In non-linear models, the effect of a given variable cannot be gauged directly from the associated coefficient. Instead, researchers typically compute the average effect in the population to assess the substantive significance of the variable of interest. Based on the average response, analysts often make policy recommendations that are to be implemented at the individual level (i.e. the unit of analysis level). Such extrapolations, however, can lead to gross generalizations or incorrect inferences. The reason for this is that the mean may obscure a large variation in individual effects, in which case the real-world applicability of the average value is limited. Correctly interpreting the average response may prevent unwarranted extrapolations but does not solve the problem of the lack of practical relevance. Particularly when cases carry special meaning (e.g. countries), the political and socioeconomic relevance of research findings should be assessed at the individual level. This article outlines the conditions under which aggregation to mean is problematic, and advocates a case-centered approach to model evaluation. Specifically, we advise researchers to compute and report the quantity of interest for each case in the data. Only by seeing the full spread of cases can the reader assess how well the average summarizes the population. Our approach allows researchers to draw more meaningful inferences, and makes the connection between research and practical applications more realistic.

In the Sahel region, disputes between farmers and herders are often linked to religious, ethnic, and resource conflicts. Farmer–herder relations are put under pressure by negative out-group perceptions and affected by resource constraints, particularly those created by changes in climatic conditions. This study makes two key contributions: first, it examines the impact of in-group and out-group identities on farmer–herder relations under uncertainty; and second, it integrates qualitative and quantitative methods. In this study, a public good experiment was conducted with 332 farmers and herders in Senegal comparing in-group and out-group identity priming effects under individual and collective risks. The experiment was paired with 14 in-depth focus group discussions (FGDs) to elicit key mechanisms for in-group and out-group cooperation. The results show that priming out-group membership reduces cooperation towards out-group members, especially among farmers. Interestingly, herders reduced cooperation in response to in-group primes, likely attributable to rivalry between local and mobile herders. FGDs revealed that negative perceptions of mobile herders (transhumant pastoralists) drive this behavior. Additionally, introducing collective risks, such as those resulting from climate change, worsens in-group–out-group biases. The findings highlight the need to address negative stereotyping of mobile herders to prevent escalations of conflicts in relatively peaceful areas like Senegal, where farmers and herders regularly interact.

Are politicians who have previously experienced human rights violations more supportive of promoting human rights abroad? Much of the literature on human rights advocacy has centered on actors at the international or state levels. By contrast, this article focuses on individual politicians and their personal life experiences. Understanding variations in commitment to global human rights among political leaders within a country is particularly important in legislative resolutions where each legislator’s roll-call vote directly impacts a bill’s outcome. I argue that legislators with firsthand experience of state repression are more likely to support promoting international human rights. Their shared experience with foreign victims fosters greater empathy and a moral obligation to stand with them. They also have electoral motivations, as human rights promotion is an issue of their ownership and aligns with voter expectations. I test my theory using original micro-level data on South Korean legislators’ state repression experiences during the country’s democratization in the 1980s and their roll-call votes on global human rights between 2020 and 2023. I address two major barriers to inference, generational and selection effects, by comparing politicians from the same generation who participated in protests based on the intensity of violence they experienced. I find that those who experienced severe forms of repression, such as torture, injury, and imprisonment, are more likely to support promoting human rights in other countries than those who faced lower-level repression. The results suggest that prior repressed experience is an important source of political elites’ preferences for international human rights, a topic that has received little attention in previous research.

The literature on election violence lacks a consistent set of core predictors for why certain elections are violent and others are not. Between 2010 and 2022, 97 scholars published 65 peer-reviewed journal articles on this topic using quantitative research designs involving over 440 predictor variables. As a distinct research area, therefore, the study of election violence has reached a size and maturity where it is useful to take stock. Through a meta-analysis of 581 models, this article makes three key contributions. First, it finds that 13 of 44 variables consistently predict election violence, which highlights both the field’s fragmentation and most promising avenues for future research. Second, it reveals that election-specific factors like fraud and competitiveness are more reliable predictors than commonly studied structural conditions like democracy or economic development. Third, it shows that many predictors operate differently at national and subnational levels, with only population size and domestic conflict significant at both levels. This article’s findings suggest a greater focus is needed on election-specific triggers, explicit discussions about perpetrators and targets, and measurement issues.

A substantial fraction of all intrastate conflict onsets are recurrences of previously active conflicts. Recent studies suggest that constitutional arrangements that constrain executive power limit the risk of conflict recurrence. This effect is theorized to be driven by minority and individual-rights protection, in which formal executive constraints act as promises to protect these rights. These promises increase the mobilization costs for any challenger to the regime. However, the promises may no longer be credible at very high levels of formal executive constraints, as excessive promises are often seen as ‘too good to be true’. Consequently, one might expect a curvilinear relationship between executive constraints and conflict recurrence, in which high levels of constraints increase the risk of conflict recurrence. Empirical analysis of post-conflict regimes between 1975 and 2019 shows evidence of such a curvilinear relationship. The effect is further illustrated by a case study of the eastern Democratic Republic of Congo, where the rebel group M23 emerged in the aftermath of unfulfilled government promises of minority and individual-rights protection. This nuances the established relationship between executive constraints and conflict recurrence, provides a cautionary note to designers of constitutional arrangements, and lends support to the theory that mobilization costs drive this relationship.

The TJET project offers a comprehensive database for exploring the supply of transitional justice (TJ) in every country of the world. TJET provides detailed descriptive information on domestic, foreign, and international prosecutions; truth commissions; reparations policies; vetting policies; amnesty laws and offers; and UN investigations. This article describes TJET’s quantitative dataset, consisting of longitudinal data from 1970 to 2020, with over 400 measures related to the design and operation of TJ mechanisms. Because TJ has become integral to discussions related to democracy and rule of law promotion, as well as peacebuilding, it is necessary that researchers and practitioners use the most comprehensive information possible for grounding their analysis and advocacy. The TJET dataset is unique not only in its global coverage, but also in its custom sampling feature, allowing users to select which types of cases to compare. This article provides descriptive data on TJ attributes, analysis of new trends, and an examination of the temporal relationship between different TJ mechanisms.

Can naming and shaming reduce domestic public support for a government that fails to comply with an international court ruling? While existing research suggests that such tactics can diminish public support for non-compliance with international law, this evidence largely stems from Western democracies. Far less is known about how naming and shaming functions in autocratic contexts. We address this gap by conducting a conjoint survey experiment with 1,500 respondents in China – an environment where naming and shaming is generally expected to have limited impact. In our experiment, respondents were informed about a ruling by the International Court of Justice against China. While we found that citizens generally favored non-compliance with the ruling, shaming by the United Nations significantly reduced public support for non-compliance. In contrast, shaming by the United States had no significant effect. These results suggest that naming and shaming may bolster domestic support for compliance with international court rulings, even in restrictive environments like China.

Existing scholarship shows that transnational human rights advocacy depends on successful framing and audience mobilization. While most of this literature has focused on human rights framing by NGOs and advocates, governments can also frame human rights interests to their advantage. Focusing on the topic of police violence in the United States government’s transnational human rights advocacy practices, this article argues that a government may frame advocacy narratives for its benefit. For the government, strategic framing could be used to mobilize pressure against its geopolitical rivals, ease condemnation against its friends, and actively define advocated issues in a way favorable to its regime. With a novel network approach for text representation based on pre-trained large language models (LLMs), this article proposes an effective method to measure strategic framing from text data. Using the US State Department’s human rights reports, the results show that police violence accusations in the US government’s human rights advocacy narratives are strategically framed with reporting in favor of countries closer to the US. This research contributes to human rights scholarship by highlighting how governments’ national interests considerations could be incorporated into transnational human rights advocacy activities through strategic framing. The proposed LLM-based text data representation method also shows promising potential for broader text analysis tasks like topic modeling.

Countries can deliberately create, manipulate and exploit cross-border population movements to induce concessions from a target. Such ‘coercive engineered migrations’ are more likely to be successful when targeting domestically unstable states. I argue that environmental stress can add to this instability and ‘swamp’ a target’s ability to cope with cross-border population movements. Ultimately, the chances of migration-driven coercion to be successful should increase when target countries are both domestically unstable and suffer from environmental shocks. This claim is tested using quantitative data on the outcomes of coercive engineered migrations since the 1950s, which I combine with information on environmental extremes, as measured by the number of environmental disasters. Controlling for several other influences that may affect the outcome and employing sample-selection estimators that account for the non-random assignment of coercive engineered migration, the results support the argument as I show that the likelihood of successful migration-related coercion increases when domestically unstable target countries also face environmental disasters. This finding contributes to our understanding of migration as a foreign-policy instrument, it sheds new light on the role of environmental stress in international bargaining, and there are direct implications for conflict as a driver of cross-border population movements.

How does attachment to the nation influence one’s perception of white nationalist terrorism in the United States? Whereas terrorism has traditionally been understood as attacking the interests of the State, the recent increase in white supremacist violence in the United States is also deeply connected to the country’s history. An emerging body of literature has begun to examine the subjectivity of what is considered terrorism, often finding that respondents are less likely to identify white perpetrators as terrorists compared to non-white perpetrators for similar crimes. We engage a survey experiment to extend the ‘relational theory of terror perception’ to one’s attachment to the nation. We test how national attachment, an ostensibly positive disposition and distinct from patriotism and nationalism, shapes how racially motivated violence is perceived. We find that those with a stronger attachment to the nation are less concerned by hypothetical incidents of white supremacist violence than those with a weaker attachment to the nation. These biases that minimize concern for white supremacist violence are held across the political spectrum and are not simply a function of race, party affiliation, or political ideology. In fact, national attachment is a stronger predictor of attitudes toward white supremacy than respondent race; we find no support for our hypothesis that white respondents would be less concerned by violence committed by white perpetrators. Recognizing the link between positive attachment to the nation and tolerance for white nationalist violence is crucial for shaping America’s response to this threat to national security and civil peace.