The Impact of Repression on Protest: Empirical evidence from the 2018-19 Sudanese Revolution

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The Impact of Repression on Protest:

Empirical evidence from the 2018-19 Sudanese Revolution

Siri Rebecca Dyhre Bjønnes [email protected]

Spring, 2021 Word count: 16,280

Master’s thesis in Peace and Conflict Studies, Department of Political Science, University of Oslo

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Abstract

For decades, scholars have studied the relationship between repression and dissent.

Researchers in this field of literature find evidence for multiple causal directions. Their findings suggest that repression simultaneously deters and inspires protests. An empirical analysis of new data concerning the Sudanese revolution show the usefulness of different models in evaluating the relationship between protest and repression. Using two dependent variables, protest frequency and participation, I analyse the associations with a negative binomial, a log-transformed OLS regression, and a vector autoregression

model. The negative binomial and log-transformed OLS regression models produce similar findings, which include a delayed increase in protest activities after repression of

previous protests. Additionally, I find evidence for the importance of the focal day for protest participation in both models. The VAR model findings include a similar pattern

for protest frequency after repression. These findings demonstrate the usefulness of modelling this relationship dynamically. Sudan is a case that demonstrates a delayed increase of protests after repression of previous protests. In other words, there is a small

backlash effect after a day of reluctance. The findings further demonstrate the dynamic relationship between repression and dissent.

Key words: protest, repression, digital repression, ols, negative binomial, vector autoregression

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Declaration

This thesis is a presentation of my own original research. Wherever contributions of others are involved, every effort is made to indicate this clearly, with due reference to the literature, and acknowledgment of collaborative research and discussions. Citations are done in the correct manner according to the guidelines of the University of Oslo and are listed in the References section. All mistakes are

my own.

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Acknowledgements

I express my sincere gratitude to my primary supervisor, Neil Ketchley, for access to the data, and to my secondary supervisor, Philipp Lutscher. I thank you both

for your honest support and guidance.

My greatest appreciation to Rahiem for all our conversations about Sudan, without whom this thesis would not be what it is today. Thank you for your

bravery in sharing your knowledge and experiences during and after the revolution.

The completion of this thesis could not have been done without my study group, Anne Kari, Sofie, and Malin, and the class of 2021. Thank you for the insightful

discussions.

It is my privilege to thank my partner for his support and encouragement.

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To my parents for always believing in me, and my family for constant encouragement.

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List of Tables

1 Frequency of repressive actions . . . 31

2 Summary statistics . . . 48

3 Negative binomial model . . . 51

4 OLS model . . . 53

5 Summary of F-statistics model 5 . . . 56

6 Summary of F-statistics model 6 . . . 62

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List of Figures

1 Types of events . . . 27

2 Protest distribution . . . 29

3 Protest duration . . . 30

4 Protests repressed in Khartoum . . . 32

5 Protests repressed outside Khartoum . . . 33

6 Protest participation in Khartoum . . . 34

7 Arrested, killed, and wounded . . . 35

8 Categories of the arrested . . . 36

9 Cause of death . . . 37

10 Categories of people killed . . . 38

11 Spread of protest frequency . . . 42

12 Spread of protest participation . . . 43

13 Impulse response matrix I: model 5 . . . 57

14 Impulse response matrix II: model 5 . . . 59

15 Impulse response matrix III: model 5 . . . 60

16 Impulse response matrix IV: model 5 . . . 61

17 Impulse response matrix I: model 6 . . . 64

18 Impulse response matrix II: model 6 . . . 65

19 Impulse response matrix III: model 6 . . . 66

20 Impulse response matrix IIII: model 6 . . . 67

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1 INTRODUCTION

1. I ntroduction

The Sudanese have long used protesting as a tool to express discontent against their government. A wave of mass mobilisation began mid-December, 2018, which resulted in the removal of Omar al-Bashir’s three decades long regime. It began in the city of Atbara, where people protested the increased prices for basic goods such as bread and fuel. The living conditions were harsh due to high inflation and the ongoing economic crisis. The protests rapidly spread to other cities, including the capital city of Khartoum. It was not long until the revolution gained international attention, particularly due to the regime’s repression of non-violent protests.

The purpose of this article is to examine how state repression impacted protests during the Sudanese revolution. The relationship between repression and protest is perplexing. Tilly outlines four directions of impact in the repression-dissent nexus: first, repression increases mobilisation, second, repression decreases mobilisation, third, mobilisation increases repression, and fourth, mobilisation decreases repression (Tilly, 1978). I limit the scope of this article by examining the first two mechanisms, the association of repression with dissent. The literature leads you to believe that almost any causal relationship is possible, mirroring Zimmermann:

There are theoretical arguments for all conceivable basic relationships between government coercion and group protest or rebellion, except for no relationship.

(Zimmerman, 1980) Under the rule of al-Bashir, protests have been repressed the past decades.

However, the recent mass movement successfully removed the head of state after massive mobilisation efforts. Sudan is hitherto quantitatively understudied.

Armed with a new data set, I aim to clarify the relationship between state repres-

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1 INTRODUCTION

sion and public protest in an attempt to place Sudan in this universe of cases where lack of consensus seem to be the consensus. I examine how two types of repression, protest policing and digital repression, are related to two aspects of dissent, protest frequency and participation. This article also sheds light on the role of internet and communication technologies in mobilisation processes. The internet plays an increasingly important role in people’s social, economic, and political lives. Here, I have an opportunity to quantitatively assess the role of digital engagement as spaces of political engagement in this particular context. In addition, I compare the observations to a global conflict data set and illustrate the advantages of using local language sources in capturing information.

Using three different models, a negative binomial model, a log transformed OLS model, and a vector autoregression model, I estimate the association between the variables. The vector autoregression model is useful when estimating how adept the variables and their six day lags are in predicting current values ofY. The results from the negative binomial and OLS models show that people displayed a delayed response to direct repression of protests. The comparison with the VAR model is useful when discussing how to analyse dynamic relationships that include endogenous variables. The VAR model finds a similar temporal pattern for protest frequency after repression in Khartoum. When visualised in an impulse-response function, the relationship is negative the first day and positive for the subsequent four days. All the endogenous variables are statistically significant when shocking them on protest events outside of the capital, with digital repression producing positive relationship for the first five days. Repression outside of Khartoum is negatively associated almost throughout. For protest participation, participants in Khartoum positively influence protesters outside of Khartoum for the first four days, after which it becomes negative for one day, before the positive association is restored. Repression in Khartoum is positively associated with protesters, similarly to the pattern seen with protest frequency.

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2 THEORISING REPRESSION

Digital repression is positively associated with participation levels outside of Khartoum the first four days. These findings demonstrate the usefulness of modelling this relationship dynamically. Taken together, the findings suggests that the Sudanese Professional Association (SPA), who were responsible for much of the coordination and mobilisation efforts, were successful in their strategic choice of when and how to protest in response to the government actions. Instead of an immediate emotional response, people awaited instructions. In terms of digital repression, there is no immediate relationship neither with protest frequency nor participation, indicating that it people in Khartoum managed to organise without digital tools.

The article begins with an integrated literature review and theory section where I delve into different hypotheses. Second, I introduce Sudan, commencing with a short overview over Sudan’s political history, describing the 2019 revolution and the protest events by drawing on data. Third, I describe the methods used, introducing the data sets. Fourth, I present the results from the three models estimated. Next, I discuss the strengths and limitations of my data and findings, and reflect upon what the results mean in context of the literature and Sudan, before concluding the article.

2. T heorising repression

The repression-dissent nexus is still puzzling researchers. Researchers in this field of literature find evidence for multiple causal directions. Their findings suggest that repression simultaneously deters and inspires protests. As such, it is interesting to examine how Sudan conforms to the literature. This article aims to contribute to the literature by examining how the relationship between protest and repression presents itself in the case of Sudan. Following is an introduction to protest and repression as understood by this article, a brief summary of

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why authoritarian states choose repressive measures, and an introduction to digital repression, before presenting the theoretical explanations for the competing hypotheses.

Protest is generally understood as a public display of discontent. It is often politically motivated, either by aiming at for example ideas, policies, or entities.

It can refer to a single protester and to larger social movements. The data in the context of Sudan limits the definition of protest to gatherings with more than ten people present at a protest.¹ This naturally excludes other forms of protest, such as individuals protesting and digital dissent, such as creating and distributing anti-regime or pro-revolutionary art or information. These types of dissent are not as easily measured.

I examine two aspects of protest, frequency and participation.² In this article, as the data does not provide a description of the different categories of dissent, the term protest is used to encompass all instances of dissent captured by the data. This includes demonstrations, picketing, protesting at funeral events, and strikes. Overwhelmingly, the events are reported as demonstrations and protests.³ Protests with ten or more participants captures all the larger events. By using the cautious definition, I am more certain that the events captured and the analyses correctly represents larger protests.

While common practice is to create an event catalogue to study protest movements, Biggs (2018) argues that size also matters. Protest events are the most prevalent measure of how protest movements appear, evolve, and diffuse. He proposes that counting events and participants will produce different conclu- sions. Protest events encompass all possible sizes, from a single-person protest

1For explanation of data, see section: 4.1.

2Revolutionary movements tend to last a shorter time, typically weeks or months, contrary to the common conception of revolutions lasting years (Aditjondro et al., 2000). This has empirical implications when studying protest days. Unsurprisingly, with fewer protest days, there are fewer observations to study in my analysis.

3See figure 1 in section 3.3.

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to the hundred thousands and the rare one million participants (Biggs, 2018).

Chenoweth and Stephan (2011) argue that larger movements are more likely to inflict higher costs on a government and makes loyalty shifts within the state apparatus more likely. Non-violent campaigns are also associated with higher turnout rates compared with violent campaigns because they are low cost for the participants, who do not have to commit, learn new skills, or hide or give up their work or lives to participate. Consequently, they argue that non-violent campaigns have a strategic advantage over violent campaigns (Chenoweth & Stephan, 2011).

By examining the two aspects that are closely linked, but not entirely the same, it provides the added benefit of a more nuanced picture of the situation. As such, I justify using two dependent variables to examine protest activities, namely protest frequency and protest participation.

State repression is a form of coercion. Commonly, it refers to state actions that include the actual or threatened use of sanctions. Carey (2006) defines it as:

It includes negative sanctions, such as restrictions on free speech, viola- tions of life integrity rights, such as torture and political imprisonment, as well as widespread state terror in the form of genocide.

(Carey, 2006) A state will carefully calculate their choices when faced with dissidents. They can choose to tolerate or accommodate the activities. If these options are considered to be more ineffective and costly than repression in deterring dissident, then repression is calculated to be the more effective option. In liberal democracies, the cost of repression is drastically higher compared with authoritarian states.

Authoritarian regimes can be destabilised by sustained mass mobilisation among the people. People within the coercive apparatus are more likely to defect or challenge the position of the leader. As such, an autocratic state leader sometimes react with repression in order to ensure loyalty within the government apparatus

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(Tolstrup, Seeberg, & Glavind, 2019). Cross-class mass mobilisations are particularly dangerous to the loyalty within the government apparatus. One reason for this is that the more people mobilised, the more likely it is that people in the protest movement reflect the identities of those in the security forces. Once the orders are issued, security forces must either oblige and potentially kill or harm people they know or identify with, or defect (Schneider, 2011). We often witness the turning point in favour of the protest movements once a substantial number of security agents refuse to obey orders.

While digital network mobilisation processes are becoming more evident in the literature on collective action, it becomes evident that they constitute a challenge for the incumbent’s status quo. This is due to the internet’s possibilities of anonymity and multilateral distribution of information. These new avenues for connection replace, to some degree, old state-run media such as radio, television, or newspapers. Digital spaces are both places of activism and mobilisation, and if co-opted, they can be used as tools for the repressive repertoire of the regime.

However, regimes face the digital dilemma, where both optioning to repress digital spaces and allowing digital spaces to remain public without hindrance can lead to the same outcome; dissent. The former by denying a population newly-accessed media for communication, and the latter for the growing amount of spaces the government cannot control (J. A. Rydzak, 2018).

Case evidence demonstrates that states often use these new technologies to monitor and censor information that have the potential to lead to dissent in the population (Gohdes, 2018). A complete network shutdown is an extreme measure of censorship against a population and causes massive disruption to the population. States do tend to use other, less detectable measures to monitor and control dissidents online, including censorship, surveillance, and throttling.

Throttling is the deliberate slowing of applications, webpages, or services the the point of not working. It is difficult to detect state interference in throttling

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given that slow internet connectivity is often blamed (Sutterlin, 2020). Some states attempt to hide any trace of digital presence, while other states make no attempts to hide their surveillance. States such as China and Sudan enforce compliance by spreading messages from their internet police forces when they attempt to access political information on web pages that have been removed. China and Iran are examples of authoritarian states that have legally punished and arrested people for online dissent (Fielder, 2012). When traditional media are censored by the regime, digital and social media can often replace it as an information source. Protesters can organise their campaigns and reach out to potential new protesters. With the rapid increase of the role of social media in the past decade, one must acknowledge how the use of digital spaces is contextualised, especially in areas where the access to internet is relatively low. Comparing Tunisia and Egypt in 2011, in the case of Tunisia, Breuer et. al. (2015) have demonstrated the importance of social media for the Tunisian organisational successes, while in the case of Egypt, Tufekci and Wilson (2012) found that nearly half of the protesters learned about the protest events through "offline" communication such as having face-to-face conversations, not through social media (Gohdes, 2018).

This paper examines two aspects of repression, protest policing and digital repression. Protest policing includes all instances of direct repression of protests that are both violent and non-violent. Potentially non-violent measures in the case of Sudan includes measure such as road blocking and vehicle searches. Violent repression includes measures such as arresting protesters, police brutality, and murder. Digital repression in general encompasses surveillance, interference, and network shutdowns. As covert digital repression is difficult to measure quantitatively and attribute the action to the regime, this article focuses on the extreme measure of network shutdown (Davenport, 2007).

A network shutdown is an intentional and significant disruption of entire channels of electronic communications within a given geographical area and/or

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affecting a predetermined group of citizens. It is often used to exert control over the information flow (Taye, 2020). The term network shutdown is sometimes used interchangeably with other terms such as internet shutdown, blackouts, network disruption, and complete blackouts, often known as using the kill switch.

There is little scholarly consensus on which term is preferable. This article uses the term network shutdown as a measure of digital repression, which includes social media blockage, suspension of telephone services through providers and complete blackouts (J. Rydzak, Karanja, & Opiyo, 2020). I analyse whether state manipulation of the digital freedom of speech is associated with protest activities.

Access Now is a global collaboration between organisations with the goal of ending government interference online. In their 2020 report of their #KeepItOn campaign, they found a global tendency for these shutdowns to last longer than previously. Sudan is one of those states that enabled a network shutdown lasting more than seven days. During the revolution, one of the network shutdowns lasted for 67 days (Taye, 2020). In my analysis, I use this data to measure the association between digital repression and protest frequency and participation. While this measure of digital repression is imperfect, it might provide an indication as to if and to what extent digital repression is correlated with protest participation and frequency.

2.1. C ompeting hypotheses

Following are three common hypotheses in the literature included for hypothesis testing. First, I hypothesise that repression has a negative relationship with protest frequency and participation. Second, I hypothesise that repression has a positive relationship with protest frequency and participation. Third, I hypothesise that repression initially has a negative relationship with protest frequency and participation, but over time has a positive relationship with protest frequency and participation.

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According to the logic of most rational actor theories, one expects repression to decrease protest frequency and participation. This follows the rationale of participants running the risk of personal injury, arrest, or death if they participate in repressed protest events. The cost runs higher than the benefit for the individual protester. Individuals that choose not to participate will often reap the same benefits if the movement is successful without risking their own health and lives, also known as free-riders (Lichbach, 1987).

Many scholars find theoretical and empirical evidence for this relationship (Muller & Weede, 1990; Opp & Roehl, 1990). Rational choice theory dictates that this happens due to the increased personal or group cost for participating when repression is anticipated. Some authors argue that while state repression might be followed by a backlash effect, where protesters are infuriated by the repression, it does not undo or reverse the negative effect of repression on protests (DeNardo, 2014).

Internet control or network shutdowns also impact protests. Authoritarian states use both technical and social filtering elements to both control access and discredit and demoralise dissidents by dominating the public sphere. States employ different mechanisms in order to ensure security. The digital age has brought about difficulties. Digital shutdowns, controlling webpages, or monitoring users are methods used by authoritarian regimes to control the population. States can implement controls that ensure that all internet traffic flows through one channel they control and block unwanted materials. Some states block IP addresses, which ensures that the device with the specific IP address is unable to access all media (Fielder, 2012). As such, I would expect that digital repression decrease protest occurrence due to an inability to organise and distribute necessary information.

If these hypotheses are correct, I expect to see a negative relationship between protest frequency and participation and the independent variable, Khartoum repression. The same will be true for the independent variable, digital repression:

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H1a: State repression of protest decreases subsequent protest frequency.

H1b: State repression of protest decrease protest participation.

Following the logic of the relative deprivation theory, one would expect repression to increase protest frequency and participation rates. Gurr (2015) argues that collective discontent or grievance is necessary for protest to occur. As such, repression of protest is likely to cause psychological effects on the protesters and their grievances, which in turn encourages mobilisation processes necessary for future protest events. This is due to social justice and solidarity accelerating when collective action is repressed, contrary to the calculations of the rational action theorists (Gurr, 2015; Traugott, 1995; Koopmans, 1997).

Backlashes might occur due to several reasons. First, repression at time t-1 might infuriate or upset the population enough to mobilise more people for protests at time t. Second, violence against protesters is likely to attract negative international attention that increases the pressure on the government. Third, repression also runs the risk of internal divides within the government and security forces (Tolstrup et al., 2019).

The internet is another area where such grievances can be cultivated and propagated. Fielder (2012) argues that the internet cultivates dissent through distance, decentralisation, and interaction. People no longer need to meet each other in person or read through local pamphlets to coordinate and learn of protests. The internet is cheap, contrary to former media forms like the radio or television, and it is harder to channel through state interference. In addition, he highlights that people have become both producers and consumers of the product due to interaction, which can turn into trust built on that interaction and spark subsequent offline action. Only between 10 and 20 per cent of the population are needed for a medium to become a mass medium.⁴ In addition, the medium does

4As of 2020, 30 out of 100 people in Sudan use the internet (UN, 2020).

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not need to be a mass medium in order to aid the organisation and coordination of protests if those who have access utilise it to spread the information further.

The proliferation of the internet has led to businesses incorporating the medium, for example hotels or internet cafés, which dissidents may use (Fielder, 2012).

Unlike liberal democracies, where information has been flowing relatively freely even before the introduction of the internet, the citizens in authoritarian states might have experienced access to a new world of information after the proliferation of the internet. Ruijgrok (2017) argues that this can cause an increase in protests through four mechanisms. First, by reducing the cost and risks for opposition groups. Second, by producing attitude changes. Third, by decreasing information uncertainty for people potentially ready to protest. Fourth, through the mobilising effect of emotional content, including videos and pictures from protests. Internet mobilisation relies on common grievances and sometimes a triggering event, but the internet enhances these issues to a broader population and can thus translate into subsequent protest events (Ruijgrok, 2017, p. 499-500).

If these hypotheses are correct, I expect to see a positive relationship between protest frequency and participation, and Khartoum repression and digital repression:

H2a: State repression of protest increases subsequent protest frequency.

H2b: State repression of protest increases protest participation.

Is it as simple as protest frequency increasing or decreasing as a result of protest repression? It is commonly noted that repression and protest are not an exogenous relationship in the literature. There are many ways to study the dynamic nature of the relationship, here following Rasler (1996) and her findings that time plays an important role. Drawing on micro-mobilisation theory, Rasler argues that the association of repression on protest events follows a dynamic temporal pattern. This describes a relationship where repression

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immediately has a negative impact on protest activities, which later increases to a positive relationship, before decreasing again in relevance due to time. Micro- mobilisation refers to a myriad of processes. Micro-mobilisation is the effort made by individuals or small groups to incentivise other individuals or small networks to mobilise for their cause (Bekkers, Edwards, Moody, & Beunders, 2011). These processes can be triggered by protest events if people are eager to protest, the state represses protests, and the protesters view repression as illegitimate. In turn, this might undo or reverse the demobilising effect that often occur immediately due to fear of high personal costs, as predicted by many rational actor theories (Opp & Roehl, 1990; Rasler, 1996).

If these hypotheses are correct, I expect to see a negative or neutral relationship between protest frequency and Khartoum repression during the first few days, and after that it should change to become a positive relationship. Rasler analyses this relationship by lagging the models by six weeks. It is not feasible to replicate this many lags with my low N data set. In my models, lags of six days have been chosen to identify possible influences within a week, while avoiding picking up other influencing variables unaccounted for that might have happened over weeks:

H3a: State repression of protest initially decreases subsequent protest frequency, but later increases protest frequency.

H3a: State repression of protest initially decreases subsequent protest participation, but later increases protest participation.

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3 THE CASE OF SUDAN

3. T he case of S udan

3.1. S udan ’ s political history

The political climate in Sudan has been turbulent since their independence in 1956, with two civil wars spanning decades, multiple coups d’etat, and very few years of democratic rule. Governments have fluctuated between pan-Arabism and Islamism. A group of army officials led by Omar al-Bashir executed a coup d’etat in 1989 and subsequently ruled the country for 30 years (Johnson, 2016).

Al-Bashir spent a substantial amount of the state budget coup proofing by prioritising the secret police, the National Intelligence Security Services (NISS).

With informants in every village and government agency, al-Bashir remained in control. He commanded multiple ethnic militias, funded a tribal paramilitary group, the Janjaweed.⁵ He also relied on buying the loyalty of the Sudanese Armed Forces (SAF). After the independence of South Sudan in 2011, the economy plummeted into crisis. Three quarters of the Sudanese oil fields were located within the new borders of South Sudan and drastically reduced exports for Sudan.

Inflation rose to over 70 per cent in December 2018 after years of steady increase.

The regime prioritised resources in urban areas. Al-Bashir could no longer affort to pay for the loyalty of his forces (Hassan & Kodouda, 2019).

The regime systematically repressed dissent throughout. They introduced a ban on labour unions and political parties. Online dissidents were monitored.

Sharia laws were implemented, including an apostasy law and an alcohol ban, under penalty of death or political exclusion. They enforced strict gendered laws

5The Janjaweed evolved to become the infamous Rapid Support Forces (RSF), directed by the NISS, who waged war on behalf of the government in the War of Darfur from 2003. Consequently, al-Bashir has been wanted by the International Criminal Court for crimes against humanity after the RSF terrorised, forcibly displaced, and massacred villagers. The RSF were also responsible for some of the extreme violence during the 2018-19 revolution, including sexual violence and massacre of protesters during a sit-in June 4th, 2019 (Hassan & Kodouda, 2019).

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3 THE CASE OF SUDAN

which also manifested itself within the population. Men would often harass women who wore trousers or did not cover their hair in public, or yell at their male companions to not allow their women to dress in this manner. Women were fined, lashed, and arrested for not adhering to the dress code. Female genital mutilation was practised. Women in general have been highly educated, but excluded from the labour market (Young, 2020).

Sudan is experiencing a growing population and a rapid urbanisation process.

The enormous state debt and the increasing unemployment rates are witnesses of a blatant economic crisis. The country is becoming increasingly connected to the global world through the internet, and 30 per cent of the population are online (UN, 2020) ⁶.

Protests have occurred regularly over the past decade. The people have protested the increased prices for basic goods and the economic crisis, while the government continually repressed most attempts at creating momentum for sustained mass mobilisation. The government relied heavily on the secret police, NISS, to gather evidence of anti-regime sentiment and punishing dissidents. The increasing power of NISS led to increasingly aggressive tactics against anti-regime protesters, including abductions and sexual violence (Ibrahim, 2019). The regime controlled and owned most of the international television stations, radio stations, and newspapers. They established the Cyber Jihad unit in 2011 to digitally monitor and hack online users, and have since arrested numerous bloggers and digital activists. They openly censored digital content including pornography, LGBTQ websites, and content that disregards the Islamic morality upheld by the state as well as content that threatens public order (Young, 2020; Hassan &

Kodouda, 2019; Ibrahim, 2019).

Ibrahim (2019) remarks that the rapid urbanisation process in Sudan was a

6Sudan has since 1993 been on the US State Sponsors of Terrorism list, subjected to strict import and investment restrictions due to the regime’s ties to al-Qaida and bin Laden. In December 2020, the US announced a removal of Sudan from the list (Wright, 2006; Johnson, 2016).

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3 THE CASE OF SUDAN

destabilising factor for the regime. The youth were largely involved in the mass mobilisation and their ethnic, class, and gender diversity was unprecedented in Sudan’s rich history of protesting their government. She argues that this accounts for the shift from rural to urban protests in the 2019 revolution, whereas previous movements have predominantly been situated in rural areas (Ibrahim, 2019).

3.2. T he 2019 revolution

The protests began in the city of Atbara in the province River Nile, Northeast of Khartoum (Hassan & Kodouda, 2019). People were furious due to the increased prizes of bread and fuel (Shendi, 2020; Ibrahim, 2019; Dahab, Abdelmagid, Kodouda, & Checchi, 2019; El-Gizouli, 2019a, 2019b). The protests rapidly spread and the deeper discontent with the government resonated throughout the country.

The Sudanese had long been angered by the widespread corruption, suppression of human rights, and the economic crisis. Younger people with higher education were particularly exasperated by the high unemployment rates⁷.

Civil society rapidly mobilised. The Sudanese Professionals Association (SPA) lead the protest movement and formalised the demands to a few key common grievances among the people. They were established in 2016 as an alliance between three professional groups, doctors, journalists, and lawyers. While not the first to demand change, they were successful in exploiting the recent outbreak of political discontent. They announced a declaration demanding freedom and change on January 1st, 2019. In this public document, they required that al-Bashir’s reign must end and his regime must be replaced by a democratic transitional government. In addition, they demanded an improvement in the socio-economic situation as well as a restructuring of Sudan’s governance and institutions (SPA,

7People were chantingtasqut bas, which translates tojust fall, that is all(Hassan & Kodouda, 2019, 98-101) andash - shaab yurid isqat an-nizam, meaningthe people want the regime to fall(Deshayes, Etienne, & Medani, 2019).

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3 THE CASE OF SUDAN 2019).

22 civil society organisation signed the declaration, including several women’s rights movements, political parties, and armed rebel groups. Mobilisation was immense. The diversity of the movement manifested itself in the geographical spread of protest events, the people who participated, and their choice of actions against the government. The protesters were relatively highly educated, progres- sive, and diverse (Deshayes et al., 2019). The youth had long been suffering from the lack of work opportunities, corruption, and lack of respect for human rights, which affected their personal rights and freedoms. The Islamic morality preached by the government did not resonate with the younger population. Young women in particular did not want to accept infringement upon their personal freedom, such as genital mutilation, child marriage, marital rape, or their freedom to make independent choices such as what to wear and travelling without an escort (Young, 2020).⁸

The continued repression of worker’s unions, political parties, and the regime’s willingness to use extremely violent means against the public made establishing a mass countermovement difficult. The solution was to establish small grass-root organisations in Khartoum that were interconnected and cooperated in minor engagements. These groups consisted largely of youth. Once protests emerged in the outskirts of Khartoum, the 30 smaller units in Khartoum were ready to mobilise as a national organisation who signed the declaration. The Small Arms Survey conclude in their 2020 report that while the youth were subjected to an Islamist socialisation during their school years, they also assumed an international identity brought upon them by globalisation processes. They used these comparative images of ways of life to rebel against their lack of opportunities, challenging the

8Women represented all ages, all ethnic, religious, and socio-economic backgrounds during the revolution. They initiated marches, spoke up about injustice during protest events, painted, and wrote poetry. Reports emerged of protest events where women accounted for almost 70 per cent of protesters (Hanna, 2020).

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3 THE CASE OF SUDAN traditional and Islamist regime (Young, 2020).

The forces for freedom and change alliance maintained a non-violent strategy against the regime. However, some protesters responded to the presence of security forces by burning tyres, throwing the tear gas back at security forces, flipping police cars, and throwing rocks and bricks. The SPA actively supported peaceful protests and attempted to stay politically neutral to compose a broad political base of unity against the regime (Young, 2020). By remaining politically neutral and by highlighting common grievances, they generated an inclusive in-group that resulted in mass mobilisation. Consequently, every punch to specific groups were met with unrest from diverse groups of people in a relatively united front.

The alliance called for protests on the 6th of April to commemorate the victory of the 1985 uprising, resulting in one of the largest protest events during the revolution. Hundreds of thousands are estimated to have mobilised in a sit-in that lasted multiple days. Al-Bashir responded with an attempt of extreme repression, where tens, even hundreds were injured and many killed. Reportedly, security agents began to disobey their orders and aided the protesters (Young, 2020).⁹

9A few days later al-Bashir was removed by the Defence Minister General Ahmed Ibn Auf on April 11th, who announced that a transnational military council would rule for two years (El- Gizouli, 2019b) This course of action was rejected and people continued to protest for substantive democratic change. Mere 36 hours later, he was replaced by another military official. People chanted tasqut tani, or your fall, a second time in the streets (Deshayes et al., 2019). At last, negotiations began between the government and the forces for freedom and change. A few months later, in the autumn of 2020, the civil organisations and the military council agreed to a power-sharing settlement that will last three years before holding an official election. It is headed by the civilian prime minister Abdalla Hamdok (Hassan & Kodouda, 2019, 98-101). The government has since signalled democratic changes. They have signed the UN treaty against torture and removed the death penalty for being a part of the LBGTQ+ community and repealed the apostacy law. Women are no longer flogged or fined for not following a strict dress code.

Non-Muslims are allowed to drink and sell alcohol. Freedom of speech is slowly becoming more accepted.

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3 THE CASE OF SUDAN

3.3. D escribing the scene

This section draws on the Daftar Ahwal and Almustagleen Independent Movement data sets.¹⁰ In this section, I graph and describe trends reported by the data sets, including geographic location, duration of protests, repressive measures used by the regime, protests reported and how many thereof were repressed, injured, arrested, and killed.

Figure 1: Types of events

Drawing on the Daftar Ahwal event data, I find that approximately nine out of ten events were demonstrations and protests, as seen in figure 1. Three events were funerals or condolences ceremonies, and the rest were strikes, pickets, and

10For more information on the specific data sets, see section 4.1.

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acts of civil disobedience. The classification might be somewhat rough, and the distinction between event categories is not always obvious. Unsurprisingly, very few events were strikes, given the governments’ history of suppressing labour unions (Johnson, 2016).

The most prevalent reasons for protesting include condemnation of the killing of protesters at previous events, dissatisfaction with the regime, the high living costs, wishing the revolution is successful, the suppression of protests, and the police brutality during protests. Some reasons appears to reflect common reports that doctors, journalists, and students were actively involved during in the movement. The protesters demands naturally reflect the reasons provided for protesting with retribution of killed protesters, toppling the regime, and the release of detainees being frequently reported demands. Lowering the living costs was also a recurring demand, as the fundamental problem for the population in general.

The frequency of protests peaked January 24th with 83 reported protest events in Khartoum. Soon afterwards the protest frequency peaked outside the capital as well, with 27 protest events the 31st of January. The intensity was higher in Khartoum, evident from the protest frequency as seen in figure 4 and 5. There were only eight days without protest in the capital, while there were 20 protest- free days outside of Khartoum. The protest events in Khartoum and outside are likely highly dependent on each other, seeing how protest encourages more protest even when spatially lagged. More than three quarters of events occurred in the province of Khartoum, visualised in figure 2. The region Central Sudan, in which the capital is situated, is overwhelmingly responsible for most protest events. This region includes the provinces Khartoum, Gezira, and North Kordofan.

Comparatively, there were much fewer instances of protest and repression outside the capital. This might be due to the continued priority of Khartoum in most government practices, such as prioritising economic investments in the capital

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Figure 2: Protest distribution

leading to the worsening of living conditions for people outside of Khartoum.

The digital infrastructure is less developed outside of Khartoum compared with the capital, possibly decreasing mobilisation efforts (Shendi, 2020).¹¹

Figure 3 visualises the duration of protests in Khartoum, excluding outliers over 20 days and zeroes. For example, we see that 22 protest events lasted 10 days, 12 lasted for 17 days, and nine events lasted for 19 days. There were only a few protest events that lasted fewer days, except those that lasted less than one day.

11Most large protest events occur in larger cities, and overcoming the barrier to participate is often easier once they see a display of like-minded people. However, I expect two additional contributing factors to the uneven distribution of protest events; first, people in rural areas are more likely to travel to larger cities to protest the government compared with the reverse scenario, and second, protest events are less likely to be reported outside of the urban centres, as such skewing the distribution even further.

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Figure 3: Protest duration

The regime readily responded with their repressive repertoire. The security forces dispersed people with a variety of measures, including using direct violence and the threat thereof.¹² Repressive actions more specifically include setting up roadblocks, beating, throwing tear gas, shooting and arresting people, and breaking up sit-ins. Most prevalent is the use of tear gas, which was reportedly used in 248 protest events. Table 1 provides an overview over the most common repressive measures used during the protests. Rather than being reported in the degree of usage of the measures, the repressive actions reportedly occurred at the frequency of events described in table 1. Multiple measures overlapped during

12Actions are divided into four main categories as the main measure in the data; first, trespassing with firearms, second, arrest. Third, forcible dispersal, and fourth, physical or sexual abuse.

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protests.¹³ Rare measures include harassment, vehicle searches, and threatening with beheading if people engaged in demonstrations. The data does not include violence perpetrated by protesters.

Table 1: Frequency of repressive actions

Measure Frequency of occurrence Live bullets 56 times

Tear gas 248 times

Beating 140 times

Arrest 152 times

Al-Bashir’s regime limited network access in 70 of the 114 days of protest.

Consequently, people were unable to access applications such as Instagram, Facebook, and Twitter, as well as communicate through several of the larger telecommunication companies in Sudan. There was also a declared state of emergency implemented on the 19th of February, constituting the last 52 days of the movement. Figure 4 shows the development of protest events in Khartoum as well as the protests repressed by security forces. It is evident here that almost all protest events were repressed in the week between February 12th and 19th, and again the week between March 26th and April 2nd. Most protests only lasted a day. However, there are many protests that were sustained for weeks and even months. The lighter grey dashes pinpoints the first period when digital repression was employed nationally, from December 21st until February 26th. The second, darker grey and longer dashes illustrate the second time digital repression was employed, this time only in Khartoum, lasting one day from April 7th until April 8th. Approximately 60 % of the protests in Khartoum were moving, not fixed at

13The degree of use is described in little detail, i.e. sometimes the measure is reported asdozens arrested, students arrestedorbeaten with batons, beaten with canes, hit, severe beating. These are reported together in the categorybeatingin table 1.

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Figure 4: Protests repressed in Khartoum

one location.¹⁴

In comparison, illustrating where efforts were put by the regime, figure 5 displays the number of protest events outside of Khartoum and the number of these protests that were repressed. Most days, protests were not repressed.

From around February 20th, there were protests almost every day, but none were repressed until the 20th of March.

Figure 6 displays the levels of protest participation in Khartoum over the same time period. It visualises the combined protest participation for each day.

Participation rates were low during the first week of protest, with only a few

14There were even 25 exclusive women’s protest events recorded, with very few protests exclusively for men. The rest were most likely gender-diverse. Women’s activist groups were reportedly prevalent in the revolution.

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Figure 5: Protests repressed outside Khartoum

protest events occurring, while the participation rates gained traction from January, fluctuating until the first weeks of April.

More than 150 people were reported injured in Khartoum two days before the ousting of al-Bashir. On the same day, 21 people were killed. Over the time period, there were reports of 174 wounded and 32 killed. Most protest events witnessed neither deaths nor wounded. 83 people were arrested. Figure 7 visualises the numbers of people arrested, killed, and wounded throughout the four-month time period.

While most protesters were categorised very broadly as people, there were a few hundred events that were categorised as employees or workers, students, and political groups.

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Figure 6: Protest participation in Khartoum

The Almustagleen Independent Movement data set records people arrested and killed until February 19th 2019. They identify over 450 observations of arrests, where politicians were overwhelmingly represented. A wide variety of political parties were represented, including the Sudanese Congress Party, the Unionist Party, the Communist Party, the Umma Party, and the Baath Party. Other political people represented were members of civil society campaigns as well as military opposition, such as the Sudan’s People Liberation Movement/Army (SPLM/A).

The other larger groups represented were teachers, students, journalists, lawyers, doctors, engineers, and activists.¹⁵ This corroborate the claim that the protesters were diverse.

15There were a few actors, poets, and academics among the rest of the arrested.

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Figure 7: Arrested, killed, and wounded

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Figure 8: Categories of the arrested

Three quarters of the people arrested were male. Three quarters occurred in the centre of Khartoum, while Omdurman saw roughly 17 per cent and Khartoum North saw 8 per cent of the arrests. Figure 8 describes the categorisation of the arrested people. All 15 people killed were male, eight of them in the centre of Khartoum, five in Omdurman. Figure 9 illustrates the causes of death. 11 were shot, one died in detention, one suffocated due to tear gas, and one was tortured to death. Figure 10 displays the categorisation of the people killed. Five were connected to a university, or are described as students. Eight of them were reported as citizens, one was a doctor, and one was connected to the Universal Peace Federation, an international network of individuals and organisations dedicated to achieving world peace. Another paper use an extended report from

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the Almustagleen Independent Movement which records data until the 9th of April, 2019. Information from the last days until the regime change are cross- tabulated data from the Sudan Doctor’s Union as well as an anonymous site, ’Lest We Forget’. They report 117 deaths. (Dahab et al., 2019, p. 2).

Figure 9: Cause of death

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Figure 10: Categories of people killed

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4. D ata and method

4.1. S ources

Since 2015, the Daftar Ahwal Data Research Institute has recorded details about protest events in Sudan.¹⁶ They are a Cairo-based institute collecting open-source data for public use. As I am investigating the mass mobilisation process that led to the ousting of al-Bashir, I focus on the time period from the 12th of December 2018 until the 11th of April 2019.¹⁷ As demonstrated in the previous section, the data provides information about the location, purpose, demands, repertoire, duration, tools of repression, and categories of participants. Previous versions of this data relied heavily on Facebook posts to gather information about the protest events, including photos and videos. The updated data set relies more on other types of media sources after the Facebook posts were deleted or censored, especially important was the newspaper DabangaSudan.org. Unsurprisingly, social media often dominates the distribution of information on protest events in real-time until the press get the opportunity to report it. In the specified time period, the data includes 2333 protest events.¹⁸

I also include a variable to test for network shutdowns. The data is collected for the #KeepItOn campaign run by Access Now in a worldwide collaboration

16The data set is not yet publicly available. For more information, contact me at: [email protected]

17Another example of data on digital mobilisation in Sudan is the use of the online crowdsourced mapping tool Ushahidi, where people can report messages or images of violence to increase attention to ongoing issues.

18To ensure the quality of the analysis, I compare the Arabic data with the widely acknowledged protest and armed conflict data set, the Armed Conflict Location and Event Data Project (ACLED).

The former is a regional research institute speaking one of the dominant languages in Sudan, while the latter is a globally-focused project. Those familiar with the language or culture have an advantage in that they might gain intimate knowledge of protest events prior to the global news begin circulating information about events. While they employ some of the same sources, the ACLED data reports only 167 protest events in Sudan in the same time period. This is equivalent of roughly 7.2 % of the observations in the Arabic data set, justifying the continued use of the local language data source. Over 2000 observations are reported as undefined, and are in the analysis defined as non-repressive actions.

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between organisations to end government interference online. In their 2020 report they find a global tendency for shutdowns to last longer than previously, and the Sudanese government implemented a 67-day long shutdown across the country during the revolution. It affected telecommunication services and targeted applications such as Twitter, Facebook, WhatsApp, and Instagram. These are popular platforms for sharing videos, pictures, and text messages, frequently used to organise and coordinate protest events as well as sharing anti-government sentiments. The government publicly acknowledged this interference, defending it by claims of public order and goals to keep the public safe, but report emerged of human rights abuses. A second shutdown was initiated only days before the ousting of al-Bashir when 45 % of telecommunication services were cut in Khartoum for two days (Taye, 2020).

4.2. M ethod

As the primary goal of this paper is to observe the relationship between state repression and protest events and participation rates each day, I collapse the protest frequency by day. Left to analyse is a sample of 121 observations, representing the 121 days in the examined time period. As such, this is time-series data. Time series are is useful to track changes over time, such as protest events. While this is a relatively small-N, it is sufficient to analyse.

Count data is commonly observed when we study empirical questions. Count data describes observations that are counts of a phenomenon, often beginning at zero and encompassing only whole numbers. When analysing count data, there are a few assumptions that should be considered when choosing a model suitable to the analysis. A typical model in social sciences is the ordinary least squares (OLS) model. Count data cannot satisfy the assumptions of the OLS model for several reasons. First, the relationship between the dependent and independent variables are linear in parameters. This is often rectified using other

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means, such as transforming either the DV or IVs to become linear. Second, there is random sampling of observations. Third, the errors are independent of the predictor variables and normally distributed. Fourth, there is no multicollinearity in the variables. Lastly, there is homoskedasticity and no serial correlation of observations (Greene, 2003).

Contrary to OLS, count data often follows a Poisson distribution rather than a Gaussian distribution. Count data violates several assumptions by being het- eroskedastic in nature and bounded by zero and limited to integer numbers.

In other words, count data has a larger variance than mean, which means that larger errors accompany the bigger observations in the data, which is known as overdispersion of the data. An OLS estimation would yield biased results including fractions and negative numbers, which is of little usefulness in this case (Ward & Ahlquist, 2018, Chapter 10). By looking at the primary dependent variable, the variance is more than ten times the mean.

I test for typical assumptions to ensure that a Poisson model is better suited.

Specifically, I test for multicollinearity, autocorrelation, and heteroskedasticity.

The data shows no conclusive signs of serial correlation between observations of protest events or collinearity, but there is clear heteroskedasticity with a larger variance than mean. For the first, third, and fifth models, where the dependent variable is protest frequency, the data indicate no conclusive signs of serial correlation between observations of protest events or collinearity, but there is clear heteroskedasticity with a larger variance than mean. For the second, fourth, and sixth models, where the dependent variable is protest participation, I find similar results indicating heteroskedasticity. Consequently, a Poisson model is the better choice for both dependent variables. The spread of the dependent variables are illustrated in figures 11 and 12.

I test whether the data show signs of overdispersion. If we model count data with overdispersion by using a simple Poisson model, we risk interpreting

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Figure 11: Spread of protest frequency

inaccurate inferences, as the Poisson model assumes equidispersion. The data can be overdispersed for several reasons. First, when the data is the result of a more variable process than the Poisson model is able to capture. Second, when the data has excess zeros. Lastly, if there is serial correlation between observations (Hardin & Hilbe, 2014). Overdispersion assumes that the variance of the residuals is greater than the mean.

I run model fit statistics which compares the model fit of a Poisson model and a negative binomial model, which convincingly favours the negative binomial model. The negative binomial model has the same mean structure as the Poisson regression, but adds a parameter which accounts for the overdispersion in the data.

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Figure 12: Spread of protest participation

Additionally, I model two other models. First, after log-transforming the dependent variables to remedy for the assumptions that count data violates, I run an OLS model. Afterwards, I run a vector autoregression model. This is a dynamic model which estimates the dynamic association between endogenous variables, such as protest activities and repression. I test for Granger-causality and use an impulse response function to visualise the direction of the relationships between the significant endogenous variables.

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4.3. V ariables

4.3.1 Dependent variables

The unit of analysis is every day in Khartoum between the 12th of December 2018 and the 11th of April 2019. While mass protests did not break out until the third week of December the protests occuring from the 12th of December week are perhaps influential and should be taken into account. I explore which factors influence the level of protest activity on any given day in order to test my hypotheses.

The first dependent variable isKhartoum protest. As the data from the capital of Khartoum is the most reliable, I limit the variable to Khartoum events. I include data from outside of Khartoum for descriptive statistics. While the data collected outside of Khartoum is likely to be correct, it was probably more difficulty identifying and reporting protest events accurately. The dependent variable is the sum of protest events in Khartoum. I create this variable by generating a variable that exclusively records protest events in Khartoum, collapsed by date.

The second dependent variable is participation in protests in Khartoum,Partic- ipation Khartoum. This allows me to model the mobilisation process to a certain degree. The data set does not accurately report how many people were at each protest event, likely due to difficulties in measuring accurately during protests.

The participation rates are reported as ’tens’, ’hundreds’, ’thousands’, and ’tens of thousands’. More than half of the observations were undefined, and these observations are coded as 101. We code the tens as 31, hundreds ad 301, thousands as 3001, and tens of thousands as 30001, following the convention of Francisco (Barrie & Ketchley, 2018). Once these are collapsed by date, I am left with the sum, or count, of protest events each day.

These two variables are primarily used in the negative binomial model. I transform the same variables when using them in vector autoregression and

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OLS models. I conform the data approximately to a normal distribution by log-transforming the dependent variables. This addresses most of the problems of skewness in the data.¹⁹ These two variables, Khartoum protest, logged and Participation Khartoum, logged, are also used as control variables for their respective models, testing whether protest at t-1 predict protest at time t, and similarly with participation rates.

4.3.2 Independent variables

I generate the first repression variable by creating a variable that exclusively captures repression events in Khartoum, not elsewhere. It excludes all instances where repression is reported as undefined. This excludes almost 2000 observations, but leaves the reported specific observations of repressive actions that are sources and verifiable. Subsequently, I log-transform the variable. This transformation method is commonly used for event count data to ensure that standard deviations are somewhat constant and to stabilise the variance. The variable is Khartoum repression, logged. I use this variable to test all the hypotheses.

I generate the second repression variable, Digital repression, by creating a dichotomous variable for the two network shutdowns that occurred in Sudan. The first network interference lasted from the 21st of December, a few days after the mass mobilisation began, until the 26th of February. The second shutdown lasted from the 7th until the 8th of April. Both shutdowns affected Khartoum, while the first affected the entire country. The variable is coded 1 if there was an ongoing network shutdown and 0 if not. I use this variable to test all the hypotheses.

19I log transform plus one, because I have counts of zero and the log of zero will always be undefined. Once I add one to the equation, Stata transforms the zeroes to ones, and the log of one is zero.

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4.3.3 Control variables

I include control variables reduce the likelihood of omitted variable bias. I include controls for protest activities the previous day in and outside of Khartoum, for the state declaring a state of emergency to ban public gatherings, and the Friday effect, the day of prayer in Sudan. I include a table which summarises the descriptions of all the variables.

I include control variables that accounts for protest activities the previous day.

For local estimation, I include a one day lag of theKhartoum protestandParticipation Khartoumvariables. To account for any spatial diffusion of protests from outside of Khartoum to the capital, I generate two variables that include protest frequency and participation rates from outside of Khartoum, log transformed, Outside Khartoum, logged andOutside Khartoum participation, logged respectively. These are included with six lags in the models, similarly to the independent variables. As we know, the first protests began outside of Khartoum and quickly spread to other cities, including Khartoum. As such, it justifies examining any influences that may derive from protest activities outside of the capital. Additionally, I generate a control variable that accounts for repression outside of Khartoum, log transformed,Outside Khartoum repression, logged.

Sudan declared a state of emergency February 19th 2019. Al-Bashir replaced cabinet members and regional governors, banned all unauthorised gatherings, and gave security forces the license to quell protests by any means necessary. The emergency lasted until the end of the presidency.²⁰ Declaring a state of emergency is often used to sidestep the legal issues to respond to urgent situations such as natural disasters, global pandemics, or political contention. It can include a temporary shift of power, restrictions of civil rights, including increased surveil-

20The transitional military government that ousted al-Bashir immediately imposed a three-month state of emergency, closing borders, airports, and suspending the constitution.

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lance, or creating other mechanisms.²¹ The variableState of emergencyis coded as a dichotomous variable, taking the value 1 if a state of emergency was in use on any given day and 0 if not.

Some protest movement benefit from protesting on certain days. This is known as a focal day or, in this case, the Friday effect. It describes the tendency for revolutionary movements to exploit the rituals of the population to mobilise for their movement. In the case of Sudan, this means Friday prayers. Intuitively, in authoritarian regimes this provides an opportunity to coordinate and mobilise without openly defying the government (Ketchley & Barrie, 2020). I include a control for the Friday effect by generating a dichotomous variable, Friday that takes the value 1 if the protest occurred on a Friday and 0 if not.

Descriptive statistics of all the variables included in all models are summarised in table 2.

21A state leader might feign emergencies to gain almost unlimited power. A state of emergency also assumes that the measures are temporary and that constitutional laws and norms go back to status quo post-emergency. However, the power shifts or the legal order from the state of emergency can endure into the government post-emergency. In addition, there are possibilities to extend the period of emergency or declare another state of emergency at a later point (Arslanalp &

Erkmen, 2020, p. 100-102). A state of emergency is also used preventively to avoid emergencies. A declared state of emergency raises the cost of the individual or group participation once a licence is given to use direct and/or violent measures against protesters. It might also alter the tactics of protesters, shifting from public gatherings to other, less monitored outlets, for example social media or single-person protests in other ways (Wahlström & de Moor, 2017, chapter 3). These types of protests are harder for the state to identify and prevent, and are not included in this data set.

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Table 2:Summary statistics

Variable N Mean SD Min. Max.

Dependent variables

Khartoum protest 121 14.537 14.175 0 83

Khartoum protest, logged 121 2.230 1.153 0 4.430817

Participation Khartoum 121 124,725.2 307,078.2 0 2,400,983 Participation Khartoum, logged 121 8.414 3.712 0 14.691

Independent variables

Khartoum repression, logged 121 .630 .945 0 4.060

Digital repression 121 .578 .496 0 1

Control variables

Friday 121 .140 .349 0 1

State of emergency 121 .429 .497 0 1

Outside Khartoum repression, logged 121 .313 .626 0 3.219 Outside Khartoum protest, logged 121 1.345 .886 0 3.332 Outside Khartoum participation, logged 121 6.631 4.179 0 13.354

The Impact of Repression on Protest: Empirical evidence from the 2018-19 Sudanese Revolution