immigrant background peer effects in italian schools dalit contini university of torino improving...
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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
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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