In this research note, we examine how Hong Kong voters respond to police violence in the recent social movement. We use causal forests, a machine learning algorithm, to estimate the impact of tear gas usage specific to each constituency. Based on the 2019 District Council Election outcome, we find that there is heterogeneity in the effect of state coercion on the vote share of pro-democracy candidates, depending on many socioeconomic characteristics of the constituency. The results imply that economic concerns still matter in the struggle to obtain democracy: citizens who sense economic insecurity in social unrest show less disapproval of state violence.

Governments’ party identifications can indicate the types of economic policies they are likely to pursue. A common rule of thumb is that left-party governments are expected to pursue policies for lower unemployment, but which may cause inflation. Right-party governments are expected to pursue lower inflation policies. How do these expectations shape the inflation forecasts of monetary policy bureaucrats? If there is a mismatch between the policies, bureaucrats expect governments to implement, and those that they actually do, forecasts will be systematically biased. Using US Federal Reserve Staff’s forecasts we test for executive partisan biases. We find that irrespective of actual policy and economic conditions forecasters systematically overestimate future inflation during left-party presidencies and underestimate future inflation during right-party ones. Our findings suggest that partisan heuristics play an important part in monetary policy bureaucrats’ inflation expectations.

Our focus is a puzzle: that ideological identification as “liberal” is in serious decline in the United States, but at the same time support for liberal policies and for the political party of liberalism is not. We aim to understand this divorce in “liberal” in name and “liberal” in policy by investigating how particular symbols rise and fall as associations with the ideological labels “liberal” and “conservative.” We produce three kinds of evidence to shed light on this macro-level puzzle. First, we explore the words associated with “liberal” and “conservative” over time. Then we take up a group conception by examining the changing correlations between affect toward “liberals” and affect toward other groups. Finally, we consider the changing policy correlates of identification.

Executive control of government is generally not a long-term job. In such cases, relatively short executive tenure should be expected to play an important role in determining the degree to which policymakers internalize the future costs associated with their current fiscal behavior. The effects of policymaker's expected planning horizons on macroeconomic outcomes, however, have been difficult to model outside of a fixed term limit context due to the unobserved likelihood of remaining in office, along with potential endogeneity problems where re-election campaigns can be enhanced with generous, deficit-financed expenditures in election years. From a globally representative sample of 79 countries over a 32-year period (1980–2012), this paper provides empirical evidence suggesting that incumbent governments who know that will not be in office in the following period with a probability of one, are found to generate significantly higher deficits in a linear discounting model, and are found to produce the least responsible fiscal outcomes where the likelihood of re-election is around fifty percent in quadratic discounting models.

Previous research finds that policy complexity affects important political processes including legislative delegation and policy diffusion. However, policy complexity is not directly observable and the search for a reasonable proxy constitutes a major challenge for scholars. This research note presents a concise and measurable definition of complex policy based on two aspects: a policy's textual sophistication and its ties to other rules and regulations. Using crowdsourcing and a pairwise comparison framework it is shown that the proposed defining features are crucial for humans’ understanding of policy text. The proposed definition is then operationalized using a large corpus of European Union rules and is shown to outperform alternative operationalizations of policy complexity in predicting the level of legislative delegation.

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

Does a universal basic income (UBI) affect voter turnout? This article argues that the introduction of an unconditional cash payment—where citizens receive money independent of employment status, age, or indigence—can have a turnout-enhancing effect. I evaluate the argument using the introduction of the Permanent Fund Dividend in Alaska. Differences-in-differences estimates covering November general elections from 1978 to 2000 provide compelling evidence that the Alaskan UBI has a significant positive effect on turnout. The results further suggest that the turnout increase was not a one-off effect but persists over a period of almost 20 years. Thus, a UBI has the potential to positively affect turnout among an entire electorate, adding to the discussion around potential welfare reforms in western democracies.

Research on Russian troll activity during the 2016 US presidential campaign largely focused on divisive partisan messaging. Here, we document the use of apolitical content—content that could counteract mobilization efforts and escape detection in future campaigns. We argue this resembled techniques used by autocratic regimes domestically, in “flooding” social media with entertainment content to distract from and displace mobilizing messaging. Using automated text analysis and hand coding to construct a timeline of IRA messaging on Twitter, we find left-leaning trolls posted large volumes of entertainment content in their artificial liberal community and shifted away from political content late in the campaign. Simultaneously, conservative trolls were targeting their community with increases in political content. This suggests the use of apolitical content might be an overlooked strategy to selectively manipulate levels of attention to politics.

Signaling games are central to political science but often have multiple equilibria, leading to no definitive prediction. We demonstrate that these indeterminacies create substantial problems when fitting theory to data: they lead to ill-defined and discontinuous likelihoods even if the game generating the data has a unique equilibrium. In our experiments, currently used techniques frequently fail to uncover the parameters of the canonical crisis-signaling game, regardless of sample size and number of equilibria in the data generating process. We propose three estimators that remedy these problems, outperforming current best practices. We fit the signaling model to data on economic sanctions. Our solutions find a novel U-shaped relationship between audience costs and the propensity for leaders to threaten sanctions, which current best practices fail to uncover.

Are cues from party leaders so important that they can cause individuals to change their own issue positions to align with the party's position? Recent work on the importance of party cues suggests they do, especially given the literature on partisanship as a strong and persistent group identity. However, in this paper we test the limits of those partisan cues. Using a unique two-wave panel survey design we find that the effect of party cues is moderated by the prior level of importance individuals place on an issue. We find that when a person believes an issue area to be more important, party cues are less likely to move that citizen's position, particularly when the cue goes against partisan ideological norms. Our results show evidence that an individual's own issue positions—at least the important ones—can be resilient in the face of party cues.

In latent scaling applications, such as the positioning of political parties, differential item functioning (DIF) may occur because of measurement issues or because of substantive differences in the association between latent and manifest variables. While the first source of DIF has received considerable attention, the second has not, although it is of potential interest to comparative scholars. In this research note, we introduce a novel hierarchical Bayesian item response model that allows us to disentangle different sources of DIF. Drawing on the 2019 Chapel Hill Expert Survey (CHES), we highlight how the same issues are unequally politicized across Western Europe, and how some issues are less ideologically determined than others. Our model can be adapted to alternate settings, allowing researchers to shine a light on variation in, e.g., ideology, issue politicization, or party competition.

The Transatlantic Trade and Investment Partnership between the European Union and the US is highly technical. Still, the negotiations triggered large-scale protests among citizens with very diverse socioeconomic backgrounds. Why has a complex issue such an enormous mobilizing effect although the economic consequences are either unclear or favorable for the participating economies? We argue that the transparency of negotiations is an important consideration for people evaluating the negotiation outcome. Conducting a survey experiment, we show that non-transparent decision-making decreases citizens' appraisal of the agreement independent of its outcome: A non-transparent negotiation is, on average, almost 16 percent less likely to find public approval than a transparent but otherwise identical agreement. Our findings have important implications for democratic decision-making.

I present a theoretical account of the politics of privatization that predicts left-wing support for the policy is conditional on the proportionality of the electoral system. In contrast to accounts that see privatization as an inherently right-wing policy, I argue that, like trade policy, it has the feature of creating distributed benefits and concentrated costs. Less proportional electoral systems create incentives for the Left to be responsive to those who face the concentrated costs, and thus for them to oppose privatization more strongly. More proportional systems reduce these incentives and increase the extent to which distributed benefits are internalized by elected representatives. Hypotheses are derived from this theory at both the individual and macro-policy level, and then tested separately. Quantitative evidence on public opinion from the 1990s and privatization revenues from Western European countries over the period 1980–2005 supports the argument.

Voters tend to be richer, more conservative, and more educated than non-voters. While many electoral reforms promise to increase political participation, these policy instruments may have multidimensional and differential effects that can increase or decrease the representativeness of turnout. We develop an approach that allows us to estimate these effects and assess the impact of postal voting on representational inequality in Swiss referendums using individual-level ($N = 79,\; 000$) and aggregate-level data from 1981 to 2009. We find that postal voting mobilizes equally across a wide range of political and sociodemographic groups but more strongly activates high earners, those with medium education levels, and less politically interested individuals. Yet, those who vote are not less politically knowledgeable and the effects on the composition of turnout remain limited. Our results inform research on the consequences of electoral reforms meant to increase political participation in large electorates.

I analyze a model in which an incumbent ruler designs a rule for propaganda disclosure that reveals information about her competence to her allies and opponents. A message that increases beliefs about the incumbent's competence is considered as propaganda. I show that for propaganda to be persuasive, it must be limited in frequency. I also demonstrate how various features of the environment affect the frequency of propaganda. Propaganda increases in frequency as the incumbent's allies become more dependent on her and as her opponents become weaker. Further, there is a non-monotonic relationship between the strength of the conflict of interest between both her allies and her opponents and the frequency of propaganda. As conflict increases, the frequency of propaganda decreases up to a threshold beyond which increased conflict is associated with more frequent propaganda.

While a number of high-level figures around the world have been prosecuted and even jailed for corruption in recent years, we know little about how such anticorruption efforts shape public opinion and patterns of political engagement. To address this question, we examine evidence from Argentina and Costa Rica involving the unprecedented sentencing of two former Presidents on corruption charges. Exploiting the coincidence in timing between these cases and fieldwork on nationally representative surveys, we find that citizens interviewed in the aftermath of these events expressed lower trust in institutions and were less willing to vote or join in collective demonstrations. Overall, these findings suggest that high-profile efforts to punish corrupt actors may have similar effects as political scandals in shaping citizens’ relationship to the political system.

How do voters in a developed economy react to political violence at the ballot box? Most of the current literature suggests that a social movement turning violent dampens its support. To this end, we examine the effect of violent clashes and indiscriminate state repression on Hong Kong's 2019 municipal election. Using original geocoded data, we proxy violence and repression by the frequency of police shooting tear gas rounds at protesters. Despite the movement turning in part violent, the presence of indiscriminate state repression reduces regime support. We offer evidence that repression de-mobilized pro-regime voters. We discuss possible explanations behind our findings and how the specificity of political violence may matter in shaping public support in protest movements.

How do governing parties respond in terms of ideological positioning when voted out of office? We study both theoretically and empirically the factors that shape parties’ responses following a loss. Studying national elections in advanced industrialized democracies over the past 70 years, we show that parties tend to counter their pre-election shifts, and do so particularly strongly following defeat. The extent of these ideological shifts is more limited in parties with a larger selectorate voting on the party leadership. Moreover, we find that subsequent to loss, parties are less likely to run on a centrist platform. Notably, shifting away from the center is associated with a higher probability of returning to power. We then introduce a dynamic model of party leadership selection and platform positioning. The model produces patterns of ideological positions over time that are consistent with our empirical findings.

We examine a problem that is confronted frequently by political science researchers seeking to model longitudinal data: what to do when one suspects a lag between the realization of a regressor and its effect on the outcome variable, but one has no theoretical reason to suspect a particular lag length. We examine the theoretical challenges posed by atheoretic lags, review existing methods for atheoretic lag analysis—most notably distributed lag specifications—and their shortcomings, and present an alternative approach for atheoretic lag analysis based on Bayesian model averaging (BMA). We demonstrate the use and utility of our approach with two examples: the litigant signal model in American politics and modernization theory in political economy. Our examples show the increasing difficulty of analyzing models with atheoretic lags as the set of possible specifications increases, and demonstrate the effectiveness of BMA for the modal type of specification in time-series cross-sectional applications.

Focusing on one specific aspect of immigrant political integration—how authorities deal with their political right to demonstrate—we show in a large-scale survey experiment that liberal policy decisions permitting demonstrations lead to a polarization in attitudes: citizens who agree with a permission become more sympathetic, while those in favor of banning become more critical of immigrants. This notion of opinion backlash to policy decisions adds a new perspective to the literature on immigration attitudes which has either assumed a congruence between public opinion and policy or ignored political sources of anti-immigrant sentiment altogether. By exploring the unintended consequences of policy decisions, we provide an alternative view and demonstrate the inherent dilemma of balancing citizen opinion and minority rights.