ABSTRACT
Research has demonstrated educational inequalities for children with migrant background. This article focuses specifically on children and families with a Turkish immigration background in four European countries. Because of the great potential of high quality Early Childhood Education and Care (ECEC) for decreasing educational disparities, we examined associations between family characteristics (structural characteristics and acculturation attitudes) and early ECEC attendance (under the age of two) for families with a Turkish immigration background in England, Germany, The Netherlands, Norway (N = 943), using data from a standardized survey. Group-wise logistic regressions revealed differences among the predicting factors across countries. Nevertheless, factors that related to family socio-economic background were found to be associated with early ECEC attendance across all countries: higher levels of maternal education (England, Germany, The Netherlands), maternal employment (Norway), and more material deprivation (England) significantly predicted early ECEC use. In addition to these SES associations, factors related to socio-cultural adoption were associated with early ECEC use in three (out of four) countries. The findings can be partly related to country-specific ECEC characteristics.
Acknowledgments
More information on the large-scale structured interview study can be found in Broekhuizen et al. (Citation2018).
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 In this paper, ECEC only refers to centre-based childcare, not to non-familiar home-based childcare.
2 See Broekhuizen et al. (Citation2018, 27f).
3 See Broekhuizen et al. (Citation2018, 64). Cultural maintenance and cultural adoption items are by Zagefka et al. (Citation2014); items for desire for contact by Zagefka, González, and Brown (Citation2011).
4 See Broekhuizen et al. (Citation2018, 44).
5 Although data were missing completely at random, we reran the models by country using Full Information Maximum Likelihood (FIML) estimation as an approach to address missing data (Enders Citation2010). The results for these four country models (it is not possible to use FIML for multi-group logistic regression models) did not deviate from the results in .