Foreign Aid, Citizen Beliefs, and Popular Protest:

Evidence from Post-Cold War Africa

Brett L. Carter∗

July 26, 2016

Abstract

Western donors routinely withhold development aid and debt relief when recipient govern-

ments violate their citizens’ human rights. In so doing, scholars find, Western donors have

constrained repression among Africa’s post-Cold War autocrats. This yields an important ob-

servable implication: When repression is less likely, then protest is less dangerous for citizens. As

a result, confronted with similar grievances, we should expect citizens of Africa’s aid dependent

autocracies to protest more often. This paper finds that, when the threat of Western sanctions

is credible, citizens in Africa’s post-Cold War autocracies have been 50% more likely to protest

on a given day than otherwise.

Word Count: 9,256

∗Assistant Professor, School of International Relations, University of Southern California. Email:blcarter@usc.edu. I thank Erin Baggott for helpful comments.

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Wherever constitutional rules are abused, wherever freedom is violated, wherever demo-

cratic transitions of power are prevented, I declare here and now that citizens will always

find in the Francophone world the support necessary to ensure that justice, rights, and

democracy prevail.

– French President François Hollande.1

The United States has a moral responsibility to protect the people of the Republic of

Congo. Madame Ambassador Sullivan, where are you???

– An opposition activist from the Republic of Congo on the US Embassy’s Facebook page.

1 Introduction

Western donors have reshaped politics in Africa’s autocracies. Since the end of the Cold War,

the international community has proven increasingly willing to withhold development aid and debt

relief when recipient governments violate their citizens’ human rights.2 The threat of financial

sanctions gives Western donors leverage over Africa’s aid dependent autocrats, which obtains even

when donors provide public goods. Since money is fungible, foreign aid enables autocrats to channel

scarce revenue to their internal security apparatus, longtime allies, and opposition rivals.3 Western

leverage has circumscribed repression among Africa’s post-Cold War autocrats,4 and rendered coups

increasingly rare.5

If Western donors have have constrained repression in Africa’s post-Cold War autocracies, then

protest should be less risky for citizens. In turn, we should expect citizens of Africa’s aid dependent

autocracies to be emboldened, knowing that they are less likely to be repressed. By sanctioning

repression in Africa’s autocracies, have Western creditors rendered protests more common?

African citizens suggest so. In the wake of the Burkinabé revolution of November 2014, French

President François Hollande reaffirmed to African citizens – in Dakar, Senegal – the democratic

1Speech at the 15th La Francophonie summit, Dakar, 29 November 2014.2This is particularly true when offending governments are not crucial geopolitical allies; see Nielsen (2013). There

is abundant evidence that Western governments have long employed foreign aid for strategic ends; see, for instance,Alesina and Dollar (2000).

3Bueno de Mesquita et al. (2003).4Aronow, Carnegie and Marinov (2012) and Carter (2015). Similarly,Bermeo (2011) finds that aid from Western

democracies, at least in the short term, generates an increase in Polity scores.5Goemans and Marinov (2014) argue that this has generated the secular decline in military coups in Sub-Saharan

Africa.

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principles that his predecessor, François Mitterand, articulated in the Baule Declaration of 1990

and that are referenced in the frontispiece. Citizens in the Republic of Congo responded. When

they took to the streets in September 2015 to protest President Denis Sassou Nguesso’s attempts

to secure an unconstitutional third presidential term – decried as a “constitutional coup d’état” –

citizens sought refuge at the American and French Embassies in Brazzaville after security officers

opened fire. When the American and French governments responded tepidly, frustrated Congolese

citizens voiced their frustration – indeed, their belief that they had been betrayed – on social media

and in the streets of Paris. For Western governments had repeatedly told African citizens that

they would intervene when their governments violated their human rights. In so doing, Western

governments had emboldened them.

Ascertaining whether Western creditors have emboldened citizens in Africa’s post-Cold War

autocracies requires accounting for “donor selection bias”: the possibility that Western donors

allocate development aid to autocrats who are most likely to respect their citizens’ human rights.

If this is the case, then citizens of Africa’s aid dependent autocracies may have fewer grievances.

With fewer grievances, these citizens may appear less likely to protest than their counterparts

elsewhere. If donor selection bias is present, then it should bias the relationship between Western

aid dependence and the probability of protest to 0.

To overcome donor selection bias, I exploit debt relief negotiations between African govern-

ments and the International Monetary Fund (IMF) and World Bank that occurred as part of the

Heavily Indebted Poor Countries (HIPC) initiative. During debt relief negotiations – which ranged

from several months to nearly 10 years – the international community’s attention to human rights

violations in Africa was focused and punishment, if human rights violations occurred, was cred-

ible. As a result, Carter (2015) finds, the rate at which Africa’s autocrats employed repression

during HIPC debt relief negotiations plummeted. I exploit two features of the HIPC debt relief

program. First, notwithstanding their rhetoric, the Bretton Woods institutions initiated debt relief

negotiations with little regard for the recipient government’s record of corruption and human rights

abuses. Since 1996, of the 33 African countries that have satisfied the economic criteria for HIPC

debt relief, the Bretton Woods institutions have granted full, irrevocable debt relief to 30. This

list includes many of Africa’s most venal autocrats, whose human rights violations are routinely

denounced by activists. Second, virtually all autocrats who received debt relief were in power long

before HIPC negotiations began and remained long after they concluded. Each population, there-

fore, constitutes its own counterfactual: how citizens would have behaved if the autocrat was not

credibly threatened with sanctions by Western donors.

There is powerful evidence that citizens knew their governments were constrained by Western

donors during HIPC debt relief negotiations. As the rate of repression slowed, citizens observed

autocrats make stunning concessions to Western donors. President Denis Sassou Nguesso, for in-

stance, has ruled the Republic of Congo for all but five years since 1979, and in the process acquired

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a reputation for massive graft. To persuade the Bretton Woods institutions of his commitment to

“good governance” and ultimately secure debt relief, Sassou Nguesso permitted quarterly audits

of the state oil company and removed his son from its senior leadership. As Congolese citizens

observed him buckle under Western pressure, they grew emboldened: “If we could just get 1,000

people in the streets,” one civil society leader told me, “[Sassou Nguesso] could never open fire on

them all. He is too reliant on the West.”6 To measure the effect of HIPC debt relief negotiations on

the probability of protest, I employ a differences in differences estimation strategy. Controlling for

a range of day- and year-level factors, I find that citizens were 50% more likely to protest during

HIPC debt relief negotiations than on any other day of an autocrat’s tenure.

This paper advances our understanding of politics in modern autocracies. Scholars generally

view disgruntled elites as constituting the chief threat to an autocrat’s survival. To prevent elite

coups, scholars argue, autocrats construct political institutions – single parties or nominally demo-

cratic institutions – that facilitate credible revenue sharing agreements with their elites.7 Given the

difficulties inherent in collective action, autocrats prevent popular uprisings by threatening repres-

sion.8 This paper joins a new wave of scholarship that suggests that modern autocrats confront

new threats to their survival, and do so with new constraints. Since the end of the Cold War,

Western creditors have largely required nominally democratic institutions and a baseline regard for

human rights in exchange for development aid and debt relief.9 As a result, the institutional envi-

ronment in Africa’s aid dependent autocracies is relatively fixed,10 coups have grown increasingly

rare,11 repression is more costly than ever,12 and citizens have learned to use regular elections to

coordinate protests.13 This paper finds that, by punishing repression and hence rendering protests

less risky, Western creditors have also emboldened citizens of Africa’s autocracies. These protests

have important political implications. Drawing on post-Cold War Africa, Aidt and Leon (2015)

find that riots are associated with increases in country Polity scores, a result that is strikingly

consistent with the accounts of democratic change in Acemoglu and Robinson (2005) and Boix

(2003). Increasingly, to understand how modern autocrats survive, scholars must also understand

how Western creditors have reshaped politics across Africa’s autocracies.

This paper proceeds as follows. Section 2 introduces the day-level dataset and probes the

relationship between Western aid dependence and the daily probability of protest. It then explains

why, because of donor selection bias, citizens in Africa’s aid dependent autocracies mayappear to

be less likely to protest than their counterparts elsewhere. Section 3 provides evidence that, during

6Interview with the author, 13 April 2012.7Geddes (2005), Arriola (2009), Svolik (2012), Gandhi (2008), Brownlee (2008), Slater (2010), and Blaydes (2011).8Tullock (1987) first argued that popular uprisings were extremely uncommon in autocracies given the difficulties

of collective action. See also Frantz and Kendall-Taylor (2014).9Dunning (2004) and Levitsky and Way (2010).

10Bratton and van de Walle (1997), van de Walle (2001), and Dunning (2004).11Goemans and Marinov (2014).12Aronow, Carnegie and Marinov (2012) and Carter (2015).13Tucker (2007), Fearon (2011), and Carter (2016a).

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HIPC debt relief negotiations, citizens in Africa’s autocracies have been far more likely to protest

than an any other day of an autocrat’s tenure. Section 4 concludes with suggestions for future

research.

2 Baseline Estimation

2.1 Data and Descriptive Statistics

As a baseline, I first probe the relationship between Western aid dependence and the probability

of protest. To do so, I combine day-level records of protest and repression with a range of day- and

year-level characteristics. Svolik (2012) provides a roster of the world’s autocrats between 1960 and

2007; it includes the dates of their entry and exit, as well as the means by which they did so. I draw

data on popular protest and state repression from the Social Conflict on Africa Database (SCAD),

introduced by Salehyan et al. (2012). SCAD records the daily number of protest and repression

events throughout Africa since 1989. Based on an exhaustive search of Lexis Nexis, Salehyan et al.

(2012) employed a research team to hand code details about each repression and protest event.

The result is the most detailed and complete record yet assembled.14 In all, the dataset includes

a total of 295,177 country-days in Africa since 1989. Of these, SCAD records a protest event on

26,166 days, or just less than 10% of total country-days. Figure 5 displays the number of days on

which a protest event occurred by country.

I draw data on Western aid from the AidData project, introduced by Tierney et al. (2011).

The AidData project records project level commitments and disbursements by year from a range

of donors to a range of countries. The AidData project defines aid commitments as the amount

promised to a country in a calendar year, whereas aid disbursements reflect the amount donors

actually transferred. Since the commitment measure more faithfully represents the anticipated

subsidies an autocrat stands to lose if aid is revoked, I employ it. I define “Western” donors as

the United States, all European countries and Anglo-Saxon offshoots, Japan, the Bretton Woods

institutions, multilateral development banks, United Nations, and private organizations such as the

Gates Foundation.15 Following Goemans and Marinov (2014), I measure Western aid dependence

by standardizing aid commitments by GDP, which I draw from the Penn World Tables16:

Western Aid Dependenceis =Total Western Aidis

GDPis

14Note that, by employing SCAD, I restrict attention to Africa’s post-Cold War autocracies. However, I argue thatthe gains from day-level precision outweigh the costs in geographic scope, particularly since nearly half of the world’scurrent autocracies – and its most aid dependent – are located in Africa.

15The overwhelming share of aid to African countries is provided by the United States, European Union, theBretton Woods institutions, multilateral development banks, and the United Nations. As a result, the results beloware not sensitive to the inclusion or exclusion of particular countries or multilateral institutions.

16Feenstra, Inklaar and Timmer (2013).

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where i indexes country and s indexes year. Since Western Aid Dependenceis is subject to skew, I

employ its natural logarithm.17

Definitions and descriptive statistics for all variables appear in Tables 7 and 8 in the Appendix.

Of the 110 autocrats in the dataset, Joseph Kabila of the Democratic Republic of Congo (DRC)

is Africa’s most Western aid dependent. He is joined by Kenya’s Daniel Arap Moi, Zambia’s

Kenneth Kaunda, Cote d’Ivoire’s Laurent Gbagbo, and Burkina Faso’s Blaise Compaoré, all of

whom governed populations that routinely took to the streets. Many of Africa’s least aid dependent

autocrats are also among its most violent. In turn, their citizens are among the least likely to

protest. Among Africa’s leading oil producers, Equatorial Guinea’s annual GDP per capita is

roughly $35,000, above Italy and South Korea.18 Meanwhile, more than 60% of Equatorial Guinea’s

citizens live on less than $2 per day. They abstain from protest not because they have no grievances,

but because President Teodoro Obiang Nguema employs repression freely, for he is unconstrained

by Western donors. His citizens dare not protest as a result.19 “Equatorial Guineans live in fear,”

Heilbrunn (2007) writes, “of arbitrary detention, harassment, beatings, and the seizure of personal

property.” Figure 5 reflects this. The SCAD dataset records not one instance of popular protest in

Equatorial Guinea. Eritrea’s Isaias Afeworki, Sudan’s Omar al-Bashir, and Angola’s José Eduardo

dos Santos also acquired similar reputations for repression in the absence of Western aid, and their

populations seldom protested.

Virtually all of Africa’s post-Cold War autocrats govern with nominally democratic political in-

stitutions: presidential term limits, multiparty legislatures, and regular elections, which are increas-

ingly required by Western creditors in exchange for development aid and debt relief.20 Accordingly,

I do not include controls for prevailing political institutions. Instead, I control for whether day t in

country i falls within the 30 days before and after election day. I refer to this 60 day period as an

election season, and I draw data on elections from the National Elections Across Democracy and

Autocracy (NELDA) dataset, which records the dates of every election around the world between

1960 and 2010.21 This is important, for the regular elections occasioned by nominally democratic

institutions constitute focal points for collective action. By enabling citizens to coordinate, election

seasons are far more likely to witness episodes of popular protests than any other day of the year.22

I control for a variety of other day-level events that could plausibly be associated with both

protest and Western aid dependence. Since weather conditions may be correlated with protests

17Note that the statistical results below are substantively unchanged if Western Aid Dependenceis is standardizedby GDP per capita rather than GDP.

18https://en.wikipedia.org/wiki/List of countries by GDP (PPP) per capita.19Klitgaard (1991), Roberts (2006), Ghazvinian (2007), Heilbrunn (2007), and Shaxson (2007).20Bratton and van de Walle (1997), van de Walle (2001), Dunning (2004), Levitsky and Way (2010), and Goemans

and Marinov (2014).21For more on the NELDA dataset, see Hyde and Marinov (2012).22McFaul (2005), Tucker (2007), Radnitz (2010), Bunce and Wolchik (2010, 2011), and Fearon (2011). Carter

(2016a) finds that protests are some 200% more likely during election seasons than on any of other day of the year,and some 300% more likely on election day itself.

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and aid commitments, I control for the amount of precipitation in the country’s most politically

prominent city on day t, as well as its average recorded daily temperature. I draw these data from

the National Oceanic and Atmospheric Administration (NOAA); the list of politically prominent

cities by country appears in Figure 9. Since political instability may foster popular protest and be

associated with Western aid, I control for whether a rebel group engaged in a violent offensive as part

of an ongoing civil war on day t, as well as whether the government engaged in a violent offensive on

day t. I draw these data from the Uppsala Conflict Data Program’s (UCDP) Georeferenced Event

Dataset,23 which includes day-level conflict events that occurred as part of broader conflicts that

did not meet the 25 battle death annual threshold. I also control for whether country i’s autocrat

employed repression on day t− 1.To accommodate structural features that might generate popular grievances, I control for a

variety of other factors at the country-year level. Poor economic conditions may render country i

a more attractive target for Western aid, and may dispose its citizens to protest. To accommodate

this, I control for the employment rate and GDP per capita during year s, both drawn from the Penn

World Tables. Substantial oil reserves may have a similar effect, particularly if natural resource

wealth both discourages Western creditors and fosters popular protests.

2.2 Model Specification

The baseline model estimates the probability of protest in country i on day t as a function of

Western aid dependence in year s:

logit [Pr (Protestit = 1)] = α+ β ln (Western Aid Dependenceis)

+ψXis + κZit + γj + � (1)

where i indexes country, j indexes autocrat, s indexes year, and t indexes day. The vector Xis

gives covariates observed at the country-year, while the vector Zit gives covariates observed at the

country-day.

A range of unobserved factors may condition both Western aid dependence and the day-level

probability of protest in Africa’s autocracies. To account for these features, I employ a full set of

autocrat-level random effects, represented by the parameters γj in (1). Since no autocrat presided

over two countries, these autocrat-level effects obviate the need for country-level effects. This is

important, for country i could be particularly well represented in the SCAD dataset because of

unobserved factors that render its affairs more interesting to Western readers.24

By shifting to a random effects estimator, I accommodate the temporal hierarchy in the data.

Since unit intercepts in random effects specifications are estimated directly, random effects models

23For more on the UCDP dataset, seeSundberg and Melander (2013).24For instance, these autocrat-level effects accommodate the possibility that country i’s affairs are over-reported

in the news sources that Salehyan et al. (2012) employ to constructed the SCAD dataset.

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can estimate the effect of variables that are observed at the unit level, such as Western Aid Dependenceis.

Indeed, because the equation in (1) estimates the day-level probability of protest as a function of

day- and year-level factors, moving to a random effects estimator avoids overstating confidence in

coefficient estimates. Estimating (1) with a standard fixed effects estimator implicitly assumes that

observations are independent. Since Western aid allocations are set annually, observations within

country-years on the explanatory variable violate this independence assumption. By ignoring this

dependence, standard fixed effects estimators yield standard errors that are considerably smaller

than appropriate.25

2.3 Estimation Results

The results appear in Table 1. Model 1 is a simple bivariate regression. Model 2 includes all

day-level control variables. Model 3 gives the full model.

Across models, citizens in Africa’s autocracies that are more dependent on Western aid appear

to be less likely to protest on any given day t. Owing to the large sample size, the effect is estimated

with precision. Substantively, however, the effect is quite small. As the odds ratios in the bottom

of Table 1 make clear, for every 1% increase in Western Aid Dependenceis, the odds of popular

protest on a given day t are between 80% and 90% as great as otherwise. To be clear, this is

precisely the opposite effect that recent literature predicts and African citizens suggest.

The coefficients for the control variables are generally consistent with theoretical expectations.

Protests are far more common during election seasons – and especially on election day – than any

other time of the calendar year. Protest is associated with political instability and, strikingly, with

weather patterns: Citizens are less likely to protest on the warmest days of the year, when temper-

atures across the continent can be sweltering, but rainfall appears to have no effect. Interestingly,

repression on day t− 1 renders protests on day t more likely.

2.4 Grievance, Protest, and Donor Selection Bias

Again, the effect of Western Aid Dependenceis is precisely the opposite of what existing literature

and anecdotal evidence suggest it should be. Citizens in Africa’s aid dependent autocracies appear,

at least observationally, to be less likely to protest than their counterparts. This result essentially

requires us to believe that African citizens somehow failed to appreciate what outsiders have: that

Western donors have circumscribed repression among Africa’s post-Cold War autocrats.

Perhaps so. But the results in Table 1 could also be driven by donor selection bias. Motivated

by ethical concerns, Western donors may direct aid to autocrats who are more inclined to respect

the human rights of their citizens. Alternatively, autocrats who receive Western aid may choose to

respect their citizens human rights or provide higher levels of public goods, for fear of losing their

25For an introduction to random effects models, see Gelman and Hill (2006).

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Table 1: Protest and Western Aid Dependence: Baseline Results

Model 1 Model 2 Model 3Logit Logit Logit

ln Western Aid Dependenceis -0.141∗∗ -0.239∗∗ -0.132∗∗

(0.012) (0.012) (0.013)Election Seasonit 0.432

∗∗ 0.427∗∗

(0.037) (0.037)Election Dayit 0.857

∗∗ 0.865∗∗

(0.215) (0.215)Repressionit−1 1.275

∗∗ 1.282∗∗

(0.058) (0.060)Civil Conflict Event: Non-Stateit 0.063

∗∗ 0.082∗∗

(0.013) (0.013)Civil Conflict Event: Stateit 0.221

∗∗ 0.231∗∗

(0.018) (0.018)Temperatureit -0.001 -0.004

∗

(0.001) (0.001)Rainfallit 0.023 0.023

(0.021) (0.018)ln GDP Per Capitais 1.638

∗

(0.074)Oil Supplyis -0.171

∗∗

(0.014)Employment Rateis 9.854

∗∗

(0.420)Entrance by Coupis 1.680

∗∗

(0.229)

Constant -0.141∗∗ -0.463† -17.397∗∗

(0.0115) (0.286) (0.737)Autocrat Effects Random Random RandomN 251,526 164,608 164,608

Significance levels: † : 10% ∗ : 5% ∗∗ : 1%

Odds Ratios[95% Confidence Intervals]

ln Western Aid Dependenceis 0.868 0.788 0.876[0.852, 0.885] [0.772, 0.804] [0.858, 0.895]

9

Table 2: Electoral correlates of Western aid dependence.

Western Incumbent Incumbent Media Expectation OppositionMonitors Competing Confident Bias Of Fraud Boycott

Logit Logit Logit Logit Logit Logit

ln Western Aid Dependenceis 0.309† 0.281† -0.278† -0.505∗∗ -0.575∗∗ -0.298∗

(0.175) (0.144) (0.181) (0.173) (0.182) (0.147)N 155 194 176 170 188 188

Significance levels: † : 10% ∗ : 5% ∗∗ : 1%

Odds Ratios[95% Confidence Intervals]

ln Western Aid Dependenceis 1.36 1.324 0.758 0.603 0.563 0.742[1.02, 1.82] [1.04 , 1.68] [0.56, 1.02] [0.45, 0.80] [0.42 , 0.76] [0.58 , 0.95]

aid commitments. For both reasons, citizens in Africa’s aid dependent autocracies may have fewer

grievances than their counterparts in aid independent Africa, and so have less cause to protest.

To ascertain whether this is the case – and hence whether donor selection bias may be driving

the results above – I employ a series of bivariate regressions to explore the electoral correlates of

Western aid dependence in Africa’s post-Cold War autocracies. To do so, I again draw on the

NELDA dataset, which includes a range of information about the conduct of elections: whether

the opposition boycotted, whether citizens believed fraud would be widespread, and whether the

incumbent was confident, among other features. The bivariate regressions take the form:

yj = α+ β ln(Western Aid Dependencej

)+ � (2)

where j indexes election season, yj gives a series of election season-level characteristics, and

Western Aid Dependencej measures the incumbent autocrat’s foreign aid position at election season

j’s outset.

The results appear in Table 2; the associated odds ratios appear in the bottom panel. Together,

they make clear that elections in Africa’s aid dependent autocracies are systematically different

than in aid independent Africa. Elections in aid dependent autocracies are more often observed by

Western election monitors. They feature less media bias, and are far less likely to be perceived as

fraudulent and boycotted by opposition parties. Perhaps reflecting the longer presidential terms in

aid independent autocracies – Congolese President Denis Sassou Nguesso and Rwandan President

Paul Kagame, for instance, engineered constitutions that stipulate seven year presidential terms –

elections in aid dependent Africa are more likely to feature the incumbent.

Put simply, elections in Africa’s aid dependent autocracies are far more likely to adhere to

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democratic norms.26 As a result, their citizens may have fewer grievances. This suggests that donor

selection bias may indeed be driving the results in Table 1, and hence obscuring the relationship

between Western aid dependence and protest in Africa’s post-Cold War autocracies.

3 Exploiting HIPC Debt Relief Negotiations

To account for donor selection bias, I employ a differences in differences estimation strategy. In

1996 the IMF and World Bank launched the HIPC Initiative to “[ensure] that no poor country

faces a debt burden it cannot manage.” In principle, the program grants debt relief to “[free] up

resources for social spending” in the world’s poorest, most heavily indebted countries. Countries

under consideration for debt relief are required to make significant governance reforms, which,

the Bretton Woods institutions hope, ensure that after debt relief governments allocate money to

anti-poverty programs that would otherwise have gone to debt service.

Carter (2015) shows that HIPC debt relief negotiations constituted a temporal shock for Africa’s

autocrats, during which Western donors enjoyed significant leverage over otherwise venal, repressive

governments. As a result, Africa’s autocrats were sharply constrained. During HIPC debt relief

negotiations, Africa’s autocrats were only 10% as likely to repress their citizens on a given day t

than on any other day of their tenure. If Western donors emboldened African citizens by constrain-

ing repression among Africa’s autocrats, then the rate of popular protest should have increased

dramatically during HIPC debt relief negotiations as well.

3.1 Program Background

HIPC debt relief negotiations begin with the publication of a “decision point” document, which

identifies a set of governance reforms that must be satisfied for debt relief. To qualify for this first

stage, candidate governments must have developed a Poverty Reduction Strategy Paper (PRSP)

in conjunction with the IMF and World Bank. Once all government reforms are implemented,

governments reach the “completion point,” when debt relief is full and irrevocable. As of July 2016,

30 African countries have qualified for HIPC debt relief, and another three have been identified

as eligible to begin the approval process based on their indebtedness and GDP levels. The HIPC

initiative has provided debt service relief worth greater than $75 billion, or an average of roughly

$2 billion per country. For most, this is a non-trivial sum relative to GDP. When Congo’s debt was

forgiven in 2010, for instance, its GDP was roughly $10 billion. For countries that received relief,

debt service payments declined by 2 percentage points of GDP between 2001 and 2013.27

For two reasons the HIPC debt relief initiative is well suited to a differences in differences

26This is consistent with Aronow, Carnegie and Marinov (2012) and Carter (2015), who find that Africa’s aiddependent autocrats are far less likely to repress their citizens.

27International Monetary Fund (2014).

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estimation strategy.28 First, the IMF and World Bank entertained debt relief negotiations with

virtually all of Africa’s autocrats.29 Of the 33 African countries that qualified for HIPC status, only

three have not yet received debt relief. The list of autocrats who have received debt relief includes

Blaise Compaoré of Burkina Faso, Paul Biya of Cameroon, Denis Sassou Nguesso of Congo, and

Yoweri Museveni of Uganda, who have collectively held power for 130 years, and whose human

rights violations are regularly criticized.30 Even Gambia’s Yahya Jammeh was granted debt relief.

In power since a 1994 coup, in 2011 Jammeh announced that he would rule for “one billion years,

God willing.” His brutal secret police and virulent homophobia earn him frequent condemnation

from the international community.31

The only qualifying countries not currently being considered for debt relief are Eritrea, Somalia,

and Sudan. Somalia has long lacked a functioning central government. Sudan is regarded as a state

sponsor of terror and its president, Omar al-Bashir, is subject to an arrest warrant from the Inter-

national Criminal Court (ICC). President Isaias Afeworki of Eritrea uses his country’s compulsory,

indefinite military service requirement to create a pool of slave labor, which the government uses

for mining.32 In short, donor selection bias is not a concern.

Although the IMF and World Bank ultimately extended debt relief to virtually all of Africa’s

autocrats, the threat of sanctions was still credible. Indeed, Africa’s autocrats were eager to finalize

debt relief negotiations to escape the scrutiny of the Bretton Woods institutions. Accordingly,

the IMF and World Bank used the duration of negotiations strategically. Africa’s most venal,

abusive autocrats were subjected to long negotiation periods, as Figure 1 makes clear. In Guinea

negotiations lasted some 12 years, in Guinea-Bissau 10 years, in DRC seven years, in Gambia seven

years, in Cameroon six years, in Congo five years, and in Burundi four years. By contrast, Uganda’s

Museveni was granted debt relief after only two months of negotiations in early 2000, befitting his

status then as a Western darling. Virtually all of Africa’s autocrats received debt relief, but the

duration of negotiations varied dramatically according to the autocrat’s economic and human rights

record.

To persuade the IMF and World Bank of their commitment to good governance and human

rights – and, therefore, to expedite debt relief – Africa’s autocrats routinely conceded major reforms.

In the Republic of Congo, for instance, Sassou Nguesso’s government reached the decision point

on January 31, 2006, despite siphoning some $300 million per year from the state treasury.33 Soon

after negotiations began, the IMF and World Bank identified two central conditions for debt relief:

that the government permit quarterly financial audits of the state oil company and that Sassou

28For a more on the HIPC debt relief initiative as an identification strategy, see Carter (2015).29Easterly (2002), Thomas (2001), Birdsall and Williamson (2002), and Easterly (2009).30For Cameroon, see Le Nouvel Observateur (2009) and Albaugh (2011). For Angola, see Global Witness (2004)

and Human Rights Watch (2012a). For Uganda, see Human Rights Watch (2012b). For Congo, see Global Witness(2004, 2005, 2007).

31Ruble (2015).32For more on Eritrea, see Amnesty International (2013) and Human Rights Watch (2011, 2013).33Global Witness (2004, 2005, 2007).

12

Nguesso’s son, Denis Christel, be removed from his position atop its marketing branch. Sassou

Nguesso conceded to both, and the proportion of oil revenue accounted for in the national budget

increased from roughly 60% prior to the decision point to 80% afterwards.34 Sassou Nguesso reached

the completion point on January 6, 2010, at which point debt relief was full and irrevocable. Sassou

Nguesso quickly reverted to previous form. Quarterly audits of the state oil company ceased, Denis

Christel was named its second in charge, and the proportion of oil revenue accounted for in the

national budget plummeted. But since Congo’s debt had been forgiven, the IMF and World Bank

had no leverage to intervene.19

8919

9019

9119

9219

9319

9419

9519

9619

9719

9819

9920

0020

0120

0220

0320

0420

0520

0620

0720

0820

0920

1020

1120

1220

13

BENBFOBUI

CAOCDI

CENCHACOMCONDRCETH

GAMGHAGNBGUILBR

MAGMAW

MLIMAAMZMNIR

RWASTPSENSIETAZ

TOGUGAZAM

Figure 1: The period of HIPC debt relief negotiations for each country in the dataset. Debt reliefnegotiations begin with publication of the decision point document and conclude with publicationof the completion point document. Of countries that participated in the HIPC debt relief initiative,70% began negotiations between 2000 and 2002, and many received debt relief soon thereafter.

Since the IMF and World Bank entertained debt relief negotiations with some of Africa’s longest

34Interviews with senior IMF official in December 2012.

13

tenured autocrats, many accumulated substantial human rights records both before negotiations

began and after debt relief was granted. Likewise, their citizens had years to accumulate lengthy

records of protest before debt relief negotiations began and after they concluded. Accordingly,

each population constitutes its own counterfactual: the rate at which it would have protested had

autocrat j not been constrained by Western pressure. As a result, the differences in differences

estimator controls for any unobserved, population-level characteristics that may be correlated with

both HIPC debt relief negotiations and protest.

3.2 Model Specification

If Western observers recognized the extent to which Africa’s autocrats modified their behavior in

response to Western pressure, we should expect Africa’s citizens to have recognized it as well. In

turn, we should expect African citizens to have responded accordingly. Since their rulers were

constrained, we should expect frustrated African citizens to protest at a considerably higher rate

than otherwise. And again, since the IMF and World Bank entertained debt relief negotiations

with Africa’s longest tenured autocrats, we can use citizens as counterfactuals for themselves. We

can compare how frequently citizens protested both before debt relief negotiations began and after

debt relief was granted with their rate of protest during negotiations themselves, when their rulers

were far less likely to employ repression.

To capture this, I construct the variable HIPC Negotiationsit, which assumes value 1 if day t

in country i occurred between the publication of its decision point document and its completion

point document. The dataset includes nearly 27,000 country-days for which HIPC Negotiationsit

assumes value 1, encompassing 21 autocrats from 19 countries. The dataset includes nearly 270,000

country-days for which HIPC Negotiationsit assumes value 0, encompassing 107 autocrats from 50

countries. From Figure 1, for some countries debt relief negotiations concluded quickly, with only

71 days elapsing between the decision point document and the completion point document. For

others, negotiations required more than a decade to complete.

Visual inspection of the data makes clear that African citizens engaged in far more protest

during HIPC debt relief negotiations than otherwise. Figure 2 gives the percentage of country-days

on which citizens in Africa’s post-Cold War autocracies engaged in protests for the three samples

along the x-axis. Prior to debt relief negotiations, citizens protested on roughly 7% of country-days.

Likewise, after debt relief negotiations concluded, citizens protested on roughly 6% of country-days.

During the most intense period of HIPC debt relief negotiations, however, the rate of protest among

citizens in Africa’s autocracies increased considerably, to roughly 9%.

The baseline estimating equation is

logit [Pr (Protestit = 1)] = α+ β (HIPC Negotiationsit)

+ψXit + κZit + γj + � (3)

14

Pre−HIPC HIPC Post−HIPC

0

2

4

6

8

10

12

Rate

of Pr

otest

(in %

Cou

ntry−

Days

)

Difference in MeansAcross Non−HIPC and HIPC Samples

t = 11.2925

Figure 2: Daily rates of protest in Africa’s autocracies since 1989. The y-axis measures the per-centage of country-days on which popular protest occurred for the three samples along the x-axis.Outside HIPC negotiations, a protest event occurred on roughly 6.5% of country-days. DuringHIPC negotiations, a protest event occurred on roughly 9% of country-days. The difference inmeans for the two samples is significant at the 0.001% level.

where Xit gives the vector of day-level control variables from equation (1), Zit the vector of year-

level control variables, and γj a full set of autocrat level fixed effects. Again, by comparing the

rate at which citizens protested during HIPC negotiations with the rate both before and after –

by focusing exclusively on within-autocrat variation – this fixed effects model ensures that the

estimated β is not a function of donor selection bias. As long as the days of debt relief negotiations

are, on average, identical in all salient respects to those on which HIPC negotiations did not occur,

the estimated effect will have a causal interpretation.

3.3 Estimation Results

The results appear in Table 3. As a baseline, Model 1 includes only HIPC Negotiationsit and the

two election season variables. Model 2 adds day-level variables that capture prevailing political

instability, while Model 3 adds daily weather records. The full model, which includes structural

control variables, appears in Model 4. As a robustness check, Model 5 reestimates the full model

with a random effects estimator and Model 6 with a rare events logit estimator.

15

The coefficient estimate on HIPC Negotiationsit is virtually constant across models. Citizens of

Africa’s autocracies are far more likely to protest during HIPC negotiations than otherwise. The

effect holds regardless of whether the government employed repression on day t− 1, any prevailingpolitical instability, the proximity of day t to an election, and the range of economic conditions that

might induce popular grievances or render power more attractive to protest leaders. The associated

odds ratios appear at the bottom of Table 3, along with 95% confidence intervals. They suggest

that, during HIPC negotiations, the daily odds of popular protest are between 40% and 50% greater

than otherwise. Owing to the large sample size and the magnitude of the effect, these estimates

are relatively precise.

3.4 Robustness Checks

Of course, protests may be correlated across days. It may be much easier, for instance, to sustain a

protest once it has emerged than to initiate one in the first place. As a result, the outcome variable

in equation (3) may be serially correlated. To ensure that the results in Table 3 are not driven by

this serial correlation, I conduct a series of robustness checks.

First, I employ a Markov transition framework.35 I restrict attention to country-days t where no

protests occurred on day t−1, and hence discard country-day observations where protests occurredon day t− 1. In so doing, I measure the effect of HIPC debt relief negotiations on the probabilitythat protests emerged on a given day t. The estimating equation is

logit [Pr (Protestit = 1|Protestit−1 = 0)] = α+ β (HIPC Negotiationsit)

+ψXit + κZit + γj + � (4)

Second, I reestimate equation (3) with a lagged outcome variable, which controls for whether

protests occurred on day t− 1.The results appear in Table 4, and they are essentially identical to the others. Across models,

the odds that citizens of Africa’s post-Cold War autocracies begin protesting during HIPC debt

relief negotiations are between 30% and 35% greater than on any other day of an autocrat’s tenure.

Controlling for whether protests occurred on day t − 1, the daily odds of protest during HIPCnegotiations are between 20% and 30% greater than otherwise. Again, these results are estimated

with precision.

The magnitude of these effects is apparent by comparing them to the effect of repression. The

Markov transition model in column 2 suggests that, when autocrats employ repression on day

t − 1, popular protests are roughly 60% more likely to emerge than otherwise. Here the Markovtransition specification is important. By not conditioning on day t− 1 protests, the baseline modelin (3), reported in Table 3, leaves open the possibility of reverse causality: that autocrats employ

35For more on Markov transition models, see Epstein et al. (2005).

16

Tab

le3:

Pro

test

and

HIP

CD

ebt

Rel

ief

Neg

otia

tion

s

Model

1M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6L

ogit

Logit

Logit

Logit

Logit

Rare

Even

ts

HIP

CN

egoti

ati

ons i

t0.4

64∗∗

0.4

59∗∗

0.3

84∗∗

0.3

12∗∗

0.3

12∗∗

0.3

12∗∗

(0.0

31)

(0.0

31)

(0.0

39)

(0.0

41)

(0.0

41)

(0.0

41)

Ele

ctio

nSea

sonit

0.4

71∗∗

0.4

04∗∗

0.3

88∗∗

0.4

34∗∗

0.4

34∗∗

0.4

34∗∗

(0.0

30)

(0.0

31)

(0.0

36)

(0.0

37)

(0.0

37)

(0.0

37)

Ele

ctio

nD

ayit

0.8

62∗∗

0.8

78∗∗

0.9

05∗∗

0.8

53∗∗

0.8

53∗∗

0.8

59∗∗

(0.1

76)

(0.1

79)

(0.2

08)

(0.2

13)

(0.2

13)

(0.2

13)

Rep

ress

ionit−1

1.1

46∗∗

1.2

96∗∗

1.3

86∗∗

1.3

85∗∗

1.3

85∗∗

(0.0

49)

(0.0

55)

(0.0

58)

(0.0

58)

(0.0

58)

Civ

ilC

onflic

tE

ven

t:N

on-S

tate

it0.0

18

0.0

04

0.0

77∗∗

0.0

77∗∗

0.0

77∗∗

(0.0

11)

(0.0

13)

(0.0

13)

(0.0

13)

(0.0

13)

Civ

ilC

onflic

tE

ven

t:Sta

teit

0.0

61∗∗

0.0

58∗∗

0.2

08∗∗

0.2

08∗∗

0.2

08∗∗

(0.0

11)

(0.0

16)

(0.0

18)

(0.0

18)

(0.0

18)

Tem

per

atu

reit

-0.0

04∗∗

-0.0

05∗∗

-0.0

05∗∗

-0.0

05∗∗

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

Rain

fallit

0.0

05

0.0

08

0.0

08

0.0

09

(0.0

20)

(0.0

21)

(0.0

20)

(0.0

21)

lnG

DP

Per

Capit

ais

1.7

58∗∗

1.7

07∗∗

1.7

57∗∗

(0.0

70)

(0.0

69)

(0.0

70)

Oil

Supply

is-0

.202∗∗

-1.1

95∗∗

-0.2

02∗∗

(0.0

14)

(0.0

14)

(0.0

14)

Em

plo

ym

ent

Rate

is8.9

25∗∗

8.8

74∗∗

8.9

21∗∗

(0.3

86)

(0.3

81)

(0.3

86)

Entr

ance

by

Coupis

1.8

32∗∗

1.7

25∗∗

1.8

33∗∗

(0.2

43)

(0.2

30)

(0.2

43)

Const

ant

-0.0

48

-0.0

24

0.3

46

-15.5

30∗∗

-18.9

80∗∗

-15.5

30∗∗

(0.2

47)

(0.2

47)

(0.2

69)

(0.5

87)

(0.6

37)

(0.5

87)

Auto

crat

Eff

ects

Fix

edF

ixed

Fix

edF

ixed

Random

Fix

edN

294,3

37

294,3

37

191,2

53

173,0

98

173,0

98

173,0

98

Sig

nifi

cance

level

s:†

:10%

∗:

5%

∗∗:

1%

Odds

Rati

os

[95%

Confiden

ceIn

terv

als

]

HIP

CN

egoti

ati

ons i

t1.5

91

1.5

82

1.4

68

1.3

66

1.3

66

1.3

66

[1.5

11,

1.6

74]

[1.5

03,

1.6

65]

[1.3

76,

1.5

65]

[1.2

78,

1.4

61]

[1.2

77,

1.4

60]

[1.2

78,

1.4

61]

17

repression in response to ongoing protests, which are sustained for a potentially wide range of

reasons. The Markov transition specification makes clear that citizens are some 60% more likely

to begin protesting after an episode of repression on day t− 1. Model 2, Table 4, suggests that theeffect of HIPC debt relief negotiations is similar.

3.5 Confounder: HIPC Negotiations and Economic Crisis

These results constitute powerful evidence that citizens of Africa’s post-Cold War autocracies were

far more likely to protest during HIPC debt relief negotiations than otherwise. However, it remains

possible that country conditions were systematically worse during HIPC debt relief negotiations. It

is possible, for instance, that autocrats chose to participate in the HIPC debt relief program only

when they had no choice: when their economies were on the verge of collapse. Alternatively, freed

from the burden of debt service, autocrats may have been able to increase public good provision

after debt relief was granted, which increased their citizens’ standards of living. For both reasons,

HIPC debt relief negotiations – as well as the period that preceded HIPC debt relief negotiations

– may be correlated with economic crisis in ways that the post-HIPC debt relief period is not. In

turn, this economic crisis – rather than citizens’ beliefs about the autocrat’s willingness to repress

– may compel popular protests and drive the results in Table 3.

To some extent, the models above already account for this. They control for a range of economic

indicators that reflect a society’s standard of living: country i’s per capita GDP in year t, its

employment rate, and its oil supply. Still, as a robustness check, Figures 3 and 4 display economic

indicators for pre-HIPC countries, countries in the midst of HIPC negotiations, and countries post-

HIPC debt relief. There is no evidence that citizens confronted worse economic conditions during

HIPC debt relief negotiations than either before or after. The employment rate is virtually identical

across samples, as is GDP per capita.

Even if aggregate economic indicators suggest no significant difference across sample periods,

it may be the case that, after debt relief, autocrats increased public good provision – or otherwise

increased financial transfers – in ways that rendered citizens better off but that do not appear in

macroeconomic indicators. To ensure that the post-HIPC period – when autocrats were able to

transfer funds otherwise earmarked for debt serve to public goods – is not driving the results in

Table 3, I restrict attention to the pre-HIPC period, when autocrats confronted similar budgetary

and credit constraints as they did during HIPC negotiations. I then reestimate (3). The results

appear in Table 4, and they are virtually identical to those from the baseline model in Table 3. In

short, there is no evidence that the increased rate of protest during HIPC debt relief negotiations

was a function of different economic conditions.

18

Tab

le4:

Pro

test

an

dH

IPC

Deb

tR

elie

fN

egot

iati

ons:

Mar

kov

Tra

nsi

tion

and

Lag

ged

Outc

ome

Mod

els

Model

1M

odel

2M

odel

3M

odel

4M

ark

ovM

ark

ovL

agged

DV

Lagged

DV

Logit

Logit

Logit

Logit

HIP

CN

egoti

ati

ons i

t0.2

83∗∗

0.2

69∗∗

0.2

43∗∗

0.1

65∗

(0.0

70)

(0.0

88)

(0.0

55)

(0.0

71)

Ele

ctio

nSea

sonit

0.4

14∗∗

0.3

70∗∗

0.2

20∗∗

0.2

14∗∗

(0.0

77)

(0.0

97)

(0.0

56)

(0.0

68)

Ele

ctio

nD

ayit

2.0

76∗∗

2.1

56∗∗

1.6

45∗∗

1.7

49∗∗

(0.2

42)

(0.2

89)

(0.2

61)

(0.2

97)

Rep

ress

ionit−1

0.4

84∗

0.6

65∗∗

(0.1

94)

(0.1

19)

Civ

ilC

onflic

tE

ven

t:N

on-S

tate

it0.1

30∗∗

0.0

94∗∗

(0.0

28)

(0.0

24)

Civ

ilC

onflic

tE

ven

t:Sta

teit

0.1

06∗

0.1

35∗∗

(0.0

47)

(0.0

33)

Tem

per

atu

reit

-0.0

06

-0.0

04†

(0.0

03)

(0.0

03)

Rain

fallit

0.0

05

0.0

42

(0.0

47)

(0.0

35)

lnG

DP

Per

Capit

ais

1.6

72∗∗

1.0

77∗∗

(0.1

77)

(0.1

30)

Oil

Supply

is-0

.020

-0.1

15∗∗

(0.0

32)

(0.0

25)

Em

plo

ym

ent

Rate

is7.6

94∗∗

5.1

90∗∗

(1.1

30)

(0.7

80)

Entr

ance

by

Coupis

2.0

62∗∗

1.1

03∗∗

(0.5

06)

(0.3

26)

Pro

test

it−1

5.6

39∗∗

5.5

66∗∗

(0.0

25)

(0.0

32)

Const

ant

-2.4

01∗∗

-16.6

94∗∗

-3.1

26∗∗

-12.3

56∗∗

(0.6

33)

(1.4

48)

(0.5

27)

(1.1

16)

Auto

crat

Eff

ects

Fix

edF

ixed

Fix

edF

ixed

N268,2

36

156,0

80

294,3

37

173,0

98

Sig

nifi

cance

level

s:†

:10%

∗:

5%

∗∗:

1%

Odds

Rati

os

[95%

Confiden

ceIn

terv

als

]

HIP

CN

egoti

ati

ons i

t1.3

27

1.3

09

1.2

75

1.1

797

[1.1

83,

1.4

88]

[1.1

33,

1.5

12]

[1.1

65,

1.3

95]

[1.0

51,

1.3

25]

19

Tab

le5:

Pro

test

an

dH

IPC

Deb

tR

elie

fN

egot

iati

ons:

Res

tric

tin

gA

tten

tion

toP

re-H

IPC

and

HIP

C

Model

1M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6L

ogit

Logit

Logit

Logit

Logit

Rare

Even

ts

HIP

CN

egoti

ati

ons i

t0.4

78∗∗

0.4

64∗∗

0.2

86∗∗

0.2

66∗∗

0.2

60∗∗

0.2

66∗∗

(0.0

39)

(0.0

40)

(0.0

50)

(0.0

55)

(0.0

54)

(0.0

55)

Ele

ctio

nSea

sonit

0.2

74∗∗

0.2

66∗∗

0.2

96∗∗

0.2

43∗∗

0.2

46∗∗

0.2

44∗∗

(0.0

51)

(0.0

51)

(0.0

61)

(0.0

63)

(0.0

62)

(0.0

63)

Ele

ctio

nD

ayit

1.2

37∗∗

1.2

51∗∗

1.2

75∗∗

1.3

23∗∗

1.3

18∗∗

1.3

32∗∗

(0.2

53)

(0.2

54)

(0.2

90)

(0.3

02)

(0.3

00)

(0.3

01)

Rep

ress

ionit−1

1.0

57∗∗

0.9

51∗∗

0.9

28∗∗

0.9

28∗∗

0.9

29∗∗

(0.1

09)

(0.1

36)

(0.1

35)

(0.1

35)

(0.1

35)

Civ

ilC

onflic

tE

ven

t:N

on-S

tate

it0.1

88∗∗

0.2

33∗∗

0.2

83∗∗

0.2

83∗∗

0.2

83∗∗

(0.0

19)

(0.0

24)

(0.0

24)

(0.0

24)

(0.0

24)

Civ

ilC

onflic

tE

ven

t:Sta

teit

0.1

75∗∗

0.1

48∗∗

0.1

90∗∗

0.1

89∗∗

0.1

90∗∗

(0.0

17)

(0.0

25)

(0.0

25)

(0.0

25)

(0.0

25)

Tem

per

atu

reit

-0.0

21∗∗

-0.0

20∗∗

-0.0

20∗∗

-0.0

20∗∗

(0.0

04)

(0.0

04)

(0.0

04)

(0.0

04)

Rain

fallit

0.0

22

0.0

27

0.0

27

0.0

29

(0.0

28)

(0.0

28)

(0.0

28)

(0.0

28)

lnG

DP

Per

Capit

ais

3.2

12∗∗

3.1

85∗∗

3.2

07∗∗

(0.1

90)

(0.1

85)

(0.1

90)

Oil

Supply

is-3

.129∗∗

-2.9

94∗∗

-3.1

23∗∗

(0.2

16)

(0.2

12)

(0.2

16)

Em

plo

ym

ent

Rate

is-7

7.9

19∗∗

-73.8

72∗∗

-77.8

40∗∗

(3.0

96)

(2.7

10)

(3.0

93)

Entr

ance

by

Coupis

-3.8

38∗∗

-3.5

26∗∗

-3.8

29∗∗

(0.3

36)

(0.3

25)

(0.3

36)

Const

ant

0.0

61

0.0

53

1.8

21∗∗

16.8

45∗∗

7.3

05∗∗

16.8

30∗∗

(0.2

47)

(0.2

47)

(0.3

94)

(2.0

27)

(1.9

16)

(2.0

26)

Auto

crat

Eff

ects

Fix

edF

ixed

Fix

edF

ixed

Random

Fix

edN

123,8

64

123,8

64

76,6

26

76,6

26

76,6

26

76,6

26

Sig

nifi

cance

level

s:†

:10%

∗:

5%

∗∗:

1%

Odds

Rati

os

[95%

Confiden

ceIn

terv

als

]

HIP

CN

egoti

ati

ons i

t1.6

13

1.5

91

1.3

31

1.3

05

1.2

97

1.3

05

[1.5

12,

1.7

21]

[1.4

91,

1.6

98]

[1.2

26,

1.4

45]

[1.1

92,

1.4

28]

[1.1

86,

1.4

18]

[1.1

92,

1.4

28]

20

Pre−HIPC HIPC Post−HIPC

0

10

20

30

40

50

60

70

80

90

100

Em

ploy

men

t (as

% T

otal

Pop

ulat

ion)

Figure 3: The proportion of the total population that is employed, according to the Penn WorldTables, before HIPC debt relief negotiations begin, during HIPC debt relief negotiations, and afterdebt relief was granted.

3.6 When Protests Are Especially Easy or Especially Difficult

Given the magnitude of the effects above, it is worth asking whether citizens of Africa’s post-Cold

War autocracies have been more likely to protest during HIPC debt relief negotiations even when

protests are especially difficult to organize. Conversely, it is also worth asking whether HIPC debt

relief negotiations fostered popular protests even when protests were especially easy to organize.

To do so, I exploit the fact that the days and weeks surrounding elections in Africa’s post-

Cold War autocracies are systematically different than other times of year. By enabling citizens

to coordinate their anti-regime activities, election seasons constitute focal moments for popular

protest. Scholars have offered a range of ways that election seasons foster protests. During election

seasons citizens are more engaged in the political process and more aware of their neighbors’

21

Pre−HIPC HIPC Post−HIPC

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

GD

P P

er C

apita

(in

US

D)

Figure 4: GDP per capita, according to the Penn World Tables, before HIPC debt relief negotiationsbegin, during HIPC debt relief negotiations, and after debt relief was granted.

discontent.36 Opposition leaders know this, and often benefit disproportionately if mass protests

generate regime change or a new distribution of patronage. As a result, they have strong incentives

to coordinate popular protests despite the inherent risks.37 By affirming the possibility of a post-

regime future, elections decrease the costs to frustrated regime elites of defecting from the coalition

and joining the opposition, which routinely causes authoritarian regimes to crumble.38 Accordingly,

Carter (2016a) finds, across Africa’s post-Cold War autocracies, the daily rate of popular protest

is some 200% greater during election seasons – both prior to election day and following – than at

other times of year. On election day, the rate of popular protest is some 300% greater.

36Kuran (1991), van de Walle (2006), Tucker (2007), and Fearon (2011). Chwe (2001) and Medina (2007) alsoemphasize the role of common knowledge in collective action, as do Hollyer, Rosendorff and Vreeland (2014).

37Beissinger (2002), Javeline (2003), McFaul (2005), Lyall (2006), van de Walle (2006), Schedler (2009), Radnitz(2010), Bunce and Wolchik (2010, 2011), and Hyde and Marinov (2014).

38O’Donnell and Schmitter (1986), Hale (2005), Langston (2006).

22

Of the 110 autocrats in the dataset, 12 confronted at least one election season during HIPC

debt relief negotiations. Of these 12, all but two experienced an election season outside HIPC debt

relief negotiations as well. The list of autocrats who confronted election seasons both during HIPC

negotiations and otherwise includes some of Africa’s most repressive: Chad’s Idriss Déby, Congo’s

Denis Sassou Nguesso, Gambia’s Yahya Jammeh, Guinea’s Lansana Conté, and Rwanda’s Paul

Kagame, all of whom rank among Africa’s most brutal, but were somewhat more respectful of their

citizens’ human rights during HIPC negotiations. As a result, we can ask whether their citizens

protested more often during HIPC negotiations than otherwise, both when organizing protests is

more straightforward, as it is during election seasons, and when organizing protests is more difficult,

as it is otherwise.

I estimate three different models. The first focuses exclusively on citizens’ behavior during

election seasons, when collective action problems are more easily overcome and organizing protests

more straightforward:

logit [Pr (Protestit = 1|Election Seasonit = 1)] = α+ β (HIPC Negotiationsit)

+ψXit + κZit + γj + � (5)

The second focuses exclusively on citizens’ behavior outside election seasons:

logit [Pr (Protestit = 1|Election Seasonit = 0)] = α+ β (HIPC Negotiationsit)

+ψXit + κZit + γj + � (6)

Finally, I reestimate equation (3) with an interaction term, which lets the effect of HIPC Negotiationsit

vary according to whether day t occurred during an election season:

logit [Pr (Protestit = 1)] = α+ β (HIPC Negotiationsit)

+φ (HIPC Negotiationsit × Election Seasonit)

+ψXit + κZit + γj + � (7)

The results appear in Table 6. Models 1 and 2 correspond to equation (5), Models 3 and

4 correspond to equation (6), and Models 5 and 6 correspond to equation (7). Across models,

citizens in Africa’s post-Cold War autocracies are far more likely to protest during HIPC debt

relief negotiations – when their rulers’ recourse to repression is constrained by Western pressure –

than at any other time of year. This effect obtains regardless of whether we focus exclusively on

election seasons, exclusively on days outside election seasons, or let the effect of HIPC debt relief

negotiations be conditional on whether day t falls during an election season.

Models 5 and 6 are particularly instructive. The associated odds ratios in the bottom half of

Table 6 make clear that, outside of election seasons, citizens of Africa’s post-Cold War autocracies

23

were between 40% and 50% more likely to protest during HIPC negotiations. During election

seasons, when protests are easier to coordinate and when the international spotlight is even more

glaring, these same citizens were even more likely to protest: roughly twice as likely as otherwise.

In short, by rendering repression costly for Africa’s autocrats, Western creditors have emboldened

African citizens.

4 Conclusion

Western governments are reshaping politics in Africa’s post-Cold War autocracies. By withholding

development aid and debt relief in response to human rights violations and endemic corruption,

Western donors have created new challenges for Africa’s autocrats, and forced them to confront

those new challenges with new constraints. As Western donors pressured newly ascendant military

dictators to cede power to elected civilians, the rate of coups across the continent fell dramatically.

As it did, the chief threats confronting Africa’s post-Cold War autocrats shifted to the streets, a

trend that regular elections – by enabling citizens to solve the collective action problem – only

reinforced. Moreover, autocrats must confront these recurrent opportunities for collective action

without the easy recourse to repression enjoyed by their predecessors.

If Western donors have constrained repression among Africa’s post-Cold War autocrats, as

recent research suggests, then citizens in Africa’s aid dependent autocracies should also be far more

willing to protest than their counterparts, for the costs of protesting should be lower. This paper

makes clear that this is indeed the case. Of course, citizens beliefs are far from immutable. As their

hopes are vindicated or disappointed, their beliefs about the democratic commitments of Western

governments will grow stronger or be discredited. As a result, the evidence presented above is a

function of the post-Cold War international environment that generated it. There are signs that

this environment is changing. As China courts African allies with low interest loans and no political

conditions, Africa’s autocrats increasingly forgo Western aid in favor of Chinese support. Likewise,

as Western governments grow preoccupied with economic crises at home and geopolitical crises

abroad, they seem more willing to ignore the democratic transgression of Africa’s autocrats as long

as the patina of stability obtains.39 If these trends continue, then just as Western aid dependence

will prove a weaker constraint on Africa’s autocrats, so too will Africa’s citizens be less inclined to

take to the streets in protest.

This paper suggests at least two directions for future research. First, this paper makes clear

that African citizens heed cues from Western governments during moments of tension. What

other cues are meaningful for citizens in Africa’s post-Cold War autocracies? Western govern-

ments, for instance, routinely condemn electoral fraud and repression by Africa’s autocrats, which

pro-democracy activists then disseminate on social media platforms. Do these public statements

39Diamond (2015).

24

Tab

le6:

Pro

test

and

HIP

CD

ebt

Rel

ief

Neg

otia

tion

s,C

ond

itio

nal

onE

lect

ion

Sea

son

Model

1M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6L

ogit

Logit

Logit

Logit

Logit

Logit

HIP

CN

egoti

ati

ons i

t0.9

07∗∗

0.3

71∗

0.4

22∗∗

0.2

76∗∗

0.4

29∗∗

0.3

29∗∗

(0.1

39)

(0.1

82)

(0.0

32)

(0.0

42)

(0.0

32)

(0.0

39)

Ele

ctio

nSea

sonit

0.4

11∗∗

0.4

04∗∗

(0.0

32)

(0.0

38)

HIP

CN

egoti

ati

ons i

t×

Ele

ctio

nSea

sonit

0.5

52∗∗

0.2

59∗

(0.0

93)

(0.1

16)

Ele

ctio

nD

ayit

1.0

06∗∗

1.0

01∗∗

0.8

60∗∗

0.8

58∗∗

(0.1

92)

(0.2

39)

(0.1

76)

(0.2

11)

Rep

ress

ionit−1

1.7

15∗∗

1.7

17∗∗

1.4

30∗∗

(0.2

31)

(0.0

65)

(0.0

58)

Civ

ilC

onflic

tE

ven

t:N

on-S

tate

it0.2

26∗∗

0.0

82∗∗

0.0

82∗∗

(0.0

53)

(0.0

14)

(0.0

13)

Civ

ilC

onflic

tE

ven

t:Sta

teit

-0.3

97∗∗

0.2

21∗∗

0.2

31∗∗

(0.1

49)

(0.0

18)

(0.0

16)

Tem

per

atu

reit

-0.0

33∗∗

-0.0

03∗

-0.0

05∗∗

(0.0

08)

(0.0

01)

(0.0

01)

Rain

fallit

0.0

11

-0.0

06

0.0

01

(0.0

80)

(0.0

22)

(0.0

01)

lnG

DP

Per

Capit

ais

0.7

50∗

1.7

38∗∗

1.7

06∗∗

(0.3

72)

(0.0

73)

(0.0

69)

Oil

Supply

is-0

.926∗∗

-0.1

88∗∗

-0.2

04∗∗

(0.1

06)

(0.0

14)

(0.0

14)

Em

plo

ym

ent

Rate

is-9

.206∗∗

9.5

93∗∗

8.9

39∗∗

(2.1

06)

(0.4

01)

(0.3

77)

Entr

ance

by

Coupis

0.5

79

1.8

91∗∗

1.7

71∗∗

(1.4

32)

(0.2

46)

(0.2

37)

Const

ant

1.9

29∗∗

3.9

64

-2.1

20∗∗

-17.9

56∗∗

-0.0

14

-15.2

12∗∗

(0.4

78)

(2.9

31)

(0.6

11)

(0.8

24)

(0.2

47)

(0.5

78)

Auto

crat

Eff

ects

Fix

edF

ixed

Fix

edF

ixed

Fix

edF

ixed

N13,3

74

8,7

46

280,9

63

164,3

52

294,3

37

182,4

85

Sig

nifi

cance

level

s:†

:10%

∗:

5%

∗∗:

1%

Odds

Rati

os

[95%

Confiden

ceIn

terv

als

]

HIP

CN

egoti

ati

ons i

t2.4

77

1.4

50

1.5

25

1.3

18

[1.9

72,

3.1

13]

[1.0

74,

1.9

57]

[1.4

47,

1.6

08]

[1.2

30,

1.4

12]

HIP

CN

egoti

ati

ons i

t:

Outs

ide

Ele

ctio

nSea

son

1.5

35

1.3

90

[1.4

57,

1.6

18]

[1.3

05,

1.4

81]

HIP

CN

egoti

ati

ons i

t:

Duri

ng

Ele

ctio

nSea

son

2.6

66

1.8

02

[2.2

93,

3.0

99]

[1.4

90,

2.1

78]

25

embolden African citizens in the days following their release? Likewise, are Africa’s autocrats less

inclined to violate their citizens’ human rights when Western governments makes their objections

clear? Of course, these question are not novel. In recent years, scholars have produced a growing

literature on the causes and consequences of “naming and shaming.”40 But its conclusions have so

far been mixed, perhaps because it relies largely on country-year data and relatively rough measures

of autocratic behavior. By employing more precise data – and focusing on regimes that rely on

Western governments for development aid and debt relief – this paper suggests a way forward.

Second, this paper joins a new wave of scholarship that recasts the challenges and constraints

confronting the world’s autocrats, especially in Africa. Drawing on the Cold War, scholars often

view the chief threats to an autocrat’s survival as emanating from disgruntled elites, who can

engineer coups. To facilitate credible revenue sharing agreements with their elites, scholars contend,

autocrats construct political institutions: single parties or nominally democratic institutions.41

When necessary, autocrats employ violence to suppress popular revolts.42 The end of the Cold

War marked a fundamental change in autocratic politics, especially in Africa, where governments

lost the ability to leverage Great Power rivalries. Largely beholden to Western creditors, Africa’s

autocrats traded the threat of the coup for the threat of the street. This paper underscores the

magnitude of that challenge.43 In an era when regular elections serve as coordinating devices

and Western governments punish human rights violations, mass collective action is no longer as

difficult to organize as it once was.44 Most broadly, the results above suggest that understanding

autocratic survival in post-Cold War Africa requires understanding how the continent’s autocrats

have inoculated themselves against popular protests, especially without easy recourse to repression.

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