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Protracted global conflicts during the past decade have led to repeated major humanitarian protection crises in Europe. During the height of the Syrian refugee crisis at the end of 2015, Europe hosted around 2.3 million people requesting asylum1. Today, the ongoing war in Ukraine has resulted in one of the largest humanitarian emergencies in Europe since World War II, with more than eight million Ukrainians seeking refuge across Europe2. Here we explore whether repeated humanitarian crises threaten to exhaust solidarity and whether Europeans welcome Ukrainian asylum seekers over other asylum seekers3,4. We conducted repeat conjoint experiments during the 2015-2016 and 2022 refugee crises, asking 33,000 citizens in 15 European countries to evaluate randomly varied profiles of asylum seekers. We find that public preferences for asylum seekers with specific attributes have remained remarkably stable and general support has, if anything, increased slightly over time. Ukrainian asylum seekers were welcomed in 2022, with their demographic, religious and displacement profile having a larger role than their nationality. Yet, this welcome did not come at the expense of support for other marginalized refugee groups, such as Muslim refugees. These findings have implications for our theoretical understanding of the drivers and resilience of public attitudes towards refugees and for policymakers tasked to find effective responses to the enduring stress on the asylum system5-8.

ObjectivesNeighborhood disadvantage may increase the risk of adverse health outcomes among older refugees. Yet few studies rigorously estimate the effects of place-based factors on later-life health, particularly dementia and mortality. Evidence about refugees is especially sparse.MethodsThis study leveraged a natural experiment in the form of a Danish policy (1986-1998) that dispersed refugees quasi-randomly across neighborhoods upon arrival. We used longitudinal registers allowing 30 years of follow-up among refugees aged 40+ years upon arrival in Denmark (N = 9,854). Cox models assessed the association between neighborhood disadvantage and risk of dementia and mortality. We examined heterogeneous effects by sex, age, and family size. We also examined associations among non-refugee immigrants and native-born Danes.ResultsNeighborhood disadvantage was not associated with dementia in any group. One unit increase in neighborhood disadvantage index (ranges -8 to 5.7) was associated with greater mortality risk among non-refugee immigrants (HR 1.06, 95%CI: 1.02, 1.10) and native-born Danes (HR 1.11, 95%CI: 1.06, 1.17). In contrast, neighborhood disadvantage was associated with lower mortality risk among refugees (HR 0.96, 95%CI: 0.93, 0.99). Neighborhood disadvantage remained negatively associated with mortality risk in subgroups: refugees who are female (on moderate-disadvantage compared to low-disadvantage), aged 60+, and who arrived with families.DiscussionWhile neighborhood disadvantage was associated with lower mortality risk among refugees, it was associated with greater mortality risk among non-refugee immigrants and native-born Danes, perhaps due to confounding in the latter groups or different place-based experiences by immigration status. Future research is warranted to explain the reasons for contrasting findings.

Abstract: Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary of voters’ multidimensional preferences that combines directionality and intensity according to a probabilistic generalization of the Borda rule. We demonstrate why incorporating both the directionality and intensity of multi-attribute preferences is essential for analyzing real-world elections, in whichceteris paribuscomparisons almost never occur. Second, and in further empirical support of this point, we show how this aggregation translates directly into a primary quantity of interest to election scholars: the effect of a change in an attribute on a candidate’s or party’s expected vote share. These properties hold irrespective of the heterogeneity, strength, or interactivity of voters’ preferences and regardless of how votes are aggregated into seats. Finally, we propose, formalize, and evaluate the feasibility of using conjoint data to estimate alternative quantities of interest to electoral studies, including the effect of an attribute on the probability of winning.

AbstractWe introduce a constrained priority mechanism that combines outcome-based matching from machine learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the probability of employment, whereas in the student assignment context, it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families and students) based on their preferences, but subject to meeting the planner’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner’s threshold.

Abstract We introduce a constrained priority mechanism that combines outcome-based matching from machine learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the probability of employment, whereas in the student assignment context, it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families and students) based on their preferences, but subject to meeting the planner’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner’s threshold.

Conjoint experiments are popular, but there is a paucity of research on respondents’ underlying decision-making processes. We leverage eye-tracking methodology and a series of conjoint experiments, administered to university students and local community members, to examine how respondents process information in conjoint surveys. There are two main findings. First, attribute importance measures inferred from the stated choice data are correlated with attribute importance measures based on eye movement. This validation test supports the interpretation of common conjoint metrics, such as average marginal component effects (AMCEs), as measures of attribute importance. Second, when we experimentally increase the number of attributes and profiles in the conjoint table, respondents view a larger absolute number of cells but a smaller fraction of the total cells displayed. Moving from two to three profiles, respondents search more within-profile, rather than within-attribute, to build summary evaluations. However, respondents’ stated choices remain fairly stable regardless of the number of attributes and profiles in the conjoint table. Together, these patterns speak to the robustness of conjoint experiments and are consistent with a bounded rationality mechanism. Respondents adapt to complexity by selectively incorporating relevant new information to focus on important attributes, while ignoring less relevant information to reduce cognitive processing costs.

AbstractRecent years have seen a renaissance of conjoint survey designs within social science. To date, however, researchers have lacked guidance on how many attributes they can include within conjoint profiles before survey satisficing leads to unacceptable declines in response quality. This paper addresses that question using pre-registered, two-stage experiments examining choices among hypothetical candidates for US Senate or hotel rooms. In each experiment, we use the first stage to identify attributes which are perceived to be uncorrelated with the attribute of interest, so that their effects are not masked by those of the core attributes. In the second stage, we randomly assign respondents to conjoint designs with varying numbers of those filler attributes. We report the results of these experiments implemented via Amazon's Mechanical Turk and Survey Sampling International. They demonstrate that our core quantities of interest are generally stable, with relatively modest increases in survey satisficing when respondents face large numbers of attributes.

Developed democracies are settling an increased number of refugees, many of whom face challenges integrating into host societies. We developed a flexible data-driven algorithm that assigns refugees across resettlement locations to improve integration outcomes. The algorithm uses a combination of supervised machine learning and optimal matching to discover and leverage synergies between refugee characteristics and resettlement sites. The algorithm was tested on historical registry data from two countries with different assignment regimes and refugee populations, the United States and Switzerland. Our approach led to gains of roughly 40 to 70%, on average, in refugees' employment outcomes relative to current assignment practices. This approach can provide governments with a practical and cost-efficient policy tool that can be immediately implemented within existing institutional structures.

What types of asylum seekers are Europeans willing to accept? We conducted a conjoint experiment asking 18,000 eligible voters in 15 European countries to evaluate 180,000 profiles of asylum seekers that randomly varied on nine attributes. Asylum seekers who have higher employability, have more consistent asylum testimonies and severe vulnerabilities, and are Christian rather than Muslim received the greatest public support. These results suggest that public preferences over asylum seekers are shaped by sociotropic evaluations of their potential economic contributions, humanitarian concerns about the deservingness of their claims, and anti-Muslim bias. These preferences are similar across respondents of different ages, education levels, incomes, and political ideologies, as well as across the surveyed countries. This public consensus on what types of asylum seekers to accept has important implications for theory and policy.

Objectives To determine whether expanding Emergency Medicaid to cover prenatal care in Oregon affected maternal health outcomes for unauthorized immigrants. Methods This study takes place in Oregon from 2003 to 2015 and includes all Emergency Medicaid and Medicaid claims for women aged 12-51 with a pregnancy related claim. To isolate the effect of expanding access to prenatal care, we utilized a difference-in-differences approach that exploits the staggered rollout of the prenatal care program. The primary outcome was a composite measure of severe maternal morbidity and mortality. Additional outcomes include adequacy of prenatal care, detection of pregnancy complications and birth outcomes. Results A total of 213,746 pregnancies were included, with 35,182 covered by Emergency Medicaid, 12,510 covered by Emergency Medicaid Plus (with prenatal care), and 166,054 covered by standard Medicaid. Emergency Medicaid Plus coverage did not affect severe maternal morbidity (all pregnancies 0.05%, CI - 0.29; 0.39; high-risk pregnancies 2.20%, CI - 0.47; 4.88). The program did reduce inadequate care among all pregnancies (- 31.75%, 95% CI - 34.47; - 29.02) and among high risk pregnancies (- 38.60%, CI - 44.17; - 33.02) and increased diagnosis of gestational diabetes (6.24%, CI 4.36; 8.13; high risk pregnancies 10.48%, CI 5.87; 15.08), and poor fetal growth (7.37%, CI 5.69; 9.05; high risk pregnancies 5.34%, CI 1.00; 9.68). The program also increased diagnosis of pre-existing diabetes mellitus (all pregnancies 2.93%, CI 2.16; 3.69), hypertensive diseases of pregnancy (all pregnancies 1.28%, CI 0.52; 2.04) and a history of preterm birth (all pregnancies 0.87%, CI 0.27; 1.47). Conclusions for Practice Oregon's prenatal care expansion program produced positive effects for unauthorized immigrant women and their children., Author(s): Jonas J. Swartz [sup.1] [sup.2] , Jens Hainmueller [sup.3] [sup.4] [sup.5] , Duncan Lawrence [sup.4] , Maria I. Rodriguez [sup.2] Author Affiliations: (Aff1) 0000000122483208, grid.10698.36, Department of Obstetrics and [...]

Political scientists have increasingly deployed conjoint survey experiments to understand multidimensional choices in various settings. In this paper, we show that the average marginal component effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary of voters' multidimensional preferences that combines directionality and intensity according to a probabilistic generalization of the Borda rule. We demonstrate why incorporating both the directionality and intensity of multi-attribute preferences is essential for analyzing real-world elections, in which ceteris paribus comparisons almost never occur. Second, and in further empirical support of this point, we show how this aggregation translates directly into a primary quantity of interest to election scholars: the effect of a change in an attribute on a candidate's or party's expected vote share. These properties hold irrespective of the heterogeneity, strength, or interactivity of voters' preferences and regardless of how votes are aggregated into seats. Finally, we propose, formalize, and evaluate the feasibility of using conjoint data to estimate alternative quantities of interest to electoral studies, including the effect of an attribute on the probability of winning. [ABSTRACT FROM AUTHOR]

A critical barrier to generating cumulative knowledge in political science and related disciplines is the inability of researchers to observe the results from the full set of research designs that scholars have conceptualized, implemented, and analyzed. For a variety of reasons, studies that produce null findings are especially likely to be unobserved, creating biases in publicly accessible research. While several approaches have been suggested to overcome this problem, none have yet proven adequate. We call for the establishment of a new discipline-wide norm in which scholars post short "null results reports" online that summarize their research designs, findings, and interpretations. To address the inevitable incentive problems that earlier proposals for reform were unable to overcome, we argue that decentralized research communities can spur the broader disciplinary norm change that would bring advantage to scientific advance. To facilitate our contribution, we offer a template for these reports that incorporates evaluation of the possible explanations for the null findings, including statistical power, measurement strategy, implementation issues, spillover/contamination, and flaws in theoretical priors. We illustrate the template's utility with two experimental studies focused on the naturalization of immigrants in the United States and attitudes toward Syrian refugees in Jordan. [ABSTRACT FROM AUTHOR]