Why don’t we see more mobilization against police corruption in Africa?

Across most of Africa, police corruption is rampant. The police often serve as predatory bandits rather than providers of security. Across the continent, police will often set up unauthorized road blocks and pull vehicles over for ‘violations’ ranging from inadequate tire pressure to improper license tags. Regardless of whether violations exist – indeed, we all likely violate some statute or rule with our driving – the primary purpose of these stops is to extract bribes from citizens. Police officers will stop a car, identify a “problem,” and then begin to describe how they would be well served by a ‘cool drink’ on such a warm day. Citizens that pay are free to go. Citizens that don’t find themselves on the wrong end of the law.

Recent scholarship on this can be found in the excellent volume from Oxford University Press. Police in Africa, edited by Jan Beek, Mirco Göpfert, Olly Owen, and Johnny Steinberg, was my first real introduction into policing at the street-level in Africa. I have worked and conducted field research in multiple African countries, but never been stopped myself, and have not had personal experience with it.

But in my most recent trip to South Africa, I began to wonder – why do we see such substantial contentious mobilization against presidential corruption and not against police corruption? After all, citizens are more directly affected by paying bribes to police officers than by the kleptocracy operating in the capital city. Looking at the Afrobarometer data, it seems like we should see more mobilization against the police. After all, when polled, Africans generally believe the police are more corrupt than the executive. Consider the figure below, which maps out concerns over police corruption (in red) and concerns over elite corruption (in blue). In most regions, citizens report higher perceptions of police corruption than elite corruption, save for Zambia.

Corruption Concerns - Gradiant - by Region

This seems to hold steady over time. Aggregating perceptions of corruption to a subnational level across time, I find that perceptions of police corruption always outstrip perceptions of elite corruption. In the figure below, each point represents a subnational first-administrative district in an African country. As more countries are introduced into the Afrobarometer data, the density of points increases. While we do observe that perceptions of elite corruption begins to rise toward the level of perceptions of police corruption, it never comes particularly close.

Corruption Over Time

So – what’s going on? Why on earth don’t we see more As I think about it, several things come to mind.

  • Concerns over safety. The biggest elephant in the room is, of course, personal safety. The costs of mobilizing against police in a region where the police are known to be corrupt entails obvious concerns over detainment, violence, and possible death.
  • Salience of the issue. Against which police precinct should one protest? We all may have paid bribes to the police, but figuring out a focal point can be difficult. There is only one president, but there are thousands of police officers. Finding a way to focus mobilization is a challenge.
  • Cross-class mobilization. In African countries, the poor overwhelmingly pay bribes. The wealthy are generally not stopped, and therefore do not internalize the costs of paying bribes. Poor people are often the least able to mobilize – they do not have the time or resources to effectively bring about an anti-police campaign, and if they cannot co-opt upper classes or the business class to join, the likelihood of success is low.

What drives variation in the implementation gap in African Anti-Corruption Efforts?


What role do formal institutions in African countries play in attenuating and controlling the rampant levels of corruption that have hampered development over the past 70 years? Scholarship and policy have not always seen eye-to-eye in determining paths toward reduced corruption and better governance. While policymakers have generally (and unsurprisingly) focused on the impact that laws and policies can have, scholars have cast a much more skeptical eye toward the role that codified anti-corruption efforts play.

Scholars have long underscored the importance that informal institutions have played in the development and maintenance of African states[i]. Bratton and Van de Walle’s seminal work on African neopatrimonialism[ii] launched a fertile field of study[iii] of the role that informal patronal linkages play in driving state development and encouraging corrupt behavior. Indeed, the failure of the African Union to make meaningful inroads in reducing corruption in its constituent states calls into doubt the effectiveness of anti-corruption laws.

The misalignment between policymakers and scholars reveals analytical territory that can be further explored. This blog serves as an entry-point into that endeavor. Here at Global Integrity, we are particularly interested in the distance between de jure laws and de facto reality. As such, we have structured our dataset to allow for meaningful 1:1 comparison between these two. The recent release of the fifth round of Global Integrity’s Africa Integrity Indicators provides enough cases (216) to allow for an entry-point into truly analyzing the distance between de jure and de facto, which we refer to as the ‘implementation gap’.

This blog post presents initial findings about whether formal laws and policies are meaningful in controlling corruption in African governments. Drawing from the perspective of policymakers, I present the alternative hypothesis that formal anti-corruption laws are correlated with lower levels of corruption in practice. In order to test this, I employ a mixed-effects ordinal response model with random intercepts at the country level. I also run a standard mixed-effects generalized linear model as a simple robustness check to ensure that the ordinal response model is working properly.

The Data

In order to answer this question, I rely on the recently-updated Africa Integrity Indicators dataset, which was recently appended with a fifth round of data. The dataset covers each African country[iv] over the course of a five-year period, from 2012 to 2016. The total number of observations is 270, and there are over 100 governance-oriented variables, built on both de jure indicators about the presence of laws, as well as de facto indicators that measure on-the-ground governmental performance and behavior. In addition to these Global-Integrity based variables, the dataset includes data from the World Bank[v] concerning country-level economic factors, such as GDP, export rents on resources, population, and access to electricity. Drawing from the UCDP data[vi] project, each observation was coded to account for the present of conflict within the past five years.

There are several challenges inherent with the data that might complicate the analysis. First, the dataset is relatively small, with only 270 observations total. Due to some missing data from the World Bank, most analyses are able to analyze between 205 and 252 observations at any given time. This has a deleterious effect on the size of standard errors, and may possibly drive Type II errors. Second, Global Integrity shifted its methodology slightly between Round 1 and Round 2, which means that the first set of data is not completely comparable to Rounds 2 through 5. In order to address this, I run regressions with both the full set as well as a limited sub-set that does not include Round 1.


The first model analyzed is a mixed-effects cumulative linked ordinal response model. Because the Africa Integrity Indicators data are recorded in ordinal categories, this model is both statistically and theoretically appropriate. Three models are presented. The first model regresses the covariates on the implementation of anti-corruption investigations by the government. The second model regresses the covariates on the general efficacy of the anti-corruption agency. The third model regresses the covariates on the measured independence of appointments to the anti-corruption body. Each model is run with random intercepts at the country-level. Random slopes are not included due to the small number of observations.

The results, presented in Table 1, demonstrate tentative support for the hypothesis that de jure anti-corruption legislation is correlated with improved anti-corruption efforts by the government. The first model indicates a positive, but statistically insignificant correlation. The second model indicates that the present of in-law anti-corruption legislation is correlated, on average and holding all else constant, with a moderate improvement in the efficacy of anti-corruption efforts. The third model indicates that, on average and holding all else constant, the presence of in-law anti-corruption legislation is strongly correlated with the appointment of independent members to the anti-corruption authorities.

Table 1 – Cumulative Linked Ordinal Response Model

In Law Corruption 0.479 0.838* 2.421***
Petroleum Rents -0.019 -0.031 -0.066*
Nat. Gas Rents -0.125 -0.390 0.375
Mineral Rents -0.011 -0.015 -0.089**
Conflict Five Years -0.704* -0.981 -1.942*
Log of GDP 0.360 0.374 0.604
Log of Population -0.188 0.078 -0.187
Observations 252 252 205
0 | 25 4.361** 8.685*** 10.499*
25 | 50 5.649** 10.339*** 12.310**
50 | 75 7.167** 12.775*** 14.644***
75 | 100 8.216** 14.753*** 15.807***


The complexity of this correlation is made more clear once the coefficients from the ordinal model are translated into predicted probabilities. The plots below indicate relative probabilities that, given either the absence or presence of a law, we will observe a certain outcome. Across the board, the absence of anti-corruption laws predicts increased probabilities that a country’s in-practice anti-corruption efforts will achieve a score of 0 or 25. This relationship becomes more ambiguous as we examine predicted probabilities for higher rankings. On average, and holding all else constant, countries without anti-corruption laws are more likely to achieve scores of 50. This reverses for scores of 75, in which the presence of anti-corruption laws is correlated with a higher likelihood of attaining a ranking of 75 than countries with no such laws. Interestingly, there is a general convergence in predicted probabilities when examining the highest score, which is 100. In the first two models, the predictions seem to converge. In the third model, pertaining to appointments, countries without anti-corruption laws have a higher likelihood of scoring 100 than countries with anti-corruption laws.

Figure 1 – Anti Corruption Investigations

Investigation Probabilities

Figure 2 – Anti Corruption Effectiveness

Effectiveness Probabilities

Figure 3 – Anti Corruption Appointments

Appointments Probabilities

This relationship emphasizes the importance of understanding the multicausal and likely interactive relationship between inputs and outcomes. To ensure the robustness of these results, I run the same data through a generalized linear model with random slopes and intercepts at the country level. The results, which are found in Table 2, provide confirmatory support to the original model. Across the board, the presence of codified anti-corruption laws is correlated with higher overall anti-corruption performance.

Table 2 – Generalized Linear Model

In Law Corruption 6.864* 9.211** 26.919***
Petroleum Rents -0.288 -0.291* -0.478**
Nat. Gas Rents -2.080 -4.161 3.166
Mineral Rents -0.129 -0.152 -0.672***
Conflict Five Years -10.167** -9.355 -19.661**
Log of GDP 5.720* 4.040* 5.363*
Log of Population -2.981 0.577 -0.702
Constant -51.217 -71.882** -89.159*
Observations 252 252 205
Note: *p<0.1; **p<0.05; ***p<0.01

Figure 4 – Predicted Scores

Predicted Scores LMER

The results indicate that the implementation gap is largest with regard to the appointment of officials to the government anti-corruption agency. These predictions hold constant a number of covariates, including GDP, population, the presence of conflict, and levels of natural resource rents.


What can we take from this initial examination of the implementation gap? Perhaps most excitingly, we find that the presence of formal, codified anti-corruption laws are positively and statistically significantly correlated with higher anti-corruption performance. This is a promising finding, as it indicates that the type of government that is likely to enact such laws is also likely to step up its efforts in enforcing anti-corruption programs. There are reasons to be hesitant about this finding. First, it is important to note that this is a correlational relationship rather than a causal one. These results show us clusters of governance rather than delineating a clear, causal link. Likely, factor analysis would find that the presence of some laws informs us about the presence (or absence) of other laws, as well as government performance in many governance-related areas. Second, the models presented do not test for endogeneity. While the single-equation model treats the presence of anti-corruption laws as exogenous, the astute reader will understand that this is an unrealistic assumption. There is an entire ‘sausage-making’ process that leads countries to codify anti-corruption policies, and it is highly likely that there is either an active endogenous relationship or a selection issue at hand. Running a simultaneous equation model or a selection model would likely provide further insight into the relationship.

[i] (Hyden 2012; Jackson and Rosberg 1982a, 1982b)

[ii] (Bratton and Walle 1994, 1997)

[iii] (Bach 2011; Cammack 2007; Erdmann and Engel 2007; Pitcher, Moran, and Johnston 2013; Von Soest, Bechle, and Korte 2011)

[iv] Excepting South Sudan and the Western Sahara.

[v] The World Bank, 2017. World Development Indicators. Dataset. Available online: http://data.worldbank.org/products/wdi. Accessed July 24, 2017.

[vi] Kreutz, Joakim. 2010. “How and When Armed Conflicts End: Introducing the UCDP Conflict Termination Dataset,” Journal of Peace Research 47(2): 243-250. Accessible online at: http://ucdp.uu.se/downloads/. Accessed June 24, 2017.


Does Corruption Affect Behavior in Ultimatum Games? Evidence from a Collective Action Pilot Study

The role of corruption in fomenting civil unrest has gained increasing attention recently. Does corruption engender action, or is it merely an excuse used to cover for contentious behavior? This research tests this question in a laboratory pilot study using a collective-action ultimatum game. The results show that participants that receive a ‘corrupt’ treatment protest more often than participants that receive a ‘not corrupt’ treatment.

Social movements, rebels, and terrorists often cite government corruption as a motivating factor for their behavior. In 2003, Nigerians took to the streets to protest massive electoral fraud following what many considered to be a rigged election (Human Rights Watch, 2004). Across North Africa and the Middle East, citizens banded together in 2011 and 2012 to challenge their governments during the Arab Spring. In 2015, following weeks of protest focused on contending President Nkurunziza’s attempts to circumvent constitutional term limits, a faction of the army attempted a coup d’état (Vircoulon, 2015).

But assessing the role of corruption in motivating large-scale political events is difficult, particularly when rebels stand to gain political power. Furthermore, corruption, by its nature a clandestine activity, is highly endogenous to flawed democratization, reduced trust in civil society, and institutional weakness. As such, corrupt places tend to be conflictual places. Teasing out the effects of perceptions of corruption are extraordinarily difficult.  This begs the question: does corruption actually motivate civil conflict, or is it simply an excuse to cover ulterior political motives?

In order to test this question, this research uses an ultimatum game that has been modified to allow a group of participants to collectively decide whether to accept or reject an offer made to them by an automated ‘government’. The results of the first trial of the experiment indicate that groups of participants that were exposed to the ‘not corrupt’ treatment were fifteen percentage points less likely to protest than those exposed to either the ‘corrupt’ treatment or control.

Corruption, Contention, and Conflict

The end of the Cold War brought about a renewed scholarly focus on issues of governance, particularly in developing nations. As such, corruption gained increased attention in both the policy and academic worlds. While initial scholarship focused largely on the relationship between corruption and economic development (Bardhan, 1997; Girling, 1997; Heidenheimer, Johnston, & LeVine, 1989; Johnston, 2005; Rock, 2009), more recent work has begun to expand the analysis to include corruption’s deleterious effects on social trust (Rothstein & Uslaner, 2011; Uslaner, 2007, 2008, 2013), as well as civil society responses to corruption (Johnston, 2012, 2014).

A third body of work focuses on the relationship between government corruption and conflict, though the relationship remains unclear. Le Billon (2003) argues that corruption is neither necessary nor sufficient in causing the onset of conflict. Fjelde (2009) finds that corruption may indeed reduce the likelihood of conflict, as elites can ‘buy off’ potential challengers. Neudorfer and Theuerkauf (2014), on the other hand, argue that corruption intensifies the likelihood of ethnic conflict due to the exacerbation of ethnic grievances. Teets and Chenoweth (2009) argue that corruption likely facilitates conflict and terror, but does not itself motivate it. Recent experimental work has endeavored to more clearly tease out the impact of corruption on behavior (De La O & Chong, 2012; De La O, 2013; Figueiredo et al., 2013; Serra & Wantchekon, 2012; Wantchekon, 2003); however, to the author’s knowledge, this work has not extended to cover collective contentious behavior.


Corruption, despite its surreptitious and complex nature, is a salient force in the lives of many citizens across the globe. Acts of financial corruption – kleptocracy and patronage – can halt economic development, exacerbate poverty, and create ethnic grievances. Acts of systemic corruption – electoral fraud – disenfranchises citizens. Significant research has demonstrated that, when faced with government malfeasance and deeply rooted inequalities, citizens act upon their grievances to challenge the government (Buhaug & Gleditsch, 2014; Cederman, Gleditsch, & Buhaug, 2013; Cederman, Weidmann, & Gleditsch, 2011; Gurr, 2011).

Following this logic, the primary argument of this article is that corruption is, in and of itself, a motivator of contentious political action. Because citizens perceive corrupt governments as illegitimate and untrustworthy, they should be more likely to protest perceived slights and deprivations. In regions such as Africa in which citizens worry overwhelmingly about issues of poverty and unemployment, corruption highlights economic plight by generating between-group disparities. This is particularly true, given the patrimonial nature in which not only economic resources, but economic opportunities are distributed. When corruption reigns, ‘losers’ not only lose today, but lose for the foreseeable future.

Hypothesis 1: holding all else equal, citizens will protest against governments perceived to be corrupt than against governments not perceived to be corrupt.

Research Design

In order to test this hypothesis, an experiment was designed to allow groups of participants to accept or protest government offers in a modified collective ultimatum game. Past research (Habyarimana, Humphreys, Posner, & Weinstein, 2004, 2007, 2009) has examined the well-known ‘collective-action problem’ (Lichbach, 1995; Olson, 1965). Drawing from this, a modified ultimatum game was developed that allows groups of up to five participants the opportunity to collectively decide how to respond to varying offers by a government regime. The game is structured as follows.

An ultimatum game software application was developed to run in a web-browser using the Python computing language. The game can accommodate up to five simultaneous players, each of which acts as a ‘citizen’ in a newly democratic regime[1]. In this game, the role of the ‘government regime’ (the part played by the ‘offerer’ in traditional ultimatum games) is controlled by the software application. Participants interact with the game via a computer or tablet device[2], and each participant is issues his or her own device.

At the beginning of the game, the participants are treated with one of three treatments explaining the rules of game. All treatments indicate that the participants are citizens in a newly democratic regime that, in its efforts to promote development, provides each citizen with a stipend of up to 100 ‘shillings’. At each round, the government will offer up to 100 shillings. Participants can either accept the offer or protest (in hopes of gaining the full 100 shillings). The manipulation revolves around a portion of the prompt that indicates whether the government is considered ‘not corrupt’ or ‘very corrupt’, or whether no indication of its corruption is given (the control).

Once participants signal that they have read the prompt, five successive rounds of the ultimatum game begin. In each round, the computer randomly offered between 0 and 100 shillings, at 25 shilling increments. Participants have two options: to accept the offer or to protest the offer in an attempt to earn the full 100 shillings. Failed protests result in payoffs of zero shillings. In order to attempt to address the issue of collective action, the user interface indicates how many other participants have decided to accept or protest the offer. For every participant that decides to protest, the likelihood of the success of that protest increases, and participants can view the probability of success. This attempts to model the cascades effect of contentious action in which the more citizens are protesting, the more likely it is for others to join (Hussain & Howard, 2013; Lohmann, 1994).

Each round ends after all participants have made a selection of whether to protest or accept. After the fifth round, the game terminates. Participants were paid a base participation fee of ten dollars, and then paid an additional amount based on their in-game behavior. The maximum additional amount possible was five dollars. This was modeled in order to encourage participants to consider the potential loss of their earnings upon a failed protest. In total, 46 undergraduate students were recruited, of which 41 (89.13 percent) showed up to participate, resulting in a total of 205 rounds of the ultimatum game being played. Each group arrived on the hour to participate, and no groups overlapped in arriving or departing. It is therefore reasonable to assume that SUTVA was not violated.


The results provide initial, though not conclusive, support for the hypothesis. On the whole, participants that were exposed to ‘not corrupt’ treatment were approximately 15 percentage points less likely to protest than those exposed to the ‘very corrupt’ treatment. This result is substantively significant, and a one-tailed t-test indicates the result to be significant at the 10 percent confidence level[3]. The substantive difference is large enough to assume that, given enough observations, the result would be significant at a higher level of confidence.

Table 1 – Between Group T-Test Comparisons




Not Corrupt


Not Corrupt

Group 1 µ 1.6333 1.5833 1.6333
Group 2 µ 1.5833 1.4588 1.4588
Upper CI -0.0988 -0.0150 0.0363
Lower CI 0.1988 0.2640 0.3127
2-tailed p value 0.5785 0.1416 0.0383
1-tailed p value 0.2983 0.0710 0.0192

Here, the results become more complicated. While the difference between the corrupt and not-corrupt treatments is significant at the ninety percent level, and the difference between the control and the not-corrupt treatments is significant at the 95 percent level[4], the difference between the control and the corruption treatment is both substantively small (five percentage points) and statistically insignificant. This indicates that, to participants, there was little theoretical difference between a newly democratic government and a corrupt newly democratic government. One explanation of the results would indicate that, to the average participant, newly democratizing regimes are synonymous with corrupt governance.

Figure 1 – Protest Behavior Across Treatments


A further examination of the data reveal a second possible explanation of this relationship. The average offer in the control and corrupt treatments are very similar (47.5 and 43 shillings, respectively), whereas the not-corrupt treatment averaged an offer of 55 shillings. However, this relationship is not entirely direct. While protest rates are highest in the control group, the lowest average offer is in the corrupt group. Therefore, while the higher average offer in the not-corrupt group may explain lower levels of protest, the average offer does not explain the relationship between the control and corruption groups.


Does corruption affect behavior in collective ultimatum games? In short, it is too soon to tell. While the initial data analysis indicates that levels of perceived corruption do matter, the sample is too small to make any conclusions. Furthermore, the likelihood that the large variation in average amounts is at least partially confounding the results. Future trials will ensure that all rounds offer the same average amount in order to remove this from consideration. Furthermore, future trials will likely remove the control group, leaving only the ‘very corrupt’ and ‘not corrupt’ treatments, mainly because of the scarcity of data.

[1] In this trial, the maximum number of participants was capped at three.

[2] Any modern computing device with a web browser will work. In this case, participants used Amazon Fire tablet computers.

[3] The p-value for a two-tailed t-test between corrupt and not-corrupt is 0.1416. Because the hypothesis is directional, however, a one-tailed test is more appropriate. This splits the p-value in half, resulting in a p-value of 0.0708.

[4] 0.03832 for the two-tailed t-test.


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You are a citizen of a newly formed country. This country is an emerging democracy, and has just completed a round of relatively fair elections. The president was elected by direct vote. Most election monitors concluded that this was a flawed, but fair election.


Though the country is underdeveloped, citizens are hopeful that the situation will improve. Due to a combination of taxes, natural resource exports, and humanitarian aid, the government is able to provide money to each of its citizens. This money is intended to help in the development of the country, including building better schools, roads, electricity, and other infrastructure.

In each turn, the government will offer you up to 100 Shillings. If you receive 100 Shillings, that means the government is giving you 100 percent of your funds. If you receive less than 100 Shillings, it likely means that the government is using some of your development funding for other projects, or keeping it for itself.




The country has a relatively equal distribution of mountains, rivers, lakes, and plains. English is the primary language. The economy is mostly agricultural, with some natural resource extraction and a growing manufacturing sector.


Many citizens consider the government to be very corrupt. People talk of government officials stealing state funds and putting the money into their private bank accounts. Others say that certain ethnic groups are favored and receive lots of public goods, while less favored groups receive almost none.


Most citizens agree that the government is not very corrupt. People generally believe that officials use state funds properly. Most agree that, even though the population is ethnically mixed, each group received generally equal distributions of public goods.

Descriptive Statistics

Table 2 – Gender Distribution Across Treatments

  Control Corrupt Not Corrupt
Male 4 5 9
Female 8 7 8


Table 3 – Protest Behavior Across Gender

  Accept Protest
Male 40 50
Female 53 62


Table 4 – Number of Rounds

  Number of Rounds
Control 30
Corrupt 25
Not Corrupt 30