Stabilizing Corruption in Societies: Group Size and Sub-group Enforcement, Lecture notes of Decision Making

Download Stabilizing Corruption in Societies: Group Size and Sub-group Enforcement and more Lecture notes Decision Making in PDF only on Docsity! Four Essays on Corruption and Cooperation Theory and Evidence Inaugural-Dissertation zur Erlangung des Grades Doctor oeconomiae publicae (Dr. oec. publ.) an der Ludwig-Maximilians-Universität München 2010 vorgelegt von Jan Theodor Schikora Referent: Prof. Dr. Martin G. Kocher Korreferent: Prof. Dr. Johann Graf Lambsdorff Promotionsabschlussberatung: 9. Februar 2011 Datum der mündlichen Prüfung: 27.01.2011 Namen der Berichterstatter: Martin Kocher, Johann Graf Lambsdorff, Joachim Winter Contents ii 2.2.1 Corruption and the NIE . . . . . . . . . . . . . . . . . . . . . . . 38 2.2.2 The 4EP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.2.3 Related literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.3 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.3.1 Bribe Splitting Effect . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.3.2 Group Decision-making Effect . . . . . . . . . . . . . . . . . . . . 48 2.3.3 B ’s behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.3.4 Gender effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.3.5 Total effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.4 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.5 Results and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . 55 2.5.1 Descriptive results . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.5.2 Conditional reciprocity . . . . . . . . . . . . . . . . . . . . . . . . 64 2.5.3 Switching behaviour . . . . . . . . . . . . . . . . . . . . . . . . . 70 2.5.4 Content analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 2.7 Appendix 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3 Bringing Good and Bad Whistle-Blowers to the Lab 87 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 3.2.1 Representation of the basic game . . . . . . . . . . . . . . . . . . 91 3.2.2 Treatment specifications . . . . . . . . . . . . . . . . . . . . . . . 94 3.2.3 Related literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 3.3 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 3.4 Analysis and hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Contents iii 3.4.1 Number of legal transactions . . . . . . . . . . . . . . . . . . . . . 101 3.4.2 Stabilizing corruption . . . . . . . . . . . . . . . . . . . . . . . . . 103 3.4.3 Gender effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 3.4.4 Officials’ whistle-blowing . . . . . . . . . . . . . . . . . . . . . . . 105 3.5 Results and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . 106 3.5.1 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . 106 3.5.2 Conditional reciprocity . . . . . . . . . . . . . . . . . . . . . . . . 114 3.5.3 Whistle-blowing and gender . . . . . . . . . . . . . . . . . . . . . 115 3.5.4 Path-dependent behaviour . . . . . . . . . . . . . . . . . . . . . . 119 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 3.7 Appendix 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 4 Cooperation with Uncertain Endowments 139 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 4.2 The Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 4.3.1 Treatment Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 4.3.2 Explaining Treatment Effects . . . . . . . . . . . . . . . . . . . . 152 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 4.5 Appendix 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Bibliography 185 List of Tables 2.1 Performance variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.2 Gini Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 2.3 Output of (M3) and (M4) . . . . . . . . . . . . . . . . . . . . . . . . 67 2.4 R-value and adjusted R-value . . . . . . . . . . . . . . . . . . . . . . 70 2.5 Success and Initial Consent, TDT1 . . . . . . . . . . . . . . . . . . 71 2.6 Success and Initial Consent, TDT2 . . . . . . . . . . . . . . . . . . 71 2.7 Success and Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 2.8 Model M1, Random effects estimation . . . . . . . . . . . . . . . . 79 2.9 Model M2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 2.10 Model M4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.1 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 3.2 Transfer levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 3.3 Gender and punishment . . . . . . . . . . . . . . . . . . . . . . . . . 118 3.4 Output for random effects estimation (M1) . . . . . . . . . . . . . 121 3.5 All relevant Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.1 Strategy Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 4.2 Absolute and Relative Conditional Cooperation . . . . . . . . . . 157 4.3 Type-Specific Relative Conditional Cooperation . . . . . . . . . . 159 Preface This dissertation contains four separate studies in the fields of corruption and coop- eration. The first chapter explicates the mechanism that links the fractionalization of society with its level of corruption within a theoretical model of relational contracting. The other three chapters describe experimental studies. The second and third chapters evaluate two popular anti-corruption policies, the ‘Four-Eyes-Principle’ and ‘Whistle- Blowing’, with respect to their effectiveness in decreasing the level of corruption and increasing social welfare. The last chapter considers the effect of endowment uncertainty on cooperative behaviour in a linear public goods game and explains it by specific condi- tional cooperation preferences. Cooperation between decision-making agents is recognized as one of the single most important mechanisms in economic research and represents one of the cornerstones of economic development. Countless economic activities have been analysed with game theoretic models of cooperation. Experimental methods may not only demonstrate the deficiencies of standard economic theory in terms of explanation and predictive power, they may also help to improve existing models. The public goods game (Isaac and Walker 1988) represents one of the most popu- lar vehicles to experimentally analyse cooperative behaviour. It models the dilemma of the opposition between selfish preferences and social efficiency. Numerous experiments have shown that participants behave in a highly cooperative way in situations for which the standard economic theory of rational payoff maximization predicts strictly selfish be- haviour. In my view, the most convincing approach to explaining the phenomenon of cooperation is the existence of conditional cooperative behaviour. This links the public goods game to games that are specifically designed to analyze trust and reciprocity, e.g. the gift exchange game (Fehr et al. 1993), the investment game (Berg et al. 1995) and Preface 2 the ultimatum game (Güth et al. 1982). Applications of these games even extend to criminal activities such as corruption. Administrative corruption, defined as ‘the misuse of public power for public gain’ (Klitgaard 1988) is recognized as the ‘single most im- portant obstacle to economic development’ (The World Bank 2008). The key to effective anti-corruption policy with respect to institutional design is the understanding of the determinants, mechanisms and weaknesses of corruption. This demands the analysis of two different levels of cooperation common to most corrupt transactions. Since any corrupt relationship is by definition illegal, the corrupt partners cannot rely on legal third parties, i.e. the courts, to enforce their contracts. The transaction between a client and a corrupt official depends on trust and reciprocity which may be fostered for example by repeated interaction. This kind of cooperation is similar to the mechanism modelled in the gift exchange game. In contrast to the original gift exchange game, where cooperation is efficient with respect to social welfare (measured e.g. in the sum of payoffs to all affected individuals), corrupt reciprocity is socially undesirable due to the reasonable assumption of its strong negative externality to the public (Shleifer and Vishny 1993, Rose-Ackermann 1999). In the case of instable corruption, it would be socially optimal for all agents to stay away from reciprocity and cooperation, and adhere to their selfish rationality. The situation involving all members of a society with respect to their choices for or against corrupt reciprocity can hence be seen as a reverse public goods dilemma in a broader sense. The first chapter focuses on the role of the fractionalization of a society in determining the level of corruption. In a series of empirical cross-country studies social fractionaliza- tion, often (crudely) measured by the Ethno-Linguistic Fractionalization Index (ELF, see Appendix 1A) has been identified as an important determinant of the level of cor- ruption measured by the Corruption Perception Index or similar indices (Alesina and Ferrara 2002, Mauro 1995, Bardhan 1997). In a cross regional analysis, providing a more controlled environment, Dincer (2008) finds an inversely U-shaped relationship between the two variables. However, none of these studies provides a model based theoretical explanation for the empirical evidence. As a basis of our analysis we use an infinitely repeated version of a standard, multi- Preface 3 stage game of administrative corruption which captures the enforcement problem of the illegal transaction. In order to describe the main mechanism underlying the relationship between the social structure and corruption we define the structure of society in terms of its fractionalization as a vector of separated sub-networks whose members share infor- mation. Assuming that clients use simple punishment strategies as devices to maintain corrupt cooperation within relational contracting, we find that the maximum level of corruption is to be expected in societies that consist of a large but not maximal number of small (but not minimal) groups. This is due to the inversely U-shaped relationship between the relative size of a sub-network (measured in the number of group members relative to the size of society) and its members’ ability to stabilize corrupt transactions. The (relative) size of a sub-group has two countervailing effects on the corruption level. On the one hand, the (average) probability of a successful corrupt transaction (expected frequency) increases in the number of group members. This is because the incentives for opportunism decrease due to growing stakes for the official. On the other hand, an increase in the relative sub-group size increases the (personal) costs for the clients through the internalization of a larger part of the negative externality. Thus, necessary compensation of a growing number of peers decreases the profitability of corruption. This chapter provides a model-based explanation of the inversely U-shaped relation- ship between social fractionalization and corruption found in Dincer (2008). The results of our model are also in line with empirical observations of cross-country comparisons (Gunasekara 2008, La Porta et al. 1999 and Alesina et al. 2003). Our model can be extended to account for considerations of the influence of different types of social capital on corrupt behaviour. Using the standard model of self-interested payoff maximization to analyse the mechanisms that underlie the determinants of corruption may only be reasonable in sit- uations in which limit values and benchmark examinations are considered, and therefore simplifying assumptions such as infinite repetitions are justified. In finitely repeated interaction (or one shot games) of corruption, neither the standard self-interested model nor models of strong reciprocity relying on social preferences such as altruism (Andreoni and Miller 2002), inequity aversion (Fehr and Schmidt 1999) or intentions (Dufwenberg and Kirchsteiger 2004) can provide a consistent explanation or predictions for corrupt Preface 6 whistle on the two main negative consequences of corruption. First, whistle-blowing may reduce the negative effect of corruption hindering ‘honest’ clients to engage in productive activity by providing a tool against demanding corrupt officials. Second, whistle-blowing may affect the stability of the corrupt transaction and influence the number of successful deals and hence the amount of realized negative externalities. We find that the total effect of symmetrically punished whistle-blowing (i.e. the punishment is independent of who has blown the whistle) is ambiguous. Confirming the findings of Lambsdorff and Frank (2010) and Abbink (2006), whistle-blowing increases the stability of a corrupt transaction. However we find that it also reduces the effect of corruption deterring productive activity, offering a safeguard for an ‘honest’ client against the extortion by a corrupt official. We demonstrate that asymmetric leniency for the official can offset the negative effect of whistle-blowing. Our results can be explained using simple arguments as to subjects’ belief structures and payoff maximization. More- over, we find that leniency is especially effective for male officials. The consideration of asymmetric punishment of illegal activities in general and leniency for whistle-blowing in particular should be considered in legislature. Our extended model of corruption provides the basis for experimental research targeting both direct and indirect effects of corruption. While the focus of the first three chapters is on the negative consequences of coop- erative behaviour in corruption, the fourth chapter, which is joint work with Johannes Maier, considers socially desirable cooperation. In an experimental public goods game using the Voluntary Contribution Mechanism (Isaac and Walker 1988) we study the effect of uncertainty as to others’ endowments on contribution behaviour. In most applications of public goods provision it is more realistic to assume heterogeneity instead of homo- geneity of initial endowments between cooperation partners. The own endowment can be private information, which means that endowment levels of fellow group members can be unknown. In situations of charitable giving, for example, endowments (individual wealth levels) are likely to be heterogeneous and information about them remains private, while information about actual contributions (donations) are often made publicly available. To quantify and explain the effect of endowment uncertainty on cooperative behaviour we use an adapted version of the experimental two-stage approach used by Fischbacher and Gächter (2010). Preface 7 In the first stage of our experiment, subjects had to state their contribution preferences conditional on their group partners’ contributions and endowments. Here we used an incentivized strategy method (Selten 1967). In the second stage, we quantify the effect of uncertainty in a repeated linear public goods game (ten periods) played in partner design with groups of three participants. While subjects knew their own endowment and the endowments of their two group partners in the certainty treatment, they only knew their own endowment, low or high, in the uncertainty treatment. However, they knew that the others were either high or low endowed. When we pool all observations of each treatment, we only find a small negative but insignificant effect of uncertainty on average contribution levels. When we separate observations according to participants’ endowments, we find that subjects with high initial endowments contribute slightly more, while participants with low endowments contribute substantially less under uncertainty. These two opposing effects of uncertainty lead to lower contribution levels in poor and higher contribution levels in rich groups. The inequality in income levels between low and high endowed subjects therefore increases through uncertainty. We explain our treatment effects by two mechanisms, the effect of deviating beliefs and the net effect of (strategic) over-contribution. We attribute both effects to condi- tional contribution preferences. One of our main results is that subjects are relative conditional contributors. In the context of heterogeneous endowments this means that they contribute more, the lower their group partners’ endowments holding their absolute contributions constant. This and the findings of systematically deviating beliefs explain the former of the two mechanisms. Under uncertainty, low endowed subjects believe that they are in a richer group than they actually are and therefore contribute less in the repeated public goods game than they would have done, had they known the correct endowments of their group partners. High endowed subjects, on the contrary, believe that their group members are poorer on average and hence contribute more. The preference for relative conditional cooperation also explains the treatment differences in (strategic) over-contribution that remains when we substract the effect of deviating beliefs. The intuition for (strategic) over-contribution is that subjects contribute higher levels in repeated games than their stated preferences should allow in order to trigger positive reciprocity and thereby sustain cooperation (see e.g. Fischbacher and Gächter 2010). In contrast to groups in the certainty treatment and groups consisting of high endowed Preface 8 individuals in the uncertainty treatment, we do not find (strategic) over-contribution in groups of low endowed individuals under uncertainty. We attribute this to their fear of sending the wrong signals (i.e. being high endowed) by making large contributions. This fear may be due to participants’ anticipation of others’ relative conditional cooperation preferences. Overall, the combination of the two mechanisms explains a large fraction of our treatment effects. This paper not only explicates contribution behaviour under uncertainty, it also expands the knowledge of conditional cooperation preferences in general. Its results motivate future research on the theoretical foundations of conditional cooperation. All four chapters contain their own introductions and appendices so they can be read independently. How do Groups stabilize Corruption? 11 stabilization of illegal corrupt transactions. Analysing simple benchmark punishment strategies, we find that the variation in the size of a group (which defines the number of agents committing to a certain punishment rule) has two countervailing effects on the ability of individual sub-group members to stabilize their corrupt transactions. On the one hand, a larger group provides its members with additional power to stabilize a corrupt deal by raising the stakes (and thereby the threat-point) for a potentially defecting public official. On the other hand, the amount of internalization of the negative external effect, which is a by-product of cognizance, and thereby the cost of corruption is increasing with the size of a sub-network. We show that the balance between these two countervailing effects causes small but not minimal groups to maximize their members’ ability to stabilize corruption. Through the implications of this result on the structure of an entire society, our model provides a theoretical rationalization of the empirical observations. The remainder of the paper is structured as follows. In Section 1.2 we introduce the basic model of corruption and explain the main mechanism of bilateral punishment strategies. Section 1.3 introduces the existence of sub-networks and describes the main consequences of group-enforcement through multilateral punishment strategies in the infinitely repeated game. Section 1.4 summarizes and concludes. 1.2 Model In its most general form a corrupt transaction is best described as a Principal- Agent-Client relationship which can be broken down into two distinct Principal-Agent (P-A) problems (Lambsdorff 2007). In the first, a benevolent government, representing the principal, delegates a (perfectly defined) task to his imperfectly controllable agents represented by potentially corrupt public officials.3 This P-A problem is in essence not specific to corruption. Hence the New Institutional Economics (NIE) of corruption focuses on the P-A problem between the public official and a private entity (e.g. a firm) that may be willing to pay for preferential treatment. A central aspect of the analysis is 3With this specifications we exclude political or grand corruption. How do Groups stabilize Corruption? 12 the enforcement problem of a corrupt transaction. 1.2.1 Framework Consider a society that consists of a small number (> 1) of potentially corrupt public officials (O) and a large number (N ) of individuals (clients). All clients are potential bribers (B).4 Assume that clients need some kind of permit (e.g. a licence) given out by the officials in order to engage in any kind of economic (e.g. productive) activity. Legally proceeded transactions never yield positive payoffs (e.g. the government-set price for the licence is equal to the net expected payoff for any given transaction). In terms of efficiency as well as social welfare, a licence should only be given out in case client B satisfies a set of conditions. Conditions are exogenous (defined by the benevolent authority) and can be thought of as safety requirements, quality standards, etc. Officials are required (by the benevolent authorities) to test whether conditions are met by the client and subsequently give out the licence or reject the request. Obtaining the licence without having to satisfy the set of conditions gives return E to the client. E may include opportunity costs of time or monetary costs saved e.g. by using sub-standard quality etc.5 Hence, the client may have an incentive to distort the behaviour of the official who is neither controlled nor monitored perfectly (by the authorities)6 by paying a bribe b. A successfully completed corrupt transaction causes the negative externality D. D is directly proportional to the return of corruption (D = iE) where i depicts the factor of inefficiency common to all corrupt transactions. The damage D is assumed to spread equally across all (N ) members of society. The assumption of a flat distribution of a fixed level of damage is a simplification that can be rationalized by considering personal dam- age as the certainty equivalent of the expected risk of damage caused by the realization of the corrupt transaction. 4This assumption follows the hypothesis of money maximizing individuals conducting illegal activity whenever this yields a positive payoff (Becker 1968). 5We assume that B ’s only motivation to satisfy the obligations set by the authorities is to receive the licence which enables production. There is no intrinsic motivation to satisfy the legal conditions. The reason for B ’s sub-optimal choice of production technology (in equilibrium) lies in the non-internalized external effect. 6We simplify our analysis considerably by assuming that the official does not face the risk of being fired. How do Groups stabilize Corruption? 13 The per-head-damage can be written as d = D N = iE N . Setting i > 1 ensures that the total damage (in form of the externality) caused by a successful corrupt deal is always larger than the sum of benefits for the corrupt partners. From a social point of view (welfare perspective), O should always force all clients to satisfy government-set (and first-best-results inducing) requirements. This constitutes corruption as socially undesirable. Delivering the corrupt service in exchange for a bribe (cooperating in the corrupt trans- action) causes costs c to the official. These costs may include the moral costs of being responsible for causing damage to the public (fellow citizens) and real (technical) costs of hiding illegal activities from the authorities. Note that in this set-up the amount of these costs cannot be modified by the official. Nor do they depend on the profitability of the corrupt transaction (E). Costs c could also be interpreted as the certainty equivalent of the lottery between no punishment in the good state and (e.g. monetary) punishment in the bad state of the world (i.e. detection by the authorities).7 By incurring c, the official is able to guarantee return E to the client.8 Consider Figure 1.1 for the timing and payoff structure of the 3-stage game. Figure 1.1: Extensive Form 7The assumption of constant costs of corruption serves as a simplification and may be changed in a more comprehensive model in which O may (endogenously) choose the amount of c to determine the probability of detection. 8This implies that the probability of being detected is assumed to be 0 if costs c are paid. How do Groups stabilize Corruption? 16 bribe b* and B’s personal expected loss from the negative externality (d). PCBPS : E − iE N − b∗BPS ≥ 0 (1.2) Solving for the minimal surplus E∗ yields: E∗BPS = c N δ(N−i) . E ∗ BPS can be interpreted as the minimal corrupt return of a transaction for which the briber will rationally choose to bribe the official. 1.2.3 Level of corruption We define the level of corruption as its (relative) frequency.13 The minimum return E∗ can be taken as a direct measure of the maximum level of corruption for the following reasons. E is likely to differ across types of economic activities. Some types are more suitable for creating the opportunity to extract rents through corruption than others. This is likely to be true across (Bardhan 1997) but also within economic sectors (e.g. special purpose construction offers more scope to extract rents through corruption than the maintenance of infrastructure, Klitgaard 1988). We assume that E takes values between E and E according to the distribution func- tion f(E). Furthermore we assume that E is stable across individuals of society (all individuals can, in expectation, extract the same rent from a given type of economic activity through the use of corruption). The expected total number of realized corrupt deals (per capita as well as for the entire society of identical clients when multiplied by N) is captured by the sum of all deals that yield (in expectation) a return E ≥ E∗. Ordering all economic transactions according to their potential for corrupt rent extraction, the fre- quency ‘Frqť of per capita (as well as overall corruption within a society) can be defined as Frq(E∗) = 1− F (E∗) , with the cumulative distribution function F (E∗) = ∫ E∗ E f(z)dz. Independent of the specific form of f(E) it is clear that the level of corruption (per capita) is strictly decreasing in E∗ (∂Frq(E ∗) ∂E∗ < 0).14 The time structure of our game implicitly assumes that B holds all bargaining power 13This is in line with the notion of CPI being strongly correlated with the relative frequency of transactions involving corruption. 14Any cumulative distribution function satisfies: ∂F (E) ∂E > 0. How do Groups stabilize Corruption? 17 since she makes a ‘take it or leave it’ offer. Irrespective of how bargaining power is distributed between client and official, the level of corruption that can be stabilized by some punishment strategy will be defined by the relative mass of potential transactions that yield a corrupt return E that is high enough to pay the equilibrium bribe b∗ and compensate for expected personal losses. Hence E∗ always defines the maximum level of corruption, independent of the distribution of bargaining power since the transaction with a corrupt return E∗ is the ‘last’ (marginal) type of transaction for which corruption is profitable and will be undertaken. See Appendix 1E for a detailed explanation. 1.3 Analysis 1.3.1 Heterogeneity of social structure The New Economic Sociology describes corruption as the result of the clash between particularized and universal norms where the institutional economics of corruption dis- tinguishes between trust to insiders and trust (or responsibility) to outsiders (Lambsdorff et al. 2005). In our view trust between individuals ultimately determines the level of corruption through its implications on the structure of society with respect to its frac- tionalization into sub-groups enabling the exchange of information between its members. Exchange of information enables joint cooperation, but also determines the degree of the internalization of external effects. This idea can be visualized by depicting the intensity of trust as the sizes of the radii of trust around individuals (Fukuyama 1995, 1999, Putnam 2000, Realo et al. 2008, Alesina and La Ferrara 2000). Considering a given pattern of individual proximity15, the sizes of the individual radii of trust define the shape of society (i.e. the number and size of its sub-groups) in the following way. Two individuals are linked as soon as their radii of trust overlap. A sub-network is defined by all individuals linked either directly or indirectly. Figure 1.2 contains four panels as examples of social structures defined by different sizes of radii of trust. In Panel 1, very small radii of trust leave society highly fraction- 15By given we mean the pre-determined geographical position of each individual on a plain. Fixed proximity is justified by exogenous variables such as heritage. How do Groups stabilize Corruption? 18 alized. There are only small sub-groups (depicted by the larger circles), which may be interpreted as nuclear entities (e.g. families). As the radii of trust increase, the size of networks grows, while their numbers decrease (Panel 2 and 3). There are fewer but larger networks sharing trust and information. At a certain size, the radii of trust overlap in such a way that a single network is created which includes all members of society (Panel 4). Social fractionalization graphically depicted by the set union of the plains of radii of trust can be defined by a vector containing the number and size (in the number of individuals) of all separable sub-networks.16 Figure 1.2: Examples of group structures 1.3.2 Multilateral Punishment Strategies (MPS) The existence of sub-networks defined by their members’ trust has an important ef- fect on the level of corruption that can be stabilized in our model since mutually trusting 16The assumption of exclusiveness of network membership based on trust can be justified by the existence of natural barriers such as language, dialects or cultural differences driven by ethnic or religious differences as they tend to foreclose link formation to outsiders and increase the (prohibitively high) costs of setting up bridging links between members of other sub-networks (Alesina et al. 2003). How do Groups stabilize Corruption? 21 sub-group members are being compensated (obtain d each). This means that a (certain type or class of) corrupt transaction will only be undertaken if it yields a return E high enough to cover both expenditures, bribe b∗ and n ∗ d.19 In the one shot version of an adapted game between a particular member of a sub- group of a certain size and an official, the uniqueness of the SNE of Section 1.2.1 remains.20 To incorporate the effects of information transmission between sub-group members within the repeated game, we consider ICC and PC under an adapted version of Grim Trigger, the benchmark punishment strategy. Our Multi-lateral Punishment Strategy (MPS) builds on B ’s threat of cancelling po- tential future economic transactions not only when defected on herself but also when observing (having observed) misbehaviour towards any of her fellow sub-group mem- bers.21 Again, all agents in the economy live for infinite periods and maximize their expected present value of life time payoff by discounting their continuation payoff with the common factor δ.22 In the simplest case, the MPS for B can be formulated as follows. ‘Pay the equilibrium bribe b∗ as long as O has never defected on any of B’s fellow sub-group members. Never engage in any corrupt transaction with O again and choose another O who is known to have never defected23 on B or any of B’s fellow sub-group members otherwise.’ 24 Appendix 1B provides more details on the incentive compatibility of the MPS. 19Modelling the peer-compensation as a fixed payment is the easiest vehicle of transporting the idea of joint payoff maximization by all members of a sub-group. In reality direct large-scale compensation payments on a project basis are unlikely. In expectation all group members engage in the same class of corrupt transactions in their infinite life time, making direct compensation redundant. The direct com- pensation makes sure that only those transactions are realized that cover all fellow sub-group members’ losses (the sub-group’s share of the total negative externality). 20Consider the same backward induction argument as applied in the one shot game of the situation of bilateral contracting in Section 1.2.1 (and Appendix 1B). 21This is consistent with the mechanism in Greif (1993). 22We ignore the potential problem of renegotiation between B and O by assuming that collusion between officials is impossible. This assumption is especially reasonable if staff relocation among officials is common and officials do not belong to the same sub-group. The existence of network ties between an official and a client provides a different form of contract enforcement device (Kingston 2007) that is not considered in this paper. 23In equilibrium there is no defection, hence there must always be at least one O who has never defected before as long as we assume that there are more officials than needed. 24As under BPS, there are more forgiving strategies, e.g. ‘Tit for Tat’, which are not considered in order to keep the analysis as simple as possible (benchmark argument). How do Groups stabilize Corruption? 22 1.3.3 Corruption and group size Compared to the case with only two agents (ICCBPS), the Incentive Compatibility Constraint under the Multilateral Punishment Strategy ICCMPS changes with respect to the expected future loss of the benefit from potential corrupt transactions in case of defec- tion of the official. Incentive compatibility has to include all O ’s potential transactions with any of B ’s fellow sub-group members.25 Since we assume clients to be homoge- neous, we consider a representative agent (B) in a sub-group of size n in order to derive the ICCMPS. ICCMPS : b− c+ T∑ i=1 δin(bi − c) ≥ b+ 0; T →∞ : b− c+ δ 1− δ n(b− c) ≥ b (1.3) Solving for the minimal incentive compatible bribe yields b∗(δ, c, n)MPS = (1−δ nδ + 1) c. The larger the sub-group, the lower the equilibrium bribe has to be in order to hinder O from defection: ∂b∗ ∂n = −1−δ n2 c < 0. For the full effect of n on the level of corruption, consider B ’s new Participation Constraint PCMPS. B will only participate if her profits are still positive after paying b∗MPS and compensating all her sub-group members for the damage realized by the corrupt transaction. PCMPS : E − niE N − b∗MPS ≥ 0 (1.4) This yields the minimum return: E∗MPS(δ,N, c, i, n) = cN 1+δ(n−1) nδ(N−in) . For a given size of the sub-group n and under the technical assumption of N > in, the partial effect of the size of society N is negative: ∂E∗ ∂N = − (1+δ(n−1)) δ(N−in)2 i c < 0 if N > i n. The larger the society, the wider the total damage is spread and the less of it has to be internalized through the compensation of all sub-group members. There are two countervailing effects of sub-group size n on E∗: E∗n(n) ≡ ∂E∗(n) ∂n = cN(i(2 + δ(n− 2))n−N(1− δ)) δn2(N − in)2 S 0. (1.5) On the one hand we can identify a positive effect, the Coalition Effect (CE). The larger the sub-group, the more future potential earnings are at stake for O when deciding 25The assumption that all n transactions potentially take place in any of the periods implies sufficient (time) capacities available to the official. How do Groups stabilize Corruption? 23 between cooperation (t) and defection (nt). This decreases the equilibrium bribe b∗MPS needed for incentive compatibility. On the other hand, there is a negative effect, the Internalization Effect (IE), which captures the direct internalization of the sum of negative external effects relevant for B ’s fellow sub-group members. The number of sub-group members (group size) increases the sum of compensation payments needed for the successful engagement in corruption. Corruption maximizing group-size We can show that the relationship between E∗ and n is U-shaped for all relevant coefficient values. This means that an intermediate group size balances the trade-off between the two countervailing effects. First, we show that an increase in the size of the sub-group (whose members useMPS ) increases the level of corruption for low values of n. Consider the marginal effect of n on E∗(n) at n = 1: E∗n(n = 1) = c δN ((2−δ)i−(1−δ)N) ((1−δ)i−δt)2 . If N > i 2− δ (1− δ) : E∗n(n = 1) < 0. (1.6) For group enforcement through MPS to be effective (E∗ is smaller in a group when using MPS than in the case of BPS), the size of society N needs to be large enough compared to the factor of inefficiency of corruption (i) and the discount factor δ.26 Second, we show that, due to the inefficiency of corruption, group enforcement (through MPS) does not provide more stability than BPS when sub-group size n is large relative to the size of society N. Note that in our set-up of perfect information transmission inside a sub-group, sub-group members cannot choose to use a bilateral punishment strategy since information cannot be withheld. This may be rationalized by the inability of group members to hide criminal activities from their peers.27 If sub-group size n reaches values smaller but close to N (N = N i ) which is, by definition (i > 1), strictly smaller than N , we can show that E∗ approaches infinity under MPS and hence 26With δ = 0.9 (a depreciation rate of 10%) society must be only ten times larger than the factor of inefficiency. 27The U-shaped curve of E∗(n) indicates that MPS is individually optimal only up to a certain sub-group size no above which BPS would be optimal if feasible (if information could be blocked). How do Groups stabilize Corruption? 26 corrupt transactions because of increasing costs of internalization. The U-shaped curve of E∗(n) translates immediately into a hump-shaped curve in the relation between per capita frequency of corruption and sub-group size n. 1.3.4 Structure of society and level of corruption We have shown that, irrespective of the behaviour of the other sub-groups in a society, sub-groups that are small relative to society are able to (and will) produce the highest levels of corruption per capita.31 This implies the following relationship between the structure of (the entire) society and the level of corruption. Let ng denote the number of members in sub-group g (g = {1, 2, ..., k}) of k different groups in a society of size N where ∑k g=1 ng = N . The relative frequency of corrupt transactions is calculated as Frqsoc = ∑k g=1 ng(1−F (E∗(ng))) N .32 We can show that a society S∗ which is fractionalized into (k∗ = N n∗ ) sub-groups of size n∗ (ng = n∗) exhibits the maximum level of corruption. For n∗ << N , the problem of a positive balance after dividing is negligible.33 Departing from the situation of ng = n∗ we consider the effects of deviations in terms of changes in the size and number of sub-groups on corruption, holding the size of society constant. In the following we discuss the properties of S∗ with respect to its connection to the notion of ELF as well as the implication of deviations of its structure on the level of corruption. First, we hold the number of sub-groups constant (k = k∗). Regard society Snewk which deviates from S∗ in the characteristics (i.e. the size) of at least one sub-group. For any sub-group larger than n∗ (nm > n∗) at least one (other) sub-group with size nl < n∗ is needed to hold the size of society constant (at N∗). Consider all other sub-groups maintaining size n∗ (ng 6=l,m = n∗). It is easy to see that Snewk will exhibit lower levels of corruption, since E∗(nl) > E∗(n∗) (the IE dominates the CE ) as well as E∗(nm) > E∗(n∗) (the CE dominates the IE ) as shown in Section 1.3.3 (n∗ defines the global minimum in E∗ and hence the global maximum in the frequency of corruption levels per capita). By 31Recall that the level of corruption within a society is defined by its expected frequency, and the return E is distributed according to the function F (E). Hence the frequency of corruption is defined by 1− F (E∗). 32Sub-groups are assumed to be homogeneous, therefore all individuals within a certain sub-group produce the same (relative) amount of successful corrupt transactions (per head) in expected terms. 33According to the definition of the Ethno-Linguistic Fractionalization Index (see Appendix 1A), S∗ would be characterized by ELF ∗ = 1− ∑k∗ g=1(n ∗ g N )2 = 1− k∗(n ∗ N )2. How do Groups stabilize Corruption? 27 the convexity of the ELF 34, any society of size N and with a number of sub-groups k will exhibit a fractionalization index of ELF < ELF ∗ (being less fractionalized) if any n 6= n∗. The further away a society shifts from the characterization of S∗ in terms of a lower ELF , the lower its level of corruption. Moreover, the total level of corruption is strictly decreasing in the sum (since n∗ constitutes a unique minimum in E∗) as well as in the variance (since E∗(n) is convex) of the sum of (individual) differences between ng and n∗. Since V ar(n− n∗) = V ar(n), the ELF and the total level of corruption must go into the same direction considering societies of equal size and equal numbers of sub-groups. Second, we consider societies of the same size but different numbers of sub-groups (of the same size). Here we have to consider two cases. If in society Snewn, the number of sub-groups is smaller than k∗ (knewn < k∗), the size of each sub-group must be larger than n∗ (nnewn > n∗). This means that each member of any sub-group exhibits a lower (per capita) level of corruption since (E∗(nnewn) > E∗(n∗)). Snew− would show an ELFSnewn < ELFS∗ 35 indicating a less fractionalized society. If the number of sub-groups in Snewn is larger than in S∗ (knewn > k∗), nnewn < n∗, the (average) critical value of the rent of coruption must be larger than in S∗ (E∗(ngnewn) > E∗(n∗)) because of the shape of E∗(n). It is clear that Snewn is more fractionalized than S∗ (ELFnewn > ELF ∗) using the reverse argument from above. The larger the sum of (individual) differences between n and n∗ (in either way), the lower the frequency of corruption. Moreover, due to the convexity of E∗(n) (E∗nn(n) > 0), the difference in the level of corruption decreases at an increasing rate. Third, we compare societies allowing for different numbers as well as different sizes of sub-groups. A society being characterized by ELF > ELF ∗ (higher fractionalization) must consist of a larger number of sub-groups (k > k∗) than S∗ (for all combinations of sub-group sizes). Holding the size of society constant (N = N∗) there must be at least two sub-groups (l,m) with nl,m < n∗. Hence the overall level of corruption must be lower than in S∗ since E∗(nl,m) > E∗(n∗). Compared to S∗, a society (of the same size) exhibiting an ELF < ELF ∗ may either consist of a smaller number of sub-groups (k < k∗ and hence n > n∗) or accommodate sub-groups of unequal size (or both). In all (three) cases there are at least two sub-groups (l,m) which exhibit lower (per capita) 34The more variance in the sub-group size, the smaller ELF holding k constant. 35ELFSnewn = 1− ∑knewn gnew−=1(ngnewn N )2 = 1− knewn(nnewn N )2 < 1− k∗(n ∗ N )2 = ELFS∗ , since Nnewn = knewnnnewn = K∗n∗ = N∗ and nnewn > n∗. How do Groups stabilize Corruption? 28 levels of corruption since nl,m 6= n∗ and hence E∗(nl,m) > E∗(n∗).36 These results provide a simple theoretical explanation for the inversely U-shaped rela- tionship between the social fractionalization of a society and the level of corruption found by Dincer (2008) using data from 48 (US-)American states. Our model interprets this observation as an effect stemming from the distribution of citizens (mass) between the sub-groups of a society. Regions with a structure close to that of S∗ exhibit the highest levels of corruption, while societies that are either more fractionalized (ELF > ELF ∗) or less fractionalized (ELF < ELF ∗) show lower levels. Generally, the larger the distance to S∗ in terms of the sum of differences between n and n∗, the lower the level of corruption. 1.3.5 Stability and convergence of group size The internalization of part of the negative external effect through the compensation of all sub-group members leads to efficient behaviour within sub-groups but causes large inefficiencies for the society as a whole. Hence, even if we consider the size of the radii of trust as endogenous, i.e. subjects can sever or establish links to citizens in their immediate proximity (by increasing or decreasing their radii of trust, see Figure 1.2) we still face a prisoner’s dilemma type situation (Fudenberg and Tirole 1991). Regard a situation of (k∗) sub-groups of corruption maximizing size n∗. It would be socially efficient to increase the radii of trust (e.g. by promoting trust to outsiders) in order to increase the internalization and thus reduce the relative number of corrupt transactions and thereby the amount of inefficiency resulting from the negative external effects. However, for the members of a particular sub-group it cannot be individually optimal to increase their radii of trust as long as their sub-group size is greater than or equal to n∗. A unilateral step of increasing the radii of trust by the members of a particular sub-group would yield a lower level of corruption in society (and thereby decrease the degree of realization of the negative external effect) but at the same time decrease the expected individual corrupt rents for all members of this sub-group. Given the behaviour of the members of all other groups, the positive effect of the decrease 36If the sub-group size n is not constant, the relationship between the level of corruption and the ELF is not monotonic since the ELF does not allow for a unique matching. How do Groups stabilize Corruption? 31 1.5 Appendix 1 Appendix 1A: Definition of the ELF The Ethno Linguistic Fractionalization Index measures the probability that two individuals, randomly drawn from the population, belong to different ethnic groups (Bossert et al. 2006). ELF = 1− ∑k g=1( ng N )2 Let k be the number of different ethnic groups in society, ng the size of group g (measured by the number of members) and N the size of society. The larger the ELF, the more fractionalized the society. The ELF is crude in the sense that societies consisting of different structures can lead to the same ELF. However, it is the most practical measure of fractionalization and widely used in empirical studies. Appendix 1B: Equilibrium properties In order to check for the equilibrium property of E∗ and b∗ as well as O and B playing according to the MPS, consider the following arguments. Since the formal proofs would be technically equivalent to the ones in Greif (1993) we will outline the intuition only. B’s behaviour Under MPS, only an ‘honest’ official (an official who has never reportedly cheated on B or any of B ’s fellow sub-group members) will be hired by B and all her sub-group members in future periods, whereas an official who has cheated (at least once) on B or any of B ’s fellow sub-group members will in equilibrium never be hired (by them) again. Implicitly, O ’s expected future payoff through bribery determines the magnitude of the equilibrium bribe b∗. The larger the potential future payoffs, the smaller is b∗. Hence an ‘honest’ official has a brighter prospect of future gains from corruption, so that a briber will always prefer an honest official to an official who is known to have cheated at least once, since the cost, i.e. the incentive compatible bribe is lower. In our model, the difference in expected future payoff between an official who has cheated and an official How do Groups stabilize Corruption? 32 who has not, is extreme, since (on the equilibrium path) a cheater will never be bribed again, which yields 0-profit for all future periods. A model that would allow for imperfect information transmission (which would not destroy the entire future perspective of a cheating official) and some probability of leaking information to outsiders would cap- ture the argument of the re-hiring of the (more) ‘honest’ official(s) in a more realistic way. O’s behaviour The official will always choose ‘a’ as long as ‘b > 0’. Moreover, she will choose ‘nt’ for all b < b∗ and ‘t’ for all b ≥ b∗ (ICC ). The existence of several officials causes competition for bribes (no monopoly for the official), hence there must be unemployment39 and positive rents for those officials who serve bribing clients. However unemployed officials who have a clean record of corrupt cooperation be- haviour with respect to at least one sub-group, may try to under-bid the equilibrium bribe b∗. This would lead to a violation of the ICC, since any b < b∗ leads to defection of the official in the first period of the relationship. Hence such a bribe will never be (rationally) chosen (or accepted when proposed) by any B. Appendix 1C: Uniqueness of the Sub game perfect Nash Equilib- rium We show the uniqueness of the SNE by using an argument of backward induction. Denote by Ii the information set in stage i (i ε {1, 2, 3}). Let p(Ii) be the probability of reaching stage ‘i’ and q(‘s’|Ii) the conditional probability of the relevant agent choosing action ‘s’ once having reached stage i. An information set contains all relevant information about all relevant players’ behaviour up to stage i. First, we show that there cannot be an equilibrium in which O chooses ‘t’ in Stage 3. Consider a strategy-set EQU1 = [s1, s2, s3] in which p(I3) > 0 and q(‘t’|I3) > 0. Compare the payoff, resulting from the realization of this strategy-set (PO(EQU1)) to that of an alternative set (EQU1new) which consists of equal strategies up to stage 3 but for 39An unemployed official does not engage in corruption. How do Groups stabilize Corruption? 33 which q(‘t’|I3) = 0 yielding payoff PO(EQU1new). Since b > b − c, PO(EQU1new) > PO(EQU1) and EQU1 cannot constitute an equilibrium. Second, we show that O will never choose ‘na’ if b > 0. Consider the strategy-set EQU2 = [s1, s2, s3] in which p(I2) > 0, q(‘t’|I3) = 0, q(‘b > 0’|I1) = 1, q(‘nt’|I3) = 1 and q(‘na’|I2) > 0. Compare PO(EQU2) to the payoff of the strategy set EQU2new which differs from the former only in q(‘na’|I2) = 0. Since b > 0, PO(EQU2new) = b > 0 = PO(EQU2) so that EQU2 is not an equilibrium. It is clear that O is indifferent between ‘a’ and ‘na’ if b = 0. Third, we show that B setting a positive bribe cannot belong to the equilibrium path. Compare the payoff levels of the strategy set EQU3 exhibiting q(‘t’|I3) = 0, q(‘na’|I1) = 0 and q(‘b > 0’|I1) > 0 with a similar set that differs only in q(‘b > 0’|I1) = 0: EQU3new. Since PO(EQU3) = −b < 0 = PO(EQU3new), EQU3 cannot describe an equilibrium. Hence the unique Sub-game perfect Nash Equilibrium is characterized by [‘b=0’; ‘a’/‘na’, ‘nt’]. Appendix 1D: Alternative explanations of corrupt stability Knapp (1986) emphasizes that third-party enforcement of a corrupt transaction can be circumvented by middlemen who perform the ‘dirty work’ of the transaction between a client and an official. These arrangements are usually labelled as commissioned services. Despite being a widely used practice, the deployment of middlemen does not solve the sta- bility problem in the corrupt model, since it only shifts the vulnerability to opportunism to a third party. A corrupt transaction then consists of two unenforceable transactions, one between the client and the middleman and the other between the middleman and the official. Hence, the theoretical problem of contract enforcement is rather duplicated than eliminated. Some individuals or groups try to solve the problem by vertical integration, i.e. in- tegrating a public official into their own social network. This may be done in two ways. A group of clients may form informal relationships with officials through reciprocal gift exchange (Klitgaard 1988), or they may encourage closely related agents such as fam- ily members to enter administrative offices which enable them to stabilize corrupt deals Chapter 2 Bringing the Four-Eyes-Principle to the Lab 2.1 Introduction With almost daily media attention of high profile scandals, corruption, generally de- fined as ‘the misuse of public office for private gain’ (OECD 2010), has been recognized as a major problem. In general, a corrupt transaction is illegal and exerts a large neg- ative external effect on outsiders, which is usually assumed to be larger than the sum of benefits to the agents who are directly involved. This defines corruption as socially inefficient (Klitgaard 1988, Rose-Ackerman 1999). In addition to traditional views of deterring an agent from engaging in criminal activity by varying the amount of penalties and the probability of detection (Becker 1968), the New Institutional Economics (NIE) of corruption concentrates on finding an institutional design that optimally exploits the instability of the corrupt transaction between a client and an official (Schulze and Frank 2003).1 The instability of a single corrupt transaction stems from the enforcement problem between a bribing agent and a potentially corrupt official. Its illegal nature precludes the assistance of legal third parties, i.e. the courts (Lambsdorff 2007). The occurrence of corruption therefore relies heavily on trust and 1There is a considerable amount of theoretical research on the Principal-Agent relationship between a (benevolent and non-corrupt) government and its public officials (Groenendijk 2004). Ignoring the lack of (legal) enforcement of a corrupt transaction between O and B boils the problem down to the analysis of an ordinary Principal-Agent model with a specific application. Bringing the Four-Eyes-Principle to the Lab 37 reciprocity and is difficult to explain in standard theoretical models. Nonetheless, national as well as international organizations such as Transparency International, the OECD and several national fiscal authorities publish lists of (institutional) policy recommendations containing measures to curb corruption. Along with ‘staff rotation’ (analyzed in Abbink 2004), the introduction of the Four-Eyes-Principle (4EP), ‘a requirement that business has to be effectively conducted by at least two individuals (four eyes)’, is one of the most prominent examples (Pörting and Vahlenkamp 1998, Rieger 2005, Wiehen 2005, Hussein 2005). As a result of general problematic tractability (let alone predictability) of corrupt behaviour, a theoretical analysis of the effectiveness of the 4EP does not exist. Nor is there any kind of traceable empirical evidence to support its usefulness. Not only in the corrupt context, but also on a general level, the distinction between individuals and small groups as decision-makers has been widely ignored in the theoretical literature. Differences between the behaviour of individuals deciding alone or in a group have only recently been addressed in the field of experimental economics, where results seem ambiguous. Some studies find that the behaviour of groups is closer to standard equilibrium predictions derived from the self interested model of payoff maximization (e.g. Bornstein and Yaniv 1998, Blinder and Morgan 2005), other studies (e.g. Kocher and Sutter 2005, 2007, Cason and Mui 1997) provide experimental evidence to the con- trary. Kocher and Sutter (2007) conclude that the direction of the group decision-making effect critically depends on the nature of the task determining which of two countervail- ing motives, the profit maximizing motive or the competitive motive, dominates. The basic set-up of our laboratory experiment is close to those used in Abbink (2004) and Lambsdorff and Frank (2010). Our experiment is designed to assess the effects of the introduction of the 4EP on observed levels of corruption. Within this framework we model the 4EP as replacing a single official (deciding individually) with a group2 of two officials deciding jointly according to a decision-making process that secures veto power for non-corrupt officials. Using four different treatments we can separate two countervailing effects of the intro- duction of the 4EP. One is due to the difference in marginal incentives resulting from the division of the transfer between the jointly deciding officials. The other effect is deter- mined by the group decision-making process alone (keeping marginal incentives constant). 2Although the entity consists of only two participants we call it a group rather than a team. Bringing the Four-Eyes-Principle to the Lab 38 Rejecting predictions taken from (self interest based) arguments within the standard model of corruption, we find that the introduction of the 4EP increases the frequency of successful corrupt transactions unambiguously. We substantiate this hypothesis in three stages. First, we consider only outcomes (actual corruption levels). Second, we investigate behaviour in the decision-making process, i.e. we compare initial and final choices. Third, we analyse the content of electronic text messages exchanged during the decision-making process between jointly deciding officials. Our results strongly suggest the dominance of the profit maximizing motive (Kocher and Sutter 2007, Blinder and Morgan 2005). Groups reveal more functional behaviour with respect to conditional re- sponding. By their higher (joint) cognitive capacity, groups of officials seem to be more capable of maximizing their payoffs by following strategies that are shown to lead to a higher frequency of corrupt transactions based on mutual reciprocity. Our explanations of the observed effects are in line with the argumentation of the persuasive argument theory (Pruitt 1971). Since groups perform better in solving the enforcement problem between briber and official, the introduction of the 4EP moves behaviour further away from the theoretical prediction of selfish behaviour, which, in the corrupt context falls in line with the social optimum. Therefore our results cast doubt on the usefulness of the introduction of the 4EP and its justification as a recommended measure against corruption. The remainder of the paper is structured as follows. Section 2.2 describes the experimental set-up giving details on the specifications of all four treatments. Section 2.3 analyses the effects of the introduction of the 4EP in the framework of the NIE of corruption and forms hypotheses. Section 2.4 gives details on the procedure of the ex- periment. In Section 2.5 we describe the main findings, provide a detailed explanation of the empirical strategies and interpret the results. Section 2.6 summarizes and concludes. 2.2 Experimental set-up 2.2.1 Corruption and the NIE In its most general form, a corrupt transaction can be described as a Principal-Agent- Client relationship, in which the principal, represented by the government or any kind Bringing the Four-Eyes-Principle to the Lab 41 choices.8 All treatments are run in a partner design so that all subjects remain in their respective unit (of B and O subjects) for all ten periods of the experiment. Separating potentially countervailing effects and still being able to compare outcomes across treat- ments, we have to consider different group sizes within the treatments. Find Figures 2.7 and Figure 2.8 in Appendix 2B for representations of the full extensive forms of the games played in the respective treatments. In our experiment, the ‘public’ is modelled in two different ways. In one set of sessions (mode 1) we model the externality on the public as payments (reductions of payments) to four randomly chosen participants of the (same session of the) experiment. For technical reasons a particular subject can never be hit more than once per period. In the second set of sessions (mode 2) we model the externalities as increases or decreases in the amount of a donation to the public aid organization ‘Doctors without Borders’. We chose ‘Doctors without Borders’ to obtain results as comparable as possible to the findings of Lambsdorff and Frank (2007, 2010, forthcoming), who use this organization in their experiment. The use of donations to a charity in experiments goes back to Eckel and Grossman (1996). As expected, we find from answers to our post-experimental questionnaire that virtually all subjects approve of this organization and take this as evidence for our working hypothesis that the reduction of a real donation represents a valid model for the reduction of public welfare. The total amount of added or deducted payments is equal across the two modes. Using two different modes of modelling the externality including real outsiders allows us to address the problem of a ‘super-game’ considering the possibility of participants forming expectations on the behaviour of participants outside their own group. IDT19 In Treatment 1, the ‘Individual Decision-making Treatment 1’ (IDT1), we consider units of two subjects, one in the role of the official O and one in the role of the potential briber B. The 3-Stage corruption game (see Figure 2.1) is played for ten consecutive periods. At the end of each period, all participants get to know their own payoffs. Additionally, 8By choosing a repeated instead of a one-shot set-up, we focus on the strategic component of the reciprocal transaction of corruption which we expect to be affected by the number of participants within a decision-making entity (Lambert-Mogiliansky et al. 2006). 9See Figure 2.7 in Appendix 2B for the extensive form representation. Bringing the Four-Eyes-Principle to the Lab 42 type B subjects get information about the (Stage 2 and Stage 3) decisions of their transaction partners (of type O). While through this information all subjects know about the negative or positive externalities they have helped to cause to the public (four randomly chosen participants in mode 1 or ‘Doctors without Borders’ in mode 2) they do not learn about the magnitude of the spill-overs that may have been caused to them by the decisions of the subjects outside their unit in mode 1. Nevertheless, there could be violations of the independence assumption due to considerations of a ‘super-game’. Participants may condition their choices on their beliefs about the other participants’ corrupt behaviour which may affect them through the negative externality. To check for the existence of effects stemming from this ‘super-game’ and provide a robustness check, we use mode 2 in which the public is modelled as a ‘real’ third party, the recipients of the donation towards the public aid organisation. At least for this mode independence of observations across units is warranted. TDT1 In the second treatment, the ‘Team Decision-making Treatment 1’ (TDT1), we form units of three subjects, one B and two O types. The B type decides in Stage 1 about her bribe b which is tripled and then transferred to both officials of her (3-player-)unit. Note that although the amount goes to two players, it is subtracted only once from B ’s account. The parameters of the game are set in such a way that the incentives for the officials are equal to the ones in IDT1, given the amount of bribe. This way we can separate the true ‘Group Decision-making Effect’ (GDE) from effects stemming from the partition of the bribe between the two officials. In Stage 2 and Stage 3, the two officials of a unit make their decisions jointly. In both stages they decide independently first. If they do not come to an unambiguous decision (e.g. one official decides for ‘reject’, the other for ‘accept’ in Stage 2), they learn about each other’s choice and decide again. If there is still no agreement, they get the opportunity to communicate with each other via a real time electronic ‘chat’ in which they can, for one minute, exchange electronic messages (see the translated instructions in Appendix 2C). If there is still no mutual consent, the corruption-unfriendly choice is taken (‘reject’ Bringing the Four-Eyes-Principle to the Lab 43 in Stage 2 and ‘defect’ in Stage 3). This rule reflects the veto-power of officials who do not want to engage in a corrupt transaction in Stage 2 and those who do not want to reciprocate in Stage 3 (in both cases avoiding damage to the public). The idea is that an official cannot force her colleagues to engage in a corrupt transaction but can try to con- vince them. As a consequence of the decision-making rule, a corrupt transaction can only be successful if both officials (finally) choose ‘accept’ in Stage 2 and ‘cooperate’ in Stage 3. IDT2 The ‘Individual Decision-making Treatment 2’ (IDT2) differs from IDT1 only in the number of possible transactions between a particular B -O pair. In this treatment we consider units of four, two type B and two type O participants. Every type B participant sends only one transfer to one of the two officials in her unit per period. This means that playing the game for ten periods makes five possible transactions per pair, producing two transactions per period and four-player-unit. The reason for running this treatment is to control for possible effects in the behaviour of subjects stemming from playing in a larger group and interacting less frequently with a particular transaction-partner. The decisions of the participants within a unit of four yield only one independent observation. In Stage 1, one of the (potential) bribers (B1) decides about her transfer b1 to one of the officials (O1), and the other briber (B2) decides about her transfer b2 to the remaining official (O2). All transfers are tripled and shown to the respective officials. In Stage 2 and Stage 3 each of the officials decides independently. The respective pairs change every period so that in the following period B1 decides about the size of her transfer to O2 while B2 interacts with O1. TDT2 In the ‘Team Decision-making Treatment 2’ (TDT2) we form again four-player-units. Each of the two type B players sends one transfer each to the group of two officials who decide jointly in Stage 2 and Stage 3, according to the same decision-making process explained for TDT1. Contrary to the case of TDT1, the transfer is split equally so that each of the officials receives only half of the tripled amount of the transfer chosen by the respective briber (3 ∗ 0.5 ∗ b). This means that each of the type B participants makes Bringing the Four-Eyes-Principle to the Lab 46 direction of the effect of group decision-making is ambiguous and depends highly on the nature of the particular situation. The majority of studies find that decisions made in small unitary groups (which is the case in our study) act more in line with the predictions of the self interested model of payoff maximization (Blinder and Morgan 2005, Bone et al. 1999, Bornstein and Yaniv 1998, Kugler et al. 2007), while there is also contrary evidence (e.g. Cason and Mui 1997). Kocher and Sutter (2007) show (using a gift exchange game) that decisions made by a small group may be either more or less in line with selfish preferences. The total effect of group decision-making seems to depend on what kind of motivation dominates the decision-making process, the competitive or the profit maximizing motive. 2.3 Hypotheses In our analysis, we distinguish between two main effects of the introduction of the 4EP with respect to the officials’ behaviour. First, the introduction of the 4EP causes a bribe to be divided between two officials instead benefiting just one. Keeping B ’s behaviour (i.e. the amount of transfer) constant, the splitting of the bribe causes each official to receive half of what a single official would have got. We call the officials’ immediate reaction to the lower benefit from a bribe in TDT1 and TDT2 the ‘Bribe Splitting Effect’ (BSE). Second, we consider the pure effect of group decision-making when we hold marginal effects constant and call it the ‘Group Decision-making Effect’ (GDE). 2.3.1 Bribe Splitting Effect From a series of experiments using comparable set-ups (e.g. Abbink 2004), we know that the probability of success increases with the level of transfer (bribe). The phe- nomenon relates closely to the findings of reciprocity in the gift exchange game (Fehr et al. 1993). For simplicity we assume that the bribe is shared equally between the two Bringing the Four-Eyes-Principle to the Lab 47 officials, ignoring potential distributional issues.12 The existence and magnitude of an effect stemming from the splitting of a bribe depends on what officials condition their behaviour on. Officials may consider the monetary benefits of the bribe only or they may include the ‘intentions’ of the briber. Leaving intentions out, the correlation be- tween the size of the bribe and the probability of positive reciprocity may be explained by the trade-off between marginal benefits and marginal costs of engaging in corruption. Moreover, considering the repeated set-up, the minimum amount of transfer (in an early period) needs to be large enough to give O an incentive (in terms of expected benefits in the future) to make her incur personal costs with the objective to trigger B ’s reciprocity in future periods. Figure 2.2 illustrates the monetary benefits (Be) and costs (C ) on the y-axis as a function of the level of transfer on the x-axis for treatments TDT1 and TDT2. In TDT2, the benefits as well as the costs are split between the officials and hence are both half as large as in TDT1. The net monetary benefits (differences between benefits and costs) are labelled as NTDT1 and NTDT2. Figure 2.2: Monetary Costs and Benefits of corruption in TDT1 and TDT2 For b < 4 3 , corrupt behaviour cannot be rationalized since costs are larger than ben- efits.13 For b > 4 3 the monetary benefit is always larger in TDT1 than in TDT2. The difference is increasing in b. If O conditions her corruptibility on the net benefit of the transaction (at all), the probability of corrupt success must be weakly larger in TDT1 12In our anonymous setting there is no reason to believe that there should be any other kind of distribution rule to be agreed by both officials. 13For b < 4 3 not even the technical costs c are covered. Bringing the Four-Eyes-Principle to the Lab 48 than in TDT2 for all b > 4 3 .14 The difference in probabilities should be even larger con- sidering not only the monetary but also the moral costs of inflicting (monetary) harm to other members of society (i.e. other participants of the experiment in mode 1 or recipients of the donation in mode 2). Unlike the technical costs (c), these are likely to apply to both officials at the full scale, since the approval of both is needed to finalize a corrupt transaction and hence they should both be held morally accountable (see Appendix 2A under ‘Responsibility and veto Power’). However, if subjects condition their behaviour on intentions and equilibrium outcomes alone (i.e. they consider the ‘kindness’ of B ’s decision only in the sense that it leads to a certain outcome, given that the transaction is successful) there should not be any difference between the conditional behaviour of type O subjects in TDT1 and TDT2. By the construction of the experiment15, strategies leading to equalized outcomes between B and O (ignoring the negative externalities to the public) are equal across treatments (require the same actions for both types), see Section 2.5.1. Hence outcome-based models of inequity aversion (e.g. Fehr and Schmidt 1999) would yield the same predictions across treatments assuming the irrelevance of the negative externality. Hypothesis 1: “Holding bribe levels constant, there will be no difference in cor- ruption levels between TDT1 and TDT2, if officials condition their reciprocal behaviour exclusively on intentions or consider equalization of payoffs only.” Hypothesis 1 will be rejected if the actual amount of bribe in a particular situation has an effect on the probability of success of a corrupt transaction (being different in TDT1 as compared to TDT2). In this case we call the effect the Bribe Splitting Effect (BSE). 2.3.2 Group Decision-making Effect In order to measure the effect of group decision-making (GDE) separated from BSE, we have to compare the behaviour of subjects deciding alone and subjects deciding 14This allows for individual heterogeneity and does not even exclude participants who would never engage in a corrupt transaction (pj(b; c) = 0 for any value of b and c) 15Including the difference in the number of transactions played per period by the different types. Bringing the Four-Eyes-Principle to the Lab 51 efficient on a social level (by the reverse public goods dilemma), a successful corrupt transaction yields the largest individual payoffs for the transaction partners (unit), given the behaviour of other groups. Groups may be more capable of suppressing short-sighted impulses of behaviour which may maximize myopic payoffs but ultimately decreases total individual payoffs of all transaction partners. This behaviour includes free-riding or de- fecting in social dilemmas (e.g. the public goods game using the voluntary contribution mechanism) and failing to foresee the breakdown of future cooperation (reciprocal rela- tionships). The Persuasive Argument Theory (PAT, see Pruitt 1971, Bishop and Myers 1974, Burnstein et al. 1973) predicts that groups are more successful in finding strategies that maximize their members’ long term payoffs. Explorative and knowledge capacities in groups (containing more than one individual) are expected to be greater than those of a single individual. Chalos and Pickard (1985) proclaim that groups are better in process- ing information load. In games where payoff maximizing strategies are as complicated as in the repeated corruption game, we expect groups to develop and follow more successful strategies than individuals with respect to maximizing their members’ monetary payoffs when we assume that groups and individuals exhibit equal preferences with respect to the trade-off between individual and social welfare maximizing. Hypothesis 3: “If the group decision-making process is dominated by the CM, outcomes in TDT1 will be closer to the game theoretical predictions than those in IDT1. If the PMM dominates group decision-making, groups will produce higher levels of corruption by following strategies that are more successful in maximizing their members’ individual payoffs.”17 2.3.3 B ’s behaviour The introduction of the 4EP may not only affect the behaviour of the officials but also that of the bribers. The direction of the effect depends entirely on the beliefs about the (effects on the) behaviour of the official(s).18 17Note that the comparison between IDT1 and TDT1 shuts off any potential effect arising from the splitting of the bribe between the two officials deciding jointly in a group since marginal incentives for all subjects are the same in both treatments. 18Relying on the assumptions of standard game theory (within the self interested model), we do not expect bribers across treatments to adhere to different beliefs about the behaviour of groups and Bringing the Four-Eyes-Principle to the Lab 52 Even if we assume very restrictive belief structures it is difficult to formulate consistent hypotheses. If subjects of type B expect groups of officials to be less likely to reciprocate (given a certain amount of bribe) than individual officials, B ’s reaction can (still) go into both directions. On the one hand, bribers who want to initiate a corrupt transaction may be discouraged by their anticipation of a higher probability of failures and therefore choose ‘0’-transfers more often in the GD treatments. On the other hand, there might be an increase in the bribe level in the GD treatments, coming from bribers who anticipate that groups of officials are more demanding to be ‘convinced’ to act in a reciprocal way than individuals are. Therefore the total effect is ambiguous and depends on which of the effects dominates the decisions. The argument for a belief structure that assumes higher reciprocity (for any bribe level) within groups of officials leads to similarly ambiguous predictions. In contrast to the inconclusive predictions on B ’s expected reaction on the anticipation of the GDE, we can form hypotheses on the direction of the effect stemming from B ’s anticipation of the BSE. This can be quantified by comparing average bribe levels between TDT1 and TDT2. If bribers anticipate the BSE (correctly) they may send larger transfers in order to compensate the splitting of the bribe.19 Hypothesis 4: “The bribe level (and distribution) will be different in TDT1 and TDT2 if type B subjects anticipate officials to behave according to the BSE and react accordingly.” 2.3.4 Gender effects An especially important finding in the empirical literature on corruption is the rela- tionship between gender and corruption. Dollar et al. (2001) and Swamy et al. (2001) find that female participation in market transactions leads to lower levels of corruption. Sung (2003) however advocates that the findings might be at least partly due to inconsistencies individuals, since in all treatments the Sub game-perfect Nash Equilibrium (see Appendix 2A) is unique and predicts neither positive transfer levels nor positive reciprocation. 19Note that such a reaction can not be explained by models of inequity aversion, since a strategy that aims at equalizing payoffs would not proclaim different levels of transfers across these treatments. Bringing the Four-Eyes-Principle to the Lab 53 related to omitted variable biases.20 Avoiding these problems, Rivas (2007) as well as Lambsdorff and Frank (forthcoming) use controlled laboratory experiments to find that women tend to reciprocate less often with the corrupt partner in the role of the official even if risk aversion, a potential source of behavioural gender difference, can be ruled out as a driving force (Schubert et al. 2009).21 The findings lead to the conclusion that the presence of women may destabilize trust and reciprocity-backed stability of the corrupt transaction. It provides a strong argu- ment for policies aiming at increasing women participation in corruption-sensitive sectors within public procurement. Lambsdorff and Frank (forthcoming) show that the gender effect is especially strong when B has a direct opportunity of negative reciprocity, i.e. costly punishment of defective behaviour (by the tool of whistle-blowing). This may be explained by the fact that female participants are less inclined to reciprocate negatively and therefore do not anticipate negative reciprocity in the behaviour of their transactions partners. In our set-up B ’s only opportunity of negative reciprocity is by reducing bribes in future periods. Relying on the results of corruption experiments using comparable set- ups we do not expect a strong gender effect in B ’s behaviour as long as O ’s gender is unknown. For the individual decision making (ID) treatments, we expect a gender effect only if the female lack of anticipation of negative reciprocity extends to cross- period reciprocity (i.e. anticipating lower transfers in periods following a defected transfer situation). In order to control for gender effects within officials in the GD treatments, we need to distinguish between pure female groups (both officials are women), mixed groups and pure male groups. Even if we assume a gender effect within the officials’ decision-making, the interaction of a male and a female official, when deciding jointly, is 20The real but unobserved driver may be the quality and level of development of institutions, affecting both, women labour market participation and corruption. Assuming a positive correlation between institutional quality and women (labour market) participation, we would expect an over-estimation of the predicted effect. Furthermore, women might self-select into sectors where corruption is less rampant. This may be motivated by a vector of non-measurable variables presenting even more complex problems of misspecification. 21Note that experiments on corruption that model the individual cost of reciprocation by adding a lottery including a large loss of payoff with small probability as a fourth stage of the corruption game (instead of modelling it as a certainty equivalent) cannot distinguish corruption-specific gender effects from gender effects that are caused through differences in individual risk aversion without controlling for individual risk attitudes separately. Bringing the Four-Eyes-Principle to the Lab 56 other participants. This interaction between groups may produce an unwanted additional in-group effect and destroy the idea of the external effect as an unreciprocated reduction in the payoff of unrelated third parties. In particular, subjects may justify their own corrupt behaviour by their belief of others’ corrupt interaction. Corrupt behaviour might even be considered as a payoff equalizing equilibrium (‘super-game’). In order to rule out effects stemming from these considerations we applied mode 2 in one session of IDT2 and one session of TDT2. In mode 2, the ‘super-game’ problem is eliminated by modelling the negative externality as a reduction of a (fixed) amount of donation to the public aid organization ‘Doctors without Borders’.23 Checking for differences we compare all relevant variables, i.e. the average total transfer-level, the transfer levels after success and failure of a corrupt deal (measure of the client’s reciprocity), the average relative number of successful deals, the percent- age of rejected bribes and the percentage of zero-value transfers between observations of the two modes and find no significant difference, taking averages over all 10 periods for each (relevant) subject and applying (pair-wise) two-sided Mann Whitney U-tests24 on group levels (p ≥ 0.363; N ≥ 16). We take this as sufficient evidence for the assumption that the design of the externality (mode) does not have any significant impact on the outcomes of the relevant decisions. We therefore pool the data from these sessions in the respective treatments for the entire analysis. The absence of a ‘super-game’ effect may be explained by subjects having difficulties in forming beliefs of higher orders (see Anderson and Holt 1997, Hung and Plott 2001). The main objective of the experiment is to evaluate the introduction of the 4EP by comparing the main performance variables in IDT2 and in TDT2. This may have been problematic since these treatments do not only differ in the decision-making process and splitting of the bribe, but also in the numbers of transactions per period for the officials. Where in TDT2 an official interacts with each of the two bribers in her unit of four once in a period, an official in IDT2 only interacts once in every two periods with any of the bribers in her unit of four. This makes a large difference with respect to the ‘horizon’ of bilateral (IDT2) or multilateral (TDT2) repetition. Considering repetition as 23We chose this organization to be able to compare our results to those obtained in Lambsdorff and Frank (2007) who use donations to this public aid organization to model the negative externality of their corruption experiment. 24Unless stated otherwise (exact, highest or lowest) p−values and numbers of observations (N) apply to the two-sided Mann Whitney U-test. Bringing the Four-Eyes-Principle to the Lab 57 a major determinant of reciprocal behaviour (see Abbink 2004) there may be differences in corrupt behaviour not related to any of the effects discussed in Section 2.3 (GDE and BSE). A comparison between the outcomes of IDT2 and TDT2 may still be interpreted as capturing the total effect of the introduction of the 4EP if we take into account that it may lead to an increase in the frequency of multilateral transactions. This consideration is not unreasonable assuming that the available number of officials is held fixed across the situations modelled in the treatments. To check whether there is an effect of the amount of repetition we compare the outcomes of IDT1 and IDT2. Although we do not find any significant differences in the main variables (U-tests; p > 0.236; N ≥ 24), we do not pool the data from these treatments, but apply separate tests. 2.5.1 Descriptive results Table 2.1: Performance variables Corrupt success Payoff Neg. Externality Transfer level Treatment mean std.dev. mean std.dev. mean std.dev. mean std.dev. IDT1 0.25 0.09 13.13 3.92 2.90 1.01 3.18 1.52 IDT2 0.25 0.06 12.72 3.78 3.49 2.12 3.16 1.25 TDT1 0.47 0.07 7.32 4.26 5.13 2.91 3.58 1.15 TDT2 0.42 0.05 9.40 4.05 5.38 1.97 4.15 1.24 All means are calculated as averages across periods and (relevant) participants of the respective treatment. Table 2.1 shows the values of the four main performance variables in all treatments. Corrupt success depicts the average share of successful transactions per unit (Nsuccess Ntotal ).25 Payoff represents the average payoff level (in Euros) after the reduction of the negative externality26 (final payoff) per subject. Neg. Externality describes the level of the 25Note that units contain two, three or four individuals, which makes a comparison between absolute levels of corrupt success inconclusive. 26To be able to accurately compare payoff levels between all treatments, we subtract the relevant share of the reduction of the donation from the actual payoff in the externality mode 2. Bringing the Four-Eyes-Principle to the Lab 58 (relevant) negative externality in Euros. Transfer level measures the average amount of bribe (in EMU) transferred by type B participants per period and unit. Corruption levels Comparing corrupt success rates (Corrupt success) between treatments, we can iden- tify a relatively small but significant Bribe Splitting Effect. The difference in corrupt success rates between TDT1 and TDT2 (0.05) is significant (p = 0.082; N = 28). We reject Hypothesis 1. Corrupt success does not seem to depend exclusively on inten- tions or final outcomes. Moreover we find a substantial Group Decision-making Effect (Hypothesis 2). The negative difference in the corrupt success-levels between IDT1 and TDT1 amounts to 0.22 (p = 0.034; N = 40) and strongly suggests the dominance of the Profit Maximizing Motive (Hypothesis 3). The large and significant difference (0.17) between IDT1/IDT2 and TDT2 (IDT1 vs. TDT2: p = 0.002; N=36, IDT2 vs. TDT2: p = 0.042; N=24) indicates a negative total effect of the introduction of the 4EP even if we control for the difference in the number of repetitions (between IDT1 and TDT2) confirming Hypothesis 627. Figure 2.3: Success-probabilities over Periods Figure 2.3 demonstrates the differences in the dynamic development of success- 27The Profit Maximizing effect dominates the Competetive Motive in group decision-making. The resulting effect is stronger than the Bribe Splitting Effect. Bringing the Four-Eyes-Principle to the Lab 61 vary systematically in their effectiveness between the treatments. We use a simple linear panel regression (random effects) to derive the profitability of bribing. To account for dynamic aspects and potentially decreasing (or increasing) profitability of transfers we use the following specification for 88 (all) participants of type B. We cluster errors on the unit level to take possible dependence of behaviour between type B participants into account. M1 : PPit = β0 + β1bit + β2b 2 it + γDi + δDi ∗ bit + ζDi ∗ b2it + εit (2.1) Index i stands for respective subject, index t for the respective period. The dependent variable PP signifies the individual payoff (in EMU) excluding subtractions from the externality caused by subjects outside a subject’s unit. Independent variable b indicates the transfer paid by briber i in period t. The quadratic variable b2 captures a potential non-linear effect of the transfer on period payoffs (decreasing marginal payoffs). Vector D stands for the treatment Dummy variables DIDT2, DTDT1 and DTDT2. Accordingly, vectors D∗b and D∗b2 contain interaction effects of b with treatment dummies for TDT2, TDT1 and TDT2. We find (see Table 2.8 in Appendix 2B for the full list of estimated coefficients for M130) that the individual payoff per period is strictly increasing in the level of transfer sent to the (group) of official(s) in all treatments. There is no significant evidence for a decreasing profitability of bribing. Neither β2 nor any element of ζ is different from 0 (t-tests; p ≥ 0.262) in either of the treatments. The main finding is that bribing is far more profitable in the GD than in the ID treatments. On average an additional unit of transfer increases the payoff per period by 1.896 EMU in IDT1 and 2.029 in IDT2 (∆ = 0.1321; t-test: p = 0.284). By contrast, the marginal effect is 3.94 EMU per unit in TDT1 and 2.63 EMU in TDT2. The differences in the marginal effects between ID and GD treatments are all highly significant (t-tests for differences between IDT1 and TDT1 and TDT2: p < 0.001 and F-tests for differences between IDT2 and TDT1/TDT2; p < 0.001). Payoff maximizing strategies of O 30Using pooled OLS (with and without period dummies) did not yield qualitatively different results. To account for the censored independent variable we also ran the model with Tobit yielding similar results. Bringing the Four-Eyes-Principle to the Lab 62 The differences in the individual profitability from engaging in corrupt activities between the treatments are not as large for the officials. We estimate a simple OLS regression, measuring the marginal effect of N , the number of successful corrupt transactions (num- ber of choices in which the (group of) official(s) has cooperated) on ‘Payoff’, the total payoff of each (group of) official(s).31 M2 : Payoffi = β0 + β1Ni + γDi + δDi ∗Ni + εi (2.2) Vectors D and D ∗N have analoguous interpretations as D and D ∗ b in Model M1. The results of the regression are reported in Table 2.9 of Appendix 2B. Using OLS as well as Tobit (as a robustness check), we find a strong positive effect of the number of successful transactions on the total payoff in all treatments. The profitability of being corrupt is significantly higher in both GD treatments than in the ID treatments (t-tests for IDT1 vs. TDT1/TDT2 and F-tests for IDT2 vs. TDT1/TDT2; p ≤ 0.004). While officials in the ID treatments earn on average only 15.892 (IDT1) and 15,075 (IDT2) EMU more for an additional successful corrupt transaction, the rate is at 19,035 (TDT1) and 20.923 (TDT2) EMU considerably higher in the GD treatments (Differences between ID and TD treatments are all significant; t-test for IDT1 vs. TDT1/TDT2, F-test for IDT2 vs. TDT1/TDT2: p ≤ 0.001). Altruism To accept a bribe instead of rejecting it (in Stage 2 of the game), means to keep the benefit of the tripled transfer for oneself instead of sharing it with the public. Moreover, the corruption game is designed in such a way that for any given level of transfer, the socially optimal decision (maximizing the sum of payoffs) is to reject the bribe in Stage 2. The number of non-zero rejections of bribes in Stage 2 is extremely low in all four treatments.32 In only 6.75% of possible cases (i.e. if b > 0) (groups of) type O subjects reject a non-zero bribe in IDT1 compared to 6.22% in TDT1, 5.40% in IDT2 and 5.48% in TDT2. Displays of altruism towards the public at a high personal cost (2b) are 31Since officials within a unit in the GD treatments decide jointly they receive the same payoff and are therefore treated as a single observation. Officials who are in the same unit but decide independently (IDT2) are treated as individual observations but we cluster their standard errors in the regression. 32Accepting a 0-level bribe should be (weakly) dominated by the option ‘reject’, since it inflicts damage to the public with no personal gain. Therefore an interpretation as altruistic behaviour cannot be justified. Bringing the Four-Eyes-Principle to the Lab 63 rare and confirm the findings of Büchner et al. (2008) with respect to altruism in the context of negative externalities. Moreover, we do not find a significant difference across treatments (pair-wise U-tests between all treatments: p ≤ 0.472; N ≥ 24). Transfer levels Average bribe levels (including 0-transfers) are substantially (and significantly, U-tests in all treatments: p < 0.001, N ≥ 12) larger than 0 for all treatments and almost identical within the ID treatments (3.18 and 3.16 EMU). Transfers are at 3.58 only insignificantly larger in TDT1 than in the individual decision-making treatments (TDT1 vs. IDT1/IDT2: p = 0.351/0.464, N = 36/28). At 4.15 EMU, the average transfer level in TDT2 is significantly (U-tests: TDT2 vs. TDT1/IDT1/IDT2; p ≤ 0.041; N ≥ 24) larger than those in any of the other treatments. The large difference in transfer levels between TDT1 and TDT2 suggests that bribers anticipate different behaviour from officials and react accordingly. Taking into account that success levels in corruption are significantly lower in TDT2 than in TDT1 despite the positive difference in transfer levels, we conclude, assuming realistic beliefs, that bribers anticipate the BSE and ‘react’ by trying to ‘convince’ officials by transferring larger bribes (Hypothesis 4). The distribution of the size of transfers reveals even more information about B ’s behaviour. Figure 2.5 shows the relative frequency of transfer levels for all treatments. Transfers are almost identically distributed in IDT1, TDT1 and IDT2. There are only few low (b < 4 EMU) and high (b > 8 EMU) transfers. We observe a very strong mode at b = 5. This particular observation may, e.g. be explained by subjects behaving according to preferences based on inequity aversion (Fehr and Schmidt 1999). The strategy [b = 5 EMU; ‘accept’; ‘cooperate’] leads to equal payoffs for B and O within a unit in all four treatments.33 The distribution in TDT2 depicts a significantly different pattern. We compare the distribution of bribes in TDT2 to those in all three other treatments with a Kolmogorov-Smirnov test (p < 0.001; observations of strictly positive bribes: N ≥ 382; all observations: N = 480). In TDT2 probability mass is shifted towards the higher 33For the results to be explained by social preferences we either need to assume that B and O ’s reference group excludes the public (other participants in mode 1 or recipients of donations from ‘Doctors without Borders’ in mode 2), or assume a certain structure of beliefs on the (corrupt) behaviour of the other units (only valid for mode 1). Bringing the Four-Eyes-Principle to the Lab 66 ability of a successful corrupt transaction conditional on the relevant transfer between the treatments is to use a linear panel regression (random effects) controlling for clustered standard errors on the unit level. Since we are primarily interested in the causal relation- ship between the level of transfer (b) and the success levels (SC ), we do not distinguish between a corrupt deal that failed in Stage 2 or in Stage 3. Treating the decisions ‘reject’ and ‘defect’ equally with respect to the outcome of a corrupt deal (success or failure), we do not have to take the selection process of reaching Stage 3 into account.35 We use the following specifications for the linear probability model: M3 : Prob(SCit = 1|ψX) = β0 + β1bit + γDi + δDi ∗ bit + θZi + εit (2.3) ψ stands for the vector of coefficients. X represents independent variables. Again, vectors D and D ∗ b stand for treatment dummies and interaction terms of treatment dummies with the transfer b, just as in Model M1. Vector Z contains individual demographic characteristics (e.g. age, gender36, an interaction term between gender and the level of transfer, etc.) obtained from the questionnaire. Since we do not find any significant effects with any of these characteristics we do not report them in the regression output (Table 2.3).37 M3 in Table 2.3 reports the results (coefficients and standard errors) of the linear probability model. In all treatments, we find that an additional unit in transfer (b) increases the proba- bility of the corrupt success significantly (1%-level). The effect is significantly stronger in both GD than in the ID treatments ( t-tests for IDT1 vs. TDT1/TDT2: p ≤ 0.003, F-tests for IDT2 vs. TDT1/TDT2: p < 0.001). There is no indication for the confirma- tion of the hypothesis that women are less reciprocal in a corrupt transaction (than men) when we consider observations from all four treatments. The behaviour of female officials (in ID treatments) and ‘all-female’ groups of officials (in GD treatments) does not appear to be different from that of their male (‘all-male’ and mixed group-) counterparts with respect to corrupt reciprocity (all coefficients including a gender dummy remain highly 35Treating the outcomes of ‘reject’ and ‘defect’ differently would require a Heckman-selection process explaining the selection of cases in which Stage 3 is reached (Heckman 1979). 36For officials in the GD treatments we use a dummy for ‘all-female’ groups and do not distinguish between ‘all-male’ and mixed groups. 37As a robustness check we ran the panel regression with a series of specifications, including a regression excluding Z and a set of pooled OLS regressions including dummy variables for periods. None of these specifications yield results qualitatively different from those reported in the left part of Table 2.3. Bringing the Four-Eyes-Principle to the Lab 67 insignificant; t-tests: p ≥ 0.397). Female participants do not seem to behave more in line with the predictions of the standard self-interested model (Hypothesis 5). An expla- nation may be found in the absence of the possibility of direct punishment of defective behaviour by the bribers (e.g. negative costly retaliation), which is believed to be a main determinant of the gender effect found in Lambsdorff and Frank (forthcoming).38 Table 2.3: Output of (M3) and (M4) Dependent variable: SC (M3) (M4) Lin. Prob Probit Coefficient Stand. error Coefficient Stand. error Constant 0.0582∗∗∗ 0.0215 1.5832∗∗∗ 0.1052 DIDT2 0.0981∗∗∗ 0.0351 0.5442∗∗∗ 0.1536 DTDT1 −0.0343∗∗ 0.016 −0.4637∗∗ 0.2091 DTDT2 −0.0722∗∗∗ 0.0232 −1.3272∗∗∗ 0.3162 b 0.0593∗∗∗ 0.0132 0.2123∗∗∗ 0.0171 DIDT2*b −0.0208 0.0175 −0.0981 0.0872 DTDT1*b 0.0521∗∗∗ 0.0162 0.2438∗∗∗ 0.0402 DTDT2*b 0.0462∗∗∗ 0.0133 0.3402∗∗∗ 0.0540 Pseudo R2 = 0.36 − *** denotes significance at the 1%-level. Number of subjects: 96, Number of clusters: 64, Number of periods: 10 The non-linear relationship of success-probabilities and transfer levels observed in Figure 2.6 can be quantified by a simple maximum likelihood model. To account for differences in the marginal effect of an additional unit in transfer on the success probability across transfer levels we run the following Probit model in its panel version (random effects).39 We use the same set of independent variables and repeat all robustness checks 38See Chapter 3 for a comprehensive discussion of the gender effect. 39See Pereira et al. (2006) and Gneezy and List (2006) for examples of the use of a panel version of maximum likelihood models in comparable settings, i.e. repeated gift exchange games. Bringing the Four-Eyes-Principle to the Lab 68 (pooled version etc.) applied to the linear probability model (M3). M4 : Prob(SCit = 1|ψX) = φ(β0 + β1bit + γDi + δDi ∗ bit + θZi) (2.4) Again, ψ stands for the vector of coefficients and X for independent variables. As ex- pected, qualitative results (direction and significance of the evaluated marginal effects at the mean of of transfers b = 3.46) do not change compared to the results from the linear probability model, see (M4) in Table 2.3. Table 3.10 in Appendix 2B reports marginal effects of the relevant40 variables as well as predicted conditional probabilities of success of model M4. The Probit model shows that marginal effects are lower in the TD than in the ID treatments for low transfers, b < 3, while they are higher for b ≥ 4. Consequently, the predicted success levels (probabilities) conditional on the transfer level are lower in the TD treatments than in the ID treatments for b ≤ 4 while they are larger for b ≥ 6 (see Table 2.10 in Appendix 2B). The pattern shown in Figure 2.6 and quantified in M4, i.e. a stronger curvature of the probabilistic cumulative distribution function for GD than for ID treatments, may be explained by differences in the strategies between groups and individuals. On the one hand, groups of officials seem to ‘defect’ (or ‘reject’) more often in the case of low transfer levels. On the other hand, they seem to be more likely to reward high transfers than their individual counterparts by corrupt reciprocity. We interpret this as strategic signals of unwillingness to return the corrupt favour in less profitable transactions (aiming at in- ducing a higher transfer in the following periods) and signals of willingness to reciprocate for high transfers (aiming at receiving further high transfer in future periods in exchange for cooperation). This strategy seems to aim at the extraction of a maximum amount of cumulative bribes. In all treatments, a large fraction of non-zero transfers over all ten periods (between 36% in IDT1 and 52% in TDT2) fall into the interval for which the probability of success is significantly larger in the GD than in the ID treatments. Hence the strategies followed by groups seem to be more successful in the sense of higher recip- rocal stability between briber and official than the strategies applied by individuals. We interpret this as a piece of strong evidence for the dominance of the Profit Maximizing Motive in group decision-making (Hypothesis 3). 40Again we do not report any coefficients that are not significant, e.g. a dummy variable for gender. Bringing the Four-Eyes-Principle to the Lab 71 Table 2.5: Success and Initial Consent, TDT1 Successful corruption Failed Corruption Total No initial Consent 23.13% 8.75% 31.88% Initial Consent 23.75% 44.37% 68.12% Total 46.88% 53.12% 100% Averages are derived from 160 transactions (16 independent groups of officials in 10 periods) in TDT1 Table 2.6: Success and Initial Consent, TDT2 Successful corruption Failed Corruption Total No initial Consent 20.42% 18.33% 38.75% Initial Consent 21.66% 39.59% 61.25% Total 42.08% 57.92% 100% Averages are derived from 240 transactions (12 independent groups of officials in 10 periods) in TDT2 Assuming independence of decisions (i.e. no influence of the process on final decisions) we would expect 100% of transactions without initial consent to fail because of the veto power of the non-reciprocating official. On the contrary, we find that the final decision was made in favour of (corruption-stabilizing) reciprocity in 72.6% (23.13 31.88 , TDT1 in Table 2.5) and 52.70% (20.42 38.75 , TDT2 in Table 2.6) of cases in which the two officials initially disagreed. Assuming that initial decisions reflect the true underlying preferences, this means that the decision-making process alone is responsible for a large share of the treatment effects with respect to corrupt success levels. We conclude that (in both treatments) those officials who are in favour of engaging in, or maintaining, a successful corrupt relationship dominate the outcome of the decision-making process although their decision-adversaries hold veto power. We take this finding as evidence for the Persuasive Argument Theory (Pruitt 1971) which suggests that those participants (in the role of O) who provide the most valuable ideas for maximizing long term individ- ual payoffs during the experiment (which in our case is the maintenance of the corrupt relationship through reciprocity, see Section 2.5.2) dominate the decisions within a group. Bringing the Four-Eyes-Principle to the Lab 72 2.5.4 Content analysis In addition to the arguments derived from the comparison of outcomes between the treatments (see Section 2.5.1 and 2.5.2) and the analysis of choices in the different phases of the group decision-making process (see Section 2.5.3), we are able to get some insight into the mechanism of group decision-making by considering the content of the messages44 exchanged during the decision-making processes of Stage 2 and Stage 3. 22 (out of 28, 16 in TDT1 and 12 in TDT2) groups exchanged electronic messages.45 First, we separate messages and identify 132 distinct statements.46 We allocate each statement (sent in either of the two stages) into four main categories: ‘Neutral’ (statements that do not contain any traceable argument, e.g. ‘Hello, nice game’); ‘Social’ (statements including arguments against the cooperation in the corrupt transaction mentioning the negative externality, e.g. ‘We have to consider the effect on the others, we should not cooperate’); ‘Strategic’ (arguments in favour of the stabilization of the reciprocal relationship with the objective of payoff maximization, e.g. ‘Let us cooperate, otherwise we won’t get any profit in the next period(s)’) and ‘Strategic Neg.’ (arguments against cooperation in a certain period to implicitly demand larger transfers in future periods, e.g. ‘Do not re-transfer, then he [the briber] will know to give more next time’).47 We add a 5th category ‘Social/Strategic’ to account for (mostly twisted) statements that included both, other-regarding (social) and strategic (payoff maximizing) arguments. Table 2.7 reports the relative frequencies of statements of the respective categories subdivided by the final outcome of the respective transaction in terms of success and failure. Of all statements, only 12.2%48 contain other-regarding arguments (Social and Social/Strategic). Their low frequency is noteworthy, and so is their lack of effectiveness (only 37.7%49) of transactions finally fail). 44We analyse all electronic chat messages exchanged by officials in the GD treatments. 456 groups either did not encounter a situation of initial disagreement or ignored the possibility of writing messages. 46A ‘conversation’ between two officials may yield more than one statement since it may be split into single entries. 47All examples are translated (word by word) into English from the original statements in German. 488.3 + 3.9%, Table 2.7 49 3.1+1.5 12.2 %, Table 2.7 Bringing the Four-Eyes-Principle to the Lab 73 Table 2.7: Success and Content Neutral Social Strategic Strategic Neg. Social/Strategic Total Success 8.3% 0.8% 27.3% 1.5% 6.8% 44.7% Failure 31.8% 3.1% 12.1% 6.8% 1.5% 55.3% Total 40.1% 3.9% 39.4% 8.3% 8.3% 100% Percentages are derived from 132 statements in TDT1 and TDT2 An explanation may be that in more than 75% of all situations a social argument was followed (in the same chat conversation) by a statement arguing in favour of strategic reciprocity. 82% of these situations ended with a successful corrupt transaction. The majority (56.0%50) of statements contained arguments in favour of some kind of strategic reciprocity. Additional to 63 statements of positive reciprocity there were 11 separate statements arguing in favour of strategic defection aimed at extracting larger bribes in future periods. In 19 (out of all 28 or 22 relevant) groups of officials we found at least one statement in favour of strategic reciprocity (positive or negative). The dominance of arguments in favour of payoff maximization is demonstrated not only by the relative frequency but also by the effectiveness as to corrupt success (71.5%51 of statements including an argument for strategic (positive) reciprocity ended in a suc- cessful corrupt transaction). This provides another piece of evidence for the hypothesis that the Profit Maximizing Motive is the driving force in the decisions made in groups. Arguments that seem persuasive in the pursuit of payoff maximizing are adopted and corresponding suggestions (i.e. maintenance of strategies aiming at payoff maximizing through corrupt reciprocity) realized, while arguments in favour of social efficiency (and fairness) are neglected, since they would lead to individually costly strategies. Again the argumentation is in line with the Persuasive Argument Theory (Pruitt 1971). We leave it to further research to separate the effect of the decision-making process from effects stemming exclusively from the nature of the exchange of arguments via electronic chat messages. For our purpose of evaluating the effectiveness of the 4EP the effort of distinguishing between those two would lead to an even more artificial setting and therefore would not help to derive conclusions. 5039.4 + 8.3 + 8.3%, Table 2.7 51 27.3+6.8 39.4+8.3%, Table 2.7 Bringing the Four-Eyes-Principle to the Lab 76 2.7 Appendix 2 Appendix 2A: Proofs Equilibrium in the 3-Stage Game Proof by (backward Induction). Denote by Ii,n the information set in stage i (i ε {1, 2, 3}) of period n (n ε {1, 2, ..., 10}). Let p(Ii,n) be the probability of reaching the respective stage and q(‘s′|Ii,n) the condi- tional probability of the relevant agent choosing action ‘s’ once reached Stage (i, n). An information set contains all relevant information about ego’s and alter’s behaviour up to the respective stage. Furthermore let PO(Ii,n) be the (sum of) payoff(s) gained up to the arrival of stage (i, n). First we show that there cannot be an equilibrium in which O chooses ‘co- operate’ in Stage 3 of the last (10th) period. Consider a Strategy-Set EQU1 = [s1,1, s2,1, s3,1, s1,2..., s3,10] in which the third stage of period 10 is reached with some prob- ability (p(I3,10) > 0) and O cooperates with some probability (q(‘cooperate’|I3,10) > 0). Compare the payoff, resulting from the realization of Strategy-Set EQU1 (PO(EQU1)) to the one of an alternative Strategy-Set EQU1new which consists of the same strategies up to I3,10 but for which q(‘cooperate’|I3,10) = 0 yielding payoff PO(EQU1new). Since the payoff for period 10 is larger for EQU1new, since 8 + 3 ∗ b < 12 + 3 ∗ b, EQU1 cannot constitute a Sub-game perfect Nash equilibrium. Second we show that, in the last (10th) period, B will never choose any Strategy-Set that includes the action ‘b > 0’ in Stage 1. Consider again a Strategy-Set EQU2 = [s1,1, s2,1, s3,1, s1,2, ..., s3,10] in which p(I1,10) > 0, q(‘cooperate’|I3,10) = 0 and q(‘b > 0’|I1,10) > 0.52 Again compare PO(EQU2) to PO(EQU2new), the payoff of a Strategy-Set that differs from the former only in q(‘b > 0’|I1,10) = 0. Since 12 − b ≤ 12, payoff PO(EQU2) must be smaller than PO(EQU2new) so that EQU2 cannot constitute an equilibrium. Hence only a Strategy-Set featuring [s1,1, ..., s9,1, ‘b = 0’, ‘accept’/‘reject’, ‘defect’] can characterize an equilibrium. 52Given that q(‘cooperate’|I3,10) = 0 must be satisfied, O will never choose ‘reject’ in Stage 2 for ‘b > 0’ and is indifferent between ‘reject’ and ‘accept’ if ‘b = 0’, see Appendix 1C in Chapter 1. Bringing the Four-Eyes-Principle to the Lab 77 Consider now a period-set PS = {k, ..., 10} of (the last 10-k) consecutive periods for which the above stated last period’s equilibrium Strategy-Set is played. Assume q(‘cooperate’|I3,k−1) > 0 for the period k − 1. By the same line of arguments as for the last (10th) period we can easily repeat the task up to the point of excluding all strategy sets that do not exhibit the strategy characteristics of the (Stage Game) equilibrium in the 10th period [‘b = 0’, ‘accept’/‘reject’, ‘defect’]. Letting k decrease from 9 down to 1, it is obvious that the Stage Game Nash Equilibrium remains the only Sub Game perfect Nash Equilibrium in the (finitely) repeated game. Responsibility and Veto Power Consider two officials, Oi and Oj, who decide jointly in Stage 3 between ‘cooperate’ (c) and ‘defect’ (d). Consider Oi’s preferences to be represented by the utility function Ui(s1, s2) where s1 is her own action and s2 that of Oj. Oi’s decision is pivotal only if s2 = c. If not (s2 = d), the outcome is (d) giving utility Ui(d, d), independent from Oi’s choice. This means that giving up responsibility works only in the cases where the socially optimal choice is taken anyway. Therefore, no official who is deciding jointly in a group can (for herself) deny responsibility for her group’s corrupt behaviour since this would need the approval of both officials. For these reasons we believe that a lack of individual responsibility within a group cannot be applied as an argument in favour of the prediction of higher levels of corruptibility among officials within a group. Bringing the Four-Eyes-Principle to the Lab 78 Appendix 2B: Figures and Tables Extensive forms of games in all treatments In all treatments except TDT2 both, O and B, decide once in every period. In TDT2 only B decides once per period while each O decides twice. Figure 2.7: Extensive forms of TDT1 and IDT1 Figure 2.8: Extensive forms of TDT2 and IDT2 Bringing the Four-Eyes-Principle to the Lab 81 Appendix 2C: Instructions from TDT2 (translated from German) Thank you very much for your appearance. In the next 90 minutes you will take part in an experiment in the laboratory of MELESSA. If you read the following instructions carefully, you can (depending on your decisions) earn money, additional to the show-up fee of 4 Euros. Additional to the money you can earn for yourself, you will affect the amount of donation to the public aid organization ‘Doctors without Borders’. The money you will earn during the experiment will be added to the show-up fee and paid out in cash at the end of the experiment. The money that is going to be donated will be transferred to the donations account of ‘Doctors without Borders’. During the experiment you are not allowed to communicate with the other participants. If you have questions, please approach one of the experimenters by raising your hand. In the case of violation of this rule we have to exclude you from any payments. During the experiment we will refer to Experimental Monetary Units (EMU) instead of Euros. Your income will be calculated in EMU. In the end of the experiment the total amount will be exchanged in Euros. The exchange rate is 1 EMU = 5 Eurocents. All 24 participants are assigned to groups of four. Neither the experimenters nor the other participants know which group you are in. Your decisions remain completely anonymous. The Decision Situation There are two types in this experiment: type A and type B. The types play different roles and make decisions that affect their own income, the income of the other participants of the experiment and the amount of donation transferred to the organization ‘Doctors without Borders’. The type of a participant is allocated randomly. A group of four consists of two type A and two type B participants who stay together for the entire experiment. The experiment has 10 periods. Procedure: All of the 10 periods consist of at most 3 Stages. Stage 1 In the first Stage, every participant of type A (type A Nr 1 and type A Nr 2) decides on the size of their transfer (T1 denotes type A Nr 1’s transfer and T2 denotes type A Nr 2’s transfer) which has to lie between 0 and 12 EMU. Next, the amount of the transfers is tripled and then split equally between the two type B participants (type B Nr 1 and type B Nr 2) of the group of four. If T1 is for example 6 EMU, type B Nr 1 receives 9 EMU (0.5 ∗ 6 ∗ 3 EMU) and type B Nr 2 receives 9 EMU. Bringing the Four-Eyes-Principle to the Lab 82 Hence there are 2 situations per group in any period: Situation 1: Type A Nr1 transfers T1 to the two type B participants (where T1 is first tripled and then shared) Situation 2: Type A Nr2 transfers T2 to the two type B participants (where T2 is first tripled and then shared) Stage 2 In Stage 2 the two type B participants decide jointly on how to react on the transfer of the respective type A participant. They have (in both situations) two alternatives. 1st Alternative: Both decide (for a specific transfer, e.g. T1) jointly for ‘keep’: In this case Stage 3 is entered 2nd Alternative: One or both decide in favour of ‘distribute’: In this case, the respective type A partici- pant (e.g. type A Nr 1) does not get a bonus (and receives only 12 - T1 EMU). The type B participants both get 6 EMU plus half of the value of the transfer (6 + 0.5 ∗ T1 EMU). Moreover, the amount of 2 ∗ T1 + 24 EMU is transferred as a donation to the organization ‘Doctors without Borders’. A joint decision between the two subjects is found as follows. First, each of the two type B participants decides individually whether to ‘keep’ or to ‘distribute’ the particular transfer. If the decision is not unanimous (one type B participant wants to ‘keep’ and the other wants to ‘distribute’ the transfer), the decision of the fellow participant appears on his or her own screen. Next, the participants decide once again separately. If there is still no agreement, the two type B participants can exchange messages via an electronic ‘chat’ (see explanation below) for one minute. After this the participants decide for the last time. Note that only if both type B participants decide in favour of ‘keep’ the third Stage is actually reached. Since there are two type A participants in every group of four (type A Nr 1 and type A Nr 2), each of the type B participants has to decide (jointly with the other type B participants) in two situations: once for T1 and once for T2. Bringing the Four-Eyes-Principle to the Lab 83 Stage 3 In Stage 3 (which is only reached if both type B subjects have chosen ‘keep’) the two type B participants decide again jointly whether to initiate a re-transfer or not. Again, both type B subjects decide separately first. If the decision is not unanimous (one type B participant wants to initiate the re-transfer and the other does not), the decision of the other participant is shown on the screen. Then the participants can decide again separately. If there is still no consent, the participants enter again a ‘chat’ in which they can exchange electronic messages for one minute. After this, there is a final decision. 1. Case: Both type B participants decide in favour of a re-transfer. Both carry the costs of 2 EMU each (independent of the amount of the respective transfer). They both get 6 EMU plus one and a half times the value of the transfer, less the costs of 2 (6 + 1.5 ∗ T1 − 2 EMU). The respective type A participant (type A Nr 1) receives a Bonus of 16 EMU in addition to the 12 EMU of initial endowment (16 + 12 − T1). In this case there is no donation to the organization ‘Doctors without Borders’. 2. Case: One or both type B participants decide against a re-transfer. In this case, there are no personal costs for the two type B participants (they get 6 + 1.5 ∗ T1 each), the respective type A participant does not receive a bonus (and gets 12− T1), and the donation to the organization is 20 EMU. In the end, all participants are shown their personal income in the period. Please note, that the type A participants can thereby reconstruct whether or not the type B participants chose for or against the re-transfer. These (maximal) 3 stages are repeated 10 times (10 periods). Since the members of groups stay together, participants always interact with the same persons in the same roles for the entire experiment. (Type A Nr 1 remains type A Nr 1. type A Nr 2 remains type A Nr 2 etc.) Chat: Type B subjects potentially have the possibility to communicate via real time electronic messaging (Chat) with their fellow type B subject to agree on a joint decision (e.g. ‘keep’ or ‘distribute’) in Stage 2 and Stage 3. The content of the communication is generally free to choose but there are some restrictions. You are not allowed to make statements about personal characteristics such as your name, age, address, gender, subject of study or any information that might lead to your identification. Moreover, strong language is strictly forbidden. Anyone who violates these rules of communication will be automatically expelled from the experiment and will not get any payments for the entire experiment. Each participant in the chat can send as many messages to the other participant as he wishes or is able to send within the time limit of one minute. Every message appears automatically on the screens of both type B participants of a group of four but cannot be seen by any other participant of the experiment. Bringing the Four-Eyes-Principle to the Lab 86 Question 2 Assume that you (type A Nr1) and type A Nr2 have both chosen a transfer of 0 (T1 is 0 EMU and T2 is 0 EMU). Neither participant of type B (neither type B Nr1 nor type B Nr2) wants to ‘keep’ any of the two transfers in Stage 2. a) What is your (type A Nr1) total payoff in this period? Your answer:________________________ b) What is the total payoff of type A Nr2 in this period? Your answer:________________________ c) What is the total payoff of type B Nr1 in this period? Your answer:________________________ d) What is the total payoff of type B Nr2 for all situations relevant to him/her? Your answer:________________________ e) What is the total amount of donation to ‘Doctors without Borders’ in this period? Your answer:________________________ f) What is the total amount of payoff generated by the decisions of your group of four? Your answer:________________________ Question 3 Assume that you (type A Nr1) have chosen a transfer of 5 EMU (T1 is 5 EMU) and type A Nr2 has also chosen a transfer of 5 EMU (T2 is 5 EMU). Both participants of type B (type B Nr1 and type B Nr2) decide to ‘keep’ the transfer and initiate a re-transfer in Stage 3. a) What is your (type A Nr1) total payoff in this period? Your answer:________________________ b) What is the total payoff of type A Nr2 in this period? Your answer:________________________ c) What is the total payoff of type B Nr1 in this period? Your answer:________________________ d) What is the total payoff of type B Nr2 for all situations relevant to him/her? Your answer:________________________ e) What is the total amount of donation to ‘Doctors without Borders’ in this period? Your answer:________________________ f) What is the total amount of payoff generated by the decisions of your group of four? Your answer:________________________ Chapter 3 Bringing Good and Bad Whistle-Blowers to the Lab 3.1 Introduction For several decades, corruption, defined as ‘the misuse of public office for private gain’ (Klitgaard 1988), has been considered as a major obstacle to growth and development (Bardhan 1997, Mauro 1995). A large number of activities falling under this definition can be modelled as a Principal-Agent-Client relationship between the government, its imperfectly controlled agents (public officials) and their private clients (individuals or firms). This relationship can be further divided into two disjoint principal agent prob- lems, one between the government and its official and the other between the official and the client. While the first is of less importance, a major objective of the New Institu- tional Economics (NIE) of corruption (Lambsdorff 2007) is to understand the illegal and therefore (legally) unenforceable transaction between a private entity (e.g. a firm paying a monetary bribe) and a potentially corrupt official who may reciprocate a payment with the delivery of a corrupt service. The goal is to draw conclusions for the design of institu- tions which will optimally destabilize and hence minimize corruption. Apart from obvious institutional measures such as applying harsh punishment and increasing detection prob- abilities of corrupt behaviour (Becker 1968, Klitgaard 1988, von Rose-Ackerman 2006), an effective way to destabilize corruption is to enable whistle-blowing. In our analysis, Bringing Good and Bad Whistle-Blowers to the Lab 88 we define whistle-blowing as ‘the act of disclosing information in the public interest’ 1. Whistle-blowing can hence be seen as the act of ending a corrupt transaction and all of its consequences by incurring non-trivial personal costs (Drew 2003).2 In their experimental studies, using the framework of a standard corruption game (see Chapter 2), Lambsdorff and Frank (2010) and Abbink (2006) find that the possibility of whistle-blowing leads to an increase rather than a decrease of the number of successful corrupt transactions.3 These results are at odds with the fact that whistle-blowing policies are in widespread use and perceived as successful measures in the abatement of the negative consequences of corruption (Hall and Davies 1999, Spagnolo 2006, Buccirossi and Spagnolo 2006, Hussein 2005). We explain this by the fact that the standard game of corruption (as used in Abbink 2006, Lambsdorff and Frank 2010) accounts for only one of the two main negative consequences of corruption potentially affected by whistle- blowing. The first consequence of a successful corrupt transaction (which is modelled explicitly in the standard game of corruption) concerns the direct negative externality on the public which is directly proportional to the level of corruption (Bardhan 1997, Rose-Ackermann 1999).4 The second, indirect, consequence (which is disregarded in the standard corruption game) concerns the ‘crowding out’ of legal productive activities (Klitgaard 1988). Rampant corruption is likely to drive ‘honest’ clients out of productive markets, forcing them to engage in alternative activities (of lower productivity).5 This may be due to individual (firm-level) differences in the moral costs, ability (criminal energy) or legal framework (foreign versus domestic firms) affecting corrupt transactions (see Foreign Corrupt Practices Act in Egger and Winner 2006, Bardhan 1997). The direct effect of corruption is independent of who initiates the transaction, the client or the official, while the indirect effect requires an actively corrupt official. The compactness 1Another definition is ‘the disclosure to the public or to authorities, usually by an employee, of wrongdoing in a company or government department’ (Drew 2003) 2By assuming that the briber consists of a single decision maker, we shut off considerations on behavioural interactions within groups of agents (e.g. group pressure). Hence we do not consider that whistle-blowing may exploit the differences in individual corruptibility within a group of decision makers (e.g. inside a firm or within a group of officials). In this paper we concentrate on the incentives and behavioural reactions of decision-makers represented by single individuals only. 3Neither Lambsdorff and Frank (2010) nor Abbink (2006) focus on the mechanism of whistle-blowing or on the assessment of its effectiveness. 4The most common example is the expected damage to outsiders caused by the deployment of sub- standard quality in construction projects realized with the help of administrative corruption. 5This line of argument is closely related to the rent seeking literature of corruption (Lambsdorff 2002).