American politics scholarship has relied extensively on self-reported measures of ideology. We evaluate these widely used measures through an original national survey. Descriptively, we show that Americans’ understandings of “liberal” and “conservative” are weakly aligned with conventional definitions of these terms and that such understandings are heterogeneous across social groups, casting doubt on the construct validity and measurement equivalence of ideological self-placements. Experimentally, we randomly assign one of three measures of ideology to each respondent: (1) the standard ANES question, (2) a version that adds definitions of “liberal” and “conservative,” and (3) a version that keeps these definitions but removes ideological labels from the question. We find that the third measure, which helps to isolate symbolic ideology from operational ideology, shifts self-reported ideology in important ways: Democrats become more conservative, and Republicans more liberal. These findings offer first-cut experimental evidence on the limitations of self-reported ideology as a measure of operational ideology, and contribute to ongoing debates about the use of ideological self-placements in American politics.

People are commonly expected not to waste their vote on parties with small probabilities of being elected. Yet, many end up voting for underdogs. We argue that voters gauge the popular support for their preferred party from their social networks. When social networks function as echo chambers, a feature observed in real-life networks, voters overestimate underdogs’ chances of winning. We conduct voting experiments in which some treatment groups receive signals from a simulated network. We compare the effect of networks with a high degree of homogeneity against random networks. We find that homophilic networks increase the level of support for underdogs, which provides evidence to back up anecdotal claims that echo chambers foster the development of fringe parties.

Methodologists and econometricians advocate the partial observability model as a tool that enables researchers to estimate the distinct effects of a single explanatory variable on two partially observable outcome variables. However, we show that when the explanatory variable of interest influences both partially observable outcomes, the partial observability model estimates are extremely sensitive to misspecification. We use Monte Carlo simulations to show that, under partial observability, minor, unavoidable misspecification of the functional form can lead to substantial large-sample bias, even though the same misspecification leads to little or no bias under full observability.

Does political office cause worse or better longevity prospects? Two perspectives in the literature offer contradicting answers. First, increased income, social status, and political connections obtained through holding office can increase longevity. Second, increased stress and working hours associated with holding office can have detrimental effects on longevity. To provide causal evidence, we exploit a regression discontinuity design with unique data on the longevity of candidates for US gubernatorial office. The results show that politicians winning a close election live 5–10 years longer than candidates who lose.

Whiteley et al. criticize our re-analysis of the valence model of party choice. In reply, we argue that they are mistaken with regard to their understanding of some of the claims made in our paper and, in one instance, the variables included in the analysis. We also point to flaws in their interpretation of the distinction between weak and contemporaneous endogeneity and the assumptions of their vector error correction model. Their argument privileging aggregate analysis introduces predictable inferential problems and also, it might be suggested, potentially casts doubt on their own research, which uses individual-level analysis to make the case for the valence model. For these reasons, we see no reason to moderate the negative implications of our analysis for the validity of the model.

We develop a model of adaptive learning with social comparisons. Actors are more likely to choose actions that recently yielded satisfactory payoffs; satisfaction is evaluated relative to an aspiration level that reflects previous payoffs and possibly other players’ payoffs. This captures the phenomenon of social comparison via reference groups. We show that if agents compare themselves to those who are receiving higher payoffs then in stable outcomes all payoffs must be equal. If, however, agents’ aspirations are driven by less ambitious social comparisons then very unequal distributions can be stable. We apply our general results to collective action problems in socio-political hierarchies and derive conditions for stable exploitation. Finally, we develop a computational model, which shows that increases in payoff inequality make outcomes less stable.

Research has shown that first-time voting experiences affect subsequent voting behavior, with salient elections boosting subsequent turnout and non-salient ones suppressing it. We challenge this view. Following research on the context-dependent nature of habit formation, we argue that all elections should affect subsequent turnout in elections of the same type. Comparing individuals that differ only in how salient their first eligible election was (Presidential or Midterm), we find support for this expectation. Individuals are more likely to vote for, and be interested in, elections of the same type as their first voting experience. Leveraging voting age laws in the US, we also show that such laws affect subsequent participation by changing the type of election individuals are first eligible for.

Many democracies with high levels of corruption are also characterized by low levels of women's political representation. Do women candidates in democracies with high levels of corruption face overt voter discrimination? Do gender dynamics that are unique to highly corrupt, democratic contexts influence citizens’ willingness to vote for women? We answer these questions using two separate sets of experiments conducted in Ukraine: two vignette experiments and a conjoint analysis. In line with existing cross-sectional research on Ukraine, our experiments reveal little evidence of direct voter bias against women candidates. Our conjoint analysis also offers novel insights into the preferences of Ukrainian voters, showing that both men and women voters place a great deal of value in anti-corruption platforms, but voters are just as likely to support women and men candidates who say they will fight corruption. Our analysis suggests that women's political underrepresentation in highly corrupt contexts is driven more by barriers that prevent women from winning party nominations and running for office in the first place, rather than overt discrimination at the polls.

In comparative studies, causal evaluations attempt to improve our understanding of the effectiveness of structural reforms by counterfactually inspecting post-treatment effects. Yet, even if comparative scholars find similar treatment and comparison units, the interpretation of the post-treatment trajectory is difficult as short-term estimates can be subject to strategic timing of reform implementation, while long-term effects are likely affected by further interventions. To illustrate these difficulties we apply the generalized synthetic control method to evaluate the introduction of a British national minimum wage. We find a short-term decreasing effect on youth unemployment that turns into an increasing effect over time. This suggests the presence of an upward biased selection effect from strategic timing. We also inspect two post-treatment interventions and find that they differ in their general and country-specific implications for the long-term trajectory.

Local politicians are often expected to mobilize voters on behalf of copartisan candidates for national office. Yet this requirement is difficult to enforce because the effort of local politicians cannot be easily monitored and the promise of rewards in exchange for help is not fully credible. Using a formal model, we show that the incentives of local politicians to mobilize voters on behalf of their party depend on the proportion of copartisan officials in a district. Having many copartisan officials means that the party is more likely to capture the district, but the effort of each local politician is less likely either to be noticed by higher-level officials or to make a difference on the election outcome, thus discouraging lower-level officials from exerting effort. We validate these claims with data from federal elections in Mexico between 2000 and 2012. In line with the argument, the results show that political parties fail to draw great mobilization advantages from simultaneously controlling multiple offices.

Grant and Lebo (2016) and Keele et al. (2016) clarify the conditions under which the popular general error correction model (GECM) can be used and interpreted easily: In a bivariate GECM the data must be integrated in order to rely on the error correction coefficient, $\alpha _1^\ast$, to test cointegration and measure the rate of error correction between a single exogenous x and a dependent variable, y. Here we demonstrate that even if the data are all integrated, the test on $\alpha _1^\ast$ is misunderstood when there is more than a single independent variable. The null hypothesis is that there is no cointegration between y and any x but the correct alternative hypothesis is that y is cointegrated with at least one—but not necessarily more than one—of the x's. A significant $\alpha _1^\ast$ can occur when some I(1) regressors are not cointegrated and the equation is not balanced. Thus, the correct limiting distributions of the right-hand-side long-run coefficients may be unknown. We use simulations to demonstrate the problem and then discuss implications for applied examples.

Although covert warfare does not readily lend itself to scientific inquiry, new technologies are increasingly providing scholars with tools that enable such research. In this note, we examine the effects of drone strikes on patterns of communication in Yemen using big data and anomaly detection methods. The combination of these analytic tools allows us to not only quantify some of the effects of drone strikes, but also to compare them to other shocks. We find that on average drone strikes leave a footprint in their aftermath, spurring significant but localized spikes in communication. This suggests that drone strikes are not a purely surgical intervention, but rather have a disruptive impact on the local population.

This article considers the hypothesis that the positive actions taken by members of Congress (MCs) influence citizens’ evaluations of them, their party, and Congress as an institution. We begin with a look at the available cross-sectional survey data on contact with legislators and legislator and institutional approval. Their legislative responsiveness appears to have a small spillover effect on institutions. However, when we employ a unique panel design that controls for prior levels of opinion and avoids recall bias, we find no evidence of spillover effects. Overall, we find that constituents who received a response from their own MC evaluate that representative more positively than those who did not receive a response, but legislator responsiveness does not predict evaluations of the MC’s political party or the Congress.

Members and parties have electoral incentives to address issues on the congressional agenda to satisfy public demand. When determining which issues to address, majorities seek to minimize their uncertainty about the costs and electoral benefits of legislating by revisiting policy areas previously addressed. This theory is tested using error-correction models that demonstrate that policy activity within each chamber is in a long-term equilibrium and that the passage of legislation, even important bills, promotes future policymaking in the same policy area. This relationship is stronger when the majority has less information about the costs of lawmaking—specifically, when it faces a chamber controlled by the opposite party and when it is a new majority.

Voters use salient issues to inform their vote choice. Using 2020 Cooperative Election Study (CES) data, we analyze how short-, medium-, and long-term issues informed the vote for president in the 2020 election, which witnessed record-setting participation. To explain the dynamics of presidential vote choice, we employ a voter typology advanced by Key (1966). Specifically, compared to standpatters, who in 2020 registered the same major party vote as in 2016, we find that new voters in 2020 and voters switching their preferences from 2016 cast their ballots in favor of Democrat Joe Biden. In the end, President Donald Trump was denied reelection by new voters and vote switchers principally because certain issues had a notable effect in moving their presidential preferences in the Democratic direction.

Can data on government coercion and violence be trusted when the data are generated by state itself? In this paper, we investigate the extent to which data from the California Department of Corrections and Rehabilitation (CDCR) regarding the use of force by corrections officers against prison inmates between 2008 and 2017 conform to Benford's Law. Following a growing data forensics literature, we expect misreporting of the use-of-force in California state prisons to cause the observed data to deviate from Benford's distribution. Statistical hypothesis tests and further investigation of CDCR data—which show both temporal and cross-sectional variance in conformity with Benford's Law—are consistent with misreporting of the use-of-force by the CDCR. Our results suggest that data on government coercion generated by the state should be inspected carefully before being used to test hypotheses or make policy.

Recent studies provide evidence that the absence of a no-confidence procedure in presidential systems is crucial for understanding why the allocation of portfolios does not follow the same pattern in both parliamentary and presidential democracies. In this study, I argue that distinctions within presidential systems must be used to explain the substantial variation in the allocation of portfolios to presidential parties. I theorize that when the president is more dependent upon the legislature to make and enact policies, the balance of power in presidential cabinets is more likely to reflect the balance of power in the legislature. In this case, the presidential cabinet can resemble the proportional cabinets usually formed in parliamentary systems. With new data from 20 presidential democracies worldwide spanning more than 70 years, the results support the expectation of a greater formateur's advantage when presidents have greater institutionally-granted powers to influence the policy agenda in the legislature.

Liberalization is a perennial topic in politics and political science. We first review a broad scholarly debate, showing that the mainstream theories make rival and contradictory claims regarding the role of political parties in (de)liberalization reforms. We then develop a framework of conditional partisan influence, arguing that and under what conditions parties matter. We test our (and rival) propositions with a new dataset on (de)liberalization reforms in 23 democracies since 1973 covering several policy areas. Methodologically, we argue that existing quantitative studies are problematic: They rely on time-series cross-section models using country-year observations; but governments do not change annually, so that the number of observations is artificially inflated, resulting in incorrect estimates. We propose mixed-effects models instead, with country-year observations nested in cabinets, which are nested in countries and years. The results show under what conditions parties matter for (de)liberalization. More generally, the paper argues that mixed-effects models should become the new standard for studying partisan influences.

Firms in the USA rely on highly skilled immigrants, particularly in the science and engineering sectors. Yet, the recent politics of immigration marks a substantial change to US immigration policy. We implement a conjoint experiment that isolates the causal effect of nativist, anti-immigrant, pronouncements on where skilled potential-migrants choose to immigrate to. While these policies have a significantly negative effect on the destination choices of Chilean and UK student subjects, they have little effect on the choices of Indian and Chinese student subjects. These results are confirmed through an unobtrusive test of subjects’ general immigration destination preferences. Moreover, there is some evidence that the negative effect of these nativist policies are particularly salient for those who self-identify with the Left.