344‑ 401.
Dechow, P. M., & Dichev, I. D. (2002). The Quality of Accruals and Earnings: The Role ofAccrual Estimation Errors. The Accounting Review, 77(s-1), 35‑ 59.
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting
Misstatements*: Predicting Material Accounting Misstatements. Contemporary Accounting
Research, 28(1), 17‑ 82.
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting Earnings Management. The
Accounting Review, 70(2), 193‑ 225.
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and Consequences ofEarnings Manipulation: An Analysis of Firms Subject to Enforcement Actions by the SEC.
Contemporary Accounting Research, 13(1), 1‑ 36.
Feroz, E. H., Park, K., & Pastena, V. S. (1991). The Financial and Market Effects of the
SEC’s Accounting and Auditing Enforcement Releases. Journal of Accounting Research, 29,
107.
Francis, J., LaFond, R., Olsson, P., & Schipper, K. (2005). The market pricing of accruals
quality. Journal of Accounting and Economics, 39(2), 295‑ 327.
Graham, J. L. (1984). The Foreign Corrupt Practices Act: A New Perspective. Journal ofInternational Business Studies, 15(3), 107‑ 121.
Healy, P. M., & Wahlen, J. M. (1999). A Review of the Earnings Management Literature and43Its Implications for Standard Setting. Accounting Horizons, 13(4), 365‑ 383.
Heider, F., & Ljungqvist, A. (2015). As certain as debt and taxes: Estimating the tax
sensitivity of leverage from state tax changes. Journal of Financial Economics, 118(3),Hines, J. (1995). Forbidden Payment: Foreign Bribery and American Business After 1977
(No. w5266). Cambridge, MA: National Bureau of Economic Research.
Huang, S., Sletten, E., & Roychowdhury, S. (2017). Does Litigation Encourage or Deter Real
Earnings Management? SSRN Electronic Journal.
Jennings, J. N., Kedia, S., & Rajgopal, S. (2011). The Deterrent Effects of SEC Enforcement
and Class Action Litigation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1868578
Jones, J. J. (1991). Earnings Management During Import Relief Investigations. Journal of
Accounting Research, 29(2), 193.
Jones, K. L., Krishnan, G. V., & Melendrez, K. D. (2008). Do Models of Discretionary
Accruals Detect Actual Cases of Fraudulent and Restated Earnings? An Empirical Analysis.
Contemporary Accounting Research, 25(2), 499‑ 531.
Karpoff, J. M., Lee, D. S., & Martin, G. S. (2008). The Cost to Firms of Cooking the Books.
Journal of Financial and Quantitative Analysis, 43(03), 581.
Karpoff, J. M., Lee, D. S., & Martin, G. S. (2012). The Impact of Anti-Bribery Enforcement
Actions on Targeted Firms. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1573222
Key, K. G. (1997). Political cost incentives for earnings management in the cable televisionindustry. Journal of Accounting and Economics, 23(3), 309‑ 337.
Koehler, M. (2012). The Story of the Foreign Corrupt Practices Act. Ohio State Law Journal,
73(5).
Koehler, M. (2015). Measuring the Impact of Non-Prosecution and Deferred Prosecution
Agreements on Foreign Corrupt Practices Act Enforcement. 49 U.C. Law Review, 497.
Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary
accrual measures. Journal of Accounting and Economics, 39(1), 163‑ 197.
McNichols, M. F. (2002). Discussion of the Quality of Accruals and Earnings: The Role ofAccrual Estimation Errors. The Accounting Review, 77(s-1), 61‑ 69.
Mukherjee, A., Singh, M., & Žaldokas, A. (2017). Do corporate taxes hinder innovation?
Journal of Financial Economics, 124(1), 195‑ 221.
Rose-Ackerman, S. (2010). The Law and Economics of Bribery and Extortion. Annual
Review of Law and Social Science, 6(1), 217‑ 238.
Shaw, B. (1988). Foreign Corrupt Practices Act: A legal and moral analysis. Journal ofBusiness Ethics, 7(10), 789‑ 795.
44

Shleifer, A., & Vishny, R. W. (1993). Corruption. The Quarterly Journal of Economics,
108(3), 599‑ 617.
Zeume, S. (2017). Bribes and Firm Value. The Review of Financial Studies, 30(5),1457‑ 1489.

45

Table 1 - Selection of the Final Sample of U.S. Firms Investigated under FCPA
Table 1 reports how have been selected the 80 cases included in the final sample of U.S. companies investigated under Foreign Corrupt Practice
Act (FCPA), the U.S. anti-bribery law for the period 1978-2015. In this sample, 6 firms are linked to 2 distinct cases, leading the final sample ofU.S. Investigated firms to 74 firms and 80 cases.
Number of Cases
Population of companies linked to a FCPA Enforcement Action

241- Total Privately-held companies excluded from the Sample-68- Total Miscellaneous cases (Anonymous, Individuals, No Company related)-23

Number of Firms investigated under FCPA worldwide

150- Population of Non-US companies linked to a FCPA Enforcement Action-38

Number of U.S. Firms investigated under FCPA112

- Total Cases with no match on Compustat or no Financial Data-18

- Total Cases for which we do not have a complete trigger event window or missing information
to calculate the volatility of Accruals-14

Final Sample of U.S. FCPA Investigated Firms (Treatment Group)

80

46

Table 2 - Summary statistics for main variables for FCPA Investigated firms and FCPA Non Investigated firms (Peer Group) selected using propensity scorematching - Winsorized measures (…)
Table 2 shows the summary statistics of the main variables for FCPA Investigated firms and FCPA Non Investigated (Peer Group) companies. To build the peer group, weselect peer companies that belong to the same SIC3 than the related investigated firm, exist during the 7-period of the analysis (3 years before and 3 years after the event year)
and disclose foreign sales during at least one of the seven years of the trigger event window, using the dispatching between domestic and foreign sales disclosed in thegeographical segment data in Compustat. Selected peer companies represent the nearest neighbors within common support and a caliper of 0.05 in the peer group at the SIC3
level matched using the 2 following criteria: Size (Log of Market Cap) and Percentage of foreign sales, both considered in year t-1, the year before the acknowledgment date(1).
After these steps, we identify 1,658 peers in the peer group for 1,054 unique companies over a 7-year period (each company is used 1.57 times on the average). For theinvestigated group, 80 observations are considered for 74 unique companies and 80 cases (6 companies are present twice in the sample). All variables are winsorized at the 1st
and the 99th percentiles and are lagged by total assets.
7-year Period

Variables
Log of Market Cap
Percentage of Foreign Sales
Market-to-Book-ratio
Leverage ratio
Accruals
DA Modified DD model

Nb Obs
80
80
80
80
80
80

Mean
8.45
0.43
3.02
0.24
-4.97
1.64

Median
8.80
0.41
2.47
0.24
-4.94
1.28

Variables
Log of Market Cap
Percentage of Foreign Sales
Market-to-Book-ratio
Leverage ratio
Accruals
DA Modified DD model

Nb Obs
1,658
1,658
1,658
1,658
1,658
1,658

Mean
6.34
0.40
2.74
0.18
-7.41
0.61

Median
6.42
0.37
2.49
0.14
-6.42
1.02

Min
2.21
0.01
-1.41
0.00
-8.60
-8.60

Investigated Firms
Max
P1
11.93
2.21
0.86
0.01
13.21
-1.41
0.76
0.00
13.56
-8.60
13.56
-8.60

P99
11.93
0.86
13.21
0.76
13.56
13.56

P25
7.03
0.32
1.63
0.16
-0.40
-0.40

P75
9.79
0.55
3.97
0.29
3.80
3.80

SD
1.89
0.20
2.23
0.12
3.63
3.62

Peer group with Propensity Score Matching
Min
Max
P1
P99
-1.98
11.88
1.35
11.50
0.00
1.00
0.01
0.99
-122.93133.16-17.57
15.90
0.00
3.47
0.00
1.08
-46.6426.48-27.76
5.29-26.32
22.67-15.72
12.14

P25
4.74
0.18
1.60
0.04
-10.10
-2.12

P75
7.91
0.57
3.97
0.26
-3.47
3.60

SD
2.30
0.26
9.70
0.23
6.36
5.20

(1)

For four investigated firms, neighbors are selected at the SIC2 level as there are no potential neighbors at the SIC3 level. For three investigatedfirms, we enlarge the caliper at 10% to find at least one neighbor at the SIC3 level. Results remain unchanged if no neighbor is selected for those cases.

47

(…) Table 2 - Summary statistics for main variables for FCPA Investigated firms and FCPA Non Investigated firms (Peer Group) selected using propensity scorematching - Winsorized measures (…)

3-year Period Before

Variables
Log of Market Cap
Percentage of Foreign Sales
Market-to-Book-ratio
Leverage ratio
Accruals
DA Modified DD model

Nb Obs
80
80
80
80
80
80

Mean
8.31
0.42
3.07
0.24
-4.77
2.02

Median
8.49
0.49
2.28
0.23
-4.82
1.06

Variables
Log of Market Cap
Percentage of Foreign Sales
Market-to-Book-ratio
Leverage ratio
Accruals
DA Modified DD model

Nb Obs
1,658
1,658
1,658
1,655
1,654
1,658

Mean
6.21
0.38
2.76
0.17
-8.18
0.39

Median
6.26
0.35
2.48
0.21
-6.55
1.21

Min
2.55
0.00
-0.60
0.00
-13.32-10.62

Investigated Firms
Max
P1
11.87
2.55
0.83
0.00
18.54
-0.60
0.54
0.00
9.44
-13.32
15.00-10.62

P99
11.87
0.83
18.54
0.54
9.44
15.00

P25
6.73
0.31
1.36
0.17
-7.68
-0.41

P75
9.88
0.54
3.77
0.29
-2.48
4.62

SD
1.94
0.21
2.73
0.12
3.98
4.25

Peer Firms with Propensity Score Matching
Min
Max
P1
P99
-1.01
11.95
1.17
11.46
0.00
1.00
0.00
0.99
-264.20181.30-16.97
20.62
0.00
2.89
0.00
0.86
-60.7937.54-38.8811.65-36.4229.09-22.93
17.04

P25
4.51
0.15
1.52
0.02
-11.34
-2.50

P75
7.85
0.56
4.05
0.27
-3.32
4.52

SD
2.31
0.27
13.93
0.20
9.06
7.42

48

(…) Table 2 - Summary statistics for main variables for FCPA Investigated firms and FCPA Non Investigated firms (Peer Group) selected using propensity scorematching - Winsorized measures

3-year Period After

Variables
Log of Market Cap
Percentage of Foreign Sales
Market-to-Book-ratio
Leverage ratio
Accruals
DA Modified DD model

Nb Obs
80
80
80
80
80
80

Mean
8.57
0.45
2.95
0.23
-4.81
1.63

Median
8.95
0.43
2.13
0.23
-4.22
1.32

Variables
Log of Market Cap
Percentage of Foreign Sales
Market-to-Book-ratio
Leverage ratio
Accruals
DA Modified DD model

Nb Obs
1,656
1,630
1,654
1,655
1,658
1,658

Mean
6.46
0.43
2.65
0.20
-6.88
0.72

Median
6.56
0.40
2.29
0.14
-5.74
1.14

Min
1.86
0.01
-1.86
0.00
-13.01-1.49

Investigated Firms
Max
P1
12.02
1.86
0.93
0.01
11.09
-1.86
0.95
0.00
5.06
-13.01
14.13
-1.49

P99
12.02
0.93
11.09
0.95
5.06
14.13

P25
7.29
0.32
1.51
0.16
-7.67
-1.08

P75
9.84
0.58
4.04
0.29
-2.73
4.30

SD
1.90
0.22
2.19
0.14
3.64
4.14

Peer Firms with Propensity Score Matching
Min
Max
P1
P99
-2.40
12.23
1.10
11.65
0.00
1.00
0.00
0.99
-271.74308.37-17.74
15.32
0.00
7.00
0.00
1.40
-62.3731.16-36.0913.88-39.6332.49-22.01
17.98

P25
4.77
0.20
1.46
0.02
-10.06
-2.34

P75
8.09
0.61
3.82
0.27
-2.62
4.39

SD
2.36
0.27
13.82
0.33
8.28
6.89

49

Table 3 - Difference-in-Differences Results for Standard Deviation over 3 years of Modified Dechow Dichev Discretionary Accruals - PSM nearest
neighbors within common support and a caliper distance of 0.05
Table 3 shows the Difference-in-Differences results for the main dependent variable, ModDD_3years, that represents the standard deviation of the Discretionary
Accruals over a 3-year period calculated using the Modified Dechow Dichev model. Two values of ModDD_3years have been calculated: A value Before (year -3
to year -1) and a value After (year 1 to year 3). The year 0 is excluded from the calculations, as it represents the year of the trigger event (the year 0 is the year when
the bribing behavior becomes public, i.e. when the company is supposed to implement actions to correct the behavior). Two dummy variables are used as
independent variables. The first dummy is called "Investigated", that takes the value of 1 if the company is an FCPA investigated company and zero otherwise. The
second dummy variable is called "After" and takes the value of 1 if the related period is year 1 to year 3, and zero if the related period is from year -3 to year -1.
We identify 1,658 peer companies for the 80 events (1,054 distinct companies, each one being used in 1.57 events on the average). The FCPA Investigated Group ismade of 80 observations, representing the 80 cases (for 74 unique companies). Observations are clustered at the company level in regressions. Results are shown formatched peer companies with the same SIC3 than one of the investigated firms existing during the 7 years of the event window, with at least once foreign sales
disclosed, using propensity score matching with size and percentage of foreign variables as matching variables, within common support and a caliper distance of0.05(1).

BeforeAfter

Difference

PSM with caliper distance 0.05

Investigated Firms

Nb ObsMean

Nb ObsMean

Nb ObsMean

80

3.09***

80

3.57***

160

0.47

t-statPeer Firms(2)

9.02
1,658

t-statDifference
t-stat7.04***

9.76
1,658

34.04
1,738

-3.95***-9.89

6.11***

1.26
3,316

33.51
1,738

-2.54***-6.26-0.93***
-4.62

3,476

1.40***
3.32(1) For four investigated firms, neighbors are selected at the SIC2 level as there are no potential neighbors at the SIC3 level. For three investigated firms, we enlarge
the caliper at 10% to find at least one neighbor at the SIC3 level. Results remain unchanged if no neighbor is selected for those cases.
(2) 1,054 unique companies

50

Table 4 - Difference-in-Differences Results for Standard Deviation over 3 years of Modified Dechow Dichev Discretionary Accruals with year Dummy 2005
and caliper 0.05
Table 4 shows the Difference-in-Differences results for subsamples in time (before and during/after 2005) for the main dependent variable, ModDD_3years, that
represents the standard deviation of the Discretionary Accruals over a 3 years period calculated using the Modified Dechow Dichev model. Two values of
ModDD_3years have been calculated: A value Before (year -3 to year -1) and a value After (year 1 to year 3). The year 0 is excluded from the calculations, as it
represents the year of the trigger event (the year 0 is the year when the bribing behavior becomes public, i.e. when the company is supposed to implement actions tocorrect the behavior). Two dummy variables are used as independent variables. The first dummy is called "Investigated", that takes the value of 1 if the company is an
FCPA investigated company and zero otherwise. The second dummy variable is called "After" and takes the value of 1 if the related period is year 1 to year 3, andzero if the related period is from year -3 to year -1. The Investigated Group is made of 80 observations, representing the 80 cases (for 74 unique companies). 27 cases
have been acknowledged before 2005, 53 cases have been acknowledged in or after 2005. Observations are clustered are the company level in regressions. Peer Firms
group is made of peer companies of the same SIC3 than the related investigated company, existing during the 7 years of the event window, with at least once foreign
sales disclosed and matched using the propensity score matching within common support and a caliper of 0.05.

Psm caliper 0.05 and Dummy 2005

(1)

BeforeBefore 2005
Investigated Firms
t-statPeer Firms(1)t-statDifference
t-statNb Obs27

During or after 2005
Investigated Firms
t-statPeer Firms (2)t-statDifference
t-statNb Obs
53

765
792

893
946

After

Mean
2.77***
8.29
8.59***
27.32-5.82***-12.79Nb Obs27

Mean
3.26***
6.89
5.71***
24.42-2.45**-4.68

Nb Obs
53

765
792

893
946

DifferenceMean
3.77***
5.50
6.52***
25.66-2.75***-3.81

Nb Obs54

Mean
3.46***
8.02
5.76***
24.32-2.30***-4.69

Nb Obs
106

1,530
1,584

1,786
1,892

Mean
1.00
1.26
-2.07***-6.35
3.07***
3.66
Mean
0.21
0.55
0.05
0.20
0.16
0.35

615 unique companies (2) 562 unique companies

51

Table 5 - OLS Regressions with Control Variables for Standard Deviation over 3 years of Modified Dechow Dichev Discretionary Accruals (…)
Table 5 shows the results of the OLS regressions for our main dependent variable, ModDD_3years, that represents the standard deviation of the Discretionary Accruals over a
3-year period (actually 6 years divided into 3 periods of 3 years, one before the event and one after the event) calculated using the Modified Dechow Dichev model, with
control variables. INV is a dummy variable set equal to 1 if the company has been investigated under FCPA and is part of our final sample of investigated companies and 0
otherwise. AFTER is a dummy variable set equal to 1 if the 3-year period if after the event and 0 otherwise. INV*AFT is the interaction variable between the 2 dummy
variables defined above. Other general independent variables follow Dechow Dichev (2002) and Chaney, Faccio, Parsley (2010). Operating cycle is defined as the log of thesum of days in receivable and days in inventory. Ln Mkt Cap is the natural log of the company market capitalization in US dollars, to control for size. σ(CFO/TA) × 100 is the
7-year standard deviation of CFO over total assets (*100). σ(Sales/AT) × 100 is the 7-year standard deviation of cash sales over total assets (*100). Sales growth is the annual
growth of sales. Market-to-book is the ratio of market capitalization to book value of equity in year t-1. Leverage is total debt as percentage of total assets in year t-1. Negative
Earnings is the frequency of negative earnings realization over the 7-year period of study. Additional variables linked to the cases themselves are also included as control
variables in the regressions. Court_DOJ is an ordinal variable that considers the district court that settles the DOJ case. Court_SEC is an ordinal variable that considers the
district court that settles the SEC case. Dummy_2005 is a dummy variable that takes the value of 1 if the case was acknowledged in or after 2005 and zero otherwise. First
Case is a dummy variable that takes the value of 1 if the case is the first one in the industry and zero otherwise. The FCPA Investigated Group is made of 160 observations (80before the event and 80 after the event), representing the 80 cases (for 74 unique companies). Results are shown for 3 levels of the Peer Group. Peer group is made ofcompanies with the same SIC3 than the related investigated firm, existing during the 7 years of the event window, with at least once foreign sales disclosed using thepropensity score matching within common support and a caliper of 0.05 (1), but also for all the peer companies available at the SIC3 level (2), and for a caliper of 0.1 (3).

52

(…) Table 5 - OLS Regressions with Control Variables for Standard Deviation over 3 years of Modified
Dechow Dichev Discretionary Accruals

Dependent Variable
Control variables
INV*AFTER
t-statINV
t-statAFTER
t-statOperating cyclet-stat
Ln Mkt Cap
t-statσ(CFO/AT)*100
t-statσ(Sales/AT)*100
t-statSales growth*100
t-statMarket-to-bookt-statLeveraget-statNegative Earnings
t-statCourt_DOJ
t-statCourt_SEC
t-statDummy_2005
t-statDummy_firstt-statIntercept
t-statIndustry Fixed Effects
Year Fixed Effects
Number of Observations
Investigated firms observations
Peer firms observations
R squared

Mod DD_3y PSM withcaliper of 0.05
(1)
1.3983***
3.27-1.0906***-3.10-0.9252***-4.55
0.4103*1.83-0.4298***-5.42
0.0626***3.36
0.0474***5.33-0.0002***-4.43-0.0106***-3.10
1.2785*
1.81
3.8924***7.99
0.00491.14
0.00641.14
0.64180.51-0.5132-0.99
4.3594***
2.75
YES
YES
3,426
160
3,266
30.97%Mod DD_3y at SIC3
level
(2)
1.7090***4.14-1.2690***-3.49-1.2359***-7.07
0.27981.51-0.4307***-6.49
0.0656***
5.95
0.0291***2.90-0.0001-0.54-0.0060-1.48-0.3990**-2.15
4.3805***11.04
0.00621.48
0.00120.40
4.9371***
3.16-0.8870**-2.07
1.3767
0.80
YES
YES
6,856
160
6,696
29.06%Mod DD_3y PSM withcaliper of 0.1(3)1.8038***4.27-1.2759***-3.60-1.3307***-6.80
0.21560.94-0.4576***-6.23
0.0765***5.13
0.0225*1.93-0.0001-1.06-0.0080**-2.37
0.8225
1.21
3.9074***8.30
0.00571.30-0.0006-0.13
1.51711.09-0.9007*-2.03
5.1273***
2.91
YES
YES
4,970
160
4,810
29.96%

53

Table 6 - Robustness test of OLS Regressions with Control Variables for alternative dependent variables (…)
Table 6 shows the results of robustness tests of the OLS regressions with control variables using alternative dependent variables. We use 3 alternative dependent variables. We
firstly use the standard deviation over 3 years of the discretionary accruals obtained with the Dechow Dichev model (DD_3y) following Dechow and Dichev (2002). Then we
use the Modified Jones Model following Dechow et al (1995). We finally use the Jones model following Jones (1991). The FCPA Investigated Group is made of 160
observations (80 before the event and 80 after the event), representing the 80 cases (for 74 unique companies). Peer group is made of peer companies existing during the 7
years of the event window, with at least once foreign sales disclosed using the propensity score matching within common support and a caliper of 0.05 (1), but also for all thepeer companies available at the SIC3 level (2), and for a caliper of 0.1 (3). INV is a dummy variable set equal to 1 if the company has investigated under FCPA and is part ofour final sample of investigated companies and 0 otherwise. AFTER is a dummy variable set equal to 1 if the 3-year period if after the event and 0 otherwise. INV*AFT is theinteraction variable between the 2 dummy variables defined above. Other general independent variables follow Dechow Dichev (2002) and Chaney, Faccio, Parsley (2010).
Operating cycle is defined as the log of the sum of days in receivable and days in inventory. Ln Mkt Cap is the natural log of the company market capitalization in US dollars,
to control for size. σ(CFO/TA) × 100 is the 7-year standard deviation of CFO over total assets (*100). σ(Sales/AT) × 100 is the 7-year standard deviation of cash sales overtotal assets (*100). Sales growth is the annual growth of sales. Market-to-book is the ratio of market capitalization to book value of equity in year t-1. Leverage is total debt aspercentage of total assets in year t-1. Negative Earnings is the frequency of negative earnings realization over the 7-year period of study. Additional variables linked to thecases themselves are also included as control variables in the regressions. Court_DOJ is an ordinal variable that considers the district court that settles the DOJ case.
Court_SEC is an ordinal variable that considers the district court that settles the SEC case. Dummy_2005 is a dummy variable that takes the value of 1 if the case was
acknowledged in or after 2005 and zero otherwise. First Case is a dummy variable that takes the value of 1 if the case is the first one in the industry and zero otherwise.

54

(…) Table 6 - Robustness test of OLS Regressions with Control Variables for alternative dependent
variables (…)

Dependent Variable: DD_3y

DD_3y PSM withcaliper of 0.05

DD_3y at SIC3 level

DD_3y PSM withcaliper of 0.1

Control variables

(1)

(2)

(3)

1.1524***3.02-1.0377***-3.06-0.9779***-4.72
3.9766**
2.46
YES
YES
YES
3,446
160
3,286
31.72%

1.4691***4.03-1.2263***-3.51-1.2946***-7.27
1.0543
0.65
YES
YES
YES
6,884
160
6,724
31.72%

1.5352***
4.09-1.2339***-3.64-1.3607***-6.87
4.4580**
2.44
YES
YES
YES
4,990
160
4,830
31.09%

Dependent Variable: ModJones_3y

ModJones_3y PSM
with caliper of 0.05

ModJones_3y at
SIC3 level

ModJones_3y PSM
with caliper of 0.1

Control variables

(1)

(2)

(3)

0.86641.61-0.8890***-2.64-0.8425***-4.15
4.1262***
2.71
YES
YES
YES
3,456
160
3,296
27.05%

1.0529**2.02-0.9841***-2.78-1.0290***-6.18
5.0716***
2.67
YES
YES
YES
6,886
160
6,726
25.76%

1.1167**2.10-1.0118***-2.92-1.0929***-5.73
5.9298***
3.69
YES
YES
YES
4,980
160
4,820
26.26%INV*AFTER
t-statINV
t-statAFTER
t-statIntercept
t-statControl Variables
Industry Fixed Effects
Year Fixed Effects
Number of Observations
Investigated firms observations
Peer firms observations
R squared

INV*AFTER
t-statINV
t-statAFTER
t-statIntercept
t-statControl Variables
Industry Fixed Effects
Year Fixed Effects
Number of Observations
Investigated firms observations
Peer firms observations
R squared55

(…) Table 6 - Robustness test of OLS Regressions with Control Variables for alternative dependent
variables

Dependent Variable: Jones_3y

Jones_3y PSM withcaliper of 0.05

Jones_3y at SIC3
level

Jones_3y PSM withcaliper of 0.1

Control variables

(1)

(2)

(3)

0.79031.49-0.7878**-2.37-0.8197***-4.17
4.0396***
2.68
YES
YES
YES
3,464
160
3,304
27.05%

0.9512*1.84-0.8469**-2.41-0.9806***-6.07
5.0528***
2.73
YES
YES
YES
6,906
160
6,746
25.72%

1.0160*1.93-0.9027***-2.64-1.0454***-5.62
5.8353***
3.66
YES
YES
YES
4,994
160
4,834
26.26%INV*AFTER
t-statINV
t-statAFTER
t-statIntercept
t-statControl Variables
Industry Fixed Effects
Year Fixed Effects
Number of Observations
Investigated firms observations
Peer firms observations
R squared56

Table 7 - Robustness test of OLS Regressions with Control Variables using Propensity Score Matching with Nearest Neighbors
Table 7 shows the results of robustness tests of the OLS regressions with control variables using nearest neighbor matching instead of caliper matching as a Propensity Score
Matching technique for our dependent variable ModDD_3years, that represents the standard deviation of the Discretionary Accruals over a 3-year period (actually 6 years
divided into 3 periods of 3 years, one before the event and one after the event) following the modified Dechow Dichev model. INV is a dummy variable set equal to 1 if thecompany has investigated under FCPA and is part of our final sample of investigated companies and 0 otherwise. AFTER is a dummy variable set equal to 1 if the 3-year
period if after the event and 0 otherwise. Other general independent variables follow Dechow Dichev (2002) and Chaney, Faccio, Parsley (2010). Operating cycle is definedas the log of the sum of days in receivable and days in inventory. Ln Mkt Cap is the natural log of the company market capitalization in US dollars, to control for size.
σ(CFO/TA) × 100 is the 7-year standard deviation of CFO over total assets (*100). σ(Sales/AT) × 100 is the 7-year standard deviation of cash sales over total assets (*100).
Sales growth is the annual growth of sales. Market-to-book is the ratio of market capitalization to book value of equity in year t-1. Leverage is total debt as percentage of total
assets in year t-1. Negative Earnings is the frequency of negative earnings realization over the 7-year period of study. Additional variables linked to the cases themselves arealso included as control variables in the regressions. Court_DOJ is an ordinal variable that considers the district court that settles the DOJ case. Court_SEC is an ordinalvariable that considers the district court that settles the SEC case. Dummy_2005 is a dummy variable that takes the value of 1 if the case was acknowledged in or after 2005
and zero otherwise. First Case is a dummy variable that takes the value of 1 if the case is the first one in the industry and zero otherwise. The FCPA Investigated Group ismade of 160 observations (80 before the event and 80 after the event), representing the 80 cases (for 74 unique companies). Peer group is made of companies existing during
the 7 years of the event window, with at least once foreign sales disclosed using the propensity score matching within common support nearest neighbors matching. Results
are shown for the 7 nearest neighbors, 5 nearest neighbors, and 3 nearest neighbors respectively.

Dependent Variable
INV*AFTER
t-statINV
t-statAFTER
t-statIntercept
t-statControl Variables
Industry Fixed Effects
Year Fixed Effects
Number of Observations
Investigated firms observations
Peer firms observations
R squared

Mod DD_3y PSM with
7 neighbors

Mod DD_3y PSM
with 5 neighbors

Mod DD_3y PSM with
3 neighbors

0.9195*1.96-0.8700**-2.45-0.4464*-1.68
0.9373
0.48
YES
YES
YES
1,076
160
916
29.28%

1.0468**2.13-0.9558**-2.44-0.5737*-1.91
0.3777
0.18
YES
YES
YES
844
160
684
26.52%

1.0201*1.94-0.9436**-2.34-0.5470-1.59
0.0576
0.02
YES
YES
YES
598
160
438
25.97%

57

58