group dynamics of corruption in public organizations

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Group Dynamics of Corruption in PublicOrganizationsOmer Gokcekus a & Adam Godet aa John C. Whitehead School of Diplomacy and InternationalRelations , Seton Hall University , NJ, USAPublished online: 01 Feb 2007.

To cite this article: Omer Gokcekus & Adam Godet (2006) Group Dynamics of Corruption in PublicOrganizations, The Journal of Policy Reform, 9:4, 275-287, DOI: 10.1080/13841280601107075

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The Journal of Policy ReformVol. 9, No. 4, 275–287, December 2006

ISSN 1384–1289 Print/ISSN 1477–2736 Online/06/040275-13 © 2006 Taylor & Francis

DOI: 10.1080/13841280601107075

ARTICLE

Group Dynamics of Corruption in Public Organizations

OMER GOKCEKUS & ADAM GODET

John C. Whitehead School of Diplomacy and International Relations, Seton Hall University, NJ, USA

Taylor and Francis LtdGPRE_A_210646.sgm10.1080/13841280601107075Journal of Policy Reform1384-1289 (print)/1477-2736 (online)Original Article2006Taylor & [email protected]

ABSTRACT When there are two groups of officials in a public organization, we showthat depending on the groups’ behavior – collusive or competitive – increasing the levelof monitoring and punishment may have different impacts on corruption. If the twogroups of public officials had been demonstrating collusive behavior, increased moni-toring or punishment reduces both the level of corrupt activities and the corrupt offi-cials’ bribe revenues. However, if the groups had not been colluding, increasedmonitoring reduces the level of corruption, but increases the corruption revenuescollected. Only after reaching the optimum level of monitoring, is this result reversed.

KEY WORDS: Monitoring, punishment, corruption, public sector

JEL CODES: K42, J16

1. Introduction

Public corruption, defined generally as using public office for private gain,undermines economic development and damages social stability.1 There area number of proposed ways to eradicate public corruption. Klitgaard(2000) has identified three stages in anti-corruption efforts: raisingconsciousness about corruption’s existence and potential harm; addingsystems analysis to consciousness-raising measures; and subvertingcorruption. Rose-Ackerman (2004) proposes five options for addressing

Correspondence Address: Omer Gokcekus, School of Diplomacy and InternationalRelations, Seton Hall University, 400 South Orange Avenue, South Orange, NJ 07079, USA.Fax: +1 973 275 2519; Tel: +1 973 313 6272; Email: [email protected]. For details, see Bardhan (1997), Grindle (1997), Gupta and Alonso-Terme (1998),

Klitgaard (1998), Mauro (1997), Rose-Ackerman (1999), Tanzi and Davoodi (1997),and World Bank (2000).

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276 O. Gokcekus & A. Godet

corruption: voice and accountability, procurement reforms, tax reforms,changes in systems of business regulation, and international efforts to limithigh-level corruption in business. Others have focused on accountability,transparency, merit-based recruitment, rewards, punishment, and bettergovernance, to name just a few. Shultz and Frank (2003) show thatincreased monitoring reduces corruption, but that it also decreases theintrinsic motivation for honesty; thus, the overall effect on corruption isundetermined. They also find that as salary increases, so too does the costof being caught; thus corruption decreases as salary increases. In one formor another, these measures suggest either improving monitoring or provid-ing incentives, and they typically have the same basic conceptual basis. Thebetter the monitoring or the more severe the punishment, the lower thelevel of corruption will be.

We develop a simple model to argue that, in addition the level of corrup-tion in an organization depends on group composition and behavior.Specifically, it depends on whether they collude or act non-cooperatively.The corruption literature tends to assume implicitly that there is one repre-sentative official in a public organization. As such, it tends to examine thebehavior of individuals. This paper focuses instead on group dynamics. Indoing so, we are able to show that group behavior in fact helps determine thepotential impact of intervention on corruption. We show that increasedmonitoring and punishment may increase the bribe revenues of corruptpublic officials – both an unexpected and undesirable outcome. This occursbecause the increased level of monitoring or punishment may reduce the levelof competition among officials for bribes, which would increase the briberevenues of corrupt public officials.

The rest of the paper is divided into four sections. Section 2 introduces themodel. Section 3 derives the equilibria for the model. Section 4 presents theresults of two comparative statics analyses. Finally, section 5 concludes.

2. A Simple Model of Group Dynamics

In this section, we describe the different agents involved within the modeland describe the different group decision-making dynamics of differentgroups of public officials.

2.1. The Agents

The model assumes that the economy consists of four agents and one goodor service. The four main parties involved are the truck drivers crossing theborder, the anti-corruption unit of government, and two different customsofficers. There is one government service, e.g. customs duty for tradablegoods. This is a homogeneous service, demanded by the truck drivers. Thecustoms office is on the border of two neighboring countries, and driversneed the same document to transfer their loads from one country to another.The cost of preparing the customs documents is completely immaterial to thecustoms officials – treated as zero – because the government is paying these

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Group Dynamics of Corruption in Public Organizations 277

costs. The tools used in inspections, such as office supplies and the customsbuilding, are all purchased and maintained with government resources. Thecustoms officials turn over the official fee for the customs duty to the govern-ment – the case without theft, as is described by Schleifer and Vishny, (1993).To focus on the issue at hand, we also adopt the assumption that buyers needone government service to conduct their business. Once the drivers pay thecustoms duty and cross the border, they deliver their shipments, completingtheir task.

Truck drivers. For the truck drivers, we write the demand function forcustoms duties as:

Q is the number of transactions, a is a scalar, P is the official price of thecustoms duty, and B is the bribe paid.

Anti-corruption unit of government. At the beginning of each period, theanti-corruption unit of the government announces the amount of resourcesit has committed for monitoring, M. Given this knowledge, both the anti-corruption unit of government and the public officials can predict the prob-ability of being caught and punished for a corrupt activity. The probabilityof getting caught and punished for a corrupt activity α, where 1≥α≥0, is anincreasing function of the resources devoted for monitoring, α = Λ(M). Forinstance, the frequency of visits by the anti-corruption police to the customsoffice depends on the resources devoted for monitoring. The more theresources devoted for monitoring, the more frequent the visits and inspec-tions by the anti-corruption police, and consequently, the higher the proba-bility of being caught. Λ(.) may be modeled as a logistic cumulativedistribution function. That means (i) up to a certain level of monitoring, theprobability of getting caught stays close to zero and (ii) after a certain levelof monitoring, additional monitoring will not make a big difference in theprobability of being caught.

The anti-corruption unit itself is assumed not to be corrupt: the governmenthires an international, independent company such as the Société Générale deSurveillance – SGS. It is also understood that if the anti-corruption unitdetects a corrupt transaction, the government will impose a punishment. Theexpected punishment is indicated by ηi Qi, where ηi is a positive scalar thatindicates the severity of the punishment.

Two public officials. For simplicity, we assume two public officials providethe government service. Accordingly, the total number of transactions can bewritten as the summation of the customs officers’ activities.

Again, the cost of preparing the customs documents is completely imma-terial to the customs officials. The customs officials simply turn over the

Q a P B= − +( ). (1)

Q Q Qi j= + (2)

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official fee for the customs duty to the government. Public official i deter-mines the optimal number of transactions to maximize expected net returnsby taking into account the given demand, the probability of getting caughtand punished, and the other customs officer’s activities:

Equation (3) states that the expected net return is the difference betweenthe expected benefits and costs. The expected benefit is written as the prob-ability of not being caught multiplied by the total bribes collected; theexpected cost is written as the probability of being caught times the totalpunishment.

2.2. Group Dynamics of Public Officials

When a custom’s officer makes a decision about the number of transactionsinvolving bribery, she also anticipates how the other group will respond toits choice. There are two possible hypotheses regarding the other officer’sbehavior: (1) Cournot-Nash Behavior; and (2) Collusive (or Monopolistic)Behavior.

We assume that there are two different institutions. In the first, there is ahigh turnover rate; the two officers assume that they will have few interac-tions with each other. There is less incentive to cooperate, i.e. they exhibitCournot-Nash Behavior. Each officer takes the other’s action as given,assuming that the other will not respond to its action. Each recognizes thatits own decision about the number of transactions affects the bribe, but doesnot recognize that the decision affects the other group’s decision about thenumber of transactions. Each group assumes that changes in its number oftransactions affect total bribe revenue received only through their directeffects, without accounting for the response of the other officer.

In the second institution, there is very low turnover, such that the twoofficers know that they will have repeated interactions with each other.Therefore, they have an incentive to cooperate. The equilibrium result is thesame as it would be with a monopolistic agreement between them. Tacitcollusion is enforced because of the nature of the service that the two officersprovide and the circumstances in which it is provided. First, officers operat-ing in the same office have no problem of detection lag. If one deviates, theother will detect it instantaneously. Thus, the threat of retaliation is real andimmediate. Second, each officer provides the identical service at the samecost Consequently, the officers recognize their interdependence and are ableto sustain the monopoly price without explicit collusion as in Tirole (2001,pp. 239–242).

3. The Optimum Number of Transactions and Bribes

We can now analyze the behavior of the public officials. We derive theoptimum (Qi, Qj) combinations in these two institutions.

Max E Net Return Q B Qi i i i i i (3)[ ] ( ) ( )= − + −1 α α η

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3.1. Cournot-Nash (CN) Solution

Combining Equations (1), (2), and (3) yields the following expected netreturn maximization problem for customs official i:

The first-order condition for a maximum is:

Rewriting 0 yields the following condition:

Equation (6) is the ‘reaction function’ for officer i. Of course, the analo-gous condition must hold for officer j. Accordingly, the ‘reaction function’for group j is the following:

The Cournot-Nash equilibrium is a (Ci, Cj) combination that satisfies bothof the groups’ reaction functions. Accordingly, combining 0 and 0 yields thefollowing total number of transactions.

For each one of these transactions, truck drivers pay the following bribe:

3.2. Collusive (or Monopolistic (M)) Solution

The collusive solution can be found by maximizing the following totalexpected net benefits:

Max E Net Return Q a P Q Q Qi i i i j i i i (4)[ ] ( ) ( )= − − − + −1 α α η( ) ( )

�

��

E Net turnQ

a P Q Qi

ii i j i i

( Re )( ) ( ) 01 2� �� (5)

Q a P Qii

ii j� � �

��

�

��

�

��

12 1

( )��

(6)

Q a P Qij

jj i� � �

��

�

��

�

��

12 1

( )�

� (7)

Q a PCN � � ��

�

��

�

��

23 1

( ) ( )��

(8)

B a PCN � � ��

�

��

�

��

13

21

( ) ( )

(9)

MAX E Net turn Q a P Q Q (10)[ ] ( ) [( ) ] ( )Re = − − − −1 α α η

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The first-order condition for a maximum is:

From 0, we derive the optimal number of transactions and the amount ofbribe for the collusive officers:

3.3. Optimal Outcome under the Two Behavioral Hypotheses

Equations (8), (9), (12), and (13) show the optimal number of transactionsand the amount of bribes under these two behavioral hypotheses.

Proposition 1: Collusive behavior always results in a smaller number oftransactions, QM < QCN and higher bribes than non-collusiveCournot-Nash behavior, BM < BCN.

3.4. Corrupt Officials’ Bribe Revenue, viz., Truck Drivers’ Bribe Payments

Corrupt customs officials are not concerned about the number of corruptactivities. They are concerned about the bribes collected – the number oftransactions multiplied by the bribe per transaction. This is also the amountof money, in addition to the official fee (P), that truck drivers have to pay inorder to cross the border. The following two equations show corrupt offi-cials’ bribe revenues under these behavioral hypotheses.

Proposition 2: Collusive behavior generates a higher level of bribes wheneither monitoring or punishment is relatively insignificant,

�

��

E Net turnQ

a P Q( Re )

( ) ( )1 2 0� ��[ ] - = (11)

Q a PM � � ��

�

��

�

��

12 1

( ) ,�� (12)

B a PM � � ��

�

��

�

��

12 1

( ) (13)

B a P a PCN � � � ��

��

�

��

�

��

��

�

��

2

9 12

1

2

2

( ) ( ) ,��

�� (14)

B a PM � � ��

�

��

�

��

�

�

�

1

4 1

2

2

( )�� (15)

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otherwise groups with Cournot-Nash

behavior generate a higher level of bribe revenue.2

Proposition 3: For groups, regardless of their behaviors, the corrupt offi-cials’ bribe revenues are zero when they stop the corrupt

activities. This happens

At what level of probability of being caught and at what level of punish-ment is bribe revenue maximized? This question should seemingly have astraightforward answer: either when monitoring is totally ineffective or thereis no punishment. Yet, as the following first order conditions for bribe reve-nue maximization show, the answer depends on the groups’ behavior.

2. Figure 1 demonstrates the main point of this and the following propositions.

αα

η1 7−

<−( )

;a P

ifα

αη

1−= −( ).a P

�

��

�

��

�

��

� � ��

�

�

� �

Ba P

CN

��

��

1

2

94

10( ) , (16)

�

��

�

��

�

��

��

�BM

1

1

2 10 (17)

0.00

0.05

0.10

0.15

0.20

0.25

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Probability of getting caught and harshness of punishment

Cor

rupt

off

icia

ls’ s

urpl

us

Cournot-Nash

Collusive

Figure 1. The probability of being caught and harshness of punishment versus corrupt

officials’ bribe revenues

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From Equation (16), for officers with Cournot-Nash behavior, theoptimum level of bribe revenue is reached when the following condition issatisfied:

Proposition 4: For colluding groups, as long as the probability of beingcaught is positive but less than one, 1 > α > 0, for the highestlevel of bribe revenue, there should not be any punishment,η = 0. However, for non-colluding groups, for the highestlevel of bribe revenue, there should be some punishment, η*> 0: For groups with Cournot-Nash behavior, the optimum

level of punishment,

Equation (18) shows that for groups with Cournot-Nash behavior, ηdepends on (i) the probability of being caught, (ii) the demand for customsduties, and (iii) the official price of the service that corrupt officials turn overto the government. For instance, if the probability of being caught is 50%, α

= 0.5, the optimum level of punishment in terms of its severity is

4. Comparative Statics

Here we consider the comparative statics of the equilibria with respect tomonitoring and punishment. We study the impact of changing monitoringand punishment both on the number of corrupt activities and corrupt publicofficials’ bribe revenues.

4.1. Monitoring

By intensifying the monitoring effort, the anti-corruption unit increases theprobability that corrupt officials will be caught. Intensified monitoringeffectively pressures the groups and forces them to reduce their illegal activ-ities. At customs, this might reveal itself as ‘more thorough’ inspections ofeach truck; longer lines and waiting; and eventually, a smaller number oftrucks passing through. However, the implications of the intensified moni-toring on the corrupt public officials’ bribe revenues depend on the official’sbehavior. For instance, since the two colluding officials have already pickedthe optimum number of transactions that maximized their bribe revenues,forcing them to move away from this point decreases their revenues. Thus,increased monitoring negatively affects the revenues of the colludingofficers.

Interestingly, however, increased monitoring helps the groups withCournot-Nash behavior. It forces them to cut the number of their activities,which in turn causes them to behave more like two colluding officials. In a

αα

η1 4−

=−( )a P

(18)

ηα

α*

( ).=

− −>

a P4

10

η*( )

.=−a P4

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sense, increased monitoring artificially reduces the level of competition, andincreases the corrupt public officials’ bribe revenues. As Figure 1 demon-strates the bribe revenues first approach the optimum level in collusion, andthereafter begin to decline.Figure 1. The probability of being caught and harshness of punishment versus corrupt officials’ bribe revenues

4.2. The Level of Punishment

Another way to deter public officials from getting involved in corrupt activ-ities is through increased punishment. The corrupt public officials will reducethe frequency of their illegal activities if they know that the punishment isharsher for the same probability of being caught). However, as in monitor-ing, increasing the level of punishment has different implications in terms ofbribe revenues.

Proposition 5: If the two public officials were demonstrating collusivebehavior, increasing monitoring (punishment) reducesboth the level of corrupt activities and the corrupt offi-cials’ bribe revenues. However, if the public officials werenot colluding, increased punishment reduces the level ofcorruption but increases the corruption revenues collected.Only after reaching the optimum level will this trend bereversed.

4.3. Substitution between Monitoring and Punishment

We have seen that monitoring and level of punishment have similar effectson public officials’ corrupt activities. Both are effective in reducing thenumber of corrupt activities. To analyze the substitution between monitor-ing and punishment, we construct an isocorrupt curve, which shows thecombinations of monitoring and punishment that can result in a givennumber of corrupt transactions, C0. Mathematically, an isocorrupt curve

records the set of monitoring and punishment that satisfies, C(M, P) = C0.

The slope of an isocorrupt shows the rate at which punishment can besubstituted for monitoring while keeping corruption at its current level, andthe negative slope of this line is called the rate of technical substitution

(RTS). On an isocorrupt curve Accord-

ingly, as Figure 2 shows, initially an isocorrupt curve has a negative slope:the rate of technical substitution is positive. This implies that by increasingmonitoring and reducing punishment simultaneously, or vice versa, theanti-corruption unit can keep the number of corrupt activities unchanged.Second, the isocorrupt = C0 is also a convex curve: the rate of technical

substitution diminishes with increased monitoring and punishment. Thediminishing rate of technical substitution implies that for high ratios ofmonitoring to punishment, the rate of technical substitution is a large

= = −

−

=−

C , 0 RTSd

d

ηα

α

αα

η

1

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positive number, indicating that a great deal of punishment can be foregoneif one more unit of monitoring becomes available. Alternatively, when a lotof monitoring is already being employed, the rate of technical substitutionis low, signifying that only a small amount of punishment can be traded foran additional unit of monitoring if the level of corrupt activities has to beheld under control, i.e. C = C0. The more monitoring (relative to punish-

ment) that is put in place; the harder it is to substitute monitoring forpunishment. In some sense, monitoring becomes less potent as a substituteas more of it used.Figure 2. An isocorrupt curve: Different combinations of monitoring and punishment to eradicate corruption

5. The Case with Theft

Up to this point, we have assumed that the officials were giving the officialrevenues from the customs duty to the government. Now consider the possi-bility that the customs officials do not turn in anything to the government atall. Schleifer and Vishny (1993) describe this as the case with theft. In thiscase, the price that the buyer pays is equal to the bribe, a special case of themodel described in section 2, with P= 0. The decline in marginal cost of thetransaction by P increases the number of transactions; lowers the price paidby the truck drivers and increases the amount of the bribe. For instance, forcolluding groups, both the number of transactions and value of bribes arehigher by 1/2P; for groups with Cournot-Nash behavior, the number oftransactions is increased by 2/3P, and the bribe is increased by 1/3P.

0

2

4

6

8

10

12

14

16

18

20

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Probability of getting caught

Har

shne

ss o

f pu

nish

men

t

Figure 2. An isocorrupt curve: Different combinations of monitoring and punishment to

eradicate corruption

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Accordingly, each truck driver’s cost of crossing the border is lower in thiscase, (P + BNT) > BT. Yet, the corrupt officials’ bribe revenues are higher by

for colluding officials and for non-colluding ones. This implies

that in the case with theft, there is more at stake for the corrupt official,especially colluding ones.

For instance, both officials with Cournot-Nash behavior and collusive

behavior bring their corrupt activities to a halt when . This is four

times larger than in the case without theft.

Proposition 6: To reduce the number of corrupt activities to a given levelwith theft, the anti-corruption unit has to spend more formonitoring and/or introduce tougher punishment.

6. Conclusion

There are three main findings in this paper. First, by examining groupdynamics, we show that the number of transactions and the amount of thebribe in an organization depend on the group dynamics of the public offi-cials, the level of monitoring, the level of punishment, and the demand forthe service provided by the public organization. For colluding public offi-cials, increasing monitoring or punishment reduces the number of transac-tions and corrupt public officials’ bribe revenues. However, increasedmonitoring or punishment reduces the number of transactions and increasescorrupt public officials’ bribe revenues if public officials have Cournot-Nashbehavior – up to the optimum number of transactions by colluding officials.After that point, there is a decline in both the number of transactions and thebribe revenues of corrupt public officials. In other words, depending on thegroups’ behavior, the same intervention may have different effects. Forinstance, seemingly effective anti-corruption efforts may increase the corruptpublic officials’ bribe revenues: Both increasing monitoring and punishmentlowers the number of transactions. Yet, unintentionally, the same interven-tions increase the corrupt public officials’ bribe revenues by reducing thelevel of competition. Second, we show that monitoring and punishment aresubstitutes for each other. The rate of substitution shows variations accord-ing to the initial levels of monitoring and punishment. There is a diminishingrate of substitution. Third, we show that bribe revenues for corrupt officialsare much higher in the case of corruption with theft compared to the casewithout theft.

These findings suggest several important policy implications. First, wefind that in cases with theft, there is a greater incentive for the corrupt offi-cials, because their revenues will be higher. Thus, efforts within these coun-tries should focus first on ending theft by the officer of the official duty.Second, the finding that monitoring and punishment are substitutable couldbe very good news, as different countries may have different resources and

14

2P29

2P

αα

η1−

= a

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different abilities. Countries that are good at monitoring should increasetheir monitoring levels (with the acknowledgement that they must greatlyincrease the likelihood of the corrupt officials being caught), and countrieswhich are better equipped for punishment should focus their anti-corruptionefforts on that aspect.

Third, and most importantly, our results imply that anti-corruptionagents should not approach public organizations with the assumption thatsuch organizations are composed of homogeneous groups of individuals.Instead, they should acknowledge that public officials within groups mightoperate with different group dynamics. Thus, the course of action foreradicating corruption is not the same for every institution or organiza-tion. Using the incorrect dosage of monitoring or punishment may resultin worsening the problem. Furthermore, according to this finding, anti-corruption efforts focused on institutions with Cournot-Nash groupdynamics must ‘go all the way’ in order to be effective. That is, a smallincrease in monitoring actually increases bribe revenues for these groupsbecause it reduces the amount of competition and thus increases monop-oly power.

As shown in Figure 2, in order to be effective in reducing the incentive forcorrupt officials, the probability of getting caught must be approximately60% or higher. While increasing the likelihood of being caught to such a highlevel may be difficult and costly for some countries to achieve, failing to doso will result in worsening the condition that the efforts were originallydesigned to ameliorate.

Acknowledgements

We should like to acknowledge helpful suggestions and comments fromMichael Connolly, Jan Knoerich, Heather Ramsey, Edward Tower, HuseyinYildirim and an anonymous referee.

References

Bardhan, P. (1997) Corruption and development: a review of issues, Journal of Economic Literature,35(3), pp. 1320–1346.

Grindle, M. (Ed.) (1997) Getting Good Government: Capacity Building in the Public Sector of DevelopingCountries (Boston, MA: Harvard Institute for International Development).

Gupta S. H. D. & Alonse-Terme, R. (1998) Does Corruption Affect Income Inequality and Poverty?IMF Working Paper 98/76 (Washington, DC: International Monetary Fund).

Klitgaard, R. (1998) International cooperation against corruption, Finance and Development, 35(1),

pp. 3–6.

Klitgaard, R. (2000) Subverting corruption, Finance and Development, 37(2), pp. 7–8.

Mauro, P. (1997) Why Worry About Corruption. IMF Economic Issues Series No. 6. (Washington, DC:

International Monetary Fund).

Rose-Ackerman, S. (1999) Corruption and Government: Causes, Consequences, and Reform(Cambridge: Cambridge University Press).

Rose-Ackerman, S. (2004) Copenhagen consensus 2004 challenge paper: the challenge of poor governance

and corruption. Available at www.copenhagenconsensus.com/Files/Filer/CC/Papers/Governance_

and_Corruptions

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Schulze, G. G. & Frank, B. (2003) Deterrence versus intrinsic motivation: experimental evidence on the

determinants of corruptibility, Economics of Governance, 4(2), pp. 143–160.

Shleifer, A. & Vishny, R. W. (1993) Corruption, The Quarterly Journal of Economics, 108(3),

pp. 599–617.

Tanzi, V. & Davoodi, H. (1997) Corruption, Public Investment, and Growth. IMF Working Paper 97/

139 (Washington, DC: International Monetary Fund).

Tirole, J. (2001) The Theory of Industrial Organization (Cambridge, MA: The MIT Press).

World Bank (2000) Anticorruption in Transition: A Contribution to the Policy Debate (Washington,

DC: The World Bank).

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group dynamics of corruption in public organizations

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