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Who Perceives Corruption? Income, Development, and Forms of Corruption

Kentaro Maeda1 Tokyo Metropolitan University

Adam Ziegfeld2 Beloit College

October 2013

Abstract

How do citizens form perceptions about corruption? In this paper, we advance a theory of

corruption perceptions in which the prevailing form of corruption in society shapes citizens’

beliefs about the pervasiveness of corruption. We begin with the observation that corruption not

only varies across countries in its levels but also in its forms. While grand corruption is the

dominant form of corruption in advanced industrial countries, petty corruption exists alongside

grand corruption in developing countries. Based on this, we predict that corruption engenders

grievances among different segments of the public in advanced and developing countries.

Specifically, the poor will perceive more corruption than the rich in advanced industrial countries,

whereas they will perceive less corruption in developing countries. These predictions are tested

on multiple cross-national surveys that reveal consistent evidence in support of the theory.

1 Associate Professor, School of Law and Politics, Tokyo Metropolitan University (email: maeda-kentaro@tmu.ac.jp). Earlier versions of this article were presented at the 2010 and 2012 Annual Meetings of the Midwest Political Science Association. For comments and suggestions, the authors thank Matthew Amengual, Jennifer Bussell, Miriam Golden, Orit Kedar, Michele Margolis, and participants in the Nuffield Postdoc Lunch Seminar. 2 Corresponding author. Visiting Assistant Professor, Department of Political Science, Beloit College, (email: ziegfelda@beloit.edu).

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“In the West, and among some in the Indian elite, this word, corruption, had purely negative connotations; it was seen as blocking India’s modern, global ambitions. But for the poor of a country where corruption thieved a great deal of opportunity, corruption was one of the genuine opportunities that remained.” −Katherine Boo, Behind the Beautiful Forevers: Life, Death, and Hope in a Mumbai Undercity.

Levels of corruption vary widely across societies. Some countries are riddled with

corruption, whereas in others, it is relatively rare. However, even within the same country,

whether comparatively corrupt or comparatively clean, citizens vary greatly in their beliefs about

the frequency of corruption.3 Why do corruption perceptions vary from one person to the next?

What explains why some citizens within the same society perceive more corruption than others?

Equally important, how do citizens form perceptions about corruption? Understanding variation

in corruption perceptions is important for two reasons. First, perceptions of a phenomenon

matter—irrespective of their accuracy—because perceptions can motivate behavior. 4 The

perception of widespread corruption can diminish one’s trust in both government and other

members of society,5 discourage political participation,6 and influence vote choice.7 In addition,

perceptions are a crucial mechanism in explaining corruption’s harmful effects on a society. For

3 Charles L. Davis, Roderic Ai Camp, and Kenneth M. Coleman, “The Influence of Party Systems on Citizens’ Perceptions of Corruption and Electoral Response in Latin America,” Comparative Political Studies, 37 (August 2004), 677-703; Benjamin A. Olken, “Corruption Perceptions vs. Corruption Reality,” Journal of Public Economics, 93 (August 2009), 950-64; Yuliya V. Tverdova, “See No Evil: Heterogeneity in Public Perceptions of Corruption,” Canadian Journal of Political Science, 44 (March 2011), 1-25. 4 Alan S. Gerber and Gregory A. Huber, “Partisanship and Economic Behavior: Do Partisan Differences in Economic Forecasts Predict Real Economic Behavior?,” American Political Science Review, 103 (August 2009), 407-26. 5 Eric C.C. Chang and Yun-han Chu, “Corruption and Trust: Exceptionalism in Asian Democracies?,” Journal of Politics, 68 (May 2006), 259-71; Bo Rothstein and Daniel Eek, “Political Corruption and Social Trust: An Experimental Approach,” Rationality and Society, 21 (February 2009): 81-112. 6 Oliver Cover, “Political Corruption, Public Opinion, and Citizens’ Behaviour, ” D.Phil. Thesis, Department of Politics and International Relations, University of Oxford, 2007. 7 Kazimierz Slomczynski and Goldie Shabad, “Perceptions of Political Party Corruption and Voting Behavior in Poland,” Party Politics, 18 (November 2012), 897-917.

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example, corruption is widely thought to slow economic growth.8 One way it does so is direct. If

businesses must pay bribes to do business, they have less money available to invest in the

economy. However, another mechanism involves perceptions; foreign investors who expect to

encounter corruption invest their money elsewhere, whether their fears are justified or not. Since

perceptions can motivate behavior, knowing who is likely to perceive more corruption can

contribute to a better understanding of a wide range of individual-level behaviors.

Second, understanding individual-level variation in corruption perceptions matters

because scholars often rely on perceptions of corruption as a measure of actual corruption.9

Some have pointed out that corruption perceptions can be biased in systematic ways.10 However,

since corruption is notoriously difficult to measure, perceptions are likely to remain an important

element in the cross-national measurement of corruption. Therefore, knowing how and why

individuals vary in their perceptions of corruption is critical for understanding potential biases in

perceptions-based measures of corruption and the implications of those biases for empirical

research.

To explain variation in corruption perceptions, we advance a novel theory of how

individuals are affected by the societal context in which they operate. Specifically, we focus on

the prevailing form of corruption in society and how it interacts with an individual’s socio-

8 Paolo Mauro, “Corruption and Growth,” Quarterly Journal of Economics, 110 (August 1995), 681-712; J. Edgardo Campos, Donald Lien, and Sanjay Pradhan, “The Impact of Corruption on Investment: Predictability Matters,” World Development 27, (June 1999), 1059-67. 9 Daniel Treisman, “The Causes of Corruption: A Cross-National Study,” Journal of Public Economics, 76 (June 2000), 399-457; Gabriella R. Montinola and Robert W. Jackman, “Sources of Corruption: A Cross-National Study,” British Journal of Political Science, 32 (January 2002), 147-70; John Gerring and Strom C. Thacker, “Political Institutions and Corruption: The Role of Unitarism and Parliamentarism,” British Journal of Political Science, 34 (April 2004), 295-330. Jana Kunicová and Susan Rose-Ackerman, “Electoral Rules and Constitutional Structures as Constraints on Corruption,” British Journal of Political Science, 35 (October 2005), 573-606; Margit Tavits, “Clarity of Responsibility and Corruption,” American Journal of Political Science, 51 (January 2007), 218-29. 10 Mireille Razafindrakoto and François Roubaud, “Are International Databases on Corruption Reliable? A Comparison of Expert Opinion Surveys and Household Surveys in Sub-Saharan Africa,” World Development, 38 (August 2010), 1057-69.

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economic status in forming her perceptions of corruption. Because different forms of corruption

should engender stronger grievances among certain segments of the population, the effect of

socio-economic status should vary according to the forms of corruption prevailing in a country,

which in turn depends on the country’s level of economic development. In advanced countries,

where petty corruption is relatively infrequent and the most well-known cases of corruption take

the form of high-level or grand corruption, the socio-economically disadvantaged should

perceive relatively more corruption. By contrast, in developing countries, where petty and grand

corruption are both pervasive, the socio-economically privileged should perceive relatively more

corruption. Thus, we theorize that the relationship between socio-economic status and corruption

perceptions varies across countries because the forms of corruption that prevail in rich and poor

countries differ, generating more intense grievances among different income groups.

We test this theory on dozens of surveys from countries around the world and find that

the socio-economically disadvantaged indeed perceive more corruption that the relatively

advantaged in advanced industrial countries. In developing countries, this relationship is

frequently reversed, with the disadvantaged perceiving less corruption. Our results therefore

suggest that the kinds of people whose behavior is likely to be affected by beliefs of widespread

corruption differ across countries. The comparatively poor in advanced industrial countries and

the comparatively affluent in developing countries are likely to perceive higher levels of

corruption that their fellow citizens. These results suggest potential problems in the use of

corruption perception measures as proxies for corruption. To the extent that perceptions-based

measures often rely on elite respondents, the biases of these respondents may run in different

directions across countries. In wealthy countries that are usually thought of as relatively free of

corruption, respondents to elite surveys may systematically understate levels of corruption

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(relative to others in their societies), whereas in poorer countries, a comparable set of elites may

overstate corruption.

The remainder of this article proceeds in five sections. The first section reviews previous

research on corruption perceptions, highlighting the literature’s inconsistent findings. Next, the

second section outlines our theory of corruption perceptions, explaining why socio-economic

status ought to shape corruption perceptions in different ways depending on a country’s level of

economic development. The third section tests our theory on data from the Comparative Study of

Electoral System (CSES), International Social Survey Programme (ISSP), and World Values

Survey (WVS). The fourth section discusses our findings, and the fifth section concludes.

1. Existing Studies of Corruption Perceptions

Since bureaucrats and politicians go to great lengths to hide corruption and many citizens

are unwilling to admit to participation in corrupt activities, scholars have long used perceptions

of corruption as an approximation of a society’s actual level of corruption, particularly in cross-

national studies. Nevertheless, some scholars have considered individual-level corruption

perceptions as an independent variable, exploring how a person’s perceptions of corruption

influence their trust in government,11 beliefs about government legitimacy,12 propensity to vote,13

and likelihood of engaging in corruption.14 Comparatively little research has, however, treated an

individual’s beliefs about corruptions as a dependent variable worth explaining in its own right.

11 James A. McCann and Jorge I. Domínguez, “Mexicans React to Electoral Fraud and Political Corruption: An Assessment of Public Opinion and Voting Behavior,” Electoral Studies, 17 (December 1998), 483-503. 12 Christopher J. Anderson and Yuliya V. Tverdova, “Corruption Political Allegiances, and Attitudes Toward Government in Contemporary Democracies,” American Journal of Political Science, 47 (January 2003), 91-109. 13 Mitchell A. Seligson, “The Impact of Corruption on Regime Legitimacy: A Comparative study of Four Latin American Countries,” Journal of Politics, 64 (May 2002), 408-33. 14 Margit Tavits, “Why Do People Engage in Corruption? The Case of Estonia,” Social Forces, 88 (March 2010), 1257-79.

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Among the relatively few studies that explain individual-level variation in perceptions of

corruption, one set of explanations involves attitudinal correlates. Research in this vein has

shown that partisanship,15 ideological leanings,16 support for the incumbent government,17 and

levels of trust18 can all predict variation in perceptions of corruption. One limitation inherent in

any observational study involving the relationship between various attitudes and beliefs is the

possibility of reverse causality. Partisanship, ideology, incumbent support, and trust can not only

influence one’s beliefs about the pervasiveness of corruption, but these same attitudes and

predispositions can almost certainly be affected by beliefs about the pervasiveness of corruption,

leading to inaccurate estimates about the causal effect of attitudes on corruption perceptions.

A second set of explanations involves socio-demographic attributes: income, education,

age, and gender. The advantage of examining the relationship between these variables and

corruption perceptions is that these relationships are less likely to suffer from reverse causality.

One’s corruption perceptions cannot plausibly affect one’s gender or age, and the likelihood that

they affect income and education is also low.19 Interestingly, research focusing on socio-

demographic attributes has arrived at very mixed findings. For instance, Tverdova’s multi-

country study finds that wealthier and older people perceive more corruption and women

perceive less.20 Consistent with her finding, Davis et al. find that in Chile and Mexico higher

incomes are associated with perceptions of more corruption; however, they also find that in

Costa Rica higher incomes are associated with perceptions of less corruption. Olken’s study of

15 Davis et al. 16 Razafindrakoto and Roubaud. 17 Tverdova. 18 Cover; Eric M. Uslaner, Corruption, Inequality, and the Rule of Law: The Bulging Pocket Makes the Easy Life (New York: Cambridge University Press, 2008). 19 Perceptions of widespread corruption may influence a person’s decision to invest in higher education, meaning that reverse causality with income and education cannot be ruled out entirely. However, the problem of reverse causality is far less severe that for attitudinal variables. 20 Tverdova.

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corruption in Indonesian road building projects shows that women are less likely to report

corruption (like Tverdova), while the educated are more likely to do so.21 However, using British

data, Cover finds no relationship between personal characteristics (other than age) and corruption

perceptions.22 Finally Redlawsk and McCann find with their American respondents that older,

more educated, and wealthier respondents perceive less corruption, while women perceive

more.23 In short, existing studies have reported inconsistent findings about the relationship

between socio-demographic traits and corruptions perceptions. Moreover, because many of these

studies are not primarily concerned with the relationship between socio-demographic

characteristics and corruption perceptions—but instead investigate other questions—the existing

literature has not addressed these inconsistent findings.

Our contribution to the literature on corruption involves exploring how the relationship

between individual attributes and corruption perceptions depends on the national context. Unlike

most prior studies that consider a variety of socio-demographic characteristics as control

variables for individual attitudes, we focus on the effect of an individual’s income and provide a

theory explaining how it is related to corruption perceptions in different ways across countries.

The varying forms of corruption that exist across countries provides a clue as to why the

literature has so far arrived at mixed findings about the relationship between individual

characteristics and perceptions of corruption.

2. A Contextual Theory of Corruption Perceptions

21 The results for education are robust across all specifications; the results for gender and age are not. 22 Cover 2007, p. 152. 23 David P. Redlawsk and James A. McCann, “Popular Interpretations of ‘Corruption’ and Their Partisan Consequences,” Political Behavior, 27 (September 2005), 261-83

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In this section, we advance a theory of corruption perceptions in which individuals’

beliefs about corruption depend on both their own economic position in society and the form of

corruption that prevails in the country in which they live. We start from the observation that

corruption varies not only in its levels but also in its forms. Corruption is most commonly

defined as the “the misuse of public office for private gain.”24 It comprises a variety of

behaviors, including when politicians and bureaucrats steal money and resources from public

coffers, demand remuneration from citizens in return for special favors, or make the provision of

government goods and services conditional on the provision of bribes or votes. Many students of

corruption draw a distinction between grand corruption and petty corruption.25 Grand (or elite-

level) corruption occurs when politicians or bureaucrats engage in outright predation or take

bribes from major financial interests in return for lucrative contracts or favorable regulation.

Grand corruption involves a small number of elite actors. By contrast, petty corruption involves

low-level bureaucrats and the average citizen, usually in small-scale bribery by citizens in an

attempt to get preferential access to state resources, secure resources to which they are entitled,

facilitate minor rule-breaking, or avoid punishment for infractions of the law.

Much research has demonstrated a strong negative association between economic

development and corruption.26 Beyond just aggregate levels of corruption, however, economic

development is also correlated with the forms of corruption that prevail in a society.27 In most

24 Treisman 2000, p. 399. 25 Uslaner; Michael Johnston, Syndromes of Corruption: Wealth, Power, and Democracy, (New York: Cambridge University Press, 2005) also refers to syndromes of corruption, which vary in the extent to which they involve elite actors versus the general public. 26 Alberto Ades and Rafael DiTella, “Rents, Competition, and Corruption,” American Economic Review, 89 (September 1999), 982-93; Treisman. 27 Uslaner. Johnston describes several syndromes of corruption. Those that involve mainly grand corruption “influence markets” and “elite cartels” are, not surprisingly, found in wealthy countries (US, Japan, Germany, Italy, Korea, Botswana), whereas those involving both grand and petty corruption “oligarchs and clans” and “official moguls” are poorer (Russia, Mexico, Philippines, China, Kenya, and Indonesia).

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advanced industrial democracies—where corruption tends to be low—some measure of grand

corruption typically persists. By contrast, in developing democracies—where corruption is

generally more pervasive—widespread petty corruption exists alongside grand corruption.

To verify this link between a country’s level of economic development and its

predominant form of corruption, we must move beyond conventional measures that report a

single corruption score for each country and instead examine data that disaggregates corruption

into multiple categories. Transparency International’s Global Corruption Barometer survey asks

respondents around the world to evaluate levels of perceived corruption associated with various

sectors in their society. We use the data from the 2010/2011 survey and compare corruption

among civil servants with corruption among businessmen. Corruption among businessmen

implies grand corruption, such as kickbacks to legislators or large bribes to government

regulators. Corruption among public officials suggests the possibility of petty corruption in

addition to grand corruption. Perceived corruption among the police or low-level officials in

bureaucracies related to education or public utilities likely reflects small bribes that citizens must

pay to avoid police harassment or get the police to investigate a complaint, secure a job as a

teacher or a spot in a school, or expedite a telephone or electrical connection.

As outcome variables, we use the percentage of citizens who viewed each of these two

sectors, businessmen and civil servants, as either “corrupt” or “extremely corrupt.” Figure 1 plots

these variables on the y-axis against the level of per capita GDP on the x-axis, and fits a

regression line for both outcomes using GDP to predict corruption perceptions. The figure shows

that in poor countries, people tend to consider public officials to be more corrupt than

businessmen. In contrast, citizens in rich countries tend to consider businessmen as relatively

more corrupt than public officials. This finding suggests that, at least from the perspective of

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citizens, corruption in poor countries is more often about petty corruption by public officials,

whereas corruption in rich countries tends to involve transactions between businessmen and the

government.

[Figure 1 about here]

Given that the form of corruption that predominates in poor countries differs from the

form that predominates in rich countries, we do not expect that corruption perceptions should

necessarily operate in the same way across all countries. Citizens in wealthy and poor countries

should, in fact, perceive different kinds of corrupt behaviors that ought, in turn, to have very

different implications for who in a society feels most aggrieved by corruption. In wealthy

countries, where grand corruption is the dominant form of corruption, relatively few individuals

actually engage in corruption, and those who engage in—and benefit from—corruption are those

for whom corruption amplifies existing wealth and power. Already powerful politicians gain

income from bribes, while wealthy businesspeople increase their profits from business. By

contrast, in poorer countries with both petty and grand corruption, the pervasiveness of petty

corruption means that far larger sections of the public are implicated in corruption. Citizens of all

stripes must engage in corruption as part of their dealings with the state. The kinds of citizens

who ultimately benefit from petty corruption are therefore less obvious. Low-level bureaucrats

who may enjoy little social and economic prestige often benefit from petty bribe taking, and

corruption may even be seen by some poor citizens as an opportunity to game a system that that

is otherwise stacked against them, as captured by the quote from Katherine Boo’s account of life

in the slums of Mumbai at the beginning of this article.

2.1 Expectations from Advanced Economies

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Based on the different forms of corruption that prevail in societies at different levels of

economic development, we expect that citizens’ perceptions of corruption should vary based on

their economic position. Uslaner succinctly summarizes the intuition behind this proposition

about an individual’s income and the form of corruption when he argues that “While petty

corruption helps a large number of people cope with broken public and private sectors, where

routine services are rarely provided routinely, grand corruption enriches a few people… People

do not associate petty corruption with inequality. They do make a clear connection between

inequity and grand corruption.”28 For the poor in an affluent society, grand corruption is a blatant

signal that the rich are further enriching themselves and excluding the already disadvantaged

from their society’s relative affluence. Corruption reinforces any existing beliefs that the political

and economic system has marginalized them.29 We therefore expect the poor in a wealthy

country to be far more troubled by corruption and for it to engender a greater sense of grievance

that for the comparatively wealthy, who have less of a proverbial ax to grind with the existing

political and economic system.

This heightened sense of grievance should translate into perceptions of more widespread

corruption for two distinct reasons. The first involves the resolution of cognitive dissonance.30 If

corruption deeply troubles an individual or if she feels that it greatly harms her, then she may

overestimate corruption’s frequency to justify her preoccupation with it, thereby bringing her

beliefs about corruption’s pervasiveness into line with her beliefs about the gravity of the issue.

28 Uslaner, p 11. 29 Redlawsk and McCann find that in the United States, poorer respondents tend to interpret corruption more broadly to include favoritism and not just activities such as bribery. This finding is arguably consistent with the idea that the poor in affluent countries tend to see corruption as part of a broader process that reinforces inequality and their own relatively low status in society. 30 See Leon Festinger, A Theory of Cognitive Dissonance, (Stanford, CA: Stanford University Press, 1957) for the original formulation of cognitive dissonance theory and Sendhil Mullainathan and Ebonya Washington, “Sticking with Your Vote: Cognitive Dissonance and Political Attitudes,” American Economic Journal: Applied Economics, 1 (January 2009), 86-111 for a recent example of cognitive dissonance theory applied to politics.

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Conversely, the relatively affluent in an advanced economy, for whom revelations of grand

corruption are likely to be less troubling, may underestimate corruption’s frequency to

unconsciously rationalize their indifference to the issue. The second mechanism involves the

selective consumption of information on corruption. 31 Those who are angered about and

aggrieved by corruption may pay more attention to information about revelations of corruption

and be more likely to recall stories about corruption that color their beliefs about corruption’s

pervasiveness. Meanwhile, those who are relatively unconcerned about corruption will pay less

attention to information about corruption, recall fewer instances of it, and therefore perceive the

phenomenon to be less widespread. All told, our first hypothesis is the following.

Hypothesis 1: In advanced economies, the poor should perceive higher levels of

corruption than the rich.

2.2 Expectations in Developing Economies

In poorer countries, as petty corruption becomes more pervasive, we expect a very

different relationship between an individual’s income and her corruption perceptions. Whereas

Uslaner shows that grand corruption is tightly bound up with inequality in most people’s minds,

petty corruption is not a phenomenon that exclusively benefits the wealthy at the expense of the

poor. For one, rich people and poor people alike can be the victims of petty corruption. When

trying to secure a phone connection from a government telephone monopoly, officials may

demand bribes from all citizens. For another, the beneficiaries of corruption can include not only

31 Much research shows that people selectively expose themselves to information that reinforces existing political attitudes. See, for example, Diana C. Mutz and Paul S. Martin, “Facilitating Communication across Lines of Political Difference: The Role of Mass Media,” American Political Science Review, 95 (March 2001), 97-114 and Natalie Jomini Stroud, “Media Use and Political Predispositions: Revisiting the Concept of Selective Exposure,” Political Behavior, 30 (September 2008), 341-66. Similarly, individuals may also pay greater attention to information that confirms pre-existing beliefs.

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those who are relatively powerful and affluent (as with grand corruption), but also low-level

bureaucrats and clerks who may struggle to make ends meet. The fact that victims and

beneficiaries of petty corruption can be found among rich and poor alike might at first suggest

that the relationship between income and corruption perceptions in developing countries ought

simply be attenuated relative to advanced economies. However, for two reasons, we expect that

the pervasiveness of petty corruption in developing countries should result in the wealthy

perceiving more corruption than the poor—the opposite of what we expect in affluent countries.

First, pervasive petty corruption is likely to be perceived by the wealthy in poor countries

as a greater affront because the wealthy pay into the state to a far greater extent than the poor. In

their study of citizen-state relations in India, Corbridge et al. note that although the poor express

“their concern about corruption...for the most part ‘what matters to villagers is perhaps how

much reaches them, not how much is siphoned off’. Where the state is seen mainly as provider of

funds rather than as a collector of taxes, as it is in much of rural India, it is perhaps

understandable that villagers would come to such a conclusion.”32 Put another way, the poor may

see the failure of the state to provide non-corrupt public services as unfortunate and worthy of

censure, but less as a breach of some kind of social contract. By contrast, for the wealthy, who

are net payers into the state, pervasive petty corruption may be an aggravating reminder of how

little they get in return for their contribution. The widespread protests in Brazil in the summer of

2013 represent a recent example of this dynamic, as mainly middle-class demonstrators took to

the streets to demand less corrupt, more efficient public services.

Second, petty corruption can also invert established social and economic hierarchies,

leading the wealthy to develop a more uniformly negative affect toward corruption than the poor. 32 Stuart Corbridge, Glyn Williams, René Véron, and Manoj Srivastava, Seeing the State: Governance and Governability in India (Cambridge, UK: Cambridge University Press, 2005), pp. 174-175.

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Those who extract bribes in petty corruption—police officers, clerks, low-level bureaucrats—

often wield little economic, social, or political clout outside of the small set of powers available

to them through their jobs with the state. Their ability to demand bribes or potentially withhold

services from those over whom they would normally have little power can lead to feelings of

frustration and aggravation among the comparatively affluent. Meanwhile, for the poor,

corruption can sometimes appear to be an equalizer in a system in which they are typically the

losers. For example, Jeffrey’s ethnography of sugar cane cooperatives in north India reveals that

“when SCs, MBCs, Muslims, or poorer Jats [all disadvantaged groups] are able to obtain

political purchase within Cane Societies, they tend to celebrate their capacity to engage in

corrupt activity. Examples of low-caste involvement in corrupt practices are considered to be

indicative of their refusal to ‘sit still’ or ability to ‘stand on their own two feet’”33 and engage in

the same kinds of practices that were long available only to privileged groups.

In short, irrespective of the actual harm done by corruption,34 the wealthy should see

petty corruption not only as a failure of the state they finance but also as an occasion when their

social and economic inferiors can extort money from them. By contrast, petty corruption may

represent opportunities for the poor, who expect little from the state in any case, to engage in the

same practices traditionally undertaken by the more privileged. Even among those who are

economically disadvantaged and see petty corruption for the economic burden that it is,

corruption may not stand out as any more egregious than the other disadvantages and inequities

that the poor often face in developing countries. Combined with the same set of mechanisms

33 Craig Jeffrey, “Caste, Class, and Clientelism: A Political Economy of Everyday Corruption in Rural North India,” Economic Geography, 78 (January 2002), 21-41, p. 38. 34 Our hypothesis does not, for a moment, suggest or rest on the assumption that the poor are actually harmed less by petty corruption. On the contrary, petty corruption can often exact a very high toll on the poor—one that they can ill afford.

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described above—resolution of cognitive dissonance and selective consumption of

information—we arrive at our second hypothesis.

Hypothesis 2: In developing countries, the rich should perceive higher levels of

corruption than the poor.

3. Data Analysis

The previous section developed a set of expectations about the relationship between an

individual’s socio-economic status and her perceptions of corruption. Our argument began with

the distinction between the forms of corruption that prevail across differing levels of economic

development. Different forms of corruption, whether petty or grand, should generate grievances

among different segments of the population. Cognitive dissonance and the selective consumption

of information about corruption should, in turn, lead certain groups to perceive higher levels of

corruption than other groups. The ultimate result is our two hypotheses about the link between

individual income and corruption perceptions in advanced and developing economies.

In this section, we test our two hypotheses against the data, examining whether and how

corruption perceptions are associated with individual-level income. After describing the data, we

first fit regression models assuming that corruption perceptions and individual-level income are

associated in the same way across all countries. We then relax this assumption and allow the

regression coefficients to vary by country, thereby permitting us to test our two hypotheses and

examine whether the relationship between income and corruption perceptions in fact varies by a

country’s level of economic development. Finally, to take the uncertainty of the estimates into

account, we repeat the analysis using multilevel models. Overall, we find convincing evidence

that poorer citizens perceive higher levels of corruption in advanced industrial countries, which

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is consistent with our first hypothesis. We also find suggestive evidence in favor of our second

hypothesis, that wealthier citizens in developing countries perceive higher levels of corruption.

3.1 The Data

To examine whether individual-level income predicts perceptions of corruption, we use

cross-country surveys that ask citizens how they evaluate the state of corruption in their

respective countries. The surveys that we use are Module 2 of the Comparative Study of

Electoral Systems (CSES), conducted between 2001 and 2005 in 39 countries, Wave 3 of the

World Values Survey (WVS) from 1994 to 1999 in 47 countries, and the International Social

Survey Programme (ISSP) from 2006 in 33 countries.35 Working with multiple surveys ensures

that our findings do not reflect the idiosyncrasies inherent in any single survey. Furthermore,

these surveys cover countries that vary widely in their levels of economic development. In each

of these surveys, we use the level of perceived corruption as the outcome variable and an

individual’s income as the predictor.

The major challenge in using multiple surveys is comparability, since the surveys vary in

how they measure corruption perceptions. CSES asks respondents “How widespread do you

think corruption such as bribe taking is amongst politicians in your country?” Respondents

choose from a four-point scale that ranges from “very widespread” to “it hardly happens at all.”

In WVS, the question is somewhat similar but does not single out corruption among politicians.

It asks: “How widespread do you think bribe taking and corruption is in this country?” To this,

respondents may select answers ranging from “Almost all public officials are engaged in it” to

“Almost no public officials are engaged in it.” ISSP asks two separate questions, one for 35 The number of countries is the number for which both the corruption perception and income variables are available. The list of countries is available in the online appendix.

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politicians and one for public officials: “In your opinion, how many [politicians/public officials]

in your country are involved in corruption?” Respondents answer on a five-point scale from

“Almost none” to “Almost all.”

The surveys also measure income in different ways. ISSP provides raw household

income figures denominated in the national currency, and in some countries the respondents are

categorized into several income groups. CSES reports a five-point scale based on income

quintiles that the interviewer converts from the raw figures reported by the respondent. Because

CSES uses quintiles, roughly the same number of respondents falls into each income category,

irrespective of the country’s income distribution. In WVS, household income is measured on a

ten-point scale based on the self-assessment of respondents.

First, we examine the raw data to uncover the broad patterns in the data. For the outcome

variables, we use the original scales for corruption perception in CSES and WVS, and take an

average of the two corruption perception variables for ISSP. As for the predictor, we use the

original income scales for the CSES and WVS. Because the ISSP figures were reported in

national currency units, we recoded the variables into US dollars and took the log as the

predictor. 36 After conducting the analysis using the raw data, the results remain mostly the same

when we standardize the variables.37

3.2 Regression with Pooled Data

36 Without taking logs, the coefficients for poor countries would be artificially inflated. The effect of increasing income by 1000 dollars should have a larger impact on the status of respondents in developing countries where incomes are generally lower than in advanced countries, even if the relationship between social status and corruption perception is the same in the two countries. 37 See the online appendix for details.

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The basic problem in using cross-country surveys to tackle our question is the huge

variation in the average level of perceived corruption in each country. In all three datasets, richer

countries tend to have lower average levels of perceived corruption than poorer countries.

Sample sizes vary across countries in each survey, but the number of respondents is reasonably

large in both rich and poor countries, indicating that differences in levels of corruption

perceptions are not likely to be generated by chance. In this case, we cannot simply regress the

level of corruption perception of each respondent on the individual level variables using the

entire dataset. If some unobserved country-level factor affects both the predictor and the outcome,

the estimates will be biased. For example, because income levels are likely to be higher and the

mean level of perceived corruption lower in rich countries, we may wrongly infer that wealthier

citizens are more likely to perceive lower levels of corruption, even if no such relationship exists

in most countries.

A common way to tackle this problem is to fit a Least-Square Dummy Variable (LSDV)

model with individual corruption perceptions as the outcome and individual characteristics as the

predictors, with a dummy variable for each country. Formally, this is:

yij=β01+ β0jDjnj=2 +β1xij+εij (1)

Estimating this equation with Ordinary Least Squares (OLS) predicts the level of

perceived corruption y for each citizen i in country j as a function of his or her attributes xij with

a common slope parameter β1. The constant term β01 is the intercept for the baseline country in

the dataset.38 For the rest of the counties, β01+β0j indicates the intercept for country j. The

dummy variable Dj takes a value of 1 for country j, and 0 otherwise.

38 In this section, we use the United States as the baseline country in all estimations.

19

Two points are worth noting here. First, we report the results for models that control

only for the unobserved effects for each country. We take this minimalist approach because

individual-level control variables may induce post-treatment bias if they are affected by the

predictor of interest.39 Second, we choose not to fit categorical choice models. Although the

categorical nature of the outcomes suggest that we fit ordered logit or probit models, these

models are most useful for predicting probabilities that are close to either zero or one. In contrast,

linear models are more straightforward when it comes to interpreting how a unit difference in the

predictor is associated with a difference in outcomes.40

The results of these initial estimations are shown in Table 1, with each column

representing an identical regression using a different data source, CSES (column 1), WVS

(column 2), and ISSP (column 3). The coefficients on income are all negative and statistically

significant, indicating that wealthy citizens tend to perceive lower levels of corruption compared

to other citizens.

[Table 1 about here]

As a first cut, these results suggest that, on the whole, wealthy citizens tend to perceive

higher levels of corruption. However, the support is no more than modest once we interpret the

results. For example, the coefficient on income in column (1) is -0.019, which translates to a

difference of only 0.08 on a four-point scale between citizens in the top income quintile and the

bottom income quintile. By pooling the data, the results in Table 1 potentially obscure variation

across countries—variations that we expect to observe based on our two hypotheses.

39 Gary King and Langche Zeng, “The Danger of Extreme Counterfactuals,” Political Analysis, 14 (Spring 2006), 131-59. 40 Joshua D. Angrist and Jörn-Steffen Pischke, Mostly Harmless Econometrics: An Empiricist’s Companion, (Princeton, NJ: Princeton University Press, 2009), p. 107.

20

3.3 Regression without Pooling

Consistent with our expectations, we now assume that the relationship between

corruption perceptions and individual income varies across countries. Allowing each country to

have its own regression coefficient requires us to move beyond the LSDV model. Here we fit

separate regressions for each country without pooling the data. In other words, we treat the data

for each country j as a separate dataset, and fit the following regression.

yij=β0j+β1jxij+εij (2)

Compared with equation (1), both the intercept and slope parameters are different for all

countries in this specification. Each country j now has its own intercept β0j and slope parameter

β1j. In order to estimate these parameters, we fit dozens of regressions for each of the datasets.

Figure 2 presents the results. The black circles show the estimated coefficients, and the bars

show the 95% confidence intervals. In countries with estimates on the left half of the figure, poor

citizens perceive higher levels of corruption, whereas in the countries on the right half of the

figure, rich citizens perceive corruption to be more widespread. The gray circles in the figure

represent estimates controlling for education and gender. We also added urban residence and

unemployment, but the estimates did not noticeably change. Since these variables were not

available for many countries, we do not include them in the analysis presented here.

[Figure 2 about here]

The figure reveals a striking variation across countries. What is more, the dotted line in

the figure indicates that the regression coefficient estimated from pooled data in Table 1 (-0.019)

is outside the confidence intervals of almost half of the countries in the dataset. In the most

extreme case, the coefficient for the United States is -0.11. This indicates that the level of

corruption perception of citizens in the richest quintile is likely to be 0.4 points lower than the

21

citizens in the poorest quintile on a four-point scale. The addition of control variables does not

drastically change the results. Despite the correlation between income and the control variables,

the estimates are roughly the same except for Finland and Portugal.

Having analyzed respondents from each country separately, the question is whether these

regression coefficients are systematically associated with the level of economic development as

we expect. To answer this question, we examine how the regression coefficients vary across

societies at different levels of economic development. For the level of economic development,

we use the per capita GDP in the year the survey was conducted. Figure 3 shows the relationship

between the regression coefficients for income and levels of economic development. GDP per

capita is plotted on the x-axis and the regression coefficients on the y-axis. The black circles

show the regression coefficient in each country, and the bars show the 95% confidence intervals.

In order to indicate the general pattern in the data, we fit a lowess line on the estimated

coefficients predicted by GDP per capita.

A clear pattern emerges. In the WVS dataset, for example, the regression coefficients for

income range from +0.08 in Nigeria with a per capita GDP of $1,211 to -0.11 in Norway with a

per capita GDP of $37,148. In this figure, most of the coefficients on the right side of the figure

(i.e., high GDP) are negative. The left side of the figure (i.e., low GDP) features a fair number of

negative coefficients, though some positive ones as well. A similar pattern emerges from the

other two datasets. In both surveys, the lowess line shows a negative slope that intersects the x-

axis at low levels of per capita GDP. In advanced countries, higher-income citizens tend to

perceive lower levels of corruption compared to other citizens. In developing countries, the

relationship runs in the opposite direction, but is substantially weaker. The pattern for each

22

survey is roughly the same if we include the other predictors as controls.41 To summarize, the

analysis in this section shows that, consistent with Hypothesis 1, economically disadvantaged

citizens perceive higher levels of corruption compared to other citizens in the rich countries. In

poorer countries, we find more modest support for Hypothesis 2, that the relationship between

income and corruption perceptions runs in the opposite direction.

[Figure 3 about here]

3.4 Multilevel Models

Although we find variation between advanced and developing countries in how citizens

of different incomes perceive corruption, we have said nothing about the uncertainty involved in

these patterns. One possibility is that the differences between rich and poor countries may be

small enough to have been generated by chance. To address this concern, we repeat the previous

analysis with a multilevel model.42 We use the corruption perceptions and income variables in

their original scales and fit a multilevel linear model allowing the intercepts and the slope

parameters to vary by country. We add per capita GDP as a country-level predictor, and interact

it with the individual level-predictors. Specifically, the model assumes that the corruption

perception for individual i living in country j is a function of household income xij:

yij=β0j+β1jxij+εij (3)

The parameters β0j and β1j enter the model as random effects that are functions of per

capita GDP in each country, z!:

41 Corresponding figures with control variables are available in the online appendix. 42 Marco R. Steenbergen and Bradford S. Jones, “Modeling Multilevel Data Structures,” American Journal of Political Science, 46 (January 2002), 218-37; Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/ Hierarchical Models, (Cambridge: Cambridge University Press, 2007).

23

β0j=γ00+γ01zj+δ0j (4)

β1j=γ10+γ11zj+δ1j (5)

Equation (4) predicts the intercept β0jin equation (3) with a constant term γ!!, a slope

parameter β01, and an error term δ0j. Equation (5) predicts the slope parameter β1j. We can now

rewrite equation (3) by substituting β0j and β1j with the second set of equations (4) and (5):

yij= γ00+γ01zj+δ0j + γ10+γ11zj+δ1j xij+εij

= γ00+γ01zj+γ10xij+γ11zjxij + δ0j+δ1jxij +εij (6)

Written in this way, the model has two components. The first four terms are the fixed-

effects for the four predictors that are assigned to each individual: the constant term, per capita

GDP of the country that respondent i lives in, household income, and the interaction between

income and GDP. The next two terms are the random intercepts and the random slopes for

household income in each country. Since our goal is to understand the difference between rich

and poor countries, the parameter of most interest is γ11, the coefficient for the interaction term

between household income and per capita GDP.

We present the results in Table 2. A negative coefficient on the interaction term between

household income and per capita GDP means that the association between income and

corruption perceptions tends to be more negative in rich countries compared to poor countries.

The standard errors show the uncertainty of the estimates. Indeed, the coefficients on the

interaction terms are statistically significant. We report the estimates for models with individual-

level control variables in the online appendix, because they are largely similar. On the whole, the

multilevel models do not change the conclusions that we reached in the previous sections. In

countries at high levels of economic development, high-income citizens are likely to perceive

24

lower levels of corruption compared to low-income citizens. As countries get poorer, the poor

increasingly perceive less corruption than the privileged.

[Table 2 about here]

4. Discussion

Our analysis in the previous section allowed us to simultaneously test our two hypotheses.

The results demonstrate support for both hypotheses. As countries’ levels of GDP per capita

increase, the relationship between individual-level income and corruption perceptions becomes

increasingly negative—that is, greater income correlates with lower levels of perceived

corruption. However, we arguably found more support for Hypothesis 1 than Hypothesis 2. The

results in Table 1 suggest that, overall, the relationship between income and corruption

perceptions is negative; the wealthier tend to perceive lower levels of corruption than the poor.

Furthermore, the number of large negative coefficients in Figure 2 far outnumbers the number of

large positive coefficients, which might suggest that although the relationship between income

and corruption perceptions changes across levels of economic development, the wealthy may

actively perceive higher levels of corruption in relatively few countries. It may be that the

relationship between income and corruption perceptions is virtually non-existent in poorer

countries.

However, another interpretation is that our data sources simply do not include a sufficient

number of very poor countries. Indeed, the three datasets used in Section 3 cover mainly high- to

middle-income countries, with few very low-income countries. We therefore turn to the second

wave of the Afrobarometer survey (2002) to explore the relationship between income and

corruption perception in a set of predominantly low-income countries. This survey includes

questions about corruption perceptions in several sectors of the society and also asks about

25

respondents’ household income. One problem with using Afrobarometer is that this survey only

includes countries at lower levels of economic development. Therefore, we need to make our

estimates from the Afrobarometer comparable to the estimates from the other surveys that we

examined in the previous section. To do so, we first create a dichotomous outcome variable

based on whether the respondent perceives higher levels of corruption compared to the national

average. Second, we create an income variable on a 5-point scale based on the household income

of the respondent. We repeat this procedure for CSES, WVS, and ISSP. Standardizing the

outcomes and the predictors in the four surveys, we repeated the analysis in section 3 by

regressing the respondents’ corruption perception on household income for each country.43

Figure 4 shows the results of this analysis by plotting the estimates from the

Afrobarometer along with the three other surveys. Black circles represent the results for

Afrobarometer, whereas the results for CSES, WVS, and ISSP are shown in color. First, the

figure shows that the estimates from CSES, WVS, and ISSP are highly comparable. Although

the results for WVS diverge from the other two surveys at low levels of economic development,

the estimates for advanced countries are approximately the same. Second, the black circles from

the Afrobarometer data fit with the overall pattern that we observed in the other datasets. Most of

the countries in the sample, which are predominantly very poor, exhibit positive coefficients in

which the rich perceive more corruption than the poor. Combined with the analysis in Section 3,

the Afrobarometer data in Figure 4 suggest more robust support for Hypothesis 2—that in

developing countries, the wealthy tend to perceive more corruption than the poor. All told, the

evidence suggests strong support for Hypothesis 1 and somewhat more qualified, though highly

suggestive, support for Hypothesis 2.

43 We describe how we transformed the variables in each of these surveys in the online Appendix.

26

[Figure 4 about here]

5. Conclusion

The findings in this article have several important implications. Empirically, we

uncovered a robust finding about the relationship between income and corruption perceptions

and how that relationship varies across countries. In practical terms, this empirical regularity—

which is consistent across multiple different data sources—is an important one that scholars

should take note of when employing corruption perceptions as a proxy for actual corruption. The

biases that respondents bring to questions about corruption will vary across countries. In

particular, the biases of the affluent, who frequently contribute to expert surveys or surveys of

businesspeople, will vary across rich and poor countries. In wealthier countries, the affluent are

likely to offer lower estimates of corruption than the comparatively poor, whereas in poorer

countries, they are more likely to offer somewhat higher estimates. When combined, perceptions

measures that rely primarily on upper-income respondents may produce a wider range of

variation in perceived corruption across countries than would be the case if one solicited the

perceptions of the poor.

Theoretically, our findings also offer some new insights. For one, our theory and

evidence can potentially make sense of the inconsistent findings in the literature about the

relationship between socio-demographic characteristics and corruption perceptions. The

argument presented in this article offers an explanation for why researchers looking at one or a

handful of countries in isolation might come to very different conclusions about the relationship

between income and corruption perceptions. What appear to be inconsistent findings may

actually be findings that vary systematically across countries. The same may be true for other

27

socio-demographic attributes, particularly those that are, like income, markers of disadvantage.44

For another, our theory and findings highlight the need for researchers to be more sensitive to the

political context in which citizens find themselves, particularly when conducting cross-national

research. We should not expect that the same personal attributes or experiences shape attitudes

and beliefs in the same way in all contexts. The political context—in this case, the form of

corruption that predominates at different levels of economic development—can powerfully shape

how otherwise identical citizens perceive the world around them.

Finally, this article points to a variety of avenues for future research that build on or

refine parts of our theoretical argument. One potentially fruitful area for research would be in

better understanding different forms of corruption. For many years, the literature on corruption—

both in its explanations and the perceptions-based data that it frequently uses—implicitly focuses

on grand corruption. 45 However, corruption comprises a wide variety of behaviors. The

distinction that we draw between petty and grand corruption is itself quite broad. An even more

fine-grained disaggregation of corrupt practices as well as systematic cross-national data on the

types of corruption found in different countries would go a long way in helping scholars better

understand the causes corruption and corruption perceptions as well as the impact that different

forms of corruption have. Direct data on the forms of corruption in different societies could

potentially produce even stronger evidence in favor of our theory. We might also find that certain

forms of petty or grand corruption are especially important in shaping perceptions of corruption.

44 Indeed, in analyses presented in the online appendix, we find that education and, to a lesser extent, gender exhibit similar patterns to income, with the well-educated and men perceiving less corruption in affluent countries and more corruption in poorer countries. At present, we do not offer a theoretically informed explanation for the relationship between education, gender, and corruption perceptions, but simply suggest that processes similar to those involved with income may be at work. 45 Montinola and Jackman; Gerring and Thacker; Kunicová and Rose-Ackerman; Eric C.C. Chang and Miriam A. Golden, “Electoral Systems, District Magnitude and Corruption,” British Journal of Political Science, 37 (January 2007), 115-37.

28

Another worthwhile area of research concerns attitude formation with respect to

corruption. How precisely do individuals understand and assign blame for phenomena such as

corruption? When exactly are citizens more or less incensed about revelations of practices that

are widely condemned in society? Finally, we suggested two mechanisms for why anger about

corruption should translate into perceptions of greater corruption: resolution of cognitive

dissonance and selective consumption of information. Which of these is, in the end, more

powerful in shaping an individual’s belief about corruption in her society? Once armed with a

better sense of how citizens form perceptions about corruption, researchers can better understand

how beliefs about corruption shape important political and economic behaviors.

29

Table 1. Regressions with Pooled Data

CSES WVS ISSP

(1) (2) (3)

Income (β1) -0.019** -0.013** -0.027** (0.002) (0.001) (0.006)

Constant (β01) 2.795** 2.698** 3.482** (0.025) (0.023) (0.064)

Observations 46042 58894 37026 Groups 39 46 33

Standard errors in parentheses. ** p<0.01, * p<0.05. Country dummies are not shown.

30

Table 2. Multilevel Models

CSES WVS ISSP (1) (2) (3)

Income (γ10) 0.060** -0.000 0.075** (0.014) (0.005) (0.019)

Income * GDP (γ11) -0.003** -0.001** -0.005** (0.001) (0.000) (0.001)

Per Capita GDP (γ01) -0.028** -0.025** 0.011 (0.006) (0.004) (0.007)

Constant (γ00) 3.642** 3.232** 3.411** (0.142) (0.059) (0.168)

Observations 46042 57574 37026 Number of groups 39 46 33

Standard errors in parentheses. ** p<0.01, * p<0.05.

31

Figure 1. Forms of Corruption in Rich and Poor Countries

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32

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33

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34

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35

Online Appendix

The datasets that we used in this paper are all available for download at the websites of

the organizations that conducted the survey. Table A1 lists the variables that we used for each of

the three datasets in Section 3. Table A2 lists the countries that were covered in each dataset. In

this Appendix, we conduct a set of further robustness tests that we omitted from the main text.

First, we examine the statistical relationship between income and corruption perception by

adding education and gender as control variables. Second, we check the comparability of the

three surveys by standardizing the variables and using multilevel logistic regression models

instead of multilevel linear models.

A1. The Impact of Household Income

In this paper, we examined the bivariate relationship between individual-level income

and levels of corruption perception. However, the statistical correlation between an individual’s

income and her beliefs about the pervasiveness of corruption does not directly capture the causal

impact of this predictor if the predictor and outcome are both affected by an omitted variable.

Household income is especially vulnerable to such bias, since it can be affected by one’s

education and gender. Educated citizens are more likely to be richer, and men tend to have an

advantage over female citizens in both education and income. Therefore, the relationship

between income and corruption perceptions can potentially be explained away by the strong

correlation between corruption perceptions and these two variables.

In order to examine how this problem affects our results, we begin with the analysis of

pooled data in Section 3.2. Here we present the results of LSDV models when education and

gender are added as control variables. Table A3 shows the results. Columns (1), (4), and (7) are

36

identical to the results in Table 1 in the body of the manuscript. Columns (2), (5) and (8) show

the results when education is added as a control variable. Columns (3), (6) and (9) are the results

when both education and gender are included. The table shows that the size of the coefficients

for income is reduced when we add the control variables.

These results raise the natural question of whether the bivariate relationship between

income and corruption perception in each country is robust to the inclusion of control variables.

Therefore, we repeat the analysis in section 3.3 by examining the statistical relationship between

income and corruption perception when education and gender are added as control variables.

Figure A1 corresponds to Figure 2 in the main manuscript. The black circles are the regression

coefficients for each country when the level of corruption perception is regressed on household

income with the two other characteristics as control variables. Although the estimates are

generally closer to zero, the pattern observed in Figure 2 is largely replicated. In rich countries,

high-income citizens tend to perceive lower levels of corruption, while the reverse tends to be

true in low-income countries.

We conducted a similar analysis for the multilevel model in section 3.4 by adding

individual social and economic characteristics as well as their interaction terms with per capita

GDP. In Table A4, columns (1), (4), and (7) are identical to Table 2, and adding control variables

do not drastically change the results. The results for the interaction term between income and

GDP are roughly identical across the three specifications in all of the datasets.

A2. Increasing Comparability

The major reason why the different surveys used for this paper are not directly

comparable is that they use different scales to measure the variables of interest. Therefore, we

transformed them in section 4.

37

For the outcome variables, we examined the perceived levels of corruption compared to

the national average in order to increase the comparability of the datasets. We transform the

corruption perception variables into dichotomous outcome variables that indicate whether the

respondent perceives corruption levels higher or lower than the average in her country. For

CSES and WVS, we create a variable that takes a value of 1 if the respondent perceives levels of

corruption higher than her country’s national average and 0 otherwise. For ISSP, we create a

similar variable taking a value of 1 if the mean value of the respondent’s two corruption

perception variables is higher than the national average.

The next challenge is to standardize the predictors. First, we transform the income

variable into a five-point scale, using CSES as our baseline. For WVS, the original ten-point

scale is based on self-assessment, and hence includes a disproportionately small number of high-

income citizens. Therefore, we divide respondents in each country into five groups so that a

roughly similar number of respondents fall into to each quintile. This means grouping citizens

with income levels equal to or above 8 (or 7 in some cases) into the highest income group (5).

Another option is to group the respondents into five groups by merging two adjacent income

groups. In this case, income groups 9 and 10 would together constitute the highest income group.

However, this would create small numbers of high-income respondents. For this reason we

instead create income groups with roughly equal numbers of respondents. For ISSP and

Afrobarometer, we divide the respondents into quintiles depending on their actual income level.

After this transformation, all datasets have five income groups of roughly equal size. Second, we

recode the eight-point education variables in CSES and WVS into six-point variables that

roughly match the categories used in ISSP. We merged the categories so that they correspond to

the highest educational degree attained by the respondent, rather than dividing them into groups

38

of equal size. For example, we merged categories 1 and 2 along with 6 and 7 in CSES. In WVS,

we merged categories 3, 4, and 5.

With these new variables, we repeat the procedure in Section 3. As in the previous

section, we use OLS instead of logit or probit models because the average values of the

corruption perceptions variable in each country are fairly close to 0.5. Although the slopes vary

from country to country, none are so steep that the models would predict values close to or

outside the range between 0 and 1. For our purposes, the additional benefit of using nonlinear

models over OLS is fairly small.

Figure A2 presents the results from using the transformed variables, plotting the

regression coefficients against per capita GDP. Each column plots the three different individual

characteristics. Compared to Figure 3, the three surveys show remarkably similar patterns. When

comparing the top row of Figure A2 (for income) with Figure 3, we observe roughly equal

magnitudes at a given level of per capita GDP. For example, in the most affluent countries, a

citizen of average income is as much as 5% less likely to perceive widespread corruption

compared to citizens that are in an income group one category below. This translates into a 20%

difference between the citizens in the lowest and highest categories. The figure also includes the

results for education and gender, which exhibit similar patterns (albeit less so for gender).

An important choice that we make here is to divide the respondents into five income

categories in all countries instead of rescaling the income variables to account for income

inequality. A difference of one income category in countries with low income inequality implies

a smaller absolute income difference and may therefore lead to smaller regression estimates than

in countries with high income inequality. Ideally we would address this problem by transforming

the income variables for CSES and WVS into the measures used in ISSP. However, since CSES

39

and WVS do not provide raw income figures, this strategy is not possible. As an alternative, we

experimented with using the share of national income earned by each income quintile as reported

in the World Development Indicators (WDI) in place of the income quintile as our income

predictor. Doing so did not change the results significantly, and since this alternative necessitated

dropping a sizeable number of countries due to lack of data, the results are not reported here.

Finally, in order to take the uncertainty of the estimates into account, we fit a multilevel

model as we did in Section 3. Here we fit a multilevel logistic regression to show that our basic

findings remain the same as fitting a multilevel linear model. The results in Table A5 show that

whereas the coefficients for the interaction terms are generally larger in magnitude in CSES, they

are largely identical in ISSP and WVS.

40

Table A1. Outcomes and Predictors in the Three Datasets � CSES WVS ISSP Outcomes Perceived Corruption of

Public Officials (1-4) Perceived Corruption of

Politicians (1-4) Average of the Two

Perception Variables (1-5) Predictors

Income Converted (1-5) Self-Assessment (1-10) Logged US Dollars Education Highest degree (1-8) Highest degree (1-8) Highest degree (1-6) Male Dichotomous (0-1) Dichotomous (0-1) Dichotomous (0-1)

41

Table A2. List of Countries in the Datasets

country CSES WVS ISSP AFB country CSES WVS ISSP AFB Albania X X � Lesotho X Argentina X � Lithuania � X � � Armenia X � Luxembourg � Australia X X X � Macedonia X � Austria � Malawi X Azerbaijan X � Malaysia � Bangladesh X � Mali X Belarus X � Mexico X X � Bolivia � Moldova X � Bosnia and Herzegovina X � Mozambique X Botswa X Namibia X Brazil X X � Netherlands X X � Bulgaria X X � New Zealand X X X � Cambodia � Nigeria X X Canada X X � Norway X X X � Cape Verde X Pakistan � Chile X X X � Panama � Colombia X � Paraguay � Comoros � Peru X X � Costa Rica � Philippines X X � Croatia X � Poland X X X � Czech Republic X X X � Portugal XX X � Denmark X X � Puerto Rico X � Dominican Republic X X � Romania X X � Ecuador � Russia X X X � El Salvador X � Senegal X Estonia X � Singapore � Ethiopia � Slovak Republic X � Finland X X X � Slovenia X X � France X X � South Africa X X X Georgia X � South Korea X X X � Germany XX X X � Spain X X X � Ghana X Sweden X X X � Greece � Switzerland X X X � Guatemala � Taiwan XX X X � Hong Kong X � Tanzania X Hungary X X � Thailand � Iceland X � Togo � India X � Turkey X � Indonesia � Uganda X Ireland X X � Ukraine X � Israel X X � United Kingdom X X X � Italy X � United States X X X � Japan X X � Uruguay X X

� Kenya X Venezuela X X � Latvia X X � Zambia

� � � X Note: The CSES datasets include two surveys from Germany, Portugal, and Taiwan. The German survey was a telephone survey and a mail-back survey. For Portugal and Taiwan.

42

Table A3. Regression with Pooled Data and Controls

CSES WVS ISSP � (1) (2) (3) (4) (5) (6) (7) (8) (9)

� � � � � � � � � � Income -0.019** -0.014** -0.011** -0.013** -0.008** -0.008** -0.027** -0.016* -0.015* (0.002) (0.003) (0.003) (0.001) (0.001) (0.001) (0.006) (0.006) (0.006) Education -0.009** -0.008** -0.015** -0.015** -0.016** -0.016** (0.002) (0.002) (0.002) (0.002) (0.003) (0.003) Male -0.078** -0.020** -0.020* (0.007) (0.006) (0.009) Constant 2.795** 2.837** 2.866** 2.698** 2.742** 2.730** 3.482** 3.416** 3.414** (0.025) (0.027) (0.027) (0.023) (0.023) (0.024) (0.064) (0.067) (0.067) Observations 46,042 45,805 45,749 58,894 58,457 58,409 37,395 36,881 36,859 39 39 39 � 46 46 46 � 33 33 33 Standard errors in parentheses. ** p<0.01, * p<0.05. Country dummies are not shown.

43

Table A4. Multilevel Linear Models with Controls

CSES WVS ISSP (1) (2) (3) (4) (5) (6) (7) (8) (9)

Income (!!") 0.060** 0.049** 0.046** -0.000 0.001 0.001 0.075** 0.051** 0.049** (0.014) (0.014) (0.013) (0.005) (0.005) (0.005) (0.019) (0.015) (0.015) Education (!!") 0.025* 0.026* 0.006 0.006 0.021 0.022 (0.013) (0.013) (0.005) (0.005) (0.018) (0.019) Male (!!") 0.067* -0.004 0.022 (0.030) (0.015) (0.032) Income * GDP (!!!) -0.003** -0.003** -0.003** -0.001** -0.001* -0.001* -0.005** -0.003** -0.003** (0.001) (0.001) (0.001) (0.000) (0.000) (0.000) (0.001) (0.001) (0.001) Education * GDP (!!!) -0.002** -0.002** -0.002** -0.002** -0.002* -0.002* (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) Male * GDP (!!!) -0.006** -0.002 -0.002 (0.001) (0.001) (0.001) Per Capita GDP (!!") -0.028** -0.025** 0.011 (0.006) (0.004) (0.007) Constant (!!!) 3.642** 3.063** 3.096** 3.232** 2.980** 2.967** 3.411** 3.602** 3.598** (0.142) (0.064) (0.061) (0.059) (0.043) (0.044) (0.168) (0.074) (0.074) Observations 46,042 45,805 45,749 57,574 57,151 57,105 37,026 36,518 36,497 Number of groups 39 39 39 46 46 46 33 33 33 Standard errors in parentheses. ** p<0.01, * p<0.05. Country dummies are not shown.

44

Table A5. Multilevel Logistic Models

� CSES WVS ISSP (1) (2) (3) (4) (5) (6) (7) (8) (9) � � � � � � � � � � Income (γ10) 0.131** 0.012 0.054 (0.036) (0.015) (0.034) Income * GDP (γ11) -0.009** -0.004** -0.005**

(0.001) (0.001) (0.001) Education (γ10) 0.081** 0.004 0.042 (0.031) (0.015) (0.035) Education * GDP (γ11) -0.006** -0.005** -0.004**

(0.001) (0.001) (0.001) Male (γ10) 0.179* 0.022 0.068 (0.076) (0.042) (0.054) Male * GDP (γ11) -0.016** -0.006* -0.006** (0.003) (0.003) (0.002) Per Capita GDP (γ01) -0.000 0.006 -0.016 -0.008 -0.007 -0.031** 0.018** 0.017* 0.006

(0.012) (0.012) (0.012) (0.011) (0.010) (0.010) (0.008) (0.008) (0.008) Constant (γ00) 0.098 0.047 0.370 0.121 0.179 0.217 -0.338 -0.291 -0.180 (0.293) (0.291) (0.293) (0.162) (0.149) (0.152) (0.207) (0.216) (0.203) Observations 46,042 55,178 55,515 57,574 63,180 63,635 37,395 43,743 44,335 Number of groups 39 39 39 46 46 46 33 33 33 Standard errors in parentheses. ** p<0.01, * p<0.05. Country dummies are not shown.

45

Figure A1. Regression without Pooling Using Controls

Note: Estimates with controls are in black, and the estimates without controls are in gray. The estimates without controls are identical to Figure 2 in the main manuscript.

-.2-.1

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46

Figure A2. Regression with Dichotomous Outcomes

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