Recent years have seen a surge of politicians campaigning on policies that aim to deter asylum applicants. We present a game-theoretic model in which foreign nationals consider applying for asylum and bureaucrats decide their case if they apply. We show that, while policies that make the asylum application less attractive decrease the probability that foreign nationals apply, they also endogenously raise the credibility of applicants' claims to political persecution and, therefore, may not decrease the number of admitted refugees. The strength of this effect depends on the distribution of preferences among bureaucrats in the asylum-granting agency. We show that, under some conditions, populist politicians choose more restrictive policies if the agency is more lenient toward applicants. Our analysis speaks to the causes of restrictive asylum policies, their (in)effectiveness to reduce immigration, and the origins of refugee-skeptic politicians' anti-elitist rhetoric.

The spread of COVID-19 has prompted governments to implement a range of restrictions to public life and the economy (broadly, 'lockdown policies'). We evaluate the short- and medium-term impacts of lockdown policies on civic attitudes relevant for the health of democracy. Using survey data collected daily between March and May 2020 in the United States and four European countries from 27,317 respondents and a difference-in-differences design, we document that lockdown policies give rise to authoritarian values and, to a lesser degree, support for autocracy. We find little evidence that lockdown policies affect satisfaction with democracy and the government, out-group hostility, and generalized trust. Additional analyses reveal that the effects on authoritarianism and support for autocracy persist for at least seven weeks, that the lifting of lockdown policies does not have a countervailing effect away from authoritarianism and support for autocracy, and that economic support packages have limited ability to alleviate the negative consequences of lockdowns on civic attitudes. Together, these findings confirm the existence of lockdowns' political repercussions, but show the need for a nuanced assessment of their scope. We discuss the implications of our findings for how governments might need to accompany lockdowns with measures that strengthen civic culture.

Governments across Europe are restricting labor market access for asylum seekers, with often detrimental consequences for the livelihood of refugees and the public finances of host societies. This raises the following questions: Are the benefits of restrictive immigrant policies political rather than economic, and do incumbent governments receive an electoral edge by implementing such policies? In this paper, we exploit a natural experiment in Germany, where, following a deterministic assignment rule, certain regions were exempted from a reform that liberalized labor market access for refugees. Using difference-in-difference and regression discontinuity designs, we find that the incumbent vote share sharply increases in regions with restrictive labor market access. Exploring different mechanisms, our results suggest that this effect is primarily driven by differential candidate entry: In regions with restrictive labor market access, fewer conservative and populist challengers are running for office. Our results suggest that not only do immigration inflows have direct electoral repercussions, but immigrant policies do also.

While far-right parties profit electorally from rising immigration, we know very little about how increases in immigration mobilize opposition outside the electoral arena. Using fine-grained, classified data from the Federal Criminal Office in Germany, we estimate the causal effect of a sizable increase in asylum-seekers in a community on the probability of xenophobic hate crimes. Exploiting county-level quota regimes governing the allocation of asylum-seekers in Germany, we find that when immigration levels rise nationally, an increase in asylum-seeker arrivals in a community causes an increase in xenophobic hate crimes. We also document that these crimes are directed against asylum-seekers and not other non-natives, which suggests that they are instrumental actions intended to dispel and deter asylum-seekers from local communities.

Although Europe has experienced unprecedented numbers of refugee arrivals since 2015, there exists almost no causal evidence regarding the impact of the refugee crisis on natives’ attitudes, policy preferences, or political engagement. We provide evidence from a natural experiment in the Aegean Sea, where Greek islands close to the Turkish coast experienced a sudden and massive increase in refugee arrivals while similar islands slightly farther away did not. Leveraging distance as an instrument for between-island variation in exposure to the refugee crisis allows us to obtain causal estimates of its impact. In our targeted survey of 2,070 islands residents, we find that immediate exposure to large-scale refugee arrivals induces sizable and lasting increases in natives' hostility toward refugee, immigrant and Muslim minorities; support for restrictive asylum and immigration policies; and political engagement to effect such policies.

The welfare magnet hypothesis holds that immigrants are more likely to settle in locations with generous welfare benefits. Yet while this assumption has motivated extensive reforms to immigration policy and social programs, the empirical evidence remains contested. In this study, we draw on individual-level data from Switzerland covering the full population of social assistance recipients between 2005 and 2015. By leveraging local variation in cash transfers and exogenous shocks to benefits, we identify how welfare benefits shape residential decisions. We find no evidence that immigrants regularly move to localities with higher benefit levels. This null result within a case characterized by high variance in benefits and low barriers to movement suggests that the prevalence of welfare migration may be significantly overstated. These findings have important implications in the European setting, where sub-national governments often possess discretion over welfare and parties frequently mobilize voters around the issue of benefit tourism.

The successful integration of refugees into the local economy has become a major policy challenge for European countries in light of the global displacement crisis. One of the key challenges is to support refugees to learn the language of their host country. Several European countries provide publicly funded language training for asylum seekers and refugees shortly after arrival, but there is scant evidence on the impact of these early training programs on subsequent economic integration. We examine the impact of two such programs policymakers in Germany used in response to the large increase in the number of asylum seekers in 2015: a rapidly developed, ad hoc program that offered basic language training to over 230,000 newly arrived refugees and a smaller, preexisting program that offered refugees comprehensive language training. We leverage register data on the entire population of asylum seekers who arrived in 2015-2016 and exploit program eligibility and temporal participation variation using regression discontinuity and difference-in-differences designs to evaluate and contrast the effectiveness of both programs. We find that the ad hoc program had no discernible effect on refugee employment over the following 24 months. In contrast, the more comprehensive, preexisting program increased refugee employment by about 13 percentage points. These findings have implications for how policymakers can design effective language training programs to foster the integration of refugees.

Many European countries impose employment bans that prevent asylum seekers from entering the local labor market for a certain waiting period upon arrival. We provide evidence on the long-term effects of these employment bans on the subsequent economic integration of refugees. We leverage a natural experiment in Germany, where a court ruling prompted a reduction in the length of the employment ban. We find that, 5 years after the waiting period was reduced, employment rates were about 20 percentage points lower for refugees who, upon arrival, had to wait for an additional 7 months before they were allowed to enter the labor market. It took up to 10 years for this employment gap to disappear. Our findings suggest that longer employment bans considerably slowed down the economic integration of refugees and reduced their motivation to integrate early on after arrival. A marginal social cost analysis for the study sample suggests that this employment ban cost German taxpayers about 40 million euros per year, on average, in terms of welfare expenditures and foregone tax revenues from unemployed refugees.

An extensive literature links the rise of populist radical right (PRR) parties to immigration. We argue that another demographic trend is also significant: Emigration. The departure of citizens due to internal and international emigration is a major phenomenon affecting election outcomes via two complementary mechanisms. Emigration alters the composition of electorates but also changes the preferences of remaining voters left behind. Empirically, we establish a positive correlation between PRR vote shares and population loss at the county level across Europe. A more fine-grained panel analysis of precincts in Sweden demonstrates that the departure of local citizens raises PRR vote shares in the places left behind and that the Social Democrats are the principal losers from emigration. We explore through elite interviews and newspaper analyses how emigration produces grievances on which populists can capitalize and that established parties do not effectively address. Emigration and the frustrations it generates emerge as important sources of populist success.

How do economic opportunities abroad affect citizens’ ability to exit an authoritarian regime? This article theorizes the conditions under which authoritarian leaders will perceive emigration as a threat and use imprisonment instead of other types of anti-emigration measures to prevent mass emigration. Using data from communist East Germany's secret prisoner database that we reassembled based on archival material, the authors show that as economic opportunities in West Germany increased, the number of East German exit prisoners – political prisoners arrested for attempting to cross the border illegally – also rose. The study's causal identification strategy exploits occupation-specific differences in the changing economic opportunities between East and West Germany. Using differential access to West German television, it also sheds light on the informational mechanism underlying the main finding; cross-national data are leveraged to present evidence of the external validity of the estimates. The results highlight how global economic disparities affect politics within authoritarian regimes.

Social scientists have long been interested in the persistent effects of history on contemporary behavior and attitudes. To estimate legacy effects, studies typically compare people living in places that were historically exposed to some event and those that were not. Using principal stratification, we provide a formal framework to analyze how migration limits our ability to learn about the persistent effects of history from observed differences between historically exposed and unexposed places. We state the necessary assumptions about movement behavior to causally identify legacy effects. We highlight that these assumptions are strong; therefore, we recommend that legacy studies circumvent bias by collecting data on people's place of residence at the exposure time. Reexamining a study on the persistent effects of US civil-rights protests, we show that observed attitudinal differences between residents and non-residents of historic protest sites are more likely due to migration rather than attitudinal change.

Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. IV analyses can be used both to analyze randomized trials with noncompliance and as a form of natural experiment. In these analyses, investigators often adopt a monotonicity assumption, which implies that the relevant effect only applies to a subset of the study population known as compliers. Since the estimated effect is not the average treatment effect of the study population, it is important to compare the characteristics of compliers and non-compliers. Profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step in evaluating the external validity (or lack thereof) of the IV estimate for compliers. Here, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the local average treatment effect if the instrument is randomly assigned. We then outline a simple and general method to characterize compliers and noncompliers using baseline covariates. Next, we extend current methods by deriving standard errors for these estimates. We demonstrate these methods using an IV known as tendency to operate (TTO) from health services research.

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by noncompliance or leverage partially exogenous treatment variation in observational studies. The potential outcome framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE) and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and noncompliers. Yet, profiling compliers and noncompliers is necessary to understand what subpopulation the researcher is making inferences about and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers, and never-takers in terms of their covariates and provide easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and noncompliers as a standard practice accompanying any IV analysis.

In the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions.

Imputing missing values is an important preprocessing step in data analysis, but the literature offers little guidance on how to choose between different imputation models. This letter suggests adopting the imputation model that generates a density of imputed values most similar to those of the observed values for an incomplete variable after balancing all other covariates. We recommend stable balancing weights as a practical approach to balance covariates whose distribution is expected to differ if the values are not missing completely at random. After balancing, discrepancy statistics can be used to compare the density of imputed and observed values. We illustrate the application of the suggested approach using simulated and real-world survey data from the American National Election Study, comparing popular imputation approaches including random forests, hot-deck, predictive mean matching, and multivariate normal imputation. An R package implementing the suggested approach accompanies this letter.

Dyadic data from UNHCR on the size of the global refugee population are widely used. However, for a large fraction of the refugee population, these data provide no information about refugees' country of origin, which contributes to a high nominal rate of unreported values in the data. In this article, I demonstrate that two imputation approaches outperform the current standard approach, which assumes that all unreported values are zero. The first approach interpolates the unreported values, while the second predicts them based on trends observed in other dyads. Drawing on different types of information, the two approaches' performance is similar. Replicating a published study on the effect of refugee crises on international war and peace, I demonstrate how both approaches strengthen the author's findings and help to minimize the risk of a null finding.

Comparative studies report the rise of left- and right-wing Eurosceptic parties that have transformed national party competition in Europe toward an inverted U-shaped configuration: Peripheral parties at the left and right of the party spectrum oppose while centrist parties support several features of European integration. To describe the tempo and timing of this transformation and the heterogeneity across countries, we develop a Bayesian finite mixture factor analysis that estimates the election-specific probability of a one-dimensional left/right versus a two-dimensional inverted U-shaped national party configuration. The results show a general trend toward 'U' but with significant variation across countries and time, including cases with a reversal of this trend.

RILE estimates based on party manifesto data suggest that political parties leapfrog on the left-right scale over time. This implausible finding has raised questions about the efficacy not only of RILE for estimating left-right positions but of coded party manifestos for political science research in general. The recently developed Manifesto Common Space Scores (MCSS), which reduce leapfrogging by accounting for the election-specific character of party manifestos, provide alternative estimates for parties left/right-positions, but little is known about their validity. This study shows that MCSS estimates exhibit greater convergent validity relative to RILE estimates when compared to other measures of parties left/right-positions. It also finds that MCSS has greater construct validity relative to RILE estimates in two prominent cases (Greece and Italy). Overall, the findings underscore the election-specific character of party manifestos and demonstrate that MCSS is a useful alternative measure of parties’ left-right positions.

This article presents a new method for estimating positions of political parties across country- and time-specific contexts by introducing a latent variable model for manifesto data. We estimate latent positions and exploit bridge observations to make the scales comparable. We also incorporate expert survey data as prior information in the estimation process to avoid ex post facto interpretation of the latent space. To illustrate the empirical contribution of our method, we estimate the left-right positions of 388 parties competing in 238 elections across twenty-five countries and over sixty years. Compared to the puzzling volatility of existing estimates, we find that parties more modestly change their left-right positions over time. We also show that estimates without country- and time-specific bias parameters risk serious, systematic bias in about two-thirds of our data. This suggests that researchers should carefully consider the comparability of party positions across countries and/or time.

Authoritarian rulers fend off revolutions by stimulating the economy. However, expanding the economy can also increase environmental pollution. If citizens value clean air and water, worsening pollution has the potential to galvanize large segments of the society against the regime---which increases the risk of a revolution. While the literature has documented how concerns over the environment upend politics in democracies, we know relatively little about the effects of these concerns in authoritarian regimes. We analyze environmental pollution as an overlooked threat to authoritarian rulers. Using unique data from Communist East Germany and exploiting variation in thermal inversions to instrument for pollution levels, we find that pollution causes both individual and collective expressions of regime dissatisfaction. Our findings suggest that rulers face a trade-off between growing the economy and worsening pollution.