Elections, Protests, and Focal Moments:

Day-Level Evidence from Post-Cold War Africa

Brett L. Carter∗

May 5, 2016

Abstract

Scholars increasingly argue that autocrats employ nominally democratic institutions – gen-

erally national legislatures populated by regular elections – to sustain their power. However, a

range of case study evidence suggests that the regular elections occasioned by nominally demo-

cratic institutions enable citizens to organize collective action. This paper measures the extent

to which regular elections foster popular protests in Africa’s post-Cold War autocracies. This pa-

per uses day-level data to construct a plausible counterfactual: a conjecture of whether protests

would have emerged on a given day t had an election not been imminent. The paper finds that

during autocratic election seasons, the daily rate of popular protest increases by between 200%

and 300%.

∗Post-Doctoral Fellow at the Center on Democracy, Development, and the Rule of Law; National Fellow at theHoover Institution; Stanford University. Email: blcarter@stanford.edu. I thank Erin Baggott and Melissa Lee forhelpful comments on an earlier draft.

1

1 Introduction

Students of autocratic politics increasingly view national legislatures as sources of stability. These

nominally democratic institutions, the arguments generally go, enable autocrats to credibly commit

to revenue sharing agreements with regime insiders or policy compromises with other prominent

political figures.1 Scholars have suggested that elections enable autocrats to equitably distribute

regime patronage,2 locate pockets of popular discontent,3 and identify effective party cadres.4 As

Lust-Okar (2006) puts it, “the logic of authoritarian elections should lead us to question the value of

pressing for, and applauding, the introduction of elections in authoritarian regimes.”5 If Lust-Okar

(2006)’s skepticism is founded, then Western creditors, by virtually requiring nominally democratic

institutions in exchange for development aid and debt relief for nearly 25 years,6 will have rendered

the world’s autocrats more stable, not less.

Nominally democratic institutions may yield these benefits to the world’s autocrats. But other

scholars, drawing on case study evidence, note the coincidence of regular elections and popular

protests. This case study evidence offers three ways that elections may enable citizens to solve the

collective action problem.7 First, during election seasons citizens are more engaged in the political

process and more aware of their neighbors’ discontent.8 When all citizens experience electoral fraud

simultaneously, Tucker (2007) observes, “people no longer have to choose whether to react alone.

Especially as crowds grow, individuals know that they will only be one of many, many people

protesting, and thus much less likely to be punished individually.” As Fearon (2011) writes, “it is

the commonly understood convention of holding elections at particular times according to known

rules, not the electoral outcome itself, that provides a public signal for coordinating rebellion in

the event that elections are suspended or blatantly rigged.” Widespread reports of fraud, in short,

create common knowledge among frustrated citizens, which provides a foundation for action.

Opposition leaders know this, of course, and stand to benefit disproportionately if mass protests

generate regime change or a new distribution of patronage. As a result, opposition leaders may

serve as political entrepreneurs, who have strong incentives to alert citizens to electoral irregularities

and coordinate protest events. McFaul (2005) traces the success of protest movements in Serbia,

Georgia, and Ukraine to a robust political opposition, which was “capable of mobilizing tens of

1Magaloni (2006), Gandhi and Przeworski (2007), Gandhi (2008), Magaloni (2008), Wright (2008).2Lust-Okar (2006) and Blaydes (2008).3Ames (1970), Magaloni (2006), Brownlee (2007), Blaydes (2008), and Cox (2009).4Birney (2007) and Blaydes (2008).5Gandhi and Lust-Okar (2009) provide an excellent overview of this research program.6Many scholars have documented the tendency for Western governments to attach “good governance” conditions

to development aid and debt relief, especially in post-Cold War Africa; see, among many others, Bratton and van deWalle (1997), van de Walle (2001), Dunning (2004), Levitsky and Way (2010), and Goemans and Marinov (2014).

7For more on protest as a collective action problem, see Olson (1977), Granovetter (1978), Popkin (1979), DeNardo(1985), McAdam (1986), Tullock (1987), Chong (1991), and Hardin (1995), Weingast (1997), Schedler (2002), Prze-worski (2006).

8Kuran (1991), Tucker (2007), van de Walle (2006). Chwe (2001) and Medina (2007) also emphasize the role ofcommon knowledge in collective action, as do Hollyer, Rosendorff and Vreeland (2014).

2

thousands or more demonstrators to protest electoral fraud.” Opposition leaders marshal the finan-

cial resources that sustain protest movements,9 persuade citizens of regime culpability for policy

mistakes,10 and draw on “lessons learned” from successful revolutions in neighboring countries.11

When electoral rules are violated, opposition leaders help popular frustration coalesce into mass

protest.12

These two mechanisms whereby regular elections help citizens act collectively yield a third. 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. As a result, regular elections may

facilitate the splits between hard-liners and soft-liners that O’Donnell and Schmitter (1986) wrote

about nearly 30 years ago.13 Hale (2005) concludes that revolutions in Georgia, Kyrgyzstan, and

Ukraine succeeded, in part, because high ranking security officials refused to suppress the opposition

leaders who “might be the authorities of the future.”14 More recently, in Africa, elite defections

occasioned by elections helped end Senegalese President Abdoulaye Wade’s ambitions for his son,

catalyzed the Burkinabe Revolution of October 2014 that toppled President Blaise Compaore, and

gave rise to Jean Ping’s candidacy against Gabonese President Ali-Ben Bongo.

In short, considerable case study evidence suggests that the regular elections occasioned by

nominally democratic institutions may constitute “focal moments” for popular protests: they may

help citizens overcome the classic collective action problem. However, the magnitude of this hy-

pothesized effect remains unclear. Moreover, ascertaining whether autocratic elections facilitate

protest also requires constructing a counterfactual: a plausible conjecture about whether protests

would have emerged on a given day t had a given election not been imminent. Indeed, when scholars

have attempted to establish such a counterfactual, they have found that elections appear not to

be associated with higher rates of popular protests. Drawing on annual level data, for instance,

Schedler (2009) concludes:

opposition actors more often choose to acquiesce to authoritarian elections than toprotest against them. For a remarkable number of cases data do not register anyinstance of contentious mass action in the five years [preceding an election year].15

To help answer Lust-Okar (2006)’s question – indeed, to help determine whether the interna-

tional community ought to “[applaud] the introduction of elections in authoritarian regimes” – this

paper measures the extent to which regular elections facilitate popular protests in Africa’s post-Cold

War autocracies. More, the paper asks whether the posited destabilizing effects of regular elections

9Radnitz (2010).10Javeline (2003).11Beissinger (2002), Lyall (2006), Bunce and Wolchik (2010, 2011).12van de Walle (2006), Schedler (2009), Hyde and Marinov (2014).13Langston (2006).14Indeed, among the primary benefits of single party regimes is their ability to stem elite defection. See Geddes

(1999), Way (2005), Brownlee (2008), and Slater (2010).15Schedler (2009), 189.

3

extend to the pre-electoral period, when expectations of fraud – rather than evidence thereof – and

past human rights violations compel popular frustration.

Answering these questions requires distinguishing the days surrounding elections from other

days within the calendar year. It requires controlling for day-level, confounding variables that may

be associated with both protests and elections. It requires recognizing that the days immediately

prior to an election may be systematically different than days a year prior to it. Indeed, this may

be the case for a number of reasons. Anticipating electoral violence, investors could withdraw from

the local economy, generate an economic contraction, and so foster protests for reasons that have

nothing to do with elections. Alternatively, international donors could increase funding during

election years, which could generate incentives for political violence.

This paper measures the effect of election seasons on the rate of protest by constructing a set

of counterfactual days: a plausible conjecture about the rate of popular protest on a given day t if

election day was not imminent. To do so, the paper employs a day-level dataset of popular protests

in post-Cold War Africa, home to nearly half of the world’s autocracies. The paper compares

the rate of popular protest during election seasons with the rate of popular protest in the weeks

immediately prior. In so doing, this research design exploits two related features of elections. First,

economic, international, and other structural forces tend to move slowly. While these structural

conditions may change dramatically over several years, they are virtually identical in the days

immediately surrounding the onset of election season. Second, autocrats schedule elections several

months in advance, and so any favorable conditions that the incumbent sought to exploit at the

moment he announced the election will likely exist in the days immediately surrounding it. The

paper finds that popular protests are twice as likely during the weeks and months surrounding

elections – and more than three times as likely on election day itself – than at any other time

during the calendar year. More, the rate of protest rises well before election day, and not simply

upon allegations of fraud after voting has occurred and the results have been announced. Owing

to the large sample size, these effects are estimated with considerable precision.

This paper contributes to the growing literature on regime change in electoral autocracies.

Scholars have suggested a variety of mechanisms through which even flawed elections might foster

democratization. Howard and Roessler (2006) and Donno (2013) find that when opposition leaders

remain united, elections are far more likely to result in more liberal politics.16 In his seminal work,

Lindberg (2006, 2009) posits that regular elections in autocratic Africa are self-reinforcing: the mere

act of holding the election may create demand for elections in the future, which over time favors

democracy. Coupled with recent work by Aidt and Leon (2015), this paper, most broadly, provides

16Relatedly, Schedler (2009) also finds evidence that pre-election protests are associated with better electoraloutcomes by opposition parties. It may be, however, that opposition parties have better information about incumbentweakness, and so are more likely to protest when electoral outcomes are likely to be better. Indeed, contributions byHoward and Roessler (2006) and Donno (2013) confront a similar endogeneity bias. In their case, it remains possiblethat opposition leaders are more likely to maintain a united front when incumbent autocrats are weak and liberalizingelectoral outcomes more likely.

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a foundation for an additional mechanism. In post-Cold War Africa, Aidt and Leon (2015) find that

the probability of a democratic transition in a given year increases with the number of protests, a

result they interpret as consistent with the theories of democratization in Boix (2003) and Acemoglu

and Robinson (2005). Together, these contributions provide cause for applauding the introduction

of regular elections in authoritarian regimes, as Lust-Okar (2006) queried. For by enabling citizens

to overcome their collective action problems, regular elections themselves enable citizens to organize

and ultimately demand political concessions. In short, elections are fundamentally destabilizing,

even when autocrats can guarantee victory with electoral fraud.

This paper proceeds as follows. Section 2 introduces the day-level dataset, as well as descriptive

statistics that illustrate the life cycle of electoral protest. Sections 3 and 4 present statistical

evidence that popular protests are far more common during election seasons than at any other

time of year. Section 5 concludes with suggestions for future research.

2 Descriptive Statistics: The Life Cycle of Electoral Protests

To explore the life cycle of popular protest, I combine day-level records of protest with a range

of election season-level characteristics. In constructing the dataset, I build on the work of others.

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.17 The National Elections

Across Democracy and Autocracy (NELDA) dataset, introduced by Hyde and Marinov (2012),

records the dates of every election around the world between 1960 and 2010. This pathbreaking

work also includes a range of information about the political season that preceded each election:

whether Western monitors observed the elections, whether the population expected widespread

fraud, whether opposition parties boycotted, whether the incumbent expected to win ex ante, as

well as a range of other information.18 I draw data on popular protests from the Social Conflict

in Africa Database (SCAD), introduced by Salehyan et al. (2012). The SCAD dataset records the

number of protest events, each day, occurring throughout Africa since 1989. Based on an exhaustive

search of the Lexis Nexis archive, Salehyan et al. (2012) employed a research team to hand code

details about each event. The result is the most exhaustive record yet assembled.19 I include all

protest events that are directed against the state. Most commonly, these are labor strikes from

all sectors of the economy, opposition political rallies that spun out of control, and spontaneous

uprisings of frustrated citizens.

For each election in post-Cold War autocratic Africa, I define an election season that begins

eight months prior to election day and ends a month following it, after the results are announced

17To extend the sample period through 2010, I employed Svolik (2012)’s criteria to classify Africa’s autocratsbetween 2007 and 2010.

18In drawing on the NELDA dataset, I follow Hafner-Burton, Hyde and Jablonski (2013, 2014).19See descriptive statistics in the appendix.

5

and newly elected officials assume office. For each of 213 election seasons, I index each day within

the season as t ∈ {−240, 30}, where day t = −240 denotes the 240th day until the election, day

t = 0 denotes election day itself, and day t = 30 denotes the 30th day after the election. To compute

the life cycle of electoral protest, I sum the number of protest events across Africa’s autocracies

for each day t. Figure 1 presents the results, with days until the election along the x-axis and the

number of protests on the y-axis. For clarity, I overlay a fitted OLS regression line.

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−240 −210 −180 −150 −120 −90 −60 −30 0 30

Days Until Election

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Num

ber o

f Pro

test

Eve

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Figure 1: The trajectory of popular protest during election season.

The results are striking. The number of protests per day across autocratic Africa rises steadily

as election day approaches, from a low of 10 on day t = −235 to nearly 30 when the election is

but two weeks away. From there, the number of protests spikes, and exceeds 60 on election day

itself. Protests remain elevated immediately following the election, and only decline a month later,

well after the results have been announced and, usually, the victors sworn in. In many cases, these

protests represent campaign rallies called by opposition leaders that gather momentum, as in the

6

2009 Congolese presidential elections. In other cases, these protests represent labor strikes, timed

by union leaders to embarrass the regime and elicit maximum concessions, as occurred just prior

to Gabon’s 2005 legislative elections.

Drawing on the post-Soviet experience, scholars routinely emphasize that allegations of fraud

on election day provide a coordinating device for popular protests. But in post-Cold War Africa,

these descriptive statistics suggest, the rate of popular protest rises long before election day: when

opposition leaders launch political campaigns, when citizens sense their shared frustrations and

their shared commitment to regime change, and when onetime regime allies sense a post-regime

future and defect to the opposition.

3 Baseline Estimation

3.1 Data and Model Specification

To measure the magnitude of these focal moments – and to assess whether these focal moments

are as strong before elections as they are after – I estimate the probability that a protest event

occurred in country i on day t as a function of whether day t occurred during an election season.

In many parts of Sub-Saharan Africa, the 15 days before and after elections are the most

sensitive.20 During the 15 days prior to election day, campaign activities are most intense; within

the 15 days following the election, the Ministry of Interior announces results, which often elicit the

ire of citizens. Accordingly, I create a series of variables that define the days prior to an election

as constituting a pre-election season, and the days after an election as constituting a post-election

season. Since these election seasons are not always well defined – and may effectively be longer in

some countries than others – I vary the length of each pre- and post-election season in the statistical

models below. In the most restrictive models, I define only the two days before the election as the

pre-election season, and the two days after the election as the post-election season. In the most

expansive models, I let the pre-election season span the 60 days before the election and the post-

election season span the 60 days after. Note that I define an election season as the aggregate of

the pre- and post-election seasons.

In probing the relationship between protest and election seasons, I control for a variety of other

factors that may be associated with both. Weather patterns may vary across years, and may also

condition a population’s choice to protest. Less rainfall, for instance, may indicate drought, which

could catalyze citizen protests. Temperature may have a similar effect. Accordingly, I control for

total rainfall and the average temperature on day t in country i’s primary political city. Usually

the political capital, in some cases, such as Cote d’Ivoire, I include the country’s more populous

20In the Republic of Congo, for instance, candidates are legally prohibited from campaigning more than three weeksin advance.

7

economic capital.21 I control for whether day t represents country i’s independence day. Since these

days are often particularly sensitive for frustrated populations – and for the president, who typically

invites foreign allies – these days may also be more likely to witness protest. At the same time,

African presidents may also be more likely to schedule elections to coincide with their country’s

independence day, particularly if, by letting the holiday coincide with their inauguration, they can

brand themselves “the father of the country.” I also include weekday fixed effects to accommodate

the possibility that elections are scheduled on days of the week that might also be more or less

favorable to protest.

In addition, I control for a range of day-level indicators of political instability, which too could

be correlated with both popular protests and election seasons. If, for instance, autocrats are more

likely to employ repression during election seasons, as substantial scholarship suggests,22 and state

repression induces popular protests, then any empirical relationship between election seasons and

protest could simply be driven by the elevated rate of repression. Accordingly, I control for whether

the incumbent government of country i employed repression against the population on day t− 1; I

draw this variable from the SCAD dataset, as recorded by Salehyan et al. (2012). Civil war events

might also be associated with both election seasons and popular protests. To accommodate this,

I control for the number of civil war events – initiated by both the incumbent government and

organized armed groups – in country i on day t− 1. All variables are described in Table 1.

The baseline estimating equation is

logit [Pr (Protestit = 1)] = α(

Pre-Election Seasondit

)+ β (Electionit)

+δ(

Post-Election Seasondit

)+ κXit

+γi + γw + ε (1)

where i indexes country, t indexes day, γi gives a full set of country fixed effects, and γw gives a set

of weekday fixed effects. The vector X represents day-level control variables that may be correlated

with both the election season and popular protests. The superscript d on the explanatory variables

of primary interest, Pre-Election Seasondit and Post-Election Seasond

it, indexes the length of the

season. Since election seasons are not well defined and may vary by country, I estimate models for

d ∈ {2, 5, 15, 30, 60}. Since the outcome variable of interest is the probability of a protest event in

country i on day t, I employ a logit model.23

21I draw these data from the National Oceanic and Atmospheric Administration (NOAA); the list of politicallyprominent cities by country appears as Figure 5 in the Appendix.

22See, among many others,Hafner-Burton, Hyde and Jablonski (2014) and Robinson and Torvik (2009).23Note that the results are unchanged with a rare events logit model.

8

Table 1: Variable Definitions

Variable Range Mean Description

Day Level VariablesProtestit {0, 1} 0.094 Assumes value 1 if a protest event occurred on day

t in country i, as reported by SCAD.Repressionit {0, 1} 0.009 Assumes value 1 if a repression event occurred on

day t in country i.Electionit {0, 1} 0.001 Assumes value 1 if day t in country i witnesses ei-

ther an executive or legislative election and value 0otherwise, as recorded by NELDA.

Pre-Election Seasondit {0, 1} Assumes value 1 if day t in country i occurs within

d days before an executive or legislative election, ford ∈ {2, 5, 15, 30, 60}.

Post-Election Seasondit {0, 1} Assumes value 1 if day t in country i occurs within

d days after an executive or legislative election, ford ∈ {2, 5, 15, 30, 60}.

Rainfallit [0, 19.02] 0.085 The amount of rainfall in inches in country i’s mostpolitically salient city on day t, as reported by theNational Oceanic and Atmospheric Administration.

Temperatureit [36, 107] 74 The average temperature in degrees fahrenheit incountry i’s most politically salient city on day t, asreported by the National Oceanic and AtmosphericAdministration.

Independence Dayit {0, 1} 0.003 Assumes value 1 if day t is country i’s IndependenceDay celebration.

Pre-Independence Dayit {0, 1} 0.076 Assumes value 1 if day t occurs within 30 days priorto country i’s Independence Day celebration.

Civil War State Eventit−1 [0, 12] 0.079 Counts the number of civil war events initiated bythe incumbent government in country i day t − 1,as reported by the Uppsala Conflict Data Project.

Civil War Non-State Eventit−1 [0, 21] 0.152 Counts the number of civil war events initiated byan organized armed group in country i day t− 1, asreported by the Uppsala Conflict Data Project.

Day Level VariablesPer Capita GDPis [197, 28425] 2487 Measures country i’s per capita GDP in year s, as

reported by the World Bank.Populationis [0.1, 120] 17 Measures country i’s population, in millions, in year

s, as reported by the World Bank.Oil Supplyis [−1, 2160] 176 Measures country i’s oil supply in year s, as re-

ported by United States Energy Information Ad-ministration.

Oil Exportsis [0, 945] 19 Measures country i’s oil supply in year s, as re-ported by United States Energy Information Ad-ministration.

Entrance by Coupis {0, 1} 0.373 Assumes value 1 if country i’s executive in year sseized power in a coup, as coded by Svolik (2012).

9

3.2 Results

The results appear in columns 1 through 5 of Table 2. Consistent with Figure 1, protests are far

more likely to occur in the days and weeks surrounding election seasons than at any other time

during the year. This effect is highly significant, both statistically and substantively. The bottom

half of Table 2 presents the predicted daily probability of protest events: outside election season,

during the pre-election season, on election day itself, and during the post-election season. Outside

election season, protests are uncommon. From the bottom half of Table 2, the daily probability of

a non-election season protest event in Africa’s post-Cold War autocracies is only 9%. During pre-

and post-election seasons, however, the predicted probability of protest doubles, to roughly 20%.

On election day itself, the predicted probability of protest reaches some 30%.

In short, the odds of a protest during both the pre-election season and the post-election season

are between 1.5 and 2 times as great as other times during the calendar year. These pre- and

post-election season effects are statistically indistinguishable from each other, which suggests that

regular elections foster protest not simply upon allegations of voting fraud leveled by opposition

leaders, but in and of themselves: by serving as coordinating devices. On election day itself, the

predicted probability of a protest event is between 3.5 and 4 times that of a typical, non-election

season day. Owing to the sample size – hovering around 200,000 – these effects are precisely

estimated.

The magnitude of the Pre-Election Seasondit and Post-Election Seasond

it effects across models

are equally instructive. In models 1 and 2, where the pre- and post-election seasons are defined

as the 2 and 5 days before and after the election, respectively, the estimated effects are strongest.

As the definitions of Pre-Election Seasondit and Post-Election Seasond

it grow more expansive, they

include calendar days that are temporally distant from the election itself, and hence distant from

the most politically sensitive moment. Accordingly, the effects of the two election season variables

are far weaker.

The coefficients on the control variables are instructive as well, especially for comparison pur-

poses. Across models, the odds of popular protest after repression on day t − 1 are 4.5 times as

great as they would be otherwise. Put otherwise, elections are roughly as powerful as episodes of

repression in facilitating popular protests across Africa’s post-Cold War autocracies.

3.3 Robustness Checks

To further establish confidence in these results, I employ a series of robustness checks. First, I

reestimate (1) as a Markov transition model by conditioning on days where no protest occurred on

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day t− 1:

logit [Pr (Protestit = 1|Protestit−1 = 0)] = α(

Pre-Election Seasondit

)+ β (Electionit)

+δ(

Post-Election Seasondit

)+ κXit

+γi + γw + ε (2)

This Markov transition model estimates the probability that a protest emerged on day t as a

function of proximity to election season.24 Next, I revise the baseline estimating equation in (1)

to include a range of control variables measured at the country-year. This extended estimating

equation is

logit [Pr (Protestit = 1)] = α(Pre-Election Season15

it

)+ β (Electionit)

+δ(Post-Election Season15

it

)+ κXit + ψWis

+γi + γw + ε (3)

where s indexes year and W is the vector of year-level control variables. The vector W includes

measures of population, oil supply and exports, and per capita GDP, which might condition popular

frustration. The vector W also includes an indicator for whether the incumbent autocrat assumed

power in a coup. Since these variables are measured at the year-level, I estimate (3) with both

country fixed effects and country random effects. Finally, I also estimate (3) as a Markov transition

model by restricting attention to days where no protest occurred on day t− 1:

logit [Pr (Protestit = 1|Protestit−1 = 0)] = α(Pre-Election Season15

it

)+ β (Electionit)

+δ(Post-Election Season15

it

)+ κXit + ψWis

+γi + γw + ε (4)

As in (3), I estimate (4) with both country fixed effects and country random effects.

The results of these robustness checks appear in Table 3. The results are virtually identical

to those in Model 3, Table 2. Model 1 corresponds to estimating equation (2), and it suggests

that popular protests are far more likely to emerge during pre- and post- election seasons than at

any other time of the calendar year, save, of course, for election day, when the odds of a protest

emerging are some 12 times as great as any other calendar day. Models 2 and 4 correspond to

estimating equation (3), with an expanded set of country-year covariates and fixed and random

effects, respectively, by country. Again, the results are virtually identical, as they are when we

estimate these expanded models in a Markov transition framework, as given equation (4); the

results for these appear in Models 3 and 5.

24For more on Markov transition models, see Epstein et al. (2005).

11

3.4 Remaining Confounders

The results in Tables 2 and 3 indicate a strong, consistent relationship between elections and

protests in Africa’s autocracies. Nonetheless, these results should be interpreted with caution,

for they may be driven by confounding forces. First, although elections may be constitutionally

required before a certain date, incumbents often have considerable latitude in precisely when to

schedule them. Consequently, incumbents may choose to schedule elections when their prospects

for victory are higher or when popular protests are less likely.25 Second, foreign investors may

anticipate political instability around elections and curtail their operations shortly before. Insofar

as this reduces the employment rate or fosters economic uncertainty, the propensity for popular

protests around elections may have nothing to do with the focal moment itself. Third, international

donors may increase foreign aid during election years, which could generate incentives for political

violence. These three possibilities underscore that election seasons may be systematically different

than non-election seasons in ways that also increase the probability of popular protests.

4 Measuring the Focal Moment

4.1 Establishing Counterfactual Days

To accommodate these potential sources of bias, I employ a series of differences-in-differences

estimators. To do so, I construct a set of counterfactual days, which are similar in all respects

to the days immediately surrounding autocratic elections. In particular, since the pre- and post-

election season effects are statistically indistinguishable, I define a single Election Season31it variable,

which assumes value 1 if day t in country i occurred within 15 days before an election or within 15

days after an election. I then define the 30 days immediately preceding the 31 day election season

– that is, between 45 days and 15 days prior to the election – as the counterfactual set of calendar

days. Because this 30 day period immediately precedes the election season, we can be confident

that all relevant conditions are identical to those in the election season itself. Economic conditions

are similar. Latent popular frustration is similar, as are popular beliefs about regime corruption.

Foreign aid is similar.

As discussed above, election seasons are, in some sense, arbitrarily defined: There is seldom a

legally defined day on which an election season begins and seldom a day on which election season

officially concludes. And, indeed, even if there was, the forces that appear to render protests more

likely during election seasons – outlined in Section 1 – neither begin with a legally defined start date

nor conclude with a legally defined end date, as the descriptive statistics in Figure 1 make clear.

Accordingly, as a robustness check, I specify a second model that treats as the counterfactual set of

calendar days the 30 day period between 75 and 45 days to the election. Since economic conditions

25See, for instance, Smith (2004). Note, however, that if elections are set by incumbents strategically – whenexternal conditions are most likely to favor them – this should bias the results against protests.

12

Table 2: Popular Protest in Africa’s Autocracies

Model 1 Model 2 Model 3 Model 4 Model 5d = 2 days d = 5 days d = 15 days d = 30 days d = 60 days

Day Level VariablesPre-Election Seasonit 0.590∗∗ 0.573∗∗ 0.461∗∗ 0.345∗∗ 0.278∗∗

(0.158) (0.102) (0.063) (0.048) (0.037)Electionit 1.220∗∗ 1.224∗∗ 1.236∗∗ 1.246∗∗ 1.255∗∗

(0.197) (0.197) (0.197) (0.197) (0.196)Post-Election Seasonit 0.544∗∗ 0.548∗∗ 0.491∗∗ 0.464∗∗ 0.336∗∗

(0.157) (0.102) (0.064) (0.048) (0.037)Repressionit−1 1.546∗∗ 1.534∗∗ 1.514∗∗ 1.499∗∗ 1.488∗∗

(0.054) (0.054) (0.054) (0.054) (0.054)Rainfallit -0.008 -0.008 -0.008 -0.007 -0.007

(0.020) (0.020) (0.020) (0.020) (0.020)

Temperatureit -0.002† -0.002† -0.003∗ -0.003∗ -0.002∗

(0.001) (0.001) (0.001) (0.001) (0.001)Independence Dayit 0.087 0.086 0.084 0.082 0.078

(0.164) (0.164) (0.164) (0.164) (0.164)

Pre-Independence Dayit -0.048 -0.050 -0.054 -0.061† -0.058†

(0.033) (0.033) (0.033) (0.033) (0.033)Civil War State Eventit 0.038∗ 0.037∗ 0.037∗ 0.035∗ 0.034∗

(0.016) (0.016) (0.016) (0.016) (0.016)Civil War Non-State Eventit -0.043∗∗ -0.043∗∗ -0.042∗∗ -0.044∗∗ -0.043∗∗

(0.013) (0.013) (0.013) (0.013) (0.013)

Constant -1.780∗∗ -1.782∗∗ -1.779∗∗ -1.783∗∗ -1.801∗∗

(0.104) (0.104) (0.104) (0.104) (0.104)Weekday Fixed Effects Yes Yes Yes Yes YesCountry Effects Fixed Fixed Fixed Fixed FixedN 203,141 203,141 203,141 203,141 203,141

Significance levels: †† : 20% † : 10% ∗ : 5% ∗∗ : 1%

Predicted probability of protest per day(all variables at sample means)

Outside Election Season 0.090 0.090 0.089 0.088 0.087During Pre-Election Season 0.167 0.163 0.147 0.134 0.127On Election Day 0.243 0.243 0.244 0.243 0.244During Post-Election Season 0.162 0.161 0.150 0.145 0.129

13

Table 3: Popular Protest in Africa’s Autocracies

Model 1 Model 2 Model 3 Model 4 Model 5d = 15 days d = 15 days d = 15 days d = 15 days d = 15 days

Markov Markov Markov

Day Level VariablesPre-Election Seasonit 0.402∗ 0.533∗∗ 0.503∗∗ 0.533∗∗ 0.501∗∗

(0.160) (0.067) (0.166) (0.067) (0.165)Electionit 2.551∗∗ 1.334∗∗ 2.681∗∗ 1.333∗∗ 2.678∗∗

(0.266) (0.208) (0.278) (0.207) (0.275)Post-Election Seasonit 0.392∗ 0.577∗∗ 0.501∗∗ 0.576∗∗ 0.496∗∗

(0.165) (0.068) (0.169) (0.068) (0.168)Repressionit−1 0.595∗∗ 1.688∗∗ 0.567∗∗ 1.690∗∗ 0.605∗∗

(0.181) (0.060) (0.201) (0.060) (0.200)Rainfallit 0.019 0.012 -0.016 0.012 -0.015

(0.044) (0.021) (0.053) (0.021) (0.052)

Temperatureit -0.001 -0.003† -0.005 -0.003† -0.004(0.003) (0.001) (0.004) (0.001) (0.004)

Independence Dayit 0.717∗ -0.015 0.644∗ -0.015 0.647∗

(0.287) (0.184) (0.328) (0.183) (0.325)Pre-Independence Dayit -0.022 -0.152∗∗ 0.020 -0.151∗∗ 0.023

(0.078) (0.037) (0.084) (0.037) (0.084)Civil War State Eventit -0.063 0.154∗∗ 0.057 0.155∗∗ 0.060

(0.044) (0.017) (0.048) (0.017) (0.047)Civil War Non-State Eventit 0.062∗ 0.011 0.083∗∗ 0.011 0.081∗∗

(0.030) (0.013) (0.030) (0.013) (0.030)

Year Level Variablesln GDP Per Capitais -0.277∗∗ -0.645∗∗ -0.237∗∗ -0.350∗

(0.065) (0.176) (0.063) (0.171)ln Populationis 1.836∗∗ 3.363∗∗ 1.748∗∗ 2.758∗∗

(0.077) (0.203) (0.076) (0.218)Oil Supplyis -0.019∗∗ -0.001 -0.019∗∗ -0.002

(0.001) (0.003) (0.001) (0.003)Oil Exportsis 0.317∗∗ 0.119∗∗ 0.317∗∗ 0.121∗∗

(0.012) (0.030) (0.012) (0.029)Entrance via Coupis 0.430∗∗ 0.437∗∗ 0.404∗∗ 0.290∗

(0.046) (0.114) (0.046) (0.115)

Constant -2.903∗∗ -4.190∗∗ -9.198∗∗ -4.997∗∗ -8.336∗∗

(0.231) (0.538) (1.330) (0.524) (1.216)Weekday Fixed Effects Yes Yes Yes Yes YesCountry Effects Fixed Fixed Fixed Random RandomN 184,159 176,251 160,067 176,251 160,067

Significance levels: †† : 20% † : 10% ∗ : 5% ∗∗ : 1%

14

and popular beliefs about public corruption are relatively slow moving, these calendar days are

sufficiently close to the election itself that they also constitute an appropriate counterfactual. As a

result, we can be confident that any change in the rate of popular protests surrounding the election

is due to the proximity of those days to the election itself, and not unobserved factors that may

drive both elections and protests. I again control for the day-level variables described in Table 1.

The estimating equation is otherwise identical to (1).

The qualitative evidence makes clear that would-be protest leaders may intentionally wait until

election seasons to call for protests. They do so, indeed, for reasons consistent with the theory of

collective action: because they believe that citizens are more engaged with the political process,

more aware of their neighbors’ shared discontent, and more confident that their neighbors will join

them. As a result, the probability of protest might be artificially low just before election season

begins. Put otherwise, would-be protest leaders might be more inclined to delay their protests until

election season if an election season is at least approaching at the moment of grievance. In this

case, the two counterfactual sets of days above might overstate the effect of the election season on

the probability of protest. To ensure this is not the case, I specify two additional counterfactual

sets of days: one that occurs about six months from election day (between 165 and 195 days from

the election) and about one year from election day (between 335 and 365 days from the election).

4.2 Results

The results appear in Table 4, and are virtually identical to those in Table 2. Column 1 presents

the results when the counterfactual set of days is defined as all days between day 45 and day 15.

Column 2 presents the results when the counterfactual set of days is defined as all days between

day 75 and day 45. Column 3 presents the results when the counterfactual set of days is defined

as all days between day 195 and day 165, or approximately six months from the election. Column

4 presents the results when the counterfactual set of days is defined as all days between day 365

and day 335, or approximately one year from the election. Column 5 presents the results from an

aggregated model, with each of these counterfactual days included as a factor to be estimated.

For all sets of counterfactual days – from columns 1 through 4 – the daily rate of popular

protest is far higher during the 30 days surrounding election day than otherwise. Owing to the

sample size, these effects are estimated with considerable precision. These effects are robust to day-

level covariates as well as weekday and country fixed effects. Again, because both counterfactual

sets of calendar days are so close to election day itself, all significant structural features that

might condition protest should be constant: international pressure,26 economic conditions, public

perceptions of corruption, and the incumbent’s capacity for repression, among others. Rather, the

chief difference between the two weeks surrounding election day and the four sets of counterfactual

days is proximity to the election itself.

26Indeed, Donno (2013) focuses on international pressure as far as four months away from election day.

15

Figure 2 presents the results from column 5 visually. The x-axis gives the number of days

until the election; the y-axis measures the predicted daily probability of protest. For each set

of counterfactual days, the solid lines record the predicted daily probability of protest, and the

dashed lines 95% confidence intervals. For the day 365-335 counterfactual set, the predicted daily

probability of protest is low, roughly 7%. This daily probability increases slightly for the six month

counterfactual set, to just over 10%. For the two counterfactual sets closest to election season –

days 75-45 and days 45-15 – the daily predicted probability of protest is roughly the same, about

12%; the 95% confidence intervals just overlap with the six month counterfactual period. But

the predicted probability of protest spikes during election season, when it reaches some 17%, and

especially on election day itself, when the predicted probability approaches 27%.

As a robustness check, I estimate the same model, but in a Markov transition framework: by

restricting attention to country-days t where no protests occurred on country-day t−1. In so doing,

I measure the effect of election seasons on the probability that protests emerge. The results appear

in column 6 of Table 4, and they are essentially identical to the others. They suggest that, during

election seasons, popular protests are nearly 1.7 times as likely to emerge as in any other of the

counterfactual sets. On election day itself, the odds that a popular protest emerges are nearly 10

times as great as on other calendar days.27

5 Conclusion

Nominally democratic institutions, scholars have recently suggested, provide a range of benefits

to the world’s autocrats. They may facilitate patronage distribution, locate pockets of frustration

within the regime, enable credible commitments, foster policy compromises with key interest groups,

and identify talented cadres. But the regular elections occasioned by these institutions, this paper

finds, are also destabilizing. By providing “focal moments” for collective action, regular elections

constitute recurring opportunities for popular protests. Across models, the paper consistently found

that 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 itself, the rate of popular

protest is some 300% greater.

Most broadly, this paper contributes to the research agenda initiated by Lindberg (2006, 2009).

27The statistical results in Tables 2 through 4 come with an important proviso: The conjecture about the coun-terfactual rate of popular protest on a given day t if election day was not imminent is conditional, technically, oncountry i holding an election at some appointed point in the future, as has been the case for every autocracy in post-Cold War Sub-Saharan Africa, save Eritrea. Accordingly, the statistical results make clear, conditional on countryi holding regular elections, the daily probability of protest will be highest in the 30 days surrounding the electionthan during any other period of the calendar year. This distinction leaves open the possibility that election seasonscause protests to cluster : election seasons draw protests into a discrete time period, rather than cause an increasein the total number of protests by country. For answering Lust-Okar’s question, however, the distinction may beirrelevant. For Aidt and Leon (2015) make clear that the probability of losing power is increasing in the intensity ofprotests during discrete periods. As a result, even if elections cluster protests, they do so effectively and regularly.They provide a focal point for collective action, without which citizens would be less likely to organize.

16

Tab

le4:

Pop

ula

rP

rote

stin

Afr

ica’

sA

uto

crac

ies

Model

1M

odel

2M

odel

3M

odel

4M

odel

5M

odel

6L

ogit

Logit

Logit

Logit

Logit

Tra

nsi

tion

Counte

rfact

ual:

Counte

rfact

ual:

Counte

rfact

ual:

Counte

rfact

ual:

Aggre

gate

dA

ggre

gate

d45-1

5day

s75-4

5day

s195-1

65

day

s365-3

35

day

sB

ase

:365-3

35

day

sB

ase

:365-3

35

day

s

Ele

ctio

nSea

sonit

0.3

44∗∗

0.4

14∗∗

0.5

68∗∗

1.0

99∗∗

0.8

48∗∗

0.5

62∗∗

(0.0

70)

(0.0

71)

(0.0

78)

(0.0

94)

(0.0

77)

(0.1

85)

Ele

ctio

nit

0.8

02∗∗

0.7

50∗∗

0.7

31∗∗

0.7

82∗∗

0.8

21∗∗

2.2

69∗∗

(0.2

19)

(0.2

17)

(0.2

14)

(0.2

16)

(0.2

11)

(0.3

01)

Rep

ress

ionit−1

1.5

40∗∗

1.2

98∗∗

1.3

27∗∗

1.4

34∗∗

1.2

60∗∗

0.5

72

(0.1

70)

(0.1

56)

(0.1

65)

(0.1

97)

(0.1

16)

(0.3

84)

Rain

fallit

-.001

0.0

28

0.0

54

0.0

99

0.0

17

0.1

32

(0.0

75)

(0.0

75)

(0.0

70)

(0.0

77)

(0.0

61)

(0.1

11)

Tem

per

atu

reit

-0.0

09

-0.0

13†

-0.0

10

0.0

20∗∗

0.0

07

0.0

18†

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

07)

(0.0

04)

(0.0

11)

Indep

enden

ceD

ayit

0.0

39

-0.2

33

-0.6

52

0.5

34

0.2

18

0.5

88

(0.6

78)

(0.7

54)

(0.8

34)

(0.6

52)

(0.4

74)

(1.0

44)

Pre

-Indep

enden

ceD

ayit

-0.0

82

0.0

04

-0.1

46

0.2

28

-0.0

82

0.4

25∗

(0.1

29)

(0.1

35)

(0.1

56)

(0.1

52)

(0.0

98)

(0.2

05)

Civ

ilW

ar

Sta

teE

ven

t it

-0.4

65∗∗

-0.7

68∗∗

-0.3

26∗∗

-0.9

45∗∗

-0.3

64∗∗

-0.2

64†

(0.1

16)

(0.1

39)

(0.1

11)

(0.1

78)

(0.0

76)

(0.1

35)

Civ

ilW

ar

Non-S

tate

Even

tit

-0.1

86∗∗

-0.3

21∗∗

-0.2

03∗∗

-0.0

99†

-0.2

34∗∗

0.1

14

(0.0

49)

(0.0

51)

(0.0

56)

(0.0

58)

(0.0

37)

(0.0

86)

Day

Gro

up

45-1

5it

0.4

58∗∗

0.3

18†

(0.0

76)

(0.1

82)

Day

Gro

up

75-4

5it

0.4

58∗∗

0.2

01

(0.0

81)

(0.2

03)

Day

Gro

up

195-1

65it

0.2

88∗∗

0.2

13

(0.0

89)

(0.2

16)

Const

ant

-0.8

91†

0.0

19

-0.6

34

-2.6

42∗∗

-1.8

15∗∗

-3.3

70∗∗

(0.5

39)

(0.4

99)

(0.5

01)

(0.5

81)

(0.3

33)

(0.7

99)

Wee

kday

Fix

edE

ffec

tsY

esY

esY

esY

esY

esY

esC

ountr

yE

ffec

tsF

ixed

Fix

edF

ixed

Fix

edF

ixed

Fix

edN

9,5

96

9,5

17

8,8

23

8,1

87

20,0

04

17,4

87

Sig

nifi

cance

level

s:†

:10%

∗:

5%

∗∗:

1%

17

−360 −300 −240 −180 −120 −60 −30 0 30

Days Until Election

0.00

0.05

0.10

0.15

0.20

0.25

0.30P

redi

cted

Dai

ly P

roba

bilit

y of

Pro

test

Figure 2: Predicted daily probability of protest associated with Model 5 in Table 4, with 95%confidence intervals surrounding point estimates.

In the decade since his seminal work, scholars have sought to understand why – and, in some cases,

when – repeated elections in autocracies foster democratic gains. Coupled with recent research

from Aidt and Leon (2015), this paper provides the empirical foundations for another mechanism.

Regular elections increase the daily probability of protests in Africa’s post-Cold War autocracies

by between 200% and 300%. And these protests, especially when they are sustained, Aidt and

Leon (2015) find, force Africa’s autocrats into political reforms. This mechanism is particularly

consistent with the models of democratic transitions proposed by Boix (2003) and Acemoglu and

Robinson (2005).

Nearly a decade after she posed it, Lust-Okar’s question remains unanswered. Determining

18

whether the introduction of elections in authoritarian regimes should be applauded, this paper sug-

gests, requires measuring their destabilizing and stabilizing effects. Although nominally democratic

institutions may help autocrats identify talented party cadres or locate pockets of discontent, in the

absence of these institutions autocrats might employ some other technique to do so. By measuring

the destabilizing effects of election seasons in a cross-country setting – by measuring the extent to

which protests are far more likely during election seasons than any other time of year – this paper

provides a baseline against which the stabilizing effects of nominally democratic institutions should

be evaluated. Measuring these stabilizing effects is a vital direction for future research. In addition,

future research might also consider whether there is variation in the intensity of focal moments:

whether, as a result of structural factors, some election seasons generate more protests than others.

Apart from its obvious policy implications, Lust-Okar’s question has important implications for

the study of autocratic politics. Whatever advantages single party regimes provided the world’s

autocrats during the Cold War,28 Western creditors now essentially require nominally democratic

institutions in exchange for development aid and debt relief.29 As a result, the institutional en-

vironment that modern autocrats confront – especially those dependent on Western aid – can be

revised only at the margins. If the destabilizing effects of regular elections outweigh the benefits

that nominally democratic institutions provide, then the chief question confronting students of

authoritarianism is how autocrats survive despite these institutions.

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23

Number of Protests

0

1−500

501−1000

1001−1500

1501 and above

Figure 3: The number of protests per African autocracy as recorded in the SCAD dataset, 1989-2010.

24

Tab

le5:

Pol

itic

ally

Sal

ient

Cit

ies

by

Cou

ntr

y

Cou

ntr

ySal

ient

Cit

yC

ou

ntr

yS

ali

ent

Cit

y

Alg

eria

Alg

iers

An

gola

Lu

an

da

Ben

inC

oton

ou

Bu

rkin

aF

aso

Ou

agad

ou

gou

Bu

run

di

Bu

jum

bu

raC

am

eroon

Yaou

nd

eC

ape

Ver

de

Pra

iaC

entr

al

Afr

ican

Rep

ub

lic

Ban

gu

iC

had

Nd

jam

ena

Com

oro

sM

oro

ni

Con

goB

razz

avil

leC

ote

d’I

voir

eA

bid

jan

Dem

ocr

atic

Rep

ub

lic

ofC

ongo

Kin

shasa

Dji

bou

tiD

jib

ou

tiT

own

Egy

pt

Cai

roE

qu

ator

ial

Gu

inea

Mal

abo

Eri

trea

Asm

ara

Eth

iop

iaA

dd

isA

bab

aG

ab

on

Lib

revil

leG

amb

iaB

anju

lG

han

aA

ccra

Gu

inea

Con

akry

Gu

inea

Bis

sau

Bis

sau

Ken

yaN

airo

bi

Les

oth

oM

ase

ruL

iber

iaM

onro

via

Lib

yaT

rip

oli

Mad

agas

car

Anta

nan

ari

voM

ala

wi

Lil

on

gw

eM

ali

Bam

ako

Mau

rita

nia

Nou

akc

hott

Mau

riti

us

Por

tL

ou

isM

oro

cco

Casa

bla

nca

Moz

amb

iqu

eM

apu

toN

am

ibia

Win

dh

oek

Nig

erN

iam

eyR

wan

da

Kig

ali

Sao

Tom

ean

dP

rin

cip

eS

aoT

om

eS

eneg

al

Daka

rS

ierr

aL

eon

eF

reet

own

Som

ali

aM

ogadis

hu

Su

dan

Kh

arto

um

Sw

azi

lan

dM

bab

an

eT

anza

nia

Dar

esS

ala

am

Togo

Lom

eT

un

isia

Tu

nis

Ugan

da

Kam

pala

Zam

bia

Lusa

kaZ

imb

abw

eH

ara

re

25

Number of Elections

0

1−4

5−9

10 and above

Figure 4: The number of elections per African autocracy as recorded in the NELDA dataset,1989-2010.

26

Number of Calendar Days

1−2000

2001−4000

4001−6000

6001−8000

8000 and above

Figure 5: The number of dataset days per African autocracy, 1989-2010.

27