In coalition governments, parties need to agree on a common policy position. Whose preferences prevail? The proportionality hypothesis, the idea that coalition partners’ influence on policy is proportional to their share of seats, has been used widely in the literature on democratic representation, ideological congruence, and coalition politics. In my analysis of competing theories aimed at determining what influences policy compromise in multiparty governments, I reject the proportionality hypothesis. My results suggest instead that coalition partners exert equal influence on policy compromises, independent of their number of seats. More extensive analysis also provides evidence for increased party influence on policies when the party is the formateur or closer to the parliamentary median, ceteris paribus. As a by-product of my analysis, I provide a simple and better proxy for measuring a government's position when this position is not directly observable.

Does providing information about the costs and benefits of automation affect the perceived fairness of a firm's decision to automate or support for government policies addressing automation's labor market consequences? To answer these questions, we use data from vignette and conjoint experiments across four advanced economies (Australia, Canada, the UK, and the US). Our results show that despite people's relatively fixed policy preferences, their evaluation of the fairness of automation—and therefore potentially the issue's political salience—is sensitive to information about its trade-offs, especially information about price changes attributable to automated labor. This suggests that the political impact of automation may depend on how it is framed by the media and political actors.

Even in the best-designed experiment, noncompliance can complicate analysis. While the intent-to-treat effect remains identified, randomization alone no longer identifies the complier average causal effect (CACE). Instrumental variables approaches, which rely on the exclusion restriction, can suffer from high variance, particularly when the experiment has a low compliance rate. We provide a framework which broadens the set of design and analysis techniques political science researchers can use when addressing noncompliance. Building on the growing literature about the advantages of ex-ante design decisions to improve precision, we show blocking on variables related to both compliance and the outcome can greatly improve all the estimators we propose. Drawing on work in statistics, we introduce the principal ignorability assumption and a class of principal score weighting estimators, which can exhibit large gains in precision in low compliance settings. We then combine principal ignorability and blocking with a simple estimation strategy to derive a more efficient estimation strategy for the CACE. In a re-evaluation of a study on the effect of GOTV on turnout, we find that the principal ignorability approaches result in confidence intervals roughly half the size of traditional instrumental variable approaches.

Does technological change fuel political disruption? Drawing on fine-grained labor market data from Germany, this paper examines how technological change affects regional electorates. We first show that the well-known decline in manufacturing and routine jobs in regions with higher robot adoption or investment in information and communication technology (ICT) was more than compensated by parallel employment growth in the service sector and cognitive non-routine occupations. This change in the regional composition of the workforce has important political implications: Workers trained for these new sectors typically hold progressive political values and support progressive pro-system parties. Overall, this composition effect dominates the politically perilous direct effect of automation-induced substitution. As a result, technology-adopting regions are unlikely to turn into populist-authoritarian strongholds.

Economic development and accompanying structural changes of the economy create new opportunities, however not everywhere and not for everyone. Development increases the demand for skilled labor, improving their welfare perceptions, but low-skilled workers feel more insecure and worse off economically. This adverse effect results from a mismatch between local labor demand and individual skill sets. To measure the development levels of people's local environments, I combine geocoded Afrobarometer data and night lights. Bayesian multilevel analysis confirms that the highly skilled are economically most dissatisfied in lagging areas, while the low-skilled are less satisfied in highly developed environments. These findings emphasize the importance of local conditions for welfare perceptions and show the unequal effect of development leaving behind large parts of the population.

Affective polarization between partisans is potentially troubling for liberal democracy. Hence, recent research has focused on how affective dislike between partisans can be reduced. Using a survey experiment in Austria, we test whether elite signals matter. Respondents exposed to fictional news stories implying that their in-party might form a coalition with an out-party show reduced dislike toward supporters of that out-party. Our experiment also shows that coalition signals can influence out-party affect even if neither of the two parties signaling cooperation are an in-party. We conclude that cooperation between rivals has an important role in reducing affective polarization.

The study of coup-proofing holds significant importance in political science as it offers insights into critical topics such as military coups, authoritarian governance, and international conflicts. However, due to the multifaceted nature of coup-proofing and empirical inconsistencies with existing indicators, there is a need for a more profound understanding and a new measurement methodology. We propose a new measure of the extent of coup-proofing, utilizing a Bayesian item response theory. We estimate the extent of coup-proofing using a sample of 76 countries between 1965 and 2005 and theoretically relevant observed indicators. The findings from the estimation demonstrate that the extent of coup-proofing varies across regime type, country, and time. Furthermore, we verify the construct validity of our measurement.

Do the choices of city leaders matter for local economic conditions? While existing literature focuses on how the preferences of local officials influence city policy, we argue that the managerial skill of local leaders should condition their ability to achieve their goals. We conduct an original phone survey of over 300 mayors and city managers across the USA to learn about their management practices. Using a two-way fixed effects design that holds fixed a rich battery of individual and city-level characteristics, we examine how changes in leadership affect economic growth, a common goal for local officials. We find that when local leaders employ the “best practices” of organizational management, their cities grow across a range of indicators. These results are strongest for the subset of leaders who mention a growth-related goal for their time in office, suggesting that managerial skill allows local leaders to more effectively achieve their objectives.

We use an online experiment to study the relative effect on voter behavior of a candidate’s voice pitch and policy stance. We demonstrate a strong voice-pitch bias: between candidates who are identical in every other aspect, voters are more likely to choose the one with the lower voice-pitch, and more so in elections between men than women candidates. We then introduce a novel phenomenon: persistence of voice-pitch bias is the amount of policy difference needed to compensate for voice-pitch bias. While persistence is also gender-dependent, the effect is now reversed: voice-pitch bias is more persistent in elections between women than men candidates. As a possible mechanism, we show that voters perceive candidates with lower voice-pitch as more competent and trustworthy.

Political polarization has become an increasingly salient issue worldwide, but a systematic examination of the variation and sources of mass polarization across countries is limited by current measurement methods. This work proposes a nonparametric, entropy-based measure of mass political polarization. It exploits the specific structure of ordinal distributions in public opinion data, makes no prior assumptions about the form and spacing of the data, and can still draw reliable measures of issue-based polarization. We demonstrate the theoretical and practical superiority of the measure with analytical comparisons and simulations. We then apply the proposed measure to questions about mass polarization in the USA, the relationship between radical parties and polarization in Europe, and cross-country trends in affective and ideological polarization.

We argue that education's effect on political participation in developing democracies depends on the strength of democratic institutions. Education increases awareness of, and interest in, politics, which help citizens to prevent democratic erosion through increased political participation. We examine Senegal, a stable but developing democracy where presidential over-reach threatened to weaken democracy. For causal identification, we use a difference-in-differences strategy that exploits variation in the intensity of a major school reform and citizens’ ages during reform implementation. Results indicate that schooling increases interest in politics and greater support for democratic institutions—but no increased political participation in the aggregate. Education increases political participation primarily when democracy is threatened, when support for democratic institutions among educated individuals is also greater.

This article explores the effects of social media on government accountability under authoritarian regimes. It examines whether online discussions have a disciplining effect on officials' scandals. We use a unique dataset containing records of scandals discussed on microblogs in China to systematically study their effects on the government response process and officials' disciplining. We find that the government employs clear strategies: higher levels of online discussion lead to quicker government responses and more severe punishment of the officials involved. Scandals involving sexual and economic factors, which initially capture more attention, involve quicker responses and more severe punishments. Even when we exploit rainfall as the instrumental variable to mitigate the endogeneity, the results are still robust. Our findings highlight the accountability mechanism facilitated by social media and the power of social media empowerment.

Governments can grant political concessions to induce quasi-voluntary compliance with taxation, yet empirical evidence probing the taxation–representation connection remains inconclusive. We contend that this association remains valid but it is primarily confined to business elites in nondemocratic regimes because the same wealth that exposes them to state predation also incentivizes them to endorse tax policies that offer greater political representation. We test our argument by evaluating preferences for hypothetical tax reforms in separate samples of business elites and ordinary citizens in China. We find that business elites show stronger preference than nonelites for tax policies that include advances in political representation. We explore various mechanisms for our results and find support for government credibility, tax ownership, and tax salience considerations.

This paper explores how factional competition shapes local media's coverage of negative political news. Employing news reports that appeared in Chinese national and local newspapers (2000–2014) coupled with data on the networks of elites, we find that local bureaucrats connected to strong national leaders tend to criticize members of weaker factions in politically damaging news reports. These adverse reports indeed harm the promotion prospects of the province leaders reported on in the articles, weakening the already weak factions and expanding the relative power of the strong factions. Our findings suggest that the loyalty-based competitive behaviors of political elites further tilt an already uneven playing field across political factions and facilitate power concentration in China.

Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model's performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31 percent) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.

Stereotypes of the two parties play an important role in political cognition, and a range of recent studies have examined the content and effects of partisan stereotypes. However, little work has studied change in partisan stereotypes over time. We address this question by comparing data on stereotypes of partisans collected before and after the Trump presidency, a time when we might expect individuals' images of the two parties to undergo significant change. Using a structural topic model, we compare responses to open-ended questions asking respondents to list words describing members of the two parties from 2016 and 2021. We find that partisan stereotypes in the 2021 sample are less group- and issue-based and focused more on personal traits. These results suggest that, during the Trump era, members of the mass public came to see the parties in more personalized, character-focused terms, potentially contributing to affective polarization.

In this note, we provide direct evidence of cheating in online assessments of political knowledge. We combine survey responses with web tracking data of a German and a US online panel to assess whether people turn to external sources for answers. We observe item-level prevalence rates of cheating that range from 0 to 12 percent depending on question type and difficulty, and find that 23 percent of respondents engage in cheating at least once across waves. In the US panel, which employed a commitment pledge, we observe cheating behavior among less than 1 percent of respondents. We find robust respondent- and item-level characteristics associated with cheating. However, item-level instances of cheating are rare events; as such, they are difficult to predict and correct for without tracking data. Even so, our analyses comparing naive and cheating-corrected measures of political knowledge provide evidence that cheating does not substantially distort inferences.

Some work in political methodology recommends that applied researchers obtain point estimates of quantities of interest by simulating model coefficients, transforming these simulated coefficients into simulated quantities of interest, and then averaging the simulated quantities of interest (e.g., CLARIFY). But other work advises applied researchers to directly transform coefficient estimates to estimate quantities of interest. I point out that these two approaches are not interchangeable and examine their properties. I show that the simulation approach compounds the transformation-induced bias identified by Rainey (2017), adding bias with direction and magnitude similar to the transformation-induced bias. I refer to this easily avoided additional bias as “simulation-induced bias.” Even if researchers use simulation to estimate standard errors, they should directly transform maximum likelihood estimates of coefficient estimates to obtain point estimates of quantities of interest.

Travel bans were a globally prevalent policy response to the COVID-19 pandemic. In the United States, travel bans against China and European countries proved a broadly popular mitigation tool among Americans. Why did Americans support COVID-19 travel bans? We fielded two novel survey experiments, surveying 3000 American citizens across five waves (between March 2020 and March 2021). In randomizing the country of origin of those potentially subject to travel ban measures, we find consistent evidence that racial attitudes drive support for travel bans. The strength of this relationship varies across political parties and across hypothetical target countries but is not explained by objective caseloads that change across countries and over the course of the pandemic.

Is populism electorally effective and, if so, why? Scholars agree that populism is a set of people-centric, anti-pluralist, and anti-elitist ideas that can be combined with various ideological positions. It is difficult, yet important, to disentangle populism from its hosting ideology in evaluating populism's effectiveness and its potential conditional effects on the hosting ideology. We conduct a novel US conjoint experiment asking respondents to evaluate pairs of realistic campaign messages with varying populism-related messages and hosting policy positions given by hypothetical primary candidates. Although party-congruent policy positions are expectedly much more popular, we find that none of the populist features have an independent or combined effect on candidate choice.