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Migrant Youths’ Educational Achievement: The Role of Institutions. Deborah Cobb-Clark (U Melbourne) Mathias Sinning (ANU) Steven Stillman (U Otago). What Questions Do We Try to Answer?.
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Migrant Youths’ Educational Achievement: The Role of Institutions Deborah Cobb-Clark (U Melbourne) Mathias Sinning (ANU) Steven Stillman (U Otago)
What Questions Do We Try to Answer? • How do reading, math and science skills for 15-year-old migrant children compare to those of native children across OECD countries? • How does this vary with the child’s age at arrival and the language spoken in their home? • To what extent do differences in parental education and socioeconomic status explain migrant-native differences in test scores? • Do different institutional arrangements mitigate or exacerbate differences in achievement?
Data: PISA • 2009 OECD Programme for International Student Assessment (PISA) is an internationally standardised assessment administered to 15-year-olds in schools. • Surveyed between 4,500 and 10,000 15-year-old students from at least 150 different schools in 67 countries. • Each student completed an assessment covering reading literacy, mathematical literacy, and scientific literacy, with the 2009 focus on reading literacy. • Students also completed a survey questionnaire • Principals completed a questionnaire about the school • Nearly identical data were collected in all OECD countries.
Data: Defining Migration Status • Native-born: those born in the test country who have no foreign-born parents • First-generation migrant: those not born in the test country who have at least one foreign-born parent • Second-generation migrant: those born in the test country who have at least one foreign-born parent • Drop small number of children w/o enough info to classify or who are foreign-born but have no foreign-born parents • First-generation migrants further classified into 3 groups based on their age at arrival: up to 4, 5–10, and 11–15 • Migrants are further stratified based on whether the primary language spoken at home is the same as the language in which the test was given
Baseline Regression Model • Pooled cross-country regression Tisc = standardised reading, math or science score of student i in school s of country c Aisc = age in months and gender Eisc= highest parent education and both foreign-born Xisc= a vector of household socioeconomic status vars αisc= country fixed effects
Full Regression Model • Pooled cross-country regression Ic = variables describing immigration policy and the education system in each country • We interact the full set of country-level variables with our population indicators (M), with native-born youth as the omitted category • Hence, the coefficients in the vector β5 are interpreted as the differential impact that each country-level characteristic has on test scores for different migrant students relative to the impact each characteristic has on test scores for native-born students.
Relationship between Country-Level Policies and Migrant Reading Test Score Gap in OECD Countries
Conclusions (1) • Achievement gaps are wider for those migrant youths who arrive at older ages and for those who do not speak the test language at home. • Differences in parental education and SES explain the worse performance for migrant children, except those who migrated at older ages and do not speak the test language at home. • Educational systems do not work equally well for native-born and migrant students, or indeed for all groups of migrant students.
Conclusions (2) • For example, • Earlier school starting ages appear to reduce the relative achievement gap for some migrant students while leaving unaffected or exacerbating the achievement gap of others. • Other arrangements, such as tracking on ability, are beneficial for migrant students when implemented in a limited way, but become detrimental when implemented across the board. • Finally, what works for native-born students does not always work for students with a migration background. In particular, migrant students’ achievement relative to their native-born peers falls as proportionately more funding is devoted to educational spending generally and teachers’ salaries in particular, but improves when examination is a component of the process for evaluating teachers.