Technical Details & Research
Dimensions of Civic Space
We have identified 20 event types that comprise the key components of Civic Space we are tracking. We define an event as an action that affects civic space openness. An event is extracted from a news article and is coded using machine learning to one of the types listed here.
Dimensions of RAI
We have identified 22 event types that comprise the key components of Resurgent Authoritarian Influence (RAI) we are tracking. We define an event as an action by an authoritarian government to wield influence within a developing economy. An event is extracted from a news article and is coded using machine learning to one of the types listed here.
The process that underpins our research involves numerous steps that ensure data quality and forecast robustness. For a detailed explanation of the end-to-end processes that drive this project, including data collection and processing and the machine learning that drives our forecasts, please see this page.
Civil Society & Civic Space
Political Conflict and COVID-19: Evidence from the Machine Learning for Peace Dataset
In March 2020, the World Health Organization officially declared COVID-19 a pandemic, sparking a global wave of emergency measures designed to combat the virus’s spread. The onset of the pandemic and the perceived need for temporary but dramatic restrictions on fundamental liberties — such as the freedoms of movement and assembly — to preserve public health, shaped political conflict in the ensuring months in important ways. We investigate the impact of COVID-19 on political conflict using high-frequency data on government declarations of emergency, levels of civic activism, and government coercion in the months before and after the onset of the pandemic in March 2020. We discuss a number of specific cases (Serbia, Albania, Belarus, Ukraine, Ethiopia and Sri Lanka) to tease out the sequencing of civic activism and government coercion after the onset of the pandemic. We find that most countries saw a sustained decline in government coercion, likely due to voluntary compliance with emergency measures, although levels generally returned to normal within one year. Most countries also saw a brief reduction in civic activity, but levels returned to normal within six months in most countries and within one year in nearly all. The return of civic activity was usually related to elections or instances of government repression, while mobilization motivated by dissatisfaction with COVID response itself was relatively rare. These findings have implications for our understanding of how crises affect political mobilization and conflict.
Elections and Democratic Backsliding
This report analyzes the relationship between elections and civic space. Many backsliding regimes enter power with narrow electoral margins. This electoral weakness might provide incentives to restrict civic space as a means of curtailing powerful opposition forces immediately upon entering office. Alternatively, aspiring autocrats might perceive approaching elections as an opportunity to consolidate power and constrict civic space in order to restrict campaigning by the opposition. We provide the first high-frequency, systematic assessment of the relationship between elections and civic space by drawing on the Machine Learning for Peace machine-coded event data. We analyze how elections influence the use of censorship, arrests, lethal and non-lethal violence, legal actions, and significant legal changes around 57 executive elections in twenty-four countries. Backsliding regimes show stronger evidence of electoral cycles in civic space, meaning that civic space changes more around elections in backsliding regimes than it does in stable democracies. Backsliding regimes tend to reduce several coercive practices in the lead-up to elections, but violence increases in backsliding regimes in the lead-up to, and month of, elections. We find little evidence that backsliders are systematically different from liberal democracies in their behavior in the immediate post-electoral period.
The Impact of Legal Restrictions on the Content and Sentiment of Media Coverage in Tanzania
One crucial feature of the ongoing global wave of democratic backsliding is that aspiring autocrats seek to influence the media, oftentimes through legal restrictions on the press and social media. Yet little research has examined how formal and social media respond to those legal restrictions targeting the free flow of information. We develop an original argument linking key characteristics of media sources to the regulatory environment and examine how the content and sentiment of their coverage responds to restrictive media laws. We test our claims using an enormous corpus of electronic media in Tanzania and employ two state-of-the-art neural network models to classify the topics and sentiment of news stories. We then estimate diff-in-diff models exploiting a significant legal change that targeted media houses. We find that critical news sources censor the tone of their coverage, even as they continue to cover the same issues; we also find that international news sources are unable to fill the hole left by a critical domestic press. The paper sheds light on the conditions under which the press can be resilient in the face of legal threats.
An Early Warning System for Democratic Resilience: Predicting Shocks to Civic Space
Civil society is a powerful force for political change and democratic accountability. Understanding this, a growing number of governments have cultivated a diverse repertoire of repressive tactics, ranging from legal sanctions to outright physical coercion. Advances in big data analytics are endowing governments with new tools, including the ability to anticipate citizen action and engage in preemptive repression. Civil society needs new tools to navigate increasingly sophisticated repression. In this research note, we report on our ability to forecast to civic space using the MLP dataset. Analyzing 9 different civic space event types and 39 countries, we find that ror most country-event pairs, we cannot reliably predict shocks. However, we are able to predict certain shocks in certain places with considerable precision. We accomplish this using interpretable models that reveal the model’s decision-making process. Interpretable models provide a way for practitioners with contextual knowledge to judge how reliable models are in the real world. Thus, we provide the basis for an `early warning system’ that could help civil society strategize around repressive government action.
The Effect of Closing Civic Space on Foreign Aid: Evidence from 2.3 Million Donor Projects
How donors respond to closing civic space has important implications for the incentives facing aspiring autocrats intent on democratic backsliding. If NGO laws are effective in cutting-off support for advocacy work and are not met with resistance or repercussions from donors, legal restrictions on civil society are likely to continue to proliferate. We examine how donors respond to such restrictions using data on 2.3 million aid projects, original global data tracking NGOs laws, and a variety of research designs. We find evidence that in response to restrictive NGO laws, advocacy-oriented donors decrease spending on advocacy and maintain spending on development. In short, restrictive NGO laws `work’ from the point of view of repressive governments: donors reduce their support for activities that aspiring autocrats find threatening, such as political advocacy, while funding for service-oriented development projects continues unabated.
Reporting on Civic Space: Differences in Coverage Between National and International Sources
Getting an accurate picture of any country’s civic space is difficult. While many analysts rely on the international news, the vast majority of news coverage on any given country is the news media in that country. The INSPIRES Machine Learning for Peace team has spent enormous time ensuring it is extracting as much news as possible from national sources. But what are the returns to all that effort?
Democratic Backsliding and Media Responses to Government Repression
A key feature of the global wave of democratic backsliding is that aspiring autocrats seek to influence the media through legal restrictions. We develop an original argument linking media characteristics to the regulatory environment and test it using a huge corpus of electronic media in Tanzania. We employ two state-of-the-art machine learning models to classify the topics and sentiment of news stories and exploit a significant legal change that targeted media houses. We find that critical news sources censor the tone of their articles but continue to cover the same topics; we also find that international news sources do not fill the hole left by a critical domestic press. The paper sheds light on the conditions under which the press can be resilient in the face of legal threats.
The Effect of Government Repression on Civil Society: Evidence from Cambodia
To limit oversight by civil society, governments often repress NGOs. However, quantitative research has yet to investigate how restricted civic space impacts the behavior of NGOs operating in diverse sectors. Surveying employees from 106 NGOs in Cambodia, we employ a conjoint experiment to identify how the prevalence of repression affects NGOs’ pursuit of funding via grant applications. We find that although increases in the perceived prevalence of harassment has a stronger deterrent effect on advocacy work, harassment also deters NGOs focused on service delivery. Our results suggest that local officials target both advocacy and service delivery NGOs, but for different reasons.
Legal Changes & Protest: Evidence from High-Frequency Data
This report uses the INSPIRES data to investigate the association between protests and the passage of laws bearing on civic space. The relationship between protest and legal changes has important policy and academic implications. Practitioners often face decisions about whether to support protest movements in support of legal openings, as well as whether to support movements protesting against legal closures. Previous research provides competing findings on the relationship between protests and legal restrictions on free assembly and civic space. That work, however, has been hamstrung by poor data on both protests and the timing and characteristics of laws bearing on civic space.
Resurgent Authoritarian Influence
The Impact of Resurgent Authoritarian Influence on Civic Space: New High-Frequency Evidence
This research memo reports assess the relationship between Resurgent Authoritarianism Influence (RAI) and changes in civic space. We present evidence that for some countries, increases in RAI activity are associated with near-term changes in civic space. In doing so, we provide one of the first tests of a claim driving high-level decision-making in foreign policy and international advocacy. We find that increases in RAI are more often associated with increasing restrictions on civic space, although increases in RAI are also predictive of decreasing restrictions in some cases. Conversely, civic space events are not predictive of RAI events. Together, these findings provide evidence that influence from Russia and China are not so much responding to civic space dynamics in target countries as they are trying to shape it.
Resurgent Authoritarian Influence: New Machine-Generated, High-Frequency, Cross-National Data
This research memo reviews the academic and policy literatures on Resurgent Authoritarian Influence (RAI), discusses existing data, and describes the MLP RAI data. We group our 22 RAI events into 5 conceptual categories and summarize the prevalence of reporting on these categories across countries and over time using visualizations, descriptive statistics, and dimensionality reduction. Thhis analysis suggest that RAI activity has been surprisingly consistent over the last ten years, that Russia and China utilize regionally-specific approaches to exerting influence, and that Russia and China deployed similar strategies when dealing with strategically important countries with which they have a strained or hostile relationship.