Gender, culture, and corruption: insights from an experimental analysis.
Alatas, Vivi ; Cameron, Lisa ; Chaudhuri, Ananish 等
1. Introduction
In recent years, a substantial body of work has explored the
differences in the behavior of men and women in various economic
transactions. This paper contributes to the literature by investigating
gender differences in behavior when confronted with a common bribery
problem.
Due to the negative impact of corruption on economic development,
eliminating corruption is a major concern for many countries. Two recent
empirical papers have examined the relationship between gender and
corruption. Dollar, Fisman, and Gatti (2001) use country-level data for
a sample of more than 100 countries and find that the greater the
representation of women in the country's legislative body, the
lower the country's level of perceived corruption. This finding is
consistent with the results of Swamy et al. (2001), who use both
micro-level survey data from a range of countries and country-level
data. They also find that, on average, women are less tolerant of
corruption than men.(1)
Our study departs from these two papers by using economic
experiments, which allows us to explore individuals' attitudes
toward corruption.(2) One issue with drawing conclusions on the basis of
surveys is that actual behavior (especially when confronted with
nontrivial amounts of money) may be quite different from survey
responses. Experiments differ from surveys and perception indices in
that the participants in the experiments receive actual monetary
payments, the amounts of which depend on the decisions they make during
the experiments. Hence, we explore whether the gender differences
reported in the previous studies on corruption are also evident in an
experimental setting. (3)
Gender differences may be the result of both biological and social
differences, that is, differences in social roles of men and women. An
individual's social role and presence in the public domain may play
an important part in that individual's exposure to corruption.
Hence, if women and men differ in their social roles, one may also
expect them to differ in their attitudes toward corruption. Higher
levels of exposure to corruption in daily life may promote a tolerance
and an acceptance of corruption that are reflected in norms of behavior.
In addition, women may be more victimized by (and, therefore, less
tolerant of) corruption in countries where their presence in the public
domain is lower. (4,5)
To investigate whether consistent gender differences are evident
across countries, we conducted experiments in four countries: Australia
(Melbourne), India (Delhi), Indonesia (Jakarta), and Singapore. Two of
the countries in our sample are consistently ranked among the least
corrupt countries in the world (Australia and Singapore, with scores of
8.7 and 9.4 out of 10, respectively), and two of them are consistently
ranked among the most corrupt (India and Indonesia, with scores of 3.3
and 2.4, respectively). (6)
Our results show that the gender differences found in the previous
studies, which are largely based on data from Western countries, are
also evident in the experimental data from Australia. That is,
Australian men are more likely to engage in and be more tolerant of
corruption than are Australian women. However, we find no systematic
gender differences in the three Asian countries included in our study.
Thus, gender differences in attitudes toward corruption appear not to be
as robust as suggested by the previous evidence and may be culture
specific.
We also investigate whether cross-country variation in behavior is
similar for men and women. We find greater variation in the behavior of
women across the four countries we study than in the behavior of men.
Cross-country variation in attitudes toward corruption may reflect the
differing levels of exposure to corruption in the different countries.
(7) Women may react differently to this exposure than men since there
may be a larger variation in the social roles of women than in the
social roles of men across countries.
The paper proceeds as follows. We discuss the experimental design
in section 2 and present the results in section 3. We then discuss the
implications of our results, possible explanations for why gender
differences may vary across cultures, and avenues for future research in
section 4.
2. Experimental Design and Procedure
Since different cultures may have different perceptions of
corruption, we wanted to capture in our experimental design behavior
that would be viewed as corrupt in all of the countries included in our
study. One of the fundamental aspects of corruption is that the parties
who engage in it benefit from it at the expense of parties external to
the corrupt transaction. We wanted to examine the behavior of parties on
both sides of the corruption problem--those who are perpetuators of it
as well as those who are victims of it. Our experiment is based on a
game in which two players can act corruptly to increase their own payoff
at the expense of a third player. The bribery that takes place between
the first two players harms the third player and is illegal. Hence, the
third player, the victim, is allowed to punish the first two players at
a cost to the victim. (8)
More specifically, the experiment is based on a three-person,
sequential-move game. The first player in the game is called the firm
and is given the option to initiate a corrupt act by offering a bribe to
a government official. The second player, whom we call the official, can
either reject or accept the bribe. If the bribe is accepted, both the
firm and the official are monetarily better off at the expense of the
third player, the citizen. The citizen can, however, respond to the
corrupt act by choosing to punish both the firm and the official. The
punishment is costly to the citizen but imposes a much larger monetary
sanction on the firm and the official. (9)
This setup allows us to examine two types of behavior: (i) the
incentive to engage in a corrupt act from which one reaps benefits and
(ii) the incentive to incur a cost to punish a corrupt act that
decreases one's payoff. This distinction enables us to examine
whether individuals behave differently depending on whether they
directly benefit from a corrupt act.
Figure 1 contains an extensive-form representation of the game,
where all of the payoffs are denoted in experimental dollars. We
constrain the amount of the bribe that the firm can offer to B [member
of] [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. This cost represents, for example, the cost of finding the
right official to bribe. (10) If a bribe is offered, the official
decides whether to accept it. If the official decides to accept the
bribe, the payoffs to the firm and the official increase by 3B. The
payoff to the citizen decreases by the amount of the bribe, B. Hence,
the net benefit to the firm from paying the bribe is 3B- 2. This may,
for example, represent the benefit the firm gets from avoiding a
regulation. We assume that the official's payoff also increases by
3B, even though the amount of bribe paid by the firm is B, due to an
assumption of difference in the marginal utilities of income. Since the
income earned in 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. (11)
[FIGURE 1 OMITTED]
If a bribe has been offered and accepted, the citizen, who moves
last after observing the choices made by the firm and the official, is
given a chance to punish the firm and the official for the corrupt
transaction. The citizen can choose an amount P in punishment. Such
punishment is costly for the citizen and reduces the citizen's
payoff by the amount of the punishment, P. We assume punishment is
costly to the citizen for two reasons. First, the cost may represent the
amount of tax the citizen has to pay for a legal system to exist.
Second, it may represent the costs of filing a police report, appearing
in court, and so forth. Since in most cases these costs are much less
than the amount of punishment actually imposed on the parties, we assume
that if the citizen chooses a punishment amount of P, the firm and the
official suffer a payoff reduction of 3P.
In the subgame perfect equilibrium of this game, regardless of the
parameters chosen, a payoff-maximizing citizen chooses not to 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
because its payoff is increasing in the amount it offers.
We have deliberately chosen to conduct a one-shot game because in a
one-shot game the punishment has no economic benefit to the citizen. The
decision to punish is not affected by the anticipation of possible
future economic gains. This implies that if we observe any punishment by
the citizens, we can infer that it is motivated by either negative
reciprocity or moral considerations. Hence, with a one-shot game, a
comparison of the citizens' willingness to punish across different
countries reveals the differences in the tolerance of corrupt acts in
those countries.
The one-shot nature of the game also helps us avoid issues
associated with repeated games, such as signaling, reputation formation,
and serial correlation in decisions. Each subject in our database
participated in the experiment only once and played only one role. (12)
The subjects playing the three roles were grouped anonymously in the
experiment to avoid conscious or unconscious signaling.
The experiments were conducted at the University of Melbourne, the
Delhi School of Economics, the University of Indonesia in Jakarta, and
the National University of Singapore using third-year undergraduate or
postgraduate students. 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. (13)
All the sessions were run as noncomputerized experiments. At the
beginning of each session, subjects were asked to come to a large
lecture theater. Each session consisted of at least 30 subjects. These
subjects, on entering the room, were randomly designated as firms,
officials, or citizens. Each group was located far apart from the others
in a recognizable cluster. Thus, each group could see the members of the
other groups, but individual subjects were unaware of which three
specific subjects constituted a particular firm-official-citizen trio.
At the beginning of each session, each subject received a copy of
the 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. After they made their decision, the record sheets with the bribe
amounts were collected by the experimenter and distributed to the
corresponding officials. After the officials made their decision on
whether to accept the bribe, the record sheets of both the firms and the
government officials were given to the corresponding citizens. Hence,
the citizens learned 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. All the subjects were then asked to fill
out a demographic survey, which included questions on age, gender,
income, education stream, employment history, and frequency of exposure
to corruption. Those in the role of the citizen were also asked to
explain the motivation for their decision.
Each experiment lasted about an hour. At the end of each session
the decisions made by all of the subjects were entered into a
spreadsheet that generated their payoffs. 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. (14) These conversion rates were public
information. To guarantee parity in the payoffs to the different types
of players (firm, official, and citizen), we used a different conversion
rate for each type. (15)
3. Results
With our experimental design, we are interested in exploring two
issues. In the first subsection we start by investigating whether,
controlling for culture (i.e., within each country), women are less
tolerant toward corruption than men. We then control for gender in the
second subsection and investigate whether larger cross-country
variations exist in the behavior of women than in the behavior of men in
the context of our game.
A total of 1326 subjects participated in the experiments. Of these,
596 (45%) were men. The number of participants in Australia, India,
Indonesia, and Singapore were 642, 309, 180, and 195, respectively. (16)
We report results based on t-tests and multivariate regression
analysis, where we estimated binary probit models for the bribe,
acceptance, and punishment rates and ordinary least square models for
the bribe and punishment amounts. (17) The regression results control
for variables not accounted for in the t-tests, such as field of study
(whether it is economics) and percentage of each Australian
subject's life that has been spent outside of Australia. (18) Of
the variables on which we collected information in the surveys, these
were the only ones that were found to be consistently significant
determinants of subject behavior. In the regressions for the
officials' and citizens' behavior, we also control for the
bribe amount.
The reported results are based on two different treatments that
were conducted. In the Indian experiments and a subset of the Australian
sessions, the citizens' punishment range was restricted to P
[member of] [2, 8]. (19) We refer to this treatment as Treatment 1. In
the other countries and the remaining Australian sessions, the
punishment range was extended to P [member of] [2, 12]. (20) This is
Treatment 2. The t-tests, the results of which are shown in the tables
below, make comparisons within treatment, and the regression results
include a control for treatment. The variation in treatment design
enabled us to examine the effectiveness of the punishment regime. We
discuss the treatment effects in detail in Cameron et al. (2006). Since
the focus of the current paper is gender differences and because gender
differences do not vary across the treatments, we do not discuss the
treatments effects here. (21)
Are Women Less Tolerant of Corruption than Men?
As stated above, both Dollar, Fisman, and Gatti (2001) and Swamy et
al. (2001) find that women are less tolerant of corruption than are men.
Within the design of our experiment, this finding is equivalent to
asking whether female participants in the four countries in which we ran
our experiment had a lower propensity to pay bribes, a lower propensity
to accept bribes, and a higher propensity to punish bribery than the
male participants.
Table 1 presents the results of t-tests for differences in the
means of the behavior of the male and female participants in the three
roles. Panel A of Table 1 pools the data and shows that overall the male
participants have a higher propensity to bribe than the female
participants (p = 0.04) but shows no other statistically significant
gender differences in behavior. However, if we break the data down by
individual countries (Panels B-E), we observe that the difference in the
bribe rates is driven by Australia. In Australia, 91.6% of male
participants offered bribes, compared with 80.4% of female participants
(p = 0.02). In none of the other countries do we see any significant
gender differences in the propensities to offer bribes. Further, in
Australia, the male subjects also had higher acceptance rates and lower
punishment rates than the female subjects. The bribe was accepted 92.1%
of the time when it was offered to a male participant in Australia,
while it was accepted 80% of the time when it was offered to a female
participant. This difference is statistically significant according to a
test of difference of means (p = 0.02). The Australian male participants
in the role of the citizen chose to punish 49.2% of the time, while the
Australian female participants chose to punish 62.6% of the time. This
difference is marginally significant at the 10% level.
In India, Indonesia, and Singapore, we find no significant
differences in the behavior of the male and female participants in the
three roles. The point estimates also do not vary systematically by
gender. For example, in India, men bribe more often, but also punish
more often.
The regression results presented in Table 2 confirm the results
from the t-tests. Panel A pools all the data across all the countries.
Overall, men offer bribes with a higher frequency (significant at the 5%
level) and punish corrupt acts by higher amounts (significant at the 10%
level). In Panel B, the effect of gender is allowed to differ by
country. For example, the coefficient on the variable male-Australia
captures the difference between men and women in Australia. The results
show that in Australia men bribe approximately 8 percentage points more
often, accept bribes approximately 8 percentage points more often, and
punish bribery about 14 percentage points less often than women.
However, if the Australian men do punish, they do so by a larger amount
than the Australian women. In the other countries, no significant gender
differences are seen in the bribe, acceptance, and punishment rates. The
only significant differences we find are in the bribe and punishment
amounts. Specifically, the Indian male subjects, when they bribe, offer
larger bribes than the Indian female subjects, and the Indonesian male
subjects, when they punish, choose higher punishment amounts than the
Indonesian female subjects.
A possible criticism of our results is that the difference we
observe in the behavior of men and women in Australia may be the result
of gender differences in other-regarding preferences, such as inequity
aversion, or in motivations for punishment, such as negative
reciprocity. To examine this issue further, we conducted a set of
experiments with Australian subjects using neutral language, where we
replaced the words "bribe" and "punishment" with
"transfer" and "forgo money to reduce others'
payoff," respectively. (22) Moreover, instead of designating
different types of players as firms, officials, and citizens, we
referred to them as players 1, 2, and 3.
Table 3 presents the results from these experiments. In comparison,
both genders offer and accept transfers more often in the
neutral-language treatment than they offer and accept bribes in the
loaded-language treatment. They also punish less often. However, the
gender differences in behavior are much less in the neutral-language
treatment than they are in the loaded-language treatment. In the
neutral-language treatment, women's propensity to offer a transfer
is not significantly different from the men's (100% of the time
instead of 94%). Their propensity to punish is not different, either
(30% in both cases). These results suggest that the use of loaded
language stimulates a reaction to corruption and that Australian women
react more strongly against a corrupt transaction than do Australian
men.
The only exception is in the acceptance rates. Women still accept
less often than men (85% vs. 100%), and the difference remains
statistically significant (p = 0.08). It is not clear why the acceptance
rate decision would differ from the other two decisions. If women are
more risk averse or more concerned about fairness than men, this would
also lead them to "bribe" less often in the neutral-language
treatment, which they do not do. The difference in the decision to
accept is driven by the behavior of only four (out of 26) women. In
fact, if we group the decision to offer and accept a transfer together,
we find that the probability of engaging in a transaction to increase
one's own payoff at the expense of another player is very similar
across the genders (92% for women vs. 97% for men, p = 0.34). Doing the
same exercise with the loaded-language data reveals that the difference
is large and statistically significant (75% for women vs. 87% for men, p
= 0.03). Hence, we conclude that the neutral-language treatment supports
our contention that the gender differences we observe in the
loaded-language experiments reflect different reactions to the corrupt
context. (23,24)
Does the Cross-Country Variation in Behavior Differ by Gender?
Our finding in the previous section is that the differences between
men and women do not necessarily lead to statistically significant
behavioral differences in terms of corruption. Another way to think of
the impact of social roles is to observe how it affects the behavior of
one gender across countries. To determine this impact, we start by
discussing the variations in the behavior of men. Table 4A, Panels i-iv,
compares the means of behavior across the Australian, Indian,
Indonesian, and Singaporean male subjects. These pairwise country
comparisons show no significant differences in the propensities to
bribe, the bribe amounts, and the propensities to accept. Hence, in
terms of the propensities to engage in corrupt behavior, the male
subjects in all four countries display similar tendencies.
It is only when we consider the propensities to punish corrupt
behavior that we see some significant differences in the behavior of
male subjects in the four countries. Specifically, the Indonesian male
subjects have the highest rate of punishment, followed by the Australian
male subjects (76.5% and 50%, respectively). This difference is
significant at the 10% level. The Singaporean male subjects punished in
39.1% of the cases. Although their rate of punishment is not
statistically significantly different from that of the Australian male
subjects (p = 0.46), it is significantly less than that of the
Indonesian male subjects (p = 0.02). The Indian male subjects have the
lowest punishment rate of all (27.3%), which is significantly less than
the punishment rate of the Australian male subjects (p = 0.06).
The regression results presented in Table 2, Panel C, confirm the
results from the t-tests. (25) We test for equality of coefficients
across the four countries for each gender. As shown in the table, the
tests indicate that we are unable to reject the hypothesis that male
behavior in each of the countries is the same, except in the case of
punishment rates (p = 0.08). In the case of punishment rates, the
regression results show that, once we control for the field of study
(that is, whether it is economics), the percentage of each Australian
subject's life that has been spent outside of Australia, and
treatment effects, the punishment behavior of the male subjects in
Australia is not significantly different from that in any of the other
countries. However, since the male subjects in Indonesia have
significantly higher rates of punishment than those in India and
Singapore, we get the result that the coefficients in this case are not
equal to each other. (26)
In contrast, the t-tests reported in Table 4B and regression
results reported in Table 2, Panel C, reveal differences in female
behavior across the four countries in all categories of comparison.
Testing for equality of regression coefficients, we find that female
behavior varies across the four countries in the case of bribe rates,
bribe amounts, and punishment rates. All of these differences are
significant at the 5% level. In the case of acceptance rates and
punishment amounts, we are only narrowly unable to reject a hypothesis
of equality of coefficients at the 10% level (with p-values of 0.12 and
0.11, respectively). Moreover, unreported pairwise tests of the
regression coefficients show that the acceptance rate in Singapore is
significantly higher than that in each of the other three countries.
The magnitude of the cross-country variation in female behavior is
quite large. For instance, the regression results show that the female
bribe rate in Australia is 16.6 percentage points lower than that in
Indonesia and 17.2 percentage points lower than that in Singapore (p =
0.02 and p = 0.007, respectively). Similarly, the female acceptance rate
in Singapore is 19.7 percentage points higher than that in Australia,
15.2 percentage points higher than that in India, and 12.9 percentage
points higher than that in Indonesia (with p = 0.016, p = 0.089, and p =
0.089, respectively). (27)
In summary, we find less cross-country variation in the behavior of
men than in the behavior of women. When we compare the behavior of the
male subjects, we find significant differences only in the propensity to
punish corrupt behavior. In contrast, when we compare the behavior of
the female subjects, we find significant differences in both the
propensity to engage in corrupt behavior (the bribe rate and amount) and
the propensity to punish corrupt behavior. Overall, the Australian
female subjects seem to have the lowest tolerance of corrupt behavior.
4. Discussion
Our goal in this paper was to examine gender differences in
behavior when confronted with a common bribery problem. We explored two
issues. First, we investigated whether women are less likely to offer
bribes and more likely to punish corrupt behavior. We find this to be
the case in only one of the four countries studied--Australia. We do not
find significant gender differences in India, Indonesia, or Singapore.
The results for the only Western country in our study are similar
to those found in the existing literature. In both Dollar, Fisman, and
Gatti (2001) and Swamy et al. (2001), the Western countries make up a
large part of their sample. (28,29) Our findings suggest that the gender
differences found in these previous studies may be culture specific.
This is important because the gender differences found in the previous
studies on corruption have prompted policy makers in many developing
countries to recommend higher rates of female participation in the
political and economic institutions. Our results indicate that, although
there may be other valid reasons for advocating policy measures that
promote female political involvement, some caution needs to be taken in
asserting that increased female participation will lower corruption in
all countries. (30,31)
Further work is needed to understand the reasons for the variations
in gender differences in attitudes toward corruption across countries
and to establish in which countries gender differences exist. It is
possible that countries with different cultural backgrounds display
gender differences to different degrees. For example, Gneezy, Leonard,
and List (2006) find that the gender differences in attitudes toward
competition that are observed in the Western countries are reversed in
matrilineal societies. Their results provide insights into how the
existing societal structure is crucially linked to the observed gender
differences in competitiveness. In the context of corruption, one
possible explanation for the different gender effects that are observed
in our data is the differing social roles of women across cultures. In
relatively more patriarchal societies where women do not play as active
a role in the public domain, women's views on social issues may be
influenced to a greater extent by men's views. In such societies,
one would expect to see less of a gender difference in behavior toward
corruption in comparison with societies where women feel more
comfortable in voicing their own opinions. (32)
The second issue we investigated is whether cross-country variation
in behavior is similar for men and women. The behavior of the male
subjects was shown to be quite similar in all four countries. In
contrast, important differences are seen in the behavior of the female
subjects across the four countries. One possible explanation for these
results is that greater variations exist in women's social roles
across countries than in men's. Understanding why the cross-country
variation in attitudes toward corruption differs by gender is another
important agenda for future research.
Appendix. The 2006 Corruption Perceptions Index
Rank Country Score
1 Finland 9.6
Iceland
New Zealand
4 Denmark 9.5
5 Singapore 9.4
9 Australia 8.7
Netherlands
11 Austria 8.6
Luxembourg
United Kingdom
20 Belgium 7.3
Chile
USA
45 Italy 4.9
54 Greece 4.4
70 Brazil 3.3
China
Egypt
Ghana
India
Mexico
Peru
Saudi Arabia
Senegal
130 Azerbaijan 2.4
Burundi
Central African Republic
Ethiopia
Indonesia
Papua New Guinea
Zimbabwe
163 Haiti 1.8
Source: Transparency International (2006).
We are grateful to the World Bank, the 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, Jonathan Thong, and Vicar Valencia have
provided excellent research assistance.
Received April 2007; accepted December 2007.
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Vivi Alatas, * Lisa Cameron, ([dagger]) Ananish Chaudhuri, ([double
dagger]) Nisvan Erkal, ([section]) and Lata Gangadharan ([parallel])
* World Bank, Jakarta 12190, Indonesia; E-mail
valatas@worldbank.org.
([dagger]) Department of Economics, University of Melbourne, VIC
3010, Australia; E-mail lcameron@unimelb.edu.au.
([double dagger]) Department of Economics, University of Auckland,
Private Bag 92019, Auckland, New Zealand; E-mail
a.chaudhuri@auckland.ac.nz.
([section]) Department of Economics, University of Melbourne, VIC
3010, Australia; E-mail n.erkal@unimelb.edu.au; corresponding author.
([parallel]) Department of Economics, University of Melbourne, VIC
3010, Australia; E-mail latag@unimelb.edu.au.
(1) Their micro-level data are based on surveys that ask
respondents about the acceptability of various dishonest or illegal
behaviors. They find that a larger proportion of women than men believe
that illegal or dishonest behavior is never justifiable. These results
are consistent with those of Glover et al. (1997) and Reiss and Mitra
(1998), who find that gender affects whether an individual regards
certain workplace behavior as unacceptable.
(2) In the experimental literature, behavioral differences between
men and women have been studied using public goods, ultimatum, dictator,
and trust games. The results have been mixed, with some studies
suggesting that women are more socially oriented, others finding that
men are more socially oriented, and still others finding no significant
gender differences. See, for example, Brown-Kruse and Hummels (1993),
Nowell and Tinker (1994), Bolton and Katoc (1995), Cadsby and Maynes
(1998), Eckel and Grossman (1996, 1998). Andreoni and Vesterlund (2001),
and Solnick (2001). Croson and Gneezy (2005) provide an excellent
survey.
(3) There is a growing literature that analyzes corruption using
experimental methodology. See Abbink (2005) for a survey. However,
except for Frank and Schulze (2000), none of these papers explores the
relationship between gender and corruption. Frank and Schulze (2000)
analyze whether economists behave in a more self-interested way than
other people. They find that economics students are significantly more
corrupt than others, with male economists being the most corrupt and
male noneconomists the least corrupt.
(4) Although all of the participants in our experiments were
upper-level undergraduate or graduate students, their expectations and
attitudes would nevertheless be influenced by the differing roles of men
and women in their society.
(5) We discuss in section 4 possible explanations for why gender
differences may vary across cultures.
(6) These rankings are based on the Corruption Perceptions Index
(CPI), released annually by Transparency International. The CPI ranks
countries in terms of the degree to which corruption is perceived to
exist among politicians and public officials, based on the views of
analysts and business people around the world. See
www.transparency.org/policy_research/surveys_indices for more
information. The Appendix contains a selective list of country rankings
from the latest (2006) Corruption Perceptions Index.
(7) See Cameron et al. (2006) for a detailed discussion of how
attitudes toward corruption vary across the four countries considered in
this study.
(8) Note that the World Values Survey also assesses the attitudes
of people in different countries by asking their views on how
justifiable it is to accept a bribe. See www.worldvaluessurvey.org/.
(9) We chose to use emotive terms such as "bribe" and
"punishment" in the instructions because our aim was to
simulate a real-life corrupt transaction. 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. On the other hand, Abbink and
Hennig-Schmidt (2002) find that the use of words like "bribe"
does not make a difference in the corruption game that they study.
(10) To offer a bribe, firms usually have to incur some transaction
costs. These costs are usually constant and have to be incurred
irrespective of the size of the bribe being offered.
(11) The choice of multipliers has the additional advantage of
helping us prevent negative total payoffs.
(12) One standard response in cases such as these is to have random
rematching of subjects. Kandori (1992) states that it is not clear
whether random rematchings actually succeed in eliminating supergame
effects. However, Duffy and Ochs (2005) consider an experiment with an
indefinitely repeated two-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.
(13) See Roth et al. (1991) and Cardenas and Carpenter (2005) for a
discussion of the methodological issues arising in multisite
experiments.
(14) The conversion rates in each country were based on (i) the
standard hourly wage paid for a student research assistant in each
country and (ii) a typical basket of goods bought by students in each
country. This approach is similar to the procedure used by other
researchers who have conducted cross-cultural studies (e.g., Carpenter
and Cardenas 2004; Cardenas and Carpenter 2005).
(15) In Australia, the conversion rates were 3 experimental
currency = 1 real currency for the firms, 2 experimental currency = I
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.
Different conversion rates for different player types are sometimes used
in experiments if the payoffs are expected to be very different across
the subjects. Davis and Holt (1993) recommend that average payments in
experiments should be high enough to compensate all participants for the
opportunity cost of their time. Having different conversion rates for
different types of players helped us achieve this goal because the
equilibrium payoffs were highly asymmetric across the different player
types (firm, official, and citizen) in the experiment.
(16) In Australia, 107 men and 107 women made decisions as firms,
89 men and 95 women as officials, and 59 men and 99 women as citizens.
In India, 49 men and 54 women made decisions as firms, 39 men and 58
women as officials, and 44 men and 43 women as citizens. In Indonesia,
32 men and 28 women made decisions as firms, 22 men and 26 women as
officials, and 17 men and 20 women as citizens. In Singapore, 23 men and
42 women made decisions as firms, 26 men and 30 women as officials, and
23 men and 27 women as citizens. As is clear from the design of the
experiment, not all officials and citizens got the opportunity to make a
decision, which is the reason we did not have a complete gender balance
across the different roles.
(17) We also estimated ordered probit models for positive bribe and
punishment amounts. These models recognize that the dependent variable
is not continuous. The results were very similar to the reported results
from the estimation of ordinary least squares models.
(18) The last variable controls for the high number of foreign
students who study in Australian universities. The majority of these
students come from Asia. We find this variable to be insignificant in
explaining behavior in most of the regressions. This outcome is possibly
because those who choose to study in Australia are more Westernized than
their counterparts and/or quickly absorb the social norms of the new
environment.
(19) Due to resource constraints, we do not have data for all
treatments from all the countries.
(20) These values were chosen to guarantee two outcomes. First, we
wanted to ensure that no one obtained a negative payoff. Second, we
wanted to make sure that the average earning was high enough to offset
the participants' opportunity cost of time (Davis and Holt 1993).
(21) Cameron et al. (2006) also present and discuss results from a
third treatment. 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. In Treatment 3, the payoffs are
altered so that the combined gains to the firm and the official are less
than the payoff loss to the citizen. Hence, the bribe is welfare
reducing. Since the gender differences are similar across all three
treatments, we chose not to discuss Treatment 3 in this paper for ease
of exposition.
(22) The neutral-language experiments were run with Treatment 2
only, in which a larger range of punishments was allowed.
(23) As further evidence, we also checked for any significant
gender differences in the reasons the citizens gave for their decision
to punish in the post-experimental survey we asked them to fill out. If
it is the case that Australian women differ from Australian men in terms
of their other-regarding preferences or motivations for punishment, one
would expect them to cite reasons of fairness or negative reciprocity
more frequently while explaining their decision to punish. However, we
find this not to be the case. On the contrary, the Australian women cite
punishing for moral reasons more often than the Australian men (39% of
the female citizens who had the chance to punish vs. 25% of the male
citizens who had the chance to punish). The difference is statistically
significant with a p-value of 0.07. Hence, our view that it is the
Australian women's lower tolerance of corruption that causes the
gender differences in behavior was further strengthened.
(24) Note that it is possible that men and women react differently
to the framed context and that what we observe is not the real
difference in their tolerance of corruption. Although it is not clear
why this would be the case, we cannot rule this reason out as a possible
explanation of the gender difference we observe.
(25) These results are the same as those presented in Table 2,
Panel B. However, they are configured (by interacting both the male and
female dummies with the country dummies) to enable an easier
interpretation of within-gender cross-country differences.
(26) The pairwise regression tests give p-values of 0.058 and
0.028, respectively. The high rate of punishment we observe among the
Indonesian male subjects is an unexpected outcome given the high level
of corruption in this country. One possible explanation for this outcome
is the recent institutional changes that have occurred in Indonesia.
Since the introduction of democracy in Indonesia in 1998 and the
relaxation of media restrictions, corruption has received a lot more
negative media attention. This trend may have resulted in a hardening of
attitudes against corruption. See Cameron et al. (2006) for a more
detailed discussion of this point.
(27) As explained in Cameron et al. (2006), one possible
explanation for the relatively higher tolerance of corruption we find in
Singapore is the top-down policy approach that has been adopted in this
country. Such an approach could have had the effect of eradicating
corruption at a faster rate than it takes to fundamentally change
society's social norms.
(28) Swamy et al. (2001) present some results disaggregated to the
country level. Interestingly, scrutiny of these results reveals no
gender differences in tolerance of corruption in the three Asian nations
in their sample (China, India, and South Korea). This is also true of
Nigeria, the only African nation, other than South Africa, in their
sample.
(29) Most of the previous experimental studies that have examined
behavioral gender differences have been based on data from the Western
nations, with the majority being from the United States.
(30) See Duflo (2005) for a discussion of the various reasons for
reserving positions for groups that are perceived as being
disadvantaged.
(31) In fact, the World Values Survey (WVS, available at
www.worldvaluessurvey.org/), which asks respondents whether someone
accepting a bribe is acceptable, yields results consistent with ours.
Specifically, the WVS also shows that while the Australian women are
significantly less tolerant of corruption than the Australian men (88%
of the women stated that accepting a bribe is never acceptable vs. 83%
of the men, p < 0.01), no statistically significant gender
differences are seen in India and Singapore. However, according to the
WVS, the Indonesian women are significantly less tolerant of corruption
than the Indonesian men (86% vs. 79%, p < 0.01). The WVS was also
conducted in Vietnam, the Philippines, Bangladesh, and China, where the
results again yield no statistically significant gender differences.
These figures are all for the most recent survey conducted in each
country.
(32) See, for example, Chan (2000), Ganguly-Scrase (2000), and
Bessell (2005) for discussions of the limited roles of women in the
public domain in Singapore, India, and Indonesia, respectively.
Australia, in contrast, has historically had a pioneering role in the
advancement of women's rights (Sawer 1994). See also Nelson and
Chowdhury (1994) for a discussion of the variation in women's
attitudes toward participation and activism in societal affairs across
different cultures.
Table 1. Gender Differences
A. All Countries, Treatments 1 and 2
Male Female p-value
% firms bribing 90.52 83.98 0.04
Bribe amount (if >0) 7.59 7.55 0.63
% officials accepting 88.64 84.21 0.21
% citizens punishing 44.06 51.85 0.16
Punishment amount (if >0) 6.05 5.37 0.24
B. Australia, Treatments 1 and 2
Male Female p-value
% firms bribing 91.59 80.37 0.02
Bribe amount (if >0) 7.63 7.72 0.42
% officials accepting 92.13 80.00 0.02
% citizens punishing 49.15 62.63 0.10
Punishment amount (if >0) 6.48 5.34 0.12
C. India, Treatment 1
Male Female p-value
% firms bribing 95.92 92.59 0.48
Bribe amount (if >0) 7.57 7.18 0.10
% officials accepting 89.74 89.66 0.99
% citizens punishing 27.27 20.93 0.50
Punishment amount (if >0) 3.25 4.33 0.30
D. Indonesia, Treatment 2
Male Female p-value
% firms bribing 78.13 82.14 0.70
Bribe amount (if >0) 7.40 7.61 0.47
% officials accepting 77.27 76.92 0.98
% citizens punishing 76.47 70.00 0.67
Punishment amount (if >0) 7.00 4.29 0.12
E. Singapore, Treatment 2
Male Female p-value
% firms bribing 91.30 83.33 0.38
Bribe amount (if >0) 7.67 7.60 0.77
% officials accepting 84.62 93.33 0.30
% citizens punishing 39.13 48.15 0.53
Punishment amount (if >0) 7.00 7.38 0.82
Table 2. Multivariate Regression Results
A. Pooled Regression Results
Bribe (0/1) Bribe Amount (>0)
1 2 3 4
M.
effect
(a) p-value Coeff. p-value
India 0.059 0.32 -0.456 0.03 **
Indonesia 0.073 0.08 * -0.254 0.23
Singapore 0.105 0.00 *** -0.096 0.64
Male 0.063 0.04 ** 0.089 0.35
Economics major 0.026 0.42 0.200 0.05 **
% life out of Australia 0.148 0.01 *** -0.119 0.55
Treatment 1 0.148 0.00 *** 0.031 0.82
Bribe amount
Constant 7.641 0.00 ***
R-squared 0.102 0.012
N 440 383
Accept (0/1) Punish (0/1)
5 6 7 8
M. M.
effect effect
(a) p-value (a) p-value
India 0.012 0.86 -0.277 0.01 ***
Indonesia 0.025 0.68 0.045 0.72
Singapore 0.100 0.06 * -0.224 0.04 **
Male 0.035 0.31 -0.062 0.29
Economics major 0.082 0.03 ** -0.159 0.01 ***
% life out of Australia 0.092 0.14 -0.060 0.56
Treatment 1 0.090 0.08 * -0.105 0.20
Bribe amount -0.007 0.71 -0.035 0.27
Constant
R-squared 0.056 0.102
N 384 332
Punishment Amount (>0)
9 10
Coeff. p-value
India -2.154 0.05 **
Indonesia -1.068 0.30
Singapore 0.665 0.53
Male 1.008 0.08 *
Economics major -0.380 0.58
% life out of Australia -0.730 0.42
Treatment 1 -0.741 0.32
Bribe amount 0.191 0.55
Constant 4.797 0.05 **
R-squared 0.046
N 161
B. Pooled Data, Gender-Country Interaction (Australian
female subjects are the reference category)
Bribe (0/1) Bribe Amount (>0)
1 2 3 4
M.
effect
(a) p-value Coeff. p-value
India 0.074 0.26 -0.725 0.00 ***
Indonesia 0.105 0.02 * -0.179 0.49
Singapore 0.110 0.01 *** -0.181 0.45
Male-Australia 0.083 0.02 * -0.044 0.74
Male-India 0.048 0.44 0.472 0.01 ***
Male-Indonesia -0.030 0.68 -0.203 0.43
Male-Singapore 0.060 0.33 0.110 0.66
Economics major 0.027 0.39 0.198 0.05 *
% life out of Australia 0.152 0.01 *** -0.135 0.50
Treatment 1 0.145 0.00 *** 0.040 0.76
Bribe amount
Const 7.719 0.00 ***
R-squared 0.110 0.022
N 440 383
Accept (0/1) Punish (0/1)
5 6 7 8
M. M.
effect effect
(a) p-value (a) p-value
India 0.036 0.61 -0.367 0.00 ***
Indonesia 0.051 0.44 -0.047 0.76
Singapore 0.135 0.02 * -0.237 0.07 **
Male-Australia 0.084 0.06 ** -0.143 0.08 **
Male-India -0.024 0.73 0.073 0.54
Male-Indonesia -0.008 0.92 0.070 0.69
Male-Singapore -0.121 0.30 -0.101 0.48
Economics major 0.083 0.03 * -0.160 0.01 ***
% life out of Australia 0.078 0.21 -0.063 0.54
Treatment 1 0.077 0.13 -0.101 0.22
Bribe amount -0.007 0.72 -0.035 0.28
Const
R-squared 0.069 0.108
N 384 332
Punishment Amount (>0)
9 10
Coeff. p-value
India -0.95 0.50
Indonesia -1.85 0.12
Singapore 1.33 0.28
Male-Australia 1.34 0.09 **
Male-India -0.95 0.54
Male-Indonesia 2.74 0.04 *
Male-Singapore 0.497 0.74
Economics major -0.364 0.59
% life out of Australia -0.793 0.38
Treatment 1 -0.777 0.29
Bribe amount 0.192 0.55
Const 4.728 0.06 **
R-squared 0.055
N 161
C. Pooled Data, Gender-Country Interaction (Australian
male subjects are the reference category)
Bribe (0/1) Bribe Amount (>0)
1 2 3 4
M.
effect
(a) p-value Coeff. p-value
Female-Australia ([a.sub.1]) -0.117 0.02 * 0.044 0.74
Female-India ([a.sub.2]) -0.011 0.89 -0.681 0.00 ***
Female-Indonesia ([a.sub.3]) 0.049 0.38 -0.135 0.60
Female-Singapore ([a.sub.4]) 0.055 0.28 -0.137 0.55
Male-India ([[beta].sub.1]) 0.040 0.59 -0.209 0.36
Male-Indonesia ([[beta.sub.2]) 0.029 0.62 -0.338 0.18
Male-Singapore ([[beta].sub.3]) 0.089 0.08 ** -0.027 0.92
Economics major 0.027 0.39 0.198 0.05 *
% life out of Australia 0.152 0.01 *** -0.135 0.50
Treatment 1 0.145 0.00 *** 0.040 0.76
Bribe amount
Const 7.719 0.00 ***
Tests:
Female: ([[alpha].sub.1] = 0.04 * 0.02 *
[[alpha].sub.2] =
[[alpha].sub.3] =
[[alpha].sub.4])
Male: ([[beta].sub.1] = 0.35 0.48
[[beta].sub.2] =
[[beta].sub.3])
R-squared 0.110 0.022
N 440 383
Accept (0/1) Punish (0/1)
5 6 7 8
M. M.
effect effect
(a) p-value (a) p-value
Female-Australia ([a.sub.1]) -0.112 0.06 ** 0.145 0.08 **
Female-India ([a.sub.2]) -0.067 0.46 -0.237 0.06 **
Female-Indonesia ([a.sub.3]) -0.044 0.64 0.098 0.53
Female-Singapore ([a.sub.4]) 0.085 0.24 -0.102 0.47
Male-India ([[beta].sub.1]) -0.036 0.69 -0.172 0.17
Male-Indonesia ([[beta].sub.2]) -0.054 0.59 0.166 0.31
Male-Singapore ([[beta].sub.3]) 0.016 0.85 -0.196 0.17
Economics major 0.083 0.03 * -0.160 0.01 ***
% life out of Australia 0.078 0.21 -0.063 0.54
Treatment 1 0.077 0.13 -0.101 0.22
Bribe amount -0.007 0.72 -0.035 0.28
Const
Tests:
Female: ([[alpha].sub.1] = 0.12 0.01 ***
[[alpha].sub.2] =
[[alpha].sub.3] =
[[alpha].sub.4])
Male: ([[beta].sub.1] = 0.86 0.08 **
[[beta].sub.2] =
[[beta].sub.3])
R-squared 0.069 0.108
N 384 332
Punishment Amount (>0)
9 10
Coeff. p-value
Female-Australia ([a.sub.1]) -1.340 0.09 **
Female-India ([a.sub.2]) -2.300 0.13
Female-Indonesia ([a.sub.3]) -3.190 0.02 *
Female-Singapore ([a.sub.4]) -0.019 0.99
Male-India ([[beta].sub.1]) -3.244 0.02 *
Male-Indonesia ([[beta].sub.2]) -0.452 0.74
Male-Singapore ([[beta].sub.3]) -0.507 0.74
Economics major -0.364 0.59
% life out of Australia -0.793 0.38
Treatment 1 -0.777 0.29
Bribe amount 0.192 0.55
Const 4.728 0.06 **
Tests:
Female: ([[alpha].sub.1] = 0.11
[[alpha].sub.2] =
[[alpha].sub.3] =
[[alpha].sub.4])
Male: ([[beta].sub.1] = 0.14
[[beta].sub.2] =
[[beta].sub.3])
R-squared 0.055
N 161
(a) We report marginal effects for the probits. *, **, and *** denote
statistical significance at the 5%, 10%, and 1% level, respectively.
Table 3. Neutral versus Loaded Language (Australia, Treatment 2)
Loaded Language
Male Female p-value
% firms offering a bribe (transfer) 87.3 71.2 0.032
Bribe (transfer) amount (if >0) 7.67 7.64 0.85
% officials accepting 85.7 78.3 0.38
% citizens punishing 50.0 68.6 0.11
Punishment amount (if >0) 7.08 5.57 0.22
% participating in a corrupt act 86.7 74.6 0.03
Neutral Language
Male Female p-value
% firms offering a bribe (transfer) 94.4 100 0.22
Bribe (transfer) amount (if >0) 7.71 7.37 0.23
% officials accepting 100 84.4 0.08
% citizens punishing 30.4 30.0 0.98
Punishment amount (if >0) 6.71 5.66 0.71
% participating in a corrupt act 97.2 92.5 0.34
Table 4A. Differences between Males across Countries
i Australia India p-value
(Treatment 1) (Treatment 1)
% of firms bribing 96.15 95.92 0.95
Bribe amount (if >0) 7.60 7.57 0.89
% of officials accepting 96.30 89.74 0.21
% of citizens punishing 48.48 27.27 0.06
Punishment amount (if >0) 6.00 3.25 0.01
ii Australia Indonesia p-value
(Treatment 2) (Treatment 2)
% of firms bribing 87.27 78.13 0.27
Bribe amount (if >0) 7.67 7.40 0.22
% of officials accepting 85.71 77.27 0.42
% of citizens punishing 50.00 76.47 0.09
Punishment amount (if >0) 7.08 7.00 0.97
iii Australia Singapore p-value
(Treatment 2) (Treatment 2)
% of firms bribing 87.27 91.30 0.62
Bribe amount (if >0) 7.67 7.67 1.00
% of officials accepting 85.71 84.62 0.91
% of citizens punishing 50.00 39.13 0.46
Punishment amount (if >0) 7.08 7.00 0.97
iv Indonesia Singapore p-value
(Treatment 2) (Treatment 2)
% of firms bribing 78.13 91.30 0.20
Bribe amount (if >0) 7.40 7.67 0.38
% of officials accepting 77.27 84.62 0.53
% of citizens punishing 76.47 39.13 0.02
Punishment amount (if >0) 7.00 7.00 1.00
Table 4B. Differences between Females across Countries
i Australia India
(Treatment 1) (Treatment 1) p-value
% of firms bribing 95.12 92.59 0.62
Bribe amount (if >0) 7.82 7.18 0.01
% of officials accepting 82.86 89.66 0.35
% of citizens punishing 56.25 20.93 0.00
Punishment amount (if >0) 5.04 4.33 0.47
ii Australia Indonesia
(Treatment 2) (Treatment 2) p-value
% of firms bribing 71.21 82.14 0.27
Bribe amount (if >0) 7.64 7.61 0.88
% of officials accepting 78.33 76.92 0.89
% of citizens punishing 68.63 70.00 0.91
Punishment amount (if >0) 5.57 4.29 0.28
iii Australia Singapore
(Treatment 2) (Treatment 2) p-value
% of firms bribing 71.21 83.33 0.15
Bribe amount (if >0) 7.64 7.60 0.83
% of officials accepting 78.33 93.33 0.07
% of citizens punishing 68.63 48.15 0.08
Punishment amount (if >0) 5.57 7.38 0.13
iv Indonesia Singapore
(Treatment 2) (Treatment 2) p-value
% of firms bribing 82.14 83.33 0.90
Bribe amount (if >0) 7.61 7.60 0.97
% of officials accepting 76.92 93.33 0.08
% of citizens punishing 70.00 48.15 0.14
Punishment amount (if >0) 4.29 7.38 0.04
