Stories of immigrant achievement could combat xenophobia

Review written by Leon Mait (PSY, G2)

This country has a complicated relationship with its immigrants. On the one hand, we pride ourselves on theoretically being the “land of opportunity,” a “melting pot” where people from all backgrounds can come to try their hand at socioeconomic success. On the other hand, we often vilify the actual individuals attempting to come to this country, likening them to criminals, viruses, pests, and resource thieves. 

In 2017, Joel Martinez (a psychology grad student at Princeton) and Mina Cikara (a psychology professor at Harvard) started the hashtag #immigrantexcellence in response to President Trump’s travel ban on refugees and immigrants from seven predominantly-Muslim countries. The hashtag aimed to combat negative, fear-inducing stereotypes against foreigners, by inviting immigrants to publicly share their achievements and contributions to American society. 

But in the face of prejudicial biases that are deeply entrenched and continuously propagated, can social media actually make a dent in how people view immigrants? To answer this question, Martinez and Cikara, along with Lauren Feldman (another Princeton psychology grad student) and Mallory Feldman (a psychology grad student at Northeastern University), tested this experimentally. 

The primary focus of their studies was to examine how people mentally represent immigrant groups. Negative images of immigrant groups are often highly racialized; we celebrate and invite White Europeans but repudiate and sanction immigrants of color from Latin America and the Middle East. Therefore, the researchers hypothesized that achievement narratives might push people’s perceptions of different groups closer together into a single encompassing “immigrant” category. Put another way, highlighting the achievements and successes of all immigrants might reduce anti-immigrant discrimination based on ethnic background. Meanwhile, narratives that feature stories of criminal behavior or material hardship might differentiate groups into a good/bad dichotomy, a dichotomy which was expected to follow a White/non-White classification.

The researchers ran three studies online, collecting data from over 1,200 Americans. (Note, while the participants were racially diverse, most did identify as White and native-born.) In the studies, participants first rated countries from which America commonly receives immigrants: Germany, Russia, Mexico, and Syria. The ratings were in regard to traits like competence, morality, laziness, dangerousness, and “Americanness”. Participants next read stories about individual immigrants from those countries (three for each country), and then rated the individuals on these same traits. After reading the immigrants’ experiences, participants went back and provided a second set of ratings for the countries. 

Although all participants read about people from the four countries, each participant saw only stories that followed one of three themes. Specifically, the stories featured tales of either excellence, criminality, or struggle. The excellence narratives came directly from the #immigrantexcellence tweets, and included stories like “He supported himself and his brother through school. He is now graduating with a PhD.” The criminal narratives were adapted from minor transgressions reported by Immigration and Customs Enforcement, such as “He owns a convenience store and sells liquor without a license.” Finally, the struggle narratives emphasized financial or resource struggles, like “He came to the U.S. to study, but dropped out to send money home and can only find temporary employment.”

The researchers found that, before reading any of the individual stories, participants demonstrated a dichotomous classification of the countries based on stereotypical majority race, either White or non-White. Specifically, the relationship among traits were more similar between Germany and Russia than between either country and Syria or Mexico, which likewise displayed the inverse pattern. These results indicate that Americans, whether knowingly or unknowingly, hold foreigners to different standards based on whether their country is majority White or non-White. 

However, the individual stories did seem to counter people’s implicit judgments of different immigrant groups. Specifically, ratings of individuals featured in the excellence stories were not only the most positive, but were also the most similar to one another across nationality. In other words, all high-achieving immigrants were held to the same standards, regardless of country of origin. Meanwhile, those featured in criminality stories had the most highly differentiated ratings by nationality. (Struggle stories fell somewhere in the middle.) 

Importantly, among the criminality stories, the ratings of the individuals were clustered into the same White/non-White dichotomy as was found in the baseline ratings of the countries themselves. Certain criminals--those from predominantly non-White countries--were judged using different standards compared to other criminals--those from predominantly White countries. This suggests depictions of immigrants as criminals might reinforce people’s pre-existing perceptions of immigrants, in that they mobilize and reaffirm stereotypes related to race.  

What the researchers label the “homogenization” of immigrant groups resulting from the excellence stories also had pronounced attitudinal impacts. When participants were asked to re-rate the countries after reading the individual stories, those who read stories of achievement subsequently rated the countries as a whole more homogeneously, a significant change from the initial dichotomization. These participants, as well as those who read stories of struggle, also reported greater support for less restrictive immigration policy. Further, the strength of these policy preferences was directly tied to how much participants homogenized their trait ratings. Criminality narratives, unsurprisingly, maintained initial race-based differentiation of the nationality groups, and led to greater endorsement of more restrictive immigration policies. 

This research suggests that perceptions of immigrant identities are inherently tied to other social hierarchies, such as race. This link enables and facilitates immigration-based stratification, as all the implicit biases regarding race are automatically evoked and coupled with representations of immigrants. However, positive depictions are a powerful weapon to combat this. Stories of achievement compel people to view immigrants as less differentiated based on race and, to the extent that the stories depict the archetypal embodiment of the “American Dream,” possibly more American. 

Nonetheless, Martinez warns against the weaponization of achievement narratives, which could bolster myths about meritocracy and hold minority groups to unreasonably high standards. He explains:

"The double-edged nature of this type of narrative that emphasizes inclusion-by-deservingness suggests we must explore alternative counter-narratives that unconditionally respect the humanity of immigrants and, most importantly, actually challenge racializing systems and people’s relationships to them. This can include narratives that emphasize qualities beyond achievement (e.g., ordinary experiences, fears, double standards, shared histories or desires) or that highlight structural conditions (e.g., links between U.S. imperial projects and migration patterns)."

Thus, one broader implication of this research is to underscore the importance of not only properly reporting the lack of association between immigration and crime rates, but also listening to immigrants’ experiences and taking them seriously. When people start seeing themselves and other groups as part of one comprehensive group that shares a common humanity, only then does the United States become more like the “melting pot” it believes itself to be.  

This article was uploaded to PsyArxiv as a preprint in December 2018, and is forthcoming in Psychological Science. Please follow this link to view the full version.