THEORETICAL EXPECTATIONS ABOUT REFUGEES AND CRIME

Existing empirical research has found varied estimates of the relationship between immigration and crime. Although the evidence suggesting that immigration increases crime, particularly violent crime, is thin (Ousey and Kubrin Reference Ousey and Kubrin2018), there is significant heterogeneity in findings across studies depending on the context and research design and types of immigration and crime (see, e.g., Berardi and Bucerius Reference Berardi, Bucerius, Bucerius and Tonry2014; Shihadeh and Barranco Reference Shihadeh and Barranco2010).

Refugees are a particular subset of immigrants and differ from economic migrants in both their observable characteristics and the drivers behind their migration decisions (Dustmann et al. Reference Dustmann, Fasani, Frattini, Minale and Schönberg2017), differences that suggest the need for special consideration of the influence of refugees specifically on crime rates. Recent studies focusing on refugees in Germany find small increases in crime rates due to the inflow of refugee migrants (Dehos Reference Dehos2017; Gehrsitz and Ungerer Reference Gehrsitz and Ungerer2017; Lange and Sommerfeld Reference Lange and Sommerfeld2018). Although the evidence is still too limited and provisional for clear conclusions, it highlights the importance of studying the question elsewhere, especially as broader evidence on the immigration-crime relationship shows that it may differ across countries, such as in the US compared with Europe (see, e.g., Berardi and Bucerius Reference Berardi, Bucerius, Bucerius and Tonry2014; Milton, Spencer, and Findley Reference Milton, Spencer and Findley2013). There is a paucity of research on the effects of refugee resettlement on crime in the US. The one exception is a recent study that broadly examines data from 2006 through 2014 and finds no evidence of an effect of refugee resettlement on crime or terrorism (Amuedo-Dorantes, Bansak, and Pozo Reference Amuedo-Dorantes, Bansak and Pozo2020). We build on their work through a novel estimation strategy, analyzing the Executive Order as a natural experiment associated with a large, sharp, and sudden variation in resettlement in 2017.

Existing theory presents ambiguous expectations about the consequences of refugee resettlement on crime. On the one hand, resettlement could increase crime rates if the refugees are a crime-prone demographic. First, people migrating from places where violence is widespread may have a relatively high propensity to commit violent crimes. Living in environments with extreme hardship, such as urban slums and refugee camps, could have long-term psychosocial effects including social disaffection, depression, or posttraumatic stress disorder, which may increase the propensity for antisocial behavior. Traumatic experiences, including living through or witnessing violence, poverty, and sexual violence, have been widely demonstrated to be predictive of aggressive and criminal behavior (Ardino Reference Ardino2012). Looking specifically at refugee resettlement in Switzerland, Couttenier et al. (Reference Couttenier, Petrencu, Rohner and Thoenig2019) find evidence that refugees who lived through war as children committed more crimes after resettlement than those who had not experienced war.

Second, even if refugees are not a crime-prone demographic, arriving in a new country could lead people to commit nonviolent crimes due to economic hardship or social alienation. Economic reasons could drive people to turn to illegal market opportunities whether through theft or the drug trade (Ousey and Kubrin Reference Ousey and Kubrin2009). Simmler et al. (Reference Simmler, Plassard, Schär and Schuster2017) find that refugees in Switzerland were more likely to commit property crime (e.g., shoplifting) than Swiss natives and other immigrant populations and argue that the difference is driven by psychosocial challenges of arriving in a new country as a refugee.

Another pathway that may link refugee resettlement to higher crime is that natives may commit more crimes targeting refugees as resettlement increases. Today refugee resettlement is a highly salient political issue in many countries, and climates of xenophobia and anti-immigrant sentiment may foster a high risk of antirefugee violence. Hangartner et al. (Reference Hangartner, Dinas, Marbach, Matakos and Xefteris2019) show that exposure to refugees in Greece can increase antirefugee hostility. There is also evidence from Germany that higher levels of refugee immigration led to more antirefugee violence (Marbach and Ropers Reference Marbach and Ropers2018). Lastly, Müller and Schwarz (Reference Müller and Schwarz2017) show that right-wing hate speech on social media can incite higher levels of antirefugee violence.

On the other hand, refugees selected for resettlement may be less likely to commit crimes than natives. Formal resettlement systems, including the US program, explicitly attempt to screen out “high-risk” individuals. Multiple agencies such as the Central Intelligence Agency and the Department of Homeland Security screen resettlement applicants and run extensive background checks. Successful applicants are often subjected to further screening once they arrive on US soil. These programs typically prioritize applicants based on family reunion or vulnerability-based criteria, including injuries, medical problems, and other forms of hardship. By “selecting in” family-based and high-vulnerability cases, countries may be indirectly selecting a subpopulation with a low propensity to commit crimes.

Moreover, the structure of refugee resettlement may depress crime rates because many refugees are seeking permanent residency and citizenship and a criminal record would undermine this objective. Refugees are more likely to naturalize within six years than nonrefugee immigrants—45% compared with 29% (Mossaad et al. Reference Mossaad, Ferwerda, Lawrence, Weinstein and Hainmueller2018). In the US, resettled refugees can apply for permanent residency and naturalization one and four years after arrival, respectively. Given that a criminal record could lead to an application being denied, refugees face higher costs for crimes than a similar native. In Germany, Lange and Sommerfeld (Reference Lange and Sommerfeld2018) compare crime propensity across nationalities of origin and find that asylum seekers who have higher ex ante probability of being granted asylum are less likely to commit crimes.

DATA

To test for a link between resettlement and local crime, we use the Federal Bureau of Investigation’s (FBI) Offenses Known to Law Enforcement series from the Uniform Crime Reports (UCR) database. UCR provides a nationwide statistical effort to collect and report data on crimes brought to the attention of various law enforcement agencies. We focus on a sample of 18,172 local law enforcement agencies that consistently report crime statistics throughout the 2010–2018 period. UCR contains information on reported incidents of violent crime (aggravated assault, rape, murder, and robbery) and property crime (burglary, theft, and motor vehicle theft). Following the crime literature, we convert the reported absolute number of crimes into crime rates per 100,000 population as our main outcome of interest and use a log transformation as an alternative specification.

We supplement this with refugee resettlement data from the Worldwide Refugee Admissions Processing System (WRAPS) database from the Refugee Processing Center, which contains yearly information on refugee arrivals to the US by country of origin and destination city. WRAPS is managed by the Bureau of Population, Refugees, and Migration and serves to provide a standardized management system and accountability to the US refugee resettlement program.Footnote ¹ We convert the refugee flow numbers to shares per 100 population as our main explanatory variable of interest and use logarithmic transformation as a robustness check. Throughout this period, 787 counties in all 50 states received refugee arrivals.

We merge the data sources together and our analysis focuses on the county-year level covering the 2010–2018 period.Footnote ² Last, we use county-level population estimates from the American Community Survey (ACS) from Manson et al. (Reference Manson, Schroeder, Riper, Kugler and Ruggles2020).

EMPIRICAL STRATEGY

Design

Figure 1 illustrates our research design. Panel A shows the large and sudden drop in refugee arrivals following the Executive Order in 2017. We exploit the fact that this nationwide reduction affected counties very differently. As shown in Panel B, the ban resulted in much larger reductions in refugee arrivals in counties that had received larger numbers of refugees prior to the ban. We use two specifications of the difference-in-differences estimator to analyze the effect of reducing refugee resettlement on crime rates. This approach compares changes in crime rates after the Executive Order in counties that received many refugees before the ban to crime rates in counties that received fewer refugees.

Figure 1.

Refugee Arrivals in the US and Research Design, 2002–2018

Note: Panel A shows that refugee arrivals dropped nationwide in early 2017 due to the Executive Order. Panel B shows that the reduction in arrivals was much larger in counties that received the most refugees prior to the ban. Green (solid), red (long dashed), and black (short dashed) lines indicate average number of arrivals for counties in the top, middle, and bottom tercile in terms of arrivals between 2002 and 2016.

The identifying assumption states that in the absence of the policy change, crime in areas with larger drops in resettlement due to the Executive Order would have followed a trajectory (or trend) similar to areas with smaller reductions. See the Appendix for formal tests showing that areas with different levels of refugee resettlement were moving along similar crime trends before the ban, supporting our identifying assumption.

First-Differences Model

The first model we estimate is

(1) $$ \Delta {crime}_c^{pre- post}={\alpha}_1+{\beta}_1\times \Delta {refugees}_c^{pre- post}+{\phi}_{state}+{\varepsilon}_c, $$

where c denotes county and s indexes states. The outcome variable, $ \Delta {crime}_c^{pre- post} $ , measures the 2015/16–2017/18 change in a separate crime type per 100,000 people (or in log number of crimes + 1). Similarly, the independent variable of interest, $ \Delta {refugees}_c^{pre- post} $ , is the corresponding change in refugee arrivals per 100 people (or in log number of refugees + 1), where $ \Delta >0 $ ( $ <0 $ ) denotes an increase (decrease) in refugee resettlement in county c from 2015/16 to 2017/18. We pool the 2015 and 2016 (2017 and 2018) data together to form an observation for the “pre” (“post”) Executive Order period. The term $ {\phi}_{state} $ controls for state fixed effects allowing for state-specific crime trends during this period. The intercept is $ {\alpha}_1 $ and $ {\varepsilon}_c $ is the error term. We cluster the standard errors by state. A positive sign on $ {\beta}_1 $ indicates that refugee resettlement is associated with an increase in crime rates. In the model where both variables are in expressed in rates, $ {\beta}_1 $ is interpreted as the change in crime rate for each additional refugee arrival per 100 people. Similarly, in the log-log model, it is the percentage of change in the number of crimes for a 1% increase in refugee arrivals.

Continuous Difference-in-Differences Model

Second, we move on to a more rigorous model in which we use data from the entire sample period 2010–2018. Specifically, we estimate

(2) $$ \begin{array}{c}{crime}_{ct}={\alpha}_2+{\beta}_2\times {refugees}_c^{2016}\times \mathbf{1}\left(t>2016\right)\\ {}\hskip-2.3em +{\gamma}_c+{\delta}_t+{X}_{ct}+{\varepsilon}_{ct},\end{array} $$

where c indexes counties, t denotes year, and $ \mathbf{1}\left(t>2016\right) $ is an indicator for years 2017 and 2018, corresponding to the period after the Executive Order. The outcome is a separate crime type measured in rate per 100,000 population (or the log number of crimes + 1). The treatment variable, $ {refugees}_c^{2016} $ , is the 2016 refugee arrivals per 100 population (or the log number of refugees + 1) and is designed to measure county-level reductions in resettlement due to the Executive Order. We include county fixed effects ( $ {\gamma}_c $ ) adjusting for permanent county-level characteristics affecting crime rates and refugee arrivals and year fixed effects ( $ {\delta}_t $ ) accounting for nationwide crime trends. The term $ {X}_{ct} $ captures county-specific linear time trends allowing for idiosyncratic trends across localities. We cluster the standard errors by state. The intercept is $ {\alpha}_2 $ and $ {\varepsilon}_{ct} $ is the error term. Note that compared with the model above, the interpretation of $ {\beta}_2 $ is switched so that a negative sign would indicate that counties with higher levels of refugee resettlement in 2016 experienced larger drops in crime rates in 2017 and 2018. Thus, a negative sign on $ {\beta}_2 $ is consistent with the hypothesis that refugee resettlement leads to higher crime rates.