How can the United Nations support peace implementation efforts following civil conflict? Prior research shows that third parties can use a variety of conflict management approaches to prevent civil war recurrence and that peace agreement implementation improves peace duration and quality. However, little research connects these two aspects of postwar peace. We argue that United Nations Security Council (UNSC) resolutions are an important tool that can be quickly used to support the peace. These resolutions can shame parties who do not comply with the agreement, deploy and coordinate resources needed for implementation, and empower local actors in postwar settings. Using implementation data for thirty-four Comprehensive Peace Agreements (CPAs) since 1989, as well as new data on the targets and language of UNSC resolutions, we show that language in a resolution that names and shames actors, connects stakeholders, or marshals resources is associated with better compliance with a CPA.
Curbstoning, the willful fabrication of survey responses by outside data collectors, threatens the integrity of the inferences drawn from data. Researchers who outsource data collection to survey collection panels, field interviewers, or research assistants should validate whether each collection agent actually collected the data. Our review of the survey auditing literature demonstrates a consistent presence of curbstoning, even at professional levels. This study proposes several general simple survey questions that have statistical distributions known a priori, as a method to detect curbstoning. By exploiting common deficiencies in statistical understanding, survey collectors imputing data to these questions can leverage empirically known distributions to determine deviation from the expected distribution of responses. We examined both authentic and fabricated surveys that included these questions and we compared the observed distributions with the expected distributions. The majority of the proposed methods had Type I error rates near or below the specified alpha level (.05). The methods demonstrated the ability to detect false responses correctly 48%-90% of the time across two samples when surveying at least 50 participants. While the methods varied in effectiveness, combining these methods demonstrated the highest statistical power, with Type I error rates lower than 1%. Additionally, even in situations with smaller sample sizes (e.g., N = 30), combining these methods allows them to be effective in detecting curbstoning. These methods provide a simple and generalizable way for researchers not present during data collection to possess accurate data.
How do leaders signal their intentions during a crisis? Scholars point to audience costs, potential political punishment for bluffing during bargaining, to explain how accountable leaders communicate. However, the empirical support for audience costs is mixed. I argue that this apparent disconnect between theory and evidence is due to different ways that audiences can threaten to use their sanctioning power during a crisis. When determining whether to punish a leader for a failed coercive threat, their domestic supporters should balance concerns over consistency and policy outcomes. As such, accountable leaders’ ability to credibly communicate is not automatic, rather it depends on their supporters’ policy preferences. I apply this insight using casualty sensitivity as a conditioning policy preference. I expect, and find, that audiences only help a leader commit to fight when fighting is low-cost, and actually prevent commitment when fighting is high-cost. Using compellent threat data, I find that audiences have countervailing effects on credibility due to their preferences for leaders who are both consistent and avoid costly conflict. This conditional effect could explain prior mixed support for audience costs in observational data, as prior studies pool together instances where I find audiences have strong, but opposing, effects.
We propose a format for presenting experimental results that combines a graph’s strength in facilitating general-pattern recognition with a table’s strength in displaying numerical results. The format supplements a conventional bar graph with additional text labels and graphics but also can be based on a dot plot. The resulting enhanced bar graph conveys general patterns about treatment effects; displays point estimates and confidence intervals for all key quantities of interest relevant to testing hypotheses (e.g., first differences in the mean of the dependent variable); and clarifies the interpretation of these quantities as treatment effects. Presenting information in a single figure avoids the need to devote scarce journal space to both a graph and a table. Moreover, an enhanced bar graph prevents readers from having to move back and forth between a graph and a table of numerical results—thereby reducing their cognitive load and facilitating their understanding of the findings.
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