Social scientists often compare survey responses before and after important events to test how those events impact respondent beliefs, attitudes, and preferences. This article offers a formal analysis of such pre-event/post-event survey comparisons, including designs that seek to reduce bias using quota sampling, rolling cross-sections, and panels. Our analysis distinguishes major sources of bias and clarifies the comparative strengths and weaknesses of each approach. We then introduce a modified panel design—the dual randomized survey—to reduce bias in cases where asking respondents to complete the same survey twice could impact their Wave 2 responses. Our formalization of bias and novel research design improve scholars’ ability to study the causal impact of events through surveys.

This paper puts forward a theoretically derived measure of firm-level political influence defined over a sample of firms from a diverse set of countries, permitting new inferences into state-business relations. We derive this measure from original surveys of 27,613 firms in 41 countries, which include information on several interactions with political actors. Using a Bayesian item response theory measurement model that incorporates non-ignorable missing data, we estimate influence scores that incorporate survey data on diverse mechanisms by which firms attempt to obtain influence. From the measurement model, we learn that membership in a business association contains the most positive information about a firm’s influence, while bribes, state ownership, firm size, and a reliance on collective lobbying tend to be substitutes for influence in equilibrium. Empirically, we are able to show for the first time how such influence is distributed across different types of political regimes using a measurement model, leading to intriguing hypotheses about how the costs and benefits of political activity structure corporate influence-seeking.

In this article, I study the effect of endogenous challenger entry on electoral accountability in the presence of adverse selection. To this end, I analyze 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 in this model is ambiguous: depending on model parameters, it can worsen or ease policy distortions. Analogously, marginally increasing the cost of running for office can deepen or reduce these distortions. This uncertainty regarding the effect of endogenous challenger entry on policymaking leads to equally ambiguous welfare implications. Nonetheless, I identify conditions under which endogenous challenger entry improves policymaking and voter welfare. This suggests that, in some circumstances, imposing higher barriers to entry in elections can improve policymaking and voter welfare.

How do the effects of climate regulation on businesses impact public attitudes toward climate policy? While emissions intensity is the primary frame for understanding the effects of climate policy on business, theoretical scholarship and public discourse often emphasize that large firms will adjust to climate regulations easily while smaller firms will struggle. Because small businesses are sympathetic and large firms are unpopular, individuals who view climate regulation’s effects in line with this firm size account should be less likely to support climate change mitigation. To test this theory, we conduct an original survey of climate policy beliefs and then a survey experiment. We find evidence that distaste for large corporations increases opposition to climate action among people exposed to the idea that big companies can more easily navigate climate regulations than small companies. This work contributes to the literature on moral political economy and on the enduring difficulty of enacting effective climate change regulation within the United States.

The Cox duration model serves as the basis for more complex duration models like competing risks, repeated events, and multistate models. These models make a number of assumptions, many of which can be assessed empirically, sometimes for substantive ends. We use Monte Carlo simulations to show the order in which practitioners assess these assumptions can impact the model’s final specification, and ultimately, can produce misleading inferences. We focus on three assumptions regarding model specification decisions: proportional hazards (PH), stratified baseline hazards, and stratum-specific covariate effects. Our results suggest checking the PH assumption before checking for stratum-specific covariate effects tends to produce the correct final specification most frequently. We reexamine a recent study of the timing of GATT/WTO applications to illustrate our points.

Do ideologically extreme candidates enjoy fundraising advantages over more moderate candidates? Extant work documents a relationship between candidates’ positions and campaign contributions subnationally and in donor surveys, yet identification challenges have hampered investigation in the congressional context. I employ a close primaries regression discontinuity design to examine how “as-if random” nominations of extreme versus moderate House candidates influence general election contributions from individual donors and corporate political action committees (PACs) from 1980 to 2020. Results at both the nominee and contributor levels demonstrate that corporate PACs financially penalize extremists, while individual donors respond similarly to extreme and moderate candidates. These findings contribute to ongoing debates regarding the extent and nature of campaign contributors’ role in congressional polarization.

We analyze a formal model of social contact and discrimination in the context of policing. Officers decide how to interact with members of two social groups while working and while socializing. The officers do not fully distinguish between their experiences of crime across these two contexts (“coarse thinking”), so they end up with excessively positive views of groups they socialize with and excessively negative views of those they police. This creates dual feedback loops as officers choose to socialize more with groups they view favorably and over-police those they view as “more criminal.” Interventions that induce positive contact with an overpoliced group can mitigate the officer’s discriminatory policing. However, this beneficial effect only persists if the policy intervention creates sustained positive contact. Our results provide a novel theoretical microfoundation for the contact hypothesis and highlight why effects of many policy interventions aimed at increasing positive contact may be short-lived.

The extent to which legislators pursue their privately held preferences in office has important implications for representative democracy and is exceedingly difficult to measure. Many models of legislative decision-making tacitly assume that members are willing and able to carry out the wishes of their constituents so as to maximize their reelection prospects and, in so doing, relegate their personal preferences. This project explores this assumption by examining the role that members’ place of birth plays in shaping legislative behavior, apart from other politically relevant factors like partisanship. We find that birthplace exerts an independent influence on members’ voting behavior. Using a variety of geographic measures, we find that members who are born in close proximity to one another tend to exhibit similar patterns in roll call voting, even when accounting for partisanship, constituency attributes, and a variety of other determinants of voting. We also demonstrate in a secondary analysis that the agricultural composition of members’ birthplace influences their support for agricultural protection. Our findings suggest that members’ personal history shapes the representational relationship they have with their constituents.

This paper demonstrates how Bayesian reasoning can be used for an analog of replication analysis with qualitative research that conducts inference to best explanation. We overview the basic mechanics of Bayesian reasoning with qualitative evidence and apply our approach to recent research on climate change politics, a matter of major importance that is beginning to attract greater interest in the discipline. Our re-analysis of illustrative evidence from a prominent article on global collective-action versus distributive politics theories of climate policy largely accords with the authors’ conclusions, while illuminating the value added of Bayesian analysis. In contrast, our in-depth examination of scholarship on oil majors’ support for carbon pricing yields a Bayesian inference that diverges from the authors’ conclusions. These examples highlight the potential for Bayesian reasoning not only to improve inferences when working with qualitative evidence but also to enhance analytical transparency, facilitate communication of findings, and promote knowledge accumulation.

Many studies argue that the World Bank grants favorable loan conditions to allies of its powerful principals. These studies typically use the count of conditions as a proxy for how demanding loans are on borrowers, even though some conditions are more difficult to comply with than others. We propose a new operationalization: a measure of conditionality stringency in Bank loans constructed using Latent Semantic Scaling. Using this new measure, we find little evidence of a generalizable influence of powerful principals. Instead, the stringency of loan conditions is associated with bureaucratic assessments of risk. To facilitate future research, we provide a new dataset of World Bank loan condition texts and our measure of text stringency for all loans in the dataset.

Covariance structure analysis or structural equation modeling is critical for political scientists measuring latent structural relationships, allowing for the simultaneous assessment of both latent and observed variables, alongside measurement error. Well-specified models are essential for theoretical support, balancing simplicity with optimal model fit. However, current approaches to improving model specification searches remain limited, making it challenging to capture all meaningful parameters and leaving models vulnerable to chance-based specification risks. To address this, we propose an improved Lagrange multiplier (LM) test incorporating stepwise bootstrapping in LM and Wald tests to detect omitted parameters. Monte Carlo simulations and empirical applications underscore its effectiveness, particularly in small samples and models with high degrees of freedom, thereby enhancing statistical fit.

Voters’ issue preferences are key determinants of vote choice, making it essential to reduce measurement error in responses to issue questions in surveys. This study uses a MultiTrait MultiError approach to assess the data quality of issue questions by separating four sources of variation: trait, acquiescence, method, and random error. The questions generally achieved moderate data quality, with 76% on average representing valid variance. Random error made up the largest proportion of error (23%). Error due to method and acquiescence was small. We found that 5-point scales are generally better than 11-point scales, while answers by respondents with lower political sophistication achieved lower data quality. The findings indicate a need to focus on decreasing random error when studying issue positions.

Governments shape policy outcomes using two distinct mechanisms: rules and discretion. A simple decomposition strategy is proposed for distinguishing between these policymaking mechanisms on income inequality in the American states from 1986 to 2020. This analytical strategy is easily applicable to other policy settings. The statistical evidence, for the most part, that income inequality observed in the American states is generally unaffected by both TELs and partisan control of state governments—the lone exception being unified Republican state governments operating under a TEL. The decomposition evidence, however, shows that this is primarily the result of discretionary policymaking differences among partisan governments. This study underscores the importance of disentangling policy mechanisms that jointly occur when evaluating the consequences of government policymaking authority.

This paper introduces a simple approach for assessing which survey questions are more likely to elicit political identity-influenced responses. We use daily data from Gallup to test which survey self-reports exhibit more or less susceptibility to politicization, finding the highest likelihood of politicization for societal-level questions. Conversely, we show that self-reported assessments of personal finances are less sensitive to partisan motivated responding. We also show how egotropic economic evaluations are influenced by the presence of other items on the same survey. Taken together, our results uncover scope conditions for how to interpret self-reported views of the economy, and we argue that measures of public opinion which have not yet been strongly politicized are better proxies for capturing the underlying welfare of the public.

A growing literature explores the effect of economic inequality in citizens’ surrounding environment on their political attitudes and behavior. This literature typically relies on measures of income concentration or gap-size, which reflect under-tested presumptions about how citizens perceive the economic conditions surrounding them. Utilizing survey data to explore perception of economic inequality in Americans’ residential environment, this note finds that measures capturing income concentration or gap-size perform poorly relative to a measure capturing the joint prevalence of “haves” and “have-nots.” These results suggest that commonly used measures of economic inequality may not fully capture the features of people’s daily environment used to perceive the existence or magnitude of inequality. The results guide future research toward using contextual indicators that treat inequality as a compound phenomenon involving manifestations of poverty and affluence.

When are far right parties punished for their extreme positions? We argue that the punishments of deviant position-taking are conditional on the degree to which a far right party is normalized or stigmatized in the party system. When the far right is treated as normal, the costs suffered from these parties’ extreme positions decrease, as moderate voters discount the authenticity of their commitment to such positions. We use a survey experiment to test this argument in Spain, finding evidence for discounting on the far right’s extreme anti-LGBTQ+ statements, but not on its embrace of authoritarian history. This study thus shows that normalization and stigmatization of the far right can change how its extreme positions are interpreted by voters.

Many recent applications of the “Unexpected Event during Survey Design” (UESD) analyze single cases of frequently occurring events. In this research note, I question the generalizability of research findings obtained this way and demonstrate the empirical benefits of the “Multiple Unexpected Events during Survey Design” (MUESD). I conduct 15 large-scale replications (total N = 101,940) of a new UESD analyzing the effects of Mediterranean shipwrecks on immigration attitudes. Previous research suggests that such events drastically reduce anti-immigration attitudes among the European public, presumably through an empathy-based mechanism. However, after 15 of the most lethal shipwrecks since 2013, anti-immigration attitudes were reduced in only one case of exceptionally high salience and to a much lesser degree than originally found.

Politicians appear to overestimate how conservative public opinion is in the U.S. and other Western democracies. Whether this “conservative bias” extends to voters remains unclear but has important implications for belief formation and behavior. I examine this in the context of abortion access after the Dobbs decision. Despite the salience of the topic, original survey data collected post-Dobbs reveal consistent underestimation of public support for abortion access. Individuals identifying as “pro-life” drive most of this underestimation, suggesting the presence of egocentric biases in which “pro-life” Americans overestimate the commonality of their views. Conservative biases among voters may contribute to a skewed information environment for politicians, potentially providing leverage for further restrictions on abortion access.