Deportations Near the Schoolyard: Examining Immigration Enforcement and Racial/Ethnic Gaps in Educational Outcomes

Abstract

With increased tensions and political rhetoric surrounding immigration enforcement in the United States, schools are facing greater challenges in ensuring support for their students of immigrant and Latino/a origin. This study examined the associations between deportations near school districts and racial/ethnic gaps in educational outcomes in school districts across the country. With data from the Stanford Educational Data Archive, the Civil Rights Data Collection, and the Transactional Records Access Clearinghouse, this study used longitudinal, cross-sectional analyses and found that in the years when districts had more deportations occurring within 25 miles, White-Latino/a gaps were larger in math achievement and rates of chronic absenteeism. No associations were found for gaps in English language arts achievement or rates of bullying. Implications for researchers, policymakers, and school leaders are discussed.

Enhancing Teacher Self-Efficacy in Multicultural Classrooms and School Climate: The Role of Professional Development in Multicultural Education in the United States and South Korea

“…In fact, teachers often teach according to their own cultural biases, with teacher preparation programs being far from successful when it comes to incorporating multicultural perspectives into their programs (Allen et al, 2017; Gorski, 2009; Sleeter, 2001). Accordingly, in both Western and Asian countries, research has shown that students from marginalized groups, including students of color, second-language learner, and immigrant students, fall behind in terms of academic performance (Kirksey et al, 2020; Lin & Lu, 2016), high school graduation rates (Heckman & LaFontaine, 2010), access to postsecondary education (Lemmermann & Riphahn, 2018), and a feeling of belonging to country (S. Choi & Cha, 2019).…”

What Is the Stanford Education Data Archive Teaching Us About National Educational Achievement?

“…Kirksey et al (2020) observe correctly that an essential contrast with Bellows (2019) is their different definitions of immigration enforcement. Bellows uses a county-level dichotomous predictor for program rollout, whereas Kirksey et al use both the number of deportations and the proximity of districts to the site of deportations.…”

“…When answers differ, the common data set helps to explain these different answers as functions of differences in questions and approach. Like Bellows (2019), Kirksey et al (2020) also contribute findings about immigration enforcement and achievement to this special collection. They find that district-proximal enforcement predicts White-Hispanic gaps that are larger in math but not ELA, an apparent contrast with the Bellows finding.…”

“…Data sets like SEDA enable researchers to not only point in a direction, but put a finger on magnitudes, in national context. Second, all of these articles linked educational data to other variables in other data sets, including the Quarterly Census of Employment and Wages (Shores & Steinberg, 2019), the Transitional Records Access Clearinghouse (Bellows, 2019;Kirksey et al, 2020), the Civil Rights Data Collection (Pearman et al, 2019), the American Community Survey , and MAP Growth (Kuhfeld et al, 2019). Whether as outcomes, covariates, or question predictors, these merged variables enable researchers to answer important descriptive and causal questions.…”

“…The two approaches have interesting tradeoffs. If their conclusions could have been causal, Bellows (2019) would describe the effects county-level policy activation, and Kirksey et al (2020) would describe the effects of district-level enforcement. I think Bellows comes closer to a plausibly causal estimate, whereas Kirksey et al provide a more descriptive finding closer to plausible mechanisms of deportation-related stress and trauma.…”