This paper explores how politicians respond to the public salience of policy issues when determining which topics to publicly address. Using new data and state-of-the-art methodology, our study provides a fresh perspective on this fundamental question. We focus on a multi-party parliamentary system, specifically the Canadian House of Commons, with a specific emphasis on the issue of climate change. To assess the attention given by political parties to various policy issues, we analyze transcripts from the Question Period spanning from April 2006 to June 2021. To gauge the public's level of concern for these issues, we incorporate data obtained from Google Trends. Employing an instrumental variable estimation strategy, our study causally estimates the extent to which the public salience of climate change influences elite attention. Our findings reveal that the public salience of climate change significantly influences the attention given to this issue by parties, albeit with noticeable partisan variations. Moreover, our research highlights the effectiveness of the Question Period in compelling the government to address challenging or potentially embarrassing issues. Lastly, we uncover evidence suggesting that the Liberal Party of Canada successfully increased the public salience of climate change during its tenure in government.

Leading up to the 2022 Congressional midterm elections, all predictions pointed to a Republican wave, given factors such as the incumbent president’s low approval rate and a struggling national economy. Accordingly, the underwhelming performance of the Republican Party surprised many, resulting in an election that became known as the “asterisk election” due to its unusual and seemingly unpredictable outcome. This study delves into the specifics of the 2022 midterms, exploring factors that may have influenced the results beyond those traditionally considered by political scientists. Our analysis particularly seeks to understand whether a sudden shift in the public salience of specific issues could have influenced voters’ preferences, leading them to consider factors they might not have otherwise. To achieve this, we analyzed data from a nationally representative sample of registered voters surveyed immediately after the midterm elections. Our findings reveal that the issue of abortion played a pivotal role during this election. The prominence of abortion was not predestined, as evidenced by a comparative analysis with data from a survey conducted after the 2020 presidential election. Indeed, it seems that the decision by the Supreme Court to overturn Roe v. Wade in June 2022 significantly increased the salience of abortion. This unexpected policy shock had a significant impact on the behavior of voters in the 2022 midterm elections.

Online competitive action games are a very popular form of entertainment. While most are respectfully enjoyed by millions of players, a small group of players engages in disruptive behavior, such as cheating and hate speech. Identifying and subsequently moderating these toxic players is a challenging task. Previous research has only studied specific aspects of this problem using curated data and with limited access to real-world moderation practices. In contrast, our work offers a unique and holistic view of the universal challenges of moderating disruptive behavior in online systems. We combine an analysis of a large dataset from a popular online competitive first-person action title (Call of Duty®: Modern Warfare®II) with insights from stakeholders involved in moderation. We identify six universal challenges related to handling disruptive behaviors in such games. We discuss challenges omitted by prior work, such as handling high-volume imbalanced data or ensuring the comfort of human moderators. We also offer a discussion of possible technical, design, and policy approaches to mitigating these challenges.

In parliamentary systems, it is common for legislatures to offer a regular opportunity for their members to question government ministers. While these institutions fulfill an essential function for democratic accountability, they also provide an occasion for incivility to creep into political discourse. This article investigates the incidence of uncivil behavior in these institutions and identifies some of its covariates. Our focus is on the Canadian House of Commons. Using cutting-edge, open-source machine learning models, we measure the incidence and evolution of incivility in Question Periods from April 2006 to June 2021. We find significant evidence of uncivil behavior, especially insults and toxicity. Through a multivariate regression analysis, we show that variations in the incidence of uncivil behavior over time and across members of various parties are correlated with the time remaining until the next general election, the institutional roles of parties, the balance of power, and the language of interventions.

In this article, I investigate the effect of endogenous challenger entry on electoral accountability in the presence of adverse selection. To this end, I formulate a two-period electoral agency model wherein a potential challenger freely chooses whether to run for office. The effect of endogenous challenger entry on policy decisions is ambiguous: depending on model parameters, it can worsen or alleviate policy distortions. Similarly, marginally increasing the cost of running for office may amplify or reduce these distortions. This uncertainty regarding the effect on policymaking leads to equally ambiguous welfare implications. Nevertheless, I identify conditions under which endogenous challenger entry improves voter welfare. This suggests that, in some circumstances, imposing higher barriers to entry in elections may improve policymaking and voter welfare.

In this paper, we investigate whether polarized issues carry greater importance in voters' electoral choices. Doing so requires a valid way to measure issue importance. To this end, we formulate a novel measurement approach using conjoint experiments to elicit issue importance. Our approach is grounded in the potential outcomes framework and designed to minimize respondents’ burden. In the aftermath of the 2022 Congressional midterm elections, we implemented this approach on a nationally representative sample of 2,109 U.S. registered voters. Using the resulting estimates, we measure the correlation between issue importance and their polarization. We consider two conceptions of political polarization: policy and partisan polarization. Our findings reveal that partisan polarization exhibits a strong and significant correlation with issue importance, whereas policy polarization does not. This offers insights into American voters' electoral behavior and raises further questions about the measurement of issue importance.

This paper presents a new approach to evaluating the quality of answers in political question-and-answer sessions. We propose to measure an answer’s quality based on the degree to which it allows us to infer the initial question accurately. This conception of answer quality inherently reflects their relevance to initial questions. Drawing parallels with semantic search, we argue that this measurement approach can be operationalized by fine-tuning a large language model on the observed corpus of questions and answers without additional labeled data. We showcase our measurement approach within the context of the Question Period in the Canadian House of Commons. Our approach yields valuable insights into the correlates of the quality of answers in the Question Period. We find that answer quality varies significantly based on the party affiliation of the members of Parliament asking the questions and uncover a meaningful correlation between answer quality and the topics of the questions.

This article seeks to provide accurate estimates of the causal effect of exposure to toxic language on player engagement and the proliferation of toxic language. To this end, we analyze proprietary data from the first-person action video game Call of Duty®: Modern Warfare® III, published by Activision®. To overcome causal identification problems, we implement an instrumental variables estimation strategy. Our findings confirm that exposure to toxic language significantly affects player engagement and the probability that players use similar language. Accordingly, video game publishers have a vested interest in addressing toxic language. Further, we demonstrate that this effect varies significantly depending on whether toxic language originates from opponents or teammates, whether it originates from teammates in the same party or a different party, and the match's outcome. This has meaningful implications regarding how resources for addressing toxicity should be allocated.

This article offers a rationale for candidates who voluntarily and preemptively place a cap on the number of terms they will eventually be in office. I build my analysis on a standard political agency model, to which I add an election campaign in which candidates can commit not to seek a second term. Pledging to term limits allows candidates to: (i) signal their private type and (ii) shield themselves from career concerns. By doing so, politicians leverage the fact that voters endogenously prefer to elect candidates who do not seek reelection because they either: (i) have, on average, more desirable characteristics or (ii) distort their policy decisions to a lesser extent. As a result, candidates who pledge to term limits have a higher probability of being elected in the first place. I show that there are plausible circumstances under which term limits pledges can be informative and simultaneously beneficial to voters.