Immigrant background peer effects in Italian schools

1. Immigrant background peer effects in Italian schools Dalit Contini University of Torino Improving Education through Accountability and Evaluation, Roma 3-5 October 2012

2. The research question Do high concentrations of immigrant background children in schools hamper the learning of native children (and of other immigrant children)?

3. Motivation Italy is a recent immigration country. Immigrant background children in primary and lower secondary schools have increased from 3% to 9% at the national level over the last decade. Widespread concern that immigrant children could be detrimental for the learning of natives. Is the concern supported by empirical evidence? The research question is relevant for the quality and equity of the schooling system and for social cohesion. It has implications on the distribution of children into schools and allocations of resources.

4. Data • INVALSI standardized learning assessment 2010 • Reading comprehension and math • Administered to the entire populations at the national level (~ 500.000 students per grade) • Info on family background provided by student questionnaire and school administrations • Children nested into classes nested into schools • I analyze children in grades 5 and 6 in the North and Centre (where the majority of immigrants live)

5. State of the art • Existing literature on peer effects mainly focuses on socio-economic status, gender and ethnic differences. Less effort directed to the estimation of peer effects related to immigrant background. • Findings from previous studies on ethnic composition of schools may not be relevant for the more recent immigrants. • EU papers on immigrant background peer effects: Cebolla-Boado (2007) achievement in lower secondary school in France Van der Silk et al. (2006) and Dumay (2008): achievement in the Netherlands Agirdag et al (2011) achievement of lower secondary school in Flemish Belgium Cebolla-Boado and Medina (2011) primary education in Spain Fekjaer and Birkelund (2007) on upper secondary graduates in Norway Brunello and Rocco (2011) upper secondary achievement (PISA. Not on Italy) Gould et al. (2009) 5° grade achievement on later educational outcomes in Israel generallysmalleffectsnotalwayssignificant no research on Italy

6. Descriptive evidence • Large immigrant/native achievement gaps. Gaps are larger for first generation, but are also large for second generation. • On average scores (of natives and of immigrants) are lower in schools with high concentrations of immigrant children. Causal effect? • Schools with many immigrant children are attended by lower SES native and immigrant children: possible confounding effect.Allocation of children in schools.

7. Structural model Assumption:peereffects operate at the classlevel other characteristics of peers achievement of peers Causal effects achievement of peerscharacteristics of peers Spurious effects school and class characteristics individual characteristics school and classunobservedeffects

8. g*is the parameterof interest • measuresclasscompositioneffects • capturespeerachievement and characteristicseffects • policy relevant Reduced form model composite error term • Problem: • Whyshouldschool or classunobservedspecificeffectsbecorrelatedwithpeercharacteristics? • schoolselection(freedomofchoice/area of residence) • classallocation(isitrandom?)

9. Addressingselection in the peereffectsliterature Hoxby (2000) exploits idiosyncratic within-school variation in peer characteristics between adjacent cohorts in given grades. Ammermueller, Pischke (2009) rely on differences in the compositions of individual classeswithin a school. Gould et al. (2009) study later educational outcomes and exploit random variation in the number of immigrants in grade 5, conditional on the number of immigrants in grades 4-6. Black et al. (2010) study post-school and labor-market outcomes, exploiting random variation in cohortcomposition within schools. Hanushek et al. (2003) use panel data to estimate peer effects on test score gains over time using student and school-by-grade fixed effects in a value-added specification. Identification is achieved by exploiting the fact that students changeschools.

10. Addressingselection INVALSI data allowthisstrategy (impossiblewith PISA, difficultwith PIRLS, TIMSS..) By exploiting within-school variability in class composition we remove school-specific effects, hence solve the school selection problem. • The class allocation problem is less relevant. Yet: • despite broad recommendations to maximize class heterogeneity there are no binding rules, so school boards may use other criteria (segregate disadvantaged children, limited ability streaming) • families are sometimes allowed to express preferences for particular classes • Random allocation of children into classes: error independent of explanatory variables

11. Randomallocation? Random allocation of immigrant background children implies school-level independence between immigrant status and class. System-level (X2 test): random assignnment rejected School-level (Fisher‘s exact test) with a=0.10: random assignnment rejected in ~ 20% schools School-level with respect to SES (Anova) with a=0.10: random assignnment rejected in ~ 30% of schools I analyzeschoolspassingbothtests:~ 60% Underlying hypothesis: the class formation process is not related to performance, given class composition.

12. Possiblebiases What if non-random allocating schools are not completely eliminated? • no bias if teachers randomly assigned to classes • overestimate peer effects if better teachers to “better” classes • underestimate peer effects if better teachers to “worse” classes Rationaleofthisoption: Ability streaming + betterresourcesto the more in need. Highlyunlikely in Italy. Streaming isnot a popularpedagogicalpractice in primary and lowersecondaryschool.

13. Variables Dependent variables Reading & math scores = % correct answers [0-1] Explanatory variables Individual Female SES (n° books, ESCS) Native repeating grade 1°generation 2°generation Sample Sample*1°generation Sample*2°generation Class composition % Females mean SES % Natives repeating grade % 1°generation % 2°generation % 1G*native % 2G*native % 1G*native*SES % 2G*native*SES • heterogenous effects allowed: • immigrants/natives • natives of different SES

14. A 10 % pointsincrease in the share ofimmigrants reduces the numberofcorrectanswersbyless than 1% (=1/20 pop stdev) Immigrant background peereffects N° children 120.000-140.000 N° classes 7000+ N° schools 1750+

15. Mainconclusions • The concentration of immigrant children in schools should not bean issue of major concern as there is little evidence of substantial detrimental effects on students’ learning. • (ii) The effect is somewhat larger for children from disadvantagedbackgrounds (immigrants and low SES) and negligible or even positive for high status native children. • (iii) On the other hand, the relative disadvantage of immigrant • children at the individual level is large.

16. Thank you for your attention!

17. Descriptive evidence (1) % immigrants in schools: North-Centre: 11-15% South-Islands: 3-4% I focus on North and Centre. 5° grade- Italian scores

18. Descriptive evidence (2) All negativeAlmost all highly significant School-level correlations between the % of immigrants and mean scores

19. Descriptive evidence (3) All negative and fairly large All highly significant School level correlations between the % of immigrants and SES

20. Robustness checks Example. 6° grade math

21. Robustnesschecks The results shown are based on schools passing randomness allocation tests with respect to: IB and SES : level a=0.10 Other subsets IB: level a=0.30, a=0.50 IB and SES : level a=0.30, a=0.50 Results No major substantive changes on immigrant background peer effects Relevant changes on peer SES effects: positive but not significant if IB and SES tests positive and significant if only IB test • Ammermueller-Pischke (2009): • peer effects understimated • with measurement error • SES affected by substantial m.e. • Underestimation of SES peer effects likely to yield to overestimation of IB peer effects Hanushek et al(2003): When historical family background and school inputs are omitted peer effects are overestimated