Propensities to Engage in and Punish Corrupt Behavior: to Engage in and Punish Corrupt Behavior:
Experimental Evidence from Australia, India, Indonesia and
Lisa Cameron,a Ananish Chaudhuri,b Nisvan Erkal,a Lata Gangadharana
a Department of Economics, University of Melbourne, VIC 3010, Australia.
(e-mail: {lcameron, latag, n.erkal}
b Department of Economics, University of Auckland, Private Bag 92019, Auckland,
New Zealand. (e-mail:
October 2007
This paper examines cultural differences in individual decision-making in an experimental
corruption game. We define culture as an individual’s accumulated experience, shaped by the
social, institutional, and economic aspects of the environment in which the individual lives.
Based on experiments run in Australia (Melbourne), India (Delhi), Indonesia (Jakarta) and
Singapore, we find that there is a greater variation in the propensities to punish corrupt behavior
than in the propensities to engage in corrupt behavior across cultures. Consistent with the
existing corruption indices, the subjects in India exhibit a higher tolerance of corruption than the
subjects in Australia. However, the subjects in Singapore have a higher tolerance of corruption
than the subjects in Indonesia. We conjecture that this is due to the nature of the recent
institutional changes in these two countries. We also vary our experimental design to examine
the impact of a more effective punishment system and the effect of the perceived cost of bribery.
JEL Classification: C91, D73, K42, O17, O57.
Keywords: Corruption; Experiments; Punishment; Cultural Analysis
* Preliminary versions of this paper have circulated under the titles “An Experimental Analysis of Third-Party
Response to Corruption,” “Corruption: An Experimental Analysis,” and “Do Attitudes towards Corruption Differ
across Countries? Experimental Evidence from Australia, India, Indonesia and Singapore.” We thank Vivi Alatas,
Jeff Borland, Tim Cason, Gary Charness, John Creedy, Rachel Croson, Cary Deck, Robert Dixon, Sisira Jayasuriya,
Pushkar Maitra, Nikos Nikiforakis, Charlie Plott, Matthew Rabin, seminar participants at the International
Workshop on Field Experiments in Economics (2006), the North American Economic Science Association Meetings
(2004), the Econometric Society Australasian Meetings (2004), International Meeting of the Economic Science
Association (2004), Deakin University and University of South Australia for their valuable comments. We are
grateful to the World Bank, Faculty of Economics and Commerce at the University of Melbourne and the University
of Auckland for their financial assistance. Lynette de Silva, Syarifah Liza Munira, Daniel Piccinin, Revy Sjahrial
and Jonathan Thong have provided excellent research assistance.
1. Introduction
Corruption is a pervasive phenomenon. Transparency International finds that of the 133
countries evaluated for its 2003 Corruption Perception Index (CPI), 70 percent score less than 5
out of a clean score of 10. Among the developing countries, 90 percent score less than 5 against a
clean score of 10.1 Corruption is a particularly troubling phenomenon in developing countries
because of its negative impact on economic growth. It undermines development by weakening
the institutional foundations on which economic growth depends and by reducing the incentives
for public and private investment (Klitgaard, 1988; Bardhan, 1997; Mauro, 1995).
Given its large negative impact, much stands to be gained from understanding the causes
of corruption and the ways in which it can be reduced.2 The aim of this paper is to contribute to
our understanding of corruption by comparing individual decision-making in a corruption
experiment across four different cultures.3 Individuals’ attitudes towards corruption are shaped
by their everyday experiences of corruption. These experiences are determined by the social,
institutional (political and legal), and economic backgrounds of the countries in which they live.
We refer to all the elements that shape individuals’ attitudes as culture. One hypothesis that we
test is whether higher levels of exposure to corruption in daily life promote a tolerance of
corruption that is reflected in norms of behavior. A corrupt environment may make it easier to
1 See for information on the CPI. Table A1 in the Appendix
contains a selective list of country rankings from the 2003 Corruption Perceptions Index (CPI). This Index ranks
countries in terms of the degree to which corruption is perceived to exist among politicians and public officials. It
reflects the views of analysts and business people around the world, including experts living in the countries
2 Previous studies have discussed, among other things, the importance of deregulation, civil service reform, intergovernmental
competition, and an effective legal system in reducing corruption. See Rose-Ackerman (2006) for a
comprehensive survey of the literature. Treisman (2000) for an assessment of the explanatory power of various
theories of the causes of corruption, and Glaeser and Goldin (2004) for a discussion of the historical factors that may
have helped reduce corruption in the United States.
3 The fact that countries with similar degrees of development may have significantly different levels of corruption
suggests that corruption may at least partially be a cultural phenomenon. For instance, Finland, with a 2002 per
capita GDP of 26,495 USD, is ranked 1st in the 2003 edition of the CPI while Italy with a 2002 per capita GDP of
25,568 USD is ranked 35th. Portugal, with a 2002 per capita GDP of 18,434 USD is ranked 25th while Greece with a
2002 per capita GDP of 18,439 USD is ranked 50th.
justify one’s own corrupt behavior. Hence, corruption may gain more acceptance as it becomes
more widespread and such acceptance of corruption may contribute to its further spread and
sustenance (Dey, 1989).
Several papers in the theoretical literature on corruption focus on the cultural
transmission of corruption (e.g., Andvig and Moene, 1990; Hauk and Saez-Marti, 2002; Lui,
1986; Sah, 1988; Tirole, 1996). However, empirical investigations of the impact of culture on
corruption are harder to find. While existing studies rely on data that is aggregated at the country
level (see, for example, Treisman, 2000 and Paldam, 2002), experimental methodology provides
us with a unique opportunity to explore how individual behavior differs across cultures.
The set of actions that fall under the rubric of “corrupt acts” is large. In our paper, we
interpret corruption as a situation where two people can act to increase their own payoff at the
expense of a third person, the victim. The transaction that takes place between the two people is
illegal, so the victim is allowed to punish them. However, such punishment is costly to the
victim. Our experimental design allows us to differentiate between the incentive to engage in a
corrupt act from which one reaps benefits and the willingness to incur a cost to punish a corrupt
act which decreases one’s payoff. This distinction enables us to examine whether individuals’
behavior differs depending on whether they directly benefit from a corrupt act. The ability to
examine punishment behavior is important because as suggested by Fehr and Gächter (2002) and
Bowles and Gintis (2002), such “altruistic” punishment by homo reciprocans, humans who are
willing to punish norm violators even when such punishment is costly to the punishers, may be
the primary driving force behind sustaining cooperative norms in a variety of social settings.
We explore whether, in environments characterized by lower levels of corruption, there is
both a lower propensity to engage in corrupt behavior and a higher propensity to punish corrupt
behavior. We report findings from experiments conducted in four countries: Australia
(Melbourne), India (Delhi), Indonesia (Jakarta), and Singapore. We have chosen to run our
experiments in two countries that are consistently ranked among the least corrupt countries in the
world (Australia and Singapore, with scores of 8.8 and 9.4 out of 10 respectively) and two
countries that are consistently ranked among the most corrupt (India and Indonesia, with scores
of 2.8 and 1.9 respectively).4
The results indicate that there is more variation in the propensities to punish corrupt
behavior than in the propensities to engage in corrupt behavior across countries. Specifically,
there are no significant differences in the propensities to engage in corrupt behaviour in
Australia, India and Indonesia. Singaporeans, however, are 8 percentage points more likely to
engage in corrupt behaviour than the participants in the other countries. With regard to punishing
corruption, Indians are much less likely than any of the other nationalities to punish. Australia
and Indonesia have the highest punishment rates.
Hence, only the comparison of the behaviour in Australia and India supports the
hypothesis that subjects in a country characterized by a low level of corruption tend to be more
likely to punish corrupt behaviour (although possibly no less likely to engage in it). The results
imply that cultural variation in attitudes towards corruption is more complex than that. We
conjecture that the relatively high tolerance of corruption in Singapore (our other low corruption
country) and the relatively low tolerance of corruption in Indonesia reflect the recent institutional
histories of these two countries. It appears Singapore’s strict top-down approach to eradication of
corruption may not have been that effective in changing the hearts and minds of the population
4 See Table A1.
whereas Indonesia’s recent grassroots rebellion against entrenched corruption coupled with our
results suggests a possibly promising change in the underlying attitudes.5
In addition to examining behavioral differences in a corruption experiment across
cultures, we varied our experimental treatment to examine whether the propensities to engage in
and punish corrupt behavior vary with the effectiveness of the existing legal system and the cost
of corruption. We modelled a more effective punishment system as one that allows the victim to
mete out a larger punishment and conducted experiments with both a low and high punishment
regime. To test for the impact of the cost of corruption, we conducted one treatment with a
welfare-enhancing bribe, where the total payoff gains from the bribe exceed the total payoff loss,
and another treatment with a welfare-reducing bribe, where the reverse is true.
The rest of the paper is organized as follows. Section 2 describes the related experimental
literature. Section 3 explains the experimental design and procedure. Section 4 states the research
questions that motivate the analysis presented in Section 5. Section 6 discusses the implications
of our results and concludes by suggesting avenues for future research.
2. Previous Experimental Literature on Corruption and Punishment
The experimental literature examining corruption is scarce.6 Abbink, Irlenbusch and
Renner (2002) model corruption as a variant of the two-person trust and reciprocation game,
where the participants play the role of a briber or a public official. They find that social welfare
considerations have no impact on the level of bribery. However, the introduction of a threat of
high penalties when discovered significantly reduces corruption. Abbink (2000) uses a similar
design and finds that varying the relative salaries received by those who engage in corruption
5 As we discuss below, these results are also supported by a recent attitudinal survey conducted by Transparency
6 See Abbink (2006) for a comprehensive survey.
does not affect its prevalence. Barr, Lindelow and Serneels (2004) focus on the decision-making
process of health workers and those appointed to monitor their performance. Using nursing
students in Ethiopia as subjects, they find that corruption in the form of the embezzlement of
public resources is less likely to take place when service providers have higher incomes and
when the risk of being caught and sanctioned is high. Frank and Schulze (2000) focus on the
individual determinants of corruptibility and find that economics students are significantly more
corrupt than others. They show that this is due to a process of self-selection rather than
Two papers which have analysed corruption using field experiments are Bertrand et al.
(2007) and Olken (2007). By studying the allocation of driving licenses in India, Bertrand et al.
(2007) show that corruption does not merely reflect transfers from citizens to bureaucrats, but
that it distorts allocation. Olken (2007) finds that increasing government audits was more
effective in reducing corruption than encouraging local-level monitoring.
Our paper differs from these papers in two main ways. First, to the best of our
knowledge, our study is the first to focus on behavioral differences across cultures in a
corruption experiment. It thus contributes to a growing experimental literature on cross-cultural
comparisons of behavior in other types of experiments.7 Second, previous studies have modelled
punishment as an exogenous lottery. In contrast, punishment is endogenous in our paper and
takes place if the victim decides to incur the cost associated with punishment. We are thus able to
examine both the incentives to engage in corruption and the incentives to punish corrupt
behavior. Understanding punishment behavior is important since societal control of corruption
7 See, for example, Carpenter and Cardenas (2004), Croson and Buchan (1999), Roth et al. (1991) and Henrich et al.
often relies on an individual bringing the act to the attention of enforcement officers.8 A further
advantage of our study is that it benefits from the increased power associated with a large sample
of 645 observations, involving 1935 participants.
3. Experimental Methodology
3.1 Design
We have designed a three-person, sequential-move game that focuses on a common
bribery problem. Figure 1 contains an extensive-form representation of the game, where all of
the payoffs are denoted in experimental dollars. The first player acts as a firm which has the
option of initiating a corrupt act by offering a bribe to a government official in order to increase
its own payoff at the expense of society. We assume the firm can offer a bribe by choosing an
amount B ∈ [4,8]. It costs the firm two experimental dollars to offer a bribe and the firm incurs
this cost regardless of whether the bribe is accepted. If a bribe is offered, the second player, who
we call the official, can either accept or reject the bribe. If the official accepts the bribe (which
implies favorable treatment of the firm), then the payoffs of the firm and the official increase by
3B while the payoff of the citizen decreases by B.9
8 The way we model punishment has some similarities with the literature on the impact of sanctions (formal or
informal) on individual behavior. In recent years, there has been a growing interest in this area. Fehr and Gachter
(2000) show that the cooperators in a public goods game display a widespread willingness to punish the free riders.
Their results reveal that in the presence of formal monetary punishment opportunities, there is less free riding in
these games. Masclet et al. (2003) find that nonmonetary sanctions can also lead to high cooperation levels. These
papers examine the impact of punishment in a game where the punishment is meted out by those who are directly
affected by others’ free riding. Fehr and Fischbacher (2004) and Carpenter and Matthews (2004) show that
punishment by a third party, whose payoffs are not affected by the norm violation, is another way of deterring noncooperation.
Bowles and Gintis (2002), Fehr, Fischbacher and Gachter (2002), and Casari and Plott (2003) provide
further evidence on how punishment can have an impact on behavior and social norms.
9 The payoff increase that the firm experiences may represent, for example, the benefit from avoiding a regulation.
The official’s payoff also increases by 3B even though the amount of bribe paid by the firm is B. This is due to a
difference in the marginal utility of income. Since the income earned in the public service is likely to be lower than
that earned in private firms, the same amount of money can be assumed to have a lower marginal utility value to the
firm than to the official. Abbink, Irlenbusch and Renner (2002) make a similar assumption in their paper. As in their
paper, this multiplier also has the additional advantage of helping us prevent negative total payoffs.
The third player is called the citizen and moves last after observing the choices made by
the firm and the official. If a bribe has been offered and accepted, the citizen is given a chance to
punish the firm and the official for the corrupt transaction by choosing an amount P in
punishment. Punishment is costly to the citizen and reduces the citizen’s payoff by the amount of
the punishment, P.10 However, it imposes a monetary sanction on the firm and official by
reducing their payoffs by 3P. Hence, the net benefit to the firm and the official from the corrupt
transaction is 3B – 2 – 3P and 3B – 3P respectively.
We have chosen to conduct a one-shot game because in a one-shot game the punishment
has no economic benefit to the citizen and so the decision to punish is not affected by the
anticipation of possible future economic gains. Hence, with a one-shot game, a comparison of
the citizens’ willingness to punish corrupt acts across different cultures reveals the differences in
the tolerance levels for corruption. The citizens who choose to punish in a one-shot game would
have even more incentives to punish in a multi-period game since by doing so, they can deter
corruption and decrease the harm they suffer.
The one-shot nature of the game also helps us avoid the issues associated with repeated
games, such as signalling, reputation formation and serial correlation in decisions. Each subject
in our database participated in the experiment only once and played only one role.11 The subjects
playing the three roles were grouped anonymously in the experiment to avoid conscious or
unconscious signalling.
10 The cost of punishment can be interpreted as the effort the citizen has to put in to file a police report or pursue
legal action. Alternatively, it can be interpreted as the amount of tax s/he is willing to pay in order to have such a
legal enforcement scheme against bribery.
11 One standard response in cases such as these is to have random re-matching of subjects. Kandori (1992) states
that it is not clear whether random re-matchings do actually succeed in eliminating supergame effects. However,
Duffy and Ochs (2005) consider an experiment with an indefinitely repeated 2-player prisoner’s dilemma game and
find that contrary to Kandori’s theoretical conjecture, a cooperative norm does not emerge in the treatments where
players are matched randomly. In the current paper we decided to adopt a conservative stance and have players
participate in pure one-shot games to avoid any repeated game effects.
We deliberately chose to use emotive terms such as “bribe” and “punishment” in the
instructions. This is a deviation from the standard practice of using neutral language in
economics experiments. However, since our aim was to simulate a real-life corrupt transaction,
we used loaded language. As indicated in Harrison and List (2004), “it is not the case that
abstract, context-free experiments provide more general findings if the context itself is relevant
to the performance of subjects (p. 1022).”12
3.2 Treatments
We conducted the following three treatments to examine whether the effectiveness of the
punishment regime and the cost of the bribe affect behavior. Treatment 1 is the low punishment
regime, where we restricted the range of the punishment to P ∈ [2,8]. Treatment 2 corresponds to
the high punishment regime, where we allowed the citizen to choose a punishment level P ∈
[2,12].13 Our goal in designing Treatments 1 and 2 was to observe whether a more effective
punishment system decreases the incentives to engage in corrupt behavior and increases the
incentives to punish corrupt behavior. Both Abbink, Irlenbusch and Renner (2002), and Barr,
Lindelow and Serneels (2004) find, in a game with exogenous punishment, that corruption is
lower when the risk of penalty is higher. We examine whether the same finding holds in an
experiment with endogenous punishment.14
12 Cooper and Kagel (2003) consider the role of loaded language in signaling games and suggest that the use of a
meaningful context might better capture behavior in field settings than the use of neutral language. Abbink and
Hennig-Schmidt (2002) however find that the use of words like “bribe” do not make a difference in the corruption
game that they study.
13 These values were chosen to guarantee two things. First, we wanted to ensure that no one obtained a negative
payoff. Second, we wanted to make sure that the payoffs were not unduly inequitable. Often, if the payoffs are
excessively unequal, it leads to confounding changes in behavior.
14 An alternative way of designing a more effective punishment system would be to increase the multiplier on the
punishment level chosen by the citizen. However, we chose to increase the punishment options available to the
citizens since we were also interested in examining “choice set” effects. Specifically, we wish to observe whether
the availability of higher punishment amounts resulted in an increase in the frequency of subjects who chose a
punishment level P ∈ [2,8].
In both Treatments 1 and 2, the bribe is welfare-enhancing, in that the total payoff gains
to the firm and the official exceed the payoff loss to the citizen. Treatment 3 is a welfarereducing
bribe game, where the combined gains to the firm and the official are less than the
payoff loss to the citizen. Specifically, in the welfare-enhancing bribe game, each dollar offered
as a bribe, if accepted, reduces the payoff to the citizen by $1 whereas in the welfare-reducing
bribe game, it reduces the payoff to the citizen by $7. Figures 1 and 2 describe the associated
payoffs to the three players in the welfare-enhancing bribe game and the welfare-reducing bribe
game respectively. We assume that in Treatment 3, as in Treatment 2, a high punishment regime
is in effect (i.e., P ∈ [2,12]).
The distinction between welfare-enhancing and welfare-reducing corruption is one that is
frequently made in the literature (see, for example, Ali and Isse, 2003; Kaufman and Wei, 1999;
Bardhan, 1997; Nas, Price and Weber, 1986; and Lui, 1986). As an example, consider the
scenario where a firm would like to import certain goods, but it needs to obtain a license to do
so. In order to acquire the license more quickly than might otherwise be the case, the firm has to
bribe a government official. Here, although undoubtedly corrupt, the immediate social cost of
this action is possibly not very high. In contrast, consider the case where the same firm manages
to bribe its way out of complying with some environmental regulations and dumps toxic waste
into the groundwater. Our goal in running Treatments 2 and 3 is to explore whether the tendency
to engage in and punish corrupt behavior is different in the latter case, where the cost of bribery
is potentially far greater.15
15 Our approach to the welfare implications of bribery is different from that of Abbink, Irlenbusch and Renner
(2002), who examine whether behavior changes when the corrupt act imposes a negative externality (in the form of
a fixed monetary damage) on the other participants in the experiment. In contrast, we explore the welfare
implications of bribery by varying the harm it imposes on the victim. We analyse how sensitive the citizen’s
behavior is to the level of harm they experience rather than the level of harm others experience.
A change in the cost of bribery may have the following effects on subject behavior. When
the bribe is welfare-reducing, the subjects may think that it is less justified. Moreover, as the
harm imposed on the citizen increases, the citizen may choose to punish due to feelings of
negative reciprocity. Both of these effects would result in lower bribe amounts and higher
punishment amounts being chosen when the bribe is welfare-reducing. Alternatively, if the harm
imposed on the citizen is sufficiently large, the citizen may not want to punish and decrease
his/her payoff by even more. As a result, punishment may occur less frequently, and if the firms
and officials anticipate this, they may act more corruptly. Hence, whether we observe higher
levels of bribery and punishment in Treatment 3 than in Treatment 2 depends on the relative
magnitude of these effects and cannot be stated a priori.
3.3 Procedure
The experiments were run at the University of Melbourne, the Delhi School of
Economics, the University of Indonesia, and the National University of Singapore using third
year undergraduate or postgraduate students.16 We recruited students from a variety of fields of
study. In order to minimize the experimenter effects, we made sure that one of the authors (the
same one) was present in all the countries where we ran the experiment.17 All the sessions were
run as non-computerized experiments. Across all four locations, a total of 1935 subjects
participated once and only once as a firm, an official, or a citizen.
Each experiment lasted about an hour. At the beginning of each session subjects were
asked to come to a large lecture theatre. Each session consisted of at least 30 subjects. These
subjects, on entering the room, were randomly designated as either firms, officials or citizens.
The subjects participating in the experiment under the same role (as firms, officials or citizens)
16 All four universities are comparable in the sense that they are ranked among the best in their respective countries.
17 Roth et al. (1991) and Cardenas and Carpenter (2005) discuss the methodological issues arising in multi-site
were asked to sit together and in one section of the lecture theatre away from the subjects
representing the other two roles. The subjects were matched anonymously with each other, so
individual subjects were unaware of which three specific subjects constituted a particular firmofficial-
citizen trio.
At the beginning of each session, each subject received a copy of the game’s instructions,
which were then read out loud to them. They were also given a number of examples explaining
how the payoffs would be calculated for specific bribe and punishment amounts. Then, the
subjects playing the role of a firm were asked to decide whether or not to offer a bribe. If they
chose to offer a bribe, they also had to choose an amount. The record sheets with the bribe
amounts were then collected and distributed by the experimenter to the corresponding officials.
After the officials made their decisions, the corresponding citizens were informed about whether
a bribe was offered and whether it was accepted. The game ended after the citizens decided
whether to punish by choosing a punishment amount. The decisions made by all of the subjects
were entered into a spreadsheet which generated their payoffs. The subjects were paid at the end
of each session after the payoffs were converted into cash using an appropriate conversion rate,
taking into consideration purchasing power parity across the countries where the experiment was
conducted.18 Since the equilibrium payoffs were highly asymmetric across the different player
types (firm, official, and citizen), we used different conversion rates for the different types.19
These conversion rates were public information.
18 The conversion rate in each country was based on 1) the standard hourly wage paid for a student research assistant
in each country, and 2) a typical basket of goods bought by students in each country. This is similar to the procedure
used by other researchers who have conducted cross-cultural studies (e.g., Carpenter and Cardenas, 2004 and
Cardenas and Carpenter, 2005).
19 The treatments described in Section 3.2 are welfare-enhancing and welfare-reducing both before and after taking
into account the relevant conversion rates. In Australia, the conversion rates were 3 experimental currency = 1 real
currency for the firms, 2 experimental currency = 1 real currency for the officials, and 1.5 experimental currency = 1
real currency for the citizens. Each subject made on average AU$20. This amount is approximately equivalent to
US$15. In India subjects were paid an average of US$11, in Singapore US$13, and in Indonesia US$9. Davis and
Holt (1993) recommend that average payments in experiments should be high enough to compensate all participants
All the subjects filled out a demographic survey, which asked them a series of questions
regarding their age, gender, field of study, work experience, income, ethnicity, exposure to
corruption, and time spent in other countries. Those in the role of the citizen were also asked to
explain the motivation for their decisions.20
In addition to the 645 observations that we report on in the paper, we also collected data
using a neutral language treatment. We eschewed words such as “bribe” or “punishment” and
replaced them with words such as “transfer” and “forego money to reduce others’ payoff”
4. Research Questions
In the subgame perfect equilibrium of the game outlined in Section 3.1, a payoffmaximizing
citizen does not punish. Knowing this, the official accepts the bribe and the firm
offers the bribe. Moreover, the firm offers the maximum amount of bribe it can since its payoff is
increasing in the amount it offers.
Since there is ample evidence in the experimental literature that punishment takes place
even in one-shot games, we expected the citizens’ behavior to differ from the theoretical
prediction. Hence, we designed our experiment with the following research questions in mind:
(i) Do subjects in countries with higher levels of corruption offer and accept bribes more
frequently, and punish bribery less frequently than subjects in countries with lower levels
of corruption?
(ii) Is there less bribery and more punishment under a high-punishment system?
for the opportunity cost of their time (pp. 24-26). Having different conversion rates for the different player types
helped us achieve this outcome. Moreover, recruiting subjects for experiments can be very difficult if payoffs are not
within the range announced for all subjects.
20 The instruction, record and survey sheets are available from the authors upon request.
21 The neutral language treatment was conducted in Australia. A total of 231 students at the University of Melbourne
participated in this treatment, which resulted in 77 neutral language observations.
(iii) Does increasing the cost of bribery on the victim, have an impact on the propensity to
engage in and punish corrupt behavior?
5. Results
5.1 Overview of the results
Table 1 summarizes the data we collected in terms of the treatments we ran at each
location and the number of subjects involved in each treatment.22 Figure 3 provides a broad
overview of our findings, pooling across all locations and treatments. Overall 1935 subjects
participated in 645 plays of the game across all treatments since three players (a firm, an official
and a citizen) are required to generate one play of the game. As can be seen from Figure 3, in
555 out of 645 (86%) plays of the game a bribe was offered by the firm. The average amount of
the bribe for those who chose to bribe was $7.50 (in a range of 4 to 8). 482 out of 555 (87%)
officials who received a bribe chose to accept it. Both the firm’s and official’s behavior is more
or less in accordance with the theoretical predictions. However, the citizens’ behavior deviates
sharply from the theoretical prediction. 238 out of 482 (49%) citizens who were harmed by the
bribe chose to incur a pecuniary cost in order to punish the firm and the official for their
22 We do not have data for all three treatments in India, Indonesia and Singapore. Given resource constraints, we
allocated the treatments such that we could compare behavior in the different treatments using data collected from at
least one low-corruption and one high-corruption country.
23 We conducted neutral language games for Treatments 2 and 3 in Australia. Behavior in the neutral-language
treatment was closer to the subgame perfect equilibrium outcome. That is, the bribe and acceptance rates were
significantly higher and the punishment rate was significantly lower. The bribe was offered in 97% of the cases
(compared to 84% in the loaded language game). The bribe was then accepted in 95% of the cases (compared to
84% in the loaded language game). 37% of the citizens in the neutral language game who were in a position to
punish did so (compared to 53% in the loaded language game).
In the next two subsections, we present our findings in more detail. We first report the
results on the cultural effects in Section 5.2. We then consider the impact of the punishment
regime and the cost of bribery on individual behavior in Section 5.3.
The reported results are based on t-tests and multivariate regression analysis. We also
conducted non-parametric rank sum tests of differences in distribution. Unless noted in the text,
the results were the same as the ones in the reported t-tests.24 We estimated binary probit models
for the bribe, acceptance and punishment rates, and ordinary least square models for the bribe
and punishment amounts. The regression results control for several aspects of the subjects’
backgrounds based on the information collected in the surveys.
5.2 Comparing Behavior in Australia, India, Indonesia and Singapore
We first examine the decisions of the firms and the officials in Section 5.2.1 and then turn
to the decisions of the citizens in Section 5.2.2.
5.2.1 Bribe and Acceptance Behavior
We start by comparing the results from Australia and India, where we conducted the low
punishment treatment (Treatment 1). India has a significantly higher level of corruption than
Australia and we are interested in examining whether this difference is reflected in the bribe and
acceptance behavior in the two countries. Table 2 summarises the behavior of the three types of
players in each country and the results of t-tests for differences in the means. Panel (i) shows that
the bribe rate does not differ across Australia and India (p = 0.63). A bribe was offered in India
in 94% of the cases while it was offered in Australia in 96% of the cases. Similarly, we find no
significant differences in the bribe acceptance rates. The bribe was accepted in 90% of the cases
in India and in 91% of the cases in Australia. The only significant difference we find between
Australia and India is in the bribe amounts. The average bribe amount offered in India is slightly
24 The rank sum test results are available from the authors on request.
lower than that offered in Australia ($7.37 and $7.70 respectively). This difference is statistically
significant according to a test of difference of means (p = 0.03).
Table 4 presents the regression results where we pool all of the data for all of the
treatments across all of the countries and control for treatment effects as well as other variables
not accounted for in the t-tests. The results confirm the results of the t-tests.25 Columns 1-6
present the results for the bribe rate, bribe amount and acceptance rate respectively.26 Of the
variables we collected information on in the post-experimental survey, only gender, field of
study (whether it is economics), and the percentage of each Australian subject’s life that has been
spent outside of Australia were found to be significant determinants of subject behavior. The last
variable controls for the high number of foreign students that study in Australian universities.
The majority of these students come from Asia. This variable is often insignificant in explaining
behavior. This is possibly because those who choose to study in Australia are more westernised
than their counterparts and/or quickly absorb the social norms of the new environment. In the
regressions for the officials’ behavior, we also control for the bribe amount.
In Australia, Indonesia and Singapore we conducted both the welfare-enhancing and
welfare-reducing treatments (Treatments 2 and 3). Table 2, Panels (ii)-(vii) compare the means
of behavior across the Australian, Indonesian and Singaporean subjects within each treatment.
As in the case of India we find that the propensities to bribe, the bribe amounts, and the
propensities to accept in Indonesia are all similar to those in Australia. This is true for both
25 We report the results for the pooled regression model only. Unless mentioned in the text, these results were
consistent with the regression results based on data from specific countries or treatments of interest. For example,
we get the same results for the comparison of behavior in Australia and India if we estimate the regressions by using
the data for Treatment 1 in Australia and India only.
26 We also estimated ordered probit models for positive bribe amounts. These recognise that the dependent variable
is not continuous. The results were very similar to the reported results from the estimation of ordinary least squares
models. We could also have estimated tobit models. However, tobits models confound the determinants of the
choice of whether to bribe with the choice of how much to bribe, which poses a problem since we are interested in
examining the two decisions separately and the results indicate that the determinants of the two decisions differ.
treatments although the point estimates are more similar for Treatment 2 than for Treatment 3.27
In Treatment 3, they suggest a lower tolerance of corruption in Indonesia, where a smaller
percentage of the subjects offered (78% in Indonesia versus 88% in Australia) and accepted
bribes (79% in Indonesia versus 89% in Australia). However, these differences are not
statistically significant (p = 0.13 in both cases). The regression results in Table 4 also indicate
that the Indonesian subjects do not differ from the Australian subjects in terms of their
propensities to offer and accept bribes although the bribe amount is marginally smaller in
Indonesia as compared with that in Australia (significant at the 10% level).
Comparing Australia and Singapore, Table 2 shows no significant difference between the
behavior of the firms and officials in each of the treatments. However, the regression results do
identify a difference between Australia and Singapore. Table 4 shows that once we control for
other variables, the Singaporean subjects have a higher probability of offering bribes than the
Australian subjects (significant at the 5% level). They are also more likely to accept bribes as
compared to the Australian subjects (significant at the 10% level).28
The bribe and acceptance rates are also higher in Singapore than they are in Indonesia. As
shown in Table 2, Panels (vi-vii), the differences in the bribe rates are not statistically significant,
but the differences in the acceptance rates are statistically significant in both Treatments 2 and 3
(p = 0.10 and p = 0.01 respectively).
In summary, we find that the Australian, Indian and Indonesian subjects display quite
similar propensities to offer and accept bribes. The Singaporean subjects are about 8 percentage
points more likely to offer and accept bribes than the Australian subjects.
27 We discuss how subject behavior varies across welfare-enhancing and welfare-reducing treatments in Section
28 Separate regressions by treatment show that this result is driven by behavior in Treatment 2.
The coefficients on the other control variables in the regressions show that the subjects
who study economics have a higher probability of accepting bribes. This result is consistent with
the findings of Frank and Schultz (2000). The Australian subjects’ probability of offering a bribe
increases with the time they have spent outside of Australia. The probability of offering a bribe
is higher for men than for women (significant at the 10% level). Men also accepted bribes more
frequently although this difference is not statistically significant.29
5.2.2 Punishment Behavior
We find larger differences between the four locations when we consider the punishment
behavior. Comparing Australia and India within Treatment 1, Table 2, Panel (i) shows that only
24% of citizens chose to punish in India compared to 53% in Australia (p = 0.0001). The
amounts that these citizens handed out in punishment were also significantly different. In
Australia, among the subjects who chose to punish, the average punishment amount was $5.40
while in India it was $3.71 (p = 0.01). These results are confirmed in Table 4. We estimated
probit models for the punishment rates and ordinary least squares models for the punishment
amounts given out by the citizens for positive punishment amounts, controlling for the
treatments, the bribe amount, and other variables as discussed in the previous subsection.30 The
regression results show that the Indian subjects are 25 percentage points less likely to punish as
compared to the Australian subjects. This is in accordance with our expectation that in
environments characterized with higher levels of corruption, individuals are less willing to
condemn corrupt acts.
29 Recent empirical papers that have analyzed the link between gender and the level of corruption find that there
exist systematic gender differences in attitudes towards corrupt behavior. For example, Swamy et al. (2001) and
Dollar et al. (2001) suggest, on the basis of survey evidence, that women are less tolerant of corruption. We report
our findings from experiments in Alatas et al. (2005).
30 Similar to the regressions on bribe amounts, we also estimated ordered probits for positive punishment amounts,
which recognise that the dependent variable is not continuous. The results are very similar to the reported ordinary
least squares results.
The punishment behavior in Indonesia also differs from that in Australia, but suggests a
lower tolerance of corruption. Table 2, Panels (ii) and (iii) show that a greater percentage of the
subjects in Indonesia chose to punish under both Treatments 2 and 3 (73% versus 62% in
Treatment 2 and 60% versus 42% in Treatment 3). However, only the difference observed in
Treatment 3 is significant (at the 10% level). The regression results in Table 4 show that when
we pool the treatments together, the difference between the Australian and Indonesian subjects
becomes insignificant. The punishment amounts do not differ significantly either.
Table 2, Panels (iv)-(vii) show that the Singaporean subjects punished significantly less
frequently than both the Australian and Indonesian subjects in Treatment 2 (p = 0.04 and p =
0.01 respectively). However, in Treatment 3 there are no significant differences in the
punishment rates and amounts observed in the three locations.
When we pool the treatments together in the regression analysis, we find that there are no
significant differences between the Australian and Singaporean subjects. This is because the
coefficient on the Singapore dummy averages the differences between the two countries in the
punishment rates across the two treatments. However, we find that the Singaporean subjects are
17 percentage points less likely to punish than the Indonesian subjects (p = 0.03) and 20
percentage points more likely to punish than the Indian subjects (p = 0.05). The largest
difference is found between the punishment behavior of the Indian and Indonesian subjects. The
Indonesian subjects are 35 percentage points more likely to punish than the Indian subjects and
this difference is strongly statistically significant (p = 0.001).31
In summary, when we compare the punishment behavior in the four locations, we find
that, as expected, the Indian subjects are much less likely to punish than the Australian subjects.
31 These findings are from an unreported regression equivalent to that in Table 4, Panel A, but with dummies for
India, Singapore and Australia (omitting the Indonesian dummy), and with dummies for India, Indonesia and
Australia (omitting the Singapore dummy).
The Indonesian subjects however display a much lower tolerance of corruption than expected
given the high level of corruption that exists in this country. In contrast, the Singaporean subjects
appear to be more tolerant of corruption than expected. They punish less than both the Australian
(in Treatment 2) and Indonesian subjects, and more than the Indian subjects.32
One can think of “culture” as having two components – one that represents those customs
and values that ethnic and religious groups transmit relatively unchanged from generation to
generation and another that reflects the values embedded in the current institutions of the society
the individual lives in.33 Although it is difficult to differentiate between these two influences, our
data allows us to explore this issue by controlling for ethnicity. Almost all of the Singaporean
sample in our data is ethnic Chinese. Indonesia has a Chinese minority who are over-represented
in Jakarta. Of our Indonesian sample, 11.4% are ethnic Chinese. Table 5 presents regression
results where we control for the Chinese ethnicity in Indonesia and test whether the behavior of
the Chinese subjects in Indonesia differs from the behavior of the rest of the Indonesian subjects
and from the behavior of the Singaporean subjects. It shows that Chinese Indonesians punished
more frequently than other Indonesians, but that this difference is not statistically significant.
However, they were on average 42.4 percentage points more likely to punish than their
Singaporean counterparts (p = 0.03).34 These results imply that the subjects’ punishment
behavior is affected by the values embedded in the institutions of the society in which they live
rather than their ethnic background.35
32 The punishment regression results also reveal that the students majoring in economics have a significantly lower
probability of punishing. Although men in our sample punished less often, the gender difference is not statistically
significant. However, of the subjects who punish, men punish by higher amounts than women (significant at the
10% level).
33 The first of these two components is how culture is defined in Guiso, Sapienza and Zingales (2006). See Bisin and
Verdier (2001) for a model where both family and society play a role in the transmission of preferences.
34 0.285 + 0.090 – (-0.049) = 0.424
35 These results are further borne out by results from unreported regressions which control for a number of different
Indonesian ethnic groups and find no significant differences in behavior. We included controls for other sizeable
ethnic groups (other than the Malay and the Javanese). These included controls for the Batak, the Sundanese, and the
To gain some insight into whether the subjects’ reasons for punishing differ across the
four locations, we examined the citizens’ responses to the question about why they chose to
punish/not to punish in the survey given to them after the experiment. Table 6 shows our
categorization of their responses. We categorized the stated reasons for punishment into four
groups: moral responsibility, reduction of corruption, fairness, and negative reciprocity.
Similarly, the reasons for not punishing were categorized into three groups depending on
whether the subject is profit maximizing, believes that it is difficult to change the system, and
thinks that the bribe may be necessary. These categories were not mutually exclusive, so the
same person may have been counted in more than one category. We chose to create nonexclusive
categories because often it was not possible to determine one single reason for the
citizens’ behavior from the statements provided in the surveys.
The reasons given reflect both the current levels of corruption in the respective countries
and the extent of concern over the problem. In general, in countries where we observed higher
rates of punishment, the proportions of subjects who gave moral responsibility or reduction of
corruption as their reasons for punishment were higher. This implies that the punishment rates
we observed reflect the attitudes of the subjects towards corruption. For example, of the citizens
who got a chance to punish, a greater proportion of citizens in Indonesia stated that they saw
punishing as a moral responsibility (39% in Indonesia versus 14% in India, 20% in Singapore,
and 32% in Australia), or as a way to reduce corruption (20% in Indonesia versus 8% in India,
8% in Singapore, and 18% in Australia). Also, less of those who chose not to punish in Indonesia
cited individual payoff maximisation as their reason (27% in Indonesia as compared to 67.7% in
India, 48.5% in Singapore and 43% in Australia). More often, they stated a concern with the
Betawi. Although the sample sizes are small, we also compared the behavior of Indians in Singapore with the
Indians in India, and the ethnic Malays in Singapore with the Malays in Indonesia. In each case the subjects acted in
accordance with their country’s participants rather than their ethnic group.
existing level of corruption in their country, and explained their behavior by arguing that it is
necessary to bribe in the environment in which they operate or that it is difficult to change the
corrupt system.
Table 6 also shows that the citizen subjects in Singapore, when compared to those in
Australia and Indonesia, were driven to a much greater extent by personal considerations rather
than moral responsibility or an attempt to reduce corruption while choosing to punish. A
relatively low proportion of those who punished in Singapore reported doing so for reasons of
moral responsibility or to reduce corruption. They were more likely to give reasons of fairness or
negative reciprocity.
The difference between the punishment rates in the neutral language and loaded language
treatments further illustrates that the subjects’ decisions were informed by their attitudes to real
life corruption. Only 37% of the citizens in the neutral language game punished as compared to
53% in the loaded language game.
5.3 Treatment Comparisons
We next analyse how sensitive subject behavior is to the effectiveness of the punishment
regime and the cost of bribery.
5.3.1 Low versus High Punishment Regimes (Treatment 1 versus Treatment 2)
By varying the punishment regime, we are able to examine whether the existence of legal
institutions which allow citizens to punish those that engage in corrupt behavior more effectively
reduces the prevalence of corruption.
Table 3, Panel A(i) presents the means summarising the behavior of the three types of
players and the results of the t-tests for differences in means across Treatments 1 and 2 in
Australia. The results show that when the citizens are given the opportunity to punish the firms
and officials more harshly, i.e., when P ∈ [2,12] as in Treatment 2 as opposed to P ∈ [2,8] as in
Treatment 1, the possibility of a greater punishment affects the firms’ behavior. 96% of the firms
offered a bribe in the low punishment treatment while only 79% did in the high punishment
treatment. This difference is statistically significant (p = 0.01). The average bribe amount
offered, however, does not differ across the two treatments ($7.70 versus $7.65). Thus, those
firms who did offer a bribe behaved in a similar manner in both of the treatments, but many more
firms seemed to have perceived the threat of punishment by the citizens as being greater in the
high punishment regime and, therefore, preferred to refrain from offering a bribe.
The reaction from the government officials is similar. The propensity to accept a bribe is
lower when there is the possibility of a more hefty punishment (81% versus 91% with a p-value
of 0.05). These findings for the firms and officials are consistent with the results in Abbink et al.
(2002) and Barr et al. (2003). The citizens’ behavior reveals that the fear of greater punishment is
not without a basis. Under the high punishment regime more citizens chose to punish (62%
versus 53%) with the average amount being slightly higher (6.0 versus 5.4) although these
differences are not statistically significant. These results are consistent with the coefficient on the
Treatment 1 dummy in Table 4.36
5.3.2 Welfare-Enhancing versus Welfare-Reducing Bribe Games (Treatment 2 versus
Treatment 3)
The final question we address is whether behavior differs when the bribe is perceived as
being harmful, i.e., when the payoff loss to the citizen exceeds the total payoff gain to the firm
and the official. As discussed in Section 3.2, one would expect those subjects who are sensitive
to the perceived cost of bribery to offer and accept bribes less frequently and to punish more
36 Figure 4 shows the distribution of the punishment amounts chosen by the subjects in Treatments 1 and 2 in
Australia. It reveals that a larger percentage of subjects chose a punishment level P ∈ [2,7] in Treatment 2. It is also
interesting to observe how the percentage of subjects who chose P = $8 differs across the two treatments (21% in
Treatment 1 versus 0.05% in Treatment 2). This difference suggests that in Treatment 1, some of these subjects
chose $8 because they could not choose a higher amount.
frequently in Treatment 3.37 On the other hand, those citizen subjects who feel impoverished and
disempowered as a result of the high cost of bribery may choose to punish less and those firms
and officials that expect this may offer and accept bribes more frequently.
Treatments 2 and 3 were conducted in Australia, Indonesia and Singapore. We find no
significant differences in the propensities to engage in and punish corrupt behavior across the
two treatments in Indonesia and Singapore.38 In contrast, there are significant differences
between the subject behavior in the two treatments in Australia. The frequency with which a
bribe was offered was higher in the welfare-reducing game (88% versus 79%) although this
difference is significant only at the 10% level. The frequency with which the bribe was accepted
was also higher in the welfare-reducing game (89% versus 81%), but this difference is not
statistically significant. There are no significant differences in the amount of the bribe offered
($7.57 in Treatment 3 versus $7.65 in Treatment 2).
The behavior of the citizens confirmed the expectations of the firms and the officials.
They had a significantly lower propensity to punish in the welfare-reducing game (42% versus
62%, p = 0.02). Interestingly, while fewer citizens punished in the welfare-reducing bribe game,
those who did punish punished by considerably larger amounts ($7.74 versus $5.98, p = 0.06).
This suggests that while the larger harm imposed on the citizen by the bribery discouraged some
citizens from choosing to punish, those that did punish felt particularly affronted by the corrupt
37 It was clear from the survey responses that we collected and the questions we received after the experiments that
for some of the subjects the purpose of the bribe, i.e., whether it was for a “good” purpose, mattered.
38 The point estimates show that the bribe rate was higher in the welfare-enhancing treatment than in the welfarereducing
treatment in both countries. The punishment rate was lower in the welfare-enhancing treatment in
Singapore and in the welfare-reducing treatment in Indonesia. However, t-tests, Wilcoxon rank sum tests, and
regression analysis show that none of these differences are statistically significant.
39 The coefficients on the Treatment 3 dummy in Table 4 capture the average treatment effect across the three
countries. Only the coefficient in the punishment amount regression is significant. However, the t-test results are
consistent with unreported regression results examining treatment effects separately for each country.
In summary, when we consider the impact of the cost of bribery on subject behavior, we
find that the results are culture-specific. In Australia, when the cost of bribery was higher, the
propensity to engage in corrupt behavior was higher, the propensity to punish corrupt behavior
was lower, but the punishment amounts were higher. These results imply that subject behavior
was shaped more by changes in individual payoffs rather than changes in social welfare. In
Indonesia and Singapore, there were no significant differences in subject behavior across the two
treatments. This result is consistent with that in Abbink, Irlenbusch and Renner (2002), who find
that social welfare considerations have no impact on corrupt behavior. The difference in behavior
between the Australian subjects and those in the other two countries could be because subjects in
Indonesia and Singapore have more immediate experiences of the negative impact of corruption
and are, therefore, relatively more willing to condemn it in the welfare-reducing treatment.
6. Discussion
We have analysed the propensity to engage in and to punish corrupt behavior in the
context of a three-person sequential-move game in four different cultures. We find significant
cross-cultural variation in the propensity to punish corrupt transactions, but little variation in the
propensity to engage in them. This finding suggests that people may be more ready to sanction
behavior socially regarded as immoral when they see it in others or when they are victimized by
it. It is in line with the arguments made in previous studies that the extent to which individuals
care about other regarding preferences like fairness or morality may depend on whether they are
predators or potential victims (Bolton and Ockenfels, 2000 and Fehr and Schmidt, 1999).
More specifically, a comparison of the Australian and Indian results suggests that
although exposure to high levels of corruption may not change the propensity to engage in
corrupt behavior, it is associated with a greater tolerance of corrupt behavior in the sense of
lower punishment rates. In Indonesia, another country with a high level of corruption, both the
tendency to engage in and the willingness to punish corrupt behavior does not differ much from
Australia. Although we are not able to identify the cause of this effect with certainty, we
conjecture that this is due to the type of corruption that exists in Indonesia and the recent
institutional changes that have occurred in this country. Corruption in Indonesia has traditionally
been more centralized (controlled largely by the Suharto family, the military leaders, and the
ethnic Chinese-run conglomerates) while corruption in India is more fragmented (Bardhan, 1997,
p. 1325). The introduction of democracy in 1998 and the increased press freedom have resulted
in this highly visible and identifiable type of corruption that exists in Indonesia receiving a lot of
negative media attention – more than in the past and more than in India.40,41 There have been
several attempts (some successful) to prosecute high profile cronies of the previous government
who were engaged in corruption to the scale of billions of dollars and the current president was
elected largely on an anti-corruption platform. Although there is no doubt that corruption
remains high in Indonesia, our results suggest that these institutional changes may have resulted
in an increase in aversion to corruption in Indonesian society.42 This finding is also supported by
the findings of a recent Transparency International survey, which assesses people’s attitudes to
40 That corruption receives more attention in Indonesia than in India is borne out by the percentage of newspaper
articles that are devoted to the topic. In the time period April to June 2004, approximately 2 per cent of the total
number of articles in Times of India relate to domestic corruption. In Indonesia nearly 9 percent of the articles in The
Jakarta Post discussed corruption issues during this same time period. As a proportion of the number of articles on
political issues, nearly 5 percent in India were on corruption as compared to 11 percent in Indonesia. The
methodology we used to calculate these numbers is similar to the one used in Glaeser and Goldin (2004).
41 India is of course a functioning democracy with a free press, but the relatively smaller scale of high-level
corruption in India has not galvanized society to forcefully oppose corruption. Further, corruption was not a major
issue at the time when democracy was introduced in India. In contrast, corruption was one of the major causes of the
downfall of President Suharto and the advent of democracy in Indonesia.
42 Our conjecture is supported by Ferraz and Finan (2005) and Brunetti and Weder (2003). Based upon the results of
Brazil’s recent anti-corruption program, Ferraz and Finan (2005) show that the media can enable voters to hold
corrupt politicians accountable and to reward non-corrupt politicians by reducing informational asymmetries. In a
study that involves a large cross-section of countries, Brunetti and Weder (2003) find evidence of a significant
negative relationship between press freedom and corruption. Gentzkow, Glaeser and Goldin (2004) also discuss how
the rise of the informative press may have been one of the reasons why corruption declined in the US.
corruption. The results indicate that, among the 45 countries surveyed, Indonesians were the
most optimistic about corruption falling in their country in the next 3 years.43
The relatively high propensity to engage in and a low propensity to punish corrupt
behavior in Singapore suggest that attitudes towards corruption may take a long time to change.
Half a century ago the level of corruption in Singapore was comparable to that in India and
Indonesia. It has successfully eradicated corruption, but this has been achieved by the imposition
of strict and heavily-enforced anti-corruption legislation. A possible explanation for our results is
that although the strict top-down approach in Singapore for the last few decades has made
Singaporeans less tolerant of corruption (e.g., vis-à-vis Indians), the attitudinal change that
accompanies such an approach occurs only slowly.44
Our paper is a first attempt to study an extremely complex phenomenon. One possible
response to our findings is that the cross-cultural variation in our results merely reflects the
differing propensities to punish across cultures, rather than the attitudes to corruption.45 While
this is an issue worthy of additional research, for a number of reasons we believe that our results
reflect attitudes to corruption rather than punishment per se. First, as discussed in Section 5.2.2,
the reasons subjects give for punishing/not punishing reveal that the majority of subjects
specifically consider the seriousness of corruption as a blight on society and often refer to the
level of corruption in their country. More than 50% of all survey responses refer explicitly to real
life corruption in their answers. Second, our results are consistent with a number of other data
43 See Indians were found to
be among the most pessimistic.
44 That attitudes take time to change was acknowledged by one of the most successful anti-corruption bodies, the
Independent Commission Against Corruption (ICAC), which was formed in Hong Kong in 1974. Their declared
goals were: “To change people’s behavior so that they will not engage in corrupt behavior initially for fear of
detection (deterrence), later because they cannot (prevention), and yet later because they do not wish to (attitude
change).” The main difference between the ICAC and the previous approaches was that the ICAC combined new
incentives with a change in values. Its success has made Hong Kong an example of how promoting ethical values
against corruption can work.
45 Even if this were the case, the results would still be relevant to anti-corruption policy making because a society’s
low propensity for punishment makes it vulnerable to corruption.
sources. As mentioned above, Transparency International finds Indonesia to be one of the most
optimistic countries in regard to lowering corruption in the future. India is found to be one of the
most pessimistic. The World Values Survey finds that more Singaporeans say that accepting a
bribe could be justified under certain circumstances (than in the other countries in our study).46
Third, evidence from other experimental work on Indonesia using the ultimatum game has not
found that Indonesians have a higher inherent propensity to punish than other cultures (Cameron,
1999). Fourth, punishment rates in our subject pool are much higher in the loaded language
treatment than in a neutral language treatment.
Some other avenues for future research are the following. First, the results from
Indonesia and Singapore suggest that it would be valuable to do further research to define more
precisely the role institutional change plays in changing attitudes towards corruption. One way to
do this is to investigate how attitudes towards corruption change over time in a given location.
Second, it would be useful to develop theoretical models to understand the mechanism through
which institutional change may help reduce corruption. Third, further experimental research
involving other countries with different levels of corruption would also be valuable, particularly
since our results suggest that the existing corruption indices might not be fully capturing how
individuals behave in corrupt environments.
In general, the differences between our results and what one would expect to observe in
these countries based on the existing corruption indices suggest that experiments can be used as
an alternative methodology for eliciting attitudes towards corruption. Corruption is difficult to
measure because it is illegal. The most frequently used measures of corruption, such as the
Transparency International Corruption Index, measure people’s perceptions of corruption in the
46 The World Values Survey is a worldwide survey of socio-cultural and political change, conducted by an
international network of social scientists. See In response to the survey, 22.4%
of Singaporeans stated that accepting a bribe could be justified under certain circumstances, compared to 18.6% of
Indians, 17.9% of Indonesians, and 14.1% of Australians.
recent past.47 Policy makers value more forward-looking measures that assess individuals’
propensity to support anti-corruption policies in the future. Our study suggests that experimental
methodology can provide such information.
47 See “Digging for Dirt,” The Economist, March 18, 2006. Several people have raised concerns about the reliability
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Figure 1: The Welfare-Enhancing Bribe Game
Figure 2: The Welfare-Reducing Bribe Game
Punish (P)
Don’t offer bribe Offer bribe (B)
F: 60
O: 30
C: 40
Reject bribe Accept bribe
F: 60 – 2
O: 30
C: 40
Don’t punish
F: 60 + 3B – 2
O: 30 + 3B
C: 40 – B
F: 60 – 2 + 3B – 3P
O: 30 + 3B – 3P
C: 40 – B – P
Punish (P)
Don’t offer bribe Offer bribe (B)
F: 60
O: 30
C: 80
Reject bribe Accept bribe
F: 60 – 2
O: 30
C: 80
Don’t punish
F: 60 + 3B – 2
O: 30 + 3B
C: 80 – 7B
F: 60 – 2 + 3B – 3P
O: 30 + 3B – 3P
C: 80 – 7B – P
Figure 3: Overview of the Results
Figure 4: Distribution of Punishment Rates in Treatments 1 and 2 in
0 .1 .2 .3 .4 .5
0 2 4 6 8 10 12
Amount of Punishment
Treatment 1
0 .1 .2 .3 .4 .5
0 2 4 6 8 10 12
Amount of Punishment
Treatment 2
FIRM (F) [n = 645]
Punish (P)
[n = 238, 49.4%]
Don’t offer bribe
[n = 90, 14.0%]
Offer bribe (B)
[n = 555, 86.0%]
Reject bribe
[n = 73, 13.2%]
Accept bribe
[n = 482, 86.9%]
Don’t punish
[n = 244, 50.6%]
Table 1: Experimental Design
Low Punishment /
(Treatment 1)
High Punishment/
(Treatment 2)
High Punishment/
(Treatment 3)
(N = 888)
N = 279
Games = 93
N = 363
Games = 121
N = 246
Games = 82
(N = 309)
N =309
Games = 103 — —
(N = 360) — N = 180
Games = 60
N = 180
Games = 60
(N = 378) — N = 195
Games = 65
N = 183
Games = 61
(N = 1935)
N = 588
Games = 196
N = 738
Games = 246
N = 609
Games = 203
Table 2: Differences in Means (t-tests) – Cultural Effects
(Treatment 1)
(Treatment 1) p-value
% of firms bribing 95.70 94.17 0.63
Bribe amount (if >0) 7.70 7.37 0.03
% of officials accepting 91.01 89.69 0.76
% of citizens punishing 53.09 24.14 0.0001
Punishment amount (if >0) 5.40 3.71 0.01
(Treatment 2)
(Treatment 2) p-value
% of firms bribing 78.51 80.00 0.82
Bribe amount (if >0) 7.65 7.50 0.30
% of officials accepting 81.05 77.08 0.58
% of citizens punishing 62.34 72.97 0.27
Punishment amount (if >0) 5.98 5.59 0.69
(Treatment 3)
(Treatment 3) p-value
% of firms bribing 87.80 78.33 0.13
Bribe amount (if >0) 7.57 7.32 0.16
% of officials accepting 88.89 78.72 0.13
% of citizens punishing 42.19 59.46 0.10
Punishment amount (if >0) 7.74 7.36 0.76
(Treatment 2)
(Treatment 2) p-value
% of firms bribing 78.51 86.15 0.21
Bribe amount (if >0) 7.65 7.63 0.83
% of officials accepting 81.05 89.29 0.18
% of citizens punishing 62.34 44.00 0.04
Punishment amount (if >0) 5.98 7.23 0.21
(Treatment 3)
(Treatment 3) p-value
% of firms bribing 87.80 83.61 0.48
Bribe amount (if >0) 7.57 7.59 0.90
% of officials accepting 88.89 96.08 0.15
% of citizens punishing 42.19 57.14 0.12
Punishment amount (if >0) 7.74 7.11 0.58
(Treatment 2)
(Treatment 2) p-value
% of firms bribing 80.00 86.15 0.36
Bribe amount (if >0) 7.50 7.63 0.48
% of officials accepting 77.08 89.29 0.10
% of citizens punishing 72.97 44.00 0.01
Punishment amount (if >0) 5.59 7.23 0.19
(Treatment 3)
(Treatment 3) p-value
% of firms bribing 78.33 83.61 0.46
Bribe amount (if >0) 7.32 7.59 0.19
% of officials accepting 78.72 96.08 0.01
% of citizens punishing 59.46 57.14 0.83
Punishment amount (if >0) 7.36 7.11 0.84
Table 3: Differences in Means (t-tests) – Treatment Effects
A. Australia
(i) Treatment 1 Treatment 2 p-value
% firms bribing 95.70 78.51 0.0003
Bribe Amount (if >0) 7.70 7.65 0.69
% officials accepting 91.01 81.05 0.05
% citizens punishing 53.09 62.34 0.24
Punishment Amount (if >0) 5.40 5.98 0.40
(ii) Treatment 2 Treatment 3 p-value
% firms bribing 78.51 87.80 0.09
Bribe Amount (if >0) 7.65 7.57 0.49
% officials accepting 81.05 88.89 0.17
% citizens punishing 62.34 42.19 0.02
Punishment Amount (if >0) 5.98 7.74 0.06
B. Singapore
(iii) Treatment 2 Treatment 3 p-value
% firms bribing 86.15 83.61 0.69
Bribe Amount (if >0) 7.63 7.59 0.83
% officials accepting 89.29 96.08 0.19
% citizens punishing 44.00 57.14 0.19
Punishment Amount (if >0) 7.23 7.11 0.92
C. Indonesia
(iv) Treatment 2 Treatment 3 p-value
% firms bribing 80.00 78.33 0.82
Bribe Amount (if >0) 7.50 7.32 0.40
% officials accepting 77.08 78.72 0.85
% citizens punishing 72.97 59.46 0.22
Punishment Amount (if >0) 5.59 7.36 0.18
Table 4: Multivariate Regression Results – Cultural Effects
A. Australia vs. India vs. Indonesia vs. Singapore, All Treatments, Pooled Regression (Australia and Treatment 2 are the reference dummies.)
Bribe (0/1) Bribe Amount Accept (0/1) Punish (0/1) Punishment Amount
1 2 3 4 5 6 7 8 9 10
M. Effect♣ p-value Coeff p-value M. Effect♣ p-value M. Effect♣ p-value Coeff p-value
Treatment 1 0.135 0.003 ⌂ 0.028 0.83 0.075 0.09 # -0.044 0.56 -0.906 0.23
Treatment 3 0.021 0.46 -0.091 0.34 0.048 0.13 -0.078 0.18 1.148 0.05 *
India 0.026 0.71 -0.424 0.02 * -0.013 0.84 -0.248 0.01 ⌂ -2.602 0.02 *
Indonesia 0.037 0.41 -0.300 0.08 # -0.043 0.45 0.123 0.18 -1.249 0.16
Singapore 0.080 0.05 * -0.085 0.61 0.086 0.07 # -0.049 0.57 -0.512 0.56
Male 0.049 0.06 # 0.064 0.42 0. 025 0.38 -0.062 0.19 0.900 0.08 #
Econ major 0.039 0.19 0.099 0.25 0.062 0.05 * -0.159 0.003 ⌂ 0.045 0.94
% life out of Australia 0.102 0.05 * -0.122 0.47 0.001 0.63 -0.019 0.83 -1.323 0.12
Bribe amount 0.009 0.53 -0.010 0.70 0.337 0.25
Const 7.693 0.00 ⌂ 4.149 0.07 #
R-squared 0.063 0.01 0.047 0.069 0.066
N 643 553 554 481 238
♣ We report marginal effects for the probits. * (#, ⌂) denotes statistical significance at the 5% (10%, 1%) level.
Table 5: Multivariate Regression Results – Controlling for Chinese Ethnicity
Punish (0/1)
M. Effect p-value
Treatment 1 -0.0115 0.35
Treatment 3 -0.0489 0.31
India -0.243 0.01
Indonesia 0.090 0.33
Chinese Indonesian 0.285 0.13
Singapore -0.049 0.57
Male -0.068 0.15
Econ Major -0.172 0.00
% life out of Australia -0.168 0.084
Bribe amount -0.0115 0.666
Indonesian + Chinese Indonesian= Singapore
R-squared 0.073
N 481
Table 6: Survey Responses: Reasons for Punishing/Not Punishing
(as a percentage of those who had a chance to punish)
A. Reasons for punishing
Overall Location
Australia India Indonesia Singapore
Moral responsibility 27.4% 32.0% 13.8% 39.2% 20.2%
reduce corruption 14.3% 17.6% 8.0% 20.3% 8.1%
fairness 13.5% 12.2% 4.6% 23.0% 15.2%
Negative reciprocity 11.0% 9.9% 0.0% 21.6% 15.2%
B. Reasons for not punishing
profit maximizing 45.9% 43.0% 67.7% 27.0% 48.5%
difficult to change
the system or
punishment system
9.5% 5.0% 16.1% 16.2% 9.1%
bribe may be for a
good purpose or may
be necessary
1.5% 1.8% 0.0% 4.1% 0.0%
Table A1: The 2003 Corruptions Perceptions Index
1. Finland 9.7
2. Iceland 9.6
3. Denmark
New Zealand 9.5
5. Singapore 9.4

7. Netherlands 8.9
8. Australia 8.8

11. United Kingdom 8.7

18. Ireland
USA 7.5

25. Portugal 6.6

35. Italy
Kuwait 5.3

Costa Rica
South Korea

86. Russia
Mozambique 2.7

122. Indonesia
Kenya 1.9

133. Bangladesh 1.3
Source: Transparency International


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