Some models of academic corruption

Richard E. Quandt

Received: 25 October 2009 / Accepted: 13 May 2010 / Published online: 27 May 2010

� Springer Science+Business Media, LLC 2010

Abstract The paper notes that while much work has been done in the past on

corruption in government and business, relatively little attention has been devoted to

corruption in academic institutions. The principal forms of academic corruption are

plagiarism and various forms of cheating, research fraud and financial fraud. Two

simple mathematical models are proposed for exploring the motivations for (1)

plagiarism, which is essentially a solitary crime, and (2) bribery. The responsiveness

of the demand for the first of these, and the demand for and supply of the second to

changes in underlying parameters are explored.

Keywords Corruption � Plagiarism � Bribery � Monitoring � Moral suasion

JEL Classification D11 � D23

1 Introduction

Considerable efforts have been devoted during the past 10 years to understanding

the problem of corruption in public life. The vast literature that has grown up about

this issue has dealt with alternative definitions of corruption, its causes and

consequences, particularly for economic development, and has provided a number

of country studies. Much attention has been devoted to possible methods for curbing

corruption (Morgan 1998).1 It is difficult to convey succinctly the scale of

corruption, and numerous sources have provided insight into this matter; see for

example Harvard Business School (1999).

R. E. Quandt (&)

Princeton University, Princeton, NJ, USA

e-mail: metrics@quandt.com

1 For a recent definitive treatment of corruption, see Lambsdorff (2007).

123

Eur J Law Econ (2012) 34:63–75

DOI 10.1007/s10657-010-9162-2

A central tool for analyzing corruption is the Corruption Perceptions Index (CPI),

which annually rates a large number of countries on a scale of 1 to 10 (1 = very

corrupt, 10 = very clean) and ‘‘focuses on corruption in the public sector and defines

corruption as the abuse of public office for private gain.’’ (http://www.icgg.org).

Other indices have also been compiled from time to time. The severity of the

problem is illustrated by the fact that the 2004 CPI covers 146 countries, of which

106 score less than 5 (Eigen 2004). The corresponding figures are 115 out of 158

countries for 2005, 118 out of 166 countries for 2006 and 133 out of 179 for 2007,

making the percentage of countries with a CPI of less than 5.0 in those years 72.6,

72.8, 71.1 and 74.3% respectively (http://ww1.transparency.org 2002, http://www.

icgg.org 2006, http://www.transparency.org 2005, 2007). The CPI is based on data

from a number of sources, such as the World Economic Forum, The Institute for

Management Development, PricewaterhouseCoopers, the World Bank Business

Environment Survey, The Economist Intelligence Unit, Freedom House and the

Political and Economic Risk Consultancy (Lambsdorff 2001). A number of

empirical studies have tried to identify the causes of corruption. Some general

conclusions that have emerged from these studies are as follows: (1) High GDP/

capita is generally associated with low levels of corruption, although some low

income countries have made good progress in reducing corruption, such as Estonia,

Colombia and Bulgaria; (http://ww1.transparency.org 2005). (2) High degrees of

economic chaos, perhaps as measured by the inflation rate, tend to be associated

with high levels of corruption; (3) High degrees of regulation tend to be paired with

high corruption (Paldam 2001, 2002). Lambsdorff reports studies that have found a

negative association between the level of corruption and the extent of press free-

dom, a negative association between corruption and the extent to which recruiting is

merit based, and a positive association between corruption and the extent to which

hierarchical religions (Catholic, Eastern Orthodox, Muslim) predominate (Lamb-

sdorff 2007; Marshall 2002). Barnowe et al. (2004) report that corruption thrives

where democratic institutions are weak. There has been much debate about whether

corruption enables the public to avoid the stultifying effects of bureaucracy or

whether it retards economic development by ‘‘lowering the security of property and

misallocating resources,’’ in other words, whether corruption acts like ‘‘sand or oil’’

in the mechanisms of the economy.2 As may be expected, identifying the possible

causes of corruption permits the political Left and neoliberals to muster politically

charged views, with the former laying the blame on ‘‘weak public ethics,’’ while the

latter pointing to ‘‘black markets caused by excessive state intervention’’ (Martı nez

2002). But note that some recent studies have found that corruption retards eco-

nomic growth.3

Most of the discussion of corruption deals with corruption in government, the

economic sphere and the use of power. A very useful typology for this is

provided by Karklins, and the headings under which she treats corruption

illustrate the prevalent focus: Bribery to bend rules, misuse of licensing and

inspection powers, asset stripping by officials, diverting public funds, profiteering

2 Many have employed this metaphor; see, for example, Rumyantseva (2005).3 Meon and Sekkat (2005).

64 Eur J Law Econ (2012) 34:63–75

123

from public assets, profiteering from privatization, nepotism, clientelism, ‘‘state

capture,’’ misuse of legislative powers, undermining elections, etc. (Karklins

2002). But a fundamental problem in studying corruption of any variety is the

difficulty of obtaining data. Corrupt acts are, by their very nature, illegal, and

are therefore difficult to find out about. The best types of data are survey data

and there certainly exist several surveys that have been fruitfully analyzed.4 But

data problems will continue to plague efforts to test statistical hypotheses about

corruption.

The present study is limited to a very special area that appears to have been

relatively neglected by researchers in the corruption field. This area is education,

and more specifically, higher education. Education provides numerous opportu-

nities for private gain at the expense of the public or at the expense of fairness,

transparency and the ideal of high academic standards. It shares with other areas

in which corruption occurs the feature that the corrupt acts are not licit and hence

tend to be hidden from public view. It differs from the more standard examples of

corruption in that the perpetrators of corrupt acts are not, strictly speaking,

politicians or bureaucrats or businessmen, but scholars, teachers and students. But

there is no reason to think that corrupt acts committed in an academic

environment are any less damaging to the social fabric of a country than any

other such acts and that the social costs of corruption in academia are smaller than

in other areas.

A taxonomy of higher educational corruption is provided by Rumyantseva

(2005). She notes that education-specific corruption can be characterized as

‘‘academic corruption’’ or as ‘‘corruption in services.’’ The former occurs in student-

faculty exchanges or student-administrator exchanges, while the latter arises in

student-administrator exchanges and student-staff exchanges. She provides numer-

ous examples of the various types; among the most notable illustrations are cases

when money changes hands between a student and a faculty member for a better

grade, when money changes hands between a (potential) student and an

administrator for the sake of guaranteeing admission to the institution, or between

a student and a staff members, such as a librarian, for the privilege of borrowing a

book. In spite of the fact that studies dealing with educational corruption are not as

numerous as those concerned with governmental corruption in general, there are

several distinguished studies dealing with either specific types of corruption (see, for

example, Hallak and Poisson 2007) or with specific countries (see Anderson 1998,

2000, 2001; Cabelkova and Hanousek 2004).

Corrupt acts may be committed by students, professors, other staff members or

even parents of students. Corrupt acts by students encompass predominantly

plagiarism and cheating on examinations. For example, a third (final) year student at

the University of Kent, Canterbury, was discovered to have plagiarized throughout

his academic career and was punished by having his degree withheld. He then sued

the University on the grounds of ‘‘negligence for failing to inform him that such

conduct is against the rules’’ (Labi 2004). Corruption by faculty primarily consists

4 See, for example, Kubiak (2001), Stefan Batory Foundation (2002), and a questionnaire that was used

in Kazakhstan and was called to my attention by Nataliya Rumyantseva.

Eur J Law Econ (2012) 34:63–75 65

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of plagiarism and research fraud.5 An especially crass example of plagiarism was

discovered when the editors of Kyklos, a distinguished economics journal, noted that

an article published in that journal by Hans W. Gottinger in 1996 was ‘‘practically

identical’’ to an article by G. J. Wyatt published four years earlier in Economics ofInnovation and New Technology, vol. 2, 1992, pp. 157–163. The one page response

to this discovery by the managing editors of Kyklos states (Frey et al. 1999) that the

journal (1) would not accept any future paper by Gottinger, (2) requested citation

and abstracting indexers to delete references and citations to Gottinger’s paper, (3)

informed the leading journals of economics and the dean and rector of the institution

where Gottinger was employed and (4) undertook to publish a summary of Wyatt’s

paper in Kyklos.

Finally, faculty and/or administrators are capable of financial fraud and may get

involved in cases of bribery. In 2002, the putative father of a young man seeking

admission to Pembroke College, Oxford, approached two fellows of the college,

the Rev. John Platt and Mary-Jane Hilton, and claiming to be a well-to-do banker,

inquired into what he could do the secure admission for his son. He was told that

his son’s place would be assured for a donation of \char’44300,000 and also

suggested that the donation be funneled through a secret trust fund lest the deal

become public knowledge. Unfortunately for them, the person they believed to be

a wealthy banker turned out to be a reporter for the (London) Sunday Times and

they must have gotten the shock of their lives when they saw his article soon after

these events took place. The appointments of Platt and Hilton were terminated

immediately upon their resignations and the then vice-chancellor of the university,

Colin Lucas, was at pains to point out that individual excellence was the only

criterion for Oxford admission (BBC News 2002; Appleton 2002; Calvert 2002;

Browne 2002). In a recent Central European case being investigated by the

authorities in October 2009, a combination of plagiarism and bribery appears to

have been committed at the Law Faculty of the University of Western Bohemia in

Plzen. Vice dean Ivan Tomazic was accused of plagiarism in his thesis. After

some debate and confrontations, the dean of the Law Faculty, also accused of

plagiarism, and both vice deans resigned. But in checking recent dissertations, it

turned out that numerous distinguished and important personages had obtained

their degrees in a 4–6 year course of study after only a few weeks or months; the

implication being the the Law Faculty wanted to curry favor with some of these

important personages.6 Cases abound, and are fodder for many statistical, political

and institutional investigations of corruption; however, there do not appear to be

many formal economic analyses of corruption. The next sections explores some

extremely simple models in a more formal way.

5 See for example Bartlett and Smallwood (2004). For a thoughtful analysis of some classic cases, see

Hoffer (2004). For research fraud, an older, but still relevant, work is Broad and Wade (1982). For more

recent cases, see for example Smallwood (2005).6 See for example Johnston (2009), Radio Praha 2009, \v Ceske noviny.cz (2009), Clifford (2009).

66 Eur J Law Econ (2012) 34:63–75

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2 Some simple models

Academic corruption differs markedly from ordinary crimes in that its commission is a

less public event and its consequences, even if the act is detected, tend to be in the ethical

sphere. With the exception of very few sample surveys7, there are no data on individual

behavior and it seems difficult to carry out studies such as those done by Witte (1980),

Viscusi (1986) and others. Numerous authors have noted that plagiarists tend to have

apologists and that the perpetrators of academic fraud are quickly forgiven.8

Not many formal models have treated the case of academic corruption. One

notable exception is a recent paper by Collins, Judge and Rickman, which treats the

choice to engage or not to engage in corrupt practices in the framework of a fairly

straightforward utility-maximizing model (Collins et al. 2007). Their principal

conclusion is that the amount of plagiarism diminishes as the probability of getting

caught and as the marginal disutility cost of plagiarizing increase. Interestingly, in

their model, if the disutility of being caught plagiarizing is invariant with the

amount plagiarized, there is no effect on the amount. We shall consider two

alternative models. The first one is a model of plagiarism, which is, by its nature, a

solitary crime, although the same could often be applied to a case of data

falsification. The second model, which may be emblematic of a case of bribery, is

one in which there is a buyer as well as a seller of some illicit good.9

A Plagiarism Model. We assume that there is a continuum of academics, with

utility function V(x), where x is the general consumption good the price of which is

normalized to unity, and we assume, as does Viscusi, that V is strictly concave;

hence V0[ 0 and V00\ 0. The agent’s endowment is M; hence in the absence of

plagiarism, utility maximization yields a utility level of V(M).

Define a variable y such that

y ¼0; if the agent does not plagiarize;

1; if the agent does plagiarize:

8<

:

Let us further assume that plagiarism leads to a financial benefit in the amount of

b, which accrues to the agent whether or not the plagiarism is detected, and that if it

is detected, a utility penalty in the amount of a must be paid.10 Then, if the agent

plagiarizes, her maximum utility is

U ¼ VðM þ bÞ; if the plagiarism is not detected;VðM þ bÞ � a; if it is detected:

�

7 Kubiak, Anna, op.cit.8 For strong arguments both pro and con in the famous case of Doris Kearns Goodwin, see Nobile (2002)

and Oliphant (2002); on the fraud perpetrated by Michael Bellesisle, see Robin (2004), where he says,

‘‘Bellesisle’s fast track to absolution is most evident withing the community of scholarly journals, where

he has already published several book reviews.’’9 These models bear some formal resemblance to Quandt (1983).10 This assumption mimics the fact that, with rare exceptions, plagiarists suffer some opprobrium but

hardly any financial penalty.

Eur J Law Econ (2012) 34:63–75 67

123

It is noteworthy and easy to verify that none of the subsequent conclusions are

altered if the benefit b does not accrue when the plagiarism is detected. Letting pdenote the probability of detection, the agent will choose to plagiarize if and only if

the expected utility from plagiarism exceeds the utility from not doing so, i.e., if

VðM þ bÞ � ap=VðMÞ

or if

a5VðM þ bÞ � VðMÞ

p:

We now assume that all agents are identical except for their value of a which is

distributed over agents with some density function f(a) for a= 0 .11 The assumption

that a is stochastic is a simple but important assumption. It reflects the assumption

that the detection of corrupt behavior has a differential utility impact on different

people; most likely because of their differing sensitivity to moral nuances or dif-

fering expectations concerning consequences. The aggregate demand for plagiarism

then is

D ¼ZðVðMþbÞ�VðMÞÞ=p

0

f ðaÞ da: ð1Þ

The response of the demand for plagiarism to parameter changes is obtained by

differentiating:

oD

oM¼ f

VðM þ bÞ � VðMÞp

� �V 0ðM þ bÞ � V 0ðMÞ

p\0

oD

ob¼ f

VðM þ bÞ � VðMÞp

� �V 0ðM þ bÞ

p[ 0

oD

op¼ f

VðM þ bÞ � VðMÞp

� �

�VðM þ bÞ � VðMÞp2

� �

\0:

ð2Þ

The first inequality holds by virtue of concavity, and hence the demand for pla-

giarism declines as the initial endowment (i.e., income or wealth) increases. The

demand increases as the benefit to plagiarism increases and declines as the proba-

bility of detection increases. A slight modification of this model arises if we assume

that the benefit b does not accrue to the plagiarist if the plagiarism is actually

detected. In that event, the utility accruing to the agent is

U ¼ VðMÞ � a

It is easy to show that the derivatives corresponding to Eq. (2) have exactly the same

signs as before and that the demand, corresponding to Eq. (1), is less than in the

previous case.

11 One might alternatively also assume that consumers differ from each other in their endowment M or in

the probability that they will be detected if they plagiarize. Then, assuming the existence of density

functions over M or p, analogous results can be derived.

68 Eur J Law Econ (2012) 34:63–75

123

This model is important for two reasons. First, because it is an introductory and

simple model leading to the model that follows, which is both more complicated and

more richly endowed with possibilities for fruitful analysis.

A Bribery Model. In the case of bribery, there are always two parties to the deed:

the consumer of the ‘‘corruption good’’ and its producer or supplier. If the

corruption concerns admission to a university, the student(or his or her parent) is the

consumer of the good and the university administrator responsible for admission is

the producer: cash changes hands from the student or his/her parent to the university

administrator who bends the rules (BBC News, March 25, 2002). The model that

follows was constructed explicitly to deal with bribery in an academic context; I am

indebted to a referee for pointing out that it applies, mutatis mutandis more

generally to bribery in other contexts as well.

Here we introduce variables y and z such that

y ¼0; if a bribe is not offered;

1; if it is;

(

and

z ¼0; if the bribe is not detected;

1; if it is detected:

(

The consumer’s utility function is now described by

U ¼VðMÞ; if y ¼ 0;VðM � CÞ þ a; if y ¼ 1; z ¼ 0;VðM � CÞ; if y ¼ z ¼ 1:

8<

:

C denotes the bribe that has to be paid to the supplier of the corruption good and a is

the utility increment if the bribe is successful. Letting p denote the probability of

detection as before, a bribe will be offered if the expected utility of doing so exceeds

the utility of not offering a bribe. Demand is then given by

D ¼Z1

ðVðMÞ�VðM�CÞÞ=ð1�pÞ

gðaÞ da

and the derivatives are given by

oD

oM¼ �g

VðMÞ � VðM � CÞ1� p

� �V 0ðMÞ � V 0ðM � CÞ

1� p[ 0

oD

oC¼ �g

VðMÞ � VðM � CÞ1� p

� �V 0ðM � CÞ

1� p\0

oD

op¼ �g

VðMÞ � VðM � CÞ1� p

� �VðMÞ � VðM � CÞ

ð1� pÞ2

!

\0:

ð3Þ

The sign of the first of these derivatives is positive (by the concavity of the utility

function), indicating that in the present case the corruption good is a normal good

Eur J Law Econ (2012) 34:63–75 69

123

for the consumer, unlike the previous case, in which it is an inferior good.12The

demand diminishes with the size of the bribe required and also with the probability

of detection.

The producer’s side is formally identical with the case of plagiarism, i.e.,

Us ¼VsðMÞ; if y ¼ 0;VsðM þ CÞ; if y ¼ 1; z ¼ 0;VsðM þ CÞ � c; if y ¼ z ¼ 1:

8<

:

where c denotes the utility penalty in the case of detection.13 By the same reasoning

as before, the bribe will be accepted if

VsðMÞ5VsðM þ CÞ � cp:

Total supply is then

S ¼ZðVðMþCÞ�VðMÞÞ=p

0

hðcÞ dc

where h is the density function of c. It also follows that the derivatives are

oS=oM\0 , oS=oC [ 0 , and oS=op\0.

The equilibrium bribe is determined by the condition that demand equal supply,

i.e., by

D ¼Z1

ðVðMÞ�VðM�CÞÞ=ð1�pÞ

gðaÞ da ¼ZðVðMþCÞ�VðMÞÞ=p

0

hðcÞ dc ¼ S: ð4Þ

The standard comparative statics analysis is carried out by differentiating ((4)

totally, which yields

oS

oMdM þ oS

oCdC þ oS

opdp ¼ oD

oMdM þ oD

oCdC þ oD

opdp; ð5Þ

and alternately setting dM or dp equal to zero, and then solving for dC/dM and dC/

dp. It is easy to verify that dC/dM is unambiguously positive, showing that the

equilibrium bribe increases with the endowment of the buyers and sellers.14How-

ever, since an increase in the detection probability decreases both demand and

supply, the sign of dC/dp is ambiguous and depends on whether an increase in the

detection probability diminishes supply more than it does demand.

12 Nothing of substance changes if we assume that the cost of the bribe is C1, but if it is detected, there is

a financial penalty to the consumer as well, so that she pays an aggregate amount of C1 ? C2.13 We have obviously assumed here that the producer has the same endowment as the consumer. This

assumption is merely for the sake of simplicity and is by no means necessary.14 We implicitly assumed that buyers and sellers have the same endowment; however, the result is the

same even if we relax this assumption.

70 Eur J Law Econ (2012) 34:63–75

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3 Numerical experiments

The numerical computations below are purely illustrative. We assume that

consumers and producers have identical utility functions V(x) = logx and that the

utility parameters a (for the consumer) and c (for the producer) have exponential

density functions

gðaÞ ¼ be�ba hðcÞ ¼ ae�ac

Performing the necessary integrations, the equilibrium condition ((4) becomes

1� exp �a1

pðlogðM þ CÞ � log MÞ

� �� �

¼ exp �b1

1� pðlog M � logðM � CÞÞ

� �� �

or, more simply,

1� M þ C

M

� ��a=p

¼ M

M � C

� ��b=ð1�pÞ: ð6Þ

It is easy to verify that Eq. (6) has a single root for C between 0 and M, and it

follows that it is easy to solve numerically using the method of regula falsi. For the

purpose of illustration, it is assumed that M = 1000. We examined the solution for

various values of a and b, for values of p = 0.01, 0.02, ..., 0.98, 0.99. In all

instances examined, the equilibrium value of C first increases with the value of pand then eventually declines. The solution values corresponding to the various

values of p are displayed in Table 1 for a = 20 and b = 10 ; i.e., for cases in which

the population mean values of a and c are relatively small.

The equilibrium amount of bribing that takes place decreases monotonically in p;

because of the assumption that the population is a continuum between 0 and 1, its

value is always less than unity. In these cases, the equilibrium C is between 0 and

24, i.e., does not exceed 2.4% of the endowment. Starting with low values of p, C at

first increases, then after p = 0.46 it decreases. The intuitive reason for this

non-monotonic behavior is that at first the reduction in the equlibrium amount of Cmakes it more valuable to consumers and they bid up its price; but eventually the

Table 1 Solution values for Cfor a = 20, b = 10 and for the

equilibrium amount of bribery

p C Equilibrium quantity

0.1 13.52 0.74

0.2 19.27 0.61

0.3 22.44 0.52

0.4 23.37 0.48

0.5 24.03 0.45

0.6 22.89 0.38

0.7 20.49 0.31

0.8 16.65 0.19

0.9 10.84 0.11

Eur J Law Econ (2012) 34:63–75 71

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detection probability becomes high enough to affect demand severely and then Chas to decline. When a and b are small (e.g., 1.0), the equilibrium bribe becomes as

large as 34 percent of the initial endowment. Monitoring increases the value of p;

hence the appropriate social policy is to monitor the behavior of the agents, which

reduces both the equilibrium value of the bribes and the amount of bribing that takes

place.

One final variant of this model emerges from the interesting work of Dan Ariely

(2009). Ariely’s work is concerned, among many other things, with cheating and the

circumstances in which people do or do not cheat. He conducted experiments in

which the experimental subjects had to solve certain numerical problems which

were so structured that cheating was impossible, which then provided a base line for

the experiments. Next, the subjects were given the opportunity to cheat, and they

did, and this provided a base line for the average amount of cheating under these

circumstances. Finally, they again were given the opportunity to cheat, but prior to

conducting the experiment, the experimenter invoked in a general way an ethical

standard (e.g., the Ten Commandments or similar). Overwhelmingly, in the

subsequent experiment the subjects did not cheat. This suggests that it might be

useful in the academic context that we are considering to strongly and frequently

invoke ethical standards.

The effect of such an invocation might be modeled by assuming that the way in

which an ethical standard works is by increasing for the briber the apparent utility

cost of paying the bribe, and reducing for the bribed person the apparent utility of

receiving the bribe. Hence, if the bribe is paid, the briber will experience the utility

V(M - k2C), with k2 [ 1, and the bribee will experience utility of V(M ? k1C),

with k1 \ 1. Eq. (6) then becomes

1� M þ k1C

M

� ��a=p

¼ M

M � k2C

� ��b=ð1�pÞ: ð7Þ

where the parameters k1 and k2 measure the effectiveness of invoking an ethical

standard.15 Repeating the calculations with k1 = 0.5 and k2 = 1.5 continues to lead

to the result that the equilibrium bribe first increases and then decreases with the

detection probability and that the equilibrium amount of bribing declines

monotonically in the detection probability. But this case also has the following

interesting consequence: for low values of the detection probability, both the

equilibrium bribe C and the equilibrium quantity of bribing increase relative to the

case in which k1 = k2 = 1; however, for large values of the detection probability,

both of these quantities decrease. It thus appears that monitoring and invoking

morality are complementary activities. This in turn suggests that invocation of

ethical standards (‘‘moral suasion’’) together with a high detection probability are

likely to be most effective in curbing bribes.

As a final comment, we need to relate this result to the analysis of Falk and

Kosfeld (2006). As they put it, ‘‘If, however, there are agents who are intrinsically

motivated to perform in the principal’s interest, controlling may actually decrease

15 A unique positive root now exists between 0 and M/k2.

72 Eur J Law Econ (2012) 34:63–75

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performance.’’ In the present context, the role of ‘‘control’’ is played by the

detection probability, whereas the ‘‘intrinsic motivation’’ of the agent is represented

by k1 and k2. Hence, our tentative conclusion appears to be at variance with that of

Falk and Kosfeld. But the two models are very different, since in the present one

probabilities play an essential role and it is further the case that a penalty (- a or a

negative wage) accrues only if detection does occur. Hence there are elements in

both models that are not present in the other, which may well explain the differences

between their respective conclusions.

4 Concluding comments

The paper has introduced two simple models in order to explore the motivations for

commiting acts of academic corruption. Of course, these are stylized models that,

for simplicity, we have called a model of plagiarism and a model of bribery. It is

well-known that other forms of academic corruption exist as well, to wit, research

fraud, credentials fraud, financial fraud and (predominantly in the United States)

sports fraud. The demand for plagiarism responds in the expected manner to

changes in the agent’s initial endowment, the benefit derived from plagiarism and

the probability of detection.

The bribery model is somewhat more complicated because in that model there is

a demand side as well as a supply side. In particular, in that model there is room for

not only monitoring and hence detection of the corrupt act, but for moral suasion,

that is appealing to the intrinsic moral attitudes of agents. For example, in some

cases there may be reason to believe that our strong condemnation of and

preoccupation with plagiarism are Western cultural artifacts. Lisa Buranen (1999)

argues that ‘‘... rather than seeing copying from books or other sources as

‘cheating,’… Asians see it as a way of acknowledging one’s respect for the received

wisdom of their ancestors and in fact are taught to copy directly from other texts

with no attribution, and Middle Eastern students see copying or ‘borrowing,’

whether from books or from friends, as a kind of community of family value…’’ and

Swearingen (1999) quotes Susan McLeod to the effect that ‘‘Students from Middle

Eastern, Asian, and African cultures are baffled by the notion that one can ‘‘own’

ideas.’’ In these instances, providing information about the prohibitions against

plagiarism may well play the role of moral suasion, as do courses and websites that

alert students to the proper way of using library and internet resources. As far as

monitoring is concerned, there is no doubt that such activities are becoming

increasing widespread in academia, as is attested to by the increasingly widespread

use of plagiarism detection software such as Turnitin, by iParadigms, LLC, or KOPI

by the Hungarian Academy of Science, or XPlag and others. The consensus in the

field appears to be that moral suasion and monitoring go hand-in-hand, and our

results confirm the usefulness of such a dual approach.

Acknowledgements I am indebted to Andreas Ortman for helpful comments. The responsibility for

errors is all mine.

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