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Why do governments severely punish some dissidents while showing mercy to others? This study argues that when constrained by limited information on dissent, states have incentives to cast the net of repression wider by executing not just key dissent actors but also members closely connected to them to ensure demobilization. States also crave information, and granting clemency to defectors who bring in information improves state intelligence. Given that tips have different values, regimes will grant clemency to defectors who are closely connected to key dissent actors and possess high-value tips, allowing the state to pursue top fugitives and dissolve resistance more efficiently. Using newly declassified data on political victims during Taiwan's White Terror authoritarian period, I find that the regime tends to execute both key actors (i.e., leaders and recruiters) and their closely connected members. Defectors who share information tend to receive mercy, but defectors closely connected to key actors are much less likely to face execution than less connected defectors. These findings shed new insight into the toolkit dictators use to gather intelligence on dissent and how strategic clemency induces defection and betrayal among dissidents, helping destroy dissent networks from within.

Why do governments severely punish some dissidents while showing mercy to others? This study argues that when constrained by limited information on dissent, states have incentives to cast the net of repression wider by executing not just key dissent actors but also members closely connected to them to ensure demobilization. States also crave information, and granting clemency to defectors who bring in information improves state intelligence. Given that tips have different values, regimes will grant clemency to defectors who are closely connected to key dissent actors and possess high-value tips, allowing the state to pursue top fugitives and dissolve resistance more efficiently. Using newly declassified data on political victims during Taiwan's White Terror authoritarian period, I find that the regime tends to execute both key actors (i.e., leaders and recruiters) and their closely connected members. Defectors who share information tend to receive mercy, but defectors closely connected to key actors are much less likely to face execution than less connected defectors. These findings shed new insight into the toolkit dictators use to gather intelligence on dissent and how strategic clemency induces defection and betrayal among dissidents, helping destroy dissent networks from within.

Why do governments severely punish some dissidents while showing mercy to others? This study argues that when constrained by limited information on dissent, states have incentives to cast the net of repression wider by executing not just key dissent actors but members closely connected to them to ensure demobilization. States also crave information, and granting clemency to defectors who bring in information improves state intelligence. However, tips have different values and regimes are particularly interested in rewarding defectors closely connected to key dissent actors and possess high-value tips, allowing the state to pursue top fugitives and dissolve resistance more efficiently. Using newly declassified data on political victims during Taiwan's White Terror authoritarian period, I find that the regime tends to execute both key actors (i.e., leaders and recruiters) and their closely connected members. Defectors who share information tend to receive mercy, but defectors closely connected to key actors are much less likely to face execution than less connected defectors. These findings shed new insight into the toolkit dictators use to gather intelligence on dissent and how strategic clemency induces defection and betrayal among dissidents, destroying the dissent networks from within.

Why do we observe mass protest in authoritarian regimes? How can we explain subnational variation within a country? This study provides an institutional approach to explain mass protest in nondemocracies. I propose that the pattern of social protest reflects the intensity of subnational elite competition within authoritarian institutions. In China, the cadre promotion system incentivizes local elites to compete in the fiscal and economic field by extracting local resources, and these efforts often trigger local protest. Using a protest dataset that records large-scale local resistance from China, I find that Chinese social protest is associated with local elite competition in a nonlinear pattern. A rising intensity in local competition encourages greater extraction efforts and triggers more resistance; however, intensified competition does not lead to excessive extraction because officials fear that too much social instability could hurt their careers. I also find that land expropriation by local governments becomes the main extractive mechanism that triggers social grievance in contemporary China. These findings highlight the important role of competitive local politics and how it shapes the subnational variation of protest in authoritarian regimes.

AbstractWhy do we observe mass protest in authoritarian regimes? How can we explain subnational variation within a country? This study provides an institutional approach to explain mass protest in nondemocracies. I propose that the pattern of social protest reflects the intensity of subnational elite competition within authoritarian institutions. In China, the cadre promotion system incentivizes local elites to compete in the fiscal and economic field by extracting local resources, and these efforts often trigger local protest. Using a protest dataset that records large-scale local resistance from China, I find that Chinese social protest is associated with local elite competition in a nonlinear pattern. A rising intensity in local competition encourages greater extraction efforts and triggers more resistance; however, intensified competition does not lead to excessive extraction because officials fear that too much social instability could hurt their careers. I also find that land expropriation by local governments becomes the main extractive mechanism that triggers social grievance in contemporary China. These findings highlight the important role of competitive local politics and how it shapes the subnational variation of protest in authoritarian regimes.

Among security institutions, police occupy a unique position. In addition to specializing in the repression of dissent, police monitor society and enforce order. Yet within research studying state repression, how police institutions are used and deployed to control domestic threats remain under-explored, particularly as it relates to the dual functionality just described. In this study, we develop and test an explanation of police repression accounting for the bifurcation of Mann's two modalities of state power: infrastructural power and despotic power. Infrastructural power allocates police resources to surveil dissidents and preemptively limit dissent's emergence or escalation. Police deploy despotic power through repressive responses to political threats. Empirically, we employ unique data to investigate police repression and the modalities of power in Guatemala. To analyze how shifting the balance between infrastructural and despotic power affects police repression, we isolate damage occurring from an earthquake that exogenously reshaped the landscape of infrastructural power. Results affirm the role of infrastructural power in regulating the despotic power of the state. Where local infrastructure was most affected by the earthquake, the security apparatus lost the capacity to surveil nascent movements and predict their activity, thereby providing opportunity for dissidents to mobilize and forcing police to (over-)react rather than shutdown resistance preemptively. However, the intensity of state violence recedes as the state recovers from the infrastructural damage and regains its control of local district.

AbstractPolitical actors often interact spatially, and move around. However, with a few exceptions, existing political research has analyzed spatial dependence among actors with fixed geographic locations. Focusing on fixated geographic units prevents us from probing dependencies in spatial interaction between spatially dynamic actors, which are common in some areas of political science, such as sub-national conflict studies. In this note, we propose a method to account for spatial dependence in dyadic interactions between moving actors. Our method uses the spatiotemporal histories of dyadic interactions to project locations of future interactions—projected actor locations (PALs). PALs can, in turn, be used to model the likelihood of future dyadic interactions. In a replication and extension of a recent study of subnational conflict, we find that using PALs improves the predictive performance of the model and indicates that there is a clear relationship between actors' past conflict locations and the likelihood of future conflicts.

Political actors often interact spatially, and move around. However, with a few exceptions, existing political research has analyzed spatial dependence among actors with fixed geographic locations. Focusing on fixated geographic units prevents us from probing dependencies in spatial interaction between spatially dynamic actors, which are common in some areas of political science, such as sub-national conflict studies. In this note, we propose a method to account for spatial dependence in dyadic interactions between moving actors. Our method uses the spatiotemporal histories of dyadic interactions to project locations of future interactions—projected actor locations (PALs). PALs can, in turn, be used to model the likelihood of future dyadic interactions. In a replication and extension of a recent study of subnational conflict, we find that using PALs improves the predictive performance of the model and indicates that there is a clear relationship between actors' past conflict locations and the likelihood of future conflicts.

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

Purpose. To determine whether young adults are interested in a Facebook intervention for smoking cessation and to inform the design of such an intervention. Design. Mixed-methods. Setting. Participants throughout the United States were recruited through Facebook. Participants. Young adults aged 18 to 25 years who had smoked at least once in the past month. Method. Participants (N = 570) completed an online survey of tobacco and social media use. A subset of 30 survey completers, stratified by motivation to quit smoking, agreed to participate in a structured interview over online chat. Themes were identified by using grounded theory. Results. Approximately a third of the full sample (31%) reported they would want to get help to quit smoking by using Facebook. Interest in using Facebook to quit was greater among those who were more motivated to quit (χ2 = 75.2, p < .001), had made a quit attempt in the past year (χ2 = 16.0, p < .001), and had previously used the Internet for assistance with a quit attempt (χ2 = 6.2, p = .013). In qualitative interviews, social support and convenience were identified as strengths of a Facebook intervention, while privacy was the main issue of concern. Conclusion. Nearly one in three young adult smokers on Facebook expressed interest in using Facebook for quitting smoking. Social media approaches that respect privacy and tailor to readiness to quit are likely to maximize participation.

International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars' ability to conduct inferential analyses on dyadic data.

AbstractInternational relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars' ability to conduct inferential analyses on dyadic data.