A ‘win-win exercise’? The effect of westward migration on educational outcomes of Eastern European children

ABSTRACT

Since the end of the Cold War, millions of migrants from Eastern Europe have sought better opportunities in Western European countries, yet few studies have assessed the impact of such moves on these migrants’ children. In the aim of isolating a ‘treatment effect’ of migration on educational outcomes, this study analyzes Programme for International Student Assessment (PISA) scores from 2012, 2015, and 2018 for adolescents born in twelve Eastern European countries and living in eight Western European countries. It employs propensity-score matching within a homeland dissimilation framework, comparing immigrants’ outcomes on reading, math, and science assessments to similar stay-at-homes in their countries of origin. In unadjusted comparisons to their counterparts who remained behind, migrant children attain lower scores across all three subjects. Once immigrant children are matched to non-immigrants with similar propensities to migrate, the disparity for math scores disappears, while those for reading and science remain. Disparities are wider for adolescents who come from within the EU, migrate at older ages, or speak a foreign language at home. This paper indicates the need for policymakers and educational administrators to better handle the negative academic effects that migration can have on children from within Europe.

KEYWORDS:

• Immigration
• education
• Europe
• causal inference
• dissimilation

Acknowledgements

I deeply appreciate the guidance I have received throughout this project from Roger Waldinger, Andrés Villarreal, Rubén Hernández-León, Jennie Brand, and Chad Hazlett, as well as the feedback I have received from UCLA's Migration Working Group (Catherine Crooke, Leydy Diossa-Jimenez, Nihal Kayali, Tianjian Lai, Andrew Le, Pei Palmgren, Ian Peacock, and Qiaoyan Li Rosenberg). I also thank the anonymous reviewers for their insightful feedback. This project was supported, in part, by the California Center for Population Research at UCLA (CCPR) with training support (T32HD007545) and core support (P2CHD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The content is solely the responsibility of the author and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health & Human Development or the National Institutes of Health.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The 2004 enlargement involved the eight Central and Eastern European countries of the Czech Republic, Estonia, Latvia, Lithuania, Hungary, Poland, Slovakia and Slovenia as well as Cyprus and Malta. In 2007, Bulgaria and Romania joined as well, followed by Croatia in 2013. The members of the EU before 2004 include Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden and the United Kingdom.

2 Xu and Xie (2015) employ propensity-score matching to estimate a causal effect of rural-to-urban migration in China on a variety of measures of child well-being. Such a matching approach, however, has been underutilized in studies of international migration.

3 In order to increase computational efficiency, I initially fit propensity score models to subsets of these data that include all immigrants and a random sample of 20,000 stay-at-homes. Blocking and matching use the full dataset.

4 For many countries in PISA, former Yugoslavian countries are aggregated into a ‘Former Yugoslavia’ category and further disaggregation is not possible. This is the case for 563 out of 621 cases used in the present analysis. For consistency and statistical power, I use ‘Former Yugoslavia’ for most analyses rather than disaggregating the few cases for which this is possible.

5 Cultural possessions include indicators for whether pupils have the following in their home: classical literature, books of poetry, and works of art for 2012, and for 2015 and 2018 those as well as musical instruments and books on art, music, or design. For educational resources, respondents indicate whether their homes contain a desk to study at, a quiet place to study, a computer they can use for school work, educational software, books to help with school work, technical reference books, and a dictionary. These indices were scaled using IRT scaling methodology (see OECD 2014, 312).

Additional information

Funding

This work was supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development: [Grant Number T32HD007545, P2CHD041022].