lying about corruption in surveys: evidence from a joint … · 2018. 1. 9. · lying about...

Lying About Corruption in Surveys: Evidence from a Joint Response Model

Virginia Oliveros Tulane University

[email protected]

Daniel W. Gingerich University of Virginia [email protected]

Word count: 5471

Abstract As is the case with other social undesirable behaviors, survey respondents tend to underreport corrupt behavior. Survey-based estimates utilizing direct questions about corruption are thus likely to underestimate the incidence of this phenomenon. More importantly, bias in reporting may be systematically related to personal characteristics, which makes deriving inferences from such data highly challenging. Recent studies have tried to address this issue by using sensitive survey techniques such as the list experiment or the randomized response technique. This paper studies how individual characteristics shape untruthful reporting in surveys about corruption. It does so by using a novel technique, the joint response model, which allows us to generate unbiased estimates of the extent of corruption among respondents as well as the proclivity towards untruthful reporting among bearers of the sensitive trait. The model permits one to estimate a parameter that captures the probability of a truthful response under direct questioning, which can be used to estimate the proportion of “liars” across groups. Examining the value of this parameter across a variety of different demographic groups, we find that truthfulness under direct questioning seems to differ by citizenship status, income, and to some degree by education, but not by gender and age.

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The literature on the causes and consequences of corruption is voluminous. Work on the topic

has shown how its incidence and form relates to factors such as government regulation, natural

resource abundance, democratic institutional design, bureaucratic structures, and even culture

(e.g., Ades and Di Tella 1999; Barr and Serra 2010; Charron et al. 2017; Dahlström, Lapuente,

and Teorell 2012; Djankov et al. 2002; Fisman and Miguel 2007; Gingerich 2013; Oliveros and

Schuster Forthcoming; Persson, Tabellini, and Trebbi 2003; Ross 2001). Equally well

documented are the negative impacts corruption has on societal wellbeing, be these in terms of

economic growth, public health, education, or democratic legitimacy (e.g., Aidt 2009; Gingerich

2009; Gupta and Abed 2002; Machado, Scartascini, and Tommasi 2011; Mauro 1995; Seligson

2002). Yet, in spite of the progress that has been made in understanding how and why corruption

manifests itself in different ways around the globe, there is still a great deal that needs to be

learned about why certain individuals and not others engage in the practice. One of the obstacles

to such understanding is misrepresentation in survey studies of this phenomenon.

As is the case with other social undesirable behaviors, survey respondents tend to

underreport corrupt behavior, a problem that has long being recognized by scholars working on

corruption (e.g., Treisman 2007). Survey-based estimates utilizing direct questions about

corruption are thus likely to underestimate the incidence of this phenomenon. More importantly,

bias in reporting may be systematically related to personal characteristics, which makes deriving

inferences from such data highly challenging. Recent studies on corruption and other sensitive

behaviors have tried to address this issue by using sensitive survey techniques such as the list

experiment (e.g., Gonzalez‐Ocantos et al. 2012; Gonzalez-Ocantos, Kiewiet de Jonge, and

Nickerson 2015; Malesky, Gueorguiev, and Jensen 2015; Oliveros 2016), endorsement

experiments (e.g., Lyall, Blair, and Imai 2013), or the randomized response technique (e.g.,

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Corbacho et al. 2016; Gingerich 2013). At the same time, scholars have devoted more attention

to the development of new methods and improvements on old ones to study these sensitive topics

(e.g., Corstange 2009; Gingerich 2010; Gingerich et al. 2016; Glynn 2013; Imai 2011; Imai,

Park, and Greene 2015; Blair and Imai 2012; Blair, Imai, and Zhou 2015; Blair, Imai, and Lyall

2014). Yet we still know very little about the characteristics of respondents who are most prone

to lie. In particular, we know very little about the characteristics of respondents who are most

prone to lie when asked about corruption.

Using original survey data from Costa Rica, this paper studies how individual

characteristics shape untruthful reporting in surveys about corruption. It does so by using a novel

technique, the joint response model, which allows us to generate unbiased estimates of the extent

of corruption among respondents as well as the proclivity towards untruthful reporting among

bearers of the sensitive trait (Gingerich et al. 2016). The model permits one to estimate a

parameter that captures the probability of a truthful response under direct questioning, a

parameter which can be used to estimate the proportion of “liars” across groups.1 Examining the

value of this parameter across a variety of different demographic groups, we find that

1 De Jonge (2015) uses list experiments to conduct a similar exercise on another sensitive topic

(vote buying); Eady (2017), in turn, develops a new method to analyze misreporting on survey

responses using the list experiment, which is applied to the study of misreporting of prejudice

across the ideological spectrum.

3

truthfulness under direct questioning seems to differ by citizenship status, income, and to some

degree by education, but not by gender and age.2

1. Who lies about corruption?

Several studies examining the effect of gender on crime and illicit behavior find that women are

less inclined towards these activities than men. For instance, in a large tax compliance

experiment conducted in Italy, Sweden, the United Kingdom, and the United States, D’Attoma et

el. (2017) find that women are significantly more compliant than men across all countries.

Similar results have been found with laboratory experiments (Kastlunger et al. 2010; Cadsby,

Maynes, and Trivedi 2006; Torgler and Schaltegger 2005) and survey data (Hasseldine 2002).

Women are also less likely to approve of tax evasion (Torgler and Valev 2010; Torgler and

Schneider 2007). In terms of delinquent behavior more broadly, the gender gap is such that

criminology scholars have argued that: “Males commit more offenses than females at every age,

2 In this paper, we focus strictly on the individual characteristics of the respondents, treating

interviewer characteristics as fixed. For a recent study that focuses on interviewer effects, see

Simpser (2017). He shows that a respondent’s second-order beliefs about the interviewer’s priors

affect the likelihood of obtaining a truthful response for questions about non voting and cheating.

Other characteristics of interviewers that seem to affect responses are ethnicity and race (e.g.

Adida L. et al. 2016; Campbell 1981; Davis 1997), the existence of a personal relationship with

the respondent (e.g. Lazarev, n.d.), perceived religiosity (e.g. Benstead 2014; Blaydes and

Gillum 2013) and gender (e.g Huddy et al. 1997; Kane and Macaulay 1993).

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within all racial or ethnic groups examined to date, and for all but a handful of offense types that

are peculiarly female” (Mears, Ploeger, and Warr 1998, 251).

In relation to corrupt behavior and attitudes towards corruption more specifically, the

literature is also quite vast. Using data from the World Values Survey, Swamy et al. (2001) show

that women are less likely to condone corruption (see also, Dollar, Fisman, and Gatti 2001). In

the same paper, but using survey data from Georgia, these authors also show that officials in

firms owned or managed by men are significantly more likely to be involved in bribe-giving than

those managed or owned by women. Similarly, using data from the World Values Survey and the

European Values Survey from eight Western European countries, Torgler and Valev (2010)

show the existence of significantly greater aversion to corruption among women. Using the same

data set but including 64 countries, Blake (2009) find the same results. Several laboratory

experiments have also found that women are less corrupt than men (e.g., Rivas 2013; Frank and

Schulze 2000).3 In the Latin American context, using survey data covering around 30 years

(1987-2015), Senter et al. (forthcoming) find that in Brazil men are more likely than women to

name corruption as an important problem facing the country. Using a conjoint experiment in the

Dominican Republic public sector, Oliveros and Schuster (Forthcoming) find that female public

employees are perceived by their colleagues to be less corrupt that their fellow male colleagues.

Gingerich et al. (2016) find that in Costa Rica men are substantially more likely than women to

indicate a willingness to bribe as well as to indicate that they have done so in the past. Following 3 Armantier and Boly (2011), in contrast, conducted a field experiment in Burkina Faso

reproducing a corruption scenario in which a grader is offered a bribe for a better grade and find

no effect of gender on the decision to accept the bribe. For an overview of the experimental

evidence, see Chaudhuri (2012).

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the existing literature, we expect social desirability bias to be positively associated with being a

woman. We therefore expect women to be more likely to lie about willingness to engage in

corrupt behavior than men (H1).

Less studied than gender is the effect of age on corruption and attitudes towards

corruption. A few studies, however, have found significant differences between older and

younger people. In the paper mentioned above, Torgler and Valev (2010) find that greater age is

associated with both lower tolerance for corruption and for tax evasion. Similarly, with data from

the World Values Survey, Swamy et al. (2001), also find lower tolerance for corruption (bribery

in their case) among older people. In the aforementioned paper that includes 64 countries, Blake

(2009) also finds that older people are less tolerant of bribe-taking. In a field experiment in

Burkina Faso in which a grader is offered a bribe and asked for a better grade, Armantier and

Boly (2011) find that older graders are significantly less likely to accept bribes. Finally, in a

study conducted in Costa Rica, younger survey respondents were found to be more inclined to

indicate a willingness to bribe than older ones (Corbacho et al. 2016; Gingerich et al. 2016).4 We

thus expect social desirability bias to be positively associated with age. We expect older

respondents to be more likely to lie about willingness to engage in corrupt behavior than

younger respondents (H2).

Another personal characteristic that has been found to be related to corrupt behavior and

attitudes about corruption is educational attainment. In the study by Torgler and Valev (2010)

4 Younger respondents were not, however, more likely to indicate that they had bribed in the past

(Gingerich et al. 2016).

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mentioned above, they find that education is negatively correlated with tolerance for corruption.5

In the Dominican Republic, Oliveros and Schuster (Forthcoming) find that more educated public

sector employees are perceived to be less corrupt than less educated employees. Similarly, in

Costa Rica, individuals with incomplete secondary school education were found to be more

inclined to bribe a police officer than individuals with some exposure to college (Corbacho et al.

2016). Using cross national survey data from the World Justice Project (WJP), Botero et al.

(2013) find that more educated people complain more about government misconduct (when it

happens).6 Interestingly, Morris and Klesner (2010) find that while the more educated are more

likely to experience corruption, the less educated are more likely to perceive it. Following the

literature, we expect social desirability bias to be higher among more educated respondents. We

then expect more educated respondents to be more likely to lie about willingness to engage

in corrupt behavior than less educated respondents (H3).

A number of studies have found an effect of income on corruption and attitudes towards

corruption. Yet, empirical findings remain mixed and contradictory. In the study of Brazilian

voters by Senters et al. (forthcoming) mentioned above, they find that wealthier respondents are

more likely to list corruption as the country’s most important problem. Using household data on

bribery of public officials in Uganda and Peru, Hunt and Laszlo (2012) find that the rich are

5 They find, however, no education effect on the justifiability of tax evasion (Torgler and Valev

2010).

6 Using the International Crime Victims Survey (ICVS), they find a similar effect of education

on the reporting of crime in general (Botero, Ponce, and Shleifer 2013). Similarly, Gonzalez-

Ocantos et al. (2014) find that more educated citizens are less tolerant of clientelism.

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more likely to bribe. Torgler and Valev (2010), in turn, find that wealthier individuals are less

tolerant of corruption.7 Similarly, using data from the World Values Survey but restricting the

sample to only Latin American countries, Blake (2009) finds a negative relationship between

income and tolerance for corruption: wealthier respondents in his sample of 10 Latin American

countries were 19 percent less likely to be tolerant of corruption than the baseline respondent.8

Winters and Weitz-Shapiro (2013), in contrast, find greater tolerance of corruption among the

upper class.9 This finding might be related to the fact that wealthier individuals appear to be less

affected by corruption, at least the type of petty corruption we study here. Indeed, in a field

experiment conducted in Mexico City, Fried, Lagunes, and Venkataramani (2010) found that

police officers engaging in traffic stops were more likely to demand bribes from poorer drivers.

Based on the mixed existing empirical evidence, we are agnostic about the direction of the effect

of wealth on the veracity of reporting. We expect wealthier respondents to be more (less)

likely to lie about willingness to engage in corrupt behavior than poorer respondents (H4,

H4’).

Finally, there are good reasons to suspect that vulnerable groups like immigrants would

be less likely to openly express a willingness to engage in illegal behavior or to admit to having

7 However, they find no effect of wealth on tolerance towards tax evasion (Torgler and Valev

2010).

8 In line with these findings, Weitz-Shapiro (2014) has found that wealthier voters are less likely

to support politicians who engage in clientelism.

9 Note that, in their case, class is constructed with an index that includes different household

goods and services, but also education.

8

been engaged in such behavior in the past. Given their more precarious status, non-citizens might

be more worried about the potential legal consequences of their answers. Indeed, in Costa Rica,

non-citizens were found to be more reluctant to respond honestly when asked directly about

paying a bribe (Gingerich et al. 2016). We expect social desirability bias and potential legal

concerns to be higher among non-citizens. We then expect non-citizens to be more likely to lie

about willingness to engage in corrupt behavior than citizens (H5).

2. Empirical Strategy

In order to estimate untruthful responses about corruption across groups, we use the joint

response model that combines individual responses about a sensitive issue based on both direct

questioning and a sensitive survey technique (SST) (Gingerich et al. 2016). Two different

questions about corruption were included in the survey. The first one (Q1) asks respondents

about their willingness to pay a bribe to a police officer in order to avoid paying a traffic ticket;

the second one (Q2) asks respondents whether they had actually bribed a police officer in the

past to avoid paying a traffic ticket. The questions were presented twice to the respondents. First,

they were presented in the format of a particular type of SST called the crosswise model (Tan,

Tian, and Tang 2009). This technique provides full anonymity to respondents by combining the

response to an innocuous question (in our case, the birth month of the respondent) with the

response to the sensitive item (in our case, corruption). At the end of the survey, the same

questions were asked directly, with the explicit option of choosing “prefer not to respond” also

offered to respondents. Responses about willingness to bribe were thus a discrete combination of

responses under the protection afforded by the SST and the absence of protection under direct

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questioning.10

Figure 1 presents the question about willingness to bribe a police officer based on the

crosswise model. The respondent were presented with two statements and asked how many were

true. The first statement (the innocuous one) refers to the respondent’s mother birth month,

stating that the respondent’s mother was born in October, November, or December.11 The second

statement (the sensitive one) denotes a willingness to pay a bribe in order to avoid paying a

traffic ticket. The privacy of the responses was protected by constraining the response options to

only two potential responses. Response A, indicating that either both statements are true or

neither statement is true, and response B, indicating that only one of the two statements is true,

without specifying which is true. The respondents’ anonymity is guaranteed since neither of the

two responses necessarily indicates willingness to bribe.

Figure 1: Crosswise Survey Question on Willingness to Bribe a Police officer

10 For a detailed description of the technique, see Gingerich et al. (2016).

11 In order to calculate the probability of having one’s mother born in October, November, or

December, we conducted a nationally representative telephone survey of 1,200 respondents. The

survey queried respondents directly about the birthday of their mother. These responses were

then checked against data from Costa Rica’s National Institute for Statistics and Censuses

(INEC) on month of birth for newborns for the 2000–11 period. The figures from the phone

survey self-reports and census data were essentially identical. See Online Appendix for more

details.

10

If we denote the outcome when respondent i is queried directly about the sensitive trait or

behavior as 𝑦!! ∈ {0 (absent),1 (present),∅ (prefer not to respond )} and the outcome when i is queried

about the sensitive trait or behavior using the crosswise technique as 𝑦!! = 0 ("A"), 1 ("B") ,

then the total observed outcomes for each respondent is captured by the vector Yi=(yiA, yiD). Any

realization of this vector belongs to the total observed response set, which is an array with six

elements, 𝒴 ∈ { 0,0 , 0,1 , 1,0 , 1,1 , ∅, 0 , (∅, 1)}. The joint response framework provides a

model for which of the six elements in this set will obtain.

The modeling strategy rests on two key assumptions. The first is called honesty given

protection: given the protection afforded by the sensitive question technique (the crosswise

model), all respondents are assumed to respond as prompted by the technique (cf. Gingerich

2010; Blair and Imai 2012). Thus, if lying occurs in the survey responses, it is assumed to occur

only when respondents are prompted to respond directly about the sensitive trait. The second

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assumption is called one-sided lying: individuals who bear the sensitive trait may either lie about

their status or refuse to respond when queried directly but those who do not bear the sensitive

trait always either tell the truth or refuse to respond, they never falsely claim to bear the sensitive

trait.

Exploiting these assumptions, the joint response model permits one to estimate a number

of relevant parameters. Most fundamentally, the model permits one to estimate the prevalence of

the sensitive trait or behavior, which we denote by 𝜋. In addition to the prevalence rate, the

model permits one to estimate the value of a set of key diagnostic parameters capturing

respondent evasiveness under direct questioning. These parameters are 𝜆!!, which represents the

probability of a truthful response under direct questioning when the respondent bears the

sensitive trait, 𝜆!!, which represents the probability of an untruthful response under direct

questioning when the respondent bears the sensitive trait, and 𝜆!!, which represents the

probability of a truthful response (as opposed to non-response) when the respondent does not

bear the sensitive trait. In examining who lies about corruption under direct questioning, we will

be examining the values of these diagnostic parameters.

To explore how individual characteristics and experiences shape untruthful reporting

about corruption, we analyze original survey data from Costa Rica.12 The survey consisted of

face-to-face interviews of 4,200 residents 18 years or older in what is known as the Gran Área

Metropolitana (GAM). This area is the principal urban center in Costa Rica (2.6 million

12 Costa Rica scores relatively well on corruption, when compared to other countries. According

to the 2016 Corruption Perception Index by Transparency International, Costa Rica is ranked

41/176 (same as Brunei and Spain).

12

residents), accounting for 60% of the country’s population.13 The survey was administered

between October 2013 and April 2014, by the firm Borge y Asociados.14

3. Results

We begin our analysis by showing that respondents, in general, do lie about corruption. After

estimating the level of truthful responses for the full sample, the rest of the analysis is focused on

how individual characteristics shape untruthful reporting. In particular, we focus on gender (H1),

age (H2), education (H3), wealth (H4, H4’), and citizenship (H5). Figure 2 presents the

prevalence estimates from direct questioning, SST questioning only, and the parameter estimates

provided by the joint response for the entire sample for both corruption questions (Q1 & Q2).15

Figure 2: Willingness to Bribe and Past Bribing, Whole Sample

13 The GAM includes 30 cantons in the provinces of Alajuela, Cartago, Heredia, and San José.

14 See the Online Appendix A for more details on the survey methodology and execution.

15 The exact numeric values are reported in Table A2 in the Online Appendix.

13

Notes: Parameter estimates for questions on corruption (whole sample). Black circles indicate

point estimates for model parameters. Ninety-five percent confidence intervals denoted by

horizontal black lines.

Figure 2 shows that, in line with our expectations and previous findings, people respond

differently when asked about corruption under a format that provides anonymity: respondents do

lie about corruption. In both cases, the estimates of willingness to bribe based on direct responses

are below those based on the joint response model and crosswise responses only. According to

the estimates based on the joint response model for Q1, the proportion of respondents who would

0.0 0.1 0.2 0.3 0.4 0.5

Prevalence estimate (π̂)

To avoid paying a traffic ticket, I would be willing to pay a bribe to a police officer

Direct only

SST only

Joint

response type

0.0 0.2 0.4 0.6 0.8 1.0

Diagnostic parameters (joint response model)

λ1T

λ1L

λ0T

0.0 0.1 0.2 0.3 0.4 0.5


I have paid, at least once, a bribe to a police officer to avoid a traffic ticket

Direct only

SST only

Joint

response type

0.0 0.2 0.4 0.6 0.8 1.0


λ1T

λ1L

λ0T

14

be willing to bribe a police officer was 0.29 [0.26, 0.32], whereas according to the crosswise

model and direct question the proportion was 0.22 [0.18, 0.25] and 0.18 [0.17, 0.19],

respectively.16 For Q2 the estimated proportion of respondents that admitted having paid a bribe

in the past was 0.16 [0.13, 0.19] based on the joint response model, compared to 0.13 [0.10,

0.16] with the crosswise model and 0.09 [0.08, 0.10] with direct questioning. Evasiveness under

direct questioning was higher for Q2, in which respondents were asked to implicate themselves

in having engaged in an illicit act. Indeed, the value of the parameter capturing the probability of

a truthful response under direct questioning was lower for Q2 (𝜆!!= 0.54 [0.45, 0.65]) than for

Q1 (𝜆!!= 0.61 [0.55, 0.69]). Respondents appeared more inclined to respond truthfully about

willingness to bribe a police officer than about having actually done so (although the difference

between the two estimates is not significant).

We now move on to focus on how personal characteristics may affect a respondent’s

willingness to provide (or not) a truthful response when asked directly. Figures 3 to 7 present the

prevalence estimates from direct questioning, SST questioning only, and the parameter estimates

provided by the joint response model for gender (female, male), age (less than 28, 29-42, 43 and

older), education (some university education, secondary education or less), wealth (low, middle,

high), and citizenship (citizens, non-citizens).17 We focus here only on the question about

willingness to bribe (Q1). Results for Q2 are similar and can be found on Online Appendix B.

16 Ninety-five percent confidence intervals in square brackets.

17 In order to measure wealth, a two-factor item response theory (IRT) model was estimated

utilizing respondent information on ownership of a cellphone, laptop, tablet, car, widescreen

television, cable television, and internet access in the home. The IRT model was estimated using

the R package ltm (Rizopoulos 2006). After estimation, factor scores for each respondent were

15

Figure 3: Willingness to Bribe, by Gender

Notes: Parameter estimates for willingness to bribe (by gender). Black circles indicate point

estimates for model parameters. Ninety-five percent confidence intervals denoted by horizontal

black lines.

First, as in the previous Figure reporting the results for the whole sample, Figure 3 shows that

both men and women respond quite differently when asked under the sensitive survey technique.

In both the case of men and women, the estimates of willingness to bribe based on direct

questioning (0.24 [0.22, 0.26] for men, 0.12 [0.11, 0.14] for women) are below those based on

crosswise model (0.28 [0.24, 0.33] for men, 0.15 [0.10, 0.19] for women) and the joint response

model (0.37 [0.33, 0.41] for men, 0.20 [0.16, 0.25] for women). At the same time, and in line

with the existing literature (e.g. Blake 2009; Gingerich et al. 2016; Swamy et al. 2001; Torgler

and Valev 2010), the Figure clearly shows that men are significantly more likely to express a

willingness to bribe across all response types. Men simply seem to be more corrupt than women. calculated using the empirical Bayes method. A three-value, ordinal indicator of wealth (low,

middle, high) was then created based on the terciles of the factor scores.

0.0 0.1 0.2 0.3 0.4 0.5



Direct onlySST only

Joint

Direct onlySST only

Joint

response type

Men (n=2,096)

Women (n=2,097)

0.0 0.2 0.4 0.6 0.8 1.0


Men (n=2,096)

Women (n=2,097)

λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

0.0 0.1 0.2 0.3 0.4 0.5



Direct onlySST only

Joint

Direct onlySST only

Joint

response type

Men (n=2,096)

Women (n=2,091)

0.0 0.2 0.4 0.6 0.8 1.0


Men (n=2,096)

Women (n=2,091)

λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

16

Finally, we find no support for H1: women are not more likely to lie about engaging in corrupt

behavior than men. Indeed, the diagnostic parameters are quite similar for men and women. The

value of the parameter that captures the probability of a truthful response under direct

questioning by those who are actually willing to bribe, 𝜆!!, is 0.63 [0.56, 0.71] for men 0.58

[0.48, 0.72] for women. We find no support for H1 either when asked about having paid a bribe

in the past (Q2) (see Table A3 in Online Appendix).

Figure 4: Willingness to Bribe, by Age

Notes: Parameter estimates for willingness to bribe (by age). Black circles indicate point


black lines.

In line with the existing literature (e.g. Armantier and Boly 2011; Blake 2009; Swamy et al.

2001; Torgler and Valev 2010), Figure 4 shows that, across the different estimation strategies,

older respondents are less likely to express a willingness to bribe a police officer (joint response

estimates: 0.39 [0.33, 0.44] for those less than 28 years old, 0.31 [0.26, 0.36] for those between

0.0 0.1 0.2 0.3 0.4 0.5



Direct onlySST onlyJoint



response typeLess than 28 years old (n=1,295)

Between 28 and 43 years old (n=1,463)

Greater than or equal to 43 years old (n=1,434)

0.0 0.2 0.4 0.6 0.8 1.0


Less than 28 years old (n=1,295)

Between 28 and 43 years old (n=1,463)

Greater than or equal to 43 years old (n=1,434)

λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

17

28 and 43 years old, and 0.18 [0.13, 0.23] for those 43 and older). We find, however, no support

for H2. The diagnostic parameters are indeed quite similar across the three age groups. The value

of the parameter that captures the probability of a truthful response under direct questioning by

those who are actually willing to bribe (𝜆!!) is 0.65 [0.58, 0.74] for those less than 28 years old,

0.60 [0.52, 0.71] for those between 28 and 43 years old, and 0.55 [0.42,0.76] for those 43 and

older. Older respondents are not more likely to lie about their willingness to pay a bribe than

younger respondents, nor are they more likely to lie about having paid a bribe in the past (Q2)

(see Table A4 in Online Appendix).

Figure 5: Willingness to Bribe, by Education

Notes: Parameter estimates for willingness to bribe (by education). Black circles indicate point


black lines.

Figure 5 reveals that there is a slight difference in willingness to bribe across education groups.

According to the joint response estimates, the prevalence of such willingness was 0.22 [0.16,

0.29] among those with some university education versus 0.30 [0.27, 0.33] among those with a

0.0 0.1 0.2 0.3 0.4 0.5



Direct onlySST only

Joint

Direct onlySST only

Joint

response type

Some university education (n=731)

Secondary school or less (n=3,308)

0.0 0.2 0.4 0.6 0.8 1.0




λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

0.0 0.1 0.2 0.3 0.4 0.5



Direct onlySST only

Joint

Direct onlySST only

Joint

response type



0.0 0.2 0.4 0.6 0.8 1.0




λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

18

secondary education or less (with some overlap in the confidence intervals of these two

estimates). However, our findings on truthfulness under direct questioning ran counter to

expectations based on the literature. Whereas in his study of vote buying De Jonge (2015, 721)

finds that “the educated, who are more likely to be aware of and understand social norms against

vote buying, are more likely to lie,” we found the opposite: that college educated respondents, if

anything, were more likely to be truthful about corruption than those with lesser levels of

education. The value of the parameter that captures the probability of a truthful response under

direct questioning by those who are actually willing to bribe, 𝜆!!, is 0.67 [0.51, 0.92] for those

with some college education and 0.60 [0.54, 0.67] for those with a secondary education or less.

Yet we would not place too much emphasis on these differences, as the confidence intervals for

the two estimates exhibit substantial overlap. We find a similar pattern when using the

alternative question (Q2) about having bribe in the past (see Table A5 in the Online Appendix).

Figure 6: Willingness to Bribe, by Wealth

Notes: Parameter estimates for willingness to bribe (by wealth). Black circles indicate point


black lines.

0.0 0.1 0.2 0.3 0.4 0.5






response typeLow material wealth (n=1,535)

Moderate material wealth (n=1,286)

High material wealth (n=1,372)

0.0 0.2 0.4 0.6 0.8 1.0


Low material wealth (n=1,535)

Moderate material wealth (n=1,286)

High material wealth (n=1,372)

λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

19

Turning to the analysis of wealth, we find no clear relationship between willingness to bribe a

police officer and wealth. Richer (poorer) respondents are not more (less) likely to express

willingness to engage in corrupt behavior. However, we do find a strong negative relationship

between wealth and evasiveness in responses. When asked directly, richer respondents are more

inclined to respond truthfully about willingness to bribe. Indeed, the value of the parameter that

captures the probability of a truthful response under direct questioning by those who are actually

willing to bribe is significantly higher for the richer group of respondents (𝜆!!= 0.80 [0.67, 0.94])

than it is for the middle group (𝜆!!= 0.55 [0.47, 0.66]) and the poorest group (𝜆!!= 0.51 [0.43,

0.61]). Poorest respondents bearing the sensitive trait were roughly equally likely to tell the truth

and lie under direct questioning (𝜆!!= 0.51, 𝜆!!= 0.45), while the richest respondents were

significantly more likely to tell the truth when asked directly (𝜆!!= 0.80, 𝜆!!= 0.17). We find

similar results when asking about having paid a bribe in the past (Q2) (see Table A6 in Online

Appendix).

Figure 7: Willingness to Bribe, by Nationality

Notes: Parameter estimates for willingness to bribe (by citizenship). Black circles indicate point

0.0 0.1 0.2 0.3 0.4 0.5





response type

Costa Rican Nationals (n=3,799)

Non-Costa Rican Nationals (n=394)

0.0 0.2 0.4 0.6 0.8 1.0




λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

0.0 0.1 0.2 0.3 0.4 0.5





response type



0.0 0.2 0.4 0.6 0.8 1.0




λ1T

λ1L

λ0T

λ1T

λ1L

λ0T

20


black lines.

Finally, Figure 7 shows strong support for our H5. Non-citizens are indeed more likely to lie

about willingness to engage in corrupt behavior than citizens. Under direct questioning, both

Costa Ricans and Non-Costa Ricans report an affirmative response proportion of 0.18 about

willingness to bribe a police officer. However, when asked under the protective formats (both the

SST and the joint response model), Non-Costa Ricans (0.31 and 0.37, respectively) show a

higher affirmative response than Costa Ricans (0.21 and 0.28). The value of the truthful response

parameter (𝜆!!) that captures the probability of an honest response when ask directly by those

who are willing to bribe is significantly lower for Non-Costa Ricans (𝜆!! =0.45 [0.34, 0.60]) than

it is for Costa Ricans (𝜆!!= 0.63 [0.58, 0.72]). Indeed, while many Costa Ricans bearing the

sensitive trait were willing to tell the truth under direct questioning (𝜆!! =0.63, 𝜆!!= 0.33), Non-

Costa Ricans were about equally likely to lie or tell the truth under direct questioning (𝜆!!= 0.45,

𝜆!!= 0.48). For the question that asked respondents to directly implicate themselves in having

paid a bribe in the past (Q2), we also found that the probability of a truthful response under

direct questioning by those who had bribed in the past is lower for Non-Costa Ricans than it is

for Costa Ricans, although this difference is not significant at conventional levels (see Table A7

in Online Appendix).

21

4. Discussion

This paper has examined how the demographic characteristics of survey respondents relate to

their inclination to lie about corruption in the context of a survey. Applying a joint response

framework—one that makes use of both protected and direct questions about corrupt activity—to

original survey data from urban Costa Rica, the paper presented estimates of the propensity to lie

about corruption under direct questioning across a wide array of demographic groups. We find

that wealth and citizenship status, and to a lesser extent, education, are relevant predictors of the

propensity to lie, but gender and age are not.

How might one make sense of these findings? We would submit that the relevant

distinction between the demographic categories that demonstrate variation in the propensity to lie

and those that do not is degree of vulnerability. Individuals belonging to vulnerable populations

are likely to believe that they have the greatest to lose from being forthcoming in direct questions

about corruption (or, for that matter, any other illegal or socially stigmatizing behavior). The

patterns of responses we observe seem to reflect this reality. For instance, non-citizens,

especially those who are undocumented, may be more concerned about any potential legal

repercussions from honest reporting than citizens, whose legal rights are almost certainly more

expansive. Similarly, poor citizens may have greater apprehension about how honest reporting in

direct surveys could potentially place them in legal jeopardy, since they are unlikely to have the

resources or contacts to navigate the legal system should the need to do so arise. Less educated

citizens may fear forthrightness about corruption for the same reason. By contrast, gender and

age—as relevant as they are for understanding the incidence of corruption—play a less important

role in variation in misreporting since, in the case of modern day Costa Rica, differences in these

characteristics do not seem to map cleanly onto differences in vulnerability.

22

The findings also cast a new light on existing results in the literature on corruption. The

well-documented aversion of women to corruption recorded in direct response surveys likely

reflects real differences in behavioral and attitudinal tendencies across genders. Whether this

difference will persist over time as more and more women populate the realms of political and

bureaucratic power remains to be seen. Nevertheless, our findings give us no reason to doubt the

descriptive snapshot provided by extant work on the link between gender and corruption.

On the other hand, our findings linking low education and wealth to evasiveness about

corruption would seem to offer two potential amendments to existing research. First, the

recorded negative effects of education on corruption may be systematically underestimated due

to the fact that lesser educated respondents are more inclined to lie about this topic. Education

may be an even more powerful antidote to corruption than previously thought. Second, the mixed

findings on the link between wealth and corruption in existing work may be due, at least in part,

to differences across income groups in the willingness to respond honestly to questions about

corruption. It is possible that wealth might have had a more consistent reductive effect on

corruption in the literature were it not for the greater inclination of poorer respondents to be

evasive in responding to direct questions on this issue.

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