“Do immigrants cause higher crime rates?” A cumulative cross-sectional general social survey data analysis

Abstract

While empirical research in the United States has largely debunked the longstanding stereotypical image of immigrants as criminals, Americans have held onto their prejudices against immigrants. Anti-immigrant sentiments and Americans’ support for restrictive and punitive immigration laws and policies have frequently been triggered by political and media rhetoric. Politicians often sensationalize the unfounded claim that immigrants, especially the undocumented, cause higher crime rates. Given the persistent nature of these beliefs and the negative consequences on immigrant communities, researchers have examined factors that predict anti-immigrant sentiments in the U.S. Many of these studies have utilized the General Social Survey (GSS), cross-sectional datasets, broadly examining anti-immigrant attitudes of Americans and their support for restrictive policies and vice versa. These studies highlight political affiliation, race, sex, age, education, socio-economic status and cultural concerns as predictors of these beliefs. However, not many studies utilized cumulative cross-sectional data to grasp the nuanced nature of these beliefs and their consequences over the years. In addition, the GSS variable measuring the immigration and crime nexus was worded differently and contained fewer response categories in the year 2000 compared to other years. This may have implications for data outcomes. Thus, the current paper analyzed cumulative cross-sectional GSS datasets, 1996, 2000, 2004, 2014, and 2022, that had the variable, “Immigrants cause higher crime rates,” as a dependent variable, in association with predictor variables highlighted by previous studies. This article explains the notable differences in those who believed that immigrants cause higher crime rates in 2000 compared to other years. The current analysis enhances previous cross-sectional studies by showing the nuanced nature of the predictors in the backdrop of changing political climate every year. It supports previous studies in emphasizing the importance of respondents’ political affiliation, race and education as predictors. The article also brings attention to possible limitations arising from differences in response categories and other GSS data issues.