Abstract
In this paper, I study educational integration of students with migration background using data from five international student assessment studies. First, Blinder–Oaxaca decompositions are used to allow for a comparison of integration of migrant students across countries and time. In a second step, integration is related to institutional characteristics of the schooling system. Pooled, country-group and country fixed effects estimations show that time in school and early education are positively related to the integration of students with migration background. Furthermore, in the OECD countries, educational integration in science is positively related to external student assessment policies.
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Notes
Priority is given to \({\hat{\boldsymbol{\upbeta}}_{\bf n}}\) and \(\mathbf{\overline{X}_{i/s} }\) over \({\hat{\boldsymbol{\upbeta}}_{\bf i/s}}\) and \(\mathbf{\overline{X}_{n}}\) as weights. It is assumed that migrants face drawbacks in education rather than natives are favored (and would get lower returns in the absence of migrant students).
For some students, not all explanatory variables are available. Since migration status, education of parents, and grade at school are important, students with missing values are dropped. For all other variables, missing dummies are included in the educational production functions. For these variables, the number of missings is small and ranges from 0.06% to 3.37%.
See (OECD 2001, 2002, 2004, 2005) and IEA (http://TIMSS.bc.edu/isc/publications.html) for detailed information on the PISA and TIMSS surveys.
Because PISA and TIMSS do not provide representative samples of schools in a country, the aggregation is based on weighted schools, with the weight for a school as simply the sum of all student weights within that school. Since the student sample is representative for the total student population, weighted school aggregates are good proxies for the school population.
I found heterogenous effects across country-groups for pre-primary enrollment and external student assessment. The coefficients of pre-primary education are statistically and economically most significant in Southern Europe and the English-speaking countries. Furthermore, the country-fixed-effects estimations show that external student assessment and integration in science are positively related in Southern Europe and the English-speaking countries, while negative associations are found in the poor Eastern European countries and the Near East. For the other variables, no systematic differences have been found.
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Acknowledgements
I thank Rudolf Winter-Ebmer, Johann Bacher, Martina Zweimüller, and Steve Machin, seminar participants in Sevilla, Padova, Steyr, Prague, Neufelden, London, and Engelberg, three anonymous referees for helpful comments, and the Austrian Center for Labor Economics and the Analysis of the Welfare State (a National Research Network funded by the Austrian Science Fund) for supporting this project.
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Appendix
Appendix
1.1 A1 Comparability of PISA and TIMSS
Although the surveys are very similar, some aspects differ between PISA and TIMSS. TIMSS measures the mastery of an internationally agreed curriculum, while PISA focuses on challenges of every-day life. The test questions span the same topics but differ in their reference to reality. Furthermore, the target population consists of teenagers in secondary education. TIMSS covers children in the grade(s) with the highest proportion of 13-year-olds (typically grade 7 or 8) and PISA covers 15-year-olds, independent of grade. It might be the case that immigrants are more likely placed in lower grades, given their age, for two reasons: they might have started later with school or repeated a grade. The inclusion of age and grade in the educational production functions should mitigate this problem. Furthermore, a sensitivity check focusing on this issue is presented in Section 4.3.
1.2 A2 Student achievement scores
The test scores in PISA and TIMSS were standardized to a mean of 500 and a standard deviation of 100, but based on a different set of countries. While PISA focuses on OECD countries, in TIMSS, participating countries are more heterogenous. Thus, a typical OECD country is likely to perform above average in TIMSS, but not in PISA, and the test scores cannot be compared without transformation.
Fifteen countries participated in both PISA 2003 and TIMSS 2003. Given the similar survey designs, it is assumed that the test score distribution in TIMSS should be equal to that in PISA in these 15 countries. Thus, the PISA scores of the common subsample were transformed to the same distribution as the TIMSS subsample. The TIMSS achievement scores are directly comparable across waves; thus, no further step for TIMSS was necessary. Because of the alternate major subject assessment in each wave, the PISA scores are directly comparable across waves in science, but not in mathematics. Thus, the science scores where just added to the scale, and for math, a second step was necessary. I calculated the score distribution of the PISA 2003 data for the subsample of countries participating in 2000 and 2003 and applied this distribution to the PISA 2000 subsample.
After this transformation procedure, the math and science scales were transformed to a mean and standard deviation of 500 and 100. Note that the transformation has no influence on the ranking of the students and does not change the distance in terms of standard deviations between any two students. Very similar approaches were used by Hanushek and Wößmann (2005), Schütz et al. (2008), and Ammermüller (2005).
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Schneeweis, N. Educational institutions and the integration of migrants. J Popul Econ 24, 1281–1308 (2011). https://doi.org/10.1007/s00148-009-0271-6
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DOI: https://doi.org/10.1007/s00148-009-0271-6