Can leveraging family history reduce xenophobia? Building on theories of group identity, we show that a family history of forced relocation leaves an imprint on future generations and can be activated to increase sympathy toward refugees. We provide evidence from Greece and Germany, two countries that vividly felt the European refugee crisis, and that witnessed large-scale forced displacement of their own populations during the twentieth century. Combining historical and survey data with an experimental manipulation, we show that mentioning the parallels between past and present differentially increases pledged monetary donations and attitudinal measures of sympathy for refugees among respondents with forcibly displaced ancestors. This differential effect is also present among respondents without a family history of forced migration who live in places with high historical concentration of refugees. Our findings highlight the role of identity and shared experience for reducing out-group discrimination.

Each year the United States resettles thousands of refugees in pre-determined locations across the country. However, refugees are free to relocate upon arrival. Although this secondary migration can fundamentally alter outcomes for both refugees and the communities that host them, policymakers lack systematic data on this phenomenon. Using novel administrative data covering all adult refugees resettled between 2000 and 2014 (N≈447,000), we provide a comprehensive analysis of secondary migration patterns. A high proportion of refugees leave their initial resettlement site and migrate to a different state, although rates vary widely by origin, family ties, and arrival state. Importantly, secondary migration is driven primarily by the presence of co-ethnic networks and labor market considerations. We find no evidence that patterns of secondary migration are driven by state partisanship and the generosity of welfare benefits.

We provide evidence that citizenship catalyzes the long-term economic integration of immigrants. Despite the relevance of citizenship policy to immigrant integration, we lack a reliable understanding of the economic consequences of acquiring citizenship. To overcome nonrandom selection into naturalization, we exploit the quasi-random assignment of citizenship in Swiss municipalities that held referendums to decide the outcome of individual naturalization applications. Our data combine individual-level referendum results with detailed social security records from the Swiss authorities. This approach allows us to compare the long-term earnings of otherwise similar immigrants who barely won or lost their referendum. We find that winning Swiss citizenship in the referendum increased annual earnings by an average of approximately 5000 U.S. dollars over the subsequent 15 years. This effect is concentrated among more marginalized immigrants.

A growing number of countries have established programs to attract immigrants who can contribute to their economy. Research suggests that an immigrant's initial arrival location plays a key role in shaping their economic success. Yet immigrants currently lack access to personalized information that would help them identify optimal destinations. Instead, they often rely on availability heuristics, which can lead to the selection of sub-optimal landing locations, lower earnings, elevated outmigration rates, and concentration in the most well-known locations. To address this issue and counteract the effects of cognitive biases and limited information, we propose a data-driven decision helper that draws on behavioral insights, administrative data, and machine learning methods to inform immigrants' location decisions. The decision helper provides personalized location recommendations that reflect immigrants' preferences as well as data-driven predictions of the locations where they maximize their expected earnings given their profile. We illustrate the potential impact of our approach using backtests conducted with administrative data that links landing data of recent economic immigrants from Canada's Express Entry system with their earnings retrieved from tax records. Simulations across various scenarios suggest that providing location recommendations to incoming economic immigrants can increase their initial earnings and lead to a mild shift away from the most populous landing destinations. Our approach can be implemented within existing institutional structures at minimal cost, and offers governments an opportunity to harness their administrative data to improve outcomes for economic immigrants.

The problem of low naturalization rates in the United States has entered policymakers’ agendas in light of the societal gains associated with citizenship and an increasing number of foreign-born residents. Nevertheless, there is little evidence on what policy interventions work best to increase naturalization rates. In this research, we show that the standardization of the fee waiver for citizenship applications in 2010 raised naturalization rates among low-income immigrants. These gains were particularly sizable among those immigrants who typically face higher hurdles to accessing citizenship. These findings have implications for policymakers interested in designing policies that help disadvantaged immigrant groups overcome barriers to citizenship.

We show that an information nudge increased the rate of American citizenship applications among low-income immigrants eligible for a federal fee waiver. Approximately half of the 9 million naturalization-eligible immigrants qualify for a federal programme that waives the cost of the citizenship application for low-income individuals. However, take-up of this fee waiver programme remains low^1,2,3. Here we use a randomized field experiment to test the effectiveness of a low-cost intervention (a ‘nudge’) that informed low-income immigrants about their eligibility for the fee waiver. We find that the information nudge increased the rate of citizenship applications by about 8.6 percentage points from 24.5% in the control group to 33.1% in the treatment group (ordinary least squares regression with robust standard errors (d.f. = 933); P = 0.015; 95% confidence interval ranged from 1.7 to 15.4 percentage points). We found no evidence that the nudge was less effective for poorer or less educated immigrants. These findings contribute to the literature that addresses the incomplete take-up of public benefits by low-income populations^{4,5,6,7,8,9,10} and suggest that lack of information is an important obstacle to citizenship among low-income immigrants who demonstrate an interest in naturalization.

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.