Security forces are one of the main targets of political violence. This paper examines the effect of their casualties on electoral outcomes. Between two general elections in 2015, Turkey experienced a series of attacks that killed 153 members of its security forces. Based on the as-if random assignment of their funerals across the country, I estimate that government vote share increases in the funeral places of security force terror victims. However, in the localities with recurring funerals, it decreases by a similar percentage. These non-linear changes provide strong evidence for the rally theory.

Researchers commonly employ multinomial logit (MNL) models to explain individual-level vote choice while treating “abstention” as the baseline category. Though many view abstainers as a homogeneous group, we argue that these respondents emerge from two distinct sources. Some nonvoters are likely to be “occasional voters” who abstained from a given election owing to temporary factors, such as a distaste for all candidates running in a particular election, poor weather conditions, or other temporary circumstances. On the other hand, many nonvoters are unlikely to vote regardless of the current political climate. This latter population of “routine nonvoters” is consistently disengaged from the political process in a way that is distinct from that of occasional voters. Including both sets of nonvoters within an MNL model can lead to faulty inferences. As a solution, we propose a baseline-inflated MNL estimator that models heterogeneous populations of nonvoters probabilistically, thus accounting for the presence of routine nonvoters within models of vote choice. We demonstrate the utility of this model using replications of existing political behavior research.

Survey experiments that investigate how voting procedures affect voting behavior and election outcomes use hypothetical questions and non-representative samples. We present here the results of a novel survey experiment that addresses both concerns. First, the winning party in our experiment receives a donation to its campaign funds inducing real consequences for voting. Second, we run an online experiment with a Dutch national representative sample (N = 1240). Our results validate previous findings using a representative sample, in particular that approval voting leads to a higher concentration in votes for smaller parties and strengthens centrist parties in comparison to plurality voting. Importantly, our results suggest that voting behavior is not affected by voting incentives and can be equally reliably elicited with hypothetical questions.

Prominent arguments hold that African states’ geography limits state capacity, impedes public service provision, and slows economic development. To test this argument, I collect comprehensive panel data on a proxy of local state capacity, travel times to national and regional capitals. These are computed on a yearly 5 × 5 km grid using time-varying data on roads and administrative units (1966–2016). I use these data to estimate the effect of changes in travel times to capitals on local education provision, infant mortality rates, and nightlight emissions. Within the same location, decreases in travel times to its capitals are robustly associated with improved development outcomes. The article advances the measurement of state capacity and contributes to understanding its effects on human welfare.

Much of the peace agreement durability literature assumes that stronger peace agreements are more likely to survive the trials of the post-conflict environment. This work does an excellent job identifying which provisions indicate that agreements are more likely to endure. However, there is no widely accepted way to directly measure the strength of agreements, and existing measures suffer from a lack of nuance or reliance on subjective weighting. We use a Bayesian item response theory model to develop a principled measure of the latent strength of peace agreements in civil conflicts from 1975 to 2005. We illustrate the measure's utility by exploring how various international factors such as sanctions and mediation contribute to the strength or weakness of agreements.

Within research on the political influence that social network members exert on one another, some studies rely on information obtained directly from different members in the network separately (self-reported measures), while others rely on information obtained from one key informant within the social network (measures based on perception). We investigate the difference between these self-reported and perceived measures by analyzing the correspondence of voting intentions within the family. On the one hand, we examine this correspondence using information obtained from only one family member. On the other hand, we use the self-reported measures obtained from all family members separately. We use data from the Parent-Child Socialization Study (PCSS), a survey conducted among 2,085 mothers, fathers and children in the Flemish region of Belgium (2012). Our analyses suggest that using perceptual measures could lead researchers to different or even opposite conclusions than using self-reported measures from all individual respondents.

This paper seeks to identify factors that may lie behind the tendency for parliamentary governments to form primarily from one side of the left-right spectrum and to adopt non-centrist policy positions. Because of measurement limitations as well as the inherent complexity of the processes involved, this exploration is undertaken through simulation experiments. The new software created for these experiments allows the potential impact of a wide variety of factors, including voter and party motivations and distributions, policy space dimensionality, and constraints on government formation, to be assessed. Although the results cannot tell us what fosters non-centrism in the real world, they do reveal some factors that appear to be conducive to that end and thus serve as a guide to further research on this neglected topic.

We examine the causal effect of legislative activity on private benefits, which have been largely neglected by previous research in legislative studies. By relying on a natural experiment in New Zealand, where randomly selected Members of Parliament (MPs) are given the opportunity to propose legislation, we find evidence for a causal relation between proposing a (successful) bill and the private benefits MPs receive, in terms of gifts and payments for services. We conclude that the allocation of private benefits depends on legislative performance.

Despite a high cross-country correlation between development and democracy, it is difficult to gauge the impact of economic development on the probability that autocracies will transition to democracy because of endogeneity, especially due to reverse causation and omitted variable bias. Hence, whether development causes democracy remains a contested issue. We exploit exogeneity in the regional variation of potato cultivation along with the timing of the introduction of potatoes to the Old World (i.e., a potato productivity shock) to identify a causal effect of urbanization, a proxy for economic development, on democratization. Our results, which hold under sensitivity analyses that question the validity of the exclusion restriction, present new evidence of the existence of a causal effect of economic development on democracy.

Across a series of experiments, I show that racial threat from a stereotypically nonthreatening racial minority group, Asian Americans, has a direct impact on white Americans' views of discrimination toward the group. When white Americans learn the group is growing, they feel a distinct racial threat which decreases support for the idea that Asian Americans experience discrimination while simultaneously increasing support for policy which actively discriminates against Asian Americans. I show this concept to be portable over context, examining support for discriminatory policy toward Asians in education policy and COVID-19 policy. I conclude by discussing the implications for how racial threat can drive more racial conflict while simultaneously decreasing the perception that this discrimination is occurring.

It is fairly well-known that proper time series analysis requires that estimated equations be balanced. Numerous scholars mistake this to mean that one cannot mix orders of integration. Previous studies have clarified the distinction between equation balance and having different orders of integration, and shown that mixing orders of integration does not increase the risk of type I error when using the general error correction/autoregressive distributed lag (GECM/ADL) models, that is, so long as equations are balanced (and other modeling assumptions are met). This paper builds on that research to assess the consequences for type II error when employing those models. Specifically, we consider cases where a true relationship exists, the left- and right-hand sides of the equation mix orders of integration, and the equation still is balanced. Using the asymptotic case, we find that the different orders of integration do not preclude identification of the true relationship using the GECM/ADL. We then highlight that estimation is trickier in practice, over finite time, as data sometimes do not reveal the underlying process. But, simulations show that even in these cases, researchers will typically draw accurate inferences as long as they select their models based on the observed characteristics of the data and test to be sure that standard model assumptions are met. We conclude by considering the implications for researchers analyzing or conducting simulations with time series data.

When analyzing data, researchers are often less interested in the parameters of statistical models than in functions of these parameters such as predicted values. Here we show that Bayesian simulation with Markov-Chain Monte Carlo tools makes it easy to compute these quantities of interest with their uncertainty. We illustrate how to produce customary and relatively new quantities of interest such as variable importance ranking, posterior predictive data, difficult marginal effects, and model comparison statistics to allow researchers to report more informative results.

Individuals' attitudes about gender roles have been shown to be associated with a wide range of political outcomes. It is therefore crucial to better understand what shapes these attitudes. This note takes advantage of a randomized survey experiment embedded in the 2018 wave of the European Social Survey (ESS) to investigate how differences in education levels between partners influence the “gender childcare bias”—the extent to which individuals disapprove more of women working full time with children under three than men. Although male and female respondents exhibit an equally strong gender childcare bias on average, we find clear-cut evidence that the bias varies asymmetrically across the household education gap for women and men. In particular, positive household education gaps lead to a smaller gender childcare bias for female respondents, whereas the opposite holds for male respondents. Our findings are more in line with a resource-bargaining approach than a gender identity approach to the formation of gender role attitudes.

Anemic demand for local news has contributed to an industry crisis. We consider whether local elections, which highlight the ability of local television stations and newspapers to provide information that is unavailable from national news outlets, increase local media use. While we show these elections are a time of increased attention to local politics in the news and among the public, we also find local media outlets do not benefit from this when considering behavioral news use measures. Relative to news outlets in cities without an election, local television remains substantively unchanged during local elections. Newspaper website traffic is largely stable, although it falls slightly the month after an election. In both cases these differences are small, even when considering close races and those happening off the federal election cycle. This shows limits on the ability of salient local political events to motivate local news use.

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.

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.

While presidents frequently create new policies through unilateral power, empirical scholarship generally focuses on executive orders and overlooks other categories of directives. We introduce data on more than 50,000 unilateral directives issued between 1877 and 2020 and use machine learning techniques to characterize their substantive importance and issue areas. Our measures reveal significant increases in unilateral activity over time, driven largely by increases in foreign affairs and through the substitution of memoranda for executive orders. We use our measures to formally evaluate the historical development of the unilateral presidency and reassess theoretical claims about public opinion and unilateral power. Our research provides new evidence about variation in the use of presidential authority and opens new avenues for empirical inquiry.

Inverse probability weighting is a common remedy for missing data issues, notably in causal inference. Despite its prevalence, practical application is prone to bias from propensity score model misspecification. Recently proposed methods try to rectify this by balancing some moments of covariates between the target and weighted groups. Yet, bias persists without knowledge of the true outcome model. Drawing inspiration from the quasi maximum likelihood estimation with misspecified statistical models, I propose an estimation method minimizing a distance between true and estimated weights with possibly misspecified models. This novel approach mitigates bias and controls mean squared error by minimizing their upper bounds. As an empirical application, it gives new insights into the study of foreign occupation and insurgency in France.