Effects in Disguise: The Importance of Controlling for Constructs at Multiple Levels in Macro–Level Immigration and Crime Research

Abstract

Contemporary research suggests that immigrant communities often have lower rates of crime despite their disadvantaged status. Yet prior work often examines the immigration and crime association using only one level of analysis without regard for how this relationship might vary when analyzed across multiple levels of analysis simultaneously. Research also suggests that the immigration‐crime link varies across spatial contexts. Using hierarchical Poisson Regression among a sample of 6,660 tracts nested within 55 cities, we examine whether the relationship between immigration and crime varies when examined at the tract and city levels simultaneously. We also include a cross‐level interaction in our model to test whether the tract‐level association between immigration and crime varies by the size of the foreign‐born population at the city level. Results show that the immigration‐crime link depends on the level of analysis, such that the relationship is positive at the tract level but negative at the city level. However, we find no support for our cross‐level interaction.

Immigration in an Era of Mass Reentry: Does Immigrant Concentration Guard Against Serious Recidivism?

“…However, it is possible that significant relationships between the other contextual-level predictors (i.e., economic disadvantage, residential instability, income inequality) and recidivism were not significant because zip codes do not approximate communities as well as smaller aggregates (e.g., census tracts), which may result in aggregation bias (Tillyer and Vose 2011). Research also shows that macro-level relationships operate at different units of analysis (Ramos and Wenger 2018;Wenger 2019). This suggests that just because the association between economic disadvantage, residential instability, income inequality and serious recidivism were not significant at the zip code level, does not necessarily mean that they will remain null when examined across other units of analysis.…”

Unemployment, Immigrants, Price Level, Population and Crime: Cointegration Analysis of Malaysia

“…An investigation of immigration-crime relationships in metropolitan areas by Adelman, Reid, Markle, Weiss and Jaret (2017) using more than 40 years of data indicate that immigration is consistently linked to decreases in violent crime and property crime throughout the time period. While Ramos and Wenger (2018) stated that the negative association between immigration and crime in contemporary research is when using only one level of analysis without regard for how this relationship might vary when analyzed across multiple levels of analysis simultaneously. Using hierarchical Poisson Regression they found that the result depends on the level of analysis; a positive immigration-crime link at the tract level but negative at the city level.…”

“…The scenario has been a source of concern and calls for attention for researchers to study the factors that influence crime rates so that effective enforcement policies can be proposed (Tang & Darit, 2015). Previous researchers highlighted the association of crime rates to unemployment (Altindag, 2012;Andresen, 2012;Britt, 1994;Ghani, 2017;Jawadi, Mallick, Cheffou, & Augutine, 2019), inflation rate (Rosenfeld, Vogel, and McCuddy, 2019;Lobont, Nicolescu, Moldovan & Kuloglu 2017), immigrants (Pinotti, 2015;Ramos and Wenger, 2018), and populations (Ha & Andresen, 2017;Janko and Popli, 2015).…”

The Multilevel Effects of Changes in Disadvantage on Changes in Crime

“…Finally, the few studies that have looked at the association between disadvantage and crime have done so only at one level of analysis at a time (e.g., neighborhoods or counties). However, empirical relationships at different levels of analysis are distinct phenomena, and research has revealed that the strength and significance of the community associations, including between disadvantage and crime, can vary by level of analysis and that city characteristics can have an effect on neighborhood crime, regardless of neighborhood characteristics (Chamberlain & Hipp, 2015;Ramos & Wenger, 2018;Wenger, 2019aWenger, , 2019bWenger, , 2021. For example, Chamberlain and Hipp (2015) used the National Neighborhood Crime Study to examine the association between disadvantage and crime rates, with disadvantage simultaneously measured at both the neighborhood and city level.…”