by Camilla Borgna

The integration of second-generation immigrants has proved to be a major challenge for Europe in recent years. Though these people are born in their host nations, they often experience worse social and economic outcomes than other citizens. This volume focuses on one particular, important challenge: the less successful educational outcomes of second-generation migrants. Looking at data from seventeen European nations, Camilla Borgna shows that migrant penalties in educational achievement exist in each one-but that, unexpectedly, the penalties tend to be greater in countries in which socio-economic inequalities in education are generally more modest, a finding that should prompt reconsideration of a number of policy approaches.

Cover

Table of Contents

Tables

Figures

Acknowledgements

1.1 Children of migrants in Europe: which equal opportunities?

1.2 The promise of diversity-oriented methods

1.3 Structure of the book

2.1 Social inequalities in education

2.2 Educational systems as opportunity structures

Figure 2.1 – Venn diagram depicting the institutional dimensions (in textboxes) theoretically relevant for one or more manifest functions of educational system

2.3 Defining children of immigrants

2.4 Case selection: comparing immigration societies

Table 2.1 – Sample sizes of G2 in all Western European countries

2.5 Educational systems in Western Europe

Table 2.2 – Indicators of schooling duration

Table 2.3 – Indicators of stratification, resources allocation, and standardization

3.1 Previous studies

3.1.1 A double disadvantage

3.1.2 The role of teachers, classrooms, and schools

3.1.3 Cross-country differences

3.2 Migrant penalties in educational achievement

3.2.1 Research questions and hypotheses

3.2.2 Analytical strategy

3.2.3 Data, operationalization, and models

Table 3.1 – Country-specific regressions of math scores estimated using replicate weights and plausible values

Figure 3.1 – Overall underachievement and migrant achievement penalty

Figure 3.2 – Migrant penalties vs. socioeconomic penalties in educational achievement

Figure 3.3 – Typology of educational systems by Inequality of Educational Opportunity (IEO) driven by SES and migratory status

Figure 3.4 – Migrant penalties vs. socioeconomic penalties, for Turkish students only

3.3 Compound disadvantages

3.3.1 Research questions

3.3.2 Analytical strategy

3.3.3 Operationalization: fuzzy-set calibration

3.3.4 Results and discussion

Table 3.2 – Advantage and disadvantage coincidence scores

Figure 3.5 – Set coincidence vs. correlation

Figure 3.6 – Differential coincidence of assets and achievement gaps

4.1.1 Educational institutions and socioeconomic disadvantage

4.1.2 Educational institutions and migrant learning disadvantage

4.1.3 Cross-country explanatory studies

4.2.1 Theoretically relevant dimensions of educational systems

4.2.2 Contextual factors

4.3 Analytical strategy

4.4.1 Variable construction

Table 4.1 – Distribution of the source variables by country

Table 4.2 – Source variables, sets, and critical thresholds for calibration

Figure 4.1 – Calibration of outcome and conditions

4.5.1 Bivariate correlations

Table 4.3 – Pearson’s correlation matrix of ‘Migrant achievement penalty,’ ‘Age at tracking,’ ‘G2 average (pre)school entry age,’ ‘Marginalization in low-performing schools,’ ‘Starting decade for mass immigration,’ and ‘Proportion of G2 with high linguist

Figure 4.2 – Linear correlation plots between ‘Age at tracking,’ ‘G2 average (pre)school entry age,’ ‘Marginalization in low-performing schools,’ ‘Proportion of G2 with high linguistic distance,’ and ‘Migrant achievement penalty.’ Peculiar cases identifie

4.5.2 Multivariate analysis

Table 4.4 – Coefficients and fit values of OLS regression of ‘Migrant achievement penalty’ on ‘Age at tracking,’ ‘G2 average (pre)school entry age,’ ‘Marginalization in low-performing schools,’ ‘Immigration decade,’ ‘Proportion of G2 with high linguistic

4.5.3 Regression-tree analysis

Figure 4.3 – Results of regression-tree analysis of ‘Migrant achievement penalty’ on ‘Age at tracking,’ ‘G2 average (pre)school entry age,’ ‘Marginalization in low-performing schools,’ ‘Proportion of G2 with high linguistic distance’

4.6.1 Assessing individual necessity and sufficiency

Table 4.5 – Results of analysis of necessity for the presence and the absence of the outcome

Figure 4.4 Fuzzy-set plots of necessary institutional conditions for the presence (left) and the absence (right) of the outcome

Table 4.6 – Results of analysis of sufficiency for the presence and the absence of the outcome

Figure 4.5 Fuzzy-set plot of LATE ENTRY as a sufficient condition for the presence of the outcome

4.6.2 Institutional configurations

Figure 4.6 Venn diagram depicting all logically possible combinations of TRACKED, EARLY TRACKED, LATE ENTRY, early entry, MARGINALIZING

4.6.3 fsQCA: model construction and robustness checks

Table 4.7 – Conservative solution of the minimization of the truth table for the presence of the outcome

Table 4.8 – Intermediate solution of the minimization of the truth table for the presence of the outcome

Figure 4.7 – Fuzzy-set plot of the whole solution for the presence of the outcome

Table 4.9 – Intermediate solution of the minimization of the truth table for the presence of the outcome (recalibrated)

4.6.4 Final fsQCA results and discussion

Table 4.10 – Conservative solution of the minimization of the truth table for the presence of the outcome (recalibrated)

Table 4.11 – Intermediate solution of the minimization of the truth table for the presence of the outcome (recalibrated)

Figure 4.8 – Fuzzy-set plot of the whole solution for the presence of the outcome (recalibrated)

Figure 4.9 – Paths to ‘SEVERE PENALTIES’

Table 4.12 – Conservative solution of the minimization of the truth table for the absence of the outcome (recalibrated)

Table 4.13 – Intermediate solution of the minimization of the truth table for the absence of the outcome (recalibrated)

Figure 4.10 – Fuzzy-set plot of the whole solution for the absence of the outcome (recalibrated)

Figure 4.11 – Paths to ‘severe penalties’

5.1 Key findings

5.2 Methodological contributions

5.3 Policy implications

5.4 Limitations and outlook

Figure A.3 – Consistency of results with alternative model specification

Table A.2 – Mean and standard deviations of math score, by country and migratory status

Table A.3 – Mean and standard deviations of ESCS, by country and migratory status

Table A.4 – Country-specific regressions of reading scores estimated using replicate weights and plausible values

Table A.5 – Country-specific regressions of science scores estimated using replicate weights and plausible values

Table A.6 – Means and standard deviations of HISCED, HISEI, CULTPOS, and WEALTH

Table A.7 – Source variables, thresholds and criteria for the fuzzy-set calibration of factors of advantage and disadvantage used in the fuzzy-set coincidence analyses

Figure B.1 – Results of regression-tree analysis of ‘Migrant achievement penalty in science literacy’ on ‘Age at tracking,’ ‘G2 average (pre)school entry age,’ ‘Marginalization in low-performing schools,’ ‘Proportion of G2 with high linguistic distance’

Figure B.2 Results of regression-tree analysis of ‘Migrant achievement penalty in reading literacy’ on ‘Age at tracking,’ ‘G2 average (pre)school entry age,’ ‘Marginalization in low-performing schools,’ ‘Proportion of G2 with high linguistic distance’

Table B.1 – Truth table for the presence of the outcome

Table B.3 – Intermediate solution of the minimization of the truth table for the presence of the outcome

Table B.5 – Truth table for the presence of the outcome (recalibrated)

Table B.7 – Conservative solution of the minimization of the truth table for the absence of the outcome

Table B.9 – Easy counterfactuals used in the truth table minimization to produce the intermediate solution for the absence of the outcome

References

Index

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