Immigrant background peer effects in Italian schools

Dalit ContiniUniversity of Torino

Improving Education through Accountability and Evaluation, Roma 3-5 October 2012

The research question

Do high concentrations of immigrant background

children in schools hamper the learning of native

children (and of other immigrant children)?

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.

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)

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

State of the art

generally small effects not always significant

no research on Italy

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.

Structural model

other characteristics of

peers

achievement of peers

individual characteristics

school and class unobserved effects

Causal effects

achievement of peerscharacteristics of peers

Spurious effects

school and class characteristics

Assumption:

peer effects operate at the class level

Reduced form model

* is the parameter of interest• measures class composition effects• captures peer achievement and characteristics effects• policy relevant

Problem:

Why should school or class unobserved specific effects be correlated with peer characteristics?

•school selection (freedom of choice/area of residence)•class allocation (is it random?)

composite error term

Addressing selection in the peer effects literature

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 classes within 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 cohort composition 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 change schools.

Addressing selection

By exploiting within-school variability in class composition we remove

school-specific effects, hence solve the school selection problem.

INVALSI data allow this strategy (impossible with PISA, difficult with PIRLS, TIMSS..)

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

Random allocation?

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 =0.10: random assignnment rejected in ~ 20% schools

School-level with respect to SES (Anova) with =0.10: random assignnment rejected in ~ 30% of schools

I analyze schools passing both tests: ~ 60%

Underlying hypothesis: the class formation process is not related to performance, given class composition.

Possible biases

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

Rationale of this option:

Ability streaming + better resources to the more in need.

Highly unlikely in Italy. Streaming is not a popular pedagogical practice in primary and lower secondary school.

Variables

Dependent variables Reading & math scores = % correct answers [0-1]

Individual

FemaleSES (n° books, ESCS)Native repeating grade1°generation2°generationSampleSample*1°generationSample*2°generation

Class composition

% Femalesmean SES % Natives repeating grade% 1°generation% 2°generation% 1G*native% 2G*native% 1G*native*SES% 2G*native*SES

Explanatory variables

heterogenous effects allowed:- immigrants/natives- natives of different SES

Immigrant background peer effects

5TH GRADEREADING

5TH GRADEMATH

6TH GRADEREADING

6TH GRADEMATH

fraction 1G on:

Immigrant -0.085*** -0.045*** -0.035** -0.005Native-SES low -0.037** -0.045*** +0.002 -0.005Native-SES med -0.037** -0.045*** +0.002 -0.005Native-SES high -0.037** -0.045*** +0.002 -0.005

fraction 2G on:

Immigrant -0.075*** -0.009 -0.046*** -0.021Native-SES low -0.075*** -0.071*** -0.046*** -0.072***Native-SES med -0.029* -0.009 -0.005 -0.002Native-SES high +0.017 +0.053** +0.036** +0.067***

A 10 % points increase in the share of immigrants reduces the number of correct answers by less than 1% (=1/20 pop st dev)

N° children 120.000-140.000 N° classes 7000+N° schools 1750+

Main conclusions

(i) The concentration of immigrant children in schools should not be

an 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 disadvantaged

backgrounds (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.

Thank you for your attention!

Descriptive evidence (1)0

.2.4

.6.8

me

an

of sc

ore

_ita

North West North East Center South Islands

N 1G 2G N 1G 2G N 1G 2G N 1G 2G N 1G 2G

Italian scores

sample not-in-sample

5° grade- Italian scores

% immigrants in schools:North-Centre: 11-15%South-Islands: 3-4%I focus on North and Centre.

Descriptive evidence (2)

All negativeAlmost all highly significant

School-level correlations between the % of immigrants and mean scores

5TH GRADE 6TH GRADEAREA MEAN

SCORES OF

ITALIAN

MATH ITALIAN MATH

North-West

N -0.14 -0.08 -0.32 -0.262G -0.11 -0.06 -0.20 -0.151G -0.12 -0.06 -0.21 -0.13

North-East

N -0.14 -0.08 -0.15 -0.132G -0.08 -0.05 -0.20 -0.151G -0.15 -0.11 -0.20 -0.20

CentreN -0.15 -0.16 -0.04 -0.00

2G -0.09 -0.08 -0.13 -0.051G -0.11 -0.07 -0.20 -0.13

Descriptive evidence (3)

All negative and fairly largeAll highly significant

School level correlations between the % of immigrants and SES

5TH GRADE 6TH GRADE

Area SESnatives

SESimmigrants

SESnatives

SESimmigrants

NW -0.17 -0.11 -0.25 -0.17NE -0.24 -0.11 -0.24 -0.20C -0.18 -0.11 -0.16 -0.16

Robustness checks

pvmig>0.3 pvmig>0.5 pvmig>0.1pvescs>0.1

pvmig>0.3pvescs>0.3

pvmig>0.5pvescs>0.5

Nstud=155348Nclass=8090

Nstud=110908Nclass=5754

Nstud=141487Nclass=7425

Nstud=78308Nclass=4121

Nstud=37523Nclass=1967

Effect of% 1G on

Immig -0.030* -0.008 -0.005 -0.010 -0.012

Nat-SES=0 -0.030* -0.008 -0.005 -0.010 -0.012

Nat-SES=2 -0.015 -0.008 -0.005 -0.010 -0.012

Nat-SES=4 0.000 -0.008 -0.005 -0.010 -0.012

Effect of% 2G on

Immig -0.046** -0.045* -0.021 -0.064** -0.084*

Nat-SES=0 -0.046** -0.045* -0.072*** -0.064** -0.084*

Nat-SES=2 0.005 0.014 -0.002 -0.007 0.012

Nat-SES=4 0.056** 0.072** 0.067*** 0.076** 0.109**

Effect of mean ESCS

0.009*** 0.009** 0.005 0.004 -0.006

Example. 6° grade math

Robustness checks

The results shown are based on schools passing randomness allocation tests with respect to:

IB and SES : level =0.10

Other subsetsIB: level =0.30,=0.50

IB and SES : level =0.30, =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