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Political Behavior (2022) 44:653–678
https://doi.org/10.1007/s11109-020-09630-5
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ORIGINAL PAPER
Corrupted Estimates? Response Bias inCitizen Surveys
onCorruption
MattiasAgerberg1
Published online: 6 July 2020
© The Author(s) 2020
Abstract
Measuring corruption has become a global industry. An important and commonly
used data source are several large-scale multi-country projects that survey citizens
directly about their perceptions and experiences of corruption. Such indicators are
regularly used by political scientists to test theories on political attitudes and behav-
ior. However, we still know little about the quality of many of these measures. This
paper deploys a large survey with two embedded experiments to investigate two
potential sources of bias in indicators based on citizens’ perceptions and experiences
of corruption, stemming from political bias and sensitivity bias. First, I draw upon
research on economic perceptions and argue that respondents are likely to respond
in a political manner when asked how they perceive the level of corruption in their
country. I test this argument by experimentally priming respondents’ political affili-
ations before asking for their perception of corruption. Second, I argue that standard
questions probing peoples’ corruption experiences are likely to be subject to sensi-
tivity bias. I test this second argument by constructing a list experiment. Overall,
the results show strong and predictable sources of response bias that also vary sig-
nificantly between important subgroups. I discuss implications for researchers and
practitioners.
Keywords Corruption perceptions· Corruption experiences· Response bias·
Survey experiment
Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1110
9-020-09630 -5) contains supplementary material, which is available to authorized users.
* Mattias Agerberg
mattias.agerberg@gu.se
1 Department ofPolitical Science, University ofGothenburg, Gothenburg, Sweden
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Political Behavior (2022) 44:653–678
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Introduction
There is now a near consensus among researchers about the destructive conse-
quences of corruption1 and the societal benefits of clean government (Bardhan 1997;
Holmberg and Rothstein 2011; Mauro 1995; Mungiu-Pippidi 2013; Rose-Ackerman
1999; Rothstein 2011). As a consequence, measuring corruption has become a
global industry, with leading actors like Transparency International (TI) spending
millions of dollars on the construction of corruption indicators and the surveying of
ordinary citizens’ attitudes about, and experiences of, corruption. These measures
are nowadays used to estimate the incidence of corruption in different countries and
to research various corruption-related questions in the social sciences. The growing
literature on corruption and political attitudes and behavior has in large part relied
on such indicators (e.g. Agerberg 2019; Anderson and Tverdova 2003; Dahlberg
and Holmberg 2014; Klasnja etal. 2016; Morris and Klesner 2010; Seligson 2002;
Xezonakis etal. 2016; Zechmeister and Zizumbo-Colunga 2013). However, we still
know little about the quality of many of these commonly used measures. This paper
aims at investigating two potential sources of bias in frequently used indicators
based on citizens’ perceptions and experiences of corruption, stemming from politi-
cal bias and sensitivity bias.
Most existing corruption indicators are either perception based or based on self-
reported experiences of corruption. While important corruption measures like TI’s
Corruption Perception Index (CPI)2 are largely based on the (aggregated) percep-
tions of business people and country experts, many large-scale multi-country pro-
jects survey citizens directly about their perceptions and experiences of corruption.3
Some researchers argue that these citizen surveys—especially experience-based sur-
veys—provide a more objective measure of corruption that can be used as a bench-
mark to which expert-based measures can be compared (Donchev and Ujhelyi 2014;
Razafindrakoto and Roubaud 2010; Treisman 2007). Citizen surveys on corruption
include, for instance, the Global Corruption Barometer (GCB)4 and the Eurobarom-
eters on corruption.5 One important advantage of these surveys is that they provide
individual-level data from ordinary people around the world. These data can then
be used to study important individual-level research questions on topics like clien-
telism, political legitimacy, and voting. Standard corruption questions are therefore
nowadays incorporated into general citizen surveys like Lapop,6 World value survey,7
1 Corruption in the World Bank’s definition is “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 etal. 2011,p. 4).
2 https ://trans paren cy.org/resea rch/cpi/overv iew.
3 Unlike corruption surveys based on citizen interviews, expert-based corruption indicators have been
widely discussed and criticized (see Hamilton and Hammer 2018 for an overview of this debate).
4 https ://trans paren cy.org/resea rch/gcb/overv iew.
5 https ://ec.europ a.eu/home-affai rs/news/eurob arome ter-count ry-facts heets -attit udes-corru ption _en.
6 https ://vande rbilt .edu/lapop /surve y-data.php.
7 https ://world value ssurv ey.org/.
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Political Behavior (2022) 44:653–678
Comparative Study of Electoral Systems,8 and the International Social Survey Pro-
gramme.9 Wysmulek (2019) identify that the number of items on corruption (both
perception and experience-based) in international survey projects has grown from
just one in 1989 to nearly a hundred in 2017. However, while data availability has
increased rapidly, little is known about how people form their perceptions of corrup-
tion and to what extent their reports of encounters with corruption are accurate.10
A large body of research has documented reporting bias among survey respond-
ents, where an individual’s reported perception of some phenomenon might be
influenced by factors other than the actual occurrence of the phenomenon (see,
for instance, Bartels 2002; Berinsky 1999; Bertrand and Mullainathan 2001; Tou-
rangeau and Yan 2007). This paper explores two potential sources of bias in individ-
ual reports on perceptions and experiences of corruption. First, I draw upon research
on economic perceptions and economic voting, and related research on political bias
and motivated reasoning, and argue that respondents are likely to respond in a politi-
cal manner when asked how they perceive corruption in their country. Corruption
is an issue that citizens view as important, especially in developing democracies
and countries with poor governance (see Eurobarometer 2017; Fisman and Golden
2017; Holmes 2015; World Economic Forum 2017). This is also an issue that often
receives intense media coverage (not least in the case of high-level corruption scan-
dals) and hence an issue where citizens can be expected to react to information on
the basis of prior affect and political affiliations (see Anduiza et al. 2013; Fischle
2000; Jerit and Barabas 2012). As a result, we should, for instance, expect incum-
bent supporters to in general report a substantially more positive view of corrup-
tion in their country, compared to other groups. In this sense, reported corruption
perceptions can be a way of justifying ones political affiliations (Flynn etal. 2017;
Leeper and Slothuus 2014).
Second, I argue that direct questions about corruption experiences are likely to be
sensitive and hence subject to ‘sensitivity bias’ (or ‘social desirability bias’) (Blair
etal. 2018). Research shows that citizens around the world in general have a strong
distaste for corruption and bribe giving (see below). Bribery is also something that
is illegal in essentially every country (Fisman and Golden 2017,pp. 26–29). Even
in countries where corruption is very widespread, most citizens still view bribe pay-
ments and the misuse of public money as a serious moral wrong that can not be
justified (Persson etal. 2013; Rothstein and Varraich 2017). Therefore, admitting
to having been part of a corrupt transaction is arguably an act of revealing sensitive
information and hence something that is likely to be under-reported. This, in turn,
makes estimates of the level of corruption in society based on experiential surveys
likely to be biased.
8 https ://cses.org/.
9 https ://issp.org.
10 An exception in the literature, further discussed below, that focuses on potential under-reporting with
regard to experiential corruption questions is Kraay and Murrell (2016).
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Political Behavior (2022) 44:653–678
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I test these conjectures with two preregistered experiments embedded in a
large survey fielded in Romania to over 3000 respondents.11 The first experiment
is designed to randomly make the political affiliations of one group of respondents
more salient before answering questions about corruption perceptions. The second
experiment, deploying a so called ‘list experiment’, is designed to minimize the like-
lihood of sensitivity bias among another group of respondents (Blair and Imai 2012;
Glynn 2013). The results show strong evidence of different types of response bias
with regard to questions about corruption. First, government supporters systemati-
cally report a much more positive view of the corruption situation in Romania. A
simple question order prime—asking about political affiliation before corruption
perceptions—makes this effect almost twice as large, suggesting that a substantial
part of the gap is the result of respondents defending and justifying their political
identity (Flynn etal. 2017; Leeper and Slothuus 2014). Second, the results suggest
that direct questions about corruption experiences are sensitive and under-reported.
For some groups, like women, the under-reporting is massive, according to the esti-
mates. For this group the true rate of corruption victimization might be three times
as high as the reported rate.
The study makes several contributions to the literature. Research has shown that
respondents often show strong political bias with regard to attitudes and percep-
tions about the economy. The study shows that questions about corruption exhibit
very similar patterns, and that responses to these questions are malleable and sus-
ceptible to political bias—even in a political system with weak party identification,
like Romania (Tatar 2013). The study also demonstrates that direct questions about
corruption experiences need to be treated like sensitive questions, where different
groups show diverging patterns of reporting bias. These results also call into ques-
tion some conclusions from previous research about who is most likely to be the
victim of corruption.
Response Bias inSurveys
I consider two commonly used individual-level corruption indicators in this article:
perception-based and experience-based measures. These two are complementary
and capture different aspects of how individuals view and experience corruption
in society. Perception-based indicators capture something more abstract since it is
hard to find an objective benchmark and determine the ‘actual’ level of corruption
in society. Rather, these measures tap into individuals’ general perceptions of how
common corruption is overall in different sectors. In the case of perceived political
corruption, for instance, these indicators are likely affected by corruption scandals
discussed in the media and overall news reporting on corruption. Experience-based
measures, on the other hand, should in theory capture something objective: whether
or not an individual has been asked to pay a bribe. This is a relatively unambiguous
11 The hypotheses and overall analysis plan was preregistered at EGAP (https ://osf.io/pwdqt /). Full repli-
cation materials are available at the Harvard Dataverse: Agerberg (2020).
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Political Behavior (2022) 44:653–678
question, even though there might be some variation in what exactly is considered a
‘bribe’. Within a specific country, however, this variation is likely to be rather mod-
est. I thus expect perception-based measures to track high-level (or ‘grand’) corrup-
tion to a larger extent, whereas experience-based measures mostly should capture
direct experiences with ‘petty’ corruption (Fisman and Golden 2017). Due to the
different nature of these two categories of measures I expect them to be subject to
different forms of bias.12 I elaborate on this in the next two sections.
Political Bias
Researchers have long acknowledged that survey responses are sometimes unstable
and inaccurate, and that there are clear psychological incentives to shape responses
in certain ways (Berinsky 1999; Bertrand and Mullainathan 2001; Zaller 1992). In
particular, research on citizens’ evaluations of the economy has uncovered strong
response biases stemming from the respondent’s political leanings (Evans and
Andersen 2006; Lau etal. 1990; Palmer and Duch 2001; Sears and Lau 1983; Wil-
cox and Wlezien 1996).
An early strand of research argued that this type of bias can arise because party
identification works as a ‘perceptual screen’ through which economic performance
is assessed, and that an individual tends to see what is favorable to his or her parti-
san orientation (Campbell 1960). Later research shows that even beliefs about basic
objective political facts can be strongly affected by political identification, partly
due to selective information processing (Bartels 2002; Gerber and Huber 2010).
Respondents might also reason their way to the conclusion that the economy is
doing better when their preferred party or politician is in power. The theory of moti-
vated reasoning holds that all reasoning is motivated in the sense that it is driven by
specific motives and goals. Taber and Lodge (2006) argue that these goals often are
directional goals (as opposed to accuracy goals) where individuals apply their rea-
soning powers in defense of a prior specific conclusion. Directional goals are thus
often defensive of particular identities, attitudes, or beliefs that are strongly held
(Flynn etal. 2017; Leeper and Slothuus 2014).
In political science a large body of research has focused on political bias in eco-
nomic perceptions. Much less is known about how political affiliations might inter-
act with corruption perceptions. Recent decades have seen a rapid increase in efforts
to measure corruption, both via expert surveys and surveys of the public (Fisman
and Golden 2017; Holmes 2015; Wysmulek 2019). Many of the survey instruments
used in the latter category clearly resemble the instruments used to tap into peo-
ple’s economic perceptions; Klasnja etal. (2016) even adopt the terms ‘sociotropic’
and ‘egotropic corruption voting’ directly from the economic voting literature.
Researchers use such measures to study, for instance, how individual corruption
12 Arguably, the term bias is more well-defined in the case of corruption experiences, where the sensitiv-
ity of the issue might lead to under-reporting and hence deviations from a true population parameter in
the aggregate. The analogue in the case of corruption perceptions is less straightforward. I still use the
term ‘political bias’ in the latter case to be consistent with the previous literature.
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Political Behavior (2022) 44:653–678
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perceptions and experiences are related to incumbent support and vote choice (Ager-
berg 2017; Gingerich 2009; Klasnja etal. 2016; Xezonakis etal. 2016; Zechmeister
and Zizumbo-Colunga 2013), and how corruption perceptions relate to political
legitimacy and support for the democratic system (Anderson and Tverdova 2003;
Dahlberg and Holmberg 2014; Morris and Klesner 2010; Seligson 2002). To be able
to estimate the effect of corruption in such settings it is important that corruption
perceptions are (exclusively) determined exogenously, to avoid biased estimates. In
this sense, I define ‘political bias’ as a situation where respondents’ reported corrup-
tion perceptions instead are partly endogenous to their political identity.
Researchers interested in the effects of corruption would hence hope that these
perceptions are only determined by external changes in an individual’s environment.
However, recent studies give us reasons to believe that this might not be the case.
Anduiza etal. (2013) show that tolerance for corruption can have a clear political
dimension. In a survey experiment fielded in Spain the authors show that respond-
ents’ judgment of the seriousness of a political corruption scandal partly is deter-
mined by whether the accused politician belongs to the respondent’s preferred party.
The authors argue, in line with the literature on economic perceptions, that this is a
way for respondents to reduce cognitive dissonance: by downplaying the importance
of corruption when it affects the own party respondents make the political world
more consistent with their political predispositions.
Jerit and Barabas (2012) show that individual-level motivated bias is present
on a wide range of topics, as long as a question has importance or strong politi-
cal implications (see also Bolsen etal. 2014). The authors show that such bias is
particularly likely for issues receiving high media coverage. I argue that corruption
perceptions are likely to be affected in this way: people—especially in developing
democracies—view corruption in society as a question of great importance (Fisman
and Golden 2017; Holmes 2015). For instance, when the World Economic Forum in
2017 surveyed individuals in 186 countries about the most pressing political issue
‘government accountability and transparency/corruption’ ranked 1st (World Eco-
nomic Forum 2017). About 25% of Europeans say that they are ‘personally affected
by corruption in their daily lives’; the number for countries like Romania, Croatia,
and Spain is as high as 60–70% (Eurobarometer 2017). In such countries, corruption
trends in society and both local and high-level scandals are often intensely covered
by the media (see Hajdu 2018). Voters in countries where corruption is widespread
also tend to associate current levels of perceived corruption with the incumbent gov-
ernment (Klasnja 2015; Klasnja etal. 2016; Xezonakis etal. 2016), and view ‘the
fight against corruption’ as one of the priorities that should be most important for
political leaders (Holmes 2015). Government supporters therefore have a ‘preferred
world-state’ where corruption levels are decreasing (this supports their political
leanings), while opposition supporters have incentives to view the situation as worse
(this would be a reason to oust the current incumbent) (Jerit and Barabas 2012). As
suggested by the literature on motivated reasoning, voters who sympathize with the
government (for whatever reason) might therefore convince themselves that the situ-
ation with regard to corruption is more positive than what is warranted by evidence.
In general, a connection between reported corruption perceptions and government
support can exist for two reasons: (1) the respondent might experience changing
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Political Behavior (2022) 44:653–678
corruption levels in society and adjust his or her support for the incumbent accord-
ingly, (2) the respondent reports perceived corruption levels that are consistent with
his or her political affiliations. If the latter is true, making political affiliations more
salient should affect reported corruption perceptions, whereas if corruption percep-
tions are only determined exogenously this should not be the case. In line with the
literature reviewed above, I argue that we have reasons to believe that some degree
of political bias is present in the reporting of corruption perceptions. In this sense, a
respondent’s reported corruption levels can be a way to defend and justify his or her
beliefs about the current government. I focus specifically on government supporters
since this group is relatively easy to define, also in a multiparty system (I discuss
this in more detail below). Drawing on this discussion, my first hypothesis has two
parts and can be stated as follows:
•
H1a.
On average, government supporters will report lower perceived levels of
corruption compared to other respondents.
•
H1b.
Increasing the salience of political affiliations will cause government sup-
porters to report even lower levels of corruption.
H1a
predicts an empirical pattern that is more likely to be true if the political bias
(PB) hypothesis is true. Finding this pattern does not, however, provide definite evi-
dence for the PB hypothesis; the pattern could, in theory, arise exclusively because
people with low corruption perceptions become government supporters. Here,
H1b
instead provides a direct test of one of the main implications of the hypothesis.
Sensitivity Bias
According to Tourangeau and Yan (2007) a survey question is likely to be ‘sensitive’
if it touches on ‘taboo’ topics, if it induces concerns that the information given will
become known to a third party, or if the question elicits answers that are socially
unacceptable or undesirable. If this is the case the respondent can be expected to
give a ‘socially desirable’ answer. That is, an answer that the respondent thinks will
be viewed favorably by others, resulting in under-reporting of ‘undesirable’ attitudes
and behavior. Such sensitivity bias (SB) has been shown to be present on a wide
range of topics based on self-reports, from questions about drug use (Fendrich and
Vaughn 1994) to questions on voter turnout (Holbrook and Krosnick 2010).13
Surveys based on self-reported experiences are also common in corruption
research. These so called ‘experiential surveys’ are one of the most direct methods
for gauging the amount of corruption in society, by simply asking citizens about
their experiences of corruption (e.g. “Have you in the past 12months had to pay a
bribe in any form?”). The method is now widely deployed by several large organiza-
tions in multi-country surveys (Holmes 2015), including, for instance, the Global
13 See Tourangeau and Yan (2007) for an overview of research on sensitive survey questions.
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Political Behavior (2022) 44:653–678
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Corruption Barometer. Should we expect citizens to truthfully report their first-hand
experiences with corruption and bribery?
Kraay and Murrell (2016) argue that some respondents might be ‘reticent’ to tell
interviewers about their experiences with corruption. The authors develop a statisti-
cal model of potential under-reporting that shows that the true rate of bribe payment
in ten developing countries might be substantially higher than the conventional esti-
mates, based on direct questions. One reason that direct questions might be sensi-
tive is that bribery—in the sense of an official demanding a bribe for a service—
is illegal in essentially every country in the world (Fisman and Golden 2017,pp.
26–29). In an overview of the research on sensitive questions Krumpal (2013) iden-
tifies several studies reporting substantial SB on topics related to criminal behav-
ior and crime-victimization. Admitting to having been part of a corrupt exchange
(for example, paying a bribe) is to admit part in an illegal transaction, and in the
light of this something that could be considered sensitive. Even a question about
whether an individual has been asked to pay a bribe should be sensitive, given that
an affirmative answer signals that the individual potentially also completed the cor-
rupt transaction.
Moreover, corruption is something that people find morally reprehensible. This is
true even in countries where corruption is ubiquitous (Karklins 2005; Persson etal.
2013; Rothstein and Varraich 2017). World value survey (WVS) has been asking
about people’s attitudes towards bribery in several waves and respondents all over
the world typically show a strong distaste for corruption.14 Figure1 shows data from
Romania—one of the most corrupt countries in Europe—based on the most recent
WVS wave for this question. It is clear that there exists a very strong norm against
bribery, even in this context where corruption is widespread: over 80% of respond-
ents say that accepting a bribe can never be justified.
Given this norm, it is hence reasonable that people would view admitting to being
part of a corrupt transaction as something that is ‘socially undesirable’ (Tourangeau
and Yan 2007). The topic of vote-buying has been extensively studied and has obvi-
ous similarities with the case of bribe taking: both acts are illegal transactions that
are generally stigmatized. Traditionally, studies on vote-buying asked direct ques-
tions about its occurrence and found limited evidence. In recent years researchers
have acknowledged that such direct questions might be sensitive and instead con-
sidered more sophisticated survey methods. As a result, several more recent stud-
ies have discovered substantial under-reporting of vote-buying due to SB (Carkoglu
and Erdem Aytac 2015; Corstange 2012; Gonzalez-Ocantos et al. 2012). Impor-
tant to note is that severe under-reporting has been found even for questions ask-
ing respondents whether someone ‘offered’ them to sell their vote—and not only
for question asking if they actually sold their vote. Given these similarities to vote-
buying, the illegality of bribery, and people’s almost unanimous distaste for its
14 In the WVS data (wave 6) the mean answer in the full sample to the question of whether accepting a
bribe can be justified is below 2 on a scale from 1 to 10, where 1 corresponds to “never justifiable”. The
mean value is below 3 for every country in the sample except South Africa (4.1), the Philippines (4.1),
and Haiti (5.2).
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Political Behavior (2022) 44:653–678
occurrence, I argue that we should expect a similar pattern with regard to reported
corruption experiences:
•
H2.
Reported experiences of bribery are subject to sensitivity bias and hence
under-reported.
Research Design andMethods
An Experimental Study inRomania
To test these hypotheses about political bias (
H1
) and sensitivity bias (
H2
) in cor-
ruption reporting I conducted a large survey in Romania, with two embedded sur-
vey experiments. Romania is one of the most corrupt countries in Europe, where
the problem is very much a current issue. After the legislative elections of 2016
the Social Democratic Party (PSD) and the Alliance of Liberals and Democrats
(ALDE) formed the governing coalition. Following massive protests in 2017 against
a bill that (among other things) would have pardoned officials imprisoned for brib-
ery offenses (see The New York Times May 4th, 2017), the government resigned
0.00
0.25
0.50
0.75
Never justifiableAlways justifiable
Share
Justifiable: Someone accepting a bribe
Fig. 1 Data from Romania, World Value Survey, Wave 6
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and was replaced by a second iteration of the coalition. After an internal power
struggle in PSD a third iteration of the PSD–ALDE coalition took office in January
2018. Due to the political turbulence, and partly related to accusations of corrup-
tion within the government, the public support for the coalition decreased over the
course of 2018. In the end of 2018 many opinion polls showed a support of around
35% for the governing coalition.15
Similar situations, with strong politicization of corruption, are not uncommon—
not least in other eastern European countries (Bågenholm 2013). Researchers even
talk about an incumbency disadvantage in many developing democracies, where
holding office seems to decrease chances of reelection. Such an effect has been dem-
onstrated in, for instance, Brazil (Klasnja and Titunik 2017), India (Uppal 2009),
and post-Communist Eastern Europe (Roberts 2008). Klasnja (2015) shows, in a
study of Romanian mayors, that one plausible explanation for this pattern is corrup-
tion, where office holders exploit their position to reap private gains—at the cost of
subsequent electoral success.
The turbulent situation during the past years in Romanian politics is thus rather
typical for many young democracies (e.g. Eastern Europe, Latin America), a group
of countries that also tend to struggle with widespread corruption in politics and in
society overall (Keefer 2007). This makes Romania a relevant case to study. Moreo-
ver, partisanship has generally been relatively weak in Romania in the post-Com-
munist period (Tatar 2013), a pattern that is common in developing democracies.
This makes the case a relatively tough test for the political bias hypothesis. Any
effects found in a context like this, with weak partisanship, are likely to be more
pronounced in contexts where partisanship is stronger. The results from the study at
hand should hence be of high relevance for developing democracies in Europe and
elsewhere, where questions about governance are on the political agenda and politi-
cization of corruption is common.
Testing thePolitical Bias Hypothesis
The aim of the study is to assess each hypothesis in turn with two different research
designs. Codings and analysis-strategies were pre-specified in the pre-analysis plan.
To test the political bias hypothesis the first design exploits question order effects
(Bertrand and Mullainathan 2001). With regard to economic voting, researchers
have shown that question order effects can be substantial (e.g. Wilcox and Wlezien
1996). Sears and Lau (1983) argue that two such effects are common: political pref-
erences might be personalized when assessed immediately after the respondent’s
own economic situation has been made salient, or perceptions of the economic situ-
ation might be politicized when assessed immediately after important political pref-
erences have been made salient. Given my hypotheses in this paper I focus on the
latter. Questions about corruption perceptions subsequent to the political questions
are assumed to exhibit stronger politically biased response patterns since asking the
15 See https ://pollo fpoll s.eu/RO.
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Political Behavior (2022) 44:653–678
political questions make the respondent’s political identity more salient (Klar 2013).
In this sense, the question ordering activates a particular political ‘frame’ around the
corruption questions (Zaller 1992). If my hypothesis about PB is correct, ‘politiciz-
ing’ corruption perceptions in this way will significantly affect the response to these
questions.
In this setup, some respondents (the treatment group) were randomly assigned
to a question ordering where the questions about their political views were asked
before a specific corruption question (political prime), while the rest of the respond-
ents (the control group) were given an ordering where the same corruption question
instead was asked before the political questions. The setup hence randomly increases
the salience of political affiliations for a group of respondents with regard to a spe-
cific corruption question.
Following Evans and Andersen (2006) I asked the following political questions:
(1) What political party would you vote for if the national parliamentary election
were today? (2) Please choose one of the following phrases to say how you feel
about the current government of Romania: ‘Strongly against’, ‘ Against’, ‘ Neither in
favor nor against’, ‘In favor’, ‘ Strongly in favor’. Based on these questions I coded
respondents as government supporters if they said that they would vote for one of
the parties in the current ruling coalition in Romania (PSD and ALDE) and answer
that they are ‘Neither in favor nor against’, ‘In favor’, or ‘Strongly in favor’ of the
current government in Romania. This way I identify government supporters in terms
of vote intention, but exclude respondents that state that they are ‘against the gov-
ernment’ from the definition. I consider different coding decisions with regard to
this variable below.
To measure corruption perceptions I asked three different questions that are com-
monly used in the literature and that are of theoretical interest. The questions are
regularly asked in multi-country surveys like the GCB and the ISSP. Together, the
three questions give a reasonably comprehensive picture of how the respondent per-
ceives current corruption in Romania, both in terms of absolute levels and in terms
of recent change. The questions ask about whether the respondent perceive that cor-
ruption has increased during the past year (corruption increase), how many politi-
cians that are involved in corruption (corruption in politics), and how worried the
respondent is about the consequences of corruption for society (corruption worry). I
also included a standard question about economic perceptions as a point of compari-
son (economy worse). All outcome questions were coded so that high values indicate
‘bad’ outcomes; increased corruption, worsened economy, high political corruption,
and high worry about corruption. See the Supplementary Materials for more details
about these questions.
To avoid artificially induced correlation between different corruption items, while
still retaining statistical power, the experiment had the following structure. First, a
respondent was randomly assigned to one of the four corruption/economy questions
above. This question was asked before any questions about political preferences.
A couple of questions later in the survey the respondent was asked the two politi-
cal questions described above, after which the respondent was randomly assigned
to one of the three remaining corruption/economy questions. This means that each
respondent is part of the control group with regard to one of the corruption/economy
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questions, and part of the treatment group with regard to another of these ques-
tions. For each specific corruption/economy question, about a fourth of the sample
was hence assigned to the control group and a fourth was assigned to the treatment
group. The basic structure of the experiment is illustrated in the Supplementary
Materials where I also test the basic assumptions underlying the design empirically.
The effects of interest with regard to the corruption/economy questions can then
be estimated with a simple regression model:
where
yi
represents the outcome variable of interest,
xi
is an indicator variable equal
to 1 if a respondent is a government supporter and 0 otherwise,
Ti
indicates if a
respondent is in the treatment group (
Ti=1
) or the control group (
Ti=0
),
(xi×Ti)
is an interaction term including
xi
and
Ti,
and
𝜖i
represents the error term. The treat-
ment, again, consists of the intervention of ‘priming’ respondents with their political
preference before answering one of the three corruption questions. In the interest
of space I simply refer to ‘corruption perceptions’ as a catch-all term referring to
all three questions (change, level, and worry—coded in the way described above).
As per
H1a,
I expect
𝛽1
to be
<0
(on average, government supporters perceive cor-
ruption to be lower) and, as per
H1b,
I expect
𝛿
to be
<0
(the effect of the prime
is negative for government supporters—that is, government supporters report even
lower perceived corruption when their political preference has been made salient).16
I consider a confirmation of these expectations for all three corruption outcomes to
be strong evidence in favor of
H1a
and
H1b.
I consider a partial confirmation of the
expectations (finding significant results in the expected direction for one or two of
the outcomes) to be somewhat weaker evidence in favor of
H1a
and
H1b.
17
To facilitate interpretability of the results I first estimate Eq.(1) using OLS with
robust standard errors as the baseline model. The Supplementary Materials include
several additional robustness checks.
Testing theSensitivity Bias Hypothesis
Kraay and Murrell (2016) develop a method to estimate under-reporting of corrup-
tion that is based on modeling respondents’ answers to a combination of a direct
corruption question and several ‘random response’ questions (see Blair etal. 2015).
While the approach definitely has strengths, it also relies on fairly strong assump-
tions about which respondents that actually are ‘reticent’ (and hence would provide
an untruthful answer to the sensitive question). Moreover, the approach also involves
(1)
yi=𝛽0+𝛽1xi+𝛽2Ti+𝛿(xi×Ti)+𝜖i,
16 Specifically,
𝛿
and
𝛽2
are estimates of the conditional average treatment effect (CATE) for government
supporters and others, respectively (Morgan and Winship 2014). A statistically significant
𝛿
indicates
that the effect for government supporters is different from the effect in the ‘others’ group.
17 The economy outcome is used as a point of comparison and does not represent a formal hypothesis to
be tested. My expectations here are analogous to those with regard to the corruption variables: I expect
government supporters to report a more positive view of the economy, and I expect these reports to be
even more positive when respondents are primed with their political affiliation.
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Political Behavior (2022) 44:653–678
asking ten different ‘random response’ questions, each involving the respondent flip-
ping a coin (see Azfar and Murrell 2009 for an earlier version of this approach).
To test the sensitivity bias hypothesis I instead deploy a list experiment, which
was implemented in the middle of the survey.18 This is a conveniently simple survey
method, previously used to estimate the prevalence of sensitive behavior like drug
abuse, cheating, and vote buying, where the respondent does not have to directly dis-
close any information about the sensitive item (see Glynn 2013).19 The list experi-
ment works by aggregating the sensitive item with a list of non-sensitive items so
that the respondent only has to indicate the number of items that apply and not
which specific items that are true. To implement this design, I asked the respondents
to do the following:
Here is a list with different things that you might have done or experienced
during the past 12months. Please read the list carefully and enter how many of
these things that you have done or experienced. Do not indicate which things,
only HOW MANY.
• Attending a work-related meeting;
• Investing money in stocks;
• Being unemployed for more than 9months;
• Discussing politics with friends or family.
The treatment group was shown the same list but with a fifth item added (the item-
order was randomized for all lists):
• Being asked to pay a bribe to a public official.
The design protects the respondents’ privacy since as long as respondents in the
treatment group answer with anything less than “five”, no one directly admits to
answering affirmative to the sensitive question (having been asked to pay bribe).
Following the advice in Glynn (2013) the control items were chosen to be negatively
correlated to avoid floor and ceiling effects (where respondents would select either 0
or all items). As shown by Blair and Imai (2012), if we assume that the addition of
the sensitive item does not alter responses to the control items and that the response
for each sensitive item is truthful, then randomizing respondents into the treatment
and control groups allows the analyst to estimate the proportion affirmative answers
for the sensitive item by taking the difference between the average response among
18 The list experiment was always implemented before the political questions. The randomization with
regard to the list experiment was independent of the randomization with regard to the political bias
experiment.
19 Research shows that the method works well for items with relatively low incidence (de Jonge and
Nickerson 2014)—like corruption experiences.
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Political Behavior (2022) 44:653–678
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the treatment group and the average response among the control group (i.e. a differ-
ence-in-means estimator).20
By asking the sensitive question directly to the control group (who did not receive
the sensitive item on their list) I can also model the amount of sensitivity bias by
comparing the direct question with the estimated proportion of affirmative answers
to the sensitive item in the list experiment. For the direct question I asked: In the
past 12months were you at any point asked to pay a bribe to a public official? The
answer alternatives given were ‘yes’, ‘no’, and ‘prefer to not answer’. I coded affirm-
ative answers as 1 and other answers as 0.21
For the basic analysis of the list experiment I rely on the linear estimator in Imai
(2011), corresponding to a standard difference-in-means estimator. To estimate the
overall level of SB I use the procedure described in Blair and Imai (2012) and com-
pare the predicted response to the direct question, modeled with a logistic regression
model, to the predicted response to the sensitive item in the list experiment.22 The
predicted response to the sensitive item in the list experiment can then be compared
to the response to the direct question to get an estimate of the amount of SB (includ-
ing 95% confidence intervals for the estimates, obtained via Monte Carlo simula-
tions). I consider a SB estimate that is positive and statistically different from 0 to be
evidence in favor of
H2.
To check for heterogeneity in SB I also perform exploratory analyses with regard
to the following variables (the variables were identified based on previous research;
see discussion below): Government supporter, Gender, University degree, Big city
inhabitant, Age, High-income household (top 20% of the distribution in the data
set). For the exploratory analyses I rely on the non-linear least squares (NLS) esti-
mator developed in Imai (2011) (see Supplementary Materials).
The Survey
After a pilot study was conducted to test the questions in the survey as well as one
of the assumptions underlying the PB experiment (see Supplementary Materials),
the final survey was fielded between 19th of December 2018 and 24th of January
2019 in collaboration with the public opinion research company Lucid.23 This is a
survey respondent aggregator that uses quota sampling to target a nationally repre-
sentative sample. Research from the US find that experimental treatment effects on
Lucid match well with effects found in US national probability samples (Coppock
21 The formulation of the sensitive item in the list experiment and the direct bribe question follows the
formulation used in Eurobarometer (2017). This is the less sensitive version of the question that is com-
monly used; the other version asks directly if the respondent have actually paid a bribe. Any estimates of
SB found with regard to the somewhat less sensitive bribe question should therefore arguably be larger
for the more sensitive question.
22 The procedure is implemented in the R package list.
23 https ://luc.id.
20 Since the treatment assignment for the political bias experiment was independent of the treatment
assignment in the list experiment a respondent can either be in both treatment groups (for both experi-
ments), in one treatment group, or in no treatment group.
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and McClellan 2019). Based on two series of power analyses (see Supplementary
Materials) the target number of respondents was set to at least 2900. The sample was
collected based on nationally representative quotas on gender, age, and region.24
3027 Respondents in total completed the survey. Descriptive statistics for the sample
are available in the Supplementary Materials.
Results
Political Bias Experiment
I start by evaluating
H1
(a and b). The unpopularity of the current government is
reflected in the survey: about 24% of the sample said they would vote for a party
in the governing coalition if the national parliamentary election were today. PSD is
still the most popular party in the sample, but its share of the total vote decreases as
many respondents indicated that they would ‘not vote’. The share true ‘government
supporters’ according to the definition above is smaller, at about 14%. While this
group is relatively small, it still contains a large number of respondents (about 410)
given the large overall sample. Below I also consider alternative ways of coding the
‘government support variable’ that utilizes the sample in a different way.
The PB hypothesis predicts that government supporters, on average, should have
a more positive view of corruption in Romania, and that this group should report an
even more positive view when primed with their political affiliation. To test this, I
estimated Eq.(1) for each of the four outcome variables (the three corruption vari-
ables + the economy variable), using OLS.25 The results are reported in Table1.
The coefficient for Government support shows the baseline difference between
government supporters and others for the control group. That is, in the group that
answered the outcome questions before the political questions. The first two models
show the results for the outcomes corruption change and economy change—the two
outcome questions that are the most similar in terms of structure. The results for
these outcomes are also very similar: government supporters in general place them-
selves about one category lower (in the direction of less corruption/better economy)
than the rest of the respondents. The corresponding coefficient for the last other two
outcomes are somewhat smaller, but still highly significant (
p<0.001
). Overall, this
is in line with
H1a
: government supporters report a much less negative view of cor-
ruption in Romania in general and say that they are less worried about the problem.
The interaction effect (Gov. support
×
Prime) estimates the effect of the ‘political
prime’—e.g. being asked about political affiliation before the corruption questions,
rather than the other way around. As shown in the table, the effect is large. For the
24 The age quota had to be relaxed to reach the desired number of completes. The final sample is thus—
as is often the case with online samples—slightly younger and also more educated than the overall
Romanian population (see Supplementary Materials).
25 Given random assignment of the treatment, the basic model in (1) yields an unbiased estimate of the
CATE (for government supporters and ‘others’) (Morgan and Winship 2014). The Supplementary Mate-
rials report several additional specifications, including models with covariates.
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Political Behavior (2022) 44:653–678
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corruption increase outcome the difference between government supporters and oth-
ers increases from 0.9 in the control group to about 1.6 (
(−0.89) + (−0.66)
) in the
treatment group (
p<0.001
). The pattern is, again, similar to that for the economy
worse outcome where the difference increases from 1.2 to 1.7 (
(−1.19) + (−0.47),
p<0.01
). In both cases are government supporters substantially more positive
(or less negative) to begin with, and become even more positive when randomly
assigned to the political prime.
The last two outcomes show the same pattern: government supporters think
corruption in politics is lower and worry less about corruption, a difference that
becomes significantly more pronounced with the political prime (both interaction
effects are statistically significant at the 0.05-level). In this experimental condition
government supporters answer on average about 0.8 to 1 categories lower. To graph-
ically display the results, predicted responses for all four outcomes are shown in
Fig.2.26
In sum, the results provide strong evidence in favor of the PB hypothesis. The
estimates show that reported corruption perceptions differ substantially depending
on whether a respondent supports the government or not. Moreover, the experi-
ment shows how a simple prime (changing the order of the questions) can strongly
affect the results and increase the ‘supporter effect’. This is clearly in line with pre-
vious research on economic perceptions (as also shown by the worse economy esti-
mates), and suggests that respondents, to a significant extent, shape their reported
perceptions to align with their stated political affiliation. This is clear evidence that
respondents’ reported corruption perceptions are not simply a reflection of external
circumstances in society. Rather, when increasing the salience of political affiliations
26 The graph excludes the treatment group for the ‘others’ category to make the graph easier to interpret.
As can be seen in Table1, this category differs little from the ‘others’ category in the control group.
Table 1 The effect of the political prime on perceived corruption
All models are estimated using OLS. Robust standard errors in parentheses (HC2). ‘Prime’ is an indi-
cator variable equal to 1 if a respondent was asked the political questions before a specific corruption/
economy question, and 0 otherwise
∗p
<
0.05;
∗∗p
<
0.01;
∗∗∗p
<
0.001
Dependent variable
Corruption increase Worse economy Corruption in
politics
Corruption worry
(1) (2) (3) (4)
Government support −0.89*** (0.12) −1.19*** (0.11) −0.61*** (0.11) −0.38*** (0.09)
Prime −0.02 (0.06) 0.14* (0.06) 0.02 (0.06) −0.07 (0.05)
Government support
× Prime
−0.66*** (0.18) −0.47** (0.16) −0.38* (0.18) −0.42** (0.13)
Constant 4.20*** (0.04) 3.94*** (0.04) 4.21*** (0.04) 3.46*** (0.03)
Observations 1492 1510 1546 1506
Adjusted R20.14 0.17 0.06 0.06
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respondents seem to engage in a ‘directional reasoning process’ where they use their
response to the corruption question to substantiate their previously stated political
preferences.
The Supplementary Materials include several robustness checks, including alter-
native codings of the supporter variable. While the robustness checks in general cor-
roborates the main results, they also confirm that the ‘prime effect’ for the outcome
corruption in politics is slightly weaker overall.27
●
●
●
●
●
●
1
2
3
4
5
Others
(control)
Gov. supporters
(control)
Gov. supporters
(prime)
Corruption Increase During Past Year
●
●
●
●
●
●
1
2
3
4
5
Others
(control)
Gov. supporters
(control)
Gov. supporters
(prime)
Worse Economy During Past Year
●
●
●
●
●
●
1
2
3
4
5
Others
(control)
Gov. supporters
(control)
Gov. supporters
(prime)
Corruption in Politics
●
●
●
●
●
●
1
2
3
4
Others
(control)
Gov. supporters
(control)
Gov. supporters
(prime)
Worry About Corruption
Estimate
Fig. 2 Predicted responses based on regression estimates in Table1. Including 95% confidence intervals
27 The interaction effect is, however, significant at the 0.01-level throughout all models and the effect
size of 0.31–0.38 scale steps is still substantial. It should also be noted that the estimates for the first part
of
H1
are highly significant in all models for this outcome.
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List Experiment
I now turn to the SB hypothesis. As argued above, it is reasonable to assume that
the often used direct question about bribe experience is sensitive and hence under-
reported. To test this, I started by estimating the proportion of affirmative responses
to the sensitive item in the list experiment using the basic difference-in-means esti-
mator (Glynn 2013). I then estimated a logistic regression intercept-only model with
the responses to the direct bribe question as the dependent variable. The two esti-
mates show what proportion of respondents giving an affirmative answer when their
privacy is protected (in the list experiment) vs when their privacy is not protected
(the direct question). I also used the procedure in Blair and Imai (2012) to compute
95% confidence intervals for the estimates and the difference between them. The
results are presented in Table2 and displayed graphically in Fig.3.
The direct estimate of 19% ‘yes’ is very close to the reported statistic in the 2017
Eurobarometer for Romania at about 18% (Eurobarometer 2017). This stand in stark
contrast to the list estimate at over 35%. The difference of more than 16 percent-
age points is highly statistically significant. This is clear evidence that respondents
under-report the sensitive item when asked directly and suggests that the true esti-
mate might be 90% higher than the estimate based on the commonly used bribe
question.28 As noted above, these estimates are based on the ‘less sensitive’ version
of the bribe question (the other version asking if the respondent actually paid the
bribe), and are also based on a survey mode that should be less likely to elicit SB
(online survey).
Voters under-reporting their experiences with corruption is obviously a serious
problem for researchers or organizations trying to estimate the occurrence of bribery
based on direct questions. However, if this sort of measurement error is randomly
distributed across the population it would still be possible to use direct questions
to explore the dynamics of bribery and assess which individuals or groups that are
most likely to be asked to pay bribes. This is for instance done in Mocan (2004). To
explore if the extent of under-reporting differs between groups I used the NLS esti-
mator to model the relationship between respondent characteristics and responses to
Table 2 Sensitive item (being
asked to pay a bribe), estimates
‘List estimate’ indicates the main estimate from the list experiment,
obtained via the difference-in-means estimator. ‘Direct estimate’ is
the result obtained for the direct question using a logistic regression
model, and ‘Difference’ is the difference between the two estimates
Proportion 95% CI low 95% CI high
List estimate 0.353 0.264 0.442
Direct estimate 0.190 0.170 0.210
Difference 0.163 0.071 0.254
28 The magnitude of this estimate is quite similar to the estimates of under-reporting in Kraay and Mur-
rell (2016).
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the sensitive item (Blair and Imai 2012), based on the six variables described above.
I also estimated a logistic regression model regressing the direct bribe question on
the same variables. Comparisons between the direct estimate and the list estimate
based on these models are shown in Fig.4. The figure displays the results based on
the variables government supporter, gender, and income. In the interest of space, the
results for the variables age, city inhabitant, and education are presented and dis-
cussed in the Supplementary Materials.
Figure4 reveals interesting differences in under-reporting among different sub-
groups. The left-hand graph indicates that government supporters tend to severely
under-report the sensitive item. When asked directly, under 9% of government sup-
porters say that someone asked them to pay a bribe, compared to the list estimate
at 58%. Given the relatively small size of this group the point estimate from the list
experiment needs to be taken with a grain of salt, given the substantial uncertainty
around the estimate.29 The results do suggest, however, that under-reporting is huge
●
●
●
●
●
●
0.0
0.1
0.2
0.3
0.4
Direct estimate List estimate Difference
Estimated Proportion
Fig. 3 Comparison of direct estimate vs list estimate for sensitive item (being asked to pay a bribe). All
respondents
29 It is hence not obvious that this estimate actually is substantially higher than the overall list estimate
of 35%. At the same time, the results strongly suggest that the estimate for the direct question is substan-
tially lower for government supporters.
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among government supporters. This is completely in line with both the SB and PB
hypothesis: government supporters might under-report the sensitive item to make
their group look better and to depict overall corruption levels as lower (Carkoglu
and Erdem Aytac 2015 find a similar pattern with regard to vote buying in Turkey).
It has long been noted that women seem to be less involved in corruption than
men. In an overview of the literature, Heath etal. (2016,pp. 67–72) find that women
have lower risk of being asked for a bribe in all nine studies reviewed. Some have
argued that one reason for this might be that women simple have fewer opportunities
to engage in corrupt activities and that women get asked to pay bribes less often than
men (e.g. Goetz 2007; Mocan 2004). This is also the pattern shown in the direct esti-
mate of about 13% for women and 21% for men. The list estimates, however, suggest
the opposite pattern; when using the indirect questioning method women seem to
be asked for bribes more often than men. The list estimate for women is over three
times as high as the direct estimate—43% vs 13%. This result is interesting, given
that it goes against what much of previous research has argued. At this point I can
only speculate about the reasons behind this pattern. One possibility is that women
as a group are more affected by sensitivity bias.30 The higher list estimate could
reflect the fact that women utilize the health care sector more than men, and that this
sector, according to many estimates, is the sector most permeated by corruption (see
Eurobarometer 2014, 2017).
Finally, Mocan (2004) argues that we should expect income to be positively
related to bribe victimization, given that it should be possible for a rent-seeking
official to extract higher bribes from a wealthier individual (see also Heath etal.
2016,pp. 67–72). This is also the pattern found in the study at hand. Interestingly,
both the list estimate and the direct estimate are substantially higher for individuals
in the top 20% of the income distribution, possibly suggesting a ‘normalization’ of
bribe-paying in this group.
●
●
●
●
●
●
●
●
0.0
0.2
0.4
0.6
0.8
Gov. supporter Others
●
●
●
●
●
●
●
●
0.0
0.2
0.4
0.6
0.8
FemaleMale
●
●
●
●
●
●
●
●
0.0
0.2
0.4
0.6
0.8
Top 20%Not top 20%
●
●
●
●
DirectList
Estimated proportion
Fig. 4 Comparison of direct estimate vs list estimate. Different subgroups: Government supporters, Gen-
der, and Income
30 This pattern is found in Gonzalez-Ocantos etal. (2012), although the estimates are uncertain.
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Overall, these results provide evidence in favor of the SB hypothesis and sug-
gest not only that bribe victimization is under-reported in general, but also that
under-reporting differs substantially between different subgroups. As a consequence,
researchers and practitioners should be very cautious in using direct, obtrusive,
questions about corruption experiences to gauge overall levels of corruption and to
model the dynamics of bribery based on these questions. As in the case of male and
female respondents, using different questioning techniques might lead to opposite
conclusions. Some researchers have suggested that direct questions (or ‘victim sur-
veys’) might be ‘the least problematic of the currently available [corruption] meas-
ures’ (Heath etal. 2016, p. 58). The results in this section suggest that this is far
from obvious.
Conclusions
Respondents’ answers to survey questions are constructed and shaped in many
different ways. Research on survey methodology and public opinion has convinc-
ingly shown that responses often are unstable and strongly affected by things like
social context, motivated reasoning, and particular frames (Bartels 2002; Berinsky
1999; Bertrand and Mullainathan 2001; Taber and Lodge 2006; Tourangeau and
Yan 2007; Zaller 1992). In this paper I argue that these findings have been under-
appreciated by corruption researchers and practitioners using individual-level sur-
vey data. Recent years have seen a steady increase in the availability of different
corruption measures and the use of corruption questions in large multi-country sur-
veys (Fisman and Golden 2017; Heath etal. 2016; Holmes 2015; Wysmulek 2019).
Many important measures and data sets are based on surveys directly probing the
perceptions and experiences of the general public. These indicators have been of
great interest to political scientists and have opened up several new research avenues
that utilizes individual-level data, like the study of corruption and political behavior.
The increase in data availability has not, however, been accompanied by sufficient
reflection about problems and potential pitfalls with regard to these survey-based
measures.
This paper departs from two potential sources of bias that have been demonstrated
in previous research: political bias and sensitivity bias. As a first test of the preva-
lence of these biases in corruption surveys I conducted an original survey fielded to
over 3000 respondents in Romania. The survey aimed at testing two specific hypoth-
eses, in two different embedded experiments, based on these suggested patterns of
response bias. The results from the first experiment provide strong evidence in favor
of the political bias hypothesis (
H1
). Government supporters report a much more
positive view when asked common corruption questions that, in principle, ask about
the objective state of society (Has corruption increased? How common is political
corruption?). Government supporters also report being less worried about corrup-
tion in general, possibly signaling that they attach less importance to the issue. Prim-
ing these respondents with their political affiliation makes this general effect even
more pronounced. This suggests that corruption reports to a significant extent might
be subject to political motivated reasoning and expressive ‘political cheerleading’.
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Researchers should hence be cautious in estimating models with individual-level
measures of corruption perceptions and individual-level political outcomes such as
incumbent support or vote intention. Relationships like these are likely to be affected
by strong feedback mechanisms and reversed causality, especially in surveys asking
political questions before corruption questions. The results also show that responses
to questions about corruption perceptions in general are malleable and affected by
simple frames. This means, for instance, that corruption perceptions among the pub-
lic should be expected to be more polarized along political lines at times when polit-
ical affiliations are more salient, for instance during an election year. From a broader
perspective, the results show that political bias can be substantial even outside of
traditionally studied topics like perceptions about unemployment and inflation (Bar-
tels 2002; Gerber and Huber 2010; Jerit and Barabas 2012), and also an important
factor shaping public perceptions in a multiparty system like Romania with tradi-
tionally weak party identification (Tatar 2013).
The findings from the second experiment on sensitivity bias strongly suggest that
direct questions about corruption experiences need to be treated as sensitive ques-
tions. According to the results, the direct question both fails to accurately capture
the overall occurrence (which is heavily under-reported), and to capture the dynam-
ics of bribery and which groups are most likely to be targeted. This is something
that anyone who uses this, or a similar question, needs to take into account. At the
same time, direct questions are an important tool to gauge actual rates of corruption
victimization—given that alternatives such as perceptions about ‘general levels of
corruption’ can be unreliable, as shown in the PB experiment. Different techniques
to unobtrusively ask sensitive questions do exist, out of which the list experiment is
one. In general, these techniques come at the cost of statistical efficiency, but when
bias is large—like in the study at hand—the bias–variance trade-off should come
down in favor of unbiased (or less biased) estimators (Blair etal. 2018). In essence,
this means that researchers will need larger samples and more sophisticated survey
designs to accurately capture sensitive topics like corruption victimization. Fortu-
nately, recent methodological developments make many of these techniques more
accessible and powerful (Blair and Imai 2012; Blair etal. 2015, 2019; Kraay and
Murrell 2016).
The results thus strongly suggest that corruption measures based on citizen sur-
veys can not be treated as an ‘objective benchmark’ for the true levels of corruption
in a country. At the same time, the findings in this paper should not be taken as a
discouragement of research on corruption or of efforts to quantify the incidence of
corruption. Rather, given the immense importance of the topic, it is crucial that we
scrutinize the methods we use and try to be cognizant of potential sources of error
and bias. I am also not saying that existing measures based on citizen surveys should
not be used, but that researchers should approach such measures with caution and
think about how reporting bias might influence a given estimate.
The experiments in this study are only a first step in identifying these potential
issues. A task for future research is to think more deeply about when and in what
contexts reporting bias is most likely to be present and which techniques that best
can mitigate the problem. As argued above, the Romanian case is rather typical of
many developing democracies, where the question of corruption plays a prominent
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role in political discourse. The patterns uncovered in the study at hand are hence
likely to be present in countries with similar dynamics, including much of Eastern
Europe and several countries in Latin America. An interesting avenue for future
studies is to extend the experiments to political systems with different dynamics
than Romania, for instance systems where party identification is stronger, like Spain.
Overall, this study suggests that paying more attention to issues of response bias is
an important part of further advancing the field of corruption research.
Acknowledgements Open access funding provided by University of Gothenburg. The author would like
to thank Lena Wängnerud, Nicholas Charron, the Quality of Government Institute, and the four anony-
mous reviewers at Political Behavior for their valuable comments and suggestions on earlier drafts of this
article.
Funding Mattias Agerberg was supported by Helge Ax:son Johnsons Stiftelse (Grant No. F18-0296) and
Kungl. Vetenskaps- och Vitterhets-Samhället i Göteborg (Grant No. W2018-0103).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
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