“Fairer Sex” or Purity Myth? Corruption, Gender, and Institutional Context

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“Fairer Sex” or Purity Myth?
Corruption, Gender, and
Institutional Context
Justin Esarey
Rice University
Gina Chirillo
National Democratic Institute
In the Congo, in order to survive, we all have to be a bit corrupt, a bit ruthless.
That’s the system here. That’s just the reality of things. If you don’t bribe a bit
and play to people’s prejudices, someone else who does will replaceyou.... Even
you, if you were thrown into this system, you would do the same. Or sink.
—Parliamentarian in the Democratic Republic of the Congo
1
Recent research finds that states with more women involved in
government are also less prone to corruption (Dollar, Fisman, and
Gatti 2001; Swamy et al. 2001). But a review of experimental evidence
indicates that “women are not necessarily more intrinsically honest or
averse to corruption than men” in the laboratory or in the field (Frank,
Lambsdorff, and Boehm 2011, 68).
2
Rather, the attitudes and behaviors
of women concerning corruption depend on institutional and cultural
We thank Heather Ondercin, Beth Reingold, and two anonymous reviewers for helpful comments
and suggestions on earlier drafts of our paper.
Published by Cambridge University Press 1743-923X/13 $30.00 for The Women and Politics Research Section of the
American Political Science Association.
#The Women and Politics Research Section of the American Political Science Association, 2013
doi:10.1017/S1743923X13000378
1. Quoted in Stearns (2011, 9).
2. There is, however, some evidence that women are more trustworthy (Buchan, Croson, and Solnick
2008).
361
Politics & Gender, 9 (2013), 361–389.

contexts in these experimental situations (Alatas, Cameron, and
Chaudhuri 2009; Alhassan-Alolo 2007; Armantier and Boly 2008;
Schulze and Frank 2003). If women’s inclination toward corruption is
contextual, then what are the contexts in which we would expect female
involvement in government to fight corruption? The answer is important
to understand where gender equality initiatives present a cost-effective
and politically feasible approach to cleaning up government.
We believe that democratic institutions activate the relationship between
gender and corruption. These institutions make corruption a risky
proposition by shrinking the potential profit, increasing the probability of
discovery, and morally stigmatizing the perpetrators (Bueno de Mesquita
et al. 2003; Kolstad and Wiig 2011; Kunicova 2006). The risks are
smaller in autocratic states where bribery and favoritism are often a
normal part of doing business (Treisman 2007); indeed, not being
corrupt may be riskier than corruption in this context. Our key argument
is that women are differentially impacted by these risks and thus feel
greater pressure to conform to existing political norms about corruption.
There are many reasons to expect this differential impact; for example,
experimental evidence indicates that women are more averse to risk-
taking than men when facing comparable incentives (Jianakoplos and
Bernasek 1998; Watson and McNaughton 2007). Women are also more
vulnerable to punishment for violating political norms because of
explicit or tacit sex discrimination (Stolberg 2011). As a result, we think
that women are less susceptible to corruption in democracies but are
equally susceptible in autocratic systems. This may explain why one
study finds that the relationship between female participation in
government and corruption is weakened once the influence of
democratic institutions is statistically controlled (Sung 2003). As a
practical consequence, we expect increasing women’s participation in
government to have uneven effects on corruption that vary widely across
political and social contexts.
In this article, we flesh out the details of our idea and present evidence to
support it. First, we examine whether men are more tolerantof bribery than
women at different levels of institutionalized democracy/autocracy. Then,
we investigate whether the negative association between corruption and
female participation in government is robust in democratic and
autocratic contexts. To preview our results, we find strong evidence that a
gender gap in corruption attitudes and behaviors is present in
democracies but that it is weaker or nonexistent in autocracies. This is
consistent with our claim that women have stronger incentives to adapt
362 JUSTIN ESAREY AND GINA CHIRILLO

to political norms because of the risks created by gender discrimination.
We conclude by exploring alternative interpretations of our empirical
findings and their policy implications. Each emphasizes a different
intersection between identity factors and institutional forces as key to the
context-specific relationship between women and corruption (Manuel
2006), but all reinforce the idea that recruiting women into government
would be unlikely to reduce corruption across the board. Furthermore,
they all support the idea that the relationship between lower corruption
and greater female participation in government is a byproduct of the
differential treatment of women (by voters or political elites).
POWER, GENDER, AND CORRUPTION
Women might be the “fairer sex” when it comes to approving of
and engaging in corrupt behaviors: evidence presented by Dollar,
Fisman, and Gatti (2001, 427) shows that “a one standard deviation
increase in [female participation in government] will result in a decline
in corruption ... of 20 percent of a standard deviation.” A more
comprehensive study by Swamy et al. (2001) examines World Values
Survey data at the individual and country level, survey data from firms in
the country of Georgia, and covariation between corruption and female
participation in government and in the labor force at the country level.
Their study finds that “(a) in hypothetical situations, women are less
likely to condone corruption, (b) women managers are less involved in
bribery, and (c) countries which have greater representation of women in
government or in market work have lower levels of corruption” (Swamy
et al. 2001, 26).
Some governments have already supported feminization initiatives
designed to fight corruption; Swamy et al. (2001, 26) give two specific
examples. In August of 1999, the police chief of Mexico City established
a women-only traffic police force to fight corruption (Moore 1999). Five
months later, no female officer had yet been accused of soliciting or
accepting bribes (Quinones 1999). Corruption also reportedly went
down after women were recruited into the police force in Lima, Peru
(McDermott 1999). Follow-up research in Lima more than a decade
after the feminization initiative found a reduction in low-level corruption
but persistent corruption among supervisors (Karim 2011).
The possibilities presented by these results are exciting. Fighting
corruption by increasing female participation in government would
“FAIRER SEX” OR PURITY MYTH? 363

diminish the need for a painful, expensive, and politically difficult process
of rooting out corruption via oversight and prosecution. Countries would
also have an economic incentive to promote gender equality, bringing
needed attention to the unequal status of women around the world.
Understanding the theoretical mechanisms behind this relationship
would be helpful to structuring policy initiatives. The two studies cited
above are largely agnostic on this point, but some recent experimental
evidence suggests that the gender differences they observed are not
universal.
3
For example, an experiment by Alatas, Cameron, and
Chaudhuri (2009) has subjects in four different countries choose
whether to punish other participants who are offering and accepting
bribes; these punishments are costly for the punisher but lower the
payoff of the punished participant. Regardless of whether he or she
chooses to punish, bribery has negative externalities (lowers payoffs) for
subjects not directly involved in the transaction. Their results show that
men’s willingness to punish bribery in this situation is similar in all four
countries, but that women’s willingness to punish is variable cross-
nationally: “[W]hile women are less tolerant of corruption than men in
Australia, no significant gender differences are seen in India, Indonesia,
and Singapore” (Alatas, Cameron, and Chaudhuri 2009, 663). Though
the authors point out that “it is possible that countries with different
cultural backgrounds display gender differences to different degrees,”
they call for further work to “understand the reasons for the variations in
gender differences in attitudes towards corruption across countries and to
establish in which countries gender differences do exist” (Alatas,
3. Swamy et al. (2001, 26) explicitly disavow any explanation for the relationship between gender and
corruption:
Claims about gender differences can easily be misinterpreted. It is, therefore, important for us to
clarify that we do not claim to have discovered some essential, permanent, or biologically
determined differences between men and women. Indeed, the gender differences we observe
may be attributable to socialization, or to differences in access to networks of corruption, or
in knowledge of how to engage in corrupt practices, or to other factors. We do not attempt to
identify these underlying factors, but rather, to document several statistically robust
relationships that point toward a gender differential in the incidence of corruption.
Dollar, Fisman, and Gatti (2001, 423–24), on the other hand, seem to rely on a series of studies
suggesting intrinsic gender differences in attitude toward the common good, but they stop short of
making an explicit claim:
Over the past couple of decades, a considerable body of work has emerged that has found
systematic differences in behavioral characteristics across gender. The basic hypothesis
proposed by this literature is that men are more individually oriented (selfish) than women.
This has been demonstrated to be the case in a wide range of institutional contexts, through
both experimental and survey-based studies. ... These results imply that women will be less
likely than men to sacrifice the common good for personal (material) gain.
364 JUSTIN ESAREY AND GINA CHIRILLO

Cameron, and Chaudhuri 2009, 678). We believe that the incentives and
opportunities of an environment influence men and women in different
ways, resulting in different behaviors in some situations but not in others.
Proposed Explanations for Gender Differences
We suspect that women will resist corruption in places where it is already
culturally and institutionally stigmatized but will be no different than men
where such practices are simply a normal part of doing business or are
even expected. Women are more powerfully subject to social norms
because systematic discrimination against them makes their position more
tenuous. Insomuch that sex discrimination means holding women to a
different (higher) standard than men for the same reward, it is riskier for
them to flout the formal and informal rules of political culture because
transgressions are more likely to invite retaliation. Thus, if a political
culture discourages corruption, then women will avoid corrupt activities
more and profess greater aversion to it (compared to men) because they
anticipate suffering more severe consequences than their male counterparts.
Some experiments support the idea that punishments activate gender
differences in corruption. For example, in an experiment assessing
subjects’ willingness to accept bribes, Schulze and Frank (2003) find
that women are less willing than men to accept bribes when there is
some chance that bribery will be detected and punished but find no
gender gap in willingness when bribery is free of risk. Similarly,
Armantier and Boly (2008) conduct a field experiment in the United
States and Burkina Faso, where they find that, compared to men,
women are equally likely to accept bribes in the absence of monitoring
but are substantially less likely to accept bribes when being monitored.
This may be because women are punished more harshly for corruption
than men because of different social expectations for their behavior, as
has been anecdotally observed in American politics: Celinda Lake, a
Democratic strategist, says female politicians are punished more harshly
than men for misbehavior.
4
“When voters find out men have ethics and
honesty issues, they say, ‘Well, I expected that,’” Ms. Lake said. “When
they find out it’s a woman, they say, ‘I thought she was better than that’”
(Stolberg 2011).
4. Karim (2011) finds evidence of a similar phenomenon in Peru: “‘Maybe 1 percent of women take
bribes, but when one female takes a bribe, we are all denounced as corrupt, when the real corrupt ones
are our supervisors,’ said another female transit officer who asked to remain unidentified.”
“FAIRER SEX” OR PURITY MYTH? 365

If voters, judges, and administrators are quicker to condemn and punish
women’s corruption (compared to men’s), then women will probably be
cleaner — but only in political systems that work to fight corruption.
This does not require that women be intrinsically cleaner but merely
that whoever is responsible for dealing out punishment expects them to
be (and that women rationally respond to these incentives).
But even if men and women are punished equally for corruption, studies
have demonstrated that women tend to be more risk-averse than men in the
same circumstances, particularly in financial matters (Jianakoplos and
Bernasek 1998; Watson and McNaughton 2007). This implies that
women will stay cleaner to avoid punishment even in the absence of
implicit or explicit sex discrimination — at least in places where
corruption is stigmatized. Indeed, this explanation implies that women
would willingly participate in bribery, nepotism, and other corrupt
practices if those practices were socially and politically expected (and
failing to participate invited sanctions). We might, therefore, expect no
gender gap (or perhaps a reversed gender gap) in corruption attitudes
and behaviors in these situations.
Our explanation focuses on gender disparities in the detection and
punishment of corruption, but it is also possible that women are simply
provided with fewer opportunities for corruption in systems where it is
surreptitious and extralegal because they are excluded from the relevant
social and political networks through which corruption flows. Alhassan-
Alolo (2007, 230) presents survey data from Ghana and draws the
following conclusions:
1. There is no gender gap in officials’ attitudes toward corruption “when
exposed to opportunities for corruption.”
2. There is also no gender gap in officials’ attitudes toward corruption when
officials are “surrounded by networks that engage in and/or condone
corruption.”
3. There is no gender gap in officials’ attitudes toward corruption “when the
society expects certain acts of corruption as a moral obligation.”
Thus, it may be that women are not inherently cleaner but are less
frequently in a position to take advantage of opportunities for corruption
(Goetz 2007). If this constraint is more binding in (democratic) contexts
where corruption is stigmatized and practiced in secret so that privileged
information is necessary in order to find these opportunities, then this could
manifest in a negative association between women in government and
366 JUSTIN ESAREY AND GINA CHIRILLO

corruption in democracies only. As we discuss more fully in the conclusion,
some implications of this argument are similar to those of our own.
Democratic Institutions as Mediator of the Relationship Between
Gender and Corruption
In sum, these studies invite us to consider how institutionalized structures
of punishment interact with gender politics to create the relationship
between corruption and female participation in government observed in
Dollar, Fisman, and Gatti (2001). In particular, we should look at
institutional arrangements that change the incentives to appropriate
public policy for private advantage. When vote-buying, favoritism in
government contracting, nepotism, bribery, personal loyalty over
obedience to law, and other such behaviors are viewed as “corruption,”
when there are incentives to expose these corrupt behaviors, and when
corruption is stigmatized and punished, we expect a gender gap: women
will express more disapproval and be more reluctant to participate. On
the other hand, when these behaviors are ignored or are integral aspects
of the structure of governance, we expect no gender gap.
Our study focuses on institutionalized democracy/autocracy measures as
indicators of the social, political, and financial stigmatization of corruption.
While far from comprehensive, we believe that our approach is sensible
and practical. It is sensible because there are reasons to believe that some
institutions associated with democracy will (on average) tend to
publicize, disincentivize, and facilitate condemnation of the private use
of power at public expense (Bueno de Mesquita et al. 2003, 102–103
and Chapter 4; Kolstad and Wiig 2011; Kunicova 2006). It is practical
because suitable measures of institutionalized democracy/autocracy are
readily available. It also speaks to an existing literature that notes
potential confounding between democracy and female participation in
government (Sung 2003).
There are four reasons why we believe that democracy/autocracy
measures are appropriate for our study. First, democracies often divide
authority among many different actors. Bribery and other forms of
influence-buying agreements are more expensive, harder to monitor for
compliance, and harder to keep secret when there are more rulers to buy
off. Autocracies have fewer veto players and are consequently more
amenable to corruption. Separation of powers might also make it
difficult for corrupt officials to coordinate on raising more money than
“FAIRER SEX” OR PURITY MYTH? 367

required and diverting it to private interests (Persson, Roland, and Tabellini
1997; Persson and Tabellini 2002, 239–41).
Second, democracies typically have electoral systems that are more
competitive and have broader suffrage than autocracies. This makes
efficient governance and provision of public goods a more effective
means of winning the necessary number of supporters to gain office than
patronage and corruption (Bueno de Mesquita et al. 2003). Competitive
elections create a powerful incentive to expose publicly and punish
corruption: viz., electoral advantage in a political campaign (Kunicova
and Rose-Ackerman 2005; Myerson 1993). The electoral risk associated
with bribery and favoritism (and the loss of all subsequent benefits of
holding public office) makes them less attractive opportunities. In
autocracies, where elections are shambolic or nonexistent, these
corruption-fighting incentives do not exist.
Third, the freedoms of speech and expression that are typically protected
in democracies provide a greater opportunity to discover and popularize
corrupt activities (Brunetti and Weder 2003; Chowdhury 2004; Freille,
Haque, and Kneller 2007). In order for corruption to be punished and
stigmatized, it must first be discovered by someone who is not a
beneficiary to the scheme. Journalists with a professional incentive to
discover and publicize newsworthy secrets would presumably be interested
in stories about corruption. Laws that protect potential whistleblowers from
ex ante surveillance and prosecution make it possible for these journalists
to recruit knowledgeable informants without placing them in personal
danger. Open records laws make it easier to compile forensic evidence of
corruption. All contribute to the dissemination of public knowledge about
corrupt activities and enable formal and informal punishments to be
demanded. In autocracies that restrict individual free speech and press
freedoms, these pressures are all reduced.
Finally, the basis of democratic authority provides a stronger moral and
conceptual basis for defining bribery, favoritism, nepotism, and personal
loyalty as corrupt when compared to the basis of authoritarian
government. Democratic governments purport to represent the interests
and welfare of their citizens, and most forms of corruption transparently
revolve around private enrichment at public expense. That is, insomuch
that democratic government is by and for the people, corruption is
philosophically inimical to democratic government. By contrast,
autocratic governments are frequently built on the concept of personal
authority, military hierarchy, or even the divine right to govern; public
welfare may be only one of the state’s many objectives. Additionally, the
368 JUSTIN ESAREY AND GINA CHIRILLO

explicitly hierarchical structure of many autocratic governments directly
rewards personal loyalty at the expense of loyalty to codified laws or the
public weal. In this context, behaviors like favoritism, legal caprice, and
bribery are not extraordinary “corruption” but rather the normal
operation of a hierarchical system of personal authority.
None of this is to say that it is impossible for democracies to coexist with a
culture of corruption: for example, India is recognized as a well-
functioning democracy
5
but has a significant corruption problem.
6
Indeed, this may explain why Alatas, Cameron, and Chaudhuri (2009)
find no gender differences in corruption attitudes in India: if corruption
is ignored by voters or treated as an integral aspect of government, then
we expect no gender gap. Other sociological and cultural influences
may overwhelm the incentives against corruption that democracy
provides. But holding these factors constant, democratic countries
stigmatize corruption more than autocratic countries, and, therefore,
institutionalized democracy is a viable measure of the political
stigmatization of corruption in a statistical analysis that controls for
potentially confounding factors.
CORRUPTION ATTITUDES AND POLITICAL INSTITUTIONS
Our theoretical framework is consistent with a gender gap in tolerance for
political corruption that varies as a function of a country’s political
institutions. In particular, women are more disapproving of corruption
than men where (democratic) institutions suppress corruption but
equally approving otherwise (in autocratic contexts). We find this pattern
in a cross-section of 68 countries in the World Values Survey (World
Values Survey Association 2009).
Dependent Variable: Gender Gap in Tolerance of Bribes
Our World Values Survey sample contains cross-sectional data from
1999–2002.
7
For our analysis, we looked at a survey question asking
5. Between 1998 and 2007, India’s Polity IV measure of democracy was 9 (on a scale of 210 ¼most
autocratic to 10 ¼most democratic). See the next section for more measurement details.
6. In 2004, India had a WBGI Control of Corruption score of 20.34, putting it at the 53rd percentile
of government cleanliness in our sample (in Table 2) and comparable to Nicaragua, Mexico, Ghana,
and Cuba. See the next section for more measurement details.
7. The WVS is collected simultaneously in a large number of countries, but there is some variation in
when data collection begins in any particular country. For more information, see http://www.wvsevsdb.
com/wvs/WVSDocumentation.jsp.
“FAIRER SEX” OR PURITY MYTH? 369

respondents about the justifiability of accepting a bribe on an ordinal
scale from 1 – 10, 1 meaning never justifiable and 10 meaning always
justifiable. We created a variable that measures the difference between
the average response to the question for men and women for each
country in the data set. Positive numbers indicate that men are more
approving of bribery than women, while negative numbers indicate that
men are less approving of bribery than women. The resulting variable
has a mean of 0.101 and a standard deviation of 0.100, consistent with
the findings of Swamy et al. (2001) that (on average) men are more
approving of bribery than women.
Independent Variable: Institutionalized Democracy and Autocracy
Our key measure of institutionalized democracy and autocracy comes
from the Polity IV Project’s revised combined Polity score (Marshall,
Jaggers, and Gurr 2010), as reported in the Quality of Government
Dataset (Teorell et al. 2009). The score places countries on a scale from
210 to 10, where 210 is strongly autocratic and 10 is strongly
democratic. The combined score is the sum of two subscores: the
institutionalized democracy score and the institutionalized autocracy
score. Institutionalized democracy measures the extent to which a state
has competitive political participation, competitive and open recruitment
of executives, and constraints on executive power. Institutionalized
autocracy measures the same characteristics, plus a measure of the
regulation of political participation. The revised score removes or
converts some special or missing cases from the normal index.
We choose the revised combined Polity score over alternatives for two
reasons. First, Polity includes institutional aspects of democracy/
autocracy that we wish to target and excludes other features of
democracy that are not of interest to our study. Some alternative
measures, such as the Freedom House measures of civil liberties and
political rights, include aspects of the dependent variable, like “Is the
government free from pervasive corruption?” and “Does the rule of law
prevail in civil and criminal matters?” (Freedom House 2012, 34–35).
Second, Polity is a reasonably continuous measure that allows for
heterogeneity on multiple institutional dimensions; this contrasts with,
for example, the measure of Chiebub, Gandhi, and Vreeland (2010) that
is based on the idea that democracy is an integrated set of institutions
functioning as a unit and is, therefore, a binary classification. We do not
370 JUSTIN ESAREY AND GINA CHIRILLO

claim that Polity’s underlying concept of democracy is better or more
accurate than others but simply that it is suitable for our purpose.
Robustness Check: Alternative Measure of Democracy
To ensure that our results are not peculiar to the Polity score, we repeat our
analyses using the democratization index of Vanhanen (2005), as reported
in the Quality of Government Dataset (Teorell et al. 2009). This index
combines
[...]two basic dimensions of democracy — competition and participation
— measured as the percentage of votes not cast for the largest party
(Competition) times the percentage of the population who actually voted
in the election (Participation). This product is divided by 100 to form an
index that in principle could vary from 0 (no democracy) to 100 (full
democracy). (Empirically, however, the largest value is 49.) (Teorell et al.
2009, 65)
While this index focuses on the electoral aspects of democracy, it provides a
helpful opportunity to check our results.
Control Variables
To prevent spurious correlation from presenting a problem for our analysis,
we control for factors that may intervene in the relationship between
political system and the gender gap in corruption tolerance. First, it
is plausible that gender discrimination is a pathway of spurious
correlation between democracy and corruption because greater gender
discrimination implies a diminished capacity for women to influence
political and economic outcomes (including corruption), and it is
plausible that gender discrimination is correlated with democratic
institutions. Thus, we need to block this pathway by controlling for the
degree of gender discrimination faced by women in a state. We
accomplish this using two measures of women’s political and economic
rights from the Cingranelli and Richards (2010) human rights dataset.
Second, it is likely that both democracy level and corruption attitudes
correlate with wealth and population. As a result, some demographic
influence on bribery attitudes might be picked up by the Polity score if
we do not separately control for these factors. We therefore included
measures of log per capita GDP and log population from the United
“FAIRER SEX” OR PURITY MYTH? 371

Nations Statistics Division as reported in the Quality of Government
Dataset (Teorell et al. 2009).
Results
We use an ordinary least squares regression to predict the bribery tolerance
gender gap using the Polity score and our control variables. Our
independent variable data come from 2002, but our results are not much
different using data from 1999– 2001.
8
A simple bivariate regression of
the relationship between a country’s gender gap in average tolerance of
corruption and that country’s Polity score is shown in Figure 1. As the
figure indicates, there is little difference in corruption tolerance between
men and women for countries that rank lowest on the Polity scale. In
more democratic countries, however, men are considerably more
tolerant of corruption than women.
To check whether this finding is influenced by spurious correlation, we
add control variables to the model and report our results in Table 1,
column 1. The table confirms a substantively and statistically significant
relationship between the Polity score and the gender gap in corruption
tolerance. A gender gap in the tolerance of corruption seems to be
associated with a country’s democracy level, with the gap largest in the
most democratic countries. As the Polity score changes from its minimum
(210) to its maximum (10), we would expect the gender gap between
men and women to grow by 0.11; this change corresponds to about one
standard deviation of the dependent variable. The results are substantively
similar for a model using our alternative democracy measure (column 2).
The results are consistent with a microfoundational story explaining why
greater female participation in government is associated with lower
corruption. Different political cultures place different pressures on
women, who have a greater incentive to conform to these pressures than
men due to sex discrimination. In consolidated democracies, whose
institutions discourage corruption, women are (on average) more
disapproving of corruption than men. In autocratic countries, where
corruption is a part of business as usual, the difference between the sexes
is considerably smaller. Thus, we can read our behavioral analysis with an
attitudinal understanding in mind: if the relationship between women in
8. Two countries (France and Uganda) are dropped from the analysis because their values for the
dependent variable (Corruption Tolerance Gender Gap) of .506 and 2.358, respectively, are
moderate outliers. Our substantive conclusions, however, do not change when we include them.
372 JUSTIN ESAREY AND GINA CHIRILLO

FIGURE 1. Gender differences in tolerance of corruption against polity score.
Table 1. Political institutions and the corruption tolerance gender gap
12
Variable
b
(se)
b
(se)
Polity score 0.01
(0.00)
Democratization index 0.00
(0.00)
Control variables
Women’s economic rights 0.00 0.01
(0.03) (0.03)
Women’s political rights 0.04 0.04
(0.03) (0.03)
Log GDP per capita 0.00 20.01
(0.01) (0.01)
Log population 20.00 20.00
(0.01) (0.01)
Constant 0.04 0.08
(0.17) (0.18)
Notes: Dependent variable ¼gender gap (male-female) in average response to WVS bribery question.
Larger numbers indicate greater relative male tolerance of bribery. Entries are coefficients and standard
errors from an OLS regression model computed using Stata 11.2. Model 1: N¼67, R
2
¼0.22. Model
2: N¼68, R
2
¼0.23. Cross-national data from the year 2002. Standard errors computed using Efron’s
HC3 heteroskedasticity-consistent VCV.
“FAIRER SEX” OR PURITY MYTH? 373

government and corruption is mediated by democratic institutions, then we
can plausibly attribute this difference to greater pressure on women to
conform to the norms and imperatives of the political system.
BEHAVIORAL FINDINGS: CORRUPTION, FEMALE
PARTICIPATION IN GOVERNMENT, AND
INSTITUTIONALIZED DEMOCRACY
Indeed, our attitudinal findings appear to have behavioral implications: we
find that female participation in government is unrelated to corruption in
autocracies, but negatively related to corruption in democracies. Our
research examines 157 countries
9
over a nine-year span, from 1998–
2007.
10
Dependent Variable: Corruption in Government
Objectively measuring corruption is challenging for a simple reason:
“[T]hose with knowledge of a corrupt act usually share an interest in
keeping it concealed” (Johnston 2005, 425).
11
As a result, many time-
series cross-section corruption indicators are measures of corruption
perception rather than reported incidences of corruption, prosecutions,
or the total number of bribes. This is certainly true for many
9. The countries are Afghanistan, Albania, Algeria, Angola, Argentina, Armenia, Australia, Austria,
Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bolivia, Botswana, Brazil,
Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Central African Republic, Chad,
Chile, China, Colombia, Comoros, Congo, the Democratic Republic of Congo, Costa Rica, Cote
d’Ivoire, Croatia, Cuba, Cyprus, Czech Republic, Denmark, Djibouti, Dominican Republic,
Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Finland, France,
Gabon, Gambia, Georgia, Germany, Ghana, Greece, Guatemala, Guinea, Guinea-Bissau, Guyana,
Haiti, Honduras, Hungary, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan,
Kazakhstan, Kenya, North Korea, South Korea, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho,
Liberia, Libya, Lithuania, Macedonia, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mauritius,
Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands,
New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua, New, Guinea,
Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, the Russian Federation, Rwanda,
Saudi Arabia, Senegal, Serbia and Montenegro, Sierra Leone, Singapore, Slovakia, Slovenia,
Solomon Islands, South Africa, Spain, Sri Lanka, the Sudan, Swaziland, Sweden, Switzerland,
Syria, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan,
Uganda, Ukraine, the United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan,
Venezuela, Vietnam, Yemen, Zambia, and Zimbabwe.
10. World Bank Governance Indicators were collected biannually until 2002, so when the WBGI’s
Control of Corruption indicator is our dependent variable, our data covers the years 1998, 2000, and
2002– 2007. The Vanhanen democratization index further limits the sample to 1998, 2000, and
2002– 2004.
11. See also Galtung (2006).
374 JUSTIN ESAREY AND GINA CHIRILLO

components of our primary measure of corruption, the World Bank
Control of Corruption index from the World Bank’s Governance
Indicators dataset (Kaufmann, Kraay, and Mastruzzi 2010).
The World Bank defines control of corruption as “the extent to which
public power is exercised for private gain, including both petty and grand
forms of corruption as well as ‘capture’ of the state by elites and private
interests” (Kaufmann, Kraay, and Mastruzzi 2010, 4). The Control of
Corruption measure combines data from multiple resources to form the
index, including expert assessments and surveys of business people or
citizens; while some experts may have privileged knowledge of
corruption activities, by and large these measures are still indirect,
perception-based measures and not direct measures of corrupt activities.
To aggregate the sources, the WGBI uses “an extension of the standard
unobserved components model, which expresses the observed data in
each cluster as a linear function of the unobserved common component
of governance, plus a disturbance term capturing perception errors and/
or sampling variation in each indicator” (Kaufmann, Kraay, and
Mastruzzi 2003, 258). The technique is designed to make scores
comparable across both countries and years, allowing researchers to
assess relative trends in corruption over time (Kaufmann, Kraay, and
Mastruzzi 2003, 261). Higher scores correspond to greater control of
corruption (i.e., less corruption) on this measure.
Robustness Check: Alternative Measures of Corruption
Because the measurement of corruption levels worldwide is difficult and
controversial, it is important to ensure that our results do not depend on
one measure of corruption, particularly because almost all corruption
measures are indirect by necessity. Thus, as a robustness check, we
replicate our core results using two alternative measures of corruption:
Transparency International’s Corruption Perceptions Index and the
International Country Risk Guide’s Corruption Index.
Transparency International combines 13 different polls and surveys from
10 independent sources, including both expert rankings and opinion
surveys of those doing international business, to develop its Corruption
Perceptions Index.
12
The index defines corruption as “the abuse of
12. The number of surveys and assessmentsmay vary year to year depending on their availability. The
surveys and assessments included are Africa Development Bank’s Country Policy and Institutional
Assessments, Asian Development Bank’s Country Performance Assessment Ratings, Bertelsmann
“FAIRER SEX” OR PURITY MYTH? 375

public office for private gain” (Transparency International 2011,2)and
constructs the measure accordingly.
13
Transparency International uses a
matching percentiles and standardization technique to construct the CPI
index, a technique designed to place the scores of the individual sources
onto the same 0–10 scale and make them comparable across countries
(Lambsdorff 2006, 88–89; Transparency International 2011, 5– 6).
Trends in the CPI, however, might be attributable to changing samples
of source material or methodological revisions, and thus “the index
primarily provides an annual snapshot of the views of businesspeople,
with less of a focus on year-to-year trends” (Lambsdorff 2006, 83). The
variable is coded so that countries with a higher score correspond to
cleaner (less corrupt) countries.
The ICRG’s Corruption Index attempts to measure “financial
corruption in the form of demands for special payments and bribes
connected with import and export licenses, exchange controls, tax
assessments, police protection, or loans. Such corruption can make it
difficult to conduct business effectively, and in some cases may force the
withdrawal or withholding of an investment” as well as “excessive
patronage, nepotism, job reservations, ‘favor-for-favors,’ secret party
funding, and suspiciously close ties between politics and business”
(Political Risk Services Group 2012). Its corruption measure is part of
the ICRG’s Political Risk Rating, which is determined by compiling an
index of 12 different components, weighted differently, on a 0– 100
scale. Points are assigned by ICRG editors from a series of preset
questions in order to ensure consistency. In order to ensure reliability
over both countries and time, each ICRG editor uses the same questions
as a basis for each rating. As before, the variable is coded so
that countries with a higher score correspond to cleaner (less corrupt)
countries.
Foundation’s Bertelsmann Transformation Index, Economist Intelligence Unit’s Country Risk Service
and Country Forecast, Freedom House’s Nations in Transit, World Markets Research Center’s Country
Risk Ratings, Institute for Management Development’s World Competitiveness Report, Political and
Economic Risk Consultancy’s Asian Intelligence, World Economic Forum’s Global
Competitiveness Report, and the World Bank’s Country Policy and Institutional Assessments for IDA
Countries. Only countries assessed by at least three sources are included in the index. See the
Methodological Brief for the CPI for more details (Transparency International 2011, 1–3).
13. The opinion survey data used to compute the index score for a given year is averaged over the prior
two years to reduce meaningless variability in what can be a noisy estimate of corruption. The
assessment scores of experts, by contrast, draw only on the current year’s data because the source is
often peer-reviewed, and, consequently, the measurement is less noisy. See pp. 1– 3 of the
Methodological Brief for the CPI for more information (Transparency International 2011).
376 JUSTIN ESAREY AND GINA CHIRILLO

Independent Variables: Female Participation in Government and
Institutionalized Democracy
This portion of our study uses two key independent variables. The first is the
Polity score used in the previous section. We also use the Vanhanen
democratization index as an alternative measure and robustness check, as
before. The second key variable is female participation in government,
which we measure using the Inter-Parliamentary Union’s data for the
percentage of women in parliament in a given country (Inter-Parliamentary
Union 2012). If data from only one house are available (e.g., if the
country’s parliament is unicameral), then we use the percentage from that
house. If data for both houses are present, then we use the percentage of
women in both houses by adding the total number of women in both
houses and dividing it by the total number of representatives in both houses.
14
Control Variables
Many studies blame economic factors for corruption (e.g., Nwabuzor
2005; Tanzi 1998). In countries with more strict economic regulations
regarding issuing licenses, permits, and other authorizations, there is a
greater opportunity for those in charge to exploit their power. This also
may increase the likelihood of bribes. Countries with low economic
freedom often have bans on imports, which promote corruption in
customs services. In less economically free societies, the government
determines prices and wages, presenting an opportunity for corruption.
Economic freedom is also often associated with the political freedoms
related to democracy, one of our key independent variables.
As a result, we include several measures of economic freedom and
openness as control variables. First, we use a modified form of the
Heritage Foundation’s Economic Freedom Index (Heritage Foundation
2012); we removed its trade freedom (we deal with this factor through
another control variable), property rights (because corruption is included
in the measurement of this value), and freedom from corruption
(because this is what we are trying to predict) components but left in the
seven remaining factors that make up the index, including business
freedom, fiscal freedom, and labor freedom.
15
We also included the
14. We used linear imputation (with the impute command in Stata) to fill in 227 observations
(10.75% of the dataset) for this variable.
15. We include the business freedom, fiscal freedom, government spending, monetary freedom,
investment freedom, financial freedom, and labor freedom components of the Heritage
“FAIRER SEX” OR PURITY MYTH? 377

United Nations Statistics Division’s Openness to Trade variable from the
Quality of Government Dataset (Teorell et al. 2009) to control for any
influence of an open economy on corruption.
Beyond these control variables, we also include all the control variables
from the attitudinal study — women’s political and economic rights, the
log of per capita GDP, and the log of population — for the reasons
previously stated. In particular, it is important to control for women’s
political and economic rights because greater gender discrimination
probably correlates with both of our key independent variables
(democratic institutions and female participation in government) and is
conceptually related to women’s ability to affect corruption (the
dependent variable) once in government. Furthermore, because there
may be systematic scaling issues in the measurement of corruption from
year to year, we include dummy variables for each year to control for
these potential intercept shifts. Finally, to remove the confounding
influence of any unit-level heterogeneity not captured by our existing
controls, we include dummy variables for each of 10 regions of the world
as coded in the Quality of Government Dataset (Teorell et al. 2009).
16
Results
As an initial look at the data, we divide our sample into two subsamples: one
with all observations for which the Polity score was less than orequal to zero
(labeled “Autocratic Leaning”) and those for which the Polity score was
greater than zero (labeled “Democratic Leaning”). We then construct a
simple bivariate plot showing the relationship between the percentage of
women in parliament and the WBGI control of corruption index. The
results are depicted in Figure 2.
In the states with a low Polity score, there is a slight, negative bivariate
relationship between the control of corruption and the percentage of
women in parliament: a line fitted via OLS indicates that every 10%
increase of women in parliament is associated with a 0.135 decline in
the WBGI Control of Corruption score, a relationship that is statistically
significant ( p¼0.002) but substantively quite small (corresponding by
construction to 0.135 of a standard deviation of the corruption variable).
Foundation’s Economic Freedom Index. We used linear imputation (with the impute command in
Stata) to fill in 443 observations (20.98% of the dataset) for this variable.
16. The ten regions coded are Eastern Europe and post-Soviet Union, Latin America, North Africa
and the Middle East, Subsaharan Africa, Western Europe and North America, East Asia, Southeast
Asia, South Asia, the Pacific, and the Carribean.
378 JUSTIN ESAREY AND GINA CHIRILLO

It may be that the greater incentive to conform to the expectations of the
political culture that women face due to their relative disempowerment
actually induces them to be more willing to participate than men. But for
practical purposes, this relationship is close to zero (and disappears with
the addition of control variables later in our analysis).
By contrast, in the states with a high Polity score, there is a strong,
statistically significant ( p,0.001), and positive bivariate relationship
FIGURE 2. Women in parliament and control of corruption.
“FAIRER SEX” OR PURITY MYTH? 379

between control of corruption and women in parliament. An OLS line
indicates that every 10% increase in female participation in parliament is
associated with a 0.515 point increase in the WBGI score, or about half
a standard deviation increase on the scale of the index. Thus, female
involvement in government is associated with lower corruption in states
that disincentivize corruption.
The same substantive conclusions are borne out in a linearregression that
includes our control variables, as shown in Table 2. Model 1 in the table
includes an interaction term between Polity score and percent women in
parliament, as we believe to be necessary, while Model 2 omits this term
in a manner consistent with Dollar, Fisman, and Gatti (2001). Model 2
Table 2. Estimating the relationship between corruption and women in
government, conditional on political institutions
12
Variable
b
(se)
b
(se)
% women in parliament 1.09 1.34
(0.22) (0.20)
Polity score 0.01 0.02
(0.00) (0.00)
% women *polity 0.06
(0.02)
Control variables
Women’s political rights 0.02 0.02
(0.03) (0.03)
Women’s economic rights 0.13 0.13
(0.02) (0.02)
Log GDP per capita 0.33 0.34
(0.02) (0.02)
Log population 20.06 20.07
(0.01) (0.01)
Openness to trade 0.00 0.00
(0.00) (0.00)
Economic freedom index 0.02 0.02
(0.00) (0.00)
Constant 23.26 23.29
(0.23) (20.23)
Notes: Dependent variable: WBGI Control of Corruption Index. Higher numbers mean less
corruption. Entries are coefficients and standard errors from an OLS regression model computed
using Stata 11.2. Year and Region dummy coefficients are omitted. N¼1208, Model 1 R
2
¼0.83,
and Model 2 R
2
¼0.83. Standard errors computed using White’s Heteroskedasticity-consistent VCV.
Model 1 includes an interaction term between % women in parliament and polity score, while
Model 2 excludes this term, as in Dollar, Fisman, and Gatti (2001).
380 JUSTIN ESAREY AND GINA CHIRILLO

yields results that are substantively similar to those of Dollar, Fisman, and
Gatti (2001): a positive relationship exists between female participation in
government and control of corruption. When the interaction between the
percentage of women in parliament and Polity score is included (in
Model 1), this interaction is positive and statistically significant. This
indicates that the relationship between female participation in
government and clean government gets stronger as the Polity score rises.
Thus, we have initial evidence from the model to conclude that the
power of female participation in government to control corruption is
contingent on democratic political institutions, with a stronger
relationship in democratic states compared to autocratic states.
We calculate the predicted marginal relationship between women in
parliament and the control of corruption over the range of the Polity
score, as described in Brambor, Clark, and Golder (2006), using the
estimates of Model 1. This marginal effect is equal to
@control of corruption
@%women in parliament ¼
b
women þ
b
productPolity
where
b
women is the coefficient on women in parliament and
b
product
is the coefficient of the interaction term. We plot this marginal effect and its
standard error in Figure 3. For the most autocratic states, the relationship
between women in parliament and the control of corruption cannot
be statistically distinguished from zero. As a state becomes more
democratic, the relationship grows stronger and more positive until
eventually it becomes statistically significant: more women in parliament
is associated with a cleaner government. Finally, at the highest levels of
institutionalized democracy, a 10 percentage point increase in women in
parliament is associated with an increase in the control of corruption
corresponding to about 0.15 standard deviations of the dependent variable.
Robustness Check: Alternative Measures of Democracy and Corruption
As we indicated earlier, we are concerned that our results may be specific to the
particular measures of democracy and corruption that we employ. We,
therefore, reestimate Model 1 from Table 2 but substitute alternative
measures of the dependent or key independent variable. The Vanhanen
democratization index is our alternative IV measure, while the ICRG and
Transparency International measures of government corruption are our
“FAIRER SEX” OR PURITY MYTH? 381

alternative DVs (where, in both cases, higher scores indicate less corruption).
Marginal effects plots derived from these models are depicted in Figure 4.
All of our alternative measures yield the same basic story as our main
measures. More female participation in government is not associated (or
is weakly associated, in the case of the democratization index) with
government cleanliness for autocratic countries. But for democratic
countries, there is a strong and statistically significant association
between more women’s participation in government and government
cleanliness. Our robustness checks bolster the credibility of our original
result, as this result appears largely insensitive to our choice of measures.
CONCLUSION
In summary, we find evidence that the relationship between gender and
corruption differs by institutional context. We think this is because
women are more averse to the risks of violating political norms and
because gender discrimination makes violating institutional norms a
riskier proposition for women than men. Where corruption is
stigmatized, women will be less tolerant of corruption and less likely to
FIGURE 3. Female participation in government and government cleanliness, by
polity score.
382 JUSTIN ESAREY AND GINA CHIRILLO

FIGURE 4. Female participation in government and government cleanliness,
alternative IV and DV.
“FAIRER SEX” OR PURITY MYTH? 383

engage in it compared to men. But if “corrupt” behaviors are an ordinary
part of governance supported by political institutions, then there will be
no corruption gender gap.
Attitudinal data from the World Values Survey and behavioral outcomes
measured by corruption indices are consistent with this story. Female
disapproval of bribe-taking is greater than male disapproval, but only in
countries with democratic institutions. We think this result can be
interpreted to mean that female attitudes are constrained to follow their
society’s political norms: the more that the society disapproves of
corruption, the more women disproportionately express disapproval of
corruption. State corruption is strongly and negatively related to female
participation in government in the context of democratic institutions but
is weaker or indistinguishable from zero in the context of autocratic
ones. This finding is consistent with the idea that women in government
are bound by social and political norms in practice, including when they
make decisions as government officials.
If our explanation is correct — that women are more sensitive to their
incentives as a consequence of their more precarious position in
government and that consequently they will engage in corruption or not
depending on which action tends to solidify their position in
government — then recruiting women into government positions will
not reduce corruption wherever participation in corrupt activities aids in
selection for and retention in government office (as in many autocratic
regimes). Female participation in government would only reduce
corruption in functional democracies where the electorate tends to
punish corruption via removal from office. Unfortunately, this is only a
subset of the total number of states where corruption is a hindrance to
economic development.
But this is not the only possible interpretation of our evidence: there are
other causal mechanisms that could plausibly underlie our findings. These
different interpretations of our evidence reflect different views of which
intersection of identity factors (aside from gender) is most relevant to
shaping women’s experience with corruption in government (Manuel
2006). But each of these mechanisms supports the inference that
recruiting women into government is unlikely to uniformly reduce
corruption, though it may have this effect in a subset of cases. Additionally,
each mechanism relies on the idea that the link between women in
government and reduced corruption is rooted in gender discrimination.
For example, suppose that a state’s democracy level correlates with its
degree of institutionalized gender discrimination. Our analysis attempts
384 JUSTIN ESAREY AND GINA CHIRILLO

to remove this source of spurious correlation by controlling for measures of
women’s political and economic rights, but it is challenging to measure the
effective degree of gender discrimination intrinsic to a social or political
system, and thus our controls may be ineffective. We may presume that
gender discrimination inhibits women’s ability to effect change in
government in a variety of ways. If this story is true, then we would
expect women’s participation in government to be uncorrelated with
corruption in high-discrimination states (that is to say, nondemocracies)
because women are not in a position to be able to effect change in these
cases. Women in government would be in a position to improve
corruption in low-discrimination states (namely, democracies), and thus
we could see a negative relationship between the two in those cases. The
underlying reasons for women’s aversion to corruption would remain
unknown, and we would thus be uncertain as to whether the
relationship would endure over time. But we would still not expect
increasing female participation in government to reduce corruption in
states where women were legally, culturally, and/or economically
unequal. A greater benefit would presumably derive from trying to
recruit women into government only as a part of a larger program to
reduce gender inequality in other ways.
If women in government engage in less corruption because they have
less access to the personal networks through which corruption flows (and
thus simply have fewer opportunities for corruption), then the fact that
female participation in government is associated with less corruption in
democracies but not autocracies could be a consequence of these
regimes’ different processes for recruiting people into government
service.
17
If recruitment into autocratic government requires contact with
and personal loyalty to people who are already involved in the
government, then these contacts and loyalties will presumably provide an
entr
´ee into corruption networks where these networks exist. Thus,
women recruited into these governments have equal opportunity for
corruption and engage in it at the same rate as their male counterparts.
Recruitment into a democratic government is ultimately a matter of
gaining more than 50% of the vote share in an electorate, and thus
outsiders who can leverage their own talents and resources outside
existing political networks may be able to get elected. If women form a
disproportionately large share of these outsiders, then we would expect
them to enter government with a smaller network of internal contacts
17. We thank Heather Ondercin for suggesting many of the ideas in this paragraph.
“FAIRER SEX” OR PURITY MYTH? 385

and thus fewer opportunities for corruption. In this scenario, recruiting
more women into government would only have a short-term effect on
corruption in democracies, one that faded as women became more
firmly integrated into insider political networks, and no effect in
autocracies.
We believe that it would be helpful for future research to focus on afiner-
grained picture of the environment beyond broad institutional context. For
example, examining the pathway through which women enter politics
could help us understand how they behave once entrenched in
government. The women who gain office through a top-down, party- or
government-directed initiative would probably be very different than a
group of women who won an open and competitive election, and we
might expect them to have systematically different attitudes toward
corruption. The personal characteristics that make a person attractive for
appointment into a hierarchy are different from those that make
someone a good electoral candidate. Furthermore, these two pathways
might lead to women holding different sorts of offices (elected vs.
appointed offices, or open seats vs. reserved gender quota seats) that
might in turn provide different opportunities to benefit from corruption.
As we note above, the differences we observe in autocracies and
democracies might be ascribed to the different ways that women enter
government in these systems. In autocracies, much participation in
government (including female participation) is imposed from above via
appointment, which could socialize appointees into existing networks of
corruption. In democracies, where elections are used to fill many offices,
women in office could be disproportionately outsiders who are not
invited into these networks. But these and other possibilities cannot be
explored without collecting detailed information about the nature of
female participation in government.
We also believe that future research should study variation in specific
aspects of both gender discrimination and corruption attitudes inside of
democratic and autocratic contexts. As we know from Alatas, Cameron,
and Chaudhuri (2009) and our own Figure 1, not all democracies have
a gender gap in corruption attitudes. It may be that public preferences
and institutional features differ among democracies in ways that put
more or less pressure on elected officials to fight corruption. The
presence of a “culture of corruption” may in turn influence whether
female government participation is associated with lowered corruption;
our theory is built on the idea that institutional incentives encourage or
discourage such a culture, but a more direct time-series cross-sectional
386 JUSTIN ESAREY AND GINA CHIRILLO

measurement of the concept may permit a more direct test. It is also
plausible that gender discrimination against women in government
differs both in nature and degree across different states and that different
kinds of discrimination will have different implications for women’s
engagement with corruption. Cross-national measures of gender
discrimination that focus specifically on political behavior (including
corruption) would be useful for clarifying how social and cultural
attitudes combine with institutional variation to influence how much
men and women in government engage in corruption.
Justin Esarey is Assistant Professor of Political Science at Rice University,
Houston, TX: justin@justinesarey.com; Gina Chirillo is Project Assistant at
the National Democratic Institute, Washington, D.C.: ginachirillo@gmail.com.
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